This dataset contains Sentinel-1 derived Surface Soil Moisture (SSM) estimates for the Alpine region, obtained using a 0th order Radiative Transfer Model (RT0). The RT0 approach, first introduced by Wagner et al. (2022) and building on the work of Quast et al. (2023), addresses the dynamic influence of vegetation on the backscatter signal by modeling the Vegetation Optical Depth (VOD) using a Leaf Area Index (LAI) climatology derived from the CLMS global LAI 300m 10-daily product (Verger et al., 2022) as a proxy, as well as frozen surface conditions by using ERA5-Land data (Munoz-Sabater et al., 2021). Open use is granted under the CC BY 4.0 license. Co-Funded by European Union and Austrian Research Promotion Agency. Views and opinions expressed are, however, those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.

Abstract

Soil moisture is a critical variable for understanding hydrological processes, land-atmosphere interactions, and climate dynamics. Mountainous regions such as the Alps present unique challenges for soil moisture monitoring due to complex topography, varied land cover, and frequent snow/ice cover. This dataset applies the RT0 retrieval method to Sentinel-1 backscatter observations to derive relative surface soil moisture at 500 m spatial sampling over the Alpine region. The algorithm addresses environmental influences including vegetation dynamics, and frozen/snow-covered surfaces. A detailed description of the RT0 retrieval algorithm and its validation will be provided in a dedicated publication (Raml et al. preprint available soon).

Method

The retrieval algorithm applies a 0th order Radiative Transfer Model (RT0) to derive relative surface soil moisture from Sentinel-1 observations at 500 m resolution. Model parameters are calibrated using ERA5-Land volumetric soil moisture reference data (2016–2020), with frozen soil and snow-affected observations masked during calibration using snow-depth (sd) and soil-temperature level 1(stl1). To optimise soil moisture sensitivity in the complex Alpine terrain, the algorithm incorporates a novel SSM sensitivity map (Raml et. al. 2023) for static masking during resampling of original 20 m Sentinel-1 imagery to the 500 m, selecting only pixels where the SAR signal is most responsive to soil moisture dynamics. Relative SSM is then estimated by directly inverting the RT0 model. For a comprehensive methodological description, including model equations, parameter derivation, and validation results, readers are referred to the planned publication (Raml et al. preprint available soon).

Data Summary

Product: Surface Soil Moisture (relative, unitless)

Spatial Resolution: 500 m

Input Data: Sentinel-1 Synthetic Aperture Radar

Temporal Coverage: 2016-01-01 to 2022-12-31

Geographic Coverage: Alpine region

Model Version: RT0-based retrieval

Value Range: Relative soil moisture (0-100%)

Data Characteristics

The SSM estimates are representative of the top soil layer (surface moisture) and are provided as relative values.

Data Limitations: Due to the selection process and the coarse resolution of ERA5-Land used for masking during calibration, the dataset exhibits increased no-data areas in regions challenging for SSM retrieval, including:

Areas with frozen soil or snow cover

Glaciers and high-elevation regions with persistent ice/snow

These limitations are particularly relevant for the Alpine region and will be addressed in future updates to the RT0 product.

Technical details

Datasets are stored in Cloud Optimised GeoTiff format using ZSTD Compression.

Files are organized and tiled following the T6 Equi7Grid tilling system at 500 m x 500 m resolution.

Each zip archive contains a GeoTiff image stack for one tile, i.e. E042N012T6, E042N018T6, E048N012T6, E048N018T6

Files are named following the Yeoda file naming convention