A Preliminary Study on a Synergistic Assimilation Scheme for Multi-band Satellite Soil Moisture Data

Abstract. 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). On the basis of a 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 significantly improves the accuracy of the CoLM land-surface model; nevertheless, the effectiveness of each product exhibits a pronounced dependency on vegetation type. Analyses of simultaneous multi-source assimilation indicate that, when SMAP and ASCAT products are already ingested, the additional introduction of MWRI data over low-stature vegetation 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: the domain-mean correlation coefficient rises by about 0.25, while the error declines from 0.068 to 0.058 m³m^-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.