B51C:
Emerging Earth Observation Technologies and Techniques in Global Agricultural Monitoring: GEOGLAM and Other Activities II Posters

Submit an Abstract to this Session



Session ID#: 24588

Session Description:
Earth Observations (EO) from satellite and in situ data sources are valuable resources for monitoring changes in global croplands and rangelands. New data sources, technologies, and techniques emerging from the public and private domains are growing the potential and realized applications of EO for global agricultural monitoring at multiple temporal and spatial scales. We invite abstracts which broadly deal with the use and application of EO for cropland and rangeland monitoring, and highlight emerging technologies and techniques related to 1) data sources (e.g. UAV, micro satellites, SAR, passive microwave, fluorescence, crowdsourcing); 2) data access and use (e.g. data fusion, big data, artificial intelligence, web-enabled tools and cloud computing); and 3) knowledge/technology transfer modalities. In-session discussions will be oriented toward coordinating monitoring efforts, such as the Group on Earth Observations Global Agricultural Monitoring (GEOGLAM), a G20 international initiative to enhance global EO-based agricultural information usage by decision and policy makers.
Primary Convener:  George Azzari, Stanford University, Stanford, CA, United States
Convener:  Alyssa K Whitcraft, University of Maryland College Park, Geographical Sciences, College Park, United States
Chairs:  George Azzari, Stanford University, Stanford, CA, United States and Alyssa K Whitcraft, University of Maryland College Park, Geographical Sciences, College Park, United States
OSPA Liaison:  George Azzari, Stanford University, Stanford, CA, United States
Index Terms:

0402 Agricultural systems [BIOGEOSCIENCES]
0430 Computational methods and data processing [BIOGEOSCIENCES]
0434 Data sets [BIOGEOSCIENCES]
0480 Remote sensing [BIOGEOSCIENCES]

Abstracts Submitted to this Session:

Estimation of Rice Crop Yields Using Random Forests in Taiwan (223884)
Chi-Farn Chen, NCU National Central University of Taiwan, Center for Space and Remote Sensing Research, Taoyuan, Taiwan, Huan-Sheng Lin, Center for Space and Remote Sensing Research, National Central University, Taoyuan City, Taiwan, Son Thanh Nguyen, NCU National Central University of Taiwan, Jhongli, Taiwan and Cheng-Ru Chen, Center for Space and Remote Sensing Research, Jhongli, Taiwan
Estimating rice yield from MODIS-Landsat fusion data in Taiwan (230700)
Cheng-Ru Chen1, Chi-Farn Chen1 and Son Thanh Nguyen2, (1)Center for Space and Remote Sensing Research, Jhongli, Taiwan, (2)NCU National Central University of Taiwan, Jhongli, Taiwan
Fusing Cubesat and Landsat 8 data for near-daily mapping of leaf area index at 3 m resolution (231833)
Rasmus Houborg, King Abdullah University of Science and Technology, Environmental Science and Engineering, Thuwal, Saudi Arabia and Matthew McCabe, King Abdullah University of Science and Technology, Hydrology, Agriculture and Land Observation (HALO) Laboratory, Division of Biological and Environmental Sciences and Engineering, Thuwal, Saudi Arabia
Prediction of winter wheat high yield from remote sensing based model: application in United States and Ukraine (232755)
Belen Franch1, Eric Vermote2, Jean-Claude Roger1, Sergii Skakun3, Inbal Becker-Reshef4 and Christopher Owen Justice5, (1)University of Maryland College Park, Department of Geographical Sciences, College Park, MD, United States, (2)NASA Goddard Space Flight Center, Terrestrial Information Systems Laboratory, Greenbelt, MD, United States, (3)University of Maryland, Geographical Sciences, College Park, United States, (4)GEOGLAM Secretariat, Geneva, Switzerland, (5)University of Maryland, Department of Geographical Sciences, College Park, United States
Mapping rice extent map with crop intensity in south China through integration of optical and microwave images based on google earth engine (241347)
Xin Zhang1, Bingfang Wu2, Miao Zhang1 and Hongwei Zeng1, (1)RADI Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China, (2)State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
Detecting spatio-temporal changes in agricultural land use in Heilongjiang province, China using MODIS time-series data and a random forest regression model (245153)
Qiong Hu, Boston University, Boston, MA, United States, Mark A Friedl, Boston University, Earth and Environment, Boston, MA, United States and Wenbin Wu, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China
MODIS Data Assimilation in the CROPGRO model for improving soybean yield estimations (289270)
Jonathan Richetti, University of Florida, Ft Walton Beach, FL, United States, Alejandro Monsivais-Huertero, ESME Ticoman/CDA, National Polytechnic Institute, Mexico City, Mexico, Ishfaq Ahmad, University of Florida, Gainesville, FL, United States and Jasmeet Judge, Center for Remote Sensing, Agricultural and Biological Engineering, University of Florida, Gainesville, United States
Integration of sUAS Imagery and Atmospheric Data Collection for Improved Agricultural Greenhouse Gas Emissions Monitoring (293503)
Lindsay Barbieri, University of Vermont, Burlington, VT, United States, Carol Adair, University of Vermont, Rubenstein School of Environment and Natural Resources, Burlington, United States, Gillian L Galford, University of Vermont, Rubenstein School of Environment and Natural Resources, Burlington, VT, United States and Janet Wyngaard, NASA Jet Propulsion Laboratory, Pasadena, CA, United States
Toward global crop type mapping using a hybrid machine learning approach and multi-sensor imagery (294785)
Sherrie Wang1, Sebastian Le Bras1, George Azzari1 and David B Lobell2, (1)Stanford University, Stanford, CA, United States, (2)Stanford University, Department of Earth System Science & Center on Food Security and the Environment, Stanford, United States
Stellite-based classification of tillage practices in the U.S. (295353)
George Azzari, Stanford University, Stanford, CA, United States and David B Lobell, Stanford University, Department of Earth System Science & Center on Food Security and the Environment, Stanford, United States
Brazilian Soybean Yields and Yield Gaps Vary with Farm Size (296804)
Graham Robert Jeffries1, Avery Cohn2, Timothy S Griffin1 and Arthur Bragança3, (1)Tufts University, Friedman School of Nutrition Science and Policy, Medford, MA, United States, (2)Tufts University, The Fletcher School, Medford, MA, United States, (3)TRACES, Rio de Janeiro, Brazil

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