As the collection of soil moisture data is often costly, it is essential to implement data‐worth analysis in advance to obtain a cost‐effective data collection scheme. In previous data‐worth analysis, the model structural error is often neglected. In this paper, we propose a robust data‐worth analysis framework based on a hybrid data assimilation method. By constructing Gaussian process (GP) error model, this study attempts to alleviate biased data‐worth assessments caused by unknown model structural errors, and to excavate complementary values of multisource data without resorting to multiple governing equations. The results demonstrated that this proposed framework effectively identified and compensated for complex model structural errors. By training prior data, more accurate potential observations were obtained and data‐worth estimation accuracy was improved. The scenario diversity played a crucial role in establishing an effective GP training system. The integration of soil temperature into GP training unraveled new information and improved the data‐worth estimation. Instead of traditional evapotranspiration calculations, the direct inclusion of easy‐to‐obtain meteorological data into GP training yielded better data‐worth assessment.

中文翻译：

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通过将顺序数据同化和机器学习相结合的强大的土壤水分流数据分析框架

由于土壤水分数据的收集通常很昂贵，因此必须预先进行数据有价值的分析，以获得具有成本效益的数据收集方案。在以前的数据价值分析中，模型结构误差通常被忽略。在本文中，我们提出了一个基于混合数据同化方法的健壮的数据价值分析框架。通过构建高斯过程（GP）误差模型，本研究试图减轻由未知模型结构误差引起的有偏数据价值评估，并在不求助于多个控制方程的情况下挖掘多源数据的互补值。结果表明，该框架可以有效地识别和补偿复杂的模型结构错误。通过训练先前的数据，获得了更准确的潜在观测结果，并提高了数据价值估计的准确性。场景的多样性在建立有效的GP培训系统中起着至关重要的作用。将土壤温度整合到GP培训中可以揭示新的信息，并改善了数据价值估算。代替传统的蒸散量计算，直接将易于获取的气象数据包含到GP培训中可以产生更好的数据价值评估。