Abstract. This article presents the latest version of the Advanced Scatterometer (ASCAT) surface soil moisture (SSM) dataset provided by the Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF) lead by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT). This new release brings the operational near real-time (NRT) product up to date with the historical offline data record. For years, the H SAF ASCAT SSM data records have benefited from successive algorithmic improvements while the H SAF ASCAT SSM NRT product has only received minor updates until its discontinuation in July 2025. A new processing chain replaces the previous service and applies the latest soil moisture retrieval algorithm to both data streams, creating a unified offline/NRT dataset and representing a major advancement for the H SAF ASCAT SSM NRT product. The H SAF ASCAT SSM climate data record (CDR) covers the time period 1 January 2007 until 31 December 2024, which is extended offline by an interim climate data record (ICDR) as well as in NRT. The new release also introduces a high-resolution 6. 25 km sampling H SAF ASCAT SSM dataset, alongside the standard 12. 5 km sampling SSM dataset. This is achieved by customising the spatial resampling process of the ASCAT Level 1B full-resolution backscatter data. A new key development in the algorithm for ASCAT SSM concerns the estimation of the dry and wet backscatter references. Specifically, a moving-window approach is now applied to mitigate artificial trends caused by long-term land cover changes. Furthermore, a new monthly subsurface scattering flag has been added to filter out unreliable SSM measurements where backscatter and soil moisture indicate an inverted relationship. Quality control of the H SAF ASCAT SSM datasets is performed by using soil moisture estimates from Noah GLDAS-2. 1 and the ESA CCI Passive Soil Moisture (SM) v09. 1 product, as well as in-situ observations provided by the International Soil Moisture Network (ISMN). The validation results show that both H SAF ASCAT SSM datasets have a comparable performance in terms of the Pearson correlation coefficient (H SAF ASCAT SSM 6. 25 km vs ESA CCI Passive SM: 17. 9 % > 0. 75 and 57. 8 % > 0. 5; H SAF ASCAT SSM 12. 5 km vs ESA CCI Passiv SM: 19. 6 % > 0. 75 and 59. 2 % > 0. 5) and signal-to-noise ratio (SNR) derived using triple collocation analysis (H SAF ASCAT SSM 6. 25 km SNR: 56. 0 % > 0 dB, 35. 6 % > 3 dB, H SAF ASCAT SSM 12. 5 km SNR: 58. 1 % > 0 dB, 38. 6 % > 3 dB). The best agreement can be found in regions with strong seasonal variability, including monsoonal, savanna, Mediterranean, and tropical wet-and-dry zones. A weaker consistency can be found in areas characterised by limited soil moisture variability (such as deserts), dense vegetation, pronounced topographic complexity, wetland areas, or higher latitudes (> 60∘ N) experiencing longer periods of frozen soil and snow cover. The H SAF ASCAT SSM CDR and ICDR datasets (6. 25 km sampling: https: //doi. org/10. 15770/EUMSAFH₀012, H SAF, 2025c, and 12. 5 km sampling: https: //doi. org/10. 15770/EUMSAFH₀011, H SAF, 2025a) are publicly available online (H SAF, 2025a, c, d, e), while H SAF ASCAT SSM NRT datasets (H SAF, 2025b, f) are distributed via the broadcasting system EUMETCast.
Hahn et al. (Fri,) studied this question.
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