High Granularity Remote Sensing and Crop Production over Space and Time: NDVI over the Growing Season and Prediction of Cotton Yields at the Farm Field Level in Texas

Abstract

Remote sensing has been applied to agriculture at very coarse levels of granularity (i.e., national levels) but few investigations have focused on yield prediction at the farm unit level. Specific aims of the present investigation are to analyze the ability of Moderate Resolution Imaging Spectroradiometer (MODIS) data to predict cotton yields in two highly homogeneous counties in west Texas. In one study county > 90% of cotton grown is irrigated, while the other study county 40 miles south has >85% non-irrigated cotton. Regression analysis by day from April to November at the county and farm levels reveals a highly significant ability for MODIS to predict cotton yields. R values ranged from 0.90 to 0.98 for irrigated cotton and 0.80 to . 90 for non-irrigated cotton practices. The objective in future studies is to algorithmically extend these analyses to the ~300 million acres of arable land under cultivation in the United States.