A preliminary study on a synergistic assimilation scheme for multi-band satellite soil moisture data

Soil moisture retrievals from satellite sensors operating at different microwave frequencies provide diverse and complementary data sources for assimilation. However, fully exploiting the advantages of each frequency band while increasing the volume of assimilated observations remains a challenge. This study assimilates soil-moisture retrievals from three dominant-frequency instruments – SMAP (Soil Moisture Active–Passive), ASCAT (Advanced Scatterometer), and MWRI (Microwave Radiation Imager) – into the Common Land Model (CoLM) via the Simplified Extended Kalman Filter (SEKF). Based on the systematic assessment of the disparate impacts of each single-band product, we propose and test a synergistic multi-satellite assimilation framework that optimally combines the complementary information inherent in the multi-frequency observations. Results show that assimilating soil-moisture retrievals improves the accuracy of the CoLM land-surface model; nevertheless, the effectiveness of each product varies with vegetation type. Analyses of simultaneous multi-source assimilation indicate that retrieval uncertainties associated with frequency-dependent differences in vegetation penetration capability are the dominant factor controlling the effectiveness of joint assimilation. When SMAP and ASCAT products are already ingested, the additional introduction of MWRI data over low-stature vegetation, where vegetation attenuation is relatively weak, provides complementary information and further enhances the joint assimilation performance. Validation against in-situ observations across China demonstrates that the largest improvements occur in the central and western parts of the country: compared with the open-loop simulation, the domain-mean correlation coefficient rises by about 0.25, while the error declines from 0.068 to 0.058 m ³  m ⁻³ . This indicates that improvements from multi-sensor assimilation stem not only from increased data volume but also from the complementary characteristics of the assimilated products. These findings provide valuable insights into the design of synergistic multi-sensor land data assimilation systems and contribute to improving land surface modeling, as well as weather and climate prediction accuracy.