Multi-temporal Data Assimilation Method Based on Filtering Optimization

When the assimilation method of open space inversion data and observation data is studied, on the basis of the physical geometric models, aerospace remote sensing and ground observation, the multi-temporal data assimilation method based on filter optimization is often used to solve different The problem of accidental deviation under the sensor. This paper focuses on rice growth prediction, and uses the complementarity of surface model and Earth observation space to study the coupling of remote sensing and RiceGrow model after combining assimilation and renewal strategies, and finally validates the timeliness of the method through three case studies. It is proved that the results detected by the proposed method are closer to the actual results.