基于遥感信息和WOFOST模型参数同化的冬小麦单产估算方法研究

为探讨遥感信息和作物生长模型在作物估产方面的优势互补特性，选取河北省藁城市冬小麦作为研究对象，采集多个关键生育时期的生理生化、农田环境、气象等数据，并获取准同步的环境减灾小卫星HJ-CCD影像数据，采用植被指数反演冬小麦叶面积指数（LAI），基于扩展傅里叶振幅灵敏度检验法(EFAST)对WOFOST作物模型的26个初始参数进行全局敏感性分析，筛选敏感性参数，调整WOFOST模型的核心参数，利用查找表优化算法构建基于WOFOST模型和遥感LAI数据同化的区域尺度冬小麦单产预测模型，并定量预测区域冬小麦单产水平。结果表明，增强型植被指数（EVI）是遥感反演LAI的最佳植被指数(开花期建模r=0.913，RMSE=0.410，灌浆期建模r=0.798，RMSE=0.470)，预测能力最强(开花期r=0.858，RMSE=0.531，灌浆期r=0.861，RMSE=0.428)；筛选出6个待优化参数，即TSUM1、SLATB1、SLATB2、SPAN、EFFTB3和TMPF4；产量预测精度r=0.914，RMSE=253.67 kg·hm_-2，找到了待优化参数的最佳取值，最终完成了单产模拟。

Estimation of Winter Wheat Assimilation Based on Remote Sensing Information and WOFOST Crop Model

In order to explore the advantages and complementary characteristics of remote sensing information and crop growth models for the estimation of crop yield, we selected winter wheat in Gaocheng city, Hebei province as the research object, and collected physiological and biochemical data, farmland environmental data and meteorological data at several key growth stages. We used vegetation index to inverse LAI based on quasi-synchronous HJ-CCD image data. The Extend Fourier Amplitude Sensitivity Test(EFAST) was used to analyze the sensitivity of 26 initial parameters of the WOFOST crop model and to adjust the core parameters of WOFOST model. The optimization algorithm was used to construct the regional-scale winter wheat yield forecasting model based on WOFOST model and remote sensing LAI data assimilation, and to quantitatively predict the winter wheat yield in the region. The results showed that the Enhanced Vegetation Index(EVI) is the best vegetation index to retrieve LAI(r=0.913, RMSE=0.410 at anthesis, and r=0.798, RMSE=0.470 at grain filling stage), whose ability of prediction is the strongest(r=0.858, RMSE=0.531 at flowering stage, and r=0.861, RMSE=0.428 at grain filling stage). In addition, six parameters has been optimized, including TSUM1, SLATB1, SLATB2, SPAN, EFFTB3 and TMPF4. Finally, the field prediction accuracy achieved r=0.914, and RMSE=253.67 kg·hm_-2, which proved that the optimal value of optimized parameters has been found to completed the yield simulation.