Monitoring winter wheat growth in North China by combining a crop model and remote sensing data

Both of crop growth simulation models and remote sensing method have a high potential in crop growth monitoring and yield prediction. However, crop models have limitations in regional application and remote sensing in describing the growth process. Therefore, many researchers try to combine those two approaches for estimating the regional crop yields. In this paper, the WOFOST model was adjusted and regionalized for winter wheat in North China and coupled through the LAI to the SAIL–PROSPECT model in order to simulate soil adjusted vegetation index (SAVI). Using the optimization software (FSEOPT), the crop model was then re-initialized by minimizing the differences between simulated and synthesized SAVI from remote sensing data to monitor winter wheat growth at the potential production level. Initial conditions, which strongly impact phenological development and growth, and which are hardly known at the regional scale (such as emergence date or biomass at turn-green stage), were chosen to be re-initialized. It was shown that re-initializing emergence date by using remote sensing data brought simulated anthesis and maturity date closer to measured values than without remote sensing data. Also the re-initialization of regional biomass weight at turn-green stage led that the spatial distribution of simulated weight of storage organ was more consistent to official yields. This approach has some potential to aid in scaling local simulation of crop phenological development and growth to the regional scale but requires further validation.