Derived from the global product SM-OBS-1 limited to the H-SAF area. Maps of the soil moisture content in the surface layer (0-2 cm) generated from the MetOp scatterometer (ASCAT) processed shortly after each satellite orbit completion and presented in common European projection format. It is generated by disaggregating the global-scale product (25 km resolution), to 1-km sampling. Unlike SM-OBS-1, which is generated in the EUMETSAT H/Q, this product is generated in Austria (ZAMG). This product is scheduled to be released in a late phase of the Project.
- Coverage: Strips of 1000 km swath crossing the H-SAF area [25-75°N lat, 25°W-45°E long]
- Cycle: 36 hours
- Resolution: Basic 25 km, constant through the field of view; sampling 1 km
- Accuracy: 0.05 m³ m-³, degrading in dependence of the presence of vegetation
- Timeliness: 135 min (potentially 35 min using the EARS service)
- Dissemination: By dedicated lines to centres connected by GTS - By EUMETCast to most other users, especially scientific ones
- Formats: Values in grid points in the orbital projection or fixed latitude-longitude grid
The product is derived by disaggregating the global product (SM-OBS-1) generated at the EUMETSAT H/Q and disseminated via EUMETCast. The disaggregation process is performed with a fine-mesh layer that is pre-computed offline. The fine-mesh scaling layer information is derived from statistical analysis of C-band SAR imagery, where a linear relationship is established between the local (fine-scale) and the regional (coarse-scale) backscatter information. These statistical parameters are derived from a multi-annual image-analysis (several hundreds of images per pixel) and stored in a parameter database, which needs to be updated only rarely. This database is then used to downscale the scatterometer pixels to the fine mesh in near real-time. The SAR imagery used for generating the database is ENVISAT ASAR in ScanSAR Global Mode (years 2004-2012) and, planned for future, imagery from the upcoming Sentinel-1. The disaggregated product, sampled at km-scale, enables better fitting of local information to better suite hydrological requirements.""