Comparison of sub-pixel classification approaches for crop-specific mapping

This paper examined two nonlinear models, multilayer perceptron (MLP) regression and regression tree (RT), for estimating subpixel crop proportions using time series MODIS-NDVI data. The subpixel proportions were estimated for three major crop types including corn, soybean, and wheat; throughout the entire 480,000 km² Laurentian Great Lakes Basin. Accuracy assessments were conducted using the cropland data layer (CDL) developed by the National Agricultural Statistics Service (NASS). The performances of the subpixel classifications were compared based on root mean square error (RMSE) and scatter plots. For MLP regression, the RMSE values at 500 m spatial resolution were 0.16, 0.14, and 0.07 for corn, soybean and wheat, respectively. The RT approach achieved slightly higher RMSE values of 0.18, 0.15, and 0.07 for corn, soybean, and wheat. The latter approach did not provide greater interpretability, because tree sizes were rather large for MODIS-NDVI subpixel crop estimation problems.