Regional winter wheat yield estimation by assimilating MODIS ET and LAI products into SWAP model

Leaf Area Index (LAI) and Evapotranspiration (ET) are two important biophysical variables related to crop growth and grain yield. This study presents a method to assimilate MODIS ET products (MOD16A2) and MODIS LAI products (MCD15A3) into the soil water atmosphere plant (SWAP) model to improve the accuracy of the winter wheat yield estimation at both the field and regional scale. Since MODIS ET and LAI products generally tend to underestimate ET and LAI values in fragmented agricultural landscape due to spatial heterogeneity in 1km resolution pixel, a new cost function was proposed through comparing the positive and negative of the first-order difference of the SWAP output and observation time series curve at certain time interval. Two model parameters (i.e. irrigation water depth and emergence date) were selected as the parameters needed to be optimized by minimizing the cost function with the SCE-UA algorithm, and then the optimized parameters were input into the SWAP model for winter wheat yield estimation. Three schemes were conducted to compare the assimilation accuracy at field and regional scale. The validation results indicate that that jointly assimilating ET and LAI achieves better accuracy than ET and LAI alone.