B24D:
Smart Farming Enabled by Remotely Sensed Big Data II


Submit an Abstract to this Session


Session ID#: 59886

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.
Primary Convener:  Yun Yang, University of Maryland College Park, ESSIC, College Park, MD, United States
Conveners:  Martha Anderson, USDA ARS, Hydrology and Remote Sensing Lab, Beltsville, MD, United States, Xiaoyuan Yang, The Climate Corporation San Francisco, San Francisco, CA, United States and Michael H Cosh, U.S. Department of Agriculture ARS, Hydrology and Remote Sensing Laboratory, Beltsville, United States
Primary Liaison:  Yun Yang, University of Maryland College Park, ESSIC, College Park, MD, United States
Chairs:  Michael H Cosh, U.S. Department of Agriculture ARS, Hydrology and Remote Sensing Laboratory, Beltsville, United States, Martha Anderson, USDA ARS, Hydrology and Remote Sensing Laboratory, Beltsville, United States, Xiaoyuan Yang, The Climate Corporation San Francisco, San Francisco, CA, United States and Zhe Zhu, Texas Tech University, Lubbock, TX, United States
OSPA Liaison:  Yun Yang, University of Maryland College Park, ESSIC, College Park, MD, United States

Cross-Listed:
  • EP - Earth and Planetary Surface Processes
  • IN - Earth and Space Science Informatics
Index Terms:

0402 Agricultural systems [BIOGEOSCIENCES]
0434 Data sets [BIOGEOSCIENCES]
0480 Remote sensing [BIOGEOSCIENCES]
1910 Data assimilation, integration and fusion [INFORMATICS]

Abstracts Submitted to this Session:

An Overview of an Evolutionary Architecture for a Data-Intensive Multi-Sensor Imagery Processing Pipeline for a Globally-Distributed Digital Platform for Agriculture (Invited) (426426)
Maria Angeles Capellades, Climate Corporation Seattle, Engineering, Seattle, WA, United States and The Climate Corporation Geospatial Engineering Group
Small UAS Remote Sensing for Assessing Corn N Status (Invited) (447739)
Craig S.T. Daughtry, USDA ARS, Hydrology & Remote Sensing Lab, Beltsville, MD, United States and Andrew L. Russ, USDA-ARS Hydrology and Remote Sensing Lab, Beltsville, United States
Estimation of Agricultural Plant Dynamics and Traits Through Integration of UAV and Other Datasets (404174)
Nicola Falco1, Haruko M Wainwright2, Baptiste Dafflon1, Craig Ulrich3, Florian Soom4, Karl Bernard Bernard Schaettle5, James Bentley Brown1, Jay McEntire6 and Susan S. Hubbard7, (1)Lawrence Berkeley National Laboratory, Berkeley, CA, United States, (2)Lawrence Berkeley National Laboratory, Department of Nuclear Science and Engineering, Berkeley, United States, (3)Lawrence Berkeley National Laboratory, Berkeley, United States, (4)Lawrence Berkeley National Laboratory (LBNL), Berkeley, United States, (5)University of California Berkeley, Departments of Chemistry, Bioengineering, Chemical and Biomolecular Engineering, Berkeley, CA, United States, (6)M2Capital, Park City, United States, (7)Oak Ridge National Laboratory, Oak Ridge, TN, United States
Fusion of satellite and UAV imagery and big data for smarter farming (415118)
Alfonso F Torres-Rua1, Mahyar Aboutalebi1, Leila Hassan Esfahani2, Manal ELarab2, William P Kustas3, Mac McKee4 and Calvin Coopmans5, (1)Utah State University, Department of Civil and Environmental Engineering, Logan, UT, United States, (2)Utah State University, Logan, UT, United States, (3)U. S. Department of Agriculture, Agricultural Research Service, Hydrology and Remote Sensing Laboratory, Beltsville, United States, (4)Utah Water Research Lab, Utah State University, Department of Civil and Environmental Engineering, Logan, UT, United States, (5)Utah State University, Electrical Engineering, Logan, United States
Monitoring Annual Farmland Expansion in the Northern Tianshan Economic Belt of China with Landsat on GEE (393615)
Qingling Zhang1, Yaoming Li2, Xi Chen3, Jieyun Zhang3 and Yuankang Xiong3, (1)Sun Yat-Sen University, Guangzhou, China, (2)Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Ulumqi, China, (3)Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, China
Remote Sensing of Corn at High Spatial, Spectral, and Temporal Resolutions (457472)
Prof. John P Kerekes, PhD1, Emily Myers2, Timothy Bauch2, Nina Raqueno2, Michael Saunders3, Jan A van Aardt4, Craig S.T. Daughtry5 and Andrew L. Russ6, (1)Rochester Institute of Technology, Chester F. Carlson Center for Imaging Science, Rochester, NY, United States, (2)Rochester Institute of Technology, Rochester, United States, (3)Rochester Institute of Technology, Rochester, NY, United States, (4)Rochester Institute of Technology, Imaging Science, Rochester, United States, (5)USDA ARS, Hydrology & Remote Sensing Lab, Beltsville, MD, United States, (6)USDA-ARS Hydrology and Remote Sensing Lab, Beltsville, United States

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