Assimilation of Remotely-Sensed LAI into WOFOST Model with the SUBPLEX Algorithm for Improving the Field-Scale Jujube Yield Forecasts

文献类型： 外文期刊

第一作者： Bai, Tiecheng

作者： Bai, Tiecheng;Wang, Shanggui;Meng, Wenbo;Zhang, Nannan;Wang, Tao;Bai, Tiecheng;Mercatoris, Benoit;Chen, Youqi

作者机构：

关键词： assimilation method; SUBPLEX; remote sensing; WOFOST model; jujube yield estimation

期刊名称：REMOTE SENSING （ 影响因子：4.848； 五年影响因子：5.353 ）

ISSN：

年卷期： 2019 年 11 卷 16 期

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收录情况： SCI

摘要： In order to enhance the simulated accuracy of jujube yields at the field scale, this study attempted to employ SUBPLEX algorithm to assimilate remotely sensed leaf area indices (LAI) of four key growth stages into a calibrated World Food Studies (WOFOST) model, and compare the accuracy of assimilation with the usual ensemble Kalman filter (EnKF) assimilation. Statistical regression models of LAI and Landsat 8 vegetation indices at different developmental stages were established, showing a validated R-2 of 0.770, 0.841, 0.779, and 0.812, and a validated RMSE of 0.061, 0.144, 0.180, and 0.170 m(2) m(-2) for emergence, fruit filling, white maturity, and red maturity periods. The results showed that both SUBPLEX and EnKF assimilations significantly improved yield estimation performance compared with un-assimilated simulation. The SUBPLEX (R-2 = 0.78 and RMSE = 0.64 t ha(-1)) also showed slightly better yield prediction accuracy compared with EnKF assimilation (R-2 = 0.73 and RMSE = 0.71 t ha(-1)), especially for high-yield and low-yield jujube orchards. SUBPLEX assimilation produced a relative bias error (RBE, %) that was more concentrated near zero, being lower than 10% in 80.1%, and lower than 20% in 96.1% for SUBPLEX, 72.4% and 96.7% for EnKF, respectively. The study provided a new assimilation scheme based on SUBPLEX algorithm to employ remotely sensed data and a crop growth model to improve the field-scale fruit crops yield estimates.

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