The assimilation of LAI measurements, repeatedly taken at sub-field level, into dynamic crop simulation models could provide valuable information for precision farming applications. Commonly used updating methods such as the Ensemble Kalman Filter (EnKF) rely on an ensemble of model runs to update a limited set of state variables every time a new observation becomes available. This threatens the model’s integrity, as not the entire table of model states is updated. In this study, we present the Weighted Mean (WM) approach that relies on a model ensemble that runs from simulation start to simulation end without compromising the consistency and integrity of the state variables. We measured LAI on 14 winter wheat fields across France, Germany and the Netherlands and assimilated these observations into the LINTUL5 crop model using the EnKF and WM approaches, where the ensembles were created using one set of crop component (CC) ensemble generation variables and one set of soil and crop component (SCC) ensemble generation variables. The model predictions for total aboveground biomass and grain yield at harvest were evaluated against measurements collected in the fields. Our findings showed that (a) the performance of the WM approach was very similar to the EnKF approach when SCC variables were used for the ensemble generation, but outperformed the EnKF approach when only CC variables were considered, (b) the difference in site-specific performance largely depended on the choice of the set of ensemble generation variables, with SCC outperforming CC with regard to both biomass and grain yield, and (c) both EnKF and WM improved accuracy of biomass and yield estimates over standard model runs or the ensemble mean. We conclude that the WM data assimilation approach is equally efficient to the improvement of model accuracy, compared to the updating methods, but it has the advantage that it does not compromise the integrity and consistency of the state variables.

中文翻译：

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将LAI值同化到作物模型集合中以改善小麦生物量估计的新方法

在子田一级反复将LAI测量值同化为动态作物模拟模型，可以为精确农业应用提供有价值的信息。每次使用新的观测值时，诸如集合卡尔曼滤波器（EnKF）之类的常用更新方法都依赖于模型运行的整体来更新一组有限的状态变量。这会威胁到模型的完整性，因为不会更新整个模型状态表。在这项研究中，我们提出了加权均值（WM）方法，该方法依赖于从仿真开始到仿真结束的模型集成，而不会影响状态变量的一致性和完整性。我们测量了法国14个冬小麦田的LAI，德国和荷兰，并使用EnKF和WM方法将这些观察结果同化为LINTUL5作物模型，其中使用一组作物成分（CC）集合生成变量和一组土壤和作物成分（SCC）集合生成集合。变量。对照田间收集的测量值，评估收获时地上总生物量和谷物产量的模型预测。我们的发现表明：（a）当将SCC变量用于集合生成时，WM方法的性能与EnKF方法非常相似，但是当仅考虑CC变量时，WM方法的性能优于EnKF方法，（b）具体表现主要取决于整体生成变量集的选择，在生物量和谷物产量方面，SCC的表现均优于CC；（c）EnKF和WM均提高了生物量的准确性，且相对于标准模型运行或整体平均值而言，产量估算更高。我们得出的结论是，与更新方法相比，WM数据同化方法在提高模型准确性方面同样有效，但是其优点是不会损害状态变量的完整性和一致性。