Jointly Assimilating MODIS LAI and ET Products Into the SWAP Model for Winter Wheat Yield Estimation

Leaf area index (LAI) and evapotranspiration (ET) are two crucial biophysical variables related to crop growth and grain yield. This study presents a crop model–data assimilation framework to assimilate the 1-km moderate resolution imaging spectroradiometer (MODIS) LAI and ET products (MCD15A3 and MOD16A2, respectively) into the soil water atmosphere plant (SWAP) model to assess the potential for estimating winter wheat yield at field and regional scales. Since the 1-km MODIS products generally underestimate LAI or ET values in fragmented agricultural landscapes due to scale effects and intrapixel heterogeneity, we constructed a new cost function by comparing the generalized vector angle between the observed and modeled LAI and ET time series during the growing season. We selected three parameters (irrigation date, irrigation depth, and emergence date) as the reinitialized parameters to be optimized by minimizing the cost function using the shuffled complex evolution method—University of Arizona (SCE-UA) optimization algorithm, and then used the optimized parameters as inputs into the SWAP model for winter wheat yield estimation. We used four data-assimilation schemes to estimate winter wheat yield at field and regional scales. We found that jointly assimilating MODIS LAI and ET data improved accuracy ( , ) than assimilating MODIS LAI data ( , ) or ET data ( , ) at the county level, which indicates that the proposed estimation method is reliable and applicable at a county scale.