Assimilation of MODIS-LAI into the WOFOST model for forecasting regional winter wheat yield

作者：Ma, Guannan; Huang, Jianxi^*; Wu, Wenbin; Fan, Jinlong; Zou, Jinqiu; Wu, Sijie

来源：Mathematical and Computer Modelling, 2013, 58(3-4): 634-643.

DOI：10.1016/j.mcm.2011.10.038

• Remote sensing
• Crop growth model
• Sensitivity analysis
• Data assimilation
• Yield

Crop growth models have been applied successfully in forecasting crop yield at a local scale, while satellite remote sensing has the advantage of retrieving regional crop parameters. The new assimilation method of integrating the crop growth model with remote sensing has presented great potential in regional crop yield assessment. In this study, the Moderate Resolution Imaging Spectrometer (MODIS) leaf area index (LAI) data product was assimilated into the World Food Studies (WOFOST) crop growth model. Using the Extended Fourier Amplitude Sensitivity Test (EFAST) global sensitivity analysis approach, several local and regional crop parameters were identified to be recalibrated. The Shuffled Complex Evolution (SCE) optimization algorithm was used to estimate the emergence date, initial biomass and initial available soil water by minimizing the differences between the corrected MODIS-LAI and simulated LAI. Results indicated that the accuracy of water-limited crop yield was improved significantly after the assimilation. The root mean square error (RMSE) reduced from 983 kg/ha to 474 kg/ha and 667 kg/ha respectively in two different optimization schemes.

• 出版日期2013-8
• 单位中国农业大学; 中国农业科学院农业资源与农业区划研究所; 国家卫星气象中心; 中南大学