Satellite Solutions: Agricultural Monitoring Through Remote Sensing

Mitkish, Mary

GC094
Satellite Solutions: Agricultural Monitoring Through Remote Sensing

Session ID#: 247467

Session Description:

NASA Harvest and NASA Acres jointly convene this session to showcase new findings, innovative methods, data sources, and key challenges in applying Earth observation (EO) to agriculture and food supply across scales and regions. Covering both domestic and global contexts, the session will also highlight emerging technologies—including AI, cloud computing, and advanced field data tools—that are shaping the future of EO-driven agricultural monitoring and decision-making.For over five decades, NASA and its partners have advanced EO tools to assess crop conditions, issue early warnings, and close information gaps. Recent progress—such as higher-resolution sensors, faster data delivery, and more accessible, analysis-ready datasets—has broadened EO’s impact in agriculture.As NASA’s global and domestic agriculture and food supply consortia, NASA Harvest and NASA Acres work across sectors and disciplines to support informed, resilient decision-making.We welcome submissions from researchers, practitioners, and stakeholders across the agricultural and remote sensing communities.

Co-Sponsor(s):

B - Biogeosciences
H - Hydrology
NH - Natural Hazards
SY - Science and Society

Index Terms:

0402 Agricultural systems [BIOGEOSCIENCES]
1630 Impacts of global change [GLOBAL CHANGE]
4333 Disaster risk analysis and assessment [NATURAL HAZARDS]
4337 Remote sensing and disasters [NATURAL HAZARDS]

Primary Convener: Mary Mitkish, University of Maryland College Park, College Park, MD, United States

Conveners: Michael Laurence Humber, University of Maryland College Park, Geographical Sciences, College Park, MD, United States, Cordelia Hiers Brady, NASA Headquarters, Washington, United States and Keelin Haynes, University of Maryland College Park, Geographical Sciences, College Park, United States

Student/Early Career Convener: Allison E Bredder, University of Maryland, College Park, United States

A multi-modal learning approach for crop canopy volume estimation using satellite SAR and multi-spectral datasets, UAV images and representation learning (1989882)

Ashutosh Tiwari, Texas A&M Agrilife Research Center, Corpus Christi, Corpus Christi, United States, Lei Zhao, Texas A&M AgriLife Research and Extension Center at Corpus Christi, Corpus Christi, United States, Benjamin Ghansah, Texas A&M University Corpus Christi, Corpus Christi, TX, United States, Scott Jose L Landivar, Texas A&M AgriLife Research Center, Corpus Christi, Corpus Christi, United States, Juan Landivar-Bowles, Texas A&M AgriLife Research, College Station, United States and Mahendra Bhandari, Texas A&M AgriLife, Corpus Christi, Washington, United States

A Phenology-Aligned Multi-Task Temporal Fusion Framework for Satellite-Based Seasonal Rice Yield Estimation in Southeast Asia (1888562)

Zhixian Lin, Kaiyu Guan, Sheng Wang and Qu Zhou, University of Illinois Urbana-Champaign, Agroecosystem Sustainability Center, Institute for Sustainability, Energy, and Environment, Urbana, United States

Advancing Satellite-based Agricultural Practices Mapping using Scale Transfer with Pseudo-Labeling (1927238)

Yuchi Ma, Stanford University, Department of Earth System Science & Center on Food Security and the Environment, Stanford, CA, United States, Yawen Shen, Corteva, Indianapolis, United States, Anuradha Swatantran, Corteva, Des Moines, United States, Courtland Kelly, Corteva Agriscience, Research and Develoment, Johnston, United States and David B Lobell, Stanford University, Department of Earth System Science & Center on Food Security and the Environment, Stanford, United States

Advancing Soil Organic Carbon Mapping Through Integrated Satellite Observations, Machine Learning, and Climatic Scaling: A Field-Based Study in Montana Agricultural Lands (2001733)

Shilan Felehgari, PhD, Montana State University, College of Agriculture, Bozeman, MT, United States, Paul Nugent, Montana State University, College of Agriculture, Bozeman, United States, Joao Souza, Montana State University, Land Resources and Environmental Sciences, Bozeman, United States and Behzad Ghanbarian, University of Texas at Arlington, Department of Earth and Environmental Sciences, Arlington, United States

AI Foundation Models for Agricultural Mapping from Satellite Observations (2004995)

Shunlin Liang, University of Hong Kong, Department of Geography, Hong Kong, Hong Kong, Wenyuan Li, University of hong kong, Hong Kong, China, Husheng Fang, Wuhan University, Wuhan, China and Han Ma, University of Hong Kong, Department of Geography, Hong Kong, China

AI-Driven Rapid Crop Mapping and Damage Assessment in Sylhet, Bangladesh using Multi-Sensor Satellite and Drone Data (1920099)

Mst. Farida Perveen, Ministry of Agriculture, Department of Agricultural Extension (DAE), Dhaka, Bangladesh, Takashi Yamano, Asian Development Bank, Economic Research and Development Impact, Manila, Philippines, Abdullah Al Mehadi, Utah State University, Electrical and Computer Engineering, Logan, United States, Maisha Amin, Orange Solutions Limited (United Group), Engineering Department, Dhaka, Bangladesh, Md. Shahadat Hossain, O.CREEDS Co. Ltd., Engineering Department, Dhaka, Bangladesh and Mohammad Shawkat Ali, Research and Entrepreneurship Development, Dhaka, Bangladesh

AVIRIS4Acres‑NY: Airborne Imaging Spectroscopy as a Path‑Finder for Satellite-Based Health Monitoring in Specialty Crops (1967426)

Kaitlin Gold¹, K. Dana Chadwick², Yu Jiang³, Philip G Brodrick⁴, Manushi Trivedi⁵, Henry Frye⁶, Shivranjani Baruah¹, Philip A Townsend⁶, David Combs⁷, Niko Janke⁸, Yiyuan Lin¹, Timothy Williams⁸, Terry Bates⁹, Yun Yang¹⁰, Nicholas Regnier¹, Louis Longchamps¹¹, Caroline Kamal¹, Luke Gregory¹², Hannah Grover¹³, Ying Sun¹⁴, Michael Gore¹⁵, Lorenzo Pippi¹, Hans Walter-Peterson¹⁶, Jennifer Russo¹⁷, Kerik Cox¹, Sara Emery¹, Madeline Oravec¹, Jason Londo¹, Clayton Schmith¹, Christine Smart¹, Faith Twinamaani¹, Andreq Garcia¹, Allison Bredder¹⁸, Courtney Herzberg⁸, Hadley Oleacrzyk¹⁹, Michael Laurence Humber²⁰ and Alyssa K Whitcraft²¹, (1)Cornell University, Cornell AgriTech, Geneva, United States, (2)NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, United States, (3)Cornell University, Cornell Agritech, Horticulture Section, Geneva, NY, United States, (4)NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States, (5)Horticulture Section, School of Integrative Plant Science, Cornell University, Ithaca, United States, (6)University of Wisconsin Madison, Department of Forest and Wildlife Ecology, Madison, United States, (7)Cornell University, Cornell Agritech, Plant Pathology Section, Geneva, NY, United States, (8)Cornell University, Ithaca, United States, (9)Cornell University, School of Integrative Plant Science, Ithaca, United States, (10)College of Agriculture and Life Sciences, Cornell University, Ithaca, United States, (11)Cornell University, Soil & Crop Sciences, Ithaca, United States, (12)Cornell University, School of Integrative Plant Science, Plant Breeding and Genetics Section, Ithaca, NY, United States, (13)US Air Force Academy, USAF Academy, United States, (14)Cornell University, School of Integrative Plant Science, Soil and Crop Sciences Section, Ithaca, United States, (15)Cornell University, School of Integrative Plant Science, Plant Breeding and Genetics Section, Ithaca, United States, (16)Cornell University, Cornell Cooperative Extension, Geneva, United States, (17)Cornell University, Cornell Cooperative Extension, Portland, United States, (18)University of Maryland College Park, College Park, United States, (19)Hobart and William Smith College, Geneva, United States, (20)University of Maryland College Park, Geographical Sciences, College Park, MD, United States, (21)University of Maryland College Park, Geographical Sciences, College Park, United States

Bayesian Autoencoder-Based Cotton Canopy Cover Estimation from Satellite Imagery Using UAV-Derived Latent Space Learning (1997572)

Lei Zhao¹, Ashutosh Tiwari¹, Sayantan Sarkar², Scott Jose L Landivar³, Juan Landivar-Bowles⁴ and Mahendra Bhandari⁵, (1)Texas A&M AgriLife Research and Extension Center at Corpus Christi, Corpus Christi, United States, (2)Texas A&M University College Station, College Station, United States, (3)Texas A&M AgriLife Research Center, Corpus Christi, Corpus Christi, United States, (4)Texas A&M AgriLife Research, College Station, United States, (5)Texas A&M AgriLife, Corpus Christi, Washington, United States

Building Trust in Agricultural Data Sharing: Anonymization of Raster Time Series at Scale (1957301)

Val Kovalsky, Organization Not Listed, Washington, DC, United States and Erik Boren

Combining PACE Hyperspectral Observations and Crop Models for In-Season Yield Prediction (1985915)

Yuval Sadeh^1,2, Mark Wronkiewicz³, Natacha Kalecinski², Rohan Sawahn⁴, Mohammad Abdul Qadir Khan², Oleksandra Oliinyk² and Inbal Becker-Reshef^2,4, (1)Monash University, School of Earth, Atmosphere and Environment, Melbourne, VIC, Australia, (2)University of Maryland, Department of Geographical Sciences, College Park, United States, (3)Jet Propulsion Laboratory, California Institute of Technology, Pasadena, United States, (4)University of Strasbourg, ICube, Strasbourg, France

Conterminous United States multi-annual crop field boundary delineation (1932676)

Lin Yan, Michigan State University, Center for Global Change and Earth Observations, East Lansing, MI, United States and David Roy, Michigan State University, Department of Geography, Environment, & Spatial Sciences, and Center for Global Change and Earth Observations, East Lansing, MI, United States

Deep learning-based detection of rust disease in switchgrass using UAV-derived multispectral imagery (1923568)

Yaqian He, Indiana University Bloomington, Bloomington, United States and Yaping Xu, Sam Houston State University, Huntsville, United States

Deep-learning transformers for panoptic delineation and monitoring of smallholder agricultural fields across Uganda’s refugee settlements (1973404)

Zhanpei Fang^1,2, Jamon Van Den Hoek³ and Fuxin Li², (1)Oregon State University, Geography Program, College of Earth, Ocean, and Atmospheric Sciences, Corvallis, United States, (2)Oregon State University, Department of Electrical Engineering and Computer Science, Corvallis, United States, (3)Oregon State University, College of Earth, Ocean and Atmospheric Sciences, Corvallis, United States

Early-season mapping of winter crops using transfer learning and remote sensing in drought-stressed agroecosystems (1981213)

Romina Díaz Gómez¹, Marina Reyes Lopez Mautner¹, Charles A. Young² and Matthew Holland³, (1)Stockholm Environment Institute, US Water, Davis, CA, United States, (2)Stockholm Environment Institute, US Water, Davis, United States, (3)California State Water Resources Control Board, Bay-Delta Section, Sacramento, United States

Enhancing Cropland Waterlogging Detection in the Prairie Pothole Region with Deep-Learning-based Large-Scale Segmentation of PlanetScope Imagery (1891960)

Kejie Zhao¹, Bin Peng², Kaiyu Guan¹, Zhao Jiang¹, Zhangliang Chen¹ and Sheng Wang¹, (1)University of Illinois Urbana-Champaign, Agroecosystem Sustainability Center, Institute for Sustainability, Energy, and Environment, Urbana, United States, (2)University of Illinois Urbana-Champaign, Urbana, United States

Evaluating Conflict Effects in Sudanese Agriculture (1929457)

Ana M Tarano¹, Gray Tappan², Hunter Blevins³, Catlynh Nguyen³, Iman Smith⁴, Barnabas Pasztor³, Christopher Atsianzale Wakhanala⁴, Gedeon Muhawenayo¹, Adebowale Daniel Adebayo⁵, Inbal Becker-Reshef⁵, Karyn M. Tabor^6,7, Amy McNally⁸ and Hannah Rae Kerner¹, (1)Arizona State University, School of Computing and Augmented Intelligence, Tempe, AZ, United States, (2)USGS Earth Resources Observation and Science (EROS) Center Sioux Falls, Sioux Falls, United States, (3)Arizona State University, Tempe, United States, (4)University of Maryland, Department of Geographical Sciences, College Park, MD, United States, (5)University of Maryland, Department of Geographical Sciences, College Park, United States, (6)Science Systems and Applications, Inc., Lanham, United States, (7)NASA Goddard Space Flight Center (USAID, SSAI), Greenbelt, United States, (8)NASA Goddard Space Flight Center, Greenbelt, United States

Farmer-Led, NASA-Enabled: Building a Verified Digital Infrastructure for Agriculture Using Earth Observation Tools (1986558)

Dwane Roth¹, Forrest S Melton², Billy Tiller³, Mark Cox³, Sam C Zipper⁴, Fahad Khadim⁵, Troy Dumler⁶, Amy Kremen⁷, Lily Hancock⁸, Susan Metzger⁹, Todd Janzen¹⁰, Michael Laurence Humber¹¹ and Alyssa K Whitcraft¹², (1)Organization Not Listed, Washington, DC, United States, (2)NASA Ames Research Center, Moffett Field, CA, United States, (3)Growers Information Services Coop, Lubbock, United States, (4)University of Kansas, Kansas Geological Survey, Lawrence, United States, (5)University of Kansas, Lawrence, KS, United States, (6)Garden City Company, Garden City, United States, (7)Irrigation Innovation Consortium, Fort Collins, United States, (8)General Mills, Golden, Valley, United States, (9)Kansas State University, Manhattan, KS, United States, (10)Janzen Schroeder Ag Law, Indianapolis, United States, (11)University of Maryland College Park, Geographical Sciences, College Park, MD, United States, (12)University of Maryland College Park, Geographical Sciences, College Park, United States

Ground truth to orbit: Bridging remote sensing and regenerative agriculture at the Flickner Innovation Farm (Invited) (1936753)

Ray Flickner, Flickner Innovation Farm, McPherson County, KS, United States, Susan Metzger, Kansas State University, Manhattan, KS, United States, Ignacio Ciampitti, Purdue University, Department of Agronomy, West Lafayette, United States and Alyssa K Whitcraft, University of Maryland College Park, Geographical Sciences, College Park, United States

Harvest2Market: Tools for Contextualizing Agricultural Production and Food Security (2001447)

Michael Laurence Humber¹, Fernanda Argueta², Christina Jade Justice³, Balaji Manikandan⁴, Xiangjie Chen⁵, Soonho Kim⁶, Blake Munshell³, John Keniston⁴, Brian Barker⁵, Kara Mobley⁴, Antonio Sanchez⁵, Mary Mitkish⁷ and Inbal Becker-Reshef⁸, (1)University of Maryland College Park, Geographical Sciences, College Park, MD, United States, (2)University of Maryland College Park, Geographical Sciences, College Park, United States, (3)University of Maryland, Geographical Sciences, College Park, United States, (4)University of Maryland College Park, College Park, United States, (5)University of Maryland College Park, College Park, MD, United States, (6)International Food Policy Research Institute, Washington, United States, (7)University of Maryland, College Park, United States, (8)University of Maryland, Department of Geographical Sciences, College Park, United States

Hybrid Smoothing of NDVI Time Series Using Whittaker and Moving Average Techniques for Agricultural Monitoring (1944463)

Amit Kumar Singh¹, Oscar Rosario Belfiore², Guido D'Urso² and Giovanni Pugliano³, (1)Sapienza University of Rome, Rome, Italy, (2)University of Naples Federico II, Department of Agricultural Sciences, Portici (NA), Italy, (3)The University of Naples Federico II, Naples, Italy

Improved NDVI Forecasting in Sub-Saharan Africa Using FLDAS Soil Moisture Forecasts and the Dynamic Agricultural Productivity Indicator Approach (1924225)

ManhHung Le¹, John D Bolten², Weston B Anderson³, Abheera Hazra² and Amy McNally², (1)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (2)NASA Goddard Space Flight Center, Greenbelt, United States, (3)University of Maryland, College Park, Geographical Sciences, College Park, United States

Improving Yield Forecasts at the Field Scale in Maryland: Integrating Remote Sensing and Crop Management to Uncover Yield Patterns (1916593)

Natacha Kalecinski¹, Sergii Skakun², Ritvik Sahajpal¹, W. D. Hively³, Alison Thieme⁴, Simon Kraatz⁵, Alyssa K Whitcraft⁶ and Michael Laurence Humber⁷, (1)University of Maryland College Park, Department of Geographical Sciences, College Park, United States, (2)University of Maryland College Park, Department of Geographical Sciences, College Park, MD, United States, (3)USGS, Lower Mississippi Gulf Science Center, Beltsville, United States, (4)USDA Agricultural Research Center, Sustainable Agricultural Systems Laboratory, Beltsville, United States, (5)USDA ARS, Hydrology and Remote Sensing Laboratory, Beltsville, United States, (6)University of Maryland College Park, Geographical Sciences, College Park, United States, (7)University of Maryland College Park, Geographical Sciences, College Park, MD, United States

In-Season Crop Growth Forecasting Using Satellite Imagery and Context-Aware Deep Learning (1983366)

Ramana Heggadal Math¹, Saransh Agrawal², Phanender Chalasani², Ashutosh Tiwari³, Lei Zhao³, Benjamin Ghansah⁴, Scott Jose L Landivar⁵, Juan Landivar-Bowles⁶ and Mahendra Bhandari⁷, (1)Texas A&M AgriLife Research, College Station, Texas, UNITED STATES, (2)Texas A&M University, College Station, United States, (3)Texas A&M AgriLife Research and Extension Center at Corpus Christi, Corpus Christi, United States, (4)Texas A&M University Corpus Christi, Corpus Christi, TX, United States, (5)Texas A&M AgriLife Research Center, Corpus Christi, Corpus Christi, United States, (6)Texas A&M AgriLife Research, College Station, United States, (7)Texas A&M AgriLife, Corpus Christi, Washington, United States

Long-term Changes in Irrigation Extent and Water Use in the Eastern Mediterranean (1919494)

Esmaeel Adrah¹, Luca Brocca² and He Yin¹, (1)Kent State University, Department of Geography, Kent, United States, (2)CNR-IRPI National Research Council, Perugia, Italy

Mapping Growth of Small-Scale Irrigation in Sub-Saharan Africa (1976978)

Christopher Bowden, University of Manchester, Manchester, M13, United Kingdom and Timothy Foster, University of Manchester, Department of Mechanical, Aerospace and Civil Engineering, Manchester, United Kingdom

Monitoring Crop Nitrogen Status Across the US Corn Belt Using Airborne-Satellite Cross-Scale Sensing (1966052)

Zhao Jiang¹, Kaiyu Guan¹, Sheng Wang¹, Emerson D Nafziger², Ignacio Ciampitti³, Alyssa K Whitcraft⁴, Jones John Jr.², Qu Zhou¹, Ziyi Li¹, John Pike⁵ and Dan Schaefer⁶, (1)University of Illinois Urbana-Champaign, Agroecosystem Sustainability Center, Institute for Sustainability, Energy, and Environment, Urbana, United States, (2)University of Illinois Urbana-Champaign, Department of Crop Sciences, Urbana, United States, (3)Purdue University, Department of Agronomy, West Lafayette, United States, (4)University of Maryland College Park, Geographical Sciences, College Park, United States, (5)Illinois Fertilizer and Chemical Association, Bloomington, IL 61701, USA, Bloomington, United States, (6)Illinois Fertilizer & Chemical Association, Bloomington, United States

Monitoring spatio-temporal dynamics of forage species using high-resolution satellite and UAV imagery under rotational stocking management strategy (1944589)

Shishir Sapkota¹, Jamie Foster², Mahendra Bhandari³, Scott Jose L Landivar⁴, Ernie Reyes⁵, Hamad Saad², Pablo Guarnido Lopez⁶ and Ernie Reyes, (1)Texas A&M University College Station, College Station, TX, United States, (2)Texas A&M AgriLife Research Center, Beeville, Beeville, United States, (3)Texas A&M AgriLife, Corpus Christi, Washington, United States, (4)Texas A&M AgriLife Research Center, Corpus Christi, Corpus Christi, United States, (5)Texas A&M AgriLife, Corpus Christi, Kingsville, United States, (6)Texas A&M Agrilife Research, Corpus Christi, United States

Multi-modal data fusion in Planet Fusion for enhanced land surface monitoring (1974961)

Julie Nutini and Rasmus Houborg, Planet Labs PBC, San Francisco, United States

NASA Acres and the Essential Agriculture Variables: Contributions and Collaborations toward a Globally-Consistent, Site-Relevant Agricultural Knowledge System (1970615)

Alyssa K Whitcraft^1,2, Michael Laurence Humber³, Kaiyu Guan⁴, Ritvik Sahajpal¹, Guanyuan Shuai⁵, Natacha Kalecinski⁶, Alison Thieme⁷, W. D. Hively⁸, Lin Yan⁹, David Roy¹⁰, Allison Bredder¹¹ and Sven Gilliams², (1)University of Maryland College Park, Geographical Sciences, College Park, United States, (2)GEOGLAM Secretariat, Geneva, Switzerland, (3)University of Maryland College Park, Geographical Sciences, College Park, MD, United States, (4)University of Illinois Urbana-Champaign, Agroecosystem Sustainability Center, Institute for Sustainability, Energy, and Environment, Urbana, United States, (5)University of Maryland College Park, Geographical Sciences, College park, United States, (6)University of Maryland, Department of Geographical Sciences, College Park, United States, (7)University of Maryland, Department of Geographical Sciences, Greenbelt, MD, United States, (8)U.S. Geological Survey, Lower Mississippi-Gulf Water Science Center, Beltsville, United States, (9)Michigan State University, Center for Global Change and Earth Observations, East Lansing, MI, United States, (10)Michigan State University, Department of Geography, Environment, & Spatial Sciences, and Center for Global Change and Earth Observations, East Lansing, MI, United States, (11)University of Maryland College Park, College Park, United States

NASA’s Agriculture Program: a Retrospective (1952043)

Christopher Owen Justice, University of Maryland College Park, Department of Geographical Sciences, College Park, MD, United States and Bradley Doorn, NASA Headquarters, Applied Sciences Program, Water Resources & Agriculture, Washington, United States

Progressive Early-Season Crop Mapping over the Contiguous United States Using Sentinel-2 Time Series and Historical Ground Data (1905546)

Haijun Li¹, Xiao-Peng Song², Bernard Adusei³, Peter Potapov^3,4, Matthew Hansen³ and Feng Gao⁵, (1)University of Maryland College Park, College Park, MD, United States, (2)Univeristy of Maryland, College Park, United States, (3)University of Maryland, Department of Geographical Sciences, College Park, United States, (4)World Resources Institute, Washington, DC, United States, (5)USDA, ARS, Beltsville, United States

Sequential Multi-Stage Gaussian Mixture Model for In-Season Cropland Mapping and Yield Forecasting in Russia Using MODIS NDVI (1893427)

Christian Abys, University of Maryland College Park, College Park, MD, United States, Natacha Kalecinski, University of Maryland, Department of Geographical Sciences, College Park, United States and Sergii Skakun, University of Maryland, Geographical Sciences, College Park, United States

The effects of lighting conditions on fractional vegetative ground cover estimation in winter cover crops (1964473)

Alison Nicole Thieme, University of Maryland College Park, College Park, MD, United States, W. D. Hively, U.S. Geological Survey, Lower Mississippi-Gulf Water Science Center, Beltsville, United States and Brian T Lamb, U.S. Geological Survey, Lower Mississippi-Gulf Water Science Center, New York, United States

The Grape Remote Sensing Atmospheric Profile and Evapotranspiration Experiment in the Finger Lakes Region, New York (1902322)

Yun Yang¹, William P Kustas², Kaitlin Gold³, John Prueger⁴, Lynn McKee⁵, Joseph G Alfieri⁶, Yu Jiang⁷, Tian Li⁸, Weina Duan⁹, William Alexander White¹⁰, Niko Janke⁸, Andrew Russ¹¹, David Combs³, Feng Gao¹⁰ and Martha Anderson¹², (1)Cornell University, Soil and Crop Sciences Section, Ithaca, NY, United States, (2)U. S. Department of Agriculture, Agricultural Research Service, Hydrology and Remote Sensing Laboratory, Beltsville, United States, (3)Cornell University, Cornell Agritech, Plant Pathology Section, Geneva, NY, United States, (4)USDA ARS, National Laboratory for Agriculture and The Environment, Ames, IA, UNITED STATES, (5)U. S. Department of Agriculture, Agricultural Research Service, Hydrology and Remote Sensing Laboratory, Beltsville, MD, United States, (6)U. S. Department of Agriculture, Agricultural Research Service, National Laboratory for Agriculture and the Environment, Ames, United States, (7)Cornell University, Cornell Agritech, Horticulture Section, Geneva, NY, United States, (8)Cornell University, Ithaca, United States, (9)Cornell University, Ithaca, NY, United States, (10)USDA-ARS Hydrology and Remote Sensing Laboratory, Beltsville, United States, (11)USDA ARS Hydrology and Remote Sensing Laboratory, Ellicott City, MD, UNITED STATES, (12)USDA ARS, Hydrology and Remote Sensing Laboratory, Beltsville, United States

The impact of biomass on the performance of remote sensing-based detection of cover crops in the Mid-Atlantic (1987555)

Yide Xu¹, Qu Zhou², Kaiyu Guan², Sheng Wang², W. D. Hively³, Alison Thieme⁴ and Zhangliang Chen², (1)University of Illinois at Urbana-Champaign, Urbana, United States, (2)University of Illinois Urbana-Champaign, Agroecosystem Sustainability Center, Institute for Sustainability, Energy, and Environment, Urbana, United States, (3)U.S. Geological Survey, Lower Mississippi-Gulf Water Science Center, Beltsville, United States, (4)USDA Agricultural Research Center, Sustainable Agricultural Systems Laboratory, Beltsville, United States

The Map Behind The Mask: Evaluating Downstream Impacts of Crop Mask Quality and Timeliness for Agricultural Applications (1955976)

Dr. Michael Cecil, PhD¹, Elinor Benami² and Ritvik Sahajpal¹, (1)University of Maryland College Park, Geographical Sciences, College Park, United States, (2)Virginia Polytechnic Institute and State University, Blacksburg, United States

User-Centered Earth Observation and AI Tools for Agricultural Resilience in Maui County (1918857)

Ana M Tarano¹, Adam Hess-Dunlop¹, Shay C. Hodges², Nicolette van der Lee³, Lauren Nelson⁴, Sommer Kimokeo³, Thomas Blamey³, Remy Romo-Valdez³, Sara M. Beery⁵ and Hannah Rae Kerner¹, (1)Arizona State University, School of Computing and Augmented Intelligence, Tempe, United States, (2)Responsible Markets, Haiku, United States, (3)University of Hawai‘i Maui College, Kahului, United States, (4)County of Maui, Department of Agriculture, Kahului, United States, (5)Massachusetts Institute of Technology, Cambridge, United States

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GC094 Satellite Solutions: Agricultural Monitoring Through Remote Sensing

Session Description:

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Index Terms:

GC094
Satellite Solutions: Agricultural Monitoring Through Remote Sensing