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Integrating remote sensing assimilation and SCE-UA to construct a grid-by-grid spatialized crop model can dramatically improve winter wheat yield estimate accuracy

文献类型： 外文期刊

作者： Li, Qiang 1 ; Gao, Maofang 1 ; Duan, Sibo 1 ; Yang, Guijun 2 ; Li, Zhao-Liang 1 ;

作者机构： 1.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, State Key Lab Efficient Utilizat Arid & Semiarid A, Beijing 100081, Peoples R China

2.Changan Univ, Coll Geol Engn & Geomat, Xian 710054, Peoples R China

3.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing 100097, Peoples R China

关键词： The grid-by-grid model; ENKF; 4Dvar; SCE-UA; Winter wheat yield estimation

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： 2024 年 227 卷

页码：

收录情况： SCI

摘要： Grain yield estimation remains a focal point in agricultural research. It's well known that crop models have very high accuracy in field application, but their scalability to a regional level encounters formidable constraints attributed to stringent input parameter demands, challenges in data acquisition, and complexities in parameter calibration. In a concerted effort to overcome these aforementioned challenges, this study endevours to formulate a spatialized crop growth model, organized grid by grid, propelled by a myriad of data sources encompassing diverse remote sensing and statistical inputs. Our approach involves the integration of a machine learning technique-the shuffled complex evolution algorithm (SCE-UA) to propose an automatic parameter optimization method for model calibration, alongside two remote sensing assimilation methods: a four-dimensional variational assimilation algorithm (4Dvar) and ensemble Kalman filter (Enkf) to optimising model trajectories to improve crop yield estimation accuracy. This innovative methodology addresses the intricacies associated with regional-scale simulation and bridges the gap between the inherent limitations of conventional crop models and the demand for high-precision yield estimations. The results show that: (1) we improved the accuracy of the regional crop model from 0.53 to 0.94 for the coefficient of determination (R2) and from 824.82 kg/ha to 148.48 kg/ha for root mean square error (RMSE), which greatly improved the accuracy of winter wheat yield estimation; (2) after comparing different optimization and assimilation strategies, the simulation strategy of complex shuffling algorithm (SCE-UA) combined with the four-dimensional variational algorithm (4Dvar) can enable the grid-by-grid model to estimate yield to achieve the highest simulation accuracy, with R2 of 0.94 and RMSE of 148.48 kg/ha; (3) we evaluated the simulation effectiveness of the algorithm and discuss the shortcomings and uncertainties of the grid-by-grid model. This study has important practical implications for the development of spatialized models for estimating winter wheat yields and bolstering our capacity for informed decision-making in the realm of food production and agricultural management.

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