Abstract:
Objectives Accurate, timely and effective monitoring of the growth and yield of winter wheat over a large area can help optimize the wheat planting structure, adjust the regional layout and ensure the country's food security. Therefore, it is very important to further improve the estimation accuracy of winter wheat yield.
Methods Vegetation temperature condition index (VTCI) and leaf area index (LAI) at the main growth period of winter wheat, which were simulated by the CERES (crop environment resource synthesis)-Wheat model and retrieved from MODIS (moderate resolution imaging spectroradiometer) data, were assimilated by using ensemble Kalman filtering (EnKF) algorithm and particle filtering (PF) algorithm. In addition, the principal component analysis combined with the Copula function was used to develop univariate (VTCI or LAI) and bi-variate (VTCI and LAI) winter wheat yield estimation models, and the optimal model was selected to estimate winter wheat yields from 2017 to 2020.
Results The experimental results show that, at the sampling-sites scale, both VTCI and LAI after assimilated can comprehensively reflect the variation characteristics of MODIS observed and model simulated values, and the application of PF algorithm has a better assimilation effect. At the regional scale, the bivariate yield estimation model developed by using PF algorithm has the highest accuracy. Compared with the accuracy of the models constructed by VTCI and LAI without assimilation, the root mean square error of the optimal assimilation model is reduced by 56.25 kg/hm2, and the average relative error is reduced by 1.51%.
Conclusions The above results indicate that the model can effectively improve the accuracy of winter wheat yield estimation and has good applicability for large area yield estimation.
下载:
