B101
Smart Farming Enabled by Remotely Sensed Big Data

Session ID#: 74685

Session Description:
Smart Farming is currently driving revolution in agriculture, aiming at more productive and sustainable production through precise and resource-efficient farming decisions. Remotely sensed Big Data from various satellite platforms, small unmanned aerial systems, airborne systems, in situ and proximal sensors, brings both challenges and opportunities for Smart Farming which requires high spatial resolution and near real-time mapping capabilities. Successes in crop health monitoring, stress identification, soil mapping, fertilizer and irrigation advisories, yield prediction and more have been achieved. This session seeks contributions across government institutions, universities, private corporations, and other nonprofit organizations. It will focus on showcasing research methodologies and applications for the use of high spatial resolution or high temporal frequency remotely sensed Big Data for Smart Farming applications. We invite you to share findings throughout the chain of data collection, storage, transfer, transformation, analytics and discuss how to achieve the goal of more productive and sustainable agriculture production.
Index Terms:

0402 Agricultural systems [BIOGEOSCIENCES]
0434 Data sets [BIOGEOSCIENCES]
0480 Remote sensing [BIOGEOSCIENCES]
1842 Irrigation [HYDROLOGY]

Primary Convener:  Yun Yang, USDA Beltsville Agricultural Research Center, Hydrology and Remote Sensing Laboratory, Beltsville, MD, United States; University of Maryland College Park, ESSIC, College Park, MD, United States
Conveners:  Martha 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; USDA Beltsville Agricultural Research Center, Hydrology and Remote Sensing Laboratory, Beltsville, MD, United States
 
Very high resolution (daily, 3 m) data cubes of surface reflectance synergizing observations from CubeSats, Sentinel-2, Landsat 8, and MODIS (Invited) (489712)
Rasmus Houborg, Planet Labs, San Francisco, CA, United States and Ignacio Zuleta, Planet Labs, San Francisco, United States
 
Getting closer to Landsat: Advances from the GRAPEX Project in the Application of UAVs for High-Resolution Evapotranspiration (Invited) (491591)
Alfonso F Torres-Rua1, Mahyar Aboutalebi2, Ayman Nassar1, Hector Nieto Solana3, Lawrence Hipps4, Mac McKee5, William P Kustas6, Joseph G Alfieri6, John H Prueger7, Lynn McKee6, Maria Mar Alsina8, Martha Anderson9, Feng Gao10, Kyle Knipper11, William Alexander White12, Pierre C Guillevic13, Luis Sanchez8, Nick Dokoozlian8, Calvin Coopmans14, Andrew J McElrone15, Nicolas E Bambach-Ortiz16 and Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX), (1)Utah State University, Department of Civil and Environmental Engineering, Logan, UT, United States, (2)Utah State University, Civil and Environmental Engineering, Logan, UT, United States, (3)Complutum Tecnologías de la Información Geográfica (COMPLUTIG), Alcala, Spain, (4)Utah State University, Plants, Soils and Climate Department, Logan, UT, United States, (5)Utah Water Research Lab, Utah State University, Department of Civil and Environmental Engineering, Logan, UT, United States, (6)U. S. Department of Agriculture, Agricultural Research Service, Hydrology and Remote Sensing Laboratory, Beltsville, MD, United States, (7)U. S. Department of Agriculture, Agricultural Research Service, National Laboratory for Agriculture and Environment, Ames, IA, United States, (8)E&J Gallo, Modesto, United States, (9)USDA ARS Hydrology and Remote Sensing Laboratory, Beltsville, MD, United States, (10)USDA-Agricultural Research Service Beltsville, Hydrology and Remote Sensing, Beltsville, MD, United States, (11)USDA Beltsville Agricultural Research Center, Beltsville, MD, United States, (12)USDA-ARS Hydrology and Remote Sensing Laboratory, Beltsville, MD, United States, (13)Jet Propulsion Laboratory, Pasadena, CA, United States, (14)Utah State University, Electrical Engineering, Logan, UT, United States, (15)USDA ARS, Department of Viticulture and Enology, UC Davis, Davis, CA, United States, (16)University of California Davis, Davis, CA, United States
 
Opportunities and challenges of crop biomass mapping based on ecosystem modeling at regional scale using high resolution Sentinel-2 data (507512)
Liming He1, Rong Wang2, Georgy Mostovoy1, Jane Liu3, Jing Ming Chen4, Jiali Shang5, Jiangui Liu5, Heather McNairn6 and Jarrett Powers7, (1)Laboratory of Environmental Model and Data Optima, Laurel, MD, United States, (2)University of Toronto, Toronto, ON, Canada, (3)Department of Geography and Planning, University of Toronto, Toronto, ON, Canada, (4)Univ Toronto, Toronto, ON, Canada, (5)Agriculture and Agri-Food Canada, Ottawa, ON, Canada, (6)Agriculture and Agri-Food Canada, Science and Technology Branch, Ottawa, ON, Canada, (7)Agriculture and Agri-Food Canada - Winnipeg, Winnipeg, MB, Canada
 
Monitoring Crop Water Use at Field Scales Using Remotely Sensed Big Data (514835)
Yoann Malbeteau1, Martha Anderson2, Christopher Hain3, Rasmus Houborg4, Bruno Aragon1, Abdelghani Chehbouni and Matthew McCabe1, (1)King Abdullah University of Science and Technology, Thuwal, Saudi Arabia, (2)USDA ARS Hydrology and Remote Sensing Laboratory, Beltsville, MD, United States, (3)National Integrated Drought Information System, Boulder, CO, United States, (4)Planet Labs, San Francisco, United States
 
Large Area Crop Type Mapping with Irregular Satellite Image Time Series Using Deep Convolutional Neural Network (537466)
Diyou LIU1, Ning Yang2, Tianwei Ren3, Quan Xiong4, Lin Zhang5, Zhenbo Du4, Zhe Liu4, Jianxi Huang6, Xiaodong Zhang4, Wenju Yun3 and Dehai Zhu4, (1)China Agricultural University(CAU), Department of Geographic Information Engineering, College of Land Science and Technology, Beijing, China, (2)China Agricultural University, Department of Geographic Information Engineering, Beijing, China, (3)CAU China Agricultural University, Beijing, China, (4)CAU China Agricultural University, College of Land Science and Technology, Beijing, China, (5)CAU China Agricultural University, Department of Geographic Information Engineering, Beijing, China, (6)China Agricultural University, College of Land Science and Technology, Beijing, China
 
County-level corn yield prediction using deep transfer learning (541879)
Yuchi Ma1, Yanghui Kang1, Mutlu Ozdogan2 and Zhou Zhang3, (1)University of Wisconsin Madison, Madison, WI, United States, (2)University of Wisconsin Madison, Center for Sustainability and the Global Environment, Madison, WI, United States, (3)University of Wisconsin Madison, Biological Systems Engineering, Madison, WI, United States
 
Spatially Complete and Daily Continuous Evapotranspiration Monitoring in an Agricultural Field Using TSEB and a Data Fusion Approach (560992)
Caijin Zhang1, Di Long1 and Yang Yang2, (1)Tsinghua University, Department of Hydraulic Engineering, Beijing, China, (2)USDA ARS, Hydrology and Remote Sensing Laboratory, Beltsville, Maryland, United States
 
NITROGEN SMART RECOMMENDATION ON FIELD SCALE: The Science Behind Nitrogen Management (561916)
Nahuel Peralta, Javier Di Matteo Sr. and Juan Matias Ferreyra, Bayer Company, Corn Market Development-Crop Science, Pergamino, Argentina
 
The first crop data layer with 10-m resolution in the Northeast China (586821)
Nanshan You, Beijing, Beijing, China and Jinwei Dong, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
 
Understanding Miscanthus production in the United States using big data and machine learning (591229)
Paul R Adler, USDA-ARS, Pasture Systems and Watershed Management Research Unit, University Park, PA, United States and Yanan Xin, Pennsylvania State University Main Campus, University Park, PA, United States
 
Agronomic Insights at Scale through Remote Sensing Data Science Framework (623117)
Xiaoyuan Yang, The Climate Corporation San Francisco, San Francisco, CA, United States, Ángeles Casas Planes, Climate Corporation San Francisco, San Francisco, CA, United States and Valeriy Kovalskyy, The Climate Corporation, Creve Coeur, MO, United States
 
Multi-sensor Monitoring of Processing Tomato at a Field-scale (627946)
Zhehan Tang, University of California Davis, Davis, CA, United States, Jahalel L Tuil, University of California, Davis, United States, Keely Roth, The Climate Corporation, San Francisco, CA, United States, Susan Ustin, Univ California Davis, Davis, CA, United States and Yufang Jin, University of California - Davis, Land, Air, and Water Resources, Davis, CA, United States
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