Accurate soil moisture (SM) data are key for climate and surface-atmosphere simulations associated with prediction and analysis of weather and climate variability. Here, we assessed in situ vs. remotely sensed SM discrepancies in a humid watershed during an extreme drought and an arid watershed under peak dry-season conditions. Using in situ SM measurements from the Soil and Climate Analysis Network (SCAN) we compared timeseries data with two remotely sensed data sources: The National Aeronautics and Space Administration's Soil Moisture Active Passive (SMAP) and the European Space Agency's Soil Moisture Ocean Salinity (SMOS) radiometers. Over a nearly three-year period (31 March, 2015 to 31 December, 2017), SMAP timeseries had a higher correlation with SCAN data (R ² = 0.24 to 0.75) compared to SMOS estimates (R ² = 0.04 to 0.68) with lower average RMSE (0.03 vs. 0.19 cm³ cm^-3). Possible sources of error were identified related to underlying assumptions in the SM retrieval algorithm, principally that soil and vegetation canopy temperatures are in equilibrium during satellite retrievals and that vegetation scattering and attenuation may be accurately represented when using static, long-term averages of NDVI. We concluded that although SMAP SM retrievals reflect dry periods observed in in situ SM timeseries, the magnitude of extreme conditions were underestimated.

准确的土壤湿度（SM）数据对于与天气和气候变异性的预测和分析相关的气候和地表大气模拟至关重要。在这里，我们评估  了极端干旱期间潮湿流域和旱季高峰条件下干旱流域的原位与遥感SM差异。利用  土壤和气候分析网络（SCAN）的原位SM测量，我们将时间序列数据与两个遥感数据源进行了比较：美国国家航空航天局的土壤水分主动无源（SMAP）和欧洲航天局的土壤水分海洋盐度（SMOS） ）辐射计。在近三年的时间段（2015年3月31日至2017年12月31日）中，SMAP时间序列与SCAN数据具有更高的相关性（R ²  = 0.24至0.75），而SMOS估算值（R ²  = 0.04至0.68），而平均RMSE较低（0.03对0.19 cm ³  cm ^-3）。误差的可能来源与SM检索算法中的基本假设有关，主要是在卫星检索过程中土壤和植被冠层温度处于平衡状态，当使用NDVI的静态长期平均值时，植被的散射和衰减可以准确表示。我们得出的结论是，尽管SMAP SM的反演反映了在原地 SM时间序列中观测到的干旱时期 ，但极端条件的程度却被低估了。