Monitoring crop growth based on assimilation of remote sensing data and crop simulation model

Remote sensing observation and crop growth model simulation are two effective ways for crop growth monitoring, and they can well complement each other. Establishing assimilation system of remote sensing data and crop growth model is an interesting research field. Assimilation involves multidisciplinary integration, so it is necessary to construct a basic assimilation system integrated with models, algorithms and datasets of many kinds to reduce the difficulty of research and applications of data assimilation. In this study, in order to establish the assimilation system prototype of crop growth model and remote sensing data, modular design was used to realize the combination of CERES-Wheat model, very fast simulated annealing algorithm and remote sensing data. Ground hyper-spectral data was chosen as remote sensing data, and the system was tested and preliminarily applied to estimate wheat LAI. The results show that the assimilated LAI can well agree with the measured LAI, and the assimilation system established is feasible. The assimilation system can provide a platform for the basic research and application of the coupling of remote sensing data and crop growth model.