USDA Foreign Agricultural Service Global Agricultural Monitoring Completed

Authors: Colin Leslie and Larisa Serbina Contact: Holly Miller

The Foreign Agricultural Service (FAS) handles USDA international activities, including the monitoring and estimation of crop supply and demand across global markets. Estimates for foreign production, supply, and demand are developed primarily through agricultural attachés. Attachés are based in foreign embassies, primarily in countries representing potential markets for U.S. crops (fig. 1). FAS attachés are further assisted by the International Production Assessment Division (IPAD), which collects and analyzes global crop condition and production. IPAD crop production analysts and FAS Global Agricultural Information Network (GAIN) reports both provide crucial inputs for the monthly World Agricultural Supply and Demand Estimates (WASDE) report.

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The FAS began monitoring global agricultural production using satellite imagery in 1972, enabled by the launch of Landsat 1 that year. Interest and need for satellite monitoring of crop production was in part driven by the Soviet Union (USSR) grain crop failure of 1972. Although the crop failure was widespread throughout the USSR, information and detection of the failure remained unknown until later that year. The lack of timely global production estimates allowed the USSR to purchase U.S. wheat at lower prices than the true market value (Curt Reynolds, USDA FAS, written commun. and oral commun. 2014).

The first large scale USDA program for monitoring global crop production (using Landsat imagery) was the Agriculture and Resource Inventory Surveys through Aerospace Remote Sensing (AgRISTARS), launched in 1979 (Caudill and McArdle, 1979). A number of objectives were outlined for AgRISTARS, but the primary focus was on early warning/crop condition assessment and foreign commodity production forecasting. In the late 1980s, AgRISTARS was officially moved to Washington, D.C., to be administered by the FAS. While AgRISTARS has since been superseded by a number of newer analysis platforms at FAS, the objectives remain largely the same and early warning/crop condition assessment covering seven countries has now expanded to most agricultural production areas of the globe (Curt Reynolds, USDA FAS, written commun. and oral commun., 2014).

Global supply and demand estimates developed by FAS and the World Agricultural Outlook Board (WAOB) are important inputs for a variety of derivative products including monthly WASDE reports and FAS Commodity Intelligence (CI) reports. While the WASDE is an important market intelligence source for agricultural commodities, CI reports from FAS provide on-demand assessments of specific regions. The ability to rapidly assess changes in a region’s agricultural status, whether due to natural disaster, drought, or political unrest, is crucial for anticipating market impact, food shortages, and humanitarian needs (Curt Reynolds, USDA FAS, written commun. and oral commun., 2014).

Currently, FAS utilizes a combination of Landsat, DMC, and MODIS imagery for monitoring crop production. Landsat and DMC, which provide 30 and 22-meter resolution respectively, are used primarily for developing crop acreage estimates. MODIS, which has a lower resolution at 250 meters but higher revisit at 1-3 days, is used for monitoring crop phenology (fig. 2).

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Benefits and Challenges of Using Landsat Imagery

As stated above, the USDA has been using Landsat to monitor crop production since the launch of Landsat 1 in 1972, making it the first and longest-running operational land-imaging satellite user in the U.S. For FAS, the main benefit of using Landsat for crop monitoring is that the imagery provides an objective and unbiased assessment of farm-level crop production. The known stability of spectral and spatial resolution across Landsat satellites as well as the high level of image-data orthorectification and radiometric correction have helped establish Landsat as a gold standard among earth observation satellites. The thirty meter image-pixel resolution of the multi-spectral data collected by Landsat since 1982 has become a standard for land cover classification, enabling a high level of classification accuracy while balancing the processing realities of classifying imagery at a global scale (Johnson and Mueller, 2010). In practical terms, the availability of consistently collected (across multiple satellites/years), accurately corrected, and as of 2008, freely available, Landsat data is an important benefit for agencies such as the FAS, which remain primarily operational in nature with limited time and budget for purchasing satellite imagery from commercial sources (Curt Reynolds, USDA FAS, written commun. and oral commun., 2014).

The recent launch of Landsat 8 in 2013 has helped ensure the continuity of the Landsat mission. However, between 2003 and 2013, the primary challenge facing agencies like the USDA were uncertainty in the long-term outlook of the Landsat program and the pending data gap due to an aging Landsat 5 and SLC-off issue with Landsat 7 (Johnson and Mueller, 2010). Although Landsat 8 became fully operational in 2013, some challenges related to agricultural monitoring remain. The primary challenge is low global repeat frequency, which continues to contribute to data gaps for global agricultural monitoring. Occlusion of the land surface by clouds, such as over Brazil’s soybean region bordering the Amazon forest or for the Ethiopian Highlands, remains the main challenge, where generally only one cloud-free image is acquired per year. Ethiopia remains one of the largest U.S. food aid recipients and therefore of high interest for crop production monitoring, while Brazil has become one of the largest soybean producers in the world. While the concurrence of agricultural production with high rainfall means occlusion by clouds, prioritization of agricultural monitoring needs within the Landsat 8 Long Term Acquisition Plan could potentially increase the number of usable Landsat images obtained within major agricultural  regions worldwide (Curt Reynolds, USDA FAS, written commun. and oral commun., 2014).

Reference:

Caudill, C.E., and McArdle, R.C., 1979, Research evaluation considerations for AgRISTARS: Washington, D.C., U.S. Department of Agriculture, Economics, Statistics, and Cooperatives Service.