Comparison of Winter Wheat Yield Estimation by Sequential Assimilation of Different Spatio temporal Resolution Remotely Sensed LAI Datasets

Abstract:

Data assimilation method combines with remotely sensed data and crop growth model has become an important hotspot in crop yield forecasting. PyWOFOST model and remotely sensed LAI were respectively selected as the crop growth model and observations to construct a regional winter wheat yield forecasting scheme with EnKF algorithm. To eliminate cloud contamination, a Savitzky-Golay (S-G) filtering algorithm was applied to the MODIS LAI products to obtain filtered LAIs. Regression models between field-measured LAI and Landsat TM vegetation indices were established and multi-temporal TM LAIs was derived. The TM LAI with time series of MODIS LAI was integrated to generate scale-adjusted LAI. Compared the assimilation accuracy using these three different spatio-temporal resolution remotely sensed data, validation results demonstrated that assimilating the scale-adjusted LAI achieved the best prediction accuracy, in potential mode, the determination coefficient (R2) increased from 0.24 which without assimilation to 0.47 and RMSE decreased from 602kg/hm2 to 478kg/hm2 at county level compared to the official statistical yield data. Our results indicated that the scale adjustment between remotely sensed observation and crop model greatly improved the accuracy of winter wheat yield forecasting. The assimilation of remotely sensed data into crop growth model with EnKF can provide a reliable approach for regional crop yield estimation.