A Data Fusion Framework for Sub-Daily Soil Moisture Mapping Using CYGNSS, SMAP, and SMOS

Accurate sub-daily soil moisture (SM) measurements at high spatial resolution on a global scale are essential for climate monitoring, agricultural management, and hydrological applications. Passive microwave missions including the Soil Moisture Active Passive (SMAP) and Soil Moisture Ocean Salinity (SMOS) satellites provide global SM products; however, their temporal revisit time (2–3 days) and coarse spatial resolution (36–50 km) limit their ability to capture short-term SM dynamics. In recent years, Global Navigation Satellite System Reflectometry (GNSS-R) has emerged as a promising alternative, offering a large number of sub-daily SM observations from low-cost, lightweight satellite platforms. Nevertheless, GNSS-R observations are spatially irregular and contain coverage gaps [1].

Here, we propose a weighted data fusion approach to integrate soil moisture estimates from NASA’s GNSS-R mission (CYGNSS) with SMAP Level-3 (36 km) and SMOS Level-2 (25–40 km) products independently, generating continuous sub-daily SM maps at a regular spatial resolution of 20 km. The weights are functions of the spatiotemporal distance between the output and input grid points, as well as the expected reliability of the input data. The proposed fusion framework aims to fill spatial gaps in CYGNSS-derived SM, improve its retrieval accuracy through the incorporation of passive microwave observations, and enhance the spatiotemporal resolution of SMAP and SMOS products.

The performance of the fusion model is evaluated over the Contiguous United States (CONUS) during the first five months of 2021. Strong spatial agreement is observed between CYGNSS–SMAP fused maps and SMOS products, as well as between CYGNSS–SMOS fused maps and SMAP products, demonstrating the model’s effectiveness in filling CYGNSS data gaps. Compared to CYGNSS-only SM estimates, the fused products show substantial improvements in accuracy. For the CYGNSS–SMOS fusion, the correlation with SMAP increases from approximately 0.64 to 0.80, while the RMSE decreases from about 0.07 to 0.04 m³/m³. Similarly, the CYGNSS–SMAP fusion improves the correlation with SMOS from about 0.43 to 0.58 and reduces the RMSE from approximately 0.10 to 0.06 m³/m³.

To further evaluate the model’s ability to generate sub-daily soil moisture observations, additional validation was performed using soil moisture time series from 11 in-situ stations obtained from the International Soil Moisture Network (ISMN) [2]. The fused products successfully capture the temporal variability observed in the in-situ measurements, with slightly better performance for the CYGNSS–SMOS fusion compared to the CYGNSS–SMAP fusion. Median correlation coefficients of approximately 0.60 and 0.56, and median RMSE values of about 0.076 and 0.083 m³/m³, are obtained for the CYGNSS–SMOS and CYGNSS–SMAP fused products, respectively.

[1] Senyurek, V., Gurbuz, A., Kurum, M., Lei, F., Boyd, D., & Moorhead, R. (2021). Spatial and temporal interpolation of CYGNSS soil moisture estimations. Paper presented at the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS.

[2] Dorigo, W., Himmelbauer, I., Aberer, D., Schremmer, L., Petrakovic, I., Zappa, L., et al. (2021). The International Soil Moisture Network: serving Earth system science for over a decade. Hydrology and Earth System Sciences Discussions, 2021, 1-83.