(Lead Computer Scientist)
| Email: | b.smith@nasa.gov |
| Phone: | 301.614.1111 |
| Org Code: | 619 |
| Address: |
NASA/GSFC Mail Code 619 Greenbelt, MD 20771 |
| Employer: | SCIENCE SYSTEMS AND APPLICATIONS INC |
Theory & Modeling
The development of Earth System Digital Twins (ESDTs) represents an ongoing journey towards more accurate and integrated simulations of Earth processes. Inherently interdisciplinary, the endeavor grapples with the challenge of melding subsystems developed by experts in different fields and organizations, requiring communication between different science domains, technology stacks, and data modalities. The Coupled Reusable Earth System Tensor (CREST) framework is a key aspect of our efforts to address these difficulties: by implementing a generic abstraction layer over existing tensor libraries (e.g. TensorFlow, PyTorch, JAX), CREST provides the software foundation for building, operating, and deploying community developed ESDTs. This framework is designed to allow scientists to easily couple together process-based and data-driven models into broader digital twin workflows, while taking advantage of significant efficiency improvements from hardware accelerators.
CREST aims to be a step forward in combining traditional modeling techniques with emerging computational methods, particularly in the context of machine learning. Machine learning plays a foundational role in our approach, both contributing to the development of new data-driven models and aiding in efficient coupling with existing models. Through CREST, we aim to enhance model integration and foster more dynamic interactions within the modeling pipeline – primarily addressing the issues of limited support in current frameworks for gradient propagation, hardware acceleration, and federation with external models. In addition, CREST operational capabilities will include data assimilation, end-to-end distributed model training, black-box model coupling, what-if scenario analysis, and an easy-to-use GUI interface for end users.
Remote Sensing
Researching and applying machine learning models to remotely sensed imagery in order to accurately estimate water quality parameters (e.g. Chl-a, TSS, etc.), absorption and scattering spectra, and other inherent optical properties. These imagery are sourced from multispectral satellite missions such as Landsat and Sentinel, as well as from a number of hyperspectral sources in preparation for PACE. The goal is to provide consistent global products across any period of time with satellite coverage, primarily targeting ecosystems in near-shore / inland bodies of water with highly turbid and eutrophic compositions.
ACIX is an international collaborative initiative to inter-compare a set of atmospheric correction (AC) algorithms for moderate-resolution optical sensors. The exercises will focus on Sentinel-2 and Landsat-8 data over a set of test areas. The end goal is to identify strengths and weaknesses of various atmopsheric correction methods and propose a hybrid approach to generate operational Level-2 reflectance products.
Development of tools which aid in monitoring and reporting of water quality across a number of sites in Africa. Water authorities and Ministry of Environment in these countries are working with us to evaluate satellite-derived products at the pilot sites.
SSAI, NASA GSFC - Greenbelt, MD
August 2017 - Present
See projects above.
NASA GSFC - Greenbelt, MD
June 2017 - August 2017
Development and publication of neural network algorithm to perform spectral band adjustments for instruments aboard NASA Earth Science satellite missions.
Georgia Institute of Technology - Atlanta, GA
September 2016 - December 2017
Knowledge-Based Artificial Intelligence (CS7637). Development of ML-based essay grading and rationale generation; mentoring graduate students through their development of conversational agents modeled on Jill Watson, via state-of-the-art cognitive frameworks (IBM Bluemix, API.ai, WIT.ai).
Artificial Intelligence (CS6601). Designed and implemented foundation for Jack Watson plagiarism detection framework; focus on teaching Hidden Markov Models via projects and exams.
Georgia Institute of Technology | Master of Science in Computer Science
University of Maryland, College Park | Bachelor of Science in Computer Science
2023. "Towards global long-term water transparency products from the Landsat archive." Remote Sensing of Environment 299 113889 [10.1016/j.rse.2023.113889] [Journal Article/Letter]
2023. "Leveraging multimission satellite data for spatiotemporally coherent cyanoHAB monitoring." Frontiers in Remote Sensing 4 [10.3389/frsen.2023.1157609] [Journal Article/Letter]
2023. "A hyperspectral inversion framework for estimating absorbing inherent optical properties and biogeochemical parameters in inland and coastal waters." Remote Sensing of Environment 295 113706 [10.1016/j.rse.2023.113706] [Journal Article/Letter]
2023. "GLORIA - A globally representative hyperspectral in situ dataset for optical sensing of water quality." Scientific Data 10 (1): 100 [10.1038/s41597-023-01973-y] [Journal Article/Letter]
2023. "Per-pixel uncertainty quantification and reporting for satellite-derived chlorophyll-a estimates via mixture density networks." IEEE Transactions on Geoscience and Remote Sensing 61 1-18 [10.1109/tgrs.2023.3234465] [Journal Article/Letter]
2022. "Monitoring Uruguay’s freshwaters from space: An assessment of different satellite image processing schemes for chlorophyll-a estimation." Remote Sensing Applications: Society and Environment 100891 [10.1016/j.rsase.2022.100891] [Journal Article/Letter]
2022. "Augmenting Heritage Ocean-Color Aerosol Models for Enhanced Remote Sensing of Inland and Nearshore Coastal Waters." Frontiers in Remote Sensing 3 [10.3389/frsen.2022.860816] [Journal Article/Letter]
2022. "Simultaneous retrieval of selected optical water quality indicators from Landsat-8, Sentinel-2, and Sentinel-3." Remote Sensing of Environment 270 112860 [10.1016/j.rse.2021.112860] [Journal Article/Letter]
2021. "Advancing cyanobacteria biomass estimation from hyperspectral observations: Demonstrations with HICO and PRISMA imagery." Remote Sensing of Environment 266 112693 [10.1016/j.rse.2021.112693] [Journal Article/Letter]
2021. "ACIX-Aqua: A global assessment of atmospheric correction methods for Landsat-8 and Sentinel-2 over lakes, rivers, and coastal waters." Remote Sensing of Environment 258 112366 [10.1016/j.rse.2021.112366] [Journal Article/Letter]
2021. "A Chlorophyll-a Algorithm for Landsat-8 Based on Mixture Density Networks." Frontiers in Remote Sensing 1 (5): [10.3389/frsen.2020.623678] [Journal Article/Letter]
2021. "Hyperspectral retrievals of phytoplankton absorption and chlorophyll-a in inland and nearshore coastal waters." Remote Sensing of Environment 253 112200 [https://doi.org/10.1016/j.rse.2020.112200] [Journal Article/Letter]
2020. "Robust algorithm for estimating total suspended solids (TSS) in inland and nearshore coastal waters." Remote Sensing of Environment 246 111768 [10.1016/j.rse.2020.111768] [Journal Article/Letter]
2020. "Monitoring Water-Related Ecosystems with Earth Observation Data in Support of Sustainable Development Goal (SDG) 6 Reporting." Remote Sensing 12 (10): 1634 [10.3390/rs12101634] [Journal Article/Letter]
2020. "Seamless retrievals of chlorophyll-a from Sentinel-2 (MSI) and Sentinel-3 (OLCI) in inland and coastal waters: A machine-learning approach." Remote Sensing of Environment 111604 [10.1016/j.rse.2019.111604] [Journal Article/Letter]
2017. "Spectral band adjustments for remote sensing reflectance spectra in coastal/inland waters." Optics Express 25 (23): 28650 [10.1364/oe.25.028650] [Journal Article/Letter]