Crop Yield Estimation at Field Scales by Assimilating Time Series of Sentinel-2 Data into a Modified CASA-WOFOST Coupled Model

Abstract

Rapidly and accurately estimating yields at field scales are very significant. Each type of currently yield estimation model has been well studied, yet all of them have certain limitations. Based on a coupled Carnegie-Ames-Stanford approach (CASA)-World Food Studies (WOFOST) model and time series Sentinel-2 imagery, we achieved daily crop simulations and crop yield estimations at two adjacent farms in China. The results indicated that the coupled model inherited the high computing speed of light use efficiency (LUE) models and the mechanistic advantages of crop growth models. The ${R}^{2}$ of yield simulation was 0.64 for the CASA model, 0.84 for the coupled model, and 0.86 for the WOFOST model, and the root mean square error (RMSE) values were 948.32, 792.11, and 623.64 kg/ha, respectively. The operating times of the CASA model, CASA-WOFOST coupled model, and WOFOST model over the growing season of wheat were 37 min, 48 min, and 1 day 5 h 7 min, respectively. Compared with the WOFOST model, the coupled model provided a much faster running speed in yield simulations and a similar accuracy; therefore, the proposed model can be applied for assessments at large farms with high-spatial-resolution images to obtain accurate yield simulations. Compared with the CASA model, the coupled model provided higher simulation accuracy in mountainous areas and regions of uneven terrain. It can be concluded that the proposed coupled CASA-WOFOST model can improve the precision, reliability, and stability of crop yield estimation; provide theoretical support for crop yield estimation at field scales; and promote the development of precision agriculture.