Soil moisture (SM) datasets are critical to understanding the global water, energy, and biogeochemical cycles and benefit extensive societal applications. However, individual sources of SM data (e.g., in situ and satellite observations, reanalysis, offline land surface model simulations, Earth system model – ESM – simulations) have source-specific limitations and biases related to the spatiotemporal continuity, resolutions, and modeling and retrieval assumptions. Here, we developed seven global, gap-free, long-term (1970–2016), multilayer (0–10, 10–30, 30–50, and 50–100 cm) SM products at monthly 0.5^∘ resolution (available at https://doi.org/10.6084/m9.figshare.13661312.v1; Wang and Mao, 2021) by synthesizing a wide range of SM datasets using three statistical methods (unweighted averaging, optimal linear combination, and emergent constraint). The merged products outperformed their source datasets when evaluated with in situ observations (mean bias from −0.044 to 0.033 m³ m⁻³, root mean square errors from 0.076 to 0.104 m³ m⁻³, Pearson correlations from 0.35 to 0.67) and multiple gridded datasets that did not enter merging because of insufficient spatial, temporal, or soil layer coverage. Three of the new SM products, which were produced by applying any of the three merging methods to the source datasets excluding the ESMs, had lower bias and root mean square errors and higher correlations than the ESM-dependent merged products. The ESM-independent products also showed a better ability to capture historical large-scale drought events than the ESM-dependent products. The merged products generally showed reasonable temporal homogeneity and physically plausible global sensitivities to observed meteorological factors, except that the ESM-dependent products underestimated the low-frequency temporal variability in SM and overestimated the high-frequency variability for the 50–100 cm depth. Based on these evaluation results, the three ESM-independent products were finally recommended for future applications because of their better performances than the ESM-dependent ones. Despite uncertainties in the raw SM datasets and fusion methods, these hybrid products create added value over existing SM datasets because of the performance improvement and harmonized spatial, temporal, and vertical coverages, and they provide a new foundation for scientific investigation and resource management.

中文翻译：

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1970-2016年基于观测的全球多层土壤水分产品开发

土壤水分 (SM) 数据集对于了解全球水、能源和生物地球化学循环至关重要，并有利于广泛的社会应用。然而，SM 数据的各个来源（例如，原位和卫星观测、再分析、离线地表模型模拟、地球系统模型 – ESM – 模拟）具有与时空连续性、分辨率和建模相关的特定源限制和偏差。检索假设。在这里，我们开发了七种全球性、无间隙、长期（1970-2016）、多层（0-10、10-30、30-50 和 50-100 厘米）SM 产品，每月 0.5 ^∘通过使用三种统计方法（未加权平均、最优线性组合和紧急约束）。当使用原位观测（平均偏差从- 0.044 到 0.033 m ³  m ^-3，均方根误差从 0.076 到 0.104 m ³  m ^-3, Pearson 相关系数从 0.35 到 0.67) 以及由于空间、时间或土壤层覆盖率不足而未进入合并的多个网格数据集。通过将三种合并方法中的任何一种应用于不包括 ESM 的源数据集产生的三种新 SM 产品比依赖于 ESM 的合并产品具有更低的偏差和均方根误差以及更高的相关性。与 ESM 相关产品相比，ESM 独立产品也表现出更好的捕捉历史大规模干旱事件的能力。合并后的产品通常对观测到的气象因素表现出合理的时间同质性和物理上合理的全球敏感性，除了依赖 ESM 的产品低估了 SM 的低频时间变异性并高估了 50-100 cm 深度的高频变异性。基于这些评估结果，三种独立于 ESM 的产品最终被推荐用于未来的应用，因为它们的性能优于依赖 ESM 的产品。尽管原始 SM 数据集和融合方法存在不确定性，但由于性能改进和协调的空间、时间和垂直覆盖范围，这些混合产品为现有 SM 数据集创造了附加值，并为科学调查和资源管理提供了新的基础。这三种独立于 ESM 的产品最终被推荐用于未来的应用，因为它们的性能优于依赖 ESM 的产品。尽管原始 SM 数据集和融合方法存在不确定性，但由于性能改进和协调的空间、时间和垂直覆盖范围，这些混合产品为现有 SM 数据集创造了附加值，并为科学调查和资源管理提供了新的基础。这三种独立于 ESM 的产品最终被推荐用于未来的应用，因为它们的性能优于依赖 ESM 的产品。尽管原始 SM 数据集和融合方法存在不确定性，但由于性能改进和协调的空间、时间和垂直覆盖范围，这些混合产品为现有 SM 数据集创造了附加值，并为科学调查和资源管理提供了新的基础。