STELLA-Q2 Archives - STELLA

NASA Acres Supports STELLA Workshop at American University

Michael Taylor — Sun, 22 Feb 2026 02:58:10 +0000

NASA Acres Supports STELLA Workshop at American University

Last month, NASA’s Science and Technology Education for Land/Life Assessment (STELLA) initiative organized a hands-on workshop for middle and high school teachers at American University focused on advancing remote sensing skills and STEM workforce development. STELLA is a low-cost, open-source, handheld spectrometer platform that participants build and use to measure light reflectance and environmental parameters, helping connect field-based observations with NASA satellite remote sensing.

STELLA Team Lead Mike Taylor led the workshop, with NASA Acres’ Drs. Mike Humber and Allison Bredder serving as contributors alongside Paul Mirel, creator and lead engineer of STELLA, who delivered a talk and Q&A, and Natalia Quinteros Casaverde, who shared a presentation on her use of STELLA to support home garden monitoring in Jamaica. The workshop was organized and planned by Chelsey Brown, Program Manager for the NASA District of Columbia Space Grant Consortium and the Integrated Space Science and Technology Institute (ISSTI). Funding was provided by a grant Chelsey received from the American Physical Society. It included contributions from Rachel Stagner, a nationally recognized STEM educator, 2023 PAEMST Awardee, 2018–19 Albert Einstein Distinguished Educator Fellow at NASA, and founder of STEMTeachersDMV, which also organized and sponsored the workshop.

During the workshop, teachers progressed from understanding how sunlight interacts with Earth’s surfaces to applying Landsat imagery, measuring spectral reflectance with STELLA-Q2 spectrometers, and analyzing vegetation health using NDVI, gaining skills they will now bring directly to their classrooms and students. The presenters highlighted techniques that support agricultural applications, demonstrating how NASA satellite data and field spectroscopy inform crop monitoring, resource management, and decision-making. Teachers assembled and programmed their own instruments, collected and analyzed field data, and gained practical experience aligned with real NASA mission applications. Participants left the event with functioning instruments, hands-on remote sensing experience, and positive feedback highlighting the value of the workshop’s practical, real-world focus.

Through initiatives like this, NASA Acres advances workforce development by equipping educators and students with applied remote sensing skills and helping bridge the gap between NASA Earth observation data and on-the-ground agricultural and environmental applications.

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Building Tomorrow’s Earth Science Workforce, One Spectrum at a Time

Michael Taylor — Fri, 20 Feb 2026 01:46:55 +0000

Building Tomorrow's Earth Science Workforce, One Spectrum at a Time

How NASA's STELLA Instrument is Multiplying Mission Impact Through Student Engagement

In a sun-drenched corner of Southwest Florida, high school students are learning to see the world the way NASA does—by measuring light invisible to the human eye. It’s August 2025, and at the Suncoast Faulhaber Fab Lab in Sarasota, twenty students are building spectrometers, collecting data from wetland plants, and discovering a career landscape they never knew existed.

This is COASTWISE (Community Observations and Analysis using Spectroscopic Techniques and Wetlands Imaging to foster Stewardship of the Environment) -making Earth observation science accessible, understandable, and inspiring to the next generation.

The Participants

Nick Barbi, founder and president of the Science and Technology Society and a NASA STEM Ambassador, sat down with Michael Taylor, NASA STELLA’s team lead to discuss the program. They were joined by Tara Bergstrom Merino, a technology professional at Bank of America and coach of the state championship Ultronic robotics team, and her son Michael Merino, one of the student participants.

Their conversation reveals how NASA’s STELLA (Science and Technology Education for Land/Life Assessment) instrument is doing more than teaching science—it’s building pathways to careers students didn’t even know existed while multiplying the impact of NASA’s Earth observation missions.

More Than Rocket Ships

“The common answer back was, doesn’t NASA just do like rocket ships?” Tara recalled. “Learning that there’s so much more to what NASA does and that they actually work on instruments and they’re doing observational things and they engage with students and they’re doing research, I think was eye opening for the students.”

For Michael Merino, who participated in COASTWISE, the revelation was profound. When asked if he knew NASA monitored Earth from space, he admitted: “I was not aware to the extent that they do.” But working hands-on with STELLA changed that understanding completely.

“The concepts, yes, as well as the type of technology that they’re using,” Michael explained. “And as well as the extent to which NASA does, you know, take notes and record data on the environment, given how direct STELLA is.”

A Mission Multiplier: Connecting Space and Ground

Nick Barbi sees STELLA as a powerful complement to NASA’s satellite missions, particularly Landsat. “It seems so out of reach with the Landsat data, other satellite data and the imagery,” Nick explained. “If you can say this is basically how it’s done. It’s just more sophisticated and a tad more expensive than what we’re trying to do. So the concept is very similar—that exactly is the point.”

This connection is crucial: STELLA uses the same spectroscopic principles as Landsat and other NASA Earth observation satellites, teaching the future workforce ground-based methods that complement and help them understand satellite observations. Students learn why ground truth matters, how spectral signatures identify vegetation, and how local measurements relate to global monitoring efforts.

“It elevated the importance of what we’re doing to say that NASA on a much larger scale is doing this, doing it at a much more scientific level, a worldwide level,” Nick noted. “It gave it a substantially stronger rationale and credibility in terms of what we’re trying to do.”

The program also introduced students to NASA’s NDVI (Normalized Difference Vegetation Index) the same metric NASA uses from satellites to assess plant health worldwide. By calculating NDVI from their own spectral measurements, students directly connect their work to NASA’s operational missions.

Nick pointed out the synergy: “We pointed out the similarity between what NASA’s doing with Landsat and what we’re trying to do.” The goal is to eventually integrate drone-based spectroscopy with ground measurements, creating a multi-scale approach that mirrors how NASA combines satellite data with field observations.

This makes STELLA a true mission multiplier, it doesn’t replace NASA’s capabilities but extends their reach into communities, classrooms, and the next generation of scientists who will use and advance those capabilities.

Workforce Development for a Earth Observation

But COASTWISE isn’t just about understanding NASA missions, it’s about preparing students for careers they might never have considered.

“I think STELLA Instruments are the greatest thing to happen—the most significant innovation to happen in STEM education since robotics,” Nick declared.

Nick pointed to a growing workforce need in the analytical instrumentation sector. “There are hundreds of thousands of instruments out there,” he explained. “Somebody’s got to run them with the technical and scientific bent and understanding.”

The answer, he explained, is “a different group of people than the engineers who build robots. This is many of the kids interested in geology and environmental chemistry, data processing. You know, math students—they may not be skilled in building robotics and electromechanical devices and so forth. Maybe scared of them.”

Tara Bergstrom Merino, who brings both a technology career background and robotics coaching experience to the conversation, emphasized the career exploration aspect: “How are you supposed to know that you want to be an architect? How are you supposed to know that you wanna do scientific data analysis if you’ve never actually been able to work alongside someone that does that with a real data set, normalize the data, put a model against it, understand the impact of that and have to do the hard work right? Not just the fun work.”

She continued: ” COASTWISE and STELLA provides them an opportunity to utilize a tool in a way that gives them information and experiences they wouldn’t have. I think then being able to work alongside real scientists and folks that built the instrument or people that use the instrument and hearing how they use it connects them to real world experiences and gives them an understanding of like how they could use what their careers could be like someday.”

Skills That Transfer Across NASA's Mission Spheres

The workforce development goes beyond just career awareness. Students gained concrete technical skills that transfer directly to careers across NASA’s Earth science, data science, and applied research spheres.

“COASTWISE allowed them to connect with a technology and a terminology that they never would have had,” Tara explained. “They got to program in a way that’s very different… They don’t do any programming over a data set to analyze data—that was something that they had never done before. A lot of them had never looked at data set that needed to be normalized before.”

She pointed out that even coming from robotics, “the amount of variables they were looking at was also much more complex than something that we would look at in robotics. I think the data component was something that is a new skill for them. I think just working with a spectrometer was definitely a new skill for them.”

These aren’t abstract educational objectives—these are the exact skills needed in environmental monitoring, agricultural technology, climate science, and countless other fields where Earth observation data drives decisions. Students who understand both satellite data and ground-based measurements become the professionals who can effectively use NASA’s Earth observation assets.

Learning from Failure, Contributing to Progress

One of the most powerful workforce development moments came when things went wrong. The new 3D-printed STELLA housings proved difficult to assemble. Rather than just finding a workaround, Nick turned it into a learning opportunity.

Two students, both freshmen at the time—prepared an engineering failure report and presented it via Zoom to Paul Mirel, STELLA’s creator and chief design engineer at NASA.

For Michael Merino, watching his classmates present technical problems to NASA engineers was transformative. “It was a very positive experience,” he recalled. “Paul was very accepting and obviously took notes of all of our feedback. And made sure to feel like our input was going to be used to better the product… We felt like we were now part of the progress that we were seeing in STELLA as well as getting a little bit of insight into what was going on behind the scenes.”

This wasn’t just a classroom exercise—it was real professional experience contributing to NASA’s outreach mission. As Tara noted: “That can be hard for a student, and even as an adult. So I think, you know, no matter what, those sort of opportunities are really educational and valuable to students. And then I think as engineers and as adults to hear from students is also allowing us to help them better their skill set.”

Multiplying NASA's Earth Science Impact

COASTWISE demonstrates how STELLA multiplies the impact of NASA’s Earth science missions across multiple dimensions:

Earth Observation Education: Students learn the same spectroscopic principles NASA uses to monitor vegetation health, wetland conditions, and ecosystem changes from space. They understand Landsat not as an abstract concept but as a more sophisticated, high-quality version of what they’re doing themselves.
Ground Truth and Validation Concepts: While COASTWISE measurements are educational rather than operational, students learn why ground truth matters and how it complements satellite observations. This understanding is crucial for the next generation who will use NASA data professionally.
Community Engagement Multiplier: NASA’s Applied Sciences program emphasizes making Earth observation data useful to communities. STELLA-equipped students become ambassadors who can help their communities understand and use NASA’s Earth observation resources.
STEM Workforce Pipeline: NASA faces workforce challenges across its Earth science portfolio. STELLA creates entry points for diverse talent in environmental monitoring, data analysis, and applied science—careers that directly support NASA’s mission.
Mission Understanding and Advocacy: Students who understand how NASA monitors Earth become advocates for continued Earth observation. They can explain to their communities why missions like Landsat, PACE, and future systems matter.
Complementary Data Networks: As STELLA use expands, networks of community-based observations complement satellite data by providing fine-scale, frequent measurements that satellites might miss—a true mission multiplier effect.

A Model for the Future

Nick Barbi envisions COASTWISE as the flagship of a larger program called IDEAS—Instruments for Discovery, Exploration and Assessments in Science. The model would use various STELLA instruments across different scientific domains, always focused on workforce development and complementing NASA’s missions.

“Robotics is aimed at future engineers and coders, while IDEAS is aimed at would-be geologists, biologists, environmentalists, chemists, astronomers,” Nick explained. It’s a different pipeline into STEM careers, one desperately needed as Earth observation becomes increasingly critical.

Tara sees the broader potential: “I think any large company should work on a model like this, right, where you’re engaging something that’s kind of cheap and accessible to as many people as possible. You can get students engaged. I have found that students, especially younger students, have such creative minds and they think so outside the box that you really want them to be engaged in projects like this.”

She added: “Students just kind of look at it however they want with their mind wide open. So I think engaging students in this and making it accessible to them with kind of a less expensive tool set can help maybe guide your overarching real modeling.”

The Bottom Line

STELLA isn’t separate from NASA’s Earth observation missions—it’s complementary to them. It’s a mission multiplier that extends Landsat’s impact from orbit to classroom, from data product to workforce development, from satellite observation to community understanding.

It teaches the scientific methods and technical skills that employers across the Earth science sector need. It helps communities connect local environmental concerns to the global perspective NASA provides from space. And it builds understanding of why NASA’s Earth observing missions matter.

As Michael Merino put it simply: “We felt like we were now part of the progress.”

That sense of connection—to NASA’s mission, to real science, to meaningful careers—is exactly what Earth observation needs as it faces the challenges of the coming decades. STELLA multiplies NASA’s mission impact by building the workforce that will carry that work forward, the communities that will use that data, and the next generation that will advance those capabilities.

Transcript

COASTWISE Interview Transcript

Participants:

Michael P. Taylor – NASA GSFC, STELLA Team Lead
Nick Barbi (Dr. Barbi) – Science and Technology Society, Program Director
Tara Merino (Tara Bergstrom Merino) – Program Coordinator, Bank of America Technology
Michael Melvin Merino – Student Participant (Senior)

00:00:08:13 – 00:00:16:12 Michael P. Taylor: Welcome. Nick. Can you introduce yourself and tell us about the Science and Technology Society and how CoastWise got started?

00:00:16:13 – 00:04:49:02 Nick Barbi: Okay, Mike, thank you very much for giving us the opportunity to talk about what we’re doing. My name is Nick Barbi. I was, at one time, a material science scientist. Then I got into making instruments because the first instrument I worked on was a scanning electron microscope and EDS x-ray detector, and we were one of the first to do that. So the story of my life was I became an instrument maker. So that’s one connection with STELLA that we have right off the bat.

I’m still involved in selling hyperspectral imaging cameras to museums. And it’s more or less a part time job, but we sell VNIR cameras, VSWIR cameras and SWIR cameras and extended SWIR cameras. So there’s a whole range, and they typically cost from 40,000 to 140,000. And which is another reason STELLA is so attractive at $200 a unit.

And so I’m still involved in that, but predominantly retired and started the Science and Technology Society about three years ago now, two and a half years and we’re initially it was to hold technology meetings and socializing with people that were in science for their careers or just interested in science, and then we just got organically interested in STEM education and actually is where we’ve been focusing most of our time these days.

And we, the first course, because of my experience, we chose to use the STELLA Q2 which is 18 channel VNIR spectrometer and you know, since I made spectrometers most of my life, various types and sold them, if I wasn’t making them and teaching people how to use them, it was a natural for me to want to do that.

One of the most important spectroscopy, one of the most important features of Sarasota and Manatee counties in southwest Florida, where we’re based is are the wetlands. And it’s an interface between the ocean and the land and a lot of neat environmental and ecosystem things happen in wetlands. So we thought we would use a spectrometer to see if we could differentiate one type of plant from another. And in so doing, learn about the importance of wetlands.

So there was an ulterior motive. Whenever you have an instrument, you need an application. And so we were sneaking in the interest and the exposure to the application, along with talking about spectroscopy, electromagnetic radiation, how to use a spectrometer, data collection, data processing, all that kind of thing. But at the same time, getting speakers to talk about our environment, the ecological system, the importance of wetlands, the damaging of wetlands that due to hurricanes and also development, hurricanes they seem to survive, you know, development they don’t, which is you know, somehow hurricanes because they’re natural do somehow work themselves out. Maybe later I’ll tell you a story about that. But so, we’re, we’re now focusing very much on STEM education for students and trying to do something exciting and different. And NASA with the family of STELLA Instruments has provided us that opportunity.

00:04:49:03 – 00:05:00:01 Michael P. Taylor: Awesome. Thank you very much. That was a heck of an introduction. So, and you answered actually answered probably like, 3 or 4 of the questions doing it.

00:05:00:02 – 00:05:12:02 Michael P. Taylor: So, short interview. Okay. All right. Exactly. So, Tara, again, thanks for joining us. And what is what’s your background and how did you get involved with this program?

00:05:12:03 – 00:09:02:08 Tara Merino: Sure. Thanks for having me. So, my name is Tara Bergstrom Merino. I officially work for Bank of America in Technology. I started in computer engineering, but I spent most of my career overseeing a suite of applications and change across those applications. About eight years ago, nine years ago, I became heavily involved in STEM with the youth in our community, mostly resulting, you know, as a result of my kids. They were very, especially Michael was very interested in STEM, and there was really not a lot in our community for him to kind of develop that interest.

We by chance kind of came into a program called FIRST, for Inspiration and Recognition of Science and Technology. We do not have any sort of presence of that organization in our community. But it was at that time the age group that my kids were in was Lego based. And so they really wanted to kind of pursue a team in there. So, Michael will share, but he, you know, I told him if you wanted to do that, you know, I would help. But, you know, I wanted him to kind of find a team. It could be about ten members. And so he aggressively went and sought out a bunch of students and came with 60 students. So we ended up having six teams because we didn’t want to turn anybody away.

And we did that for quite some time. But when we published that, it caught the interest of Lakewood Ranch proper. And so they’re the number one selling master plan community in the nation. And there’s, you know, a lot of organization that goes behind making sure that it continues to be the number one master plan community. And so they do a lot of activities and events there. So they were like, oh, what is your vision to bring STEM to the community for our youth? I was like, I don’t have a vision. But the whole we really want to bring STEM. There’s like this very significant demand in our community and we think, you know, it’s obviously an up and coming area.

So that ended up I started, you know, kind of like what Dr. Barbi’s doing, but more for youth. I started running meetings and presentations and connecting them with the experts. And I started running math 24 leagues. And we were running all sorts of like we were running the STEM tent at the farmer’s market, and we were doing all sorts of things, when Covid happened, a lot of that went away, just because everything was locked down and we weren’t able to go anywhere. But I remained very engaged in FIRST with Michael and Natalie’s team. We continued that, they continued even running activities over the Lakewood Ranch Facebook page. So like STEM activities, we’d get like every week, we give a STEM challenge out to the students to kind of keep them engaged and connected because that was, you know, an isolating time, I think, for our young folks.

So we did that. But when we came out of Covid, I knew I was going to be moving away from Lakewood Ranch, just because my kids were going to be going to a school that was about 45 minutes south of it. So I didn’t want to re-rigor all of that. When I moved down here though, I got connected with the Suncoast Science Center, the Fab Lab, which is about 15 minutes from my house. And that’s where I met Doctor Barbi. And I was like, oh, well, Doctor Barbi has a vision that was kind of like what Lakewood Ranch wanted to do. And so I said, you know, I’d love to support you in your vision instead of, you know, trying to restart that again. And that’s how we became connected. And then he’s, you know, brought a lot of great programs to the students and fully supportive and COASTWISE is definitely been a staple of that. And I think it’s taught the kids a lot. You know, I don’t think any of those kids even knew what spectrometers were before that. So I think, you know, it’s been an amazing opportunity. The kids have been involved in it for two years now. And I’m just, you know, I’m really just connecting Doctor Barbi with the students, and we have a great relationship doing that.

00:09:24:12 – 00:09:34:04 Michael P. Taylor: Excellent. Yeah. Michael, we’re glad to have you in the student perspective. What grade are you in and what made you want to do coastwise this summer?

00:09:34:05 – 00:09:59:05 Nick Barbi: And don’t say your mother made you do it.

00:09:34:05 – 00:09:59:05 Tara Merino: No.

00:09:34:05 – 00:11:17:06 Michael Melvin Merino: Yeah. So I’m in 12th grade right now, so I’m a senior. And two years ago when it was my mom that brought up the opportunity to do CoastWise, I was actually very intrigued in that. And I shared that with a bunch of other people that I knew. We were all in the same robotics team, so we share that common interest in trying to learn new things. So I originally approached it with like, the, oh, this is just going to be something new to learn, like something I can just add to my skill set. And then going to the meetings when we realized that this was actually something that was very important to the, I want to say the area like the environment, I guess, and helping the wetlands and, you know, understanding how they, their health, how they thrive, how to preserve them. And I got very invested in that. The more that I started going to these meetings. So, yeah, I mean, it’s, I think it was a great opportunity. I think it was definitely a very great chance to learn some new skills and learn a lot about the environment, how it works, how to measure certain things, how to predict the future of those environments, and to help preserve the Everglades for the future.

00:11:17:07 – 00:12:02:14 Nick Barbi: I do want to thank Tara and Michael. Our first group. You know, I contacted Tara, and I said, Tara, I don’t know how to get kids into this. And she said, don’t worry. The robotics team will be there for you. So our first group was almost completely the robotics team. So, I think not only Tara and Michael and Natalie and all the people, but just the spirit, the community spirit they have and the STEM, the love of STEM courses and efforts and experiences and so forth. So it was really critical to us. So I want to thank them for that.

00:12:03:01 – 00:12:11:10 Michael P. Taylor: Before the summer, have you ever heard of, you know, spectroscopy or worked with these particular types of scientific instruments?

00:12:11:11 – 00:12:55:02 Michael Melvin Merino: No. And that was our look at the camera. No. So before before I ever got involved in STELLA, I never heard of any of that terminology before in my entire life. And that’s, I think, part of the reason that me and all my other teammates were so intrigued, was because even though we are so invested in commonly used STEM instruments and, you know, looking at these innovative solutions quite often, that this was a brand new branch that we had never even seen before. So becoming involved in it was, you know, branching into new territory. And I think that’s what originally caught us all and got us engaged into STELLA.

00:12:55:03 – 00:13:02:07 Michael P. Taylor: What made this program appealing for your kids to participate in? So, you know, what excited you about the program?

00:13:02:09 – 00:14:36:12 Tara Merino: Oh, sorry. When the students were younger and they’re part of the Lego branch, that program included an innovation project. So every year they were asked to look at a challenge and find a real world solution for that challenge. And that was always like one of their favorite parts beyond just the robot part, really. The innovation project was one of their favorite parts. But for the league they’re at right now when they’re doing this level of robotics, there is no innovation project. There’s no real world kind of presentation or innovation kind of component. So when Doctor Barbi brought this to the community, it was like, hey, you know what? I have this CoastWise plan. We’re going to be using spectrometry. We’re going to be looking at the Everglades and the wetlands. And we want to understand if we can utilize this tool to actually do something in the real world. I knew that the kids would be interested in that. Right. It’s something that has an impact in the real world, right? That they get to work alongside experts, you know, getting to work alongside you and Dr. Barbi and people that have, you know, real knowledge I think is invaluable to students to get to kind of work alongside real engineers and real scientists, to kind of see their thought process, just learn from them, like how they grow, how they work through problems, I think is invaluable. And then, you know, also the impact of being able to have the potential of doing something that truly has an effect on the wetlands, I think is an incredible experience. Right. Like, who wouldn’t want a student to be part of it?

00:14:36:13 – 00:14:44:04 Michael P. Taylor: Did working with STELLA lead you and the students to discover various NASA data sources and stuff you didn’t know about before? And what did you find?

00:14:44:05 – 00:17:24:05 Tara Merino: Yes. Like, I think, mostly, you know, I’ll have to give credit to Craig Phillips, who really dove in to the NASA applications and pointed out to me and to others who were interested in this, that there’s some phenomenal data sources. We went up and visited a professor at USF, who has access to all the, you know, the direct satellite data and some high resolution satellite data. And Craig pointed out the benefits of space. You know, he, Craig was the type of scientist who, you know, who kept getting ahead of everybody. And instead of keeping pace, he was out at the next level already. Unfortunately, Craig and his wife moved back to Colorado. They’re not much. It’s hard to get them involved. Hopefully we’ll get them involved remotely as time goes on.

So one of the things that we tried to do was show how you could use Google Earth and get the aerial images and look at the changes to the vegetation over time, and pointed out the similarity with Landsat and the PACE which is the ocean satellite, essentially, is extremely interesting to us. The reason we couldn’t use more of the data itself is that it’s pretty complicated, very large to deal with and so forth. So for our first course, which was just four days long, we pointed out the similarity between what NASA’s done with Landsat and what we’re trying to do.

And this year, I think we’re actually going to achieve a drone flying possibly of STELLA itself, but certainly the nature that Bianca Cilento has done where in her case, I believe Mike, she had a spectroscopic drone and then sort of matched up the STELLA 18 channel information with the three channel spectroscopic drone so that they could say, this spectrum from the drone gives us this spectrum from STELLA. So now we can look in more detail and do false color imaging. And I think false color imaging is an interesting concept for young students as well.

00:17:24:08 – 00:18:24:03 Nick Barbi: So this year, we’re fortunate enough, if all goes well, to have a student who, for his science project, designed the control system for drone hardware and software. Have a student that might be able to, you know, help be a not only a student, but be an instructor in some sense. And then if we had our own control system, we have more options to modify it and link the flight of the drone with the collection of the spectrum and so forth. And the goal is to get more involved in and directly relating our work to NASA’s database and you guys. It just needs more than four days to really delve into that.

00:18:24:04 – 00:21:31:02 Michael P. Taylor: Yeah, yeah. So my question was did working with STELLA introduce you and the students to NASA’s broader work in Earth science beyond just rockets? And did it change how students viewed NASA’s work?

00:21:31:03 – 00:22:22:02 Tara Merino: Yeah. I think, you know, when I first brought this to the students that I spoke to, you know, the first year we predominantly did the robotics team, and then the second year we went to some school students as well that I know were engaged in STEM because we had a bit more time. The common answer back was, doesn’t NASA just do like rocket ships? Like what is this? And like, what do they do? And I think, you know, just learning that there’s so much more to what NASA does and that they actually work on instruments and that they’re doing educational things and they engage with the students and they’re doing research. I think was eye opening for the students. Right. But I agree with Dr. Barbi then that like then saying you’re going to get to work with a NASA instrument immediately was exciting to the students are like, oh, we’re going to work with something that NASA made, like, we’re working with something that’s, you know, really, really must be like forward looking.

00:22:22:03 – 00:23:10:12 Nick Barbi: And now they’re engaged like, oh, what else does NASA do, right? That kind of like created a spark to like is there other things that NASA works on and like what other kind of research programs do they do? You know, so I think that was kind of like a lynch pin for them to kind of start looking at that NASA does more than just rocket ships.

00:23:10:13 – 00:24:28:13 Tara Merino: You can get students engaged. I have found that students, especially younger students, have such creative minds. And they think so outside the box that you really want them to be engaged in projects like this, that they can really look at problems in ways that we haven’t been taught yet. Like this is the process you follow to look at a problem. Students just kind of look at it however they want with their mind wide open. So I think engaging students in this and making it accessible to them with kind of a less expensive toolset then can help, maybe guide, you know, your overarching real modeling and things because you really do never know what can come out of a student’s mindset.

I know for us, several years ago, we were looking at space travel and, you know, the challenges that exist in space travel. And our students came up with this whole suit that had like resistant bands to kind of keep muscles engaged so that they wouldn’t lose bone density and all of that. And I think maybe like five years later, that was like a real product. Like now people actually use that for physical therapy and things. So but it’s just you know, it was incredible that ten year olds came up with that concept, like right off the bat within a couple hours or like, oh, this is, you know, these are the challenges and like, this is what we could do. So I think, you know, having youth and students engaged in real world challenges like this, they can look at it in a different way. And they might come up with ideas that, you know, some of us that have been doing this for many years wouldn’t come up with.

00:24:44:09 – 00:26:14:12 Nick Barbi: I was going to say something, Mike, if I’m still allowed a minute. Now, we don’t want to hear from you. Yeah. So. No, your comment was about linking it to the NASA data inspired me a little bit because we are talking about doing a freshman in college level course that would actually be, you know, a semester, trimester, whatever works out to be. And in that course, that’s where we could really spend a lot more time on that kind of association. It makes it a much higher level course. And be it, you know, we got the time to do it. So I think that that’s a perfect sort of vertical addition. You know, we want to do things laterally in this course across the different instrumentation that you’re doing or different applications of the same instrument, but also vertically above the, you know, the science behind it and the use of the data further beyond it. So hopefully that’s something we’re working on now and may come to fruition in a year or two, not sure. But thank you.

00:26:14:13 – 00:27:27:03 Michael P. Taylor: Yeah, so Michael, can you talk about the experience of assembling the STELLA and what challenges you ran into?

00:27:27:04 – 00:30:12:09 Nick Barbi: So we, initially we ordered the parts to do a build class, meaning we bring in, you know, 15 kids and 15 instruments, and we go through building it all at once. But when you do that, you’ve got, you know, you’ve got to be somewhat of a control freak, right? Because everybody’s got to be on the same page, doing the same thing at the same time, and the parts have to be laid out. And in this case, it was me and one other instructor, and we just didn’t get the job done. Kind of a classic mistake and not fully doing the build prior to the teaching exercise. So you know it has the trappings of looking very easy and yet there’s some experience level things that you need to gain. The other thing that caused us a problem was just ordering parts and finding out that they weren’t available and the notice that we had given ourselves. And because of that, we had to modify a part. We essentially had to build a part that came as an integrated unit into from two pieces. And that piece ended up not being of the proper dimension for the slot and the housing. So that caused the case not to close properly. And we didn’t realize it until we spent hours on it. And so it was not, I think the conclusion was it would be nice to have a little more room in there for those wires and also some suggestions about the placement of the buttons and that kind of thing. But by and large, the problem that we experienced while building was mostly of our own doing. And now we have, you know, we have a couple of. I will show you right here. I just got delivered new Q2 units. But, yeah. So, you know, we even though we understand it was needed to be, you know, the design goal was to be small. We didn’t think it had to be quite as small as it was and that we could have used a little more leeway with the wires.

00:31:48:12 – 00:33:29:12 Nick Barbi: Although the problem was not itself the wires. The problem was in the oversized component that we had a rig up from because the part wasn’t available directly. So on time later, they all came in, but they didn’t come in on time. We then had the students do failure analysis and the whole thing. And a couple of students, including Tara’s daughter, Natalie, and Colton, who was the exceptional young man, the two of them together, really led the failure analysis and came up with some great suggestions, some of which were solving a problem that didn’t exist, as I said. But nevertheless, we didn’t know that at the time. But the exercise of going through that and then listening to Paul explain why things were done a certain way, why he didn’t think, you know, recommendations were necessary in some cases, but also why he thought they were very good recommendations in other cases. That was a great moment for us.

00:33:29:13 – 00:33:41:03 Michael P. Taylor: Yeah. In fact, he sent you all a failure analysis report. Yes. He did. Right. And so it was there for that.

00:33:41:04 – 00:35:47:03 Tara Merino: And I do recall him, like, you know, stopping the students and really having them explain what they meant and why and all that, that it was, yeah. Not only the students, but the instructors as well. And I think that’s also a very good experience for students, right? To be questioned and have to kind of give their logic and reasoning for what they thought or what they presented and areas of improvement that they see. And then how do you defend why they think a certain thing, or why they felt a certain way about something. So I think, you know, that can be hard for a student and even as an adult. So I think, you know, that no matter what those sort of opportunities are really educational and valuable to students. And then I think, you know, as engineers and as mentors to hear from students is also allowing us to help them better their skill set. So I think the students learned a lot. I hope, you know, I think Paul also hopefully took some takeaways from it. You know, I don’t think, you know, I know there were some things that were necessarily actually wrong or complicated with it or a challenge. And it was something else, like the chip we bought or something. But I think, you know, hopefully there was some other value in what they presented. But I think any time you’re having a student engage with a real professional and you’re connecting them, I think there’s a lot of growth. I think being able to struggle, and I agree with Doctor Barbi, sometimes the challenges or the problems or the struggles are much bigger learning opportunities than successes. So I think, you know, for them, you know, CoastWise and STELLA provides them an opportunity to utilize a tool in a way that gives them information and experiences they wouldn’t have.

00:36:11:10 – 00:36:51:07 Tara Merino: I think then being able to work alongside real scientists and folks that built the instrument, or people that use the instrument, and hearing how they use it, connects them to real world experiences and gives them an understanding of like how they could use, you know, what their careers could be like someday, and if that would be interesting to them. And I think overall, it gives them a breadth of exposure to things that they may never have known to consider in their careers. I feel like a lot of times students are like, okay, I want to be a lawyer or I want to be a doctor, or I want to be an engineer, because those are things that they see all the time in movies or they hear about.

00:36:51:08 – 00:39:29:07 Michael P. Taylor: What surprised you about the connections between this local project and NASA’s broader Earth science work?

00:39:29:08 – 00:42:30:14 Nick Barbi: I think that, you know, it elevated the importance of what we’re doing to say that NASA on a much larger scale is doing this, doing it at a much more scientific level, a worldwide level. And zillions of applications of it, depending upon which area of the world people are interested in. And so it gave it a substantially stronger rationale and credibility in terms of what we’re trying to do. I think one of the really interesting things was the NDVI, the normalized difference vegetative index. And this is a way to compare the vegetation from year to year or month to month or season to season. And the concept that this is done on NASA satellites, that this is common practice, I think is extremely interesting. And the fact that the waterkeeper is very interested in the changes of sea grass density, especially in the aftermath of all the, you know, the red tide and all the other things that have polluted the, the sea grass area. And so we can do a relatively small version, mini version of the same kind of thing. So yeah, I think all of this lent credibility from what we’re doing on a small scale to what the waterkeeper’s doing on a much larger regional scale and what NASA’s doing on a worldwide scale.

00:42:30:15 – 00:44:08:04 Michael P. Taylor: For Michael, could you walk us through what it was like collecting data from plants, from actual plants? And could you tell different plants apart from their light signatures? Did anything surprise you? And what were your takeaways from that?

00:44:08:05 – 00:46:29:03 Michael Melvin Merino: The data collection portion was actually very simple and very straightforward. We set up multiple plants outside, and we did a series of measurements on each plant. So it wasn’t just one measurement per plant. It was a couple. And then we imported that data into, I think it might have been Excel. And then we, you know, graphed it, organized it, looked at it, looked through trends. It definitely gave me a lot more insight into how data collection is actually done. Before then, I had really only collected data like with like single variable like experiments, not with something that had this many components to it that we had to account for, like the sunlight, which is one that I didn’t remember until you mentioned it. Like the position of the sunlight changes obviously over time. And like that, that’s not something that I’m used to accounting for. Like working with that many variables was definitely something that was new to me. And although it didn’t, like STELLA itself didn’t change the direction of mechanical engineering that I wanted to go into, it definitely gave me a lot more insight into like how the collection of data might actually happen in my field, rather than just like, you know, oh, we’re going to run this tiny little experiment. It might be like, okay, we’re going to run this experiment. Here’s all the variables we have to take into consideration. And I think using, you know, having gone through the STELLA program, although it was short, gave me a little more insight into how that process and data collection, collection and analysis might actually work.

00:46:29:04 – 00:48:53:00 Tara Merino: Yeah. I mean, I think that COASTWISE allowed them to connect with technology and terminology that they never would have had. Right, terms they never would have had. They got to program in a way that’s very different. Right? So, right now they only program for hardware, so they only do hardware programming. They don’t do any programming over a data set to like kind of analyze data that was something that they had never done before. A lot of them had never looked at a data set that needed to be normalized before. I think understanding kind of how they would have to present information and how they could utilize information and also like how data can be manipulated to kind of not maybe necessarily always give the truth was also something that was unique to them through CoastWise. So I think those were all very unique skills that they didn’t have exposure to before and without going through the CoastWise program with STELLA, they wouldn’t have had access to. Just to show, again, I wanted to mention the Fab Lab that was so generous and crucial to our pulling the first couple of years off. And then here is Natalie, Michael’s sister, and there’s Michael.

00:48:53:01 – 00:55:32:02 Nick Barbi: So I wanted to show some results of our spectra from the black mangrove, the red mangrove and the white mangrove. Now, these were all obviously saplings, but we knew exactly what they were, and we could get, you know, color wise, they’re all very similar. Here we can see the spectra that we were getting from these. And here’s the wavelength axis from 400 to 1000 nanometers, reflectance. And then these were the areas of some differentiation that I pointed out on the spectra that clearly allowed us to differentiate, you know, one spectrum from another. And there are plenty of regions that give us differentiation. And then here’s the color bar where the black is infrared and the rest is as shown here. So I think this seemed to be consistent. Consistency is always an issue. But I think it’s very interesting that the green color was very common.

And these were done one, two, three. So that was not a lot of there was no time difference. The sun was at the same angle. Essentially throughout the whole thing we used the white card and so there’s clearly differences between this, especially when you look at the infrared here, but also just in the colors. I mean, the spectrometer pointed out some very different levels of color amounts of blue and green and orange and so forth that it was hard to see by the eye. And then here we have seven plants that we looked at. And I’m going to jump to just looking at the five green plants. I took out, if you see here, I threw in a couple of ringers because I knew they were going to be quite different in the visible light spectrum because their color is different. And so these were the five green plants, and then these were the two that I threw in to make sure we got some differences.

And so the, if you go to the five plants, you can see that there’s, these are all normalized at an orange color. And so all these plants, all these spectra were made to be equal at 580 nanometers. And then you can see there’s ratio differences that can be, if consistent, can be used to distinguish one plant from another. Now, here’s a situation between two groups where we looked at the group called Viper and the group called Crocs. And we saw the green box here, which is below. And these spectra between the two groups were very similar. Remarkably so. Maybe, this one, they’re all three very, very good. So this is the bougainvillea, the jasmine and the Persian shield and so two different groups, two different spectrometers being used. And then the blue box up here was pretty good, pretty good match from one group to another. And then finally the Heliconia was really not so good of a match where we would have to do a lot more data collection. So, but of the group we looked at, we had some pretty good consistency. And this will end up being the true test as to how consistent we’ll be able to get. So is the deviation from time to time, group to group, in the system greater than the inherent deviation of the spectrum? If it is, then we’re in trouble. If not, then we’re in good shape. So this is part of the research aspect of what we’re trying to do.

00:55:32:03 – 00:56:29:10 Tara Merino: Yeah. I mean, I think if we’re going to expand it to more areas, there needs to be sort of like a curriculum guide. And I don’t know if that exists, but something that allows someone maybe that doesn’t have a Doctor Barbi, that’s utilized this sort of technology before that’s going to be able to explain it. Right. So if you’re going to want somebody else like a me to be able to run a course like this or like a fab lab to be able to run and utilize STELLA, there needs to be some sort of instruction set that says like, okay, you know, day one, this is what you want to do. Day two, this is what you want to do. Day three and maybe there’s multiple variations of that. And depending on the duration that you’re going to work with the technology with, so that would be like what I would suggest if we want to expand. Right. There just needs to be some sort of high level explanation and instruction so that you can make it accessible to people that don’t have a Dr. Barbi that has experience with instruments like this.

00:56:29:11 – 01:04:16:02 Michael P. Taylor: What do you want to tell parents or educators about programs like this?

01:04:16:03 – 01:10:35:05 Nick Barbi: And the way to do that is to make sure there are opportunities. And so we want to build the technical community, the technical strength of the community, make it a place where technical families and their companies can feel comfortable locating. Start ups can locate. One of the best things we can do in my position is to have opportunities for their kids and opportunities that can be modeled nationwide and worldwide. So if we can say that a program that is now worldwide began in Sarasota and Manatee counties in Florida, that would make people aware of the things that we’re doing. So that’s what it does for the area. It would do that, you know, we can’t, we’re not going to change much of the political spectrum or the funding spectrum or we’re not going to do much of that. But if we can make it a known place for starting really advanced STEM education programs for kids that get national and international recognition. To me, that is a phenomenal contribution.

01:05:47:15 – 01:05:53:15 Michael P. Taylor: Finally, any closing thoughts about where do you want to take this program and why people should support it?

01:05:54:03 – 01:08:46:15 Nick Barbi: You know, one of the things we’re doing next month is we’re having some kids that, you know, one of the, besides CoastWise, a very big program we do is send kids to Italy for three weeks at an AI session at, you know, Fondazione Bruno Kessler, which is in Trento, Italy. And they pay 100% of the fees once kids get to Italy. So what we do and what the parents do now is the parents have to have the money to send a kid. The airfare. And so we would like to open this up more for other kids that may not be able to buy the airfare, whose family may not be able to send them over, even though it doesn’t cost them anything when they get over there. Airfare is not cheap. So we are trying to say that we give a technical experience that kids can’t get anywhere else. It helps the career opportunities decision by opening up, you know, if kids have a bent toward science but never heard of instruments, never heard of spectroscopy, don’t know anything about data analysis. At grades, you know, at say rising freshman level where they’re just going from eighth grade to freshman. This is the time really to hit those kids with look at these opportunities.

So it will help the standard of living, as I say, draw more people into the community that have, that are tech aware people and are worried about their kids education. And then we have this international tie which is an experience that opens up all new kinds of opportunities for kids. They meet friends from not only Italy, but from, you know, there’s many countries represented in this program. So next year we’re going to be able to send 6 or 7 kids to this program. We’ve been upgraded because of the level of kids that we’ve been able to send. But we want to expand that international collaboration. And we want Italian kids to come here, and we want more of our kids to go to Italy and all of this, you know, builds the community, builds opportunities for kids, brings in families that are concerned about that. And so we can make kids aware of the environmental issues of the affected development.

01:08:46:16 – 01:10:35:05 Nick Barbi: So they can make their own decisions. And this is our CoastWise program about whether development is a good thing or whether it’s nonsense. They can look at images, they can compare images before and after. They can see mangroves decreasing in volume or changing in normalized difference vegetative index. And then we could expand this to water quality, agricultural analysis, sustainable farming, air quality to a lesser extent. We’re fortunate enough not to have severe air quality issues here because of the ocean and the breezes and all that kind of thing. So, but the things that care about the community, it teaches the kids not only science, but more about the environment in which they live and how what the real difference between an organically grown product and an organic soil versus non organic and help them understand the choices that they make throughout life. So, you know, the the launches are done and so forth can be, Jacksonville is pretty industrial area, but, you know, we’re noted for our beauty. And so environment becomes a big issue. But we want to also have tech companies, new companies, start ups or relocations of companies here. And we have to show them that there’s some amazing informal STEM education programs and maybe other education programs, not so necessarily STEM oriented, entrepreneurial things, which we’re looking into, an entrepreneurial fair, for example. And so this is to make the area a more prime location for the tech families and tech companies to locate.

01:10:35:06 – 01:12:15:00 Michael P. Taylor: Yeah. Thank you so much. You’ve been great. I’m really pleased and happy you guys were able to join us today. Thank you. Well, again you know any time we’re going to get to interact with, you know, real experts and hear feedback I’m going to sign my kids up for that. Yeah. Even after surgery. Yeah I didn’t get up there. Well, they’ll see it. And that was that was an emergency like we did. I didn’t know that that was going to happen until Monday, so I didn’t actually sign him up like knowing that that was going to be the case.

01:12:15:00 – 01:13:19:01 Tara Merino: Thank you so much, Mike, for all that you do and engaging with students in your time. I mean, it really has been an amazing opportunity. I look forward to seeing what comes out of it this summer. But, I know, you know, not just Michael, but all the students, they really, really enjoyed the summer. They really enjoyed being able to speak to you and to Paul. I know that as Doctor Barbi said, he didn’t really just say that. Like it really was the highlight. They all talked about it for weeks about how they got to talk to NASA. They thought it was the coolest thing. So, you know, I definitely, you know, I appreciate you guys taking the time, making it a memorable experience for them. You know, listening to them and taking their feedback, making them feel part of, you know, a process, making them feel part of a solution and part of, you know, impacting something that’s beyond themselves. I think that was a really inspiring moment for them. So, so thank you for that. And Dr. Barbi, you know, always, you know, thank you for making all the opportunities for our students and the community. You know, it’s awesome to work with you.

Nick Barbi: Yeah. Well thank you. And, great. Back at you, Tara. Thank you very much.

Related Resources:

The post Building Tomorrow’s Earth Science Workforce, One Spectrum at a Time appeared first on STELLA.

Students Build AI-Powered Drone for Earth Observation | NASA SEES Internship Journey

Michael Taylor — Fri, 30 Jan 2026 04:32:29 +0000

Students Build AI-Powered Drone for Earth Observation | NASA SEES Internship

Video Summary

In this interview, four high school students from the 2025 NASA SEES (STEM Enhancement in Earth Science) internship program discuss EarthLens, an innovative drone-based system they developed to complement satellite Earth observations with high-resolution ground data. Samuel Bawden, Nandini Khaneja, Neev Tamboli, and Jordan Rodriguez share their journey from classroom learners to hands-on innovators, demonstrating the power of experiential STEM education in workforce development.

Project Origins and Technical Design

EarthLens emerged from the students’ experience using GLOBE Observer to compare ground-truth land cover data with satellite datasets from Landsat and Sentinel. Recognizing the challenges of manual data collection and the vast difference in spatial scales between ground and satellite observations, the team designed a drone platform that bridges this gap. Their system integrates STELLA-Q2 and Helio-STELLA spectrometers for measuring surface and atmospheric properties, RGB cameras, a Raspberry Pi-based data system, and a preliminary AI classification tool for real-time land cover analysis. The goal is to make Earth science data collection more accessible for citizen scientists, educators, researchers, and agricultural applications.

Learning Through Hands-On Experience

The students candidly discuss their learning journey, emphasizing that none of them began with significant remote sensing or drone-building experience. Through hands-on problem-solving, mentorship from University of Toledo graduate students, and self-directed learning, they assembled the drone from accessible components, integrated unfamiliar technologies like Raspberry Pi systems and flight controllers, and developed software solutions. Their discussion of comparing satellite and ground observations provides authentic insight into how students build conceptual understanding of remote sensing principles through real-world experience. The team emphasizes that their work complements satellite missions, filling an important niche in spatial resolution and data validation.

Future Directions and Impact

Looking forward, the students plan to create build guides for others to replicate their system, integrate additional environmental sensors, and refine both hardware and software components. They envision applications in agricultural monitoring, environmental research, and education, where the system could provide detailed observations at scales between satellites and ground measurements. Each team member reflects on how the project influenced their career goals in engineering and Earth science, demonstrating that high school students can develop meaningful tools for real-world environmental challenges. Presented at the American Geophysical Union conference, EarthLens exemplifies how project-based learning and mentorship cultivate the next generation of Earth science professionals while students continue building their foundational knowledge in remote sensing and related fields.

Transcript

Introduction & Team Members

I’m here with the, NASA SEES group that I met at AGU. Working on the EarthLens project.

And I’ll save that for you to tell us all about what EarthLens, is and what you did and all that.

But, with, that being said, I’m going to ask you to, introduce yourself.

Where you’re from again, just the city is fine. And your role in the EarthLens project.

Okay. And I’m just going to go in order from how I see it in my screen over here, from right to left.

Okay. And so first up, I have Sam. Yeah. Hi.

I’m Sam Bodman from Washington, DC area.

I’m, going to Dartmouth next year. I’m going to be a freshman there. And my main responsibilities

with the Within the EarthLens team was, essentially doing,

a lot of the background research for essentially identifying the problem we were seeking to solve, which was improving,

citizen science tools for land cover. So we were interns, in a program this past summer,

which I’m sure we’ll talk about. And a lot of the tools we used were challenging and give great data.

And so kind of assessing what those challenges were and then how to solve them with EarthLens was kind of

where I came in then next up I have Neeve. Yeah. So I’m Neeve, from the Bay Area in California,

about like 20 minutes out of San Francisco. And, my main role within the EarthLens project was

sort of, helping design and plan out the drone as well as designing

how our entire system would kind of be integrated with the computational part. next up, I have Nandini.

Hi, I’m Nandini, I’m from Long Island, New York. My role on the EarthLens team was, was, hardware lead.

So I did the physical construction of the drone and the STELLA integration. And we’ll be continuing to work on various iterations of the drone.

And last but not least, Jordan. Hi. My name is Jordan Rodriguez, and I’m from Westlake, Texas.

And, on the EarthLens project, I focused on software and data architecture. My role involves designing how observation data,

is captured, from the drone and transmitted to the actual app.

NASA SEES Program & Project Origins

first off, I want to understand what NASA SEES is and how, you know, folks from all across the country,

you got to get together and work on the project. So can you tell me just a little bit more about NASA’s SEES

and, and how that happened? Sure. I love to take that question. So NASA SEES

is, really was a gift of a program. I think for a lot of us, they do,

all kinds of programing for high school, students interested in engineering and earth and space sciences

and physics and, so students from all over the country, every corner.

You know, this group is representative of that, but not in the least, limited to how many places people came from,

including, you know, territories like Puerto Rico and things like that. People from all over the country,

got together and worked on a lot of really cool projects. Or specifically was about,

essentially assessing the agreement between ground truth land cover data and satellite derived land cover data.

But there were groups who worked on designing future missions to moons of Jupiter

and Saturn or other really, you know, just crazy cool things like that. And so I think it was a really awesome way to get introduced,

to, you know, kind of what careers in Stem look like. And I think I speak for everybody when I say

it really encouraged us to pursue this kind of work in the future.

What is Earth Lens?

Awesome. Awesome. That’s fantastic. And, so we’ve touched on a bit of what EarthLens is, but,

could I bother, one of you to, tell me exactly what EarthLens is? What did you do for the project?

You know, sort of just, the in broad, definition, a broad scope of what the project is.

Yeah, I could take this one. So, I think from a very basic quick perspective, EarthLens,

we came across the idea once we realized how, a lot of times, inaccurate

and inefficient data collection of land cover was with the existing technology

because it relied mostly on, like, manual or just simply very far off inaccurate satellite satellite data.

And we wanted to change this by making it a more accurate and be more reliable, because one of our friends, Jordan, over here, I think he lost

over 200 images that he took over like ten plus hours. And we also had the data that a lot of the pictures

that we had taken, as part of our internship, they were I believe, less than one megapixel in resolution.

So we wanted to see how we could improve that. And we came up with the idea that a drone could address

both of these problems, because a, a drone is kind of like a happy medium between the very inaccurate high up satellites

and the having to go on your feet with your phone manual like, NASA GLOBE.

And with our computational parts, we can also make sure that the data can be stored very safely, and we’re not going to be losing it.

Okay. And so you said inaccurate, satellites. What do you mean by that?

So I think very briefly, one of as Sam mentioned, our internships goals

was to see the difference between, the land cover

that we can see on the ground and what satellites are, showing them to be on the main NASA data sets.

And I think we can probably pull this up later sometime, but we had these large charts that we made that compared these different, strips of data.

And we were able to see that the, the data that the satellite gave about the land,

specifically stuff like spectral resolutions and what was exactly being covered by that land was often inaccurate.

And it would be inaccurate in the way that, let’s say, it would classify something that’s grass as trees that were nearby wouldn’t be accurate

enough to see that, hey, this is a patch of grass over here in the midst of a forest.

Sometimes even, like, you know, shadows that would be picked up, it just wouldn’t give any information.

Or maybe it might even say was like water because of the darkness. It compared with other, water bodies to those, shadows.

Yeah.

Coming Together

tell me how this whole process, sort of came together and how you all started working and collaborating together.

Sure. So the NASA SEES internship program as a whole is open to high school juniors and seniors from across the country.

So we had had the ability to select teams we were interested in, and we ended up getting sorted into those as we got accepted into the program.

So we were part of a 50 person team called Earth System Explorers that met virtually over two months. June to July 2025.

We had a variety of mentors, including rusty, Peter and all the scientists who, you know, and since our main goal was to compare land cover data that we recorded

using the GLOBE, the GLOBE Observer application in our own community, and comparing that to data existing in ArcGIS

and manual, data that we’ve classified in Collect Earth Online.

We, a lot of groups were deciding what to do with that data that we had generated throughout the internship,

since we had like 37 different points within a three kilometer grid in our own communities. So a lot of people wanted to analyze the data that they had generated.

But we took a closer look at our, data quality itself. And then once we saw what a big issue that data quality was and how Jordan lost

such a significant number of the photos that he had taken, we realized that GLOBE Observer had been a very valuable, tool for the time that we were using it.

But it had, you know, there were challenges that were beginning to surface, especially since it hadn’t been

updated as frequently. And we thought we wanted to combine the benefit of the bird’s eye view that a satellite gives with the increased granularity

you would get just going by, manually taking the photos. So we wanted to, take a more unconventional hardware

based approach to doing this. And it was definitely a challenge because we’re all across the country.

So, it was a group decision to go all in on this one leading up to AGU.

Combining Hardware & Citizen Science

what interested you all about combining drones, remote sensing and citizen science all together into one package?

the technologies that have become accessible to, you know, the average person have, improved greatly.

I think, you know, these STELLA sensors and the ability to build a drone in your garage and access to AI tools is something that’s new.

And so why not take a new approach to, land observation, Earth observation.

Yeah, that hits the nail on the head. So for me, I just, we remember how initially we,

I think when we were working with our working group to see what we could take the project further with.

We definitely wanted to keep citizen science in mind, because that was kind of the core value that was ingrained with NASA GLOBE

and, you know, working after the NASA SEES project where we were so deeply engrained with land cover

and wanting to revolutionize that sort of sector, we decided that we had like a few different options.

And I remember Jordan had this great computational idea. And Nandini brought to, a hardware based idea where we had,

like, instruments like STELLA and then we looked at each other and we were like, wait a minute. We can kind of combine both of these to make like an intelligent hardware

based system that uses this type of instrumentation to solve the problems that we need to.

Building the Drone – Hardware Development

what was your experience with, like, say, drone technology, remote sensing and or the, STELLA instruments themselves?

Sure. I’d like to talk about that from from, like, a hardware perspective. I had never built a, never built a drone before.

I actually, my, mechanical engineering related experience, even before the internship program, I had done a research project,

working on a martian simulation chamber in my basement with a friend of mine. And that, you know, made me very familiar with that low cost optimizing,

being responsible for basically every type of engineering in one project kind of experience.

So from there, I, we were able to, I researched how, I guess these kinds of drones have been built in the past.

I visited my local hobby store to learn how to mount the motors. Right. And, you know, try to use online resources and my own community

to try to make this hardware as accurate as possible. And for example, we were we ourselves were not familiar with STELLA tools beforehand.

And I personally also had never used a spectrometer board. Actually, we started using STELLA because it gave us a place to start

exploring this. The first step, the very first step we took hardware wise, was ordering every part

that is required for the Helio-STELLA and the STELLA-Q2 designs. Attaching the circuitry, making sure it worked

on the SparkFun boards that it’s designed for. And then once I realized we needed to integrate that

into a Raspberry Pi system to record that microSD card and be part of the transferring to Jordan’s app system,

we were able to slowly strip away components like the real time clock didn’t need to be needed anymore.

The, of course it’s slipping my mind right now, but there’s displays, there’s much, many components we were able to eliminate

to make it to a more barebones style model that hopefully any, lab worker or hobbyist could build in their basement for a,

reasonable sum of money. Yeah, I think the cool part of it is none of us came in

with any significant experience directly related to this. And I think in, in a, in a cool way,

that’s an advantage on the citizen science end of things. Because if we can make something that is intuitive,

then it can be widely used. Yeah. And just focusing on like even though we were so inexperienced

with hardware like this, I think one of the beauties of science and in specific here with STELLA was that there’s like this community

around people that know what they’re doing. And for example, we were able to reach out to grad students all the way out in Ohio, and they were able to help us

because they were like STELLA experts, and we were able to use their help. And that kind of brought us forward to a stage where we wouldn’t

have been able to without them. Oh yeah. And of course, of course. It all ties back to you know, the, the SEES program giving us

this opportunity to explore what we probably wouldn’t have otherwise known,

if it wasn’t actually for the SEES program.

Graduate Student Mentorship & Support

Rusty connected us, with two graduate students at the University of Toledo, who go by Femi and Feisal,

who, they work under a professor that she knew very closely. And they were both very helpful in two different ways.

So for example, Faisal was able to help verify, our technical planning,

while Femi was very helpful in making sure that we knew the, just generally how to plan a project like this, because it’s

such a big undertaking and especially considering we’re for high schoolers, trying to figure it out because, I mean, in a way,

we are the citizen scientists trying to put together this kind of a system. So I think having so much support on that side from,

not just the scientists that we directly worked with in the SEES program, but scientists, we were able to be connected to, during the design process and even after.

Technical Challenges – Raspberry Pi & Flight Systems

were there any other, like, major components of the EarthLens systems that you developed, that you didn’t previously mentioned?

I think, you know, the we did a lot of work,

specifically with how to fly the drone. With Nandini, you can talk more about it, but essentially,

you know, you want it to be, clean user interface, and you don’t want it to be, you know, clunky controls

where you have to type an input for the for the drone drone to go up or down. And so there was a lot of tinkering we had to do on that.

And Nandini you want to talk about that, that’s cool. And it one that feeds right into the next question, which would be like and I figured to go right into there,

what was the sort of, most challenging technical problem you had to solve? And how did you approach it?

So I got to say, it’s like, I think it’s the combination of all the little problems that came in along the way.

Like, for example, we’re using this Raspberry Pi based system. We haven’t used Raspberry Pi’s in the past, past all my experiences

with Arduinos. So I think understanding the Raspberry Pi itself was going to be like a mini computer and required its own monitor

and keyboard, and that kind of stuff was not something I expected. So we ended up working around that so I could run it from

my own computer connected to a hotspot on my phone. So that it can be done literally anywhere, because this was because

we had to do early stages before it gets fully connected to just an app. So like that’s one of the challenges that came in the way our flight controller,

for example, they come in with preloaded software sometimes. And, you know, these things are usually designed

for like the first person view racing drones. And it was definitely hard to see how big of a how big a propeller,

what kind of flight navigation hardware, what kind of, GPS system is required for an actual observation drone.

So we had to take risks like deleting the software already on that, on that flight controller,

like $140 flight controller, and hoping that I downloaded the, the GPS navigation appropriate software.

INAV correctly instead. So I think it was just the combination of, you know, we can very clearly plan

this out and go, okay, step A, step B you know, these are going to be all the steps that we’re going to take.

But there are so many little not necessarily detours but little bumps in the way that I guess

you become more familiar with once you or a small group is responsible for the entirety of a project like that at this scale.

So I think that was definitely the biggest thing to get used to.

Dual Spectrometer System

So yeah. And you were integrating the STELLA-Q2 and the Helio-STELLA spectrometers into your platform.

And you also hinted of this, but, what did you learn from that process and why did you do it?

So the reason I think we chose those two in particular was that we we noticed that the GLOBE Observer app was not just land cover.

It also had a cloud cover function too, even though we ourselves had used it. So by we already planned to have a camera facing down and a camera facing up,

which that’s kind of being worked on still, with the multiplexer,

but so we wanted something that was going to measure the upward and downward, both because you wanted to have something, you know, comparing it to.

So it’s not just one data source. So like for example, like I have the actual thing here too.

We designed it using the same cables in the STELLA system. I don’t know if the blur will let you see it. So that we have the triad spectrometer used in the Q2 going down

and giving us more channels and more and more information or data like we would need for that land cover observation.

While we would have, I believe, 8 or 9 channels something, something a smaller number of channels

in the Helio-STELLA one and those two combined. Well, because we were able to do that,

like the daisy chaining of storage with the quick cables. So I through the STELLA system, I learned how to even do that,

because if I just looked at the spectrometer board alone before that, I would be asking myself, where’s the ground?

You know, where’s the ground? Where’s the power? Just working and very like, simple. Like the normal DuPont wire setup.

So I think, especially that STELLA the the guides for STELA online

help me see how intuitive the connection, especially between these two specific ones, would be and how valuable they’d be for implementation.

Yeah, and it’s not just how intuitive the actual hardware is, but also just the data that it spits out.

So like for our initial tests, like using the spectrometers and photos,

we were literally able to create charts of our of our first data collections by just copy and pasting values

from the output into a Google spreadsheet, Google, Google Sheets spreadsheet. So it’s not so, so STELLA.

The STELLA devices, you know, they served our purpose really well of wanting to get, you know, all of the data about light and air

and not just the ground and not just the sky, but how the lighting, essentially mixes with the irradiance between those,

two sources, interact with each other. But also just how easy it is to get the data from the STELLA hardware

to, a format where we can interpret it.

Uncertainty, Data Processing & AI Classification

so you used basically the Helio-STELLA to help normalize the data you were getting from the surface reflectance and all that.

Okay. So sort of instantaneous reflectance. So you just bypassed, the whole irradiance problem itself.

I’m sure you have quite a bit of uncertainty with those, because those are not the most sophisticated sensors ever.

Are you taking a look into those, into the uncertainties as well? So, yeah, there there was a certain amount

of normalization we had to do, for example, the, the, the, the wavelengths that each, of light

that each of the two spectrometers collect are not exactly the same.

They’re only off by a little bit, which means in a graph, you know, we can we can, pretty accurately compare them.

But there is that small contingency you have to deal with. And then, you know, like you said, it’s we’re not working with,

you know, massive, you know, super accurate spectrometers. So it really is the other thing that we’re working on with

the drone is how we can collect the maximum amount of metadata, how high we are, how fast or moving

temperature, time date and all of those things

combined to kind of account for anything that might not make sense. And so you get a much better picture by using the other aspect,

the other pieces of data that the drone itself collects to get a better picture of what the STELLA spectrometers are seeing as well.

Yeah. And one of our like, further works that we’ve planned out as well is kind of integrating an AI model into our Sis system.

And what this could do is make use of all of that metadata, as well as the raw spectral data

that we have to kind of eliminate as much as possible those uncertainties.

The Software Application – Real-Time Insights

can you tell me what was involved in developing the AI classification system?

Yeah. So I can go ahead and, take a lead on that. So when it comes to the AI classification,

we’re kind of talking more about the software side, right? So, we’re going through the route of making the app.

So EarthLens, is going to be an app, that works with the sensors and cameras to look at land in real time.

Basically, it’ll take the information like the vegetation, soil and water, and then it will use AI to give you, like, the insights right away.

So, when we were using the GLOBE Observer, we had to, use like these little scales and we had to kind of like guesstimate.

What percentage of the photo was, let’s say water tree or shadow or grass and whatnot.

So instead of waiting to process all that data later, you can see it right when happens.

Right. So, I really wanted it to be easy to use, so you don’t have to, like, be a scientist or an engineer to understand what’s going on.

Of course, that’s kind of the main point of citizen science. Yeah. So it’s meant to work in different places, too, whenever it’s,

farmland or wetlands or even urban areas so that the app can adapt, to different environments.

The coolest part is seeing the data, like, actually turn into something useful, right? So I’ve learned a lot about how the softwares and the sensors

and the AI can work together. Of course, it’s still in the development. Preliminary stages, right. Because it’s not super easy to do that.

But, also figuring out, what’s realistic for a tool like this and what it can actually do.

And what’s better to simplify it, right. So it’s been a fun challenge to make something that’s powerful, but, also very useful for citizen science.

Yeah. And, you know, developing an AI tool is, you know, it’s more doable than ever,

but it really is, kind of a space that AI hasn’t,

fully impacted, I think, in the way that it could. And so, you know, all all of the challenges that come along

with training a model, validating data to train it on, is is, you know, one of the probably one of the biggest challenges,

one that having to face, throughout the course of this project. But we think it’s very much worth it for the end goal.

So that’s one of the things that we’re excited to work on in the future. Yeah. And while we’re on the topic of both challenges and AI, I think

which is really important to note that one of our main goals was to also keep the whole thing really affordable.

And that means making sure that all the computational power that it would take is kept to as little as possible.

So we have to sort of balance how heavy of an AI model we can use without having to make sure that,

oh, there has to be like a big fat computer onboard the drone or something.

NASA Mission Data Comparisons

what particular say NASA missions would you be comparing the data to? If you’re or are you comparing the data to, if any?

I believe right now, in our adopt a pixel, in the adopt a pixel methodology

like chart that we’ve been using so far in this internship. I think we compare Landsat and Sentinel data

specifically to, what we’ve observed in GLOBE. And I remember in our poster, we proposed adding an additional column

with the, with the photos from down, another one for the up photo, and then like a bunch for those graphs individually.

So it could be seen as like an expansion to the currently existing, like comparisons that are being done to Landsat and Sentinel data.

So, so far I was going to say I completely agree that we we had a few conversations

with people at the AGU conference that brought up, similar topics about,

kind of thinking about how EarthLens itself could expand to slightly more than just land cover, observations.

And I think that integrates nicely, comparing a lot of different,

sources of satellite data. And also it sounds like STELLA has a few,

a few more really cool tools that we could work with.

Division of Labor & Team Roles

can you tell me directly, how did you divide responsibilities among your team members, and what was your specific contribution?

So you can go, one at a time, if you don’t mind. And, Sam, sincerity like on screen. Or at least you’re in my widest view here.

I can go ahead and start with yourself, if you don’t mind. Yeah. So, before CES, before this project,

I, really can’t say I’ve had that. I had that much technical experience.

Experience? I’ve learned a lot throughout this project. I do have, some pretty significant experience

working with the Smithsonian and the communication of science.

And so, a lot of what I contributed to this on, on this project was,

kind of a how to, talk about how we are kind

of converting from GLOBE to what we think could be a better system. And then also

kind of trying to optimize for citizen science is sometimes, you know, when you have these grand ideas for projects, it’s easy to let them,

slip away from you or become much bigger or more expensive than you really have the capacity for, than what’s even intended.

And so I have found a lot of joy and, helping to kind of

stay on track towards a citizen science goal. I think in terms of division of labor,

like we were talking about earlier, I definitely had, limited technical experience.

Nandini has had, a good amount, but none of us have had an exorbitant amount of technical experience.

And so we kind of just fell where our interest lied. And, you know, we’re,

you know, it’s a small team, so everybody gets a bit of a little bit of everything anyway, but,

you know, we we all have our unique passions and I’ve worked hard to integrate those into the project overall.

Fantastic. Neeve. Yeah. So, as Sam mentioned, like, I was kind of,

going into that little bit of everything type of flavor. So I personally have experience with both

the hardware and mechanical engineering as well as software and specifically and how like both of them can be integrated.

So I helped sort of design and plan how and what the drone could do,

as well as how we would integrate that with some specific systems

that our AI and software models could use to make sure that the entire system

solves the problems that we want to, while making sure that everything’s reliable and affordable.

Fantastic. Nandini Yeah. So,

I definitely agree with Sam, and you’ve talked about how we kind of did go where our interests, lay in the very beginning.

We started our working group is actually about ten person group. And, the very first thing that I had to do

so that we could get some progress in deciding what projects we would do was, you know, introduce ourselves and introduce what we like to do in a technical standpoint.

So we had a little Google document with a list of all that and all the ideas. And that’s kind of how we split which people went to do our friends,

which people went to do a different AI based project and that kind of stuff. I would say in terms of vision and division of labor,

I always liked the hands on building component the most. I, I mean, I was I’m grateful my parents have allowed me

to take over my half my basement and, build, I guess whatever I need.

I used to do a lot more art down there, but I end up switching it into more of a little basement lab setup. So,

and, you know, I want to go into mechanical engineering. The physical hands on stuff is, what, my favorite part of it.

So that’s why I decided to build the drone itself. And, also, like, I have familiarity with being up at

like 2 or 3 in the morning and saying, guys, this thing’s finally work. So I’m sure Sam, Jordan and Neev remember the, every so often, like a 1

or 2 minute video of like, demonstrating something, working in that kind of stuff. So I mean, I feel like it, I felt what would be the highlights of my day,

I’m very happy to hear that it’s it’s so. Yeah. Fantastic.

And then Jordan. Yeah. So, so before EarthLens. Right. And I also have to say Nandini. Yes.

I did love watching those videos. But, before working on EarthLens, you know, I also didn’t have much

hands on experience with a project at this caliber. So I know I had done some programing and,

but of course, again, it wasnever at the extent that we did it with this project with EarthLens.

Right. So, specifically my role in EarthLens was really focused on the application itself.

So, I worked in designing how the app processes data from the sensors in the cameras and, you know, turns it into real time insights.

About land cover, of course, going back to things like vegetation and, soil and water, we also tested a lot of troubleshooting

to make sure the system was reliable and, accurate. So, yeah, I have to say,

I think the thing we’re most proud of was how we all grew in the process. So, from almost no experience to a project

that caught a lot of attention at that, conference. Right. And it taught us, you know, how to build

from nothing to making such, we took a big stride, I have to say.

Building on a Mature Program

What do you think GLOBE added to the project? I’d honestly say without GLOBE there is no project because that’s what really gave us

the foundation of what already exists and what we want to improve upon. And from that introductory sense, I think GLOBE is phenomenal.

And what it does, it’s a very accessible technology, and what it means to do is to get that scientists,

if like they get citizen scientists activated and mobilized in a way to give our actual professional scientists the data that they need.

And what we just saw was the opportunity to make that whole system better and more optimized.

And that is in no way is they saying that GLOBE is not doing what it was meant to do.

Yeah. And I also have to say, you know, science is in science without problems, right? So of course, using the GLOBE, it was a really strong introduction

to remote sensing. I personally had no idea what remote sensing was.

And then we, of course, we got into the SEES program. We got accepted into the Earth systems explorers.

And they taught us about, you know, the GLOBE app. And working working with the GLOBE app taught us, you know, to collect consistent and accurate data.

And of course, speaking from my experience, doing all that data aggregation and all that, it took close to eight hours in total.

And then I took all my photos and I was praying that they all, you know, saved to the database.

It was 216 photos I took. I lost 202 with the GLOBE Observer app.

But of course, it again, it is phenomenal at what it does. But it was just a little bit of a misstep.

Gotcha. Yeah, yeah. And it it showed, you know, it got your hands dirty.

You had to go out and do the field work and you had to do the ground truthing. And it was a, it was a

messy process of how to, you know, compare what you’re seeing versus what sensors orbiting the Earth are telling you.

And it’s great. You know, highlighting that friction.

We just think, you know, there the there’s a system that could improve upon what GLOBE has already been able

to do for citizen scientists that we can pursue.

Skills Development & Teamwork

are there any other skills that you would like to mention that you developed through the project that you haven’t already mentioned?

I’d say personally, for me, one of the main soft skills that we were able to build was definitely like working with a team.

I think this was a big emphasis that our mentors at the SEES internship had given us, and they given us resources

like their one team building module and how to do research in teams. And I feel like

it was definitely really important for us being spread out all across the country. But the fact that we were still able to be so productive and have that sort of bond

in that connection between us to make sure that we were all on the same page. You know, those videos that I was sending, like, that’s the type of stuff

that makes us all know what’s going on and be, like, happy with what’s going on and make sure that we’re all communicating perfectly.

Yeah. And I’d say also the skills to take what’s happening internally

in our team and be able to communicate that externally. So from, you know,

Nandini building the drone and Jordan building the app. And so collaborating on,

you know, how to quantify, results from the, the research we did with GLOBE and comparing what the drone gives us.

Like that’s all the technical stuff that that goes on inside. And then being able to

put that all down on a poster in an abstract or in a paper and put that out into the world for people to get an idea of what we’re working on

is it’s it’s a lot of abstraction. And that is a skill that we’ve definitely developed through this project.

Agricultural Applications

how do you see EarthLens being used, say, for agricultural monitoring or environmental research?

How would you like to see it use as well? I mean, I would say the way I envision EarthLens,

especially in these earlier stages right now, is, we plan to put together, I’ve started drafting this, a guide similar to the inspired by the STELLA

Guide of components of how to build each, one of these drone systems and all that kind of stuff.

I mean, now, since each year we’ve started communicating more with researchers across the country who have expressed interest in it,

and we hope by getting it into a larger audience like that, continuing that integration with STELLA, we could have it be something

that a hobbyist or classroom, or that kind of stuff. People could actually start building these and using them in their own

communities, kind of like, kind of like how we use GLOBE. But I think the benefit of it being a drone is that as we adapt it,

to be able to fly higher and have a bigger field of view, it’ll be useful for maybe if there needs to be a weekly observation

of certain fields or agricultural areas, like you were saying with the coasts earlier and that kind of stuff.

I think it could be useful. Starting from this research perspective and continuing

from seeing like environmental stability and things like that. So, yeah, yeah, just

just to add to that, you know, you hit on the agricultural piece a bit, but the the sheer advances in technology that have,

occurred in agriculture over the last few decades are amazing. And it’s what allows us to feed, you know, the,

the modern population of the Earth and a growing population of the Earth.

That’s really important. And technology is one of the main ways we’re going to be able to continue to do that.

The way I envision EarthLens kind of being able to do that is right now you have, you know, you can survey your land to to an extent

and you can use the satellite data that’s made available to farmers to, you know, inform

the parts of land that tractors go over to fertilize or the different seeds that are planted on different portions of land.

And those are all based on satellite data as it is now. And, you know, in the research we did in the seed

program, comparing ground data to satellite derived data, we saw that there are inaccuracies in that seasonal inaccuracies,

spatial inaccuracy, inaccuracies, temporal inaccuracies. And if you have if you have something like EarthLens that’s small,

that’s modular, that’s easily deployable, that’s personalized and easy to use. But that still turns out quality data.

More immediately, you can, you know, imagine a farmer launching a few EarthLens drones and having them fly over their field

like Nandini said once or twice a week and get maybe even a more detailed image of their land than they would for modern satellite, data.

And so there’s a I really like this question about how to use EarthLens or similar technologies in agriculture.

building off of that, have you, played around with any vegetation indices since you’ve been, doing, or at least thinking about,

like, plants and agriculture and land use and all that? We’re not fully there yet.

Right now for this first iteration. Right now. I mean, right now we’re working on more mechanical challenges. Like, for example, a big thing is that the light bulb battery

for example, to power this is pretty heavy. It’s pretty heavy. And it’s time. And, right now, our maximum estimated flight time,

I’ve been working towards is probably about 20 to 30 minutes a piece right now. Now, we ought to work towards extending that or making

that battery system more interchangeable. And then in that next level we would integrate, though still, like the air quality systems and things like that.

Thankfully, our flight controller gives us things like accelerometer and, you know, other specific motion and gyroscopic related data.

But I think as we keep adding on different layers, we’re going to start, you know, maybe seeing if we can train it like, as Jordan,

you were saying earlier in the AI standpoint towards data sets with healthier or non healthier vegetation,

knowing what to flag in an image and that kind of stuff. Yeah. And I have to say, I also I really like the question

when you touch based on the, agricultural side. So I am from the Rio Grande Valley and, Texas, and it’s a region

where agriculture plays a very important role, of course, not just to the economy, but to the community as a whole.

So I’ve seen firsthand how much farmers rely on knowledge of the land, you know, the weather, the crop cycle to make, these decisions every day.

And that’s, of course, where we see EarthLens making that difference, because it can collect data from sensors

and, you know, cameras and processes, as, Sam had explained. So you know, coming from the region so connected to agriculture,

EarthLens has been exciting, of course, because it can, really improve this, agri agriculture that we have here in the Valley.

Comparing with NASA Satellite Data

Are you inspired by any of the, different, NASA missions and Earth science that you’ve, you know, sort of, probed at?

And then which ones have you really taken a look at? Is it just Landsat or are there others out there as well?

And if you are doing Landsat, you know what? What might have inspired you with Landsat as well. So go ahead.

Yeah. So we’ve we’ve worked with quite a few different earth science projects, I think from the original data sets

we were comparing our ground truth data to, we were pulling from Landsat. We were pulling from,

a few different data sets, with Dynamic World and, yes, array and world cover and,

a few different tree canopy data sets.

And then we also, you know, again, worked with Landsat time series data, which was cool to interact with.

I for me personally, I wouldn’t say there’s one specific, Earth science project, that I’ve been,

you know, singularly inspired, but inspired by the we did hear from one of the main engineers who’s working on the NISAR mission,

which is a very newly launched telescope that uses some really cool technologies to get information you never seen before.

But I think in, in when you take them all together and you get, you know,

a more complete image of Earth systems and science on Earth, that’s kind of the part that truly, has inspired me.

You know, the goal of NASA to continue that kind of research, I personally feel is, is very valuable.

And so that’s kind of what’s inspired me throughout all of this. for it. Now the only thing right now least existing, the only thing we’re

comparing our global observer data to is that kind of Landsat and Sentinel data. And as things continue to change, I mean, having satellites that are is in the hand

of, labs on their own or hobbyists and that kind of stuff. Still being able to contribute some to some sort of shared thing,

maybe like an ArcGIS layer or something like that gives us, you know, another thing to verify against.

And I think that’s, definitely a motivating factor to keep using that

in conjunction with the satellites while we, you know, while we can and, allowing that to be a technology that continues to evolve

alongside it. what would you like to develop further if you could continue this project?

Future Developments

And do you have plans to. Yeah. So I’d say, on top of, like, that whole AI and automatic classification goals

that we have where we wouldn’t have to manually classify each pixel of the data that our drone takes, the AI would take care of that.

On top of that, we also have an idea of perhaps adding intelligence to the flight patterns of the drone So in a way,

making it an automated flight, which would make the entire, system a lot more efficient and make it easier for people like average

Joe’s to be able to use the entire system. And I’d say as as EarthLens as a whole, like, scales up,

there’s a lot of potential for it to integrate with external platforms that already provide data sets and have this sort of connected

infrastructure, which would just make the data that EarthLens gets. And people using EarthLens have been taking and putting it out there

for a lot more people to use, and having a lot more eyes on that same data.

Yeah, I definitely think that’s a very great way to keep progressing it. And I think from the hardware perspective, I think, like I was saying before,

integrating that carbon dioxide centered, sensor and integrating some of the things that are being added in some of the new STELLA, variations

would be something that we plan on implementing back into drone and automating flight patterns is actually, the reason why we switched to that idea

of, flight software. So that’s the easiest way to go about that in the future. So I think it’s definitely, good that we’re starting to explore

these possibilities of what we’re going to do in the future now, so we can kind of make those steps as easy as possible, like starting

from where we are today. But we definitely all have plans to keep continuing this project, at least for the next couple of years and that kind of stuff, because now

we see how valuable a continually updating or continually improving, application or system like this can be. So

yeah. And then any, any kind of tapped on it but earlier.

But the end goal is the citizen science access and the whole point of making tools

available for average people to collect scientific data is to make that

data available to researchers who, you know, we’ll know what to do with it, know how to draw conclusions.

And so we hope so. Also continue work with EarthLens in terms of,

how to get that data out to researchers and how to allow users of EarthLens to make their data accessible.

The app is the main way we want to do that, but also we hope to maybe one day have enough data to make a meaningful,

geographic data layer on some other software like ArcGIS or something like that.

Experience Influencing Future Pathways

how does this experience influenced your career interests or academic plans? So, like I said

earlier, the, the SEES program, you know, it was my first real kind of technical and scientific experience.

And I definitely want to continue that,

I want to study engineering in college, and I think I’m going to try and continue with that. I’m planning on going to a school

that allows you to do a lot of interdisciplinary study. And, you know, being involved in EarthLens is also,

generating a lot more curiosity for me about Earth science and, kind of the associated

technologies and space that, help us conduct our science. So I’m really excited to,

continue in that sort of direction. Yeah. For me, I’d say the process of developing EarthLens

and the SEES internship as a whole, it’s kind of me, made me firm that I want to keep being like this sort of inventor.

I’d say the fact that we were able to design and create something that didn’t exist before and this like, completely our brainchild, is so cool.

And that kind of motivates me to keep on doing that sort of tinkering, that sort of problem solving.

And especially in this, like using this interdisciplinary nature, but making sure that we’re grounded in solving real world environmental issues.

Really beautiful. Yeah, I would say for me, it’s made me more solid in my decision to pursue mechanical engineering for my undergraduate degree.

But I because, you know, I’ve I’ve had such a wonderful time, integrating this building with so many different other aspects of it.

And, like Sam, I mean, I think it’s made me a much more interdisciplinary builder, considering my prior experience is more aerospace connected.

I think that exploring Earth science and more applications here, it’s made me more willing to explore,

all different things under NASA and scientific purview. And, I mean, I plan to continue studying, hopefully

in grad school, neither aerospace or nuclear engineering. And I feel that EarthLens and this experience in Earth science

has given me, the willingness to keep exploring in that regard,

you know, and keep seeing which type of engineering is close to me at that point. Yeah. So, I totally agree with what everyone else has said.

I am a junior, so I still have, you know, a year to think about, at least a year to think about it, but not a lot of time.

Right. But working on EarthLens, you know, through NASA SEES you know, really shaped my interest in what I do want to become and that is, aerospace engineer.

So before this, I hadn’t, of course, I have already said, I hadn’t had much experience with projects at this scale.

And, you know, SEES exposed me to that. You know, the real world engineering challenges such as, you know,

collecting aggregation and analyzing, complex environmental data, you know, designing systems to process that data efficiently, too.

And, making decisions based on the real insights. Right. So I think the experience also taught me how to approach problems method.

With the method. Right. So to adapt things that didn’t go as planned and to think critically about, you know, how technology,

can be applied to solve, big real world problems. Right? So, participating in SEES influenced my goal of going to UT as well.

UT Austin. It made me realize what, I want to be in an environment where I can work,

you know, with great ambitions, you know, impactful projects, and continuing to grow as a thinker and, of course, a problem

solver. what was the most rewarding aspect of working on this project?

Most Rewarding Aspects & Takeaways

You know, like, what do you hope people take away, from learning about EarthLens? And is there basically anything else about the project that you’d like to know?

So, some sort of combination of those three?

I think, you know, these have been pretty comprehensive questions. You talked a lot about what EarthLens is for and how we hope it gets used.

If, you know, if you if if, you know, God forbid, the projectnever gets finished

or if younever get your hands on an EarthLens drone.

The thing I would hope that people learning about it take away is just that, these things

don’t have to be, stagnant. The way we study Earth can evolve with technology.

And I think it should, because, you know, we have better ways to collect data,

better ways to interpret that data, better pipelines for that data and more data

and all of that in in together just means we get a better picture of our Earth.

How it’s changing. There’s a lot of ways that influences policy economics.

And, you know, just in general, we hope EarthLens gets people excited

about studying the Earth for change. Yeah, I’m 100% agree with that.

And I would say, I think a big thing that I guess anyone, especially maybe high schoolers or college students or people newer to these kinds of things, can,

I guess, learn, is that, I mean, we all came with relatively small amounts of experience in here and kind of used whatever we learned through this.

Internship program to formulate this kind of mission. And it felt it felt nice to kind of, I guess, like,

as you said, to be a group of, you know, inventors in this regard. And something we weren’t as familiar with.

So I think it hopefully should encourage to keep learning and I guess keep tinkering about this kind of stuff, considering, you know,

I mean, you can you can build a drone that can do these kinds of things in your own basement. You know, there’s there is more capability to do these kinds of things

outside of the traditional laboratory or, environment.

That’s typically seen for more professionals or more for adults. Yeah, I’d say personally for me, touching on like that

most rewarding part, I’d say some of the most rewarding, like emotions that I felt were when we were all on call

and we’d have like, we’d be throwing out different ideas and then as soon as we’d hear one, then we think of one that stuck.

It’d be so rewarding just to feel like, hey, you know, I can see this. I can envision this actually coming into existence.

And I’d say one of the beauties of like, the whole process of EarthLens in general

is that in a way, it has like endless possibilities, right? Like so far, how we’ve thought about it.

We have like a few different ideas that you can go into use here. You can go to use there.

But at the end of the day, we’re still high schoolers. And that’s one of the goals we have with reaching out to experts in the area

like Michael Taylor, like people at NASA, at like for over at different universities,

because they’re going to give us so many new insights about where we could take this, what we could add and how we could keep building.

And for me personally, right, I think the most rewarding experience of working on this project was,

seeing how we could develop, what we could develop can actually make a difference. And again, reiterating we’re all just high school students, right?

I mean, we made this project from scraps, right? So one thing that really excited me,

of course, was also the power of citizen science and how a tool like this project can let anyone contribute to,

you know, collecting data and understanding environmental data. So it’s amazing, I think, that this project could really connect

communities, you know, students and researchers across the country. In terms of goals for EarthLens, I really hope it continues to expand.

You know, I think we all really think we’re just gonna, you know, keep driving, right? Of course, we’re from all literally all four corners of the United States.

Right. So, sometimes there’s a little bit of a challenge in getting, communications,

but we work it out with our, with our, time zones and stuff like that. Right. But, I also wanted to take a moment to, thank NASA SEES right.

And of course, you, Michael Taylor, for allowing us to, present this project.

Yeah, it’s it’s been an amazing opportunity. You know, being part of that program was a huge learning experience.

And, it inspired all of us to keep exploring, right? Building and contributing to projects that, you know, have meaningful real

world applications.

NASA SEES Students Showcase EarthLens-STELLA Integration at AGU 2025

Michael Taylor — Sun, 04 Jan 2026 01:25:19 +0000

NASA SEES Students Showcase EarthLens
-STELLA Integration at AGU 2025

Advanced Drone Platform Combines Dual STELLA Insruments (Q2 Spectrometer & Helio) and AI Analysis for Enhanced Land Cover Monitoring

NASA STEM Enhancement in Earth Sciences (SEES) interns Nandini Khaneja, Neev Tamboli, Samuel Bawden, and Jordan Rodriguez, part of the SEES Earth System Explorers Team sponsored by NESEC and the Institute for Global Environmental Strategies presented their innovative EarthLens project at the American Geophysical Union (AGU) Fall Meeting 2025, demonstrating exceptional technical achievement in developing a drone-and-app system that addresses limitations in existing citizen science tools.

Student Innovation and Workforce Development

The EarthLens team exemplifies the caliber of innovation emerging from NASA’s SEES program, successfully integrating complex systems across drone engineering, spectral sensing, mobile app development, and AI analysis. Their ability to identify real-world problems in platforms like NASA’s GLOBE Observer app and develop sophisticated solutions showcases the high-level technical skills being developed through NASA’s workforce development initiatives.

Understanding Why NASA Maintains the Cutting Edge

Working with EarthLens provides firsthand experience with sensor limitations that highlight why NASA must stay at the very cutting edge of science, engineering, and technology. The system uses two STELLA spectrometers – an AS7341 8-channel (415-680 nm) and AS7265x 18-channel (410-940 nm) – that reveal measurement variability, calibration challenges, and atmospheric interference effects.

By confronting these uncertainties with accessible instruments, users develop appreciation for why NASA missions require years of development, extensive calibration protocols, and sophisticated technology. This hands-on experience demonstrates why precision satellite sensors like Landsat and PACE represent the pinnacle of remote sensing engineering and why NASA must continue pushing technological boundaries to maintain scientific leadership.

Technical Innovation and Problem Solving

EarthLens addresses significant challenges identified in current citizen science platforms. Research revealed that 63.2% of users took more than 9 hours to complete land cover observations using traditional GLOBE tools, often losing data or struggling with poor image quality. The team’s solution combines:

Drone platform with dual STELLA spectrometers for comprehensive spectral monitoring
High-resolution Raspberry Pi cameras (3280 x 2464 pixels)
Mobile app with real-time drone feeds, AI-powered land cover analysis, and social features
GPS-accurate data collection that prevents the data loss plaguing existing platforms

Building on Research Foundation

The project extends work by Russanne Low, Peder Nelson, Cassie Soeffing, Andrew Clark, and the SEES 2020 Mosquito Mappers Research Team, whose 2021 study demonstrated connecting ground observations with satellite imagery. EarthLens advances this framework through drone-based data collection that bridges the gap between citizen science and professional Earth observation.

Advancing Science and Technology Leadership

The system incorporates AI for automated land cover classification while enabling users to learn remote sensing principles used in NASA missions. Though STELLA and EarthLens aren’t peer-reviewed research instruments, they provide valuable experience with measurement challenges that drive NASA’s commitment to advancing calibration methods and developing more sophisticated technology.

EarthLens demonstrates how understanding sensor limitations motivates continuous advancement in Earth observation capabilities. NASA’s investment in precision-calibrated missions and cutting-edge engineering ensures American leadership in space-based science and technology, maintaining the nation’s competitive advantage in understanding our planet through the most advanced remote sensing systems ever developed.

References

Nandini Khaneja, Neev Tamboli, Jordan Rodriguez, et al. EarthLens: A Novel Drone-based Citizen Science Tool for Land Cover Observation and Analysis. ESS Open Archive . December 26, 2025.
DOI: 10.22541/essoar.176677108.80716415/v1

Low RD, Nelson PV, Soeffing C, Clark A and SEES 2020 Mosquito Mappers Research Team (2021) Adopt a Pixel 3 km: A Multiscale Data Set Linking Remotely Sensed Land Cover Imagery With Field Based Citizen Science Observation. Front. Clim. 3:658063. doi: 10.3389/fclim.2021.658063

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AAES Brings NASA’s STELLA Remote Sensing to Virginia’s Eastern Shore

Michael Taylor — Thu, 11 Dec 2025 01:18:00 +0000

AAES Brings NASA’s STELLA Remote Sensing
to Virginia’s Eastern Shore

Story by Rachel White AAES

The Aerospace Academy of the Eastern Shore (AAES)—a STEM-focused lab school led by Old Dominion University in partnership with Accomack County Public Schools, Northampton County Public Schools, and Eastern Shore Community College—is expanding hands-on aerospace learning through remote sensing using NASA’s STELLA (Science and Technology Education for Land/Life Assessment) instruments. STELLA is an open-source, low-cost suite of DIY instruments (including student-build spectrometers) that introduces learners to the same Earth-observation concepts used in NASA missions—connecting local measurements to satellite-based remote sensing and analysis.

Most recently, AAES ninth-grade students built a STELLA-Q2 device in teams and collected preliminary spectral data. The STELLA-Q2 is designed for classroom accessibility (connector-based, no soldering) and includes multiple spectrometer sensors that measure irradiance in the visible to near-infrared range, making it an ideal entry point for students new to scientific instrumentation and data collection. Students celebrated the moment their devices “came to life,” and the build process intentionally reinforced productive struggle—requiring careful assembly, debugging, and iteration as they learned to trust both the engineering process and the data.

AAES students using STELLA-Q2 spectrometer to understand field of view (FOV).

AAES students using STELLA-Q2 spectrometer collecting 3 spectral data timelines for comparison.

AAES students using STELLA-Q2 spectrometer to understand field of view (FOV).

Looking ahead, AAES plans to scale STELLA across the full four-year program as a student-built toolset for authentic, place-based investigations on the Eastern Shore—where coastal ecosystems, agriculture, and NASA Wallops’ aerospace context create a powerful living laboratory. Over time, students will use STELLA to connect field measurements to NASA Earth observation datasets (e.g., Landsat) and apply remote sensing to real questions, building the quantitative, technical, and analytical skills that translate directly to aerospace and geospatial careers.

Planned applications at AAES include:

Coastal & wetland monitoring: vegetation health and change detection in marsh systems
Agriculture & land management: crop/plant reflectance patterns and seasonal trends
Student-designed investigations: hypothesis-driven field studies that pair STELLA readings with satellite imagery and mapped results
Future expansion into aerial data collection: exploring how lightweight STELLA instruments can support UAV-enabled investigations as students’ skills mature

Rachel White, Joy Phillips and Mike Taylor at AAES

AAES instructors and visitors building STELLA-Q2s.

AAES students building STELLA-Q2s and receiving some guidance from Mike Taylor.

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Petya in the Field

Michael Taylor — Wed, 15 Oct 2025 19:40:00 +0000

Petya in the Field

Video Summary

Watch Dr. Petya Campbell, NASA Goddard Space Flight Center research scientist, lead an agricultural field campaign using STELLA spectrometers and professional measurement protocols. This documentation shows how ground-based data collection supports NASA’s Landsat program and Earth observation missions while training the next generation of remote sensing professionals.

Featured Expert and Workforce Development

Dr. Petya Campbell brings over 20 years of NASA field campaign experience to this training exercise, demonstrating the professional skills and technical protocols required for Earth science careers. With a doctorate in Remote Sensing for Environmental Applications, Dr. Campbell teaches at the University of Maryland Baltimore County while conducting vegetation research at NASA Goddard. University students participate directly in data collection, gaining hands-on experience with the same measurement techniques used to validate NASA satellite data and support Landsat mission applications.

NASA Mission Applications and Landsat Connections

The field campaign demonstrates how ground measurements validate NASA satellite observations, particularly supporting the Landsat program’s agricultural monitoring capabilities. Dr. Campbell explains the process of converting satellite digital numbers into scientific products like chlorophyll content and canopy height, showing students how their field measurements directly contribute to NASA Earth observation science. The team collected data on agricultural crops with varying nitrogen treatments, illustrating how NASA remote sensing technology addresses food security and agricultural productivity challenges that inform policy and farming practices.

Professional Field Protocols and Technical Training

Students learn comprehensive field safety procedures, emergency communication protocols, and measurement coordination techniques used in NASA Earth science campaigns worldwide. The video documents proper calibration methods using reference panels, instrument operation procedures, and quality control standards that ensure research-grade data collection. NASA ACRES personnel joined the team for several data collection sessions, providing additional professional expertise and demonstrating interagency collaboration in Earth science research.

Career Pathway Development

This field campaign provides students with direct exposure to NASA Earth science career requirements while building technical competencies in remote sensing and agricultural applications. Students operate STELLA spectrometers alongside professional instruments, learning measurement protocols that prepare them for NASA workforce opportunities. The integration of university research with NASA mission objectives demonstrates clear pathways from academic training to professional Earth observation careers, supporting NASA’s need for skilled remote sensing professionals.

Transcript

NASA Earth Science Field Campaign Transcript – Dr. Petya Campbell

Section 1: Field Safety and Emergency Preparedness (0:05-3:14)

Field safety includes a list of everyone in the party, where you’re going, and who’s the emergency contact that’s not with you. We designate a communications point person who has an InReach for satellite text messaging because we’re going to be in places without cell coverage. Actually, we’re pretty much out of cell coverage here at Patuxent, but seriously, when you’re on the North Slope of Alaska, you want to have some way of getting information out.

I go through with everyone on the team how the communication device works, how to turn it on, how to set off the alarm. If I’m incapacitated, then somebody else can use it. We identify a health response person – first aid or first responder. As we introduced ourselves, we found out that Caitlyn has the highest level of medical training among the group, and she has a very complete first aid kit. If somebody has been bitten by a bear or whatever – I’m serious – she has the tourniquet and bleed stop kit and everything. Liz and I have the second level of medical training, so if Caitlyn is incapacitated, we’re prepared to handle that.

One of the really important things is driving. I always check – Caitlyn, you drove the Suburban up the haul road with this team in it. Where are the keys? We identify a second in command like Liz, who knows how to drive a Suburban on the haul road, knows all the safety procedures and CB radio calls. We document all this with a field pencil.

Would you bring a second one for redundancy?

Absolutely. One is none and two is one. We would have at least two complete first aid kits.

Section 2: Field Campaign Planning and Instrumentation (3:26-8:39)

We’re going to use ASD and STELLA to look at pigments. For instruments we need STELLA – which STELLA are we going to use? I want to use a 1.2 and a Q2. So we have those two, plus ASD and chlorophyll meters. Ideally we want to measure leaf area index (LAI), but we’ll put that as TBD for now.

Who’s going to measure with STELLA? Mike will set up the STELLAs and prepare them. Jesse will set up the ASD and panels. We need tripods for the panels. Let’s meet directly at OPE at 9 AM. I’ll go earlier to mark the five sites with flags, separating three measurement points so we can assemble the data properly.

This is why we do the panel measurement sandwich – STELLA measures irradiance, not digital numbers. We do this because we want reflectance. Reflectance is a ratio between the panel measurement and the measurement from the target. We do multiple measurements because the sun is changing position – at 9 AM the sun is here, but at 9:30 it moves up. We need to know the incoming solar radiation at the time of measurement. If you take too long after the panel measurement, you need to take another panel reading.

Section 3: Field Preparation and Experience (8:44-12:03)

For longer campaigns, we’d prepare food. Everyone comes with covered toes – no sandals and flip-flops. I’ve had students show up in shorts and flip-flops, and there are ticks. Socks need to go over the pants so ticks climb up, then when you go home, you check carefully and remove everything. You need hats, water, sunscreen.

Interviewer: How many field campaigns have you been on?

I’m a forester by background. I started in college, then worked for the Ministry for Environmental Protection in Bulgaria, surveying protected areas and looking at plants and animals. I did my dissertation in the Czech Republic doing forest health surveys. I have a doctorate in Remote Sensing for Environmental Applications. I teach at the University of Maryland Baltimore County and do research here at NASA Goddard on vegetation function and health. That’s what makes me happy and drives me in science. I started at Goddard in 2000.

Section 4: Site Selection and Agricultural Context (12:03-17:05)

What makes a good spot to measure? We want it representative of the field. Look at the field out there – we want to measure spots that represent the field. If you turn to the other field, you’ll see it’s nothing like this field. They’re very different. We’ll measure three repetitions here because some areas are denser, some are not, some have more soil cover.

Why are we taking three measurements at each spot? If we have three measurements and some are not good and representative, we can get rid of them. These are soybeans. This field has nitrogen treatments – they fertilize with different amounts: 0, 25, 50, 100. They know what’s optimal for this soil, water, and natural nitrogen content for corn to grow. This area looks like 150 nitrogen treatment because it’s very tall. Over there the corn is smaller and yellowish because the nitrogen is low.

What we do is create equations for satellite products. Satellites get zeros and ones – digital numbers. We need to make it into products like chlorophyll or amount of water in leaves or height of canopy. People need to measure on the ground and make equations to derive the product and make the maps.

Section 5: Technical Measurement Coordination (17:05-25:07)

We’ll assemble the data in files. Make sure Mike tells you the file name for STELLA data. Going from irradiance to radiance is non-trivial – you need distance and field of view for that calculation. But Digital Numbers to radiance is a simple multiplier table. The ASD has a different sensor than STELLA, so the amount of photons it collects is different, but the curves should be the same because they’re relative. That’s why reflectance is always going to be the same.

We’ll coordinate measurements carefully. Before every plot, we put instruments over the white spectralon panel to get the baseline 100% number for reflectance calculations. The measurement sequence is important because the sun is rising and we’ll have differing values. We need the same order of operations between instruments. I suggest people with Q2s first, then the 1.2 last because it takes longer to cycle.

When I take the first data point in the plot, the next person should be over the panel. We want one data point per panel on STELLAs because they take much longer – 20 measurements each.

Section 6: Active Field Data Collection (25:07-33:13)

We’re taking chlorophyll measurements to understand if STELLA can provide information relevant to chlorophyll. Don’t shade the white panel when taking data. If we shade part of it, it’s not getting 100% sunlight like the plants. The white panel is essentially 100% reflective. This is a perfect day because there are no clouds and minimal haze.

Keep the white panel clean without touching it because touching smears things into the porous surface. This is called a Lambertian surface – once light gets in, it reflects in all directions evenly. A pure Lambertian surface reflects at all angles evenly, but nothing is pure Lambertian. This is the closest there is.

Keep your STELLAs at the same height to get similar field of view areas. We want to measure soybeans, not dirt, so position carefully over the plants. We measure the smaller corn because the leaves are less green – they didn’t get as much nitrogen as the others.

This is what I do – this is my work.

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Student Voices: Real Engineering Success with STELLA Instruments

Michael Taylor — Mon, 15 Sep 2025 15:44:54 +0000

Student Voices: Real Engineering Success with STELLA Instruments

Video Summary

When Inna Shapovalenko and Alexa Matson first encountered NASA’s STELLA instruments, neither expected the transformative 10-week journey that lay ahead. Working alongside Alexa Ross and under the mentorship of Dr. Sam Batzli through the WisconsinView program, what began as summer research positions became a powerful example of how hands-on technology can build both confidence and career readiness.

Breaking Down Barriers to NASA Technology

“I always thought that NASA was something so far from me that I maybe could get access to this only after getting a PhD,” reflects Inna, an international student studying physics and computer science. “But this summer I found out that you don’t have to finish your PhD to work with some NASA materials. You can be even a high school kid.”

This ease of access was central to the students’ experience with STELLA (Science and Technology Education for Land/Life Assessment), NASA’s low-cost spectrometer project designed to make remote sensing technology available to learners at all levels.

The AmericaView Connection

The project came about through WisconsinView’s partnership with AmericaView, a nationwide network that empowers Earth observation through remote sensing education. As Dr. Batzli explains, “We’ve learned through AmericaView that one of the best ways to grow curriculum and outreach is by teaching teachers. When we can have summer workshops where we work with teachers, they can take it into their classrooms.”

The Power of Interdisciplinary Learning

Over their 10-week collaboration, Inna and Alexa discovered that STELLA served as what Dr. Sam Batzli calls “a catalyst for learning”—connecting multiple disciplines in ways neither student had anticipated.

“It was so different from week to week… just the variety of things you get to do,” explains Alexa, a mechanical engineering major. “I didn’t realize it till we did our final presentation—just how much stuff we were able to do. And that was so cool, ’cause it was only 10 weeks.”

The project seamlessly integrated spectroscopy, 3D printing, soldering, programming, and data analysis. When Inna brought soldering expertise and Alexa contributed 3D printing skills, they naturally taught each other, demonstrating the collaborative learning that makes STELLA projects so effective.

Real Science, Real Impact

Perhaps the most powerful moment came when the students compared their handheld instrument data with actual Landsat satellite measurements. “When we were comparing our data with Landsat data… I was surprised when our results got really like they were very, very close,” recalls Inna. “I had the feeling like I touched something… something super unusual.”

This connection between classroom technology and space-based science exemplifies STELLA’s mission: providing authentic scientific experiences that build both technical skills and scientific understanding.

Building Confidence Through Authentic Challenges

For Inna, the project became about more than technical skills: “For me, STELLA is about being confident in engineering. You’re working with many fields, and you can combine your programming skills with soldering—everything. Just enjoy the process and don’t be pressured that it’s something super difficult.”

Alexa found particular value in the problem-solving aspects: “It is really cool to just have so much control over that and then also just be able to problem solve yourself and work with a partner and kinda see it applied to the real world.”

Open Source Innovation

The students were particularly impressed by STELLA’s accessibility. “It’s open source. And you can do it even from your home. And it doesn’t require some super expensive equipment. You can just buy it on Digikey or Amazon and build on your own,” notes Inna.

This open-source approach allows access to sophisticated instruments, enabling students worldwide to engage with similar technologies used by NASA researchers.

Looking Forward

Dr. Batzli sees this as just the beginning: “STELLA is almost like a catalyst for learning. It’s like the spokes of a wheel—STELLA can be the hub in the middle, and there are all these topics and possible questions and things to explore that radiate out from that.”

The success of Inna and Alexa’s collaboration demonstrates how authentic, hands-on projects can simultaneously build technical competency, scientific understanding, and career confidence—preparing students for the interdisciplinary challenges of modern STEM careers.

Their story exemplifies STELLA’s broader mission: making NASA technology accessible to learners everywhere while building the critical thinking and problem-solving skills essential for tomorrow’s engineering, manufacturing, and scientific workforce.

Transcript

00:00:02:13 – 00:00:08:13
Unknown
Thank you all for joining me and discussing your project. Could you please tell me a bit more about your background?

00:00:08:13 – 00:00:13:05
Unknown
Yeah. So, I’m international student at, Franklin

00:00:13:05 – 00:00:28:01
Unknown
and Marshall College in Pennsylvania. I’m currently senior doing physics and computer science. And, in my previous summer, I was doing a lot of robotics stuff for my summer internship.

00:00:28:01 – 00:00:53:09
Unknown
And, then I came to the college, and I was. So was doing, robotics. Like, I built my own drone, and, I also built the unmanned surface vessel. So I really wanted to go to robotics. And, I was just exploring for different opportunities. And, I found, this opportunity from Space Science and Engineering Center about, building the STELLA device.

00:00:53:11 – 00:01:12:04
Unknown
And, for me, my goal was to just develop more skills into, like, so different in 3D modeling. So during also, programing of microcontrollers as well. So CSS major and this hub was my first.

00:01:12:04 – 00:01:13:15
Unknown
Next. How about Alexa?

00:01:13:15 – 00:01:40:05
Unknown
Yeah. So I am from Wisconsin. I am a mechanical engineering major with a certificate in computer science. UW Madison. I have a lot of experience, building my own things and building projects. I love anything I can get my hands on. So I’ve, like, built a small robot, I mean Legos.

00:01:40:05 – 00:02:01:06
Unknown
Everything that, I could possibly do. And then on top of that, I’ve had a lot of 3D printing. Both in class and out of class. So when I found this internship, I thought it sounded great because there are just so many skills that I could apply. I could do the hands on of the actual building on the spectrometer.

00:02:01:06 – 00:02:11:12
Unknown
I could do coding with my CS. And then on top of that, I could, actually take measurements and analyze them. So yeah,

00:02:11:12 – 00:02:15:15
Unknown
So I typically do research at Space Science and Engineering Center.

00:02:15:15 – 00:02:45:01
Unknown
I got my master’s degree there and then was lucky enough to stay on as a full time researcher. I love doing research, but sometimes looking at a computer screen for so long can get a little tiring. So I decided to take on some outreach tasks. So that’s how I started teaching this STEM class in collaboration with, the greater University of Wisconsin-Madison.

00:02:45:02 – 00:03:22:01
Unknown
So they have a program called Pre-College programs, where they basically prepare K through 12 students for the college experience. My group was working with high schoolers from all around the world. Actually, we had students from as far as Kazakhstan to here, and Madison and I taught a two week STEM class. So that covers everything STEM. And I thought it would be a great opportunity to test out our newly bought, STELLA spectrometers.

00:03:22:02 – 00:03:51:13
Unknown
So SSC had hired two undergraduate engineering students who came and order, ordered all the STELLA parts, put them together, did testing with them, and then in collaboration, in collaboration with me, we kind of came up with this short little curriculum that lasted maybe one and a half to two hours in the classroom with our high school students, where we did a little hands on lab with the instruments.

00:03:51:13 – 00:04:18:07
Unknown
And, yeah, the students got to actually test them out. So that was really fun. I had gone over some basics of remote sensing with them beforehand, so just basics of like, what is the electromagnetic spectrum and how, you know, things can give off different wavelengths and energies depending on what they consist of. So we tested out the cell spectrometers in class with the high schoolers.

00:04:18:07 – 00:04:28:11
Unknown
The class was 13 students and they had a really good time getting some hands on experience with the instruments.

00:04:28:12 – 00:04:30:04
Unknown
last but not least, Doctor Sam.

00:04:30:04 – 00:04:51:09
Unknown
my my background is really, I’ll, I’ll start with when I got involved in remote sensing, which was at Michigan State University in the year 1999, actually. And, I got interested in Landsat then, and I was working with the, tropical rainforest information Center that had a huge Landsat archive.

00:04:51:09 – 00:05:21:09
Unknown
And so I got involved in kind of managing the archive, working with, Dave Skoll and, yeah, that that was when I got excited about remote sensing. Then I moved to Madison to work at the Environmental Remote Sensing Center. And, we did have, a handheld radiometer that we used to do upwelling and down milling of, the, gradients over the, over over water.

00:05:21:09 – 00:05:47:03
Unknown
Actually, we were doing a project where we were looking at Lake clarity and we wanted to have some in situ measurements. And, we had a pretty expensive, fancy, spectrometer with fiber optic leads. And it was pretty, pretty cool. I didn’t actually use it at all, though. I was developing the website for it, so I never actually got to play with it with the instrument.

00:05:47:03 – 00:06:14:13
Unknown
And, but I, I think I, you know, I heard about the STELLA program through, you know, going to conferences and, seeing the booth at various places. So, I knew that the back of my mind, it was something I always wanted to, to do. And I thought, well, I’m never going to find the time to do it myself, but if I could get a couple of students who were interested, then I could supervise that and learn about it myself.

00:06:14:13 – 00:06:21:11
Unknown
So, yeah, I, I think I learned as much as as these two did. So yeah. Thank you.

00:06:23:15 – 00:06:25:08
Unknown
How did you first learn about the,

00:06:25:08 – 00:06:28:02
Unknown
instrument and project?

00:06:28:02 – 00:06:30:02
Unknown
Maybe I should start.

00:06:30:03 – 00:06:41:12
Unknown
Okay. So I think, you know, I saw a STELLA display. Probably an AmericaView conference. There was,

00:06:41:12 – 00:06:43:11
Unknown
most recently in that I,

00:06:43:11 – 00:06:47:01
Unknown
made an impression on me. Was in Denver.

00:06:47:01 – 00:06:57:01
Unknown
And it was, I think an ASPRS conference. And maybe win or not, you my. You were. I’m pretty sure you were there, Michael.

00:06:57:02 – 00:07:20:10
Unknown
Yeah. At least your booth was. This was represented a lot of kids groups coming through it was Denver schools and stuff. Anyway, and I thought, you know, I really, I, I just think it’s such a great idea to be able to build your own spectrometer and learn the principles of remote sensing, but a hands on tool like that.

00:07:20:10 – 00:07:28:09
Unknown
And so, I when I had the opportunity to, create a, an I,

00:07:28:09 – 00:07:50:07
Unknown
a couple positions in our summer, internship program at the Space Science and Engineering Center at UW Madison, we, I signed up to to sponsor one student. But, when I got the resumes and the, letters of interest, these two was tied for the best.

00:07:50:07 – 00:08:14:11
Unknown
So I decided I should just take on two instead of one. And one of the cool things was I saw they had different skill sets, and they, taught each other during this process. So having two people was really great, I think. Inna had some soldering skills and, Alexa had some 3D printing skills and they shared those with each other, and now they both know how to do both.

00:08:14:14 – 00:08:20:02
Unknown
So yeah. Pretty cool. And and then I’ll let them explain how they learned about it.

00:08:20:05 – 00:08:24:04
Unknown
What initially drew you to integrate STELLA instruments into your educational programing?

00:08:24:04 – 00:09:06:09
Unknown
Yeah. So, Sam and I work on a project called AmericaView. So a lot of states have their own WisconsinView. So we’re WisconsinView, we’re part of America view. And our mission is to empower, you know, Earth observation through remote sensing. So we do a lot of satellite remote sensing, but, at SCC, we do some other, ground based remote sensing, but we you know, as, as the outreach person for WisconsinView wanted to come up with ways, you know, not just to talk at students, but to let them actually get their hands on the science, which can be

00:09:06:09 – 00:09:47:09
Unknown
really hard to do with satellite data. Just because you need some programming knowledge. Some. Yeah, just a lot of computational and technical background, to actually play around with that. But with the stellas, you know, they could actually hold the instrument in their hands. You can’t really do that with a satellite. So that was our main motivation was how do we expand Wisconsin View’s outreach efforts to continue giving students more of a hands on experience with remote sensing concepts and remote sensing instruments with the Stella.

00:09:51:06 – 00:09:57:11
Unknown
What were your specific roles during your internship at the Space Science and Engineering Center?

00:09:58:05 – 00:10:30:09
Unknown
Yeah. So, our main role, we worked through a lot of it together. And, we essentially used to get step by step, I know, you know, started her internship actually, before me. So, she, started with actually ordering all the parts. So, she technically did that all for me. Which is great. So I on my first day, we actually already had all the parts we needed, so, that was pretty cool.

00:10:30:10 – 00:11:03:03
Unknown
And then, from there, we, essentially, walked through, like, we started with the Q2 since I was the simplest one. And, our process just, going step by step, building our spectrometers, designing the case for the Q2 working on the 1.1. And then once we had all of that incorporated, then we got to do data, and then we got to programming, so that we could actually, you know, interpret that data.

00:11:03:04 – 00:11:16:09
Unknown
So it really changed from week to week, which was really cool because you got to test out so many new skills. And you weren’t doing the same thing every day.

00:11:16:09 – 00:11:35:09
Unknown
I came two weeks earlier before Alexa, start with her internship, and I just ordered parts. Everything else we did together. And, especially Alexa was very helpful in debugging, apparently. Alexa didn’t mention, but some of her details were not working.

00:11:35:10 – 00:11:57:05
Unknown
And, yeah, Alexa can go over this. Like, why is that work? So yeah, we just had a lot of debugging as well. And, maybe it would be nice regrade the small note for future people in case of the got into some the same troubles. They spent a lot of time there will already like see the solution of this.

00:11:57:09 – 00:12:12:02
Unknown
So excellent. And so to the to those different points. One thing you know, the experience of actually purchasing the parts. Did you have any troubles there? Oh no. It’s pretty much straightforward. Okay.

00:12:13:14 – 00:12:30:04
Unknown
just add that, you know, as the university has pretty specific ways of buying things. And so we had to work with our purchasing department and they showed us how to, to, use that.

00:12:30:04 – 00:12:54:01
Unknown
We had to first demonstrate that we couldn’t buy the some of the parts from existing vendors that served the university, but some of these are very specialized. And so we were able to, justify using the sources that were possible for us to get all the parts. And, but and then there were a couple of defective parts that had to be returned.

00:12:54:01 – 00:13:18:08
Unknown
And, you know, we went through a couple of cycles of that, but, it was, so it was part of the learning process. And I wanted in know the experience that, because that’s real, you know, like when you’re dealing in, in you and within some sort of corporate structure or whatever, you have to go, you have to follow the rules of the, of the game.

00:13:18:08 – 00:13:33:15
Unknown
And sometimes that’s inconvenient, but, we, so that was, that was part of the learning thing that maybe Alexa missed that, but that’s okay. Alexa got involved in returning parts though, so I think, got you a suitable amount of red tape that was game.

00:13:37:12 – 00:13:41:12
Unknown
was this your first dip into, using the STELLAs for teaching?

00:13:41:12 – 00:14:01:01
Unknown
Yes. We had been wanting to use the stylus for a while with WisconsinView. And we, you know, finally had enough money in our budget to hire engineering students to build them. So this was our first little foray into using the STELLA’s at all.

00:14:01:02 – 00:14:06:11
Unknown
So, yeah, this was this was, rookie experience for me. Okay.

00:14:06:11 – 00:14:13:01
Unknown
how would you describe the STELLA project to someone unfamiliar with it, particularly from an educational perspective?

00:14:14:02 – 00:14:24:11
Unknown
Yeah, that one’s hard. I’m gonna have to think about it a little bit. The STELLA experiment.

00:14:24:13 – 00:14:50:07
Unknown
Well, I would definitely use the word remote sensing. And then, depending on their background, you know, you might have to explain what remote sensing is, which, you know, I like to say is always exactly what it sounds like. Something. Something from far away from a remote location. I really like the phrase that you just used using saying a spectral signature is kind of like a light fingerprint.

00:14:50:08 – 00:15:29:08
Unknown
I think that’s a good way of explaining what a signature is. So each object has their own, you know, special signature. Yeah. If they if they knew what the electromagnetic spectrum was, I would say that we’re, we’ve designed a handheld instrument that. Views the energy that an object gives off at different wavelengths and then relate wavelengths back to, you know, the spectrum.

00:15:32:12 – 00:15:36:03
Unknown
Yeah. And then try to connect that with,

00:15:36:04 – 00:16:05:11
Unknown
I guess I’m always trying to connect it back to, like, what I do, which is satellite stuff. So I would maybe show them or bring them an example of like a Landsat image and, you know, move a cursor or my hand over different aspects of that Landsat image and say, you know, each color or each different area has a spectral signature, and we can measure that from space, or we can measure it from the ground.

00:16:05:11 – 00:16:43:04
Unknown
So we can do it like discretely with a STELLA If we were on the ground. But this Landsat image kind of does it all at once from space. I’d say that it’s. Yeah, a really good handheld, simple tool to use in the classroom that students can use with a little bit of instruction and inform them about, different types of surfaces and properties and paying attention to, to detail and light and optics and how all of that relates.

00:16:43:04 – 00:17:08:05
Unknown
So I would hopefully say it a lot more eloquently than I just did. But yeah, I think something along those lines of a handheld instrument that students can use to get a taste of Earth observations using fundamental science and physics concepts, and adding in a little bit of optics science as well.

00:17:08:05 – 00:17:21:10
Unknown
so yeah. And you mentioned, like, engineering students and all that. Can you tell me, like, you know, sort of like the overall structure of how you, so you had some engineering students come in and build it and all that, and you had the students actually go and use it and all that.

00:17:21:11 – 00:17:35:12
Unknown
How was that whole system set up, like, you know, did, like, you know, did you all do the purchasing and the students do the purchasing, all that sort of thing? And, you know, basically from finished. And what kind of system did you use to actually. Yeah. You know, make this a success.

00:17:35:12 – 00:17:41:15
Unknown
Yeah. So, Sam, was there a official like, advisor or supervisor?

00:17:42:01 – 00:18:09:02
Unknown
So he might have a little bit more details about this, but basically we had them come in with, maybe like a 10 or 8 week plan with different tasks to try to accomplish during that plan. We had there was, of course, some waiting time while parts came in. But the students, the students came in, and then Alexa came in and they, learned about what parts they needed to order.

00:18:09:02 – 00:18:43:14
Unknown
So they placed an order for the parts, through our purchasing office. And we have a great admin team that makes our life really, really easy. So our purchasing and financial staff was a big help here. You know, made sure that AmericaView had or WisconsinView had all the funds to cover what they needed, which, of course, Sam and I had had budgeted for, while they were waiting for the parts to come in, that was their time to do like a literature review and just learn more about remote sensing and the sell itself.

00:18:44:00 – 00:19:19:01
Unknown
At that point, I excuse me, I had reached out to other AmericanView members about what kind of curriculum they had done with the STELLA because AmericaView we we try to meet annually and, we have little poster sessions and I had seen a, a poster on still activities from LouisianaView. So I reached out to their staff and kind of asked them what they did in the classroom, to just start getting ideas of how we wanted to design our curriculum.

00:19:19:02 – 00:19:46:07
Unknown
So the STELLAs were built. They they got the parts, they built the instruments, they 3D printed all the cases and everything they needed. And then came time for, like, calibration and test measurements. I think it took for a while we only had three working machines, but Alexa Matson was able to get that all fixed up before our classroom experience.

00:19:46:08 – 00:20:14:10
Unknown
So yeah, I can’t really go into further detail other than that. For the engineering details, you would have to ask Inna, or Alexa. But yeah, I just, I met with them and wanted to know how many instruments we would have, you know, how many would be be functioning. So we knew how to split up the class, and I let them, you know, know, like the bones of of the activity I wanted to do.

00:20:14:10 – 00:20:48:05
Unknown
But I asked them to, like, design a little worksheet with the tables that they would fill out in the graphs that they would plot. They already had a small presentation, as part of their greater SCC wrap up presentation. So they kind of adapted that to a high school audience. We reviewed that and changed a couple of the details just so it would be easier for younger students to understand, relating it back to other applications such as, you know, looking at celestial bodies and whatnot.

00:20:48:06 – 00:21:10:09
Unknown
So that was pretty much the layout of how it went. We did some prep work leading up to the actual classroom experience. They came in beforehand and just wanted to get an idea of like, what the ambient light would look like in the classroom. So they came in a week prior just to get a feel of the space and how it would work.

00:21:10:10 – 00:21:22:08
Unknown
But they did a lot of the work themselves as far as like designing, the worksheet under, under my guidance and, and having a plan of what we were actually going to be doing in the classroom.

00:21:25:01 – 00:21:26:05
Unknown
You know,

00:21:26:05 – 00:21:31:10
Unknown
could you walk us through the development process for the STELLA data analysis website?

00:21:31:14 – 00:22:05:01
Unknown
Yeah. So this website I developed on my own, because the, like, this entire STELLA project was for organizing, for high school kids, like, and actually, they have, like, say, like summer school. So we really needed, like, a tool which can, very quickly analyze data and, show students final result and, at first we decided to use, the star data viewer work.

00:22:05:02 – 00:22:26:05
Unknown
But the problem is that it doesn’t support the STELLA-Q2 version. So after this, we started to play around the curve to have still to make it possible. And at first we thought just to write, like own Python program, but it was not user friendly. So after this, we decided that maybe it’s better just to make a website.

00:22:26:06 – 00:23:06:03
Unknown
And, this how we come out with this website, and it has, like a select, circuit circuit that, yeah. And, we had two versions of the website. One was, a graph, another one just a table. And, in the end, we decided just to use was a table as, we wanted, that kids could make, could plot their graph on their own on pencil and paper, not only like of tomatoes everything, but also gives them opportunity to to live in 21st century without all possible devices.

00:23:09:05 – 00:23:15:08
Unknown
Alexa, what are the what were the main challenges in designing the case modifications for the Q2?

00:23:18:07 – 00:23:47:00
Unknown
Making sure you’re getting the exact correct, dimension you need. There are times like, I think my first case was like, by some miracle, all of it fit except for the microprocessor, which, like the alignment holes, were just like, two millimeters off. So that’s that’s a lot of, the struggle in it is just making sure it’s very exact.

00:23:47:01 – 00:24:10:10
Unknown
And then on top of that, just making sure that it holds up. Well, that it you’re able to click it on and off. You’re able to take it apart and do that multiple times so you don’t have to be worried about it breaking. And then I played around with I had an SD, panel on the back and I was playing.

00:24:10:10 – 00:24:29:01
Unknown
I was having to decide that because I haven’t, I hadn’t designed that before. And I ended up, I was using my mouse, on the back of my mouse, there’s the, removable panel where the battery is. And I remember, like, I kept taking it on and off trying to figure out how it, how they designed it.

00:24:29:01 – 00:24:45:04
Unknown
And so it works. And then I kind of just played around with it on my own, to make that part. So it was a really fun challenge. And there’s a lot of detail in it. But it just made it when it finally came out. Right. That much more satisfying.

00:24:49:01 – 00:24:56:13
Unknown
Inna could you explain the technical differences between the first and simplified versions of the data analysis website?

00:24:56:13 – 00:25:07:13
Unknown
Yeah, it’s a difference. Just, because the first version, you can see a graph, for the second version, there is no graph, there is just a table with data.

00:25:07:15 – 00:25:16:09
Unknown
And, yeah, as explained before, we need this because, we wanted students to work with pencil and paper to draw themselves these graphs,

00:25:16:09 – 00:25:21:00
Unknown
And then, what programing languages and tools did you use in developing the web interface?

00:25:21:00 – 00:25:23:11
Unknown
Yeah, I used just, python

00:25:23:11 – 00:25:56:03
Unknown
The. They have, such nice library as possible. It’s very, very nice library because, you can combine, HTML website, the comic books also with, to assess how exactly you want to your, like, all your buttons work and, you can also program on your own, some functions. For example, in our case, we can, extract some data from the, spreadsheet, but we need then go to what would be like the final irradiance.

00:25:56:04 – 00:26:05:05
Unknown
So the final reflectance is or so I just used HTML like Python and CSS.

00:26:09:04 – 00:26:53:06
Unknown
Well, I was, approached by our, center leadership to help run the program this summer with, with a colleague Dave Hayes and, and so, one of the things that we did was, we had all of the interns that were about, between 10 and 12 throughout the summer. Meet once a week at a, roundtable discussion and go around the table and, and, the students would update on the various projects they’re working on.

00:26:53:07 – 00:27:16:04
Unknown
And I was fortunate because I had a project and I was in that room every week. So I would get the update from both, and, and, and Alexa as well as hear about all the other projects, but and then we would touch base after the, after the meeting to, to talk about any issues or, you know, requirements.

00:27:16:05 – 00:27:45:06
Unknown
I, I leaned heavily on my, colleagues to help. We were our fifth floor of our building is kind of kind of where hardware is, is, created where they developed, the scanning his spectrometer for and sounder for, satellites, you know, so there’s it’s there’s actually a whole clean room and a lot of a lot of high end, technology and people who know how to do all of that stuff.

00:27:45:06 – 00:28:23:15
Unknown
And so, we were lucky that, Rob Lemke, one of my colleagues, was willing and able to help get the, workspaces set up with anti-static mats and, soldering setups for, for both. And I think probably provided some instruction on that to also provided the multimeter that, Alexa talked about needing for and then, so, yeah, I had had buy in from people on, colleagues who work in the area of instrument, instrumentation, which I don’t, you know, I’m more of a, visualization person.

00:28:23:15 – 00:28:54:09
Unknown
So, I needed that help from my colleagues, but. So I think the two things were having regular meetings and then making sure connecting, Alexa and I with the, with the people who could answer their questions or help. And fortunately, both of these these students are. So, they’re self-starters and didn’t require a lot of, micromanaging at all.

00:28:54:09 – 00:29:11:11
Unknown
And I prefer that style. I prefer saying, okay, you can figure it out, and tell me when you do, you know, and, and, and if you can’t, then, tell me and we’ll find someone who can help, you know? So that was just kind of, how we how we did it.

00:29:15:08 – 00:29:23:01
Unknown
the. Not the development. What inspired you to, use the spectroscopic mystery activity? And how did it go?

00:29:23:01 – 00:30:00:01
Unknown
we were looking at the activities that were on the website, and that one sounded really fun and that I got across the different, how to read graphs and, you know, how light works and how you can read reflectance to see the color of things. And on top of that, mysteries are fun. I feel like, kids can connect with that and, get that hands on, experience with what we built.

00:30:00:02 – 00:30:09:03
Unknown
Yeah. So can you describe, like, what your setup was for the for the mystery? What? You, you know, you all did what you all found, useful, so on, so forth,

00:30:09:03 – 00:30:23:01
Unknown
Yeah. So spectroscopic mystery, is pretty much where you have the kids, take their own spectral measurements, using the spectrometers of a variety of different materials.

00:30:23:02 – 00:30:54:15
Unknown
And then, you can put up, different spectral graphs, and they get to identify, which one they think it is based on their own measurements. And yeah. So when we did it, I believe we had a couple different things. We had, healthy leaves. We tried to get brown leaves or decaying leaves. And we had, like, folders, notebooks,

00:30:55:00 – 00:31:04:02
Unknown
What else am I forgetting? Something? 3D printed cases, which. Yeah. Which, you know, which failed.

00:31:04:03 – 00:31:31:05
Unknown
Yeah. Yeah. Our first drafts of our 3D printed cases, we were used as, measurements because they’re black and white. So we tried to get, things of variety of colors to, and it was a lot of fun. We had four working spectrometers that we had the kids and split up into four groups. And each group got their own spectrometer.

00:31:31:07 – 00:31:34:08
Unknown
And it worked really well. Yeah.

00:31:34:08 – 00:31:48:09
Unknown
Yeah. So the lab was basically set up so that we had different, materials, different surfaces, and the kids were given a little demo of how to use the spectrometers.

00:31:48:09 – 00:32:12:05
Unknown
I think it was a little hard for them to pick up on. At first. We had four instruments and we split up in like four groups of 2 or 3. I think was was how we set it up. So we took in one group at a time, and we just had them stand around the table. And then we did a demo of this is how you use the instrument.

00:32:12:06 – 00:32:32:05
Unknown
Usually we did sandwich observations. So we do a calibration reading and then a reading of the object and then the calibration reading. So that was a little hard to, to drill in. You know, there used to just like pointing and clicking and having it be that easy, but it’s, a little bit more complicated than that.

00:32:32:06 – 00:32:57:13
Unknown
So we yeah, we went around and did that one group at a time and then gave them the rest of the materials. We had a list of materials that they had to measure. So one was like a healthy leaf, a dying leaf. And then things that we just found around the office. So like red binder, orange binder, black binder, black case, white case, just random things that we could get our hands on.

00:32:57:15 – 00:33:25:00
Unknown
And they took, they took measurements of each of those, and then, yeah, it was a little hard teaching them all how to use the instrument. I couldn’t teach them all how to use the instrument as a class. So we broke up into those subgroups, and I just taught, like 2 or 3 students at a time so they could actually see us clicking the buttons.

00:33:25:00 – 00:33:49:10
Unknown
And holding the instruments. And they were a little confused with, how they were actually going to get the data because we had to use data readers to get the information off the machine after all the measurements had been taken. So what they did was they did like calibration, measurement, calibration, and then they wrote down batch numbers to reference.

00:33:49:10 – 00:34:15:09
Unknown
And they were like, why are we writing down these batch numbers instead of writing down data? So maybe we could have done a better job explaining that. But afterwards we gave them, SD readers that they could plug in to their laptops. Each student had a laptop. We took the SD readers out of the Stella’s, and then one of the engineering students wrote a really easy,

00:34:15:10 – 00:34:46:03
Unknown
She put it up a website so that they could extract the data pretty easily. So after they got the data, they had these tables that was just like wavelength versus, reflectance. And so they wrote down each wavelength and they wrote down each reflectance. And then with that data, they had to draw like an X-Y graph of the pattern of reflectance versus or wavelength versus reflectance.

00:34:46:04 – 00:35:19:00
Unknown
So it was a lot of steps to get to the end product. And we ended up not having enough time because they were learning a lot all at once. But it was a really good experience. I think they still took away some knowledge from it. We had some issues with the data not reading and correctly like we were getting, I don’t know, numbers in the thousands for reflectance, which you know, doesn’t make sense because we’re looking we’re supposed to be between 0 and 1.

00:35:19:01 – 00:35:49:11
Unknown
So we had some issues reading in the data where they just had to do the step over again to get their, their valid data and yeah, we had a couple students resulting in trends that actually made sense. So what we were planning on doing was, showing the shapes of wavelength versus reflection up on the board and saying, you know, guess which object this is based on the shape.

00:35:49:13 – 00:36:15:01
Unknown
And then kind of explaining, like, oh, this is more reflective in the green channel because etc., etc., or this is more reflective in red because, it’s close to the infrared, which has to do with heat. So, you know, that’s why the number goes up there. Yeah. We were a little bit rushed at the end. We did run out of time, and since we ran the time, a lot of the kids didn’t get to draw their X-Y plots all the way.

00:36:15:02 – 00:36:35:13
Unknown
So I think we only had one group, with one object came out with it, like correctly. Oh, yeah. So yeah, I would, I would probably better control for, for time next time and just come up with a better way to communicate what they were supposed to do with the time given. But it was still really fun.

00:36:35:13 – 00:36:43:08
Unknown
I think the students had a good time and and walked away with it from it with something. So I consider that mission accomplished.

00:36:46:14 – 00:36:47:12
Unknown
areas where

00:36:47:12 – 00:36:55:07
Unknown
you had issues, can you, can you describe what issues you all ran into in this goes for both or all three of you.

00:36:55:07 – 00:36:55:14
Unknown
Really?

00:36:55:14 – 00:37:14:01
Unknown
so. Well, with my Q2 at least? The problem with the LED button, which ended up being a very simple fix, it was just we had to return it and get a new one. But beforehand it was troubleshooting to make sure that that was the actual problem.

00:37:14:01 – 00:37:38:14
Unknown
So, we used a multimeter to see if voltage was actually getting through and it wasn’t. And then, I actually it was nice having a second person because then I actually, was able to take the button off the second Q2 and then put it on mine to make sure that, it was actually the button. Then it worked so we could be like, okay, this is definitely the issue.

00:37:38:15 – 00:38:11:03
Unknown
And then we went through the return process and luckily the new one worked. And then a bit of, I guess, I also had a sensor, I believe it was the, this spectral sensor on the 1.1 that, didn’t connect. It wouldn’t, connect to. I took a bus, for whatever reason. And it was getting voltage.

00:38:11:04 – 00:38:39:04
Unknown
A light was blinking on it, but, again, it was nice to compare to, you know, because I could see that mine just continued blinking while, hers would turn on and off, and hers was working. So I couldn’t find anything wrong with it. It just wasn’t showing up. When I did the test codes. And, so that’s kind of where the part where I had to do research and see if other people had this problem and other people did have this problem.

00:38:39:04 – 00:39:04:11
Unknown
And apparently it was a somewhat common issue with this specific sensor. So then I, had to go through the return process. Again, I got a second one, but it was from the same batch. So the second one was also faulty. And then it took a few weeks to get the third one, which finally did work.

00:39:04:12 – 00:39:34:04
Unknown
So it was a much longer process. Technically, the spectrometer worked in that time. It just, couldn’t actually, read Visible light, which was, a little unfortunate, but, when I finally did get it, it was very satisfying. And then, yeah, that was, also a sensor that was soldered on, too. Right. So this wasn’t just a matter of plugging it and unplugging it each time.

00:39:34:04 – 00:39:54:06
Unknown
Yeah. So this was the one of the more complicated issues. Yeah. Well, I learned my lesson the first time, because I had sorted it on and tested it and realized it didn’t work. So then for the next two sensors, because the soldering, the whole process I used, I have my own, like, Arduino, control board.

00:39:54:07 – 00:40:07:07
Unknown
And then I just used my own breadboard and tested it on there before soldering it, which made my life a whole lot easier. So that was a good lesson to learn.

00:40:07:07 – 00:40:13:00
Unknown
Excellent. Anyone else? Any any troubles that, weren’t mentioned?

00:40:14:00 – 00:40:40:12
Unknown
Since Scott. No, I just had at first some troubles with screen, but it was just. I just had drawn wiring it first, but then it worked. So there was also, one the first 3D printing of the case for the Q2 the battery didn’t fit, so we had to redesign the case for the Q2 to fit the battery that was on the that we had ordered.

00:40:44:09 – 00:41:00:12
Unknown
what was the most challenging technical obstacle you encountered and how do you overcome it. And this can go not only just to the web interface, but to the STELLA itself. And what was it, the, the chips? Was it, you know, the the redesign of the case?

00:41:00:13 – 00:41:03:01
Unknown
What do you think was the most challenging

00:41:03:06 – 00:41:42:04
Unknown
I remember, building the, 1.1, is that was really cool, but also very stressful because, you know, you’re soldering wires and, resistors and capacitors and, you’re so worried that you’re going to put it in the wrong place and then, like you, soldering is a pain. So I think that, was really cool, but definitely, just going through it and going through every step and like, triple checking yourself, it was really hard.

00:41:42:04 – 00:42:15:10
Unknown
I remember, the wiring, the actual display screen, I think was the most difficult part because, you know, you have to go through the holes and, and do all that. I think at one point I, switched, two wires around and my display wasn’t working. And then I realized I did that. And, it was it was easier to actually, because they’re colored to what the directions had said to make, you know, it easier to identify.

00:42:15:10 – 00:42:42:13
Unknown
And I think I switched the colors, because it was just easier to change, swap the ends than to actually, make them the right colors that it was. So, that part was very technically difficult. But it also practiced a lot of, skills and soldering and, accuracy. Yeah. Excellent. What about you for you in

00:42:42:13 – 00:42:43:11
Unknown
was the most.

00:42:43:11 – 00:42:53:03
Unknown
Yeah. Absolutely same. Absolutely the same. It was just about the checking everything. Because several times making sure that that’s how it’s supposed to be.

00:42:57:00 – 00:43:01:08
Unknown
What would you change or improve about STELLA based on your experience?

00:43:03:04 – 00:43:24:13
Unknown
Or for me, like for STELLA-1.1 I would like to have this in light which you had for STELLA-Q2. It just. It’s so convenient when you can just, hold, the device just in front of the object, which you want to detect. And, the second thing I really liked is a touch screen, which you have on STELLA-1.1.

00:43:25:10 – 00:43:41:10
Unknown
It would be. It would be amazing if you could put this on STELLA-Q2 and you can, read, at the same time, when you’re taking the data, you can read what’s, what’s the data points to make sure that your like really taking data.

00:43:42:10 – 00:44:12:01
Unknown
Excellent. I think you mentioned this before, with the 1.2, but I think having the troubleshooting as you go is a huge help. This because when you’re working with so many sensors and one doesn’t work, I think that would be a huge benefit. But otherwise I completely agree with, Inna, the light on the Q2 was great, especially for taking, like, measurements on leaves.

00:44:12:02 – 00:44:15:12
Unknown
So. Yeah, I think that would be it.

00:44:19:09 – 00:44:32:14
Unknown
How did the hands on nature of solo work affect their ability to ask good scientific questions? Yeah, I know you said there’s only two days, so they might not have a really ask that many, but it’s worth a shot under.

00:44:32:14 – 00:44:42:07
Unknown
I think that it really challenge them to improve their technical language.

00:44:42:09 – 00:45:04:11
Unknown
STELLA was really the only like lab experience that we got to do in class because a lot of it, like we do a lot of touring of the campus and visiting museums and hearing from guest lectures, we did do a lot of programing the first week. So I guess you could kind of consider that, like computer lab time.

00:45:04:12 – 00:45:39:14
Unknown
But as far as, like a traditional lab experience that I think of, Stella was the only one that we did. So I think it challenged them to when asking clarifying questions of, how do I get this to work? How do I upload my data? Like, what exactly is the purpose of writing down these batch numbers? I think it allowed them to improve their their technical communication just by elaborating more on like what exactly are we doing here?

00:45:39:15 – 00:46:16:05
Unknown
Because yeah, like I said, we probably could have communicated like the goal and the steps a little bit more, more clearly to them. Yeah. So just following up and figuring out like, what is the purpose of what we’re even doing? I think it made them a little bit more, more comfortable asking those questions and, yeah, phrasing, phrasing things in a technical way that their instructors and helpers could, could understand and better answer their questions.

00:46:20:03 – 00:46:25:01
Unknown
how did this experience differ from typical classroom or laboratory learning?

00:46:28:15 – 00:46:52:15
Unknown
But I can tell that, for me is the difference was, first of all, you don’t have, like, any, like, deadlines. And of course you have like some weeks at a time when you need to do, but you don’t have to rush about the deadline. So you’re just doing this stuff and, you can really just enjoy the, the process when you’re doing.

00:46:53:01 – 00:47:20:10
Unknown
And the second thing which was from different is that, it’s more like a team work. It’s not just assignment, which just you have, you can work with a team. You can ask, different the type of people about how to debug something as, Sam told We had, and, his colleague Robert Lemke, you helped us very well, so much in electrical engineering.

00:47:20:11 – 00:47:31:04
Unknown
So you can just expand your network with other people and, just enjoy and, learn new skills.

00:47:31:04 – 00:47:34:00
Unknown
Excellent.

00:47:34:01 – 00:48:03:01
Unknown
Yeah. I completely agree with, you know, it’s it’s very, different in the way that it’s very flexible. You can, you can you’re making a lot of, choices and designs yourself, and that’s really cool. You it’s you’re given essentially a goal, but how you get there, you can vary. And that’s a really cool process.

00:48:03:01 – 00:48:18:14
Unknown
And even, coding and, just deciding how you want your graphs to look and figuring out how you want to take the data from the spectrometer and how you want to turn out. It’s just.

00:48:18:15 – 00:48:42:11
Unknown
It is really, really, I was really cool to just, have so much control over that and then also just be able to problem solve yourself and work with a partner and kind of see it applied to the real world.

00:48:46:08 – 00:48:50:05
Unknown
What new skills did you develop through your work with STELLA that you hadn’t anticipated?

00:48:52:03 – 00:49:37:00
Unknown
I think for me, I got to, get a lot of practice with soldering, with, you know, help, which was great. I also, got to, like, I got to learn a lot of trouble troubleshooting with the electronics. With the problems I ran into, which was a very helpful skill. And then on top of that, just the, trying to come up with how to present, spectrometers to, group of students, in a way that they can understand and how to use, and how to read them.

00:49:37:02 – 00:49:39:06
Unknown
That was a fun challenge.

00:49:41:09 – 00:50:13:00
Unknown
Yeah. For me, I, you know, like before, I was usually using, like, fusion 364 street modeling. And for this project, it was my first time when I used SolidWorks was to the model. Also, it was very interesting to explain, about the spectrometer also to my family, not only to kids, because before I was preparing to explain this to my parents and my sisters, and it was very, very interesting conversation.

00:50:13:02 – 00:50:29:00
Unknown
Yeah. Because they’re doing biology. That’s right. But my mom is teaching biology and chemistry, and, she asked me a lot of questions, like how exactly agree with, impacto to my work. Like, how can I explain this to my students? It was it was interesting.

00:50:32:13 – 00:50:42:14
Unknown
based upon your experience, you know, you said you’d like a little bit more time. What kind of teaching strategies, do you think worked well? And which kind of do you think, didn’t work quite so well, you know,

00:50:43:05 – 00:51:19:13
Unknown
I think that hands on learning, supplemented with visual material, always works really well in the classroom because at that age, I don’t really like talking at them for more than maybe ten minutes. Like, I think if we have guest lectures, they’ll sometimes go 30 minutes. But, you know, if we talk and then do something and then talk and then do something, that is the best way for for them to learn, because I think they can really, tune out easily otherwise.

00:51:19:14 – 00:51:40:02
Unknown
So yeah, I think I would definitely break up, lecture material and any like audio and visual learning with an activity or a hands on example. In order for the material to really sink in, better.

00:51:41:09 – 00:51:47:06
Unknown
What advice would you give to other educators wanting to implement similar programs?

00:51:48:12 – 00:52:15:11
Unknown
I would say that it’s probably a good idea to do like, a dress rehearsal, so to speak. Just do a dry run of, exactly how you’re going to teach the students. And one one thing that was kind of a hiccup in our class was that when we were teaching each subgroup how to use the instruments, the other groups were just kind of like sitting and waiting around.

00:52:15:11 – 00:52:46:11
Unknown
It didn’t have like a lot to do. So I would maybe come up with some sort of activity, like where we’re while we’re teaching one subgroup, how to use the instruments and take a reading, the other subgroups can have a collection of materials and kind of take those field notes that we were talking about. So just writing down, observations that they’re making about the materials that they are planning on measuring with the STELLA’s, yeah.

00:52:46:11 – 00:53:00:05
Unknown
So writing about color, texture, surfaces, things like that. I think that would be kind of a good filler activity. So they’re not just like waiting around, waiting to, to take their turn.

00:53:00:05 – 00:53:06:00
Unknown
What aspects of this work do you find most rewarding from an educator’s perspective?

00:53:07:09 – 00:53:34:12
Unknown
Yeah, I think the most rewarding part is when, the students get a result that makes sense to them and looks correct. So we had, one group, you know, draw the actual spectrum line, and the shape, the shape match, match, what it was supposed to look like. And, you know, they this is the first time they’ve ever done this.

00:53:34:12 – 00:53:55:04
Unknown
So they drew their line and made their points and they showed it to, me and Alexa, and they were like, is this right? And we were so excited because they had gotten it right. You were like, yeah, you did it. You did it. And they were kind of confused as to like the fact that they got it right.

00:53:55:05 – 00:54:26:01
Unknown
And so we had to like kind of anthem up and, and be like, yeah, you actually did it correctly, like you did what you were supposed to do. So I think that’s the most rewarding is kind of cheering them on and like, yeah, telling them that they did the activity correctly and then, you know, of course relating in the end back to like why that mattered and why, like what they should have taken away from it.

00:54:26:01 – 00:55:06:09
Unknown
I think when they. Yeah, when they get the right answer on their own, it can be really, really satisfying. When they’re excited about it. I think in this case, they were like confused as to why why they should be excited about it. And we had to, like, tell them why it was exciting. But yeah, I think if we had done a better, a better job of, just the the logistics of the classroom, then we could have had more of that, like satisfying experience of the students, like being really excited that they’ve gotten to the the correct answer or they had gotten something to work.

00:55:10:06 – 00:55:14:09
Unknown
How do you see STELLA evolving as an educational tool in the coming years?

00:55:14:11 – 00:55:17:07
Unknown
I’ll go with Doctor Sam first for this one.

00:55:19:08 – 00:55:48:01
Unknown
Well, I you know, this was such a good experience. I could see, doing this each year with, with, and just building on the results of this group, you know, building I might take on another intern or two next summer and have them do, whatever the next model is of Stella and then build on what, what, Inna and Alexa have done, but also, I think,

00:55:48:02 – 00:56:28:01
Unknown
Yeah, I’d love to see. So I work mostly with atmospheric scientists, meteorologists. And so, land remote sensing is more my background. And I’d like to make sure my colleagues know more about that, because we’re starting to use weather satellites to look at things like fire and smoke and things that are related to land processes. So, I kind of see it professionally, actually helping educate my own colleagues to so I think it could be kind of a I could imagine growing this program within my own, my own work.

00:56:33:06 – 00:56:47:10
Unknown
Okay. How much your contributions to help expand STELLA’s reach. Help expand STELLA’s reach to, more student populations, more in different student populations.

00:56:49:00 – 00:57:24:06
Unknown
You get the question? I think so, I think so this would probably be a good one for Alexa Ross, but, it, I can just say that one of our. We’ve through a new program, we’ve learned that one of the best ways to get to grow, curriculum or, our outreach is, by teaching teachers. And so when, if we can have summer summer workshops or we could work with teachers, then they can take it into their classrooms.

00:57:24:07 – 00:57:48:12
Unknown
And that’s kind of a way to, you know, an avenue for expanding. But I think Alexa Ross has pretty good connections within the Madison community here. And that was in Wisconsin. And, I believe that that, she got plans for her, expanding curriculum and using these tools.

00:57:52:09 – 00:57:55:14
Unknown
so you worked with, Inna and Alexa,

00:57:55:14 – 00:58:16:15
Unknown
what changes did you observe in, intellectual engagement and enthusiasm for STEM learning throughout their STELLA experience? Yeah. I didn’t really work. I worked with them closely towards the end, when we were ready to implement everything they had done into classroom curriculum.

00:58:17:00 – 00:58:39:06
Unknown
But for the most part, they were just really good, self-starting. And, you know, we gave them, like, a schedule of, you know, like week one you should do this week to do this. And they were they didn’t really need much oversight at all. They knew the mission. They were really efficient at, finding what they needed to find.

00:58:39:07 – 00:59:29:03
Unknown
I think it was helpful because Alexa mattson was a UW and or is a UW engineering student, so she already had knowledge of things like where to find the 3D printer and how to get into that makerspace and design and print what they needed. So that was really helpful. I think that I was really impressed at the end with their ability to communicate to the students, you know, not only the background of what like spectroscopy, spectroscopy is, but when they were teaching the students how to use the instruments, just their communication skills, because, yeah, I didn’t interact with them a ton leading up to this.

00:59:29:04 – 00:59:51:11
Unknown
And in a especially seemed a little quiet. So I wasn’t sure how she was going to do like presenting to students, because now you have to speak loudly. There are these loud ventilators in the back of the classroom, so sometimes it’s hard to hear. But they did a really great job. So I was impressed in the end by their science communication skills.

00:59:51:11 – 01:00:25:11
Unknown
And I know that they also had to do like an internal SCC presentation that all of our, student undergraduate interns have to do. So I think maybe, maybe if they had been like preparing or practicing for that, it helped. But they did a great job in the end in the classroom. And, yeah. So I was impressed by their science communication skills, both presenting in front of the class, but also just working one on one with the students who needed a little bit of help on how to, take the measurements correctly and then how to extract the data from the SD cards.

01:00:25:11 – 01:00:37:03
Unknown
They were really willing to sit down and troubleshoot with the student and work with them one on one to, accomplish their tasks and goals. While

01:00:37:03 – 01:00:41:08
Unknown
was the most important thing. You want people to know about your STELLA project?

01:00:45:04 – 01:01:10:07
Unknown
It was such a fun and educational, project. I had so much fun this summer doing it that it didn’t even feel like a job. It, genuinely all the different skills, I got to learn from week to week and, just the way I was so different from week to week, was really great.

01:01:10:07 – 01:01:34:14
Unknown
And this is the variety of things you get to do. It’s. I, I didn’t realize it, till we did, like, our final presentation at the end. Or even, like, putting that presentation together is just how much stuff we were able to do. And that was so cool because it was only ten weeks. And it didn’t feel like ten weeks.

01:01:34:14 – 01:01:36:12
Unknown
So. Yeah.

01:01:36:12 – 01:02:07:04
Unknown
Okay. So yeah, for me, still is. It’s also like about, like be confident about that either, like engineering and, I want to continue doing engineering and, it’s also like about, that you are working with as many fields as I’ve mentioned. That’s right. You you can combine your programing skills like your, like soldering, like everything.

01:02:07:05 – 01:02:24:12
Unknown
And, just enjoy the process and and don’t be, like, pressured. That it’s something super difficult. It’s not like everything, is in Google or ChatGPT So you can just ask them and they will answer you.

01:02:25:01 – 01:02:54:12
Unknown
Excellent. I think the memorable thing for me is the, is, is, is watching these two students teach each other things and learn from each other, and, and, I think, yeah, the stuff STELLA is in this kind of a it’s almost like a catalyst for learning. It makes a lot of learning happen, and it’s all you know, centered around this device.

01:02:54:12 – 01:03:18:09
Unknown
But like Alexa pointed out, it crosses all these boundaries into other disciplines and other, topics. And, so it’s, yeah, I think of it almost like the spokes of a wheel, you know, it’s like this. So STELLA can be the hub in the middle. And there’s all of these topics and possible questions and things to explore that, that radiate out from that.

01:03:18:09 – 01:03:23:14
Unknown
And so yeah, it was just really a pleasure to work, work on this project.

01:03:23:14 – 01:03:42:04
Unknown
How has, working with STELLA changed your perspective on, NASA technology or space science or just, you know, remote sensing and all that? So how does it change your perspective basically on, on the various technologies that are out there?

01:03:42:04 – 01:04:03:12
Unknown
I can do that for me. I always thought that NASA it’s something so far from me that I maybe can get access to this on only after getting PhD, but, this summer I you find out that you don’t have to finish your PhD to work with some NASA materials. You can be even a high school kid.

01:04:03:13 – 01:04:19:06
Unknown
It’s. Yeah, it’s open source. And, you can do it even from your home. And, it doesn’t require some, like, super expensive equipment. You can just buy it on, on Digikey or Amazon and build on your own.

01:04:19:06 – 01:04:42:15
Unknown
I think for me, I, the part of the NASA was focused on is the, like other planets, space access, aspect of it. And getting to work spectrometers and also learning about how it can be used to track wildfires, vegetation health and all these different, applications.

01:04:43:00 – 01:05:03:02
Unknown
Just on Earth was really cool. And then having also, the ability to build an instrument, that’s not super expensive. That’s super simple to use. To actually see that on a smaller scale was was really cool.

01:05:06:15 – 01:05:11:05
Unknown
surprised you most about working with solar technology? And again, this goes to anyone.

01:05:11:05 – 01:05:34:15
Unknown
for me, it was really surprising. The, in the end, when we were comparing our data with, Landsat data, we had the the satellite. What? The name phone was the one set, right? Yeah. So when we were comparing results with when I said I was surprised when our results got really like they were very, very close.

01:05:35:00 – 01:05:57:01
Unknown
And that’s why I was, I was really surprised that, with this very cheap machine, like, can take same result as something that is in the, in space, which is like very, very far away. And, I had a feeling like I touched something like, like something super Samsung unusual.

01:05:57:01 – 01:06:21:12
Unknown
I, I think that thing that was, I was, I was surprised how I wasn’t sure what to expect, honestly, with, like, where are we going to be able to get all the parts we needed? You know, I mean, at the beginning of this, I really wasn’t sure if we were, you know, the goal is to make for spectrometers, but I had no idea if it was really going to happen or not.

01:06:21:12 – 01:06:44:14
Unknown
You know, we had ten weeks and we had all these other goals like, okay. And so I think, you know, we got the parts took about a week. We got the and then they built them so quickly that we had plenty of time to work on other things, which was so great. And, and basically every time we met, it was kind of like, okay, what are we going to do next?

01:06:44:14 – 01:07:05:01
Unknown
You know, like and and so I guess for me, it just what was surprising about the whole program is how it kind of, there was kind of a feedback loop and, almost a snowballing effect where it got better and better as we went along because we got, we got to do more and learn more and discover as we went.

01:07:05:01 – 01:07:15:07
Unknown
So I think that was I thought it was just going to be making spectrometers. Honestly, I didn’t think we were going to do everything else that we ended up doing.

01:07:16:08 – 01:07:49:03
Unknown
Excellent. And Alexa, I think what surprised surprised me the most, was just, how much? You could do with the spectrometers and what kind of different experiments you can use them for, because there’s so much more even than what we did. I know one of the things I ended up doing was I collected a variety of leaves, and then I, left them on my desk for, like, a week.

01:07:49:04 – 01:08:16:03
Unknown
And then I compared the spectral measurements of when I first got them, and then a week later, which is really cool, because then you get to see the drop in the infrared and then, the, absorption, absorption. And like, the blue and red, parts of the spectrum on the graph. And it was cool to physically see that just on something I left on my desk for a week.

01:08:16:04 – 01:08:37:07
Unknown
But even that, like when I went through the GitHub, some of the suggestions of different experiments, there’s so much that you can do and there’s so, so much you can learn. Just like with any spectrometer. Excellent. No. Good question. Did you, did you use a, any kind of white reference? Normalize your,

01:08:37:08 – 01:08:58:02
Unknown
Okay. What did you what did you all use? What was your. We had a we essentially took a piece of construction paper. And put it we taped it to a box, so that, when we were outside that it could just, sit on the grass. And then we, took calibration measurements from it.

The post Student Voices: Real Engineering Success with STELLA Instruments appeared first on STELLA.

Inna Shapovalenko and Alexa Matson: Open-Source Engineering for the Future STEM Workforce

Michael Taylor — Fri, 22 Aug 2025 15:14:00 +0000

Inna Shapovalenko and Alexa Matson:
Open-Source Engineering for the
Future STEM Workforce with STELLA

Building Real-World Engineering Skills Through Open Source

Inna Shapovalenko’s and Alexa Matson’s Journey with the STELLA project exemplifies how open-source engineering projects can provide students with authentic workforce development experiences. During their summer internship at the Space Science and Engineering Center at the University of Wisconsin–Madison, they contributed to both hardware and software development while gaining exposure to collaborative engineering practices that mirror professional environments.

Shared files include the Spectroscopic Mystery activity with worksheet and presentation and Q2 case designs by Inna and Alexa:

Inna Shapovalenko, Space Science and Engineering Center at the University of Wisconsin–Madison intern

Inna showing off modified STELLA-Q2 case with larger battery

Alexa taking spectral measurements and measuring disatance,

Modified STELLA-Q2 case with larger battery

Completed modified STELLA-Q2 to house larger battery

Alexa soldering STELLA-1.1

Inna soldering STELLA

STELLA-1.1 wiring front

STELLA-1.1 wiring back

STELLA-1.1 completed wiring of main breadboard and screen breakout board.

A working STELLA-1.1 without 3D printed case.

Inna showing off STELLA-1.1 with GPS

Completed STELLA-1.1 with GPS

Inna Taking calibration measurements.

Classroom using STELLA instruments

Hardware Engineering Experience

Working as a team under the supervision of Dr. Sam Batzli, CIMSS/Space Science & Engineering Center, and Alexa Ross, Research and Outreach at the Cooperative Institute for Meteorological Satellite Studies, Inna and Alexa Matson tackled a practical engineering challenge: designing cases for the STELLA-Q2 spectrometer that could accommodate a 2200mAh battery. This project required them to apply 3D modeling skills, understand electronic component integration, and consider user interface design principles.

The case design work provided Inna and Alexa with experience in mechanical engineering fundamentals, including component packaging, thermal management considerations, and manufacturing constraints for 3D printing. Each version they developed addressed different user needs, teaching valuable lessons about design iteration and requirement analysis.

Software Development in Real Applications

Beyond hardware work, Inna developed web-based tools that would be used by educators and students worldwide. Her first website, built using Python Flask and HTML, supported both STELLA-1.1 and STELLA-Q2 systems. The platform enabled users to upload CSV files from the device’s SD card, input measurement parameters, and generate spectral graphs with accompanying data tables.

This development work exposed Inna to full-stack web development, database management, and user interface design – all critical skills in today’s technology workforce.

Custom STELLA-Dataviewer created by Inna from HTML and Python Flask.

Community-Driven Development Process

Inna and Alexa’s work contributed to the broader STELLA community, which operates through an open-source development model hosted on GitHub. Their work, funded in part by AmericaView and WisconsinView, demonstrates how student contributions to open-source projects can have immediate practical impact while providing valuable professional development experience. The STELLA project maintains an active community forum where users share experiences, request features, and collaborate on improvements.

They documented their contributions in the community forum, sharing their case designs and software tools with the global STELLA user base. Their story demonstrates how students can contribute meaningfully to open-source projects while developing professional skills.

Iterative Design Based on User Feedback

A crucial workforce development experience came when Inna and Alexa received feedback from educators using their tools. After discussions with teachers, they recognized that “a simpler version of the tool was needed—one that focuses on generating a table of irradiance and reflectance values for each material at specific wavelengths.”

This feedback led to the development of a streamlined version, which was subsequently deployed to Render for easier classroom access. The experience of receiving user requirements, redesigning based on feedback, and deploying improved solutions mirrors common professional software development cycles.

Open-Source Collaboration Skills

Through their STELLA work, Inna and Alexa gained experience with collaborative development practices essential in modern engineering environments:

Version Control: Managing code and design files in shared repositories
Documentation: Creating clear instructions and specifications for other users
Community Engagement: Participating in forums and responding to user needs
Cross-disciplinary Communication: Working with educators, scientists, and fellow engineers

Preparing for Technical Careers

Inna and Alexa’s internship provided exposure to the type of interdisciplinary problem-solving that characterizes modern STEM careers. They worked across hardware and software domains, interfaced with end users, and contributed to a project with global reach. Their solutions needed to be both technically sound and practically implementable by users with varying technical backgrounds.

The open-source nature of the STELLA project meant that Inna and Alexa’s work would continue to be used, modified, and improved by others – providing them with a portfolio of real-world applications and community impact that extends beyond traditional academic projects.

Skills for the Modern Workforce

Through their STELLA contributions, Inna and Alexa developed competencies increasingly valued in STEM careers:

Systems Thinking: Understanding how hardware, software, and user requirements interconnect
Agile Development: Iterating designs based on user feedback and changing requirements
Technical Communication: Documenting work for diverse audiences and skill levels
Open Collaboration: Contributing to community-driven projects with distributed teams

Community Impact and Continued Development

Inna and Alexa’s case designs and software tools became part of the STELLA open-source library available to people worldwide. Their work demonstrates how student contributions to open-source projects can have immediate practical impact while providing valuable professional development experience.

The GitHub community forum continues to serve as a platform where students, educators, and researchers share improvements and innovations, creating a sustainable model for workforce development through authentic engineering practice.

A Model for STEM Workforce Preparation

Inna and Alexa’s experience illustrates how open-source engineering projects can bridge the gap between academic learning and professional practice. By contributing to real tools used by a global community, students gain experience with the collaborative, iterative, and user-focused approach that characterizes modern engineering work.

Their journey from interns to contributors in the STELLA community demonstrates how open-source projects can provide students with portfolios of meaningful work, professional networks, and practical skills that translate directly to STEM career success.

The post Inna Shapovalenko and Alexa Matson: Open-Source Engineering for the Future STEM Workforce appeared first on STELLA.

From Environmental Science Student to STELLA Innovator: Bianca’s Journey

Michael Taylor — Sat, 09 Aug 2025 03:59:43 +0000

From Environmental Science Student to STELLA Innovator: Bianca's Journey

Video Summary

“Because it’s a low cost instrument, I feel comfortable messing with different sensors and trying to build my own version of STELLA with different codes and different parameters.”

A Serendipitous Meeting with Innovation

Bianca Cilento’s path to becoming a STELLA advocate began with a simple desire to build her own spectrometer. As an environmental science student pursuing a combined bachelor’s and master’s degree program, she had discovered her passion for interdisciplinary approaches to scientific problems during an internship in Sweden. There, she worked with plant compositions using a pseudo spectrometer called the Colorsnap tool, combining remote sensing with ecology to study changing Arctic landscapes.

“I was really excited about this project and thought, this is great – I’m going to get my masters and get a PhD and make my own spectrometer that works a little bit better, looks at near infrared,” Bianca recalls. But fate had other plans. When she met Mike Taylor and was introduced to STELLA, she realized she had found “everything I wanted to build and more.”

This discovery transformed her master’s thesis into “Quantifying Landscape Change using different spectrometers and imagery,” allowing her to compare different tools, collection methods, and mapping techniques. The project perfectly matched her love of looking at problems from multiple angles using interdisciplinary thinking.

Overcoming Initial Intimidation

Despite STELLA’s reputation for accessibility, Bianca’s first encounter with the device was daunting. “Honestly, I was a little overwhelmed. I did not have a very strong coding or manufacturing background, so I was a little scared at first,” she admits. The solution came from an unexpected source – her older brother helped her build her first STELLA.

But the initial fear quickly gave way to appreciation. “It actually was not as bad as I was expecting. It was really easy to put together and use,” Bianca discovered. Even when she encountered problems in the field due to her own mistakes, the troubleshooting process became part of the learning experience rather than a barrier to success.

This experience opened her eyes to STELLA’s potential for other early career scientists and researchers who might not have access to high-cost spectrometers – a realization that would shape her ongoing work with the platform.

Transcript

Could you please tell us,

tell me about your educational background and your current role?

Yeah, definitely.

So I started my degrees or my education back at right.

I started in 2019.

I was enrolled in the environmental science,

bachelor’s program, and about three years.

And to that, I was enrolled in the BSMS program.

So bachelors.

Masters, and I kind of had a hone down and pick a project that,

that I was going to do my thesis on.

And I’ve always been somebody who has like to do,

like to look at projects and problems,

from a lot of different angles and use a very interdisciplinary

ways of thinking to approach things.

So I ended up going to Sweden on an internship

or on a, like a stipend position, through a program called Emerge

that allowed me to look at, plant compositions

and how those are changing using a pseudo spectrometer called the Colorsnap tool.

And I was like, this is great.

I can use, you know, a little bit of remote sensing, a little bit of ecology.

And I can look at how Arctic landscapes are changing over time.

And I was really excited about this project and I was like, this is great.

I’m going to actually get my masters and get a PhD, which I did not do.

And make my own spectrometer that works a little bit better.

Looks at near infrared.

And then I met you and you introduced me to the STELLA,

which was everything I wanted to build and more.

And I incorporated that into my masters.

So my the title of my thesis ended up being Quantifying Landscape

Change using different spectrometers anyway, as imagery.

Yeah.

And so we were able to compare different tools and different

collection methods, different mapping methods,

even different

like GIS systems to

quantify how landscapes were changing over time.

And so that’s kind of what led me to where I am.

I’m working at EagleView right now.

It’s a aerial imagery company based out of, Rochester, New York.

And so I do a lot of the image processing stuff on that side.

But I’m also still working with Paul to kind of see how this fellow works

as a spectrometer and where its value as, like a science tool is.

So I’m testing it, compared to different spectrometers compared to

like its consistency over time, a whole bunch of different, parameters.

So I’m really excited about that.

What was your first impression when you started working with STELLA devices?

Honestly, I was a little overwhelmed.

I did not have a very strong coding

or manufacturing background, so I was a little scared at first.

I actually had my older brother help me do it, or help me build it.

But it actually was not as bad as I was expecting.

It was really easy to put together in use, and I did a little bit

of troubleshooting in the field because I messed a few things up.

But yeah, no, I had a really great time using it and

I think it can be really helpful to like a lot of early career

scientists, early career researchers who might not have access to,

high cost spectrometers.

Fantastic.

And that leads into the next question.

What career skills do you feel that STELLA has helped you develop?

Oh, gosh.

What hasn’t it helped me develop?

I feel I don’t know if I’d a confident programmer,

but I definitely am at least better than where I started.

I really think I’ve been able.

Look at what I like to look at and things from a lot of different

like perspectives and angles and different ways of thinking.

And I think that STELLA has really helped me do that

because there’s like the manufacturing angle and the machining angle

and the programing angle, and it can help

help solve so many problems, not even just environmental.

So yeah, I would say that it’s a very great tool.

It teaches a lot.

Whatever, whatever you want.

So STELLA can

What career skills do you feel STELLA has helped you develop?

Oh, gosh.

What what career skills hasn’t STELLA helped me develop?

I think, honestly, STELLA.

STELLA is what you make of it.

I’m somebody who was not a very confident programmer.

So being able to have the code

set up for me, I can really look into it and learn,

you know, what commands are helpful, where, how to modify.

I’ve been able to add a few different commands in there to make my research

easier.

Again, I’m not somebody who has very much technical experience.

So it’s really helpful that the build guide is there.

But there’s also options to, you know,

add your own sensors and add GPUs and anything that’s really helpful to you.

You can, you can mix and match.

So that’s been super helpful.

What transferable skills from solo work

do you see applying to other areas of your career?

Oh, that’s a great question.

I think that

using the STELLA has allowed me to see a project from start to finish.

So I’ve seen, you know, the development of different tools and,

you know, even, like the process of writing grants

to try to get more funding,

to pursue more research all the way down to data collection and data analysis.

So I think seeing

like a comprehensive project from start to finish

has really allowed me to hone in on things I like and things I don’t like.

Data collection specifically is a huge one, so

you only have a limited amount of time and a little limited amount of resources.

It’s really important to come up with a great data plan ahead of time.

So I think that’s something that’s really important that I’ve learned.

What skills did you develop

that you didn’t expect when you first started with STELLA?

Oh, I learned how to fail gracefully.

I would say I think that now, this isn’t specifically tied to STELLA,

but working with STELLA has given me the opportunity.

Because it’s a low cost instrument,

I feel comfortable messing with different sensors and trying to build

my own version of STELLA with different codes and different, I guess, parameters.

So, for example, I was trying to write a code in STELLA

that will, run through all the different gain and integration time combinations

so I don’t have to change it every time I run, every time I turn on the STELLA.

I just wanted one to turn it on once, and it’ll run continuously

and just cycle through.

And it was really hard.

I did not expect,

and I didn’t expect how many

times I’d have to, like, try something, test the code.

It didn’t work.

Try it again.

So it really taught me how to just

be patient.

And it’s okay that it doesn’t work the first few times.

And it’s.

It’s okay.

I can fail gracefully and then still sexy later on.

What’s been the most challenging skill to master?

And how did you overcome that challenge?

Was it the programing you were just mentioned?

Yeah, that’s definitely a big one.

Was the programing.

I am very, very little experience and the experience

I do have was hard, hard to even obtain.

Yeah, I would say the programing, but working with Paul

has definitely helped a lot.

He’s been a great coach to kind of gently

guide me without just giving me the answer on how to program things.

And like I

said, seeing the code, like in front of the working code in front of

you with like, already annotated with which steps do what.

That’s been super helpful too.

So I know, like, oh, if I want to mess with the gain

or the integration time, it’s right there in the code.

So I can kind of just take a bite sized piece and not have to

start from scratch if I want to edit it at all.

what motivated you to focus,

specifically on,

developing, these, these protocols for.

So and what are they?

So I’ve been working with Paul

to do sort of a classroom calibration protocol.

And that’s something that I wish I had learned a little bit

more of while I was doing my thesis.

I think it would have given my measurement a little bit more credibility.

So I think that’s something that

if we can teach it earlier on with something like the STELLA,

I think that that’s something that can advance science further.

I think my measurements that I took with STELLA were great and valuable,

but I also think that they could have

been helped if I had done a few measurements first, especially

because I was testing, in a very uncertain environment

like the outdoors, with different light levels and different times of day.

So having controls,

such as like seeing how

the STELLA reacts in different light levels in different

environments can help give.

A more consistent data

set and that can be easier can be compared

to different data sets year over year, different times a lot easier.

So I think that’s where that motivation really came.

And also just to see how the STELLA performs.

It’s still a relatively new instrument.

So having different people test different things and seeing if, you know, seeing.

So the STELLA is still a relatively new instrument.

So having lots of different groups of people testing

similar parameters under different environments kind of gives us a sense

of how reliable the STELLA is as a science tool,

rather just an educational tool.

Okay.

So what are some key considerations when designing your protocols?

That, so that other folks can follow it

independently?

So when designing a protocol, it’s

really important to make sure that it’s clear

and detailed while still being concise.

So there’s some steps that are a little bit implied

that you don’t necessarily need to repeat.

That just bogs things down and makes people probably overwhelmed.

Looking at a 20 step protocol.

But it’s also really important to make sure that the steps are repeatable.

So anything that could change the variability

or that could change the setup at all needs to be mentioned.

So if I say, for example,

I’m taking measurements of room darkness, I think it’s important

to make sure to mention how I darken the room.

So like the lights need to be off, curtains

need to be up.

You know, no light sources

so that it’s repeatable for other people.

So, yeah.

So you’re developing a classroom, calibration protocols.

Now, how do you balance say scientific accuracy

with classroom practicality in your protocols?

So a lot of the, constraints in the classroom

are those similar to those I’ve seen as an early career researcher.

So, low cost that

not a lot of time and, honestly, like, cramped space.

So when I’m designing it, I’m doing everything in my apartment,

my one bedroom apartment, to try to make sure that this is repeatable by kids.

This is repeatable.

Low budget.

And this is repeatable under time constraints.

So every

section of the protocol, every section of the calibration that I’m setting up,

I’m practicing it in my apartment to try to understand how,

what materials I can kind of scrounge together,

to make it, you know, low cost

and to make sure that

it can be done on time constraint and make sure there’s enough repeatability

where that can account for any uncertainty.

So it’s really I’m practicing it before I’m writing the protocol.

So I’m going through lots of different trials, lots of different experiments

before I really make sure I get it right.

So, speaking of which,

what calibration materials have you experimented with thus far?

This is a great question because it’s been a ton.

Most recently, the one that comes to my mind is Paul

and I are trying to see, reflector reliability as we’re calling it.

So we are trying to test styrofoam capability,

land version capability as, a reliable reflector.

So we want to see.

So let me back up.

We want.

So a good reflector will reflect light

equally from any incoming angle.

So we want to see is regular regular styrofoam

that anybody can get their hands on.

A good way to do that.

So we wanted to test the,

irradiance we’re getting from all these different angles.

So I have my nightstand set up to be at the right height.

I have a lazy Susan on top of it.

And then I have the styrofoam, crosshatch.

So I know where the center is with a protractor on top, so I know the angle.

And then I have floss tied to it

so I can actually turn it to get the angles that I need.

So that has been honestly probably the best way I found to do that.

In general, I also have it’s like a selfie stand

kind of, or like a tripod for photos that I use to keep the STELLA up.

Paul has a great 3D

printed part that holds the STELLA, and it just clicks right in.

And then I have an art easel that I wasn’t using at the time, so I just

that’s how I keep my target up.

That’s awesome.

Were there any other, materials that you’ve tried so far?

Other material, like in terms of reflectors or just like tools?

Reflectors? Yeah.

I have my tools.

Anything else that you found that works?

Yeah.

I have a white reference from,

I can’t remember the name of the photo studio,

but just like a local one in Atlanta that I’ve been using.

I don’t know the results off the top of my head,

but I have a bunch of different things I plan on doing.

But that’s all I have actually done is just give me a name.

Some of the other calibration mediums you plan on using.

Yes. I know we’ve talked about different types of paper,

so like construction paper specifically because it has more texture to it.

We’ve talked about a whiteboard and even just like a mirror

just to see what we’re looking at.

So those are some some things

that we might see upcoming.

Okay.

Yeah.

So what improvements or refinements are you still working on for your protocols?

That’s another great question.

There’s definitely a few methods that I.

I’m having a hard time,

I guess, nailing down.

But those are probably the more detailed ones where I

actually don’t know.

I actually kind of like my protocols.

Let me start over.

So it’s part of the classroom calibration.

We’re creating different protocols to really calibrate, the STELLA.

And so there’s a lot of different ways you can calibrate it.

You can see how it reacts in different temperatures and different light levels

with, different targets, you know, at different incident angles.

So one thing that’s really hard to balance

is how many different protocols or how many different parameters do

we need to test for this to be comprehensive?

So that’s probably

something that we’re working on refining right now.

I would say the biggest, most important things were working.

The most important ones to include, are definitely accounting

for light and then, having a good reflector.

So those are the two biggest, but also, how many can we supplement

to really have a comprehensive analysis while we’re still under these constraints?

We’ve talked about like budget in time and uncertainty levels

and repeatability.

Where do you see this work heading next?

Any exciting developments on the horizon?

In a perfect world,

I think that a paper is on the horizon.

I think,

Paul and I are working really hard to to,

you know, test all these calibration methods and see

how, you know, consistent the STELLA is across

all these different parameters we’ve talked about.

So I think once we hone down on what specific parameters

we need to include and we can get the data analyzed,

I think we might have an exciting paper in the works.

Who knows?

Yeah.

No measuring extraneous light.

Tell me about it. What is it?

so extraneous.

Light is, light in a completely dark room that we just kind of can’t get rid of.

So all the windows are down, all the curtains are up,

all the lights are off.

But there’s still

there’s still light coming from somewhere because we don’t live in a vacuum.

So that’s something to account for because it can be different

at different points of the day, different days of the week,

different seasons of the year.

So one way we do that is by saturating the lens.

So we max out, gain an integration time and a completely dark room.

And we see how much of this extraneous light we’re seeing.

And we measure that across different conditions.

And if it’s consistent, we can account for it. And,

we can account for it at once

and just kind of do some subtraction.

But if it’s not, there’s different ways we can account for it individually.

So it’s something you can

So you can measure extraneous light at the start of your measurements.

And see if it’s something

that needs to be accounted for or if it’s something that can be

that’s just negligible because of the small amount

compared to the other measurements you’re taking.

what has you most excited about STELLA?

My favorite thing about STELLA is

how easy it is for anybody to access it.

It’s available for students, researchers, teachers, professors.

It’s a great way for anybody to get involved in science.

You know,

I think there’s potential for environmental stuff, but

there’s also capabilities for engineering and machining and programing.

And any teacher who wants to get involved and implement it into their classroom,

there’s a way to do it.

Any sort of math, any sort of science, any sort of

Stem field.

There’s the opportunity for it.

And I think having something so hands on in a real world

application is what will get kids and people excited about science,

and that’s what we need.

I guess one thing that I think is really cool

is that everybody I talk to is really interested.

Like, my mom works in a school, so all the different teachers

I talk to are like, wow, tell me more about this tool.

So it’s really cool to say, like,

yeah, I’m I’m actually pretty comfortable with it, you know?

I know it really well.

Or I can explain it to people and give a demonstration and,

you know, do a quick data analysis right in front of them to show

the difference between different plants and asphalt and concrete.

So I would say that’s pretty cool, everybody.

I think once it’s out there, people will

like once people know about it, I think they’re really excited about it.

And that’s something that’s really cool to be a part of.

“I think it can be really helpful to like a lot of early career scientists, early career researchers who might not have access to high cost spectrometers.”

A Tool That Grows With You

What makes STELLA particularly powerful, according to Bianca, is its adaptability. “STELLA is what you make of it,” she explains. As someone who wasn’t a confident programmer, she found the pre-written code accessible while still offering opportunities for customization. “Being able to have the code set up for me, I can really look into it and learn what commands are helpful, where, how to modify.”

The platform’s modularity proved essential to her research. She could add different sensors, incorporate GPS capabilities, and mix and match components based on her specific needs. This flexibility allowed her to enhance her programming skills incrementally, adding commands to make her research easier without being overwhelmed by starting from scratch.

Learning to Fail Forward

Perhaps the most unexpected skill Bianca developed was what she calls “failing gracefully.” This lesson came while attempting to write code that would automatically cycle through different gain and integration time combinations, eliminating the need to manually adjust settings each time she turned on STELLA.

“It was really hard. I didn’t expect how many times I’d have to try something, test the code – it didn’t work, try it again,” she reflects. “It really taught me how to just be patient. It’s okay that it doesn’t work the first few times.” This experience with iterative problem-solving became invaluable preparation for professional research environments where persistence and patience are essential.

The low cost of STELLA made this experimentation possible. “Because it’s a low cost instrument, I feel comfortable messing with different sensors and trying to build my own version of STELLA with different codes and different parameters,” Bianca notes. This comfort with experimentation, enabled by affordability, became a crucial part of her learning process.

From User to Developer

After completing her master’s degree, Bianca joined EagleView, an aerial imagery company in Rochester, New York, where she applies her image processing skills professionally. But her relationship with STELLA didn’t end with graduation. Instead, she began collaborating with Paul Mirel and Mike Taylor to evaluate STELLA’s potential as a serious scientific instrument, testing its consistency over time and comparing it against other spectrometers.

This work led to an important realization: while her STELLA measurements were “great and valuable,” they could have been enhanced with better calibration protocols. “I think it would have given my measurements a little bit more credibility,” she reflects. This insight sparked her current focus on developing classroom calibration protocols that could advance science education and research.

Bringing Science to Everyday Spaces

Bianca’s approach to protocol development reflects her commitment to accessibility. She designs and tests everything in her one-bedroom apartment, ensuring that procedures can be replicated by students with limited space, time, and budgets. “I’m practicing it in my apartment to try to understand what materials I can kind of scrounge together to make it low cost and to make sure that it can be done under time constraints,” she explains.

Her creative solutions demonstrate the ingenuity that STELLA encourages. For testing reflector reliability, she uses a nightstand adjusted to the right height, a lazy Susan for rotation, styrofoam crosshatched to identify the center, a protractor for angle measurement, and dental floss for manual rotation. A selfie stand holds the STELLA, while an unused art easel supports targets.

This DIY approach isn’t just about cost savings – it’s about proving that quality science can happen anywhere, with materials anyone can access.

The Science of Calibration

Bianca’s calibration work addresses a fundamental challenge in scientific measurement: how do you ensure your instrument is giving you accurate, comparable data? Her protocols tackle multiple variables that can affect STELLA’s performance, from different light levels and temperatures to various target materials and incident angles.

One particularly important aspect is measuring “extraneous light” – the unavoidable illumination present even in seemingly dark rooms. “All the windows are down, all the curtains are up, all the lights are off, but there’s still light coming from somewhere because we don’t live in a vacuum,” she explains. By saturating the sensor and measuring this background light under different conditions, researchers can account for it in their data analysis.

Her work with reflector materials has tested everything from basic styrofoam to professional white references from photo studios, with plans to evaluate construction paper, whiteboards, and mirrors. Each material offers different advantages in terms of cost, availability, and optical properties.

Balancing Rigor with Practicality

Creating protocols that maintain scientific accuracy while remaining classroom-friendly requires careful balance. Bianca’s approach emphasizes clarity and repeatability while avoiding overwhelming detail. “It’s really important to make sure that it’s clear and detailed while still being concise,” she notes. “There’s some steps that are a little bit implied that you don’t necessarily need to repeat – that just bogs things down.”

The key is identifying which variables truly matter for reproducible results. When describing room darkness measurements, for example, she specifies that lights must be off and curtains must be closed – details that might seem obvious but are crucial for consistent results across different users and environments.

A Vision for Broader Impact

Bianca’s excitement about STELLA extends far beyond her own research. Working now at EagleView while continuing her STELLA research, she sees the instrument’s potential to democratize scientific access across multiple fields. “It’s available for students, researchers, teachers, professors. It’s a great way for anybody to get involved in science,” she enthuses.

Her mother’s work in schools has given her direct insight into educators’ reactions. “All the different teachers I talk to are like, ‘Wow, tell me more about this tool,'” she reports. Being able to demonstrate quick data analysis showing differences between plants, asphalt, and concrete creates immediate engagement and understanding.

Looking Toward the Future

The culmination of Bianca’s calibration work may be a research paper that establishes STELLA’s credibility as a scientific instrument rather than just an educational tool. “Paul and I are working really hard to test all these calibration methods and see how consistent the STELLA is across all these different parameters,” she explains.

This research addresses a crucial question for any emerging scientific instrument: How reliable is it? By having different groups test similar parameters under various environmental conditions, the scientific community can better understand STELLA’s capabilities and limitations.

The Ripple Effect of Accessibility

What excites Bianca most about STELLA is its potential to get people excited about science through hands-on, real-world applications. “I think having something so hands on in a real world application is what will get kids and people excited about science, and that’s what we need,” she reflects.

Her journey from intimidated student to confident developer illustrates STELLA’s power as both an educational tool and a research platform. By removing traditional barriers – high cost, complex programming requirements, need for specialized facilities – STELLA opens scientific exploration to anyone willing to learn and experiment.

A Model for Accessible Innovation

Bianca’s story demonstrates how accessible scientific tools can transform not just individual learning experiences, but entire approaches to research and education. Her work developing calibration protocols ensures that future STELLA users can achieve greater scientific rigor while maintaining the platform’s essential accessibility.

Her apartment-based testing protocols prove that quality science doesn’t require expensive laboratories or specialized facilities – just creativity, persistence, and the right tools. As she continues her professional work in aerial imagery while advancing STELLA’s scientific capabilities, Bianca exemplifies how accessible innovation can create pathways for the next generation of scientists and engineers.

Through her journey from overwhelmed student to confident researcher and protocol developer, Bianca has become living proof that STELLA’s promise of democratizing science education and research is not just aspirational – it’s achievable, measurable, and already happening.

The post From Environmental Science Student to STELLA Innovator: Bianca’s Journey appeared first on STELLA.

Preparing Tomorrow’s STEM Workforce: STELLA Technology in Education

Michael Taylor — Mon, 21 Jul 2025 19:44:46 +0000

Bringing NASA Technology to the Classroom: One Educator's Mission to Transform STEM Learning

Video Summary

An innovative teacher is using NASA’s STELLA instrument to bridge the gap between space technology and K-12 education, creating hands-on learning experiences that prepare students for tomorrow’s workforce.

The Foundation – TPACK Framework in Action

Lynn Nichols draws from her dissertation research on makerspaces to explain how NASA’s STELLA technology fits into proven educational frameworks. Using the TPACK model (Technology, Pedagogy, and Content Knowledge), she emphasizes that teachers need hands-on experience with STELLA before successfully incorporating it into their classrooms. This approach ensures that technology integration enhances rather than disrupts effective teaching practices.

Real-World Science in the Classroom

Students using STELLA engage in authentic scientific research that mirrors professional environmental work. Through spectral analysis and NDVI (Normalized Difference Vegetation Index) measurements, they can instantly determine plant health and distinguish between vegetation and non-vegetation surfaces. This immediate feedback creates engaging learning experiences while building technical competencies in remote sensing and data analysis.

From Local to Global – Connecting Classroom Data to Satellite Observations

The STELLA program creates powerful learning connections by combining hands-on measurements with Landsat satellite data. Students study real environmental challenges, such as comparing urban heat island effects between Providence and Buffalo, or measuring chlorophyll levels in the Chesapeake Bay. This approach helps students understand how local observations connect to broader environmental patterns.

“I think the connections between their community and everything that Landsat is doing, I think are really powerful and an important takeaway. You know, so much of what we’re able to do here, at ground level is because of advancements that NASA has made and then, you know, shared with the country and the world.”

Innovation and Problem-Solving

Nichols envisions students using STELLA for community-focused projects that develop both technical skills and civic engagement. From building and monitoring water irrigation systems to improving school air quality, students identify local problems and use STELLA to evaluate their solutions. Advanced applications include attaching STELLA sensors to robots for environmental monitoring, creating direct connections to NASA’s Mars rover missions.

Workforce Development Impact

This educational approach directly addresses the growing need for skilled professionals in environmental science, precision agriculture, and geospatial technology. Students develop job-ready skills in data collection, analysis, and scientific methodology while working with the same tools used by NASA professionals and industry leaders. The result is a pipeline of STEM-literate graduates prepared for high-demand careers in emerging technology sectors.

Transcript

Lynn Nichols: So incorporating STELLA into the classroom, I think follows just general good pedagogy in general. Sort of. Sorry. Let me think about this for a second. So incorporating STELLA into the classroom uses regular classroom pedagogy, I think.

Lynn Nichols: So it ties into my research. I do, as part of my dissertation work, I’m looking at, how makerspaces tie in with our traditional sort of understanding of te pack, which is technology pedagogy and content knowledge. It’s a framework, a theoretical framework created by Mishra and Kolar. And it talks about how essentially, in order to incorporate technology effectively into teaching, teachers need to have an individual full technological knowledge of whatever they’re working with and individual content, knowledge of their content area and, and pedagogical knowledge.

Lynn Nichols: But in addition to those three separate buckets, they also need a combined bucket of how all of those interact. And, so my work sort of extends that to makerspaces. And knowledge of broadening access to Stem. And so my sort of thought is that they need those that sort of try combined knowledge, but they also need knowledge of how to get students into Stem.

Lynn Nichols: And they also have it all situated sort of in makerspace contexts. So teachers using these makerspace technologies in an effective way to sort of do all of the above. And I think that the STELLA and incorporating the STELLA into the classroom very much follows, those sort of frameworks that, that I just mentioned. It is something that to get teachers to use it, they really need an opportunity to play with it and think about how they would design for their own classrooms using using STELLA’s.

Lynn Nichols: They also, you can do this through design challenges without me. Sharon Kolar originally sort of, created this. They gave teachers challenges to incorporate a technology into their pedagogy. And through that challenge, teachers really started to to build that t pack that combined knowledge. And I think, by working with you sort of in thinking about how I could incorporate STELLA into my classes, into my robotics classes, into the National Science Foundation, Noyce grant teacher work.

Lynn Nichols: That sort of served as the challenge to figure out how the STELLA would sort of naturally enhance the content, how I would structure my classes to bring it in and how I could, you know, sort of fit it into my traditional sort of pedagogical approach to teaching and learning.

Mike Taylor: So can you walk me through some, say, STELLA activities that you’ve developed for your study?

Lynn Nichols: Yeah, absolutely. Can you give me one second? I’m just going to open them up so that I can talk more. I can give you the specifics of everything that that we’ve done. So the first thing that we talked about with, STELLA was how it interacts with plants. And so we. And you also were part of this, talked about how when, light from the sun is interacting with a plant leaf, it is, producing and reflecting, near infrared wavelengths. It’s also, you know, we’re seeing that sort of, reflecting of the green, sort of wavelengths, too.

Lynn Nichols: But that near infrared pieces is what I think is really interesting. And so in doing, in developing these activities, I was doing some research and stumbled across a, it was a, an apple tree study where they were looking at blight with an apple tree, and they saw that there was a very, significant and pretty immediate dip in the near infrared, compared with the green sort of wavelengths.

Lynn Nichols: I think it took I’m looking at the data correctly. It took like about seven days to see a dip, sort of in the, in the green. But it was, I think, day. Gosh, what was that? They were starting to see sort of weird things happening with the near infrared after day one. Which was really interesting.

Lynn Nichols: So that was sort of my first thought was, how can we bring those connections in? And so from there, I really, you know, it’s sort of a back and forth between, hearing from you about what STELLA can do and then also doing research into how STELLA can reinforce content and reinforce, sort of important sort of takeaways that we want students to have.

Lynn Nichols: So the activities that we developed and I’m still opening them up, I build slideshow and slideshow on slideshow. So the first activity was, an activity on monitoring vegetation health. And so we know that, access to nutritious food, you know, impacts your ability to grow, but it also impacts your brain development. And we know that access to nutritious food early on can really make a difference in terms of IQ and sort of long term health.

Lynn Nichols: And so if you don’t have enough food in the first thousand days of your life, your brain may never reach its full potential is, sort of a takeaway from one of the podcasts that we listened to. We also know that, in farming, it’s something like 70% of water, is used by farming. And of that 70%, 40% is lost to the environment due to poor irrigation.

Lynn Nichols: And so if we can, better sort of target what needs water when we can help reduce that sort of loss of water. Let’s see. And so the we used STELLA to collect data on, plants, and we looked at reflectance, irradiance and ndVi data to determine the health of the plant. And then sort of discussed that, with our groups, and that was a nice introductory kind of activity to how to use the STELLA and the data viewer and all of those pieces in order to, introduce teachers and students to the functionality of STELLA, while also, sort of reinforcing some sort of biology content with that.

Lynn Nichols: We also there was also some additional readings on using Landsat to measure Chlorophyl in the Chesapeake Bay and then, chlorophyl and sea surface temperature. That was, interesting. We also talked about urban heat islands. And so, we know that I’m going back to my stats, so the Landsat seven satellite helps to determine the impact of, lack of trees on a region.

Lynn Nichols: So there was a comparison done between Providence, Rhode Island and Buffalo, New York. And, there are similar latitudes. Providence is 83% densely developed in terms of urban development. And Buffalo, I think is 43% densely developed. Providence had temperatures that were nearly 20 to 22 degrees warmer, than the surrounding towns, whereas Buffalo was only 13 degrees warmer than the surrounding towns.

Lynn Nichols: So we were really like looking at that urban heat island effect, with that sort of Landsat study. And to, sort of use STELLA to hammer these points home. We talked about how we used a heat lamp and compared, the temperature of a surface, that had some foliage blocking it in comparison, without foliage blocking it, and looked at the sort of temperature comparisons.

Lynn Nichols: We also looked at green spaces and downtown Baltimore. So there’s a study and it indicates that, an increase in tree density of one standard deviation for, a region can be associated with a 24% lower prevalence of asthma in that region. And so we talked about how, you know, STELLA could be used to monitor temperatures in cities, based on, sort of increased green spaces and where we could add green spaces into our cities in order to, to reduce asthma and, and reduce those heat island kind of effects, because asthma is sort of associated with some of those heat island pieces.

Lynn Nichols: And then the last activity, we looked at air quality. And so this was related to that, the Landsat piece, the Landsat eight Operational Land Imager, that collected this green vigor, collected it was used to predict the concentration of particulate matter less than ten micrometers in Delhi, India. And so great, we know that pollution and air quality are well-documented issues in India.

Lynn Nichols: And atmospheric visible spectrum reflectance data collected from Landsat 8 passes was reviewed and compared with data from the Central Pollution Control Board for corresponding dates. And that was a study from 2017. And so for that activity, we looked at how, STELLA, air quality pieces could be used to look at air quality in classrooms and help sort of benefit that.

Lynn Nichols: And so these were all designed as sort of introductory activities with STELLA. They all use graphs. And so looking at the data, graphing the data, from the spectrometer, some of it is, you know, just very clearly, you know, following a trend that students can make predictions on other pieces are just really helpful for them for making long term sort of judgment calls, the NDVI value, for example, is something that, you know, students can get an immediate sort of snapshot of how a plant is doing or if they’re even looking at a plant or if they’re looking at something else, like cement.

Lynn Nichols: And so I think that in terms of developing the activities, it’s a really cyclical process. Looking at previous Landsat data, looking at how STELLA works, looking at content and how those pieces can be combined and sort of a more authentic way that can help students engage with STELLA and engage with Stem in their community.

Lynn Nichols: So, I mean, off the top of my head, I could see students building a water irrigation system and still being used to monitor which, system is sort of working best for the plants based on, how how happy those plants are from an ndVi standpoint. I could also, you know, see something similar, for air quality for students, you know, using something like moss to, try to improve air quality and improve, you know, something, within their, their classrooms and within their schools and use, STELLA to, to monitor that.

Lynn Nichols: I could see yeah. It just essentially having students identify problems within their community and then use it to help evaluate the efficacy of their planned intervention to address that problem. And I could also see, students using STELLA in conjunction with, you know, sort of other, digital fabrication kinds of pieces, you know, like attaching a STELLA to a robot and using, you know, designing a robot that would be able to go into, sort of more extreme environments and then use the STELLA to take readings, that kind of a thing and that, you know, sort of, mimics what we’re doing with, Mars rovers, which is a really cool, I think, connection for students to see.

Mike Taylor: what do you hope teachers take away from using STELLA in their classrooms?

Lynn Nichols: I think the connections between the, their, their community and everything that Landsat is doing, I think are really powerful and an important takeaway. You know, so much of what we’re able to do here, at ground level is because of advancements that NASA has made and then, you know, shared with with the country in the world.

Lynn Nichols: I think that’s a really important takeaway is, is that our space program is so important. And really benefiting all of us in ways that we don’t immediately realize and understand. I know I’ve learned a lot about Landsat and how it benefits me and my community. And, my family through, the work that we’ve done together, which I think is really important.

Lynn Nichols: I also think, anytime we can get teachers to see that incorporating as sort of like handheld, spectrometers and microcontrollers and Arduino into their teaching, in an organic and sort of meaningful way, just reiterates how accessible these tools are. I know a lot of teachers fear when they don’t immediately know everything about what they’re incorporating. And I think that while, you know, that can be really scary, I think it’s also just so important that teachers are trying new things and taking this sort of, risk with this accessible technology.

Lynn Nichols: It’s not really a risk, right? Taking, you know, a chance on this accessible technology in their classroom, I think is, an important takeaway, that it’s it’s something that’s easy to do and, and can really impact learning in a meaningful way. I think that, showing students that they can be citizen scientists, and use, use these tools in order to impact their communities is really important.

Lynn Nichols: And, good takeaway for teachers. And I think that, just reminding teachers that 3D printing can be used to create these really useful tools, for their classroom and community. Are is also really important.

Mike Taylor: putting you on the spot here because of course, I’ll be listening. So what has it been like working directly with NASA scientists on an educational research? What has it been like working directly with, with NASA folks on, on STELLA’s? I guess we can rephrase it that way or whatever.

Lynn Nichols: It’s been a lot of fun. I really love talking with you, Mike. I love talking with Elana I’ve loved talking with Matt Pearce. And really, everybody that I’ve chatted with has been so interesting to, to talk to and to learn about what they’re doing with NASA. I have been just so appreciative of the time that you’ve you’ve just given so freely to help our, teacher fellows at Towson, learn about Landsat, learn about STELLA.

Lynn Nichols: And I really appreciate you giving up, you know, some of your weekend time to, to help us really, learn more about everything that is happening with NASA. I feel like everyone has been so kind and, so accessible and really helped us. And our students learn whenever we ask, which I appreciate, because I know I ask a lot.

Lynn Nichols: I, I appreciated you coming, on zoom to speak with my students about everything with NASA and, Landsat and GIS. So thank you so much for that. It’s been really wonderful to work with everyone.

The post Preparing Tomorrow’s STEM Workforce: STELLA Technology in Education appeared first on STELLA.

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