Fusing ERA5-Land and SMAP L4 for an Improved Global Soil Moisture Product

Abstract. Accurate, high-resolution soil moisture data are critical for hydrological modeling, climate studies, and ecosystem management. Unfortunately, current existing global products suffer from inconsistencies, coverage gaps, and biases. In this study, we evaluated the surface layers of three widely used soil moisture products, including ERA5-Land, ESA-CCI (v09.1 Combined), and SMAP L4 with resolutions ranging from 0.1° to 0.25°, against in situ measurements from 1,615 stations across five networks, including ISMN, CMA, Cemaden, COSMOS-Europe, and SONTE-China. The in situ dataset, to our knowledge, represents the most extensive global soil moisture compilation to date. It is found that ERA5-Land exhibits high correlation between measured and predicted soil moisture but the data also shows significant bias. SMAP L4 provides the highest accuracy, exhibiting low bias and root mean square error (RMSE), but is limited by its temporal coverage from 2015 to the present. To address these gaps, we developed an adjusted ERA5-Land dataset by fusing ERA5-Land and SMAP L4 using a mean-variance rescaling method optimized for long time-series alignment, which enhanced the spatiotemporal coverage and reduced bias. Validation against measured data demonstrates improved correlation with an increase correlation coefficient (r) of ~5 %, RMSE reduction of ~20 %, and NNSE improvement of ~15 % compared to the original products. The adjusted ERA5-Land dataset, which is publicly available, can be used as benchmark for future research and support drought monitoring, weather prediction, and water resource management, contributing to global climate resilience and informed decision-making across diverse ecosystems. The dataset is provided for the surface layer with global coverage at a spatial resolution of 0.1° and daily temporal resolution, spanning from 2015 to 2020, at https://zenodo.org/records/15816832.