The SMOSAR algorithm

The concept of SM retrieval from radar observation arises from the fact that the dielectric properties of soil ‐ which act as a proxy for SM ‐ affect the backscattering properties of soil surfaces in microwave frequencies. In particular, Spaceborne Synthetic Aperture Radar (SAR) sensors are currently the most suitable systems to retrieve SM at high spatial resolution at spatial scales ranging from local to regional and continental.

As example, SAR observations come from the European Radar Observatory Sentinel-1 (S-1), developed in the framework of the Copernicus programme. S-1 systematically provides C-band SAR imagery from two identical spacecraft, (S-1 A & S-1 B), at high spatial and moderate temporal (6-day exact repeat cycle) resolutions with a sustained observation strategy for the next decades which foresees first the S-1C & S-1D satellites and then the S-1 Next Generation satellites from 2028 onwards.

CNR-IREA has developed a pre-operational product of SM with high spatial resolution (1km) derived from S-1 constellation. The SM product has been validated according to international protocols using measurements of hydro networks distributed between Europe, the United States, Canada and Australia. SM forecasts from S-1 can help in agronomic and hydrological applications, such as, for example, the early identification of drought conditions of the soils or the extension of irrigated areas, the alert of flood events through new generation weather forecasts that work on grids with resolutions of ~ 1 km and efficient management of irrigation resources at the basin scale.

The implemented SM retrieval algorithm exploits a change detection approach requiring SAR time series with a short revisit time and, for this reason, can be referred to as a short-term change detection (STCD) approach. Its rationale is that temporal changes of surface parameters influencing the radar backscatter, apart from SM, (e.g., soil roughness, canopy structure, vegetation water content, etc.) usually take place at longer temporal scales than SSM changes (excluding cultivation practices). Therefore, SAR time series with a sufficiently short repeat cycle are expected to track changes in SM only, since other parameters affecting radar backscatter can be considered constant. This approximation makes robust and relatively simple the retrieval approach and also expedites the processing.

The algorithm can be applied to bare and vegetated surfaces dominated by soil attenuated scattering that enables good radar sensitivity to SSM throughout the growing season. This land cover restriction implies that before the retrieval, a masking process obscuring those areas showing a poor radar sensitivity to SM (i.e., areas dominated by volume scattering) is required. Masking is implemented as a two-step process. The first one consists of using global land cover maps (e.g. GlobCover, CCI LandCover) to mask areas such as forests, urban areas, water bodies, etc. The second masking level exploits an adaptive thresholding method applied to S-1 cross-polarized observations. As a result of the masking process, the SM retrieval algorithm is applied only over those land surfaces dominated by soil attenuated scattering that show an adequate radar sensitivity to SSM.