Multi-Sensor Data Assimilation for Global Soil Moisture Estimation Using the Local Ensemble Transform Kalman Filter

<jats:p>Abstract. This study investigates whether assimilating soil moisture (SM) observations from multiple sensors can increase spatiotemporal coverage and improve SM estimates. A global SM data assimilation (DA) system based on the Local Ensemble Transform Kalman Filter is used to merge Level-2 SM retrievals from the Soil Moisture Active Passive (SMAP) mission, the Soil Moisture and Ocean Salinity (SMOS) mission, the Advanced Scatterometer (ASCAT), and the Advanced Microwave Scanning Radiometer 2 (AMSR2) into the Joint UK Land Environment Simulator (JULES) land surface model. Retrievals are assimilated both individually and jointly over the boreal warm seasons (2015–2021). An additional experiment assimilates the gridded European Space Agency (ESA)-Climate Change Initiative (CCI) multi-satellite product to provide a benchmark comparison. The resulting SM estimates are validated using an Instrumental Variable approach at the global scale and against in-situ observations across North America, Europe, and East Asia. The four single-sensor DA experiments result in a global average improvement of 0.045 in the anomaly correlation coefficient (R) compared to the model-only (Openloop) simulation, with SMAP assimilation yielding the highest skill over 48 % of global land areas. All single-sensor experiments improve both surface and root-zone SM estimates relative to the Openloop. The multi-sensor DA system further enhances performance, outperforming both single-sensor experiments and the assimilation of ESA-CCI products. These benefits are also evident at the sub-daily timescale. The skill improvement of the multi-sensor DA over single-sensor DA at each sub-daily time step is associated with the overpass times of the individual sensors and their respective performance. Overall, this study demonstrates that expanding the spatiotemporal coverage of satellite observations through multi-sensor assimilation provides more accurate and robust SM estimates.</jats:p>