Long-term and high-quality surface soil moisture (SSM) and root-zone soil moisture (RZSM) data is crucial for understanding the land-atmosphere interactions of the Qinghai-Tibet Plateau (QTP). More than 40% of QTP is covered by permafrost, yet few studies have evaluated the accuracy of SSM and RZSM products derived from microwave satellite, land surface models (LSMs) and reanalysis over that region. This study tries to address this gap by evaluating a range of satellite and reanalysis estimates of SSM and RZSM in the thawed soil overlaying permafrost in the QTP, using in-situ measurements from sixteen stations. Here, seven SSM products were evaluated: Soil Moisture Active Passive L3 (SMAP-L3) and L4 (SMAP-L4), Soil Moisture and Ocean Salinity in version IC (SMOS-IC), Land Parameter Retrieval Model (LPRM) Advanced Microwave Scanning Radiometer 2 (AMSR2), European Space Agency Climate Change Initiative (ESA CCI), Advanced Scatterometer (ASCAT), and the fifth generation of the land component of the European Centre for Medium-Range Weather Forecasts atmospheric reanalysis (ERA5-Land). We also evaluated three RZSM products from SMAP-L4, ERA5-Land, and the Noah land surface model driven by Global Land Data Assimilation System (GLDAS-Noah). The assessment was conducted using five statistical metrics, i.e. Pearson correlation coefficient (R), bias, slope, Root Mean Square Error (RMSE), and unbiased RMSE (ubRMSE) between SSM or RZSM products and in-situ measurements. Our results showed that the ESA CCI, SMAP-L4 and SMOS-IC SSM products outperformed the other SSM products, indicated by higher correlation coefficients (R) (with a median R value of 0.63, 0.44 and 0.57, respectively) and lower ubRMSE (with a median ubRMSE value of 0.05, 0.04 and 0.07 m³/m³, respectively). Yet, SSM overestimation was found for all SSM products. This could be partly attributed to ancillary data used in the retrieval (e.g. overestimation of land surface temperature for SMAP-L3) and to the fact that the products (e.g. LPRM) more easily overestimate the in-situ SSM when the soil is very dry. As expected, SMAP-L3 SSM performed better in areas with sparse vegetation than with dense vegetation covers. For RZSM products, SMAP-L4 and GLDAS-Noah (R = 0.66 and 0.44, ubRMSE = 0.03 and 0.02 m³/m³, respectively) performed better than ERA5-Land (R = 0.46; ubRMSE = 0.03 m³/m³). It is also found that all RZSM products were unable to capture the variations of in-situ RZSM during the freezing/thawing period over the permafrost regions of QTP, due to large deviation for the ice-water phase change simulation and the lack of consideration for unfrozen-water migration during freezing processes in the LSMs.

长期和高质量的表层土壤水分 (SSM) 和根区土壤水分 (RZSM) 数据对于了解青藏高原 (QTP) 的陆气相互作用至关重要。超过 40% 的 QTP 被永久冻土覆盖，但很少有研究评估来自微波卫星、地表模型 (LSM) 和该地区再分析的 SSM 和 RZSM 产品的准确性。本研究试图通过评估一系列卫星和再分析对 QTP 中覆盖多年冻土的解冻土壤中的 SSM 和 RZSM 估计值来解决这一差距，使用原位来自十六个站的测量。在这里，评估了七种 SSM 产品：土壤水分有源无源 L3 (SMAP-L3) 和 L4 (SMAP-L4)、土壤水分和海洋盐度 IC (SMOS-IC)、陆地参数检索模型 (LPRM) 高级微波扫描辐射计 2 (AMSR2)、欧洲航天局气候变化倡议 (ESA CCI)、高级散射计 (ASCAT) 和欧洲中期天气预报中心大气再分析 (ERA5-Land) 的第五代陆地组件。我们还评估了来自 SMAP-L4、ERA5-Land 和由全球土地数据同化系统 (GLDAS-Noah) 驱动的 Noah 地表模型的三种 RZSM 产品。评估使用五个统计指标进行，即Pearson 相关系数 ( R)、偏差、斜率、均方根误差 (RMSE) 和 SSM 或 RZSM 产品与原位测量之间的无偏差 RMSE (ubRMSE) 。我们的研究结果表明，ESA CCI，SMAP-L4和SMOS-IC SSM产品胜过其他SSM产品，由更高的相关系数（表示- [R ）（中位- [R的0.63，0.44和0.57，值），并降低ubRMSE（ ubRMSE 中值分别为 0.05、0.04 和 0.07 m ³ /m ³）。然而，发现所有 SSM 产品都高估了 SSM。这可能部分归因于反演中使用的辅助数据（例如高估了 SMAP-L3 的地表温度）以及产品（例如当土壤非常干燥时，LPRM) 更容易高估原位SSM。正如预期的那样，SMAP-L3 SSM 在植被稀疏的地区比植被茂密的地区表现更好。对于 RZSM 产品，SMAP-L4 和 GLDAS-Noah（R  = 0.66 和 0.44，ubRMSE 分别为 0.03 和 0.02 m ³ /m ³）的性能优于 ERA5-Land（R  = 0.46；ubRMSE = 0.03 m ³ /m ^{3 ）}）。还发现所有 RZSM 产品都无法捕捉原位的变化 青藏高原多年冻土区冻融期间的 RZSM，由于冰水相变模拟偏差较大，且未考虑 LSM 冻结过程中的未冻水迁移。