Publications

Huang, JX; Ma, HY; Sedano, F; Lewis, P; Liang, S; Wu, QL; Su, W; Zhang, XD; Zhu, DH (2019). Evaluation of regional estimates of winter wheat yield by assimilating three remotely sensed reflectance datasets into the coupled WOFOST-PROSAIL model. EUROPEAN JOURNAL OF AGRONOMY, 102, 1-13.

Abstract
To estimate regional-scale winter wheat (Triticum aestivum) yield, we developed a data-assimilation scheme that assimilates remotely sensed reflectance into a coupled crop growth-radiative transfer model. We generated a time series of 8-day, 30-m-resolution synthetic Kalman Smoothed reflectance by combining MODIS surface reflectance products with Landsat surface reflectance using a KS algorithm. We evaluated the assimilation performance using datasets with different spatial and temporal scales (e.g., three dates for the 30-m Landsat reflectance, 8-day and 1-km MODIS surface reflectance, and 8-day and 30-m synthetic KS reflectance) into the coupled WOFOST-PROSAIL model. Then we constructed a four-dimensional variational data assimilation (4DVar) cost function to account for differences between the observed and simulated reflectance. We used the shuffled complex evolution-University of Arizona (SCE-UA) algorithm to minimize the 4DVar cost function and optimize important input parameters of the coupled model. The optimized parameters were used to drive WOFOST and estimate county-level winter wheat yield in a region of China. By assimilating the synthetic KS reflectance data, we achieved the most accurate yield estimates (R-2 = 0.44, 0.39, and 0.30; RMSE = 598, 1288, and 595 kg/ha for 2009, 2013, and 2014, respectively), followed by Landsat reflectance (R-2 = 0.21, 0.22, and 0.33; RMSE = 915, 1422, and 637 kg/ha for 2009, 2013, and 2014, respectively) and MODIS reflectance (R-2 = 0.49, 0.05, and 0.22; RMSE = 1136, 1468, and 700 kg/ha for 2009, 2013, and 2014, respectively) at the county level. Thus, our method improves the reliability of regional-scale crop yield estimates.

DOI:
10.1016/j.eja.2018.10.008

ISSN:
1161-0301