Combining Crop Proportion Phenology Index models with machine learning algorithms for estimating winter wheat areas

摘要

Monitoring crop areas is a key issue in remote sensing studies. A Crop Proportion Phenology Index (CPPI) model has previously been developed for estimation of winter wheat areas. Here we test the CPPI model in different areas using remote sensing data for varied kernel functions, including linear regression (LR), Artificial Neural Network (ANN), and Support Vector Regression (SVR). The differences of the model performances among different kernel functions were found to be small for areas with simple planting structure. For areas where multiple crop types have similar phenology cycles, the non-linear model of ANN was found to perform the best. This study indicates that the CPPI model can be applied to map winter wheat distribution in areas with complex planting structures, thus it holds promises for estimating fractional areas of winter wheat areas over large geographic areas.