| Accurate crop growth monitoring and yield prediction are significantly important to food security and sustainable development of agriculture. Crop yield estimation by remote sensing and crop simulation models have highly potential application in crop growth monitoring and yield forecasting. However, both of them have limitations in mechanism or regional application, respectively. Therefore, approach and methodology study on combination of remote sensing data and crop growth models are concerned by many researchers.Based on the related studies in this area and taking consideration of techniques and data available, this dissertation focuses on the method studies of assimilating remote sensing data into crop growth model. The research work could be summarized as follows:(1) Data preparation, include field observation data and remote sensing data. The study area is located in Shunyi county, Beijing, China. An integrated field campaign was carried out there, during the winter wheat growing season in 2001. Measured data used in this paper is collected from four fields in different locations and planting conditions in Shunyi district. MODIS data were collected and used for inversing leaf area index(LAI) of winter wheat by Remote Sensing Inversion System(RSIS).(2) Calibration of the crop growth model.. CERES_Wheat model under DSSAT v4.0 shell was used as research tool. Field observation data were used in calibrating the crop growth model to actual growing conditions of "winter wheat 411".(3) Development and validation of methodology of monitoring and estimating crop yield by means of satellite and ground-based data. It was established on the basis of assimilation theory. CERESWheat model was initialized and re-parameterized to fit simulated LAI to external LAI data. External LAI data include LAI simulated with CERES_Wheat model using actual input, LAI measured in the four fields, and LAI from...
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