作物模型与遥感信息的结合有助于利用遥感监测的大范围植被信息解决作物模型区域应用时模型初始状态和参数值难以确定的问题。该文借助叶面积指数（LAI）将经过华北冬小麦（Triticum aestivium）适应性调整的WOFOST模型与经参数调整检验的SAIL_PRO SPEC T模型相嵌套，利用嵌套模型模拟作物冠层的土壤调整植被指数（SAVI），在代表点上 借助F SEOPT优化程序使模拟SAVIs与MODIS遥感数据合成SAVIm的差异达到最小，从而 对WOFOST模型 重新初始化。结果表明，借助于遥感信息，出苗期的重新初始化使模拟成熟期与按实际出苗期模拟的结果相差在２天以内，模拟的LAI和总干重的误差比按实际出苗期模拟结果的误差降低3～8个百分点；返青期生物量的重新初始化使模拟LAI和地上总干重在关键发育时刻的误差降至16%以内，模拟LAI和贮存器官重在整个生育期内都更加接近实测值；对返青期生物量的动态调整显示返青到抽穗期间较少次数的遥感数据即能有效地提高作物模型的模拟效果。 与国外同类研究相比，该文在作物模型本地化、重新初始化变量和优化比较对象的选择上都有所不同，而利用遥感数据动态调整作物模型初始状态或参数值更具有新意。该文对区域尺度上利用遥感信息优化作物模型的研究具有基础性、探讨性意义。
Crop growth simulation models have extensive application in crop growth monitoring, yield forecasting and utilization of enviromental resources. However, problems arise when scaling crop simulation models from the field to regional scales, especially in acquiring initial conditions and parameters for the model. Fortunately, satellite remote sensing has the potential to improve some of the model parameterization for monitoring crop growth at regional scales. Thus, there is interest in developing an approach and methodology for incorporating remotely_sensed information with crop growth simulation models. In this paper, the crop model WOFOST (World food study) was adapted for simulating growth of winter wheat by using field experimental data from North China, and the radiative transfer model SAIL_PROSPECT was adapted by adjusting the sensitivity of its parameters. The two models were then coupled using LAI to simulate the vegetation indices SAVI. Finally, WOFOST was re_initialed by minimizing differences between SAVI s simulated by coupling the model and SAVIm synthesized from MODIS remote sensed data using an optimization software program (FSEOPT). The results were validated by using field experimental data (including leaf area index , dry weight of leaves, stems and storage organs) in Zhengzhou, Henan Province, Tai‘an, Shandong Province, and Gucheng, Hebei Province, and some MODIS data during the growing season of winter wheat from 2001 to 2003. The main results in this study were as follows: (1) Differences between the simulated mature date, after re_initializing the emergence date using remote sensing data and simulated values, with the actual ermergence date was within 2 days, and simulated LAI and gross above_ground dry matter weight were 3-8 percent of actual values; (2) By re_initializing biomass weight in the turn_green stage, rela tive errors of the simulated LAI and gross above_ground dry matter weight were within 16% at key development stages, and simulated LAI and storage organ weight were closer to measured values during the entire growing period; (3) Appropriate remote sensing data during the period from the turn_green stage to earing stage was more critical for improving crop modeling when biomass in the turn_green stage was adjusted. We presented a novel method for validating and adjusting crop models to regional scales. Optimization of the crop simulation model by dynamical adjustment of the initial variables and parameters based on remote sensing data produced highly satisfactory results. This research provides a basis for optimizing crop models by using remotely sensed data at regional scales. However, errors in the simulation results due to uncertainty of remote sensing data and SAIL_PROSPECT paramete rs still exist and further study is needed.