Evaluation of large area crop estimation techniques

Abstract

The performance of the USDAs EDITOR system is evaluated. The system processes Landsat imagery and estimates crop hectarage for large areas based on a regression estimator developed on a sample with known ground truth. It is found that use of multitemporal data over unitemporal significantly improves the hectarage estimates, and a 15% reduction in the r-squared of the regression occurs when independent and jackknifed test sets are used to evaluate the performance of the estimator. When an alternative clustering algorithm, CLASSY, is substituted for the current EDITOR method, estimator performance is improved with reduced need for analyst decisions. It is recommended that the CLASSY clustering algorithm and some form of jackknifing be implemented on EDITOR.