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Improving crop yield estimation by unified model parameters and state variable with Bayesian inference

文献类型： 外文期刊

作者： Song, Jianjian 1 ; Huang, Jianxi 1 ; Huang, Hai 1 ; Xiao, Guilong 1 ; Li, Xuecao 1 ; Li, Li 1 ; Su, Wei 1 ; Wu, Wenbin 3 ; Yang, Peng 3 ; Liang, Shunlin 4 ;

作者机构： 1.China Agr Univ, Coll Land Sci & Technol, Beijing 100083, Peoples R China

2.Minist Agr & Rural Affairs, Key Lab Remote Sensing Agrihazards, Beijing 100083, Peoples R China

3.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Beijing 100081, Peoples R China

4.Univ Hong Kong, Dept Geog, Jockey Club Lab Quantitat Remote Sensing, Hong Kong 999077, Peoples R China

关键词： Crop yield estimation; Data assimilation; Bayesian inference; Ensemble Kalman filter; WOFOST model

期刊名称：AGRICULTURAL AND FOREST METEOROLOGY （ 影响因子：5.6； 五年影响因子：6.3 ）

ISSN： 0168-1923

年卷期： 2024 年 355 卷

页码：

收录情况： SCI

摘要： Data assimilation techniques integrating crop growth models and remote sensing technologies offer a feasible approach for large-scale crop yield estimation. Previous research has primarily focused on either recalibrate the uncertain model parameters or updating model state variables independently using remotely sensed observations. In this study, we developed a two-step inference algorithm that couples the parameter inference and the state update, to solve the joint posterior distribution of uncertain parameters and state variables given the remote sensing observations. An Observing Simulation System (OSS) experiment was first performed based on the WOFOST crop model to validate the effectiveness of the parameter inference method. The results indicate that, the parameter inference method successfully improved the estimation of different types of model parameters and enhanced yield estimation. Furthermore, leaf area index (LAI) retrieved from Sentinel -2 was assimilated into the WOFOST model to simulate winter wheat yield at the plot scale in the northeastern part of Henan Province. The results demonstrated that the proposed two-step inference algorithm can more effectively correct model simulation biases and improve winter wheat yield estimation accuracy (R 2 =0.58, MAPE =12.75 %, and RMSE =1112 kg center dot ha - 1 ), outperforming the standard EnKF algorithm (R 2 =0.51, MAPE =14.62 %, RMSE =1328 kg center dot ha - 1 ). Overall, attributed to its unified approach to estimating both model parameters and state variables, the proposed two-step inference algorithm shows promising application prospects for data assimilation -based crop yield estimation.

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