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United States Department of Agriculture

Agricultural Research Service

Research Project: USING REMOTE SENSING & MODELING FOR EVALUATING HYDROLOGIC FLUXES, STATES, & CONSTITUENT TRANSPORT PROCESSES WITHIN AGRICULTURAL LANDSCAPES Title: Operational prediction of crop yields using MODIS data and products

Authors
item Doraiswamy, Paul
item Akhmedov, Bakhyt - SSAI
item Beard, Larry - USDA RSCH & DEV DIV
item Stern, Alan
item Mueller, Richard - USDA RSCH & DEV DIV

Submitted to: Book Chapter
Publication Type: Book / Chapter
Publication Acceptance Date: June 10, 2007
Publication Date: October 1, 2007
Citation: Doraiswamy, P.C., Akhmedov, B., Beard, L., Stern, A.J., Mueller, R. 2007. Operational prediction of crop yields using MODIS data and products. In: Chen, J., Saunders, S.C., Brosofske, K.D. and Crow, T.R. editors. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences Special Publications, Commission Working Group VIII WG VIII/10, European Commision DG JRC-Institute for the Protection and Security of the Citizen, Ispra, Italy. p. 1-5.

Interpretive Summary: Accurate and timely monitoring of agricultural crop conditions and yields are essential for operational programs. Assessment of particularly decreased production caused by a natural disaster, such as drought or pest infestation, can be critical for countries or localities where the economy is dependent on the crop harvest. Early assessment of yield reductions could avert a disastrous situation and help in strategic planning to meet demands. This research describes the potential use of satellite remote sensing data and products in algorithms to provide regional map corn and soybean crop yields. The goals of this research are to develop operational methods to predict crop yields accurately in a timely and consistent manner. This research was conducted in cooperation with the National Agricultural Statistics Service of USDA who is responsible for providing end-of-year estimates of crop yield and production. The results demonstrate the potential use of moderate resolution satellite imagery for operational assessment of crop yields.

Technical Abstract: Official crop progress, condition and production estimates for the United States are responsibilities of the U.S. Department of Agriculture’s, National Agricultural Statistics Service (NASS). In addition to weekly and monthly survey-based data, biweekly composite maps of the normalized difference vegetation index (NDVI) from the NOAA AVHRR sensor (1 km resolution) are produced by NASS’s Research and Development Division (RDD) for monitoring vegetative change. This provides a qualitative assessment of differences in crop condition that may be an indication of potential yields. There is need for a more quantitative assessment of crop yields and spatial variability. Currently, NASS acquires crop yield indications via ground-based sample surveys (objective plant and fruit counts, fruit weights and farmer reports) which are collectively used to develop tools for its decision support system to assess weekly crop progress, monthly crop yield estimates for each state and the U.S, and annual county yield estimates. This paper describes the joint research between RDD and the Agricultural Research Service (ARS) of USDA for the development of simplified process models and algorithms to supplement the NASS field data collection. Potential advantages to using remote sensing include integration of spatial variability into county yields, enhanced timeliness, and efficient use of resources. In the preliminary phase, MODIS data and products for the states of Iowa and Illinois were used to develop an operational assessment of crop yield forecasts for corn and soybeans. Spatial estimates of crop yields at county and sub-county levels offer a major improvement of current capabilities. The timeliness in producing these estimates is a vast improvement over the present assessment capability at the county level. Potential use of the estimates will supplement current tools and improve NASS’ crop condition and yield decisions. Results of the pre-harvest forecasts developed for the 2005 and 2006 crop seasons are presented.

Last Modified: 12/19/2014
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