PROJECT TITLE :

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

ABSTRACT:

Leaf space index (LAI) and evapotranspiration (ET) are two crucial biophysical variables connected to crop growth and grain yield. This study presents a crop model-information assimilation framework to assimilate the one-km moderate resolution imaging spectroradiometer (MODIS) LAI and ET merchandise (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 usually underestimate LAI or ET values in fragmented agricultural landscapes due to scale effects and intrapixel heterogeneity, we made a replacement cost perform by comparing the generalized vector angle between the observed and modeled LAI and ET time series throughout the growing season. We selected 3 parameters (irrigation date, irrigation depth, and emergence date) because the reinitialized parameters to be optimized by minimizing the price perform using the shuffled advanced evolution technique-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 have a tendency to found that jointly assimilating MODIS LAI and ET information improved accuracy (R2 = zero.43, RMSE = 619 kg · ha-one) than assimilating MODIS LAI knowledge (R2 = 0.twenty eight, RMSE = 889 kg · ha-1) or ET knowledge (R2 = 0.thirty six, RMSE = 1561 kg·ha-one) at the county level, that indicates that the proposed estimation method is reliable and applicable at a county scale.