Physically Constrained Fusion of Sentinel-1 and ERA5-Land for Daily 500-m Snow-Depth Retrieval Across Northern Hemisphere Mountains
Abstract
Quantifying the spatiotemporal distribution of mountain snowpack is critical for hydrology, as it represents a vital freshwater resource for over a billion people. While C-band synthetic aperture radar (SAR) from Sentinel-1 provides high-resolution estimates over mountains, its use for daily snow monitoring is hindered by infrequent revisits and signal attenuation in wet snow. To address these limitations, this work proposes the adaptive snow-phenology shape model fitting (ASP-SMF), a physically constrained fusion algorithm that generates all-weather continuous daily 500-m snow-depth maps for Northern Hemisphere mountains. Designed for data-scarce regions, ASP-SMF integrates Sentinel-1, ERA5-Land, and Interactive Multisensor Snow and Ice Mapping System (IMS) data independently of in situ calibration. The algorithm first adaptively segments the highly dynamic snow time series into three distinct phenological phases using physical rules of snowfall and snowmelt. Subsequently, it utilizes Sentinel-1 retrievals and IMS snow-free constraints to correct the temporal, amplitude, and offset biases of the reanalysis data via a robust reduced major axis (RMA) regression and uncertainty-aware sequential least-squares programming (SLSQP) optimization. Comprehensive validation against 565 sites and 55 airborne LiDAR/photogrammetry surveys (2017–2021) demonstrates that ASP-SMF: 1) reduces errors relative to Sentinel-1 under dry snow conditions, reducing root-mean-square error (RMSE) by 26% (from 0.27 to 0.20 m) and increasing R2 from 0.49 to 0.72; 2) exhibits pronounced improvement under wet snow conditions, reducing RMSE by 23% (from 0.61 to 0.47 m) and boosting R2 by 145% (from 0.22 to 0.54); and 3) shows comparable performance to the SNOw Data Assimilation System (SNODAS) product without in situ data assimilation, with a higher Kling–Gupta efficiency (KGE) (0.57 versus 0.37) against Airborne Snow Observatory (ASO) LiDAR in the Rocky Mountains. Critically, this independence from in situ inputs enables ASP-SMF to deliver spatiotemporally consistent snow monitoring in ungauged regions, providing a useful framework for high-resolution assessment of mountain snow water resources.