Surface soil moisture (SSM) estimates from different sources have distinct error characteristics. Multi-source data combination is an efficient way to obtain improved SSM data with reduced uncertainties. Previous data merging studies based on the linear weight averaging scheme rarely considered the impacts of data error covariance (EC) and usually needed a reference dataset, which can lead to suboptimal merging weights. This study applied the quadruple collocation (QC) to estimate EC and combine four SSM datasets simultaneously without the need for a reference. Specifically, two passive microwave satellite datasets (the L3 Soil Moisture Active Passive (SMAP)-V7 and the L3 Soil Moisture and Ocean Salinity -INRA-CESBIO (SMOS-IC)-V2), one active microwave dataset from the Advanced Scatterometer (ASCAT), and one model dataset from the Modern-Era Retrospective analysis for Research and Application, Version 2 (MERRA2) were combined. Generally, QC-based data combination reduced SSM data uncertainties with significantly reduced unbiased Root Mean Square Error (ubRMSE) scores against in situ observations and globally decreased fMSE scores. Moreover, in situ evaluation showed that the QC-based fusion products exhibited better skills than the Tripe Collocation (TC)-based products without considering EC. There were statistically significant differences in Pearson correlation coefficients and ubRMSE metric between the QC and TC -based products. Ignoring the EC between SMAPV7 and SMOS-ICV2 caused overestimations in their relative contributions to fusion data and degraded fusion accuracy. Specifically, the QC-based merging weight was reduced averagely by 0.27 (0.28) for SMAP (IC) when their error cross-correlation increased roughly from −0.42 to 0.9. This study can provide guidance for the generation of improved merged datasets at a global scale.

来自不同来源的地表土壤水分 (SSM) 估计值具有明显的误差特征。多源数据组合是获得改进的 SSM 数据并减少不确定性的有效方法。以前基于线性权重平均方案的数据合并研究很少考虑数据误差协方差（EC）的影响，通常需要参考数据集，这可能导致合并权重不理想。本研究应用四重搭配 (QC) 来估计 EC 并同时组合四个 SSM 数据集，而无需参考。具体来说，两个无源微波卫星数据集（L3 土壤水分有源无源 (SMAP)-V7 和 L3 土壤水分和海洋盐度 -INRA-CESBIO (SMOS-IC)-V2），一个来自高级散射计 (ASCAT) 的有源微波数据集), 和来自现代时代回顾性研究和应用分析的一个模型数据集，第 2 版 (MERRA2) 被合并。一般来说，基于 QC 的数据组合降低了 SSM 数据的不确定性，显着降低了现场观测的无偏均方根误差 (ubRMSE) 分数，并在全球范围内降低了 fMSE 分数。此外，原位评估表明，在不考虑 EC 的情况下，基于 QC 的融合产品比基于 Tripe Collocation (TC) 的产品表现出更好的技能。在基于 QC 和 TC 的产品之间，Pearson 相关系数和 ubRMSE 度量存在统计学上的显着差异。忽略 SMAPV7 和 SMOS-ICV2 之间的 EC 会导致高估它们对融合数据的相对贡献并降低融合精度。具体来说，基于 QC 的合并权重平均减少了 0。27 (0.28) 对于 SMAP (IC)，当它们的误差互相关大致从 -0.42 增加到 0.9 时。该研究可以为在全球范围内生成改进的合并数据集提供指导。