An FPAR Retrieval Algorithm Based on Deep Learning for Modis Visible Band Surface Reflectance

The Fraction of photosynthetically active radiation (FP AR) absorbed by vegetation is the important biophysical variables of the most climate, hydrological, biogeochemical, and ecological models. In this paper, the simulative surface reflectance and FP AR were used to train the artificial neural network, which retrieved the FP AR value using the Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance in visible spectrum. The retrieval results were comprehensively evaluated using MODIS LAI/FP AR products and available field measurement data. Compared with the ground measurement FP AR, the correlation coefficient (R) is 0.727, the relative average error (RMB) is 1.62, the mean absolute error (MAE) is 0.16, the mean relative error (MRE) is 0.896, and the root mean square error (RMSE) is 0.214. The R of MODIS FPAR products was 0.850, the RMB was 1.715, the MAE was 0.197, the MRE was 1.046, and the RMSE was 0.263. The comparison results show that retrieval FP AR can be performed effectively in the ground site area without the type of vegetation.