Maize Yield Estimation at County Level Based on World Food Studies Model and Remote Sensing Data Assimilation
Dynamic monitoring of crop growth and yield forecasting at regional scale provide important references for ensuring food security and formulating agricultural policies.The application of remote sensing data assimilation significantly enhances the timeliness and accuracy of crop yield estimation.In order to timely and accurately estimate the grain yield at the county level and improve the accuracy of the yield estimate,Tieling County in Liaoning Province was selected as the research area.The world food studies(WOFOST)model combined with remote sensing data assimilation was employed to estimate maize yield in Tieling County.In this study,the extened Fourier amplitude sensitivity test(EFAST)sensitivity analysis method was utilized to analyze and locate the sensitivity parameters of maize yield estimation.The parameter estimation(PEST)parameter optimization program was adopted to optimize the parameters.The verification results demonstrated that the average error of yield at sampling points was 852.39 kg·hm-2,and the accuracy of model simulation reached 92.82%.To further improve and optimize the yield estimation accuracy of the model,the leaf area index obtained from remote sensing inversion was assimilated with the leaf area index simulated by the model using the ensemble Kalman filter algorithm.The average error decreased from 852.39 kg·hm-2 before assimilation to 435.01 kg·hm-2 after assimilation,and the yield estimation accuracy increased from 92.82%to 96.33%.The yield estimation accuracy of the WOFOST model was effectively improved.The results showed that the growth and development of maize were not limited by water,and the yield was mainly affected by light and temperature,and the parameters related to temperature,light use efficiency and maximum assimilation rate were highly sensitive.The optimized model can simulate the growth and development of maize in Tieling county.The yield verification shows that the optimized model has a good simulation effect,but there are still some errors.The correlation between the ratio vegetation index and the leaf area index was the highest,and the inversion model was accurate.The inversion results showed that the leaf area index had a large difference in the drawing stage,but little difference in the mature stage.After assimilation of crop model and remote sensing data,the accuracy of yield estimation was obviously improved,indicating that assimilation of remote sensing and crop model is an effective method to improve the accuracy of crop yield estimation and yield prediction.
world food studies modelremote sensingdata assimilationmaize yield estimationTieling County
国家科技期刊平台
NSTL
万方数据
维普
