To minimize systematic differences between soil moisture (SM) time series derived from remote sensing (RS) and land surface model (LSM), RS-based SM climatology information is typically discarded during land data assimilation (DA). However, recent studies have demonstrated that SM climatology estimates provided by L-band microwave RS retrievals can significantly outperform comparable estimates derived from LSMs. Consequently, neglecting a RS-based SM climatology may lead to degraded SM spatial patterns generated by land DA. Here, we propose a climatology-optimized SM DA framework, which first calibrates LSM parameters to leverage the RS SM climatology information. Next, multi-sensor RS SM retrievals are assimilated into the calibrated LSM using Ensemble Kalman Filter (EnKF). This framework is demonstrated using the Variable Infiltration Capacity (VIC) model and RS SM retrievals derived from the L-band Soil Moisture Active Passive (SMAP) and C-band Advanced SCATterometer (ASCAT) sensors. Here, both SMAP and ASCAT SM retrievals are assimilated into the VIC model after calibrating VIC to match spatial variations captured in the SMAP SM climatology. The DA SM results are validated using in-situ SM observations derived from 820 stations. Results show that SMAP-based SM climatology calibration directly improves the quality of SM spatial patterns estimated by VIC. In addition, the SMAP-based calibration also benefits our model-error representation and thereby yields better Kalman gains that improve temporal SM DA accuracy. Relative to typical DA approaches, this newly proposed DA framework improves both spatial (0.26 versus 0.51 (−)) and temporal correlations (0.48 versus 0.52 (−)) versus in-situ SM observations. In addition, SM improvements are effectively propagated into improved streamflow estimates – leading to an average increase of Nash and Sutcliffe coefficient from 0.74 to 0.76 (−). Overall, we demonstrate that RS SM climatology information is valuable for land DA and our climatology-optimized framework successfully retains such information to the benefit of land DA performance.

为了最小化从遥感 (RS) 和地表模型 (LSM) 得出的土壤水分 (SM) 时间序列之间的系统差异，基于 RS 的 SM 气候学信息通常在土地数据同化 (DA) 过程中被丢弃。然而，最近的研究表明，由 L 波段微波 RS 反演提供的 SM 气候学估计可以显着优于来自 LSM 的可比较估计。因此，忽略基于 RS 的 SM 气候学可能会导致土地 DA 产生的 SM 空间模式退化。在这里，我们提出了一个气候优化的 SM DA 框架，它首先校准 LSM 参数以利用 RS SM 气候信息。接下来，使用集成卡尔曼滤波器 (EnKF) 将多传感器 RS SM 检索同化到校准的 LSM 中。该框架使用可变入渗能力 (VIC) 模型和源自 L 波段土壤水分主动无源 (SMAP) 和 C 波段高级 SCATterometer (ASCAT) 传感器的 RS SM 检索进行演示。在这里，在校准 VIC 以匹配 SMAP SM 气候学中捕获的空间变化后，SMAP 和 ASCAT SM 检索均被同化到 VIC 模型中。使用来自 820 个台站的现场 SM 观测验证了 DA SM 结果。结果表明，基于 SMAP 的 SM 气候学校准直接提高了 VIC 估计的 SM 空间模式的质量。此外，基于 SMAP 的校准也有利于我们的模型误差表示，从而产生更好的卡尔曼增益，从而提高时间 SM DA 精度。相对于典型的 DA 方法，这个新提出的 DA 框架改进了空间 (0. 26 对 0.51 (-)) 和时间相关性 (0.48 对 0.52 (-)) 与原位 SM 观察。此外，SM 改进有效地传播到改进的流量估计中——导致 Nash 和 Sutcliffe 系数从 0.74 平均增加到 0.76 (-)。总体而言，我们证明 RS SM 气候学信息对土地 DA 很有价值，并且我们的气候优化框架成功地保留了这些信息，从而有利于土地 DA 性能。