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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： Jiang, Liang 1 ; Zhang, Feilong 1 ; Chi, Jianan 1 ; Yan, Pingping 3 ; Bu, Xiangxin 5 ; He, Yong 2 ; Bai, Tiecheng 1 ;

作者机构： 1.Tarim Univ, Coll Informat Engn, Alar 843300, Peoples R China

2.Zhejiang Univ, Coll Biosyst Engn & Food Sci, Hangzhou 310058, Peoples R China

3.Liaoning Tech Univ, Coll Software, Huludao 125100, Liaoning, Peoples R China

4.Tarim Univ, Key Lab Tarim Oasis Agr, Minist Educ, Alaer 843300, Peoples R China

5.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, State Key Lab Efficient Utilizat Arid & Semiarid A, Beijing 100081, Peoples R China

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

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.9； 五年影响因子：9.3 ）

ISSN： 0168-1699

年卷期： 2025 年 233 卷

页码：

收录情况： SCI

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 RIMELSSVM 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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