The Global Agriculture Monitoring (GLAM) Project: Enhancing Crop Production Forecasting and Agricultural Monitoring Capabilities of the Foreign Agriculture Service using Moderate Resolution Satellite Data
Inbal
Reshef, UMCP, ireshef@hermes.geog.umd.edu
(Presenting)
Mark
Sullivan, UMCP, mbs@hermes.geog.umd.edu
Matthew
Hansen, SDSU, Matthew.Hansen@sdstate.edu
Assaf
Anyamba, NASA/GSFC, assaf@ltpmail.gsfc.nasa.gov
Jen
Small, SSAI-NASA/GSFC, jsmall@pop900@gsfc.nasa.gov
Compton
Tucker, NASA/GSFC, compton@ltpmail.gsfc.nasa.gov
Jacques
Descloitres, SSAI-NASA/GSFC, jack@ltmail.gsfc.nasa.gov
Jackie
Kendall, SSAI, jackie_Kendall@ssaihq.com
Jeff
Schmaltz, SSAI-NASA/GSFC, jeff.schmaltz@gsfc.nasa.gov
Brad
Doorn, USDA/FAS, doorn@fas.usda.gov
Chris
Justice, UMCP, justice@hermes.geog.umd.edu
The Global Agriculture Monitoring (GLAM) Project aims to enhance the agricultural monitoring and the crop-production estimation capabilities of the USDA Foreign Agricultural Service (FAS) using NASA’s moderate resolution satellite data. The project is a collaboration between NASA/GSFC, USDA/FAS, and University of Maryland Department of Geography.
The primary mission of the FAS is to deliver objective, timely and regular assessments of global agricultural production and of the conditions which affect it. To help achieve this goal, the GLAM project provides the FAS with multiple remotely sensed data sets and derived products from moderate resolution sensors for target agricultural regions worldwide.
To monitor crop conditions and to locate and track the factors impairing agricultural productivity, a web-based information-analysis and data-delivery system has been developed. This system provides the FAS crop analysts with a suite of MODIS temporal composites of vegetation index (VI) data, false color imagery, and a dynamic, interactive crop likelihood mask. The system’s web interface provides a range of analysis tools that allow crop analysts to interrogate these data and to drill down to the pixel level of detail. As a result, analysts can better characterize land surface conditions and monitor changes in the key agricultural areas. For near real time assessment and evaluation of disaster events, daily global data are provided from the MODIS Rapid Response system, which delivers data within 2-4 hours of satellite acquisition. These data and tools help FAS analysts track the evolution of the growing season, and inform decision makers of agricultural conditions and agricultural impediments to worldwide food-security.