Comparing Modelled and Remotely Sensed Soil Moisture Products Using In Situ Observations in Liguria, Italy: Evaluation via SWI Filtering and Rescaling Techniques

Abstract

Soil Moisture (SM) represents the temporary storage of water within the shallow layers of the Earth’s upper surface and plays a key role in a wide range of applications, including hydrological processes, numerical weather prediction models and landslide prediction. This study evaluates the comparability of multiple satellite- and model-based SM products against in situ volumetric water content (VWC) measurements collected by a regional monitoring network in Liguria, Italy. The analyzed dataset comprises satellite-based products from the SMAP mission and the ASCAT sensors, as well as modelled Soil Moisture outputs from the HTESSEL land surface model and the Root Zone Soil Moisture (RZ SM) estimates from the continuous, distributed and physically based hydrological model Continuum. Aiming to reduce the systematic differences between the SM products and the ground network measurements, Soil Water Index (SWI) filtering and various rescaling techniques are applied and evaluated. Finally, the agreement between the rescaled SM products and the in situ measurements was assessed using standard performance scores aggregated into a single multi-objective function. Within the specific context of the study area, results suggest that rescaled model-based soil moisture products generally outperform satellite-derived surface soil moisture in reproducing in situ observations. Furthermore, among the tested rescaling techniques, Cumulative Distribution Function (CDF) matching and linear regression provide the best performance in mitigating systematic biases. Additionally, the optimization of the characteristic time length (τ) for satellite-derived SWI significantly enhances the agreement with in situ root-zone dynamics.