Multiple soil moisture (SM) products have been produced based on observations from microwave satellite sensors nowadays, allowing for the acquisition of global SM dynamics in a timely manner. Currently, only two blended microwave SM products, namely the Climate Change Initiative (CCI) from the European Space Agency and the Soil Moisture Operational Product System (SMOPS) from the National Oceanic and Atmospheric Administration, are available with either better temporal or better spatial coverage than those of other SM products derived from a single sensor. However, an assessment and especially a synchronous comparison of these two products are still lacking, making it difficult to determine a better alternative in actual applications. In the present study, a comprehensive assessment of the two blended products was conducted with reanalysis SM data from the European Centre for Medium-Range Weather Forecasts and in-situ measurements from the International Soil Moisture Network. The scaling strategy of cumulative distribution function matching was used to remove the systematic differences in spatial mismatch between the satellite pixels and ground in-situ observations. The results indicated that CCI reveals overall better accuracy than that of SMOPS with both in-situ measurements and reanalysis data under different climate patterns. Specifically, the overall root mean square error (RMSE) with the in-situ measurements were 0.042 m³/m³ and 0.046 m³/m³ for CCI and SMOPS, respectively. Further investigation also confirmed that SMOPS could be a potential alternative over the regions where CCI is not available, since SMOPS has better spatial coverage than CCI.

基于当今微波卫星传感器的观测结果，已经生产出多种土壤水分（SM）产品，从而可以及时获取全球SM动态。目前，只有两种混合的微波SM产品，即欧洲航天局的气候变化倡议（CCI）和美国国家海洋与大气管理局的土壤水分操作产品系统（SMOPS），具有更好的时间或更好的空间覆盖范围比其他源自单个传感器的SM产品要高。但是，仍然缺少对这两种产品的评估，尤其是同步比较，因此很难在实际应用中确定更好的替代方案。在目前的研究中，使用欧洲中距离天气预报中心的再分析SM数据和国际土壤水分网络的现场测​​量结果，对两种混合产品进行了全面评估。累积分布函数匹配的缩放策略用于消除卫星像素和地面原位观测之间空间不匹配的系统差异。结果表明，在不同气候模式下，CCI的现场测量和再分析数据均显示出比SMOPS更高的总体精度。具体来说，现场测量的总均方根误差（RMSE）为0.042 m 累积分布函数匹配的缩放策略用于消除卫星像素和地面原位观测之间空间不匹配的系统差异。结果表明，在不同气候模式下，CCI的现场测量和再分析数据均显示出比SMOPS更好的总体精度。具体来说，现场测量的总均方根误差（RMSE）为0.042 m 累积分布函数匹配的缩放策略用于消除卫星像素和地面原位观测之间空间不匹配的系统差异。结果表明，在不同气候模式下，CCI的现场测量和再分析数据均显示出比SMOPS更高的总体精度。具体来说，现场测量的总均方根误差（RMSE）为0.042 mCCI和SMOPS分别为³ / m ³和0.046 m ³ / m ³。进一步的调查还证实，由于SMOPS的空间覆盖范围比CCI好，因此SMOPS可能是在没有CCI的地区的潜在替代方案。