Regional yield prediction of winter wheat based on retrieval of Leaf area index by remote sensing technology

In this paper, the authors had a research on the winter wheat yield estimation using retrieved LAI from remote sensing in typical 11 counties in Huanghuaihai Plain in North China. In order to improve the quality of data and reduce error of yield estimation, Savitzky-Golay filter (S-G filter) was used to smooth the NDVI series to reduce influences of cloud contamination and abnormal data. At the same time, Gaussian model was used to simulate daily crop LAI to get average LAI at each growth stage. Using these average LAI, the authors established relationships between LAI and yield of winter wheat at main growth stage. After optimization of yield estimation model, the best period of time and best model was selected out. Finally, the authors depended on retrieved LAI from MODIS-NDVI to estimate winter wheat yield. The results showed that average relative error was 1.21% and that RMSE was 257.33 kg ha^-1 comparing predicted yield with ground truth data. We draw a conclusion that we could accurately get winter wheat yield about 20-30 days ahead of harvest time.