数字农科院2.0

Evaluation of pear orchard yield and water use efficiency at the field scale by assimilating remotely sensed LAI and SM into the WOFOST model

文献类型： 外文期刊

作者： Liang Jiang;Feilong Zhang;Jianan Chi;Pingping Yan;Xiangxin Bu;Yong He;Tiecheng Bai

作者机构：

关键词： Data assimilation;RIME-LSSVM;Water use efficiency;WOFOST model;Yield estimation

期刊名称： Computers and Electronics in Agriculture

ISSN： 0168-1699

年卷期： 2025 年 233 卷

页码：

收录情况： SCIE（2025版） ; ; EI（2025版）

摘要： The pear trees, significant both ecologically and economically, play a crucial role in arid and semi-arid areas such as Xinjiang, which make the study of its growth simulation and water use efficiency vital. Few studies have integrated remote sensing with crop growth models to simulate fruit tree growth and plant water transport at the field level. However, plant growth simulations encounter challenges such as uncertain input parameters and regional variability when they are applied to different regions. To address these challenges, we proposed to assimilate inversion data from satellite remote sensing (Sentinel-1, Sentinel-2, and DEM) into the WOFOST model based on Ensemble Kalman Filter (EnKF) techniques to simulate pear tree growth and evaluate water use efficiency at the field scale. We validated this approach at the regional scale by analyzing leaf area index (LAI), yield, and water use efficiency data from 118 pear orchards across five regions during four key growth periods. The results indicated that the NDVI, NDREI, SAVI, and EVI indices were well correlated with LAI. The RIME-LSSVM algorithm enhanced LAI and soil moisture (SM) inversion models, outperforming traditional regression methods. In the four phenological periods, the R2 of LAI inversion model ranged from 0.76 to 0.96, NRMSE ranged from 3% to 6.6%, and SM inversion R2 values ranged from 0.27 to 0.41, NRMSE values ranged from 10.9% to 16.6%. The results showed that the joint assimilation of SM and LAI into the calibrated WOFOST model significantly improved the simulation performance of regional yield and water use efficiency compared to univariate assimilation and non-assimilation. Specifically, the yield estimation R2 increased from 0.37 to 0.58, and the NRMSE decreased from 8.2% to 6.2%. Similar improvements were achieved in the water use efficiency simulations, with R2 rising from 0.52 to 0.70 and NRMSE declining from 9.1% to 7.1%. The proposed assimilation method can simulate growth processes and analyze water transport across four critical periods in pear orchards in various regions, aligning with regional observations. The proposed method facilitated the quantitative simulation of pear growth and water transport, providing a promising method for water management in other orchards in arid and semi-arid regions.

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