B037
Smart Farming and Natural Resource Management Enabled by Remotely Sensed Big Data II Posters

Wednesday, 9 December 2020: 04:00-20:59
Poster
Primary Convener:  Yun Yang, USDA Beltsville Agricultural Research Center, Hydrology and Remote Sensing Laboratory, Beltsville, MD, United States
Conveners:  Martha B. Anderson, USDA ARS, Hydrology and Remote Sensing Laboratory, Beltsville, MD, United States, Xiaoyuan Yang, The Climate Corporation San Francisco, San Francisco, CA, United States and Zhe Zhu, University of Connecticut, Department of Natural Resources and the Environment, Storrs, CT, United States
Primary Liaison:  Yun Yang, University of Maryland College Park, ESSIC, College Park, MD, United States
Chairs:  Yun Yang, University of Maryland College Park, ESSIC, College Park, MD, United States, Martha B. Anderson, USDA ARS, Hydrology and Remote Sensing Laboratory, Beltsville, MD, United States, Zhe Zhu, University of Connecticut, Department of Natural Resources and the Environment, Storrs, CT, United States and Xiaoyuan Yang, The Climate Corporation San Francisco, San Francisco, CA, United States
OSPA Liaison:  Zhe Zhu, University of Connecticut, Department of Natural Resources and the Environment, Storrs, CT, United States
 
A field-scale, ET-based crop stress indicator for yield estimates: an application across the Corn Belt, USA (714773)
Yang Yang, Hydrology and Remote Sensing Laboratory, Beltsville, MD, United States, Martha B. Anderson, USDA ARS, Hydrology and Remote Sensing Laboratory, Beltsville, MD, United States, Feng Gao, USDA-ARS, Hydrology and Remote Sensing Laboratory, Beltsville, United States, Yun Yang, University of Maryland College Park, ESSIC, College Park, MD, United States and Wayne Dulaney, USDA ARS, Beltsville, MD, United States
 
A New Method to Map Croplands in Pakistan based on Dynamic Time Warping and Density-based Spatial Clustering of Applications with Noise using Landsat Time Series on GEE Platform (691292)
Ziyan Guo1, Kang Yang1, Chang Liu1, Liang Cheng2 and Manchun Li2, (1)Nanjing University, School of Geography and Ocean Science, Nanjing, China, (2)Nanjing University, Nanjing, China
 
A Novel Approach on Smart Farming: Combining Past, Present and Future EO, Climatic and NWP datasets Towards an Integrated Crop Monitoring, Risk-Assessment and Yield Forecasting Suite (725371)
Nikolaos S Bartsotas1, Vasileios Sitokonstantinou1, Charalambos Kontoes1, Alkiviadis Koukos1, Alexia Tsouni1, Savvas Rogotis2, Dimitrios Sykas2 and Nikolaos Marianos2, (1)National Observatory of Athens, Institute for Astronomy and Astrophysics, Space Applications and Remote Sensing - BEYOND Center of Earth Observation Research and Satellite Remote Sensing, Athens, Greece, (2)NEUROPUBLIC S.A., Piraeus, Greece
 
Alfalfa Yield Prediction Using UAV-Based Hyperspectral Imagery and Ensemble Learning (669795)
Zhou Zhang1, Luwei Feng2, Yuchi Ma3, Qingyun Du4, Parker Williams2, Jessica Drewry2 and Brian Luck2, (1)University of Wisconsin Madison, Biological Systems Engineering, Madison, WI, United States, (2)University of Wisconsin-Madison, Madison, United States, (3)University of Wisconsin Madison, Madison, WI, United States, (4)Wuhan University, Scool of Resource and Environmental Sciences, Wuhan, China
 
Characterization of Soil-Plant Spatial Relationships and their Impact on Crop Yield Using Remote Sensing and Geophysics from Crop to Farm Scale (703322)
Karina Nugent1, Haruko M Wainwright2, Baptiste Dafflon2, Craig Ulrich1, Florian Soom3, Jay McEntire4, James Bentley Brown2,4 and Nicola Falco2, (1)Lawrence Berkeley National Laboratory, Earth and Environmental Sciences, Berkeley, CA, United States, (2)Lawrence Berkeley National Laboratory, Berkeley, CA, United States, (3)Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA, United States, (4)ARVA Intelligence, Salt Lake City, UT, United States
 
Crop Yield Estimation at Field Scale in Sub-Saharan Africa Using Multisource Earth Observation Data (727034)
Chengxiu Li1, Ellasy Chimimba2, Jadu Dash1, Oscar Kambombe2, Tendai Chibarabada3, Levis Eneya2, Cosmo Ngongondo2, Daniela Anghileri1 and Justin Sheffield4, (1)University of Southampton, Geography and Environmental Science, Southampton, United Kingdom, (2)University of Malawi, Zomba, Malawi, (3)WaterNet, Harare, Zimbabwe, (4)University of Southampton, Geography and Environment, Southampton, United Kingdom
 
Effects of Landsat 7’s Orbit Drift and a Solution to Preserve Its Science Capabilities (700744)
Zhe Zhu, University of Connecticut, Groton, CT, United States, Shi Qiu, University of Connecticut, Department of Natural Resources and the Environment, Groton, CT, United States, Rong Shang, University of Connecticut, Storrs, CT, United States and Christopher J Crawford, USGS Earth Resources Observation and Science (EROS) Center Sioux Falls, Sioux Falls, SD, United States
 
High Spatial Resolution Soil Moisture Mapping using S2-SMM System (774277)
Eryan Dai, University of Colorado at Boulder, Boulder, CO, United States, Albin John Gasiewski, Univ of Colorado, Boulder, CO, United States, Jack Steward Elston, Black Swift Technologies, Boulder, CO, United States and Maciej Stachura, Black Swift Technologies, Boulder, United States
 
Improved Daily ET Estimation Using Remotely Sensed Data in a Data Fusion System (731564)
Yun Yang, University of Maryland College Park, ESSIC, College Park, MD, United States, Martha B. Anderson, USDA ARS, Hydrology and Remote Sensing Laboratory, Beltsville, MD, United States, Feng Gao, USDA-ARS, Hydrology and Remote Sensing Laboratory, Beltsville, United States, Christopher Hain, National Integrated Drought Information System, Boulder, CO, United States and Kyle Knipper, University of California Davis, Davis, CA, United States
 
Improving the ability of Landsat 8 NDVI in-season yield prediction by integrating the sequences of weather data to support smart farming (720182)
Jianxiu Shen1,2 and Fiona H. Evans1,2, (1)Big Data in Agriculture, Murdoch University, Murdoch, WA, Australia, (2)Centre for Digital Agriculture, Curtin University, Bentley, WA, Australia
 
Mapping Canopy Nitrogen Concentration across Ryegrass and Barley Crop using Random Forest Regression (687246)
Manish Kumar Patel1,2, Dongryeol Ryu2, Andrew William Western1, Glenn Fitzgerald3,4, Eileen M. Perry3,5, Helen Suter6 and Iain Young7, (1)University of Melbourne, Department of Infrastructure Engineering, Parkville, VIC, Australia, (2)The University of Melbourne, Department of Infrastructure Engineering, Parkville, VIC, Australia, (3)Agriculture Victoria, Horsham, VIC, Australia, (4)University of Melbourne, Centre for Agricultural Innovation, Faculty of Veterinary and Agricultural Sciences, Parkville, VIC, Australia, (5)University of Melbourne, Department of Infrastructure Engineering, School of Engineering, Parkville, VIC, Australia, (6)The University of Melbourne, School of Agriculture and Food, Parkville, VIC, Australia, (7)The University of Sydney, School of Life and Environmental Sciences, Sydney, NSW, Australia
 
Modeling Miscanthus Biomass from UAS-LiDAR Data Using Machine Learning (772499)
Paul R Adler, USDA-ARS, Pasture Systems and Watershed Management Research Unit, University Park, PA, United States, Yanan Xin, Pennsylvania State University Main Campus, University Park, PA, United States, Matthew W Myers, USDA-ARS, Pasture Systems and Watershed Management Research Unit, University Park, United States and Yanjun Su, University of California at Merced, Merced, United States
 
Towards large-scale mapping of tree crops with high-resolution satellite imagery and deep learning algorithms: a case study of olive orchards in Morocco (716242)
Chenxi Lin1, Zhenong Jin2, David Mulla3, Rahul Ghosh4, Kaiyu Guan5, Yaping Cai6 and Vipin Kumar4, (1)University of Minnesota Twin Cities, Department of Bioproducts and Biosystems Engineering, Minneapolis, MN, United States, (2)University of Minnesota-Twin Cities, Department of Bioproducts and Biosystems Engineering, Saint Paul, MN, United States, (3)University of Minnesota Twin Cities, Department of Soil, Water, and Climate, Minneapolis, MN, United States, (4)University of Minnesota Twin Cities, Department of Computer Science/Engineering, Minneapolis, MN, United States, (5)University of Illinois at Urbana Champaign, College of Agricultural, Consumer and Environmental Sciences, Urbana, IL, United States, (6)University of Illinois at Urbana Champaign, College of Agricultural Consumer and Environmental Sciences, Urbana, IL, United States
 
Web Geoprocessing Services for Disseminating and Analyzing SMAP Derived Soil Moisture Data Products (768721)
Chen Zhang1, Zhengwei Yang2, Liping Di1, Eugene Yu1, Li Lin1 and Haoteng Zhao1, (1)George Mason University, Fairfax, VA, United States, (2)USDA National Agricultural Statistics Service, Research and Development Division, Washington, DC, United States
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