Cotton growth monitoring and yield estimation based on assimilation of remote sensing data and crop growth model

Predicting cotton growth and yield accurately is significantly important to farmland management and sustainable development of agriculture. Remote sensing and crop growth model both have its advantages in crop growth monitoring and yield estimation, however, they also have limitations in mechanism or acquisition of the input parameters. This study combines the satellite remote sensing data and crop growth models by using data assimilation technique. The research uses global optimization algorithm called shuffled complex evolution-University of Arizona (SCE-UA) to constantly inverse and correct the values of model input parameters with the leaf area index (LAI) as the combination point, selects decision support system for agrotrchnology transfer (DSSAT) to build growth model of cotton in Jianghan plain in the middle reaches of the Yangtze River. The results of the research show that the precision of simulation is effectively improved after cotton model is assimilated by remote sensing data.