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 (R²=0.58, MAPE=12.75 %, and RMSE=1112 kg·ha⁻¹), outperforming the standard EnKF algorithm (R²=0.51, MAPE=14.62 %, RMSE=1328 kg·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.

中文翻译：

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通过贝叶斯推理统一模型参数和状态变量改进作物产量估计


作物生长模型与遥感技术相结合的数据同化技术为大规模作物产量估算提供了可行的方法。以前的研究主要集中在重新校准不确定的模型参数或使用遥感观测独立更新模型状态变量。在本研究中，我们开发了一种将参数推断和状态更新相结合的两步推理算法，以求解给定遥感观测的不确定参数和状态变量的联合后验分布。首先基于WOFOST作物模型进行了观测模拟系统（OSS）实验，以验证参数推断方法的有效性。结果表明，参数推断方法成功地改进了不同类型模型参数的估计并增强了产量估计。此外，从Sentinel-2中检索的叶面积指数（LAI）被同化到WOFOST模型中，以模拟河南省东北部样地尺度的冬小麦产量。结果表明，所提出的两步推理算法能够更有效地纠正模型模拟偏差，提高冬小麦产量估算精度（R²=0.58，MAPE=12.75 %，RMSE=1112 kg·ha⁻1），优于标准EnKF算法（R²=0.51，MAPE=14.62 %，RMSE=1328 kg·ha⁻1）。总体而言，由于其统一的模型参数和状态变量估计方法，所提出的两步推理算法显示了基于数据同化的作物产量估计的良好应用前景。