Jointly assimilating MODIS LAI and ET products into SWAP model for winter wheat yield estimation

Authors: HUANG, JIANXI - Chinese Agricultural University; MA, HONGYUAN - Chinese Agricultural University; WEI, SU - University Of Maryland; ZHANG, XIAODONG - University Of Maryland; Huang, Yanbo; LIU, JUMMING - Chinese Academy Of Agricultural Sciences; FAN, JINLONG - Chinese Academy Of Agricultural Sciences

Submitted to: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/28/2015
Publication Date: 4/1/2015
Citation: Huang, J., Ma, H., Wei, S., Zhang, X., Huang, Y., Liu, J., Fan, J. 2015. Jointly assimilating MODIS LAI and ET products into SWAP model for winter wheat yield estimation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 8(8): 4060-4071.

Interpretive Summary: For improved estimation of winter wheat yield, a scheme that incorporates remotely sensed data into a crop growth model has been tested. Scientists from China Agricultural University, USDA-ARS, Crop Production Research Unit, Stoneville, Mississippi, and Chinese Academy of Agricultural Science developed a framework for winter wheat yield prediction. The framework assimilates leaf area index (LAI) and evapotranspiration (ET) values derived from MODIS (Moderate-Resolution Imaging Spectroradiometer) satellite LAI products into the soil water atmosphere plant (SWAP) crop model with field-measured LAI. The results indicated that assimilating LAI and ET achieves better accuracy in yield estimation of winter wheat than assimilating LAI and ET individually. This study suggests that the proposed framework for winter wheat yield estimation is reliable and can be adapted in crop production management.

Technical Abstract: Leaf Area Index (LAI) and Evapotranspiration (ET) are two key biophysical variables related to crop growth and grain yield. This study presents a framework to assimilate MODIS LAI products (MCD15A3) and MODIS ET products (MOD16A2) into the soil water atmosphere plant (SWAP) model to improve estimates of the winter wheat yield at both the field and regional scales. Since MODIS LAI and ET products generally tend to underestimate LAI and ET values in fragmented agricultural landscape due to scale mismatch between observations and crop models, a new cost function was proposed through comparing the generalized vector angle of the observation and modeled LAI and ET time series curve with certain time interval through the growing season. Two key model parameters (i.e. irrigation water depth and emergence date) were selected as the reinitialized parameters needed to be optimized by minimizing the cost function with the SCE-UA (Shuffled Complex Evolution – University of Arizona) optimization 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 of winter wheat yield at field and regional scales. The validation results of county scale (R2 = 0.432, RMSE = 721 kgha-1) indicate that jointly assimilating LAI and ET achieves better accuracy than assimilating LAI and ET alone, which indicating that the proposed winter wheat yield estimation method was reliable and applicable in different spatial scales.