Research on yield estimation method of winter wheat based on Sentinel-1/2 data and ma-chine learning algorithms
Aiming at the problem that optical images are easily affected by cloud and rain weather,resulting in low accuracy of crop yield estimation,in this study,the Sentinel-1/2 spectral information and backscattering coefficient at winter wheat heading stage were combined,and three machine learning regression methods of extreme gradient boosting,random forest and support vector machine were used to establish the winter wheat yield estimation model in Tangshan,the best model was selected to realize the winter wheat yield inversion in Tangshan.The results show that:the extreme gradient boosting model based on vegetation index and backscattering coefficient had the best estimation effect,with the determination coefficient(R2)of 0.654,the root mean square error(RMSE)of 0.499 t·hm-2,and the normalized root mean square error(nRMSE)of 10.02％.Among the 24 remote sensing feature variables,the importance of NDMI,NDVIre3 and NDVIre2 was much higher than that of the backscattering coefficient.Inverse spatial distribution of winter wheat yield in Tangshan based on optimal yield estimation model,the yield range of win-ter wheat was mainly concentrated in 7.00-8.00 t·hm-2,accounting for 40.75％,the distribution of winter wheat yield was generally similar to the ground truth.This study proposed Sentinel-1/2 data and integration of machine learn-ing algorithms of winter wheat yield estimation method,effectively improve the inversion accuracy of winter wheat yield and machine learning method to strengthen the explanatory of the model,the method has certain feasibility.
NETL
NSTL
万方数据
