Evaluating the use of NRB Sentinel-1 product for reconstructing high resolution SWE in mountains

Seasonal snow in mountain catchments is highly heterogeneous, yet snow water equivalent (SWE) is rarely available at spatial and temporal scales useful for hydrology and ecosystem applications. We present a multi-source retrospective SWE reconstruction framework that produces daily 30-meter resolution SWE over mountains by integrating (i) SAR-based wet-snow information from Sentinel-1 Normalised Radar Backscatter (NRB) product; (ii) optical snow-cover dynamics given using Sentinel-2 and Sentinel-3 data and (iii) in-situ meteorological forcing. A key advantage is that the method does not rely on spatially distributed precipitation fields, which remain a dominant uncertainty in mountain snow modelling.

The approach is built around a pixel “state” concept (accumulation, equilibrium, ablation) that constrains physically plausible SWE evolution through the season. Snow presence is represented by a daily high-resolution snow-cover-area (SCA) time series obtained by gap-filling and downscaling coarse snow-cover fraction with high-resolution optical observations, followed by a state-aware regularization that removes implausible transitions. Snow melt is computed using an enhanced temperature-index (ETI) model driven by air temperature and incoming shortwave radiation. However, ETI formulations do not explicitly resolve cold content and internal energy storage; as a result, they can trigger melt earlier than expected, as they do not account for delays imposed by the snowpack thermal inertia. To constrain the onset of true meltwater conditions, we integrate Sentinel-1 wet-snow maps derived from the new NRB time series, using multi-temporal backscatter changes to detect wet-snow conditions.

The Sentinel-1 NRB product provides radiometrically terrain-corrected backscatter (γ⁰) using the local incidence angle and mapping the data onto a reference coordinate system [1]. This improves consistency over complex topography compared to conventional Level-1 GRD processing. In addition, a novel cloud-native Zarr format enables fast, chunked access to long time series, facilitating regional-scale analyses.

We demonstrate the method in the Maipo region (Andes), where shortwave radiation dominates snowmelt. Preliminary results show that combining daily optical snow-cover dynamics with NRB-informed wet-snow timing enables SWE reconstructions that are temporally consistent across full seasons and, critically, prevents ETI-driven melt before liquid water is detected. Additionally, in the presentation, the NRB products and their assessment for the analysis of timeseries over mountains will be provided.

References

[1]  G. H. X. Shiroma, M. Lavalle and S. M. Buckley, "An Area-Based Projection Algorithm for SAR Radiometric Terrain Correction and Geocoding," in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-23, 2022.