GC51F:
Advances in Earth Observation for Agricultural Monitoring (GEOGLAM): Food Security and Early Warning I

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Session ID#: 15541

Session Description:
Earth observations (EO) can be a critical input to help food insecure nations dynamically monitor food production, forecast production changes, and inform decision making to support effective responses to emerging food scarcity. The international GEO Global Agricultural Monitoring (GEOGLAM) Initiative promotes the utilization of EO data for crop monitoring in main producer countries and countries-at-risk for food insecurity by:

  • addressing gaps in national capacity;
  • improving availability of, quality of, and access to satellite data;
  • fostering operational R&D of ‘best-practices’ for agricultural monitoring in production and smallholder contexts; and,
  • producing global monthly, multi-source consensus reports on crop conditions (via the GEOGLAM Crop Monitors).

This session invites abstracts on EO-based tools, methods, and analyses related to agricultural monitoring and forecasting in the context of national, regional, and global food security. This session will highlight recent advances in monitoring techniques and activities focusing on early warning in countries at risk of food insecurity.

Primary Convener:  Alyssa K Whitcraft, University of Maryland College Park, Geographical Sciences, College Park, United States
Convener:  Christopher Owen Justice, University of Maryland College Park, College Park, MD, United States
Chairs:  Christopher Owen Justice, University of Maryland College Park, College Park, MD, United States, Alyssa K Whitcraft, University of Maryland College Park, Geographical Sciences, College Park, United States and Felix Rembold, Institute for Environment and Sustainability JRC, ISPRA, Italy
OSPA Liaison:  Alyssa K Whitcraft, University of Maryland College Park, Geographical Sciences, College Park, United States

Cross-Listed:
  • B - Biogeosciences
  • PA - Public Affairs
Index Terms:

0402 Agricultural systems [BIOGEOSCIENCES]
0480 Remote sensing [BIOGEOSCIENCES]
1640 Remote sensing [GLOBAL CHANGE]
1934 International collaboration [INFORMATICS]

Abstracts Submitted to this Session:

Strengthening Agricultural Decisions in Countries at Risk of Food Insecurity: The GEOGLAM Crop Monitor for Early Warning (Invited) (159194)
Inbal Becker-Reshef1, Brian Barker2, Katie McGaughey2, Michael Laurence Humber3, Antonio Sanchez2, Christopher Owen Justice2, Felix Rembold4 and James P Verdin5, (1)University of Maryland, Department of Geographical Sciences, College Park, United States, (2)University of Maryland College Park, College Park, MD, United States, (3)University of Maryland College Park, Geographical Sciences, College Park, MD, United States, (4)Institute for Environment and Sustainability JRC, ISPRA, Italy, (5)University of Maryland, Boulder, United States
Developing an automatic classification system of vegetation anomalies for early warning with the ASAP (Anomaly hot Spots of Agricultural Production) system (172942)
Felix Rembold, European Commission Joint Research Centre, Ispra Vey, Italy, Michele Meroni, European Commission Joint Research Centre, Food Security, Ispra, Italy, Ferdinando Urbano, Institute for Environment and Sustainability JRC, ISPRA, Italy and Guido Lemoine, Joint Research Center Ispra, Ispra, Italy
Earth Observations for Early Detection of Agricultural Drought: Contributions of the Famine Early Warning Systems Network (FEWS NET) (176369)
Michael E Budde1, Christopher Funk2, Gregory J Husak3, Pete Peterson4, James Rowland1, Gabriel B Senay5 and James P Verdin6, (1)U.S. Geological Survey, Sioux Falls, United States, (2)USGS, Baltimore, MD, United States, (3)University of California Santa Barbara, Department of Geography, Santa Barbara, CA, United States, (4)University of California Santa Barbara, Geography, Santa Barbara, CA, United States, (5)USGS Earth Resources Observation and Science (EROS) Center Sioux Falls, Fort Collins, United States, (6)University of Maryland, Boulder, United States
Modelling and Forecasting of Rice Yield in support of Crop Insurance (121868)
Willem van Verseveld1, Albrecht Weerts2, Patricia Trambauer3, Sander de Vries4, Sjaak Conijn4, Eric van Valkengoed5, Dirk Hoekman6, Huib Hengsdijk4 and Aart Schrevel7, (1)Deltares, Inland Water Systems, Delft, Netherlands, (2)Deltares, Operational Water Management & Early Warning, Delft, Netherlands, (3)Deltares, Delft, Netherlands, (4)Plant Research International, Wageningen, Netherlands, (5)TerraSphere BV, Amsterdam, Netherlands, (6)Wageningen University, Wageningen, Netherlands, (7)Alterra Wageningen UR, Wageningen, Netherlands
Convolutional Neural Network for Multi-Source Deep Learning Crop Classification in Ukraine (187928)
Mykola S. Lavreniuk, National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", Kyiv, Ukraine; Space Research Institute NAS Ukraine and SSA Ukraine, Kyiv, Ukraine
Comparative analysis of agricultural drought monitoring based on climate and soil moisture indicators in the Huang-Huai-Hai Plain of China (132547)
Jianjun Wu, Hongkui Zhou and Xueli An, Beijing Normal University, Beijing, China
The shared and unique value of optical, flourescence, thermal and microwave satellite data for estimating large-scale crop yields (165356)
Kaiyu Guan, University of Illinois Urbana-Champaign, Agroecosystem Sustainability Center, Institute for Sustainability, Energy, and Environment, Urbana, United States, Jin Wu, University of Arizona, Tucson, AZ, United States, John S Kimball, University of Montana, Numerical Terradynamic Simulation Group, College of Forestry & Conservation, Missoula, MT, United States, Martha Anderson, USDA ARS, Hydrology and Remote Sensing Laboratory, Beltsville, United States, Steve E Frolking, University of New Hampshire, Durham, NH, United States, Bo Li, University of Illinois at Urbana Champaign, Urbana, United States and David B Lobell, Stanford University, Earth System Science, Stanford, CA, United States

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