Near real-time prediction of U.S. corn yields based on time-series MODIS data

Abstract

Annual variation of U.S. corn production is an important matter of world concern. To assure immediate response to large-scale harvest failure in crop exporting regions, as was the case during the severe U.S. drought in 2012, and to enhance global food security, a practical crop growth monitoring system based on satellite data is required. This study developed a practical method for near real-time prediction of U.S. corn yields using Moderate Resolution Imaging Spectroradiometer (MODIS)-derived Wide Dynamic Range Vegetation Index (WDRVI) taken 7days before the corn silking stage. We incorporated two algorithms into the MODIS-based corn yield prediction method; namely, (1) a MODIS-based crop classification algorithm in consideration of differences in emergence dates between corn and soybean, and (2) a simple bias correction algorithm for correcting region-dependent yield prediction errors. The method is able to predict the annual variation of national and state level corn grain yields with high accuracy in early August and detect corn yield reductions and poor-harvest regions due to drought damage, as in 2002 and 2012, on a near real-time basis in advance of those provided by the U.S. Department of Agriculture's National Agricultural Statistics Service. The method also predicted a national-level U.S. corn grain yield for 2013, which was 3.8% lower than the NASS-statistical data.