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Torresani, M., Moudry, V., Hakkenberg, C.R., Goetz, S.J., Coomes, D., Cotrina-Sanchez, D.A., Armston, J.D., Vogeler, J.C., Roy, D.P., Rocchini, D., Laurin, G.V., Perrone, M., Tognetti, R., 2026, The role of GEDI spaceborne lidar in biodiversity analyses: a topical review, Environmental Research Letters, 21, 093003. 

Roy, D.P., Wulder, M.A., Gorelick, N., Hansen, M., Healey, S., Hostert, P., Huntington, J., Radeloff, V.C., Scambos, T., Schaaf, C., Woodcock, C.E., Zhu, Z., 2026, The next Landsat: Mission turning point?, Remote Sensing of Environment, 332, 115087. 

Chen, D., Roy, D.P., Zhu, Z., McVicar, T.R., Sheffield, J.,  Atkinson, P.M., AghaKouchak, A., Blaschke, T., Borrelli, P.,  Dorigo, W., Fensholt, F., Gorelick, N., Guanter, L., Hubacek, K., McCabe, M.F., Miralles, D.G., Rammig, A., Reichstein, M., Rignot, E., Rogers, B.M., Tyukavina, A., Veraverbeke, S., Zarco-Tejada, P.J., Berthier, E., 2025, A call for integrated and cooperative global sharing of China’s Earth observation data, Nature Geoscience, 18, 1071-1073. 

Yan, L., Roy, D.P., Huang, H., 2026, A least-squares spatio-temporal adjustment algorithm (LSSTAA) for radiometric normalization of PlanetScope time series, ISPRS Journal of Photogrammetry and Remote Sensing, 231, 394-413.

Giglio, L., Boschetti, L., Roy, D.P., Hall, J., Zubkova, M., Humber, M., Huang, H., Oles, V., 2025, The NASA Visible Infrared Imaging Radiometer Suite (VIIRS) burned area product, global validation, and inter-comparsion with the NASA Moderate Resolution Imaging Spectroradiometer (MODIS) burned area product, Remote Sensing of Environment, 331, 115006.

Luo, D., Zhang, H.K., De Lemos, H., Li, J., Roy, D.P., 2025, A globally applicable deep learning model for Sentinel-2 cloud and shadow detection, Science of Remote Sensing, 12, 100278.

Zhang, H.K., Shen, Y., Zhang, X., Li, J., Yang, Z., Xu, Y., Zhang, C., Di, L., Roy, D.P., 2025, Robust and timely within-season conterminous United States crop type mapping using Landsat Sentinel-2 time series and the transformer architecture, Remote Sensing of Environment, 329, 114950.

Roy, D.P., Wulder, M.A., Woodcock, C., 2025, Independent oversight crucial to Landsat Next, Science, 388 (6751),1033.

Yan, L., Roy, D.P., Belton, B., Baldiga, L., Thaneerat, A., Shen, M., 2025, Characterizations of spatial evolutions of aquaculture in Central Thailand 1990 – 2020, chapter in Remote Sensing of Land Cover and Land Use Changes in South and Southeast Asia, Volume 1 Mapping and Monitoring, Eds. Vadrevu, K.P., Justice, C.O., Gutman, G., Taylor & Francis Group, U.K.  

Ju, J., Zhou, Q., Freitag, B., Roy, D.P., Zhang, H.K., Sridhar, M., Mandel, J., Arab, S., Schmidt, G., Crawford, C.J., Gascon, F., Strobl, P.A., Masek, J.G., Neigh, C.S.R., 2025, The harmonized Landsat and Sentinel-2 version 2.0 surface reflectance dataset, Remote Sensing of Environment, 324, 114723.

Cho, M-S., Roy, D.P., Kashongwe, H.B., Yan, L., Shen, M., 2025, Assessment and improvement of GEDI canopy height estimation in tropical and temperate forests, Science of Remote Sensing, 11, 100221. 

Huang. H., Roy, D.P., De Lemos, H., Qiu, Y., Zhang, H.K., 2025, A global Swin-Unet Sentinel-2 surface reflectance-based cloud and cloud shadow detection algorithm for the NASA Harmonized Landsat Sentinel-2 (HLS) dataset, Science of Remote Sensing, 11,  100213 . 

Yan, L. and Roy, D.P., 2025, Using Landsat 8 and 9 Operational Land Imager (OLI) data  to characterize geometric distortion and improve geometric correction of Landsat Multispectral Scanner (MSS) imagery, Remote Sensing of Environment, 321, 114679 . 

McGregor, S., Cromsigt, J.P.G.M., te Beest, M., Chen, J., Roy, D.P., Hawkins, H-J., Kerley, G.I.H., 2024, Grassland albedo as a nature-based climate prospect: the role of growth form and grazing, Environmental Research Letters, 19, 124004. 

Gao, S., Zhang, X., Zhang, H.K, Shen, Y., Roy, D.P., Wang, W., Schaaf, C., 2024, A new constant scattering angle solar geometry definition for normalization of GOES-R ABI reflectance times series to support land surface phenology studies, Remote Sensing of Environment, 315, 114407.

Roy, D.P., De Lemos, H., Huang, H., Giglio, L., Houborg, R., Miura, T., 2024, Multi-resolution monitoring of the 2023 Maui wildfires, implications and needs for satellite-based wildfire disaster monitoring, Science of Remote Sensing, 10, 100142. 

Liu, H., Zhang, H.K., Huang, B., Yan, L., Tran, K. H., Qiu, Y., Zhang, X., Roy, D.P., 2024, Reconstruction of seamless harmonized Landsat Sentinel-2 (HLS) time series via self-supervised learning, Remote Sensing of Environment, 308,  114191. 

Giglio, L., and Roy, D. P., 2024, Satellite artifacts modulate FireCCILT11 global burned area, Nature Communications, 15, 2079.

Zhang, H.K., Luo, D., Roy, D.P., 2024, Improved Landsat Operational Land Imager (OLI) cloud and shadow detection with the learning attention network algorithm (LANA), Remote Sensing, 16(8), 1321.

Radeloff, V. C., Roy, D.P.,  Wulder, M.A.,  Anderson, M., Cook, B., Crawford, C. J., Friedl, M., Gao, F., Gorelick, N., Hansen, M., Healey, S., Hostert, P., Hulley, G.,  Huntington, J.L.,  Johnson, D.M., Neigh, C., Lyapustin,  A., Lymburner, L., Pahlevan, N., Pekel, J.-F. , Scambos, T. A., Schaaf, C., Strobl, P., Woodcock, C.E., Zhang, H.K., Zhu, Z., 2024, Need and vision for global medium-resolution Landsat and Sentinel-2 data products.  Remote Sensing of Environment, 300, 113918.

Qiu, Y., Roy, D.P., Zhang, H.K., Southern Africa demonstration  of self-supervised deep learning to forecast future fire, 14th EARSeL Workshop on Forest Fires, Forest Research Centre, School of Agriculture University of Lisbon, Lisbon, Portugal, 25-26 June 2026.

Boschetti, L., Oles, V., Roy, D.P., Giglio, L., From small fires to detection probability: sub-pixel effects on coarse-resolution burned-area accuracy, 14th EARSeL Workshop on Forest Fires, Forest Research Centre, School of Agriculture University of Lisbon, Lisbon, Portugal, 25-26 June 2026.

Giglio, L., Hall, J., Roy, D.P., Boschetti, L., Humber, M., Chen, T., Schroeder, W., Status of NASA’s Fire Products and Future Plans, 8th Global Wildfire Information System (GWIS) and Global Observations of Forest Cover – Global Observation of Landcover Dynamics (GOFC-GOLD) Fire Implementation Team meeting, Centro Científico e Cultural de Macau (CCCM), Rua da Junqueira, Lisbon, Portugal, 23 – 24 June 2026.

Boschetti, L., Roy, D.P., Oles, V., MODIS and VIIRS burned area data in the Global Wildfire Information System (GWIS) supporting wildfire reporting  and decision making, 8th Global Wildfire Information System (GWIS) and Global Observations of Forest Cover – Global Observation of Landcover Dynamics (GOFC-GOLD) Fire Implementation Team meeting, Centro Científico e Cultural de Macau (CCCM), Rua da Junqueira, Lisbon, Portugal, 23 - 24 June 2026.

Roy, D.P., Giglio, L., Huang, H., Qiu, Y.,  Boschetti, L., Current status of satellite fire monitoring and the emerging role of AI for future satellite wildfire disaster monitoring and prediction, M-IS15 Wildfire as a Geoecological Driver in a Changing Climate, Japan Geoscience Union – American Geophysical Union (JpGU-AGU) Joint Meeting 2026, Makuhari Messe, Chiba, Japan, 24-29 May 2026.

Cho, M., Roy, D.P., Kashongwe, H., Oles, V., Bwangoy, J.R., Quantification of the Democratic Republic of Congo humid tropical forest carbon sink due to three decades of post-deforestation/degradation forest regrowth, A-CG61 Carbon Cycle Observation and Analysis, Japan Geoscience Union – American Geophysical Union (JpGU-AGU) Joint Meeting 2026, Makuhari Messe, Chiba, Japan, 24-29 May 2026.

Li, J., Zhang, H.K., Roy, D.P., HLS-GPT: A Generative Pretrained Transformer (GPT) for continental-scale NASA Harmonized Landsat and Sentinel-2 (HLS) reflectance reconstruction across all bands on arbitrary dates, Second ESA-NASA workshop on AI foundation models for Earth Observation, Huntsville, Alabama, 19–22 May 2026.

Roy, D.P., Newman, T., St. Germain, K., Zurbuchen, T., Invited panelist: Pixel perfect: How Landsat and commercial capabilities are changing the view, ASCEND 2026, Commercial Space Policy Summit, Washington, D.C., 19–21 May 2026.

Roy, D.P.,  Zhang, H.K., Yan, L., Pathfinding global analysis ready consistent reflectance from the Landsat MSS to the next era, Spring Landsat Science Team Meeting, USGS Earth Resources Observation and Science (EROS) Center, Sioux Falls, SD, 5–7 May 2026.

Roy, D.P., Why CSME should be working with satellite Earth Observation data, Department of Computational Mathematics, Science and Engineering (CMSE), Big Conference  Room, Engineering Building,  Michigan State University, Research Seminar, 26 January 2026.

Roy, D.P., Zhang, H.K., Yan, L., Pathfinding the steps to ensure global analysis ready consistent reflectance from the Landsat MSS to Landsat Next era, Invited Presentation and then panelist in 90 minute panel on Public Data, National Priorities: Unlocking Value with Landsat, Fall Meeting, AGU, New Orleans LA, 14 - 19 December 2025.

Cho, M.S., Roy, D.P., Kashongwe, H., Skole, D.L., Combining GEDI and Landsat-based satellite forest cover loss data to improve understanding of Democratic Republic of Congo humid tropical and subtropical forest regrowth and carbon sequestration, Poster in Tropical Forests Under a Changing Environment, Fall Meeting, AGU, New Orleans LA, 14 - 19 December 2025.

Kashongwe, H., Roy, D.P., Cho, M, Hakkenberg, C.R., Bwangoy, J.R., GEDI observations of foliage height diversity track regrowth trends in the humid tropical forests of the Democratic Republic of Congo, Presentation in Forest Structural Diversity: Metrics, Methods, and Link to Ecosystem Functions session, Fall Meeting, AGU, New Orleans LA, 14 - 19 December 2025.

Qiu, Y., Roy, D.P., Zhang, H.K., Huang, H., Self-supervised explainable deep learning to forecast future fire at monthly, seasonal, and annual, landscape scale, Poster in Advancements in Wildland Fire Science, Management, and Engagement: Integrating Earth Observation Technologies and Collaborative Development, Fall Meeting, AGU, New Orleans LA, 14 - 19 December 2025.

Li, J., Zhang, H.K., Roy, D.P., Qui, Y, HLS-GPT: A Generative Pretrained Transformer (GPT) model for accurate Harmonized Landsat and Sentinel-2 (HLS) annual reflectance time series reconstruction, Poster in Science and Applications Enabled by Remote Sensing Data Fusion, Time Series Analysis, and AI, Fall Meeting, AGU, New Orleans LA, 14 - 19 December 2025.

Yan, L. and Roy, D.P., Conterminous United States multi-annual crop field boundary delineation, Poster in Satellite Solutions: Agricultural Monitoring Through Remote Sensing, Fall Meeting, AGU, New Orleans LA, 14 - 19 December 2025.

Whitcraft, A.K., Humber, M.L., Guan, K., Sahajpal, R., Shuai, G., Kalecinski, N., Thieme, A., Hively, W.D., Yan, L., Roy, D.P.,  Bredder, A., Gilliams, S., Acres and the Essential Agriculture Variables: Contributions and Collaborations toward a Globally-Consistent, Site-Relevant Agricultural Knowledge System, Presentation in Satellite Solutions: Agricultural Monitoring Through Remote Sensing, Fall Meeting, AGU, New Orleans LA, 14 - 19 December 2025.

Boschetti, L., Roy, D.P., Oles, V., Systematic fire products: meeting users’ needs from global to local, 12th AIT International Conference, Milan, Italy, November 12-14 2025.  

Roy, D.P., Huang, H., Boschetti, L., Zhang, H., Globally applicable high and medium resolution burned area mapping with deep learning, Invited Presentation, Food and Agriculture Organization of the United Nations, Rome, Italy, November 10 2025.

Roy, D.P., Non-prescriptive thinking concerning satellite fire product endorsement needs and potential processes, 39th Committee on Earth Observation Satellites (CEOS) Plenary, Natural Resources Canada side meeting - coordination of global wildfire monitoring using EO,  Bath, U.K., November 4 2025.

Dr. Myung Sik Cho, Postdoctoral research associate 

Dr. Haiyan Huang, Assistant research professor

Dr. Herve Kashongwe, Postdoctoral research associate

Dr. Jilin Men, Postdoctoral research associate

Yuean Qiu, Ph.D. student 

Dr. Lin Yan, Associate research professor

Roy, D.P.,  Zhang, H.K., Yan, L., Pathfinding the steps to ensure global analysis ready consistent reflectance from the Landsat MSS to the next Landsat era, Funded by US Geological Survey - Landsat Science Team - Contract awarded under RFP 140G0125R0001.

Roy, D.P., Kashongwe, H., Cho, M., Using GEDI data to improve understanding of species-specific central Africa tropical forest regrowth. Funded by NASA NNH23ZDA001N-GEDI Global Ecosystem Dynamics Investigation Science Team program. 

Giglio, L., Roy, D.P., Hall, J.V., Zubkova, M., Santamaría-Artigas, A.E., Huang, H., Yan, L. A Five-Decade Multi-Sensor Global Burned Area Data Record. Funded by NASA NNH22ZDA001N-MEASURES Making Earth System Data Records for Use in Research Environments program.

Roy, D.P., Democratic Republic of Congo satellite forest canopy cover, loss and swamp extent mapping, demonstration pilot in support of Wildlife Works Carbon LLC REDD+ projects.  Funded by Wildlife Works Carbon LLC (https://www.wildlifeworks.com/mai-ndombe).   See https://sites.google.com/view/msu-drc-forest/home. 

Whitcraft, A.K. (University of Maryland) et al., Roy, D.P.,  NASA ACRES: A Climate Resilient Ecosystem Approach to Strengthening US Agriculture. Funded by NASA Applied Science Program, solicitation A. 36 Earth Science Applications: Agriculture, NNH22ZDA001N-AG. program. 

GEO 827 Theory and Practice of Terrestrial Remote Sensing

Mon & Wed : 5:00 PM-6:20 PM   

8/26/2026 - 12/4/2026

Environmental remote sensing systems have evolved significantly over the last two decades fueled by pressing needs to monitor human impacts on the Earth system and to develop a comprehensive understanding of Earth system functioning.  Space agency and commercial satellite remote sensing systems are in orbit and planned for launch with associated computing systems to derive biophysical and geophysical data products on a global, systematic basis. This course describes the science theory, algorithms, and computational approaches to generate and assess derived satellite products for long term Earth system monitoring. Emphasis is on the principles of optical remote sensing (0.4-14 microns) and quantitative algorithms for estimating biophysical and geophysical land surface variables from remotely sensed observations. The course provides insights into how space agencies, and in particular NASA, goes about these tasks, using the NASA MODIS land products as a lens for what we learn throughout the course.  Yes, deep learning is mentioned in some of the later lectures.

This is 3 credit graduate level course. Understanding of the fundamental principles of remote sensing, some physics, calculus, statistics, and also computer literacy, is expected.  Lecture material will be provided to students after each lecture as .PDF files.

Students who desire an introductory Remote Sensing course should take GEO-324 or GEO-424.

1990 - 1993   Ph.D. Remote Sensing,  University of Cambridge, Department of Geography, Cambridge, U.K.  

(second row black scarf,  front row/first left) 

1987 - 1988   M.Sc.  Remote Sensing and Image Processing Technology, University of Edinburgh, Department of Meteorology, U.K. 

1984 - 1987    B.Sc. Geophysics, Lancaster University, Department of Environmental Science, Lancaster, U.K.