Lievens H, Reichle R H, Liu Q, De Lannoy G J M, Dunbar R S, Kim S B, Das N N, Cosh M, Walker J P, Wagner W
Laboratory of Hydrology and Water Management, Ghent University, Ghent, Belgium.
Global Modeling and Assimilation office, NASA Goddard Space Flight Center, Greenbelt, MD, USA.
Geophys Res Lett. 2017 Jun 28;44(12):6145-6153. doi: 10.1002/2017GL073904. Epub 2017 Jun 9.
SMAP (Soil Moisture Active and Passive) radiometer observations at ~40 km resolution are routinely assimilated into the NASA Catchment Land Surface Model to generate the 9-km SMAP Level-4 Soil Moisture product. This study demonstrates that adding high-resolution radar observations from Sentinel-1 to the SMAP assimilation can increase the spatio-temporal accuracy of soil moisture estimates. Radar observations were assimilated either separately from or simultaneously with radiometer observations. Assimilation impact was assessed by comparing 3-hourly, 9-km surface and root-zone soil moisture simulations with measurements from 9-km SMAP core validation sites and sparse networks, from May 2015 to December 2016. The Sentinel-1 assimilation consistently improved surface soil moisture, whereas root-zone impacts were mostly neutral. Relatively larger improvements were obtained from SMAP assimilation. The joint assimilation of SMAP and Sentinel-1 observations performed best, demonstrating the complementary value of radar and radiometer observations.
分辨率约为40公里的土壤湿度主动和被动（SMAP）辐射计观测数据通常被同化到美国国家航空航天局（NASA）集水区陆地表面模型中，以生成9公里分辨率的SMAP四级土壤湿度产品。本研究表明，将哨兵-1号的高分辨率雷达观测数据添加到SMAP同化过程中，可以提高土壤湿度估计的时空准确性。雷达观测数据要么与辐射计观测数据分开同化，要么同时同化。通过将2015年5月至2016年12月期间每3小时一次、9公里分辨率的地表和根区土壤湿度模拟结果与9公里分辨率的SMAP核心验证站点和稀疏网络的测量结果进行比较，评估同化影响。哨兵-1号同化始终能改善地表土壤湿度，而对根区的影响大多呈中性。从SMAP同化中获得了相对更大的改善。SMAP和哨兵-1号观测数据的联合同化效果最佳，证明了雷达和辐射计观测数据的互补价值。
