Shao, Y, Lunetta, RS (2009). Comparison of Sub-pixel Classification Approaches for Crop-specific Mapping. "2009 17TH INTERNATIONAL CONFERENCE ON GEOINFORMATICS, VOLS 1 AND 2", 183-186.
This paper examined two non-linear models, Multi layer Perceptron (MLP) regression and Regression Tree (RT), for estimating sub-pixel crop proportions using time-series MODIS-NDVI data. The sub-pixel proportions were estimated for three major crop types including corn, soybean, and wheat; throughout the entire 480,000 km(2) 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 sub-pixel 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 sub-pixel crop estimation problems.