{"id":2990,"date":"2024-06-29T01:38:00","date_gmt":"2024-06-29T01:38:00","guid":{"rendered":"https:\/\/science.gsfc.nasa.gov\/stella\/?p=2990"},"modified":"2025-07-08T01:59:41","modified_gmt":"2025-07-08T01:59:41","slug":"dr-huitzimengari-campos-garcias-stella-q2-sensor-classroom-exercises","status":"publish","type":"post","link":"https:\/\/science.gsfc.nasa.gov\/stella\/dr-huitzimengari-campos-garcias-stella-q2-sensor-classroom-exercises\/","title":{"rendered":"Dr. Huitzimengari Campos Garcia&#8217;s STELLA-Q2 Sensor Classroom Exercises"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"2990\" class=\"elementor elementor-2990\">\n\t\t\t\t<div class=\"elementor-element elementor-element-23143cb e-flex e-con-boxed e-con e-parent\" data-id=\"23143cb\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-f39539c elementor-widget elementor-widget-heading\" data-id=\"f39539c\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Dr. Huitzimengari Campos Garc\u00eda presentations June, 29 2024<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c2f2822 elementor-widget elementor-widget-video\" data-id=\"c2f2822\" data-element_type=\"widget\" data-settings=\"{&quot;youtube_url&quot;:&quot;https:\\\/\\\/www.youtube.com\\\/watch?v=y2BbM8XWvHo&quot;,&quot;video_type&quot;:&quot;youtube&quot;,&quot;controls&quot;:&quot;yes&quot;}\" data-widget_type=\"video.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-wrapper elementor-open-inline\">\n\t\t\t<div class=\"elementor-video\"><\/div>\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-38bf861 e-n-tabs-mobile elementor-widget elementor-widget-n-tabs\" data-id=\"38bf861\" data-element_type=\"widget\" data-settings=\"{&quot;tabs_justify_horizontal&quot;:&quot;center&quot;,&quot;horizontal_scroll&quot;:&quot;disable&quot;}\" data-widget_type=\"nested-tabs.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"e-n-tabs\" data-widget-number=\"59504737\" aria-label=\"Tabs. Open items with Enter or Space, close with Escape and navigate using the Arrow keys.\">\n\t\t\t<div class=\"e-n-tabs-heading\" role=\"tablist\">\n\t\t\t\t\t<button id=\"e-n-tab-title-595047371\" class=\"e-n-tab-title\" aria-selected=\"true\" data-tab-index=\"1\" role=\"tab\" tabindex=\"0\" aria-controls=\"e-n-tab-content-595047371\" style=\"--n-tabs-title-order: 1;\">\n\t\t\t\t\t\t<span class=\"e-n-tab-title-text\">\n\t\t\t\tSummary\t\t\t<\/span>\n\t\t<\/button>\n\t\t\t\t<button id=\"e-n-tab-title-595047372\" class=\"e-n-tab-title\" aria-selected=\"false\" data-tab-index=\"2\" role=\"tab\" tabindex=\"-1\" aria-controls=\"e-n-tab-content-595047372\" style=\"--n-tabs-title-order: 2;\">\n\t\t\t\t\t\t<span class=\"e-n-tab-title-text\">\n\t\t\t\tTranscript\t\t\t<\/span>\n\t\t<\/button>\n\t\t\t\t\t<\/div>\n\t\t\t<div class=\"e-n-tabs-content\">\n\t\t\t\t<div id=\"e-n-tab-content-595047371\" role=\"tabpanel\" aria-labelledby=\"e-n-tab-title-595047371\" data-tab-index=\"1\" style=\"--n-tabs-title-order: 1;\" class=\"e-active elementor-element elementor-element-c62c447 e-con-full e-flex e-con e-child\" data-id=\"c62c447\" data-element_type=\"container\">\n\t\t<div class=\"elementor-element elementor-element-5fb9e94 e-flex e-con-boxed e-con e-child\" data-id=\"5fb9e94\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-94450c1 elementor-widget elementor-widget-heading\" data-id=\"94450c1\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Video Summary<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c937f80 elementor-widget elementor-widget-text-editor\" data-id=\"c937f80\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Dr. Huitzimengari Campos Garcia, a plant physiologist from Mexico and professor at Universidad de las Americas, Puebla, presented his classroom experiences using the <a href=\"https:\/\/science.gsfc.nasa.gov\/stella\/instruments\/spectral\/stella-q2\/\">STELLA-Q2<\/a> sensor for plant spectral analysis. He shared two compelling student projects that demonstrated both the challenges and potential of portable spectral sensing technology in educational settings.<\/p><p>The foundation of Dr. Campos&#8217; work centers on the concept of spectral signatures &#8211; unique patterns of light reflection that serve as &#8220;fingerprints&#8221; for different plant species. He explained that while human eyes only perceive visible light from 400-700 nanometers, plants absorb and reflect various wavelengths across the spectrum, creating distinctive signatures that can be measured and analyzed. This principle forms the basis for remote sensing applications and species identification through spectral analysis.<\/p><p>The first student project, conducted by Mariana Alvarado Mulligan, focused on plant species identification using spectral signatures. The students built a portable spectrometer incorporating the STELLA-Q2 sensor and collected measurements from various plant species including Agave, Bruhinia, Yucca, Cupressus, and Asparagus. Initially working with raw, uncorrected data due to time constraints at semester&#8217;s end, they encountered significant challenges with data quality, noise, and variation.<\/p><p>When analyzing the raw data using Principal Component Analysis to reduce the 18 spectral bands to key components, the students found that while the first two components explained 89% of the data variation, the species signatures overlapped considerably. This poor data quality resulted in only 14% accuracy when using multinomial logistic regression for species classification &#8211; meaning 86% of their predictions were incorrect. However, this initial failure became a valuable learning experience about the importance of data quality and proper methodology.<\/p><p>The dramatic improvement came when Dr. Campos showed results from other researchers who had performed proper white card correction on similar data. With corrected measurements, the same analytical approach yielded completely different results. The Principal Component Analysis showed clear, distinct clusters for each plant species with no overlap, and the classification accuracy jumped to 100%. This transformation illustrated how proper calibration techniques could convert seemingly unusable data into highly accurate identification tools.<\/p><p>The second student project examined whether spectral analysis could detect differences between fertilized and unfertilized plants using Portulaca oleracea (purslane). Students grew plants under two conditions &#8211; soil only versus soil with chemical fertilizer &#8211; and collected spectral measurements throughout the growth period. While working with raw, uncorrected data again, they were able to detect small but measurable differences between the two treatment groups, suggesting that spectral analysis could potentially monitor plant nutritional status.<\/p><p>Throughout the presentation, Dr. Campos and his audience discussed various technical aspects of spectral measurement, particularly the critical importance of white reference correction. While the exact methodology wasn&#8217;t fully detailed, the process involves taking measurements of a white reference surface alongside plant measurements and using these readings to normalize the data. The discussion also touched on alternative white reference materials for improved accessibility, including polystyrene foam as a substitute for expensive Spectralon, white cutting boards, construction paper, and even white sand that connects to Landsat satellite calibration standards.<\/p><p>Dr. Campos emphasized the exceptional educational value of these exercises, noting how they integrated multiple skill sets including experimental design, data collection, computational analysis, and statistical interpretation. Students gained hands-on experience with the entire scientific process while learning to distinguish between quality data and problematic measurements. The projects effectively demonstrated real-world applications of remote sensing principles and provided practical experience with both the potential and limitations of spectral analysis technology.<\/p><p>The presentation concluded with recognition of both the challenges and opportunities presented by portable spectral sensors in educational settings. While initial measurements may suffer from noise and variation, proper calibration and methodology can transform these tools into powerful instruments for species identification and plant physiological assessment. The work represents an excellent example of how cutting-edge scientific instruments can be successfully integrated into classroom learning, providing students with valuable experience in experimental science while advancing their understanding of plant biology and remote sensing applications.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div id=\"e-n-tab-content-595047372\" role=\"tabpanel\" aria-labelledby=\"e-n-tab-title-595047372\" data-tab-index=\"2\" style=\"--n-tabs-title-order: 2;\" class=\" elementor-element elementor-element-88de15a e-con-full e-flex e-con e-child\" data-id=\"88de15a\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-7827e67 elementor-widget elementor-widget-heading\" data-id=\"7827e67\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Transcript<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-111f3dc elementor-widget elementor-widget-text-editor\" data-id=\"111f3dc\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Okay.<\/p><p>My name is Huitzimengari Campos Garcia.<\/p><p>I&#8217;m a plant physiologist from Mexico,<\/p><p>and I&#8217;m also a professor<br \/>in two universities.<\/p><p>One of them is Universidad<br \/>de las Americas, Puebla.<\/p><p>today<br \/>I&#8217;m going to talk about the exercises<\/p><p>that we did with the STELLA-Q2 sensor<br \/>in the classroom.<\/p><p>So I&#8217;m<\/p><p>going to<\/p><p>talk about the two,<\/p><p>examples, examples<br \/>that I took from my students.<\/p><p>One of them is Mariana Alvarado Mulligan.<\/p><p>what she did was two<\/p><p>relays, the expected signal<\/p><p>to take it from the<\/p><p>STELLA-Q2 sensor.<\/p><p>And you see two correlated with the,<\/p><p>name spaces of several plants.<\/p><p>So let&#8217;s begin.<\/p><p>the light, some physical characteristics,<br \/>the sticks from light.<\/p><p>And we can see that<\/p><p>the visible light is only from<\/p><p>400 nanometers to 700 nanometers.<\/p><p>But there are also other bands<\/p><p>that are happening<br \/>right now in the light. So<\/p><p>these<\/p><p>these bands could carry<\/p><p>information about the surface,<br \/>some faces that reflected that light.<\/p><p>So that is one of the, the.<\/p><p>Usefulness of the.<\/p><p>Of measuring,<\/p><p>light or quantify light,<\/p><p>which sensor like STELLA could also.<\/p><p>When we illuminate a surface<br \/>this whole face,<\/p><p>then we absorb some light and reflect<br \/>another one.<\/p><p>creating,<br \/>a spectral signature that we can see here.<\/p><p>So we have this difference,<br \/>different species,<\/p><p>and it&#8217;s spectral signature<br \/>that is very specific for each species.<\/p><p>So it&#8217;s very useful<br \/>or it will be very useful to,<\/p><p>To these,<\/p><p>taxonomic colors to this.<\/p><p>I mean, I don&#8217;t know if you<\/p><p>have tried to.<\/p><p>Identify,<br \/>plant species to the species live.<\/p><p>It is very difficult.<\/p><p>You need a lot of knowledge<br \/>about taxonomy in plant botany,<\/p><p>but we essentially stick to those.<\/p><p>We can, foster, measure<\/p><p>and try to figure out which species it is.<\/p><p>And this is where<\/p><p>Mariana did So we build the picture,<\/p><p>the spectrometer,<br \/>put the sensor in the classroom,<\/p><p>and then we go outside to take several<br \/>measures in different plant species.<\/p><p>Some of the ratios<\/p><p>where these ones<\/p><p>we find.<\/p><p>Different kind of the spectral signatures<\/p><p>you can see here for Agave,<\/p><p>Bruhinia, Yucca and it&#8217;s<\/p><p>picture or photograph.<\/p><p>here down.<\/p><p>We have Cupressus and Asparagus.<\/p><p>So also the<\/p><p>the other important thing that we find<br \/>is that we have bad data<\/p><p>with a lot of noises in, in it.<\/p><p>So we have to improve<br \/>or measure in measurements<\/p><p>and we want to spec<\/p><p>these kind of measures that are here down.<\/p><p>So the variation is smaller<br \/>than these ones.<\/p><p>Another importance is that we have to do<\/p><p>the white color correction<br \/>for each measurement.<\/p><p>But in this case we have very little time.<\/p><p>The semester was ending.<\/p><p>So we used the the road data direct<\/p><p>from the on the STELLA colors.<\/p><p>For that reason and another ones<\/p><p>like these highly variation.<\/p><p>We found a lot of noise in our data<\/p><p>when we the the principal component<\/p><p>analysis principal component<br \/>analysis is reduction.<\/p><p>And information procedure.<\/p><p>So we have 18 bands.<\/p><p>those information of the 18 bands<\/p><p>we reduce to 18 principal components.<\/p><p>So we we that reduction<\/p><p>we have 89% of all data variation.<\/p><p>Then we plot the first two<br \/>principal components.<\/p><p>The they explain 89%<\/p><p>of the of the variation.<\/p><p>So we can see that every ellipse for is<\/p><p>is the signature of each plant.<\/p><p>Here we can see that<\/p><p>because the highly variation in the data,<\/p><p>they overlap one in another.<\/p><p>So the discriminant function<br \/>was not very well indeed.<\/p><p>When when we applied<br \/>the classification method.<\/p><p>In this case, we applied<br \/>multinomial logistic regression<\/p><p>to predict the species.<\/p><p>Because all highly variation<br \/>data, we found only.<\/p><p>The accuracy of<\/p><p>that model was only of the 14%.<\/p><p>This means that 86% of<\/p><p>Of the classification went<\/p><p>was wrong.<\/p><p>But all these could be improved.<\/p><p>Indeed, we we are going to see.<\/p><p>Later that with another dataset<br \/>from another researchers<\/p><p>that they did the white card correction,<\/p><p>they improve a lot.<\/p><p>The classification<br \/>and the discriminant function<\/p><p>of the principal component analysis.<\/p><p>That&#8217;s amazing.<\/p><p>That&#8217;s really it is so good.<\/p><p>I think that.<\/p><p>The even thinking<\/p><p>in the work that my students did<\/p><p>for, semester in the classroom,<\/p><p>this example is is great for data working.<\/p><p>He&#8217;s one for his experimental skills.<\/p><p>Also computers computational skills<br \/>and statistics skills.<\/p><p>So it&#8217;s great that they.<\/p><p>Carry on an experimental<\/p><p>procedure in his background.<\/p><p>So it&#8217;s great to to did these kind of<\/p><p>exercises in the in the classroom as well.<\/p><p>That&#8217;s amazing.<\/p><p>It&#8217;s great to you know<\/p><p>find out again you know what&#8217;s good<br \/>data versus what&#8217;s bad data.<\/p><p>You know.<\/p><p>And you know<br \/>So they also take several plant species.<\/p><p>Would they correct the date the data<br \/>so we can see<\/p><p>little variation between the data?<\/p><p>We do.<\/p><p>Quick question.<\/p><p>how did they, how did they correct it?<\/p><p>can you go a little bit more into detail<br \/>about the, the, the process of what?<\/p><p>I don&#8217;t know very well the process,<br \/>but I know that they, they take<\/p><p>when they take these measurements also,<br \/>they take<\/p><p>the measurement of, weight to face.<\/p><p>I don&#8217;t know what kind of white to face.<\/p><p>And then they normalize<br \/>with the, with that 3D<\/p><p>every, every measurement in here.<\/p><p>I don&#8217;t know really well how they did it.<\/p><p>Well it&#8217;s a good question to<\/p><p>if they can share more details<br \/>about how they did the<\/p><p>the white card White card correction<br \/>that they call it.<\/p><p>with this data, with the<br \/>the principal component analysis.<\/p><p>And it was even.<\/p><p>There this is what I can show you now.<\/p><p>Every ellipse.<\/p><p>It&#8217;s well apart from the others.<\/p><p>This means that these<\/p><p>signatures, these information<\/p><p>is very particular to each species.<\/p><p>So it&#8217;s like, fingerprint signature,<\/p><p>the light signature<br \/>or the spectral signature<\/p><p>is it&#8217;s talking to us about<\/p><p>how one species specifically.<\/p><p>So that that is great.<\/p><p>We can see here.<\/p><p>Oh, how well the data or the each species.<\/p><p>Separates from, from the other one.<\/p><p>here is even they<\/p><p>the white card data.<\/p><p>So when we did the, the spectral<\/p><p>sorry the classification method,<br \/>we find an accuracy.<\/p><p>We did this data set of 100%.<\/p><p>Doing that.<\/p><p>Yes. This means that we can use<br \/>these spectra signature and predict<\/p><p>with, 100% of probability<\/p><p>that this is the coral.<\/p><p>For example.<\/p><p>That&#8217;s fantastic.<\/p><p>That is just, well, great work.<\/p><p>this is something that, you know, again,<br \/>goes to the fundamentals,<\/p><p>you know, of what we&#8217;re trying to do,<br \/>you know, just, what can you see?<\/p><p>What do you want to measure?<\/p><p>And, you know, again, you know,<br \/>and of course, the calibration as well.<\/p><p>So if,<\/p><p>at a certain point,<\/p><p>I definitely want to see what the,<br \/>their method of, white carding is.<\/p><p>you know, what what,<br \/>what material they use,<\/p><p>all that fun stuff, you know,<br \/>we&#8217;re using polystyrene foam generally<\/p><p>as our, as our substitute for,<br \/>Spectralon<\/p><p>because, you know, not everyone can afford<br \/>Spectralon and, you know much.<\/p><p>Yeah, exactly. we have that cutting board.<\/p><p>so like, we had, gotten like,<br \/>so looking at on the light blue and slope,<\/p><p>we had, our, lead scientist, Dr.<\/p><p>Petya Campbell,<br \/>she was out in Alaska, and she had trouble<\/p><p>keeping the the, polystyrene foam clean.<\/p><p>So she was using a white cutting board.<\/p><p>Okay. there.<\/p><p>So that&#8217;s another one<br \/>that we&#8217;re we&#8217;re looking at.<\/p><p>And then we&#8217;re also looking at,<br \/>because again,<\/p><p>yeah, we&#8217;re looking for accessibility<br \/>and all that we&#8217;re looking at, just like,<\/p><p>you know, like, you know,<\/p><p>pieces of paper, white construction paper,<br \/>and then finally<\/p><p>to tie it all back in with, Landsat,<br \/>as we should, white sand, we,<\/p><p>we will have to go to Quintana Arroyo,<br \/>Quintana Roo.<\/p><p>That&#8217;s white sand.<\/p><p>It would be cool to have white sand,<br \/>but it would be awesome.<\/p><p>Cool to have like the other, you know,<\/p><p>just various<br \/>different types of sand as well.<\/p><p>but there&#8217;s, a whole, like, list of,<\/p><p>you know, places where Landsat calibrates<br \/>against different sands.<\/p><p>and it&#8217;s, fantastic list.<\/p><p>And so if there&#8217;s any kind of sand that&#8217;s,<br \/>you know, anywhere<\/p><p>close to being or replicable,<br \/>to replicate it,<\/p><p>that would be, you know, just a great,<br \/>you know,<\/p><p>learning tool, an instrument,<br \/>fun, fun way of doing this type of stuff.<\/p><p>something similar.<\/p><p>What do you see this time?<\/p><p>Since partial loss vulgaris?<\/p><p>what we did was to<\/p><p>put two treatments,<\/p><p>one without only soil<\/p><p>and one with,<br \/>with some Chemical fertilizer.<\/p><p>So the plants grow<\/p><p>we take the measurements in the plants.<\/p><p>We have a lot of variation also.<\/p><p>But this could be improved.<\/p><p>They can.<\/p><p>I mean, this could be.<\/p><p>Improve it,<br \/>as you say, taking the measurement more<\/p><p>more near to the full face of the leaf.<\/p><p>But we find some differences.<\/p><p>Very little difference between the<\/p><p>fertilizer,<\/p><p>treatments.<\/p><p>What was was the.<\/p><p>without fertilizer.<\/p><p>Fertilizer and b with fertilizer.<\/p><p>So they are readings<\/p><p>where these ones<\/p><p>and then this one very similar.<\/p><p>the fertilizer readings<\/p><p>were a little different from,<br \/>from the other ones.<\/p><p>So we can speak better improvement,<br \/>improvements in the<\/p><p>taking of the measurements<br \/>so we can discriminate<\/p><p>between the fertilizer<br \/>plants, fertilizer plants and the<\/p><p>plants without fertilizer.<\/p><p>The that was<\/p><p>what she did.<\/p><p>That&#8217;s that&#8217;s great.<\/p><p>And then so that was so those measurements<br \/>were before like correction.<\/p><p>Is that what you&#8217;re saying?<\/p><p>before.<\/p><p>No. Also after<br \/>you said the the raw data. Yes.<\/p><p>Oh yeah. Yeah. That&#8217;s. Yeah. Okay.<br \/>So you&#8217;re using the raw data.<\/p><p>So the irradiance data,<br \/>and not the corrected.<\/p><p>Okay.<\/p><p>But on that is I mean, again, that is it&#8217;s<\/p><p>fantastic to know it&#8217;s,<br \/>that&#8217;s really freaking useful. So<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Revolutionary Plant Science: How Students Used NASA STELLA low-cost instruments to ID Plants with Great Accuracy!<\/p>\n","protected":false},"author":2,"featured_media":2991,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_lmt_disableupdate":"","_lmt_disable":"","footnotes":""},"categories":[15,37,39,82,7,41,85,23,72,87],"tags":[63,61,67,77,62],"class_list":["post-2990","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-applications","category-data","category-education","category-engagement","category-instruments","category-news","category-plants","category-stella-q2","category-use-case","category-video","tag-agriscience","tag-education","tag-science","tag-spectroscopy","tag-stem"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v25.4 - 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