Knowledge about spatiotemporal error characteristics of remotely sensed soil moisture (SM) products is essential for correctly interpreting observational information and optimally assimilating them into hydrological models. This work aims to (i) investigate the relative difference between time-invariant and time-variant daily SM errors of Advanced Scatterometer (ASCAT) and Soil Moisture Active Passive (SMAP) products and (ii) analyze correlation of the daily SM errors with surface biomass quantified by the Leaf Area Index (LAI) and rainfall. The time-invariant error denotes an aggregate error magnitude during the whole investigation period and the time-variant error at daily time scale also refers to an aggregate error magnitude but for the period of the 100-day time window centered at the day to be processed. The time-invariant and daily SM errors are estimated using the Triple Collocation Analysis (TCA) applied to ASCAT and SMAP SM retrievals along with the Global Land Data Assimilation System version 2.1 (GLDAS2) SM product for the period of April 2015 – January 2020. Results indicate the relative difference between time-invariant and time-variant daily errors is notable for both ASCAT and SMAP SM products. The relative difference in units percentage denotes the ratio of difference value between time-invariant and daily errors to time-invariant error itself. The daily TCA error fluctuated at 43% value from time-invariant error for ASCAT SM and at 47% value for SMAP SM on a global average. When averaged globally, temporal mean of time-variant daily errors was relatively smaller than time-invariant error by -27% for ASCAT SM and -18% for SMAP SM, respectively. In tropical areas, the relative difference between time-invariant and daily errors is large during the dry season and becomes small when rainy season comes. When the peak lagged correlation is used, daily errors exhibit a stronger correlation with rainfall than they do with LAI in 61% of landmass pixels for ASCAT SM and 66% of landmass pixels for SMAP SM. LAI cannot be used to predict temporal variability of time-variant SM errors in barren land. Lagged correlation analysis reveals rainfall peak coincides with SM error peak in areas featured with low vegetation cover, including barren land, grasslands, and open shrublands. By contrast, the LAI peak comes after the SM error peak in all cases. Savannas and woody savannas are a special case as SM error peak comes first, followed by rainfall then LAI peaks. In summary, ASCAT and SMAP time-variant error varies with a large deviation from time-invariant errors and its temporal variation shows a stronger association with rainfall than changing LAI.

有关遥感土壤水分（SM）产品的时空误差特征的知识对于正确解释观测信息并将其最佳地吸收到水文模型中至关重要。这项工作旨在（i）研究高级散射仪（ASCAT）和土壤水分主动被动（SMAP）产品的时不变和时变每日SM误差之间的相对差异，以及（ii）分析每日SM误差与表面的相关性由叶面积指数（LAI）和降雨量化的生物量。时变误差表示整个调查期间的总误差量，而每日时间标度的时变误差也指总误差量，但以以要处理的日期为中心的100天时间范围。使用应用于ASCAT和SMAP SM检索的三重搭配分析（TCA）以及2015年4月至2020年1月期间的全球土地数据同化系统2.1版（GLDAS2）SM产品，估计时不变和每日SM错误。结果表明，对于ASCAT和SMAP SM产品，时不变和时变每日误差之间的相对差异是显着的。单位百分比的相对差异表示时不变和每日误差之间的差值与时不变误差本身的比值。从全球平均水平来看，ASCAT SM的每日TCA误差为时不变误差的43％，而SMAP SM的每日TCA误差为47％。如果进行全球平均，则时变每日误差的时间均值相对于时变误差相对较小（对于ASCAT SM为-27％，对于SMAP SM为-18％，分别。在热带地区，时变和每日误差之间的相对差异在干旱季节较大，而在雨季到来时较小。当使用峰值滞后相关时，对于ASCAT SM，61％的陆地像素和对于SMAP SM的66％的陆地像素，每日误差表现出比降雨更强的与LAI的相关性。LAI不能用于预测贫瘠土地上时变SM错误的时间变异性。滞后的相关分析表明，在植被覆盖度低的地区，包括贫瘠的土地，草地和开放的灌木地，降雨峰值与SM误差峰值一致。相反，在所有情况下，LAI峰值都在SM误差峰值之后。稀树草原和木质稀树草原是特例，因为首先出现SM误差峰，然后是降雨，然后是LAI峰。总之，