Soil moisture is a key variable controlling the energy and water balance at the land surface and satellite-derived soil moisture (SDSM) products can therefore be helpful to constrain parameters of (eco)hydrological models. Due to coarse resolution of many SDSM products (>25 km), previous studies mostly focused on large catchments (>500 km²). However, recent developments in soil moisture remote sensing resulted in SDSM products with improved spatio-temporal resolution, which are potentially better suited for model calibration in small to meso-scale catchments. This study therefore aims at assessing the value of three recent SDSM products (SMAPL3E, SCATSAR and ASCAT DIREX SWI) with high spatio-temporal resolution (spatial sampling 0.5–9 km; temporal resolution 1–3 d) for the calibration of the process-based ecohydrological model EcH₂O in a 66-km² catchment in NE Germany.

Satellite-derived soil water index (SWI) data agreed well with in-situ soil moisture observations (Pearson correlation coefficient: 0.67–0.86), with a slightly better performance for SMAPL3E than for the other two products (SCATSAR, ASCAT DIREX SWI). Calibrating the ecohydrological model EcH₂O to SWI data based on SMAPL3E improved the dynamics of simulated soil moisture (increase of Pearson correlation coefficient by 0.03), while model performance for streamflow slightly deteriorated (decrease of Nash-Sutcliffe efficiency by 0.02). Including volumetric soil moisture data based on in-situ data or SDSM scaled to in-situ data in the model calibration was necessary to further improve the model with respect to absolute soil moisture levels (increase of Nash-Sutcliffe efficiency for scaled soil moisture from 0.23–0.25 to 0.56–0.59). Comparing spatial patterns of simulated soil moisture and SDSM revealed shortcomings of the simulated and the SDSM data. Simulated soil moisture spatial patterns are influenced by artefacts of the interpolated precipitation patterns, while spatial patterns of SDSM seem too strongly damped. Based on this study, the following recommendations may be derived for practitioners who consider including soil moisture data for model calibration: 1) integrating SDSM in model calibration is beneficial for improving model internal consistency; 2) where available, in-situ soil moisture data should be included in order to better constrain simulated absolute soil moisture levels; 3) at the scale of small to meso-scale catchments (∼100 km²), the temporal dynamics of the SDSM products evaluated in this study are likely more helpful for model calibration than their spatial patterns.

土壤水分是控制地表能量和水分平衡的关键变量，因此卫星衍生的土壤水分（SDSM）产品有助于约束（生态）水文模型的参数。由于许多 SDSM 产品（>25 km）的粗分辨率，以前的研究主要集中在大流域（>500 km ²）。然而，土壤水分遥感的最新发展导致 SDSM 产品具有更高的时空分辨率，这可能更适合于小到中尺度流域的模型校准。因此，本研究旨在评估具有高时空分辨率（空间采样 0.5-9 公里；时间分辨率 1-3 d）的三种最新 SDSM 产品（SMAPL3E、SCATSAR 和 ASCAT DIREX SWI）对过程校准的价值——基于生态水文模型 EcH ₂ O 在德国东北部 66 公里^{2集水区。}

卫星衍生的土壤水分指数 (SWI) 数据与现场土壤水分观测结果一致（Pearson 相关系数：0.67-0.86），SMAPL3E 的性能略好于其他两种产品（SCATSAR、ASCAT DIREX SWI）。基于 SMAPL3E将生态水文模型 EcH ₂ O 校准到 SWI 数据改善了模拟土壤水分的动力学（Pearson 相关系数增加了 0.03），而径流模型的性能略有下降（Nash-Sutcliffe 效率降低了 0.02）。包括基于原位数据的体积土壤水分数据或按原位缩放的 SDSM模型校准中的数据对于进一步改进模型在绝对土壤水分水平方面是必要的（将土壤水分比例的 Nash-Sutcliffe 效率从 0.23-0.25 增加到 0.56-0.59）。比较模拟土壤水分和 SDSM 的空间模式揭示了模拟数据和 SDSM 数据的不足之处。模拟的土壤水分空间格局受插值降水格局的影响，而 SDSM 的空间格局似乎受到过强阻尼。基于这项研究，对于考虑将土壤水分数据用于模型校准的从业者，可以得出以下建议： 1）将 SDSM 纳入模型校准有利于提高模型内部一致性；2) 在可用的情况下，就地应包括土壤水分数据，以便更好地限制模拟的绝对土壤水分水平；3）在小到中尺度流域（~100 km ²）的尺度上，本研究中评估的 SDSM 产品的时间动态可能比它们的空间模式更有助于模型校准。