The timing of snowmelt runoff is critical for water resource applications, but its spatiotemporal evolution remains poorly understood. We present a scalable approach to map snowmelt runoff onset using Sentinel‐1 synthetic aperture radar data for the past 8 years with 10 m spatial resolution and a median temporal resolution of 3.9 days. A systematic analysis of stratovolcanoes in the Western United States showed that snowmelt runoff onset is strongly dependent on elevation (r = 0.81), with a median runoff onset lapse rate of 4.9 days per 100 m of elevation gain. During the 2015 snow drought, we observed snowmelt runoff onset 25 days early relative to the 2015–2022 median. We document a median shift in snowmelt runoff onset of +2.0 days later in the year per year between 2016 and 2022. Our open‐source tools can be used to create snowmelt runoff onset maps anywhere on Earth.

Snow depth retrievals from spaceborne C-band synthetic aperture radar (SAR) backscatter have the potential to fill an important gap in the remote monitoring of seasonal snow. Sentinel-1 (S1) SAR data have been used previously in an empirical algorithm to generate snow depth products with near-global coverage, subweekly temporal resolution and spatial resolutions on the order of hundreds of meters to 1 km. However, there has been no published independent validation of this algorithm. In this work we develop the first open-source software package that implements this Sentinel-1 snow depth retrieval algorithm as described in the original papers and evaluate the snow depth retrievals against nine high-resolution lidar snow depth acquisitions collected during the winters of 2019–2020 and 2020–2021 at six study sites across the western United States as part of the NASA SnowEx mission. Across all sites, we find agreement between the Sentinel-1 snow depth retrievals and the lidar snow depth measurements to be considerably lower than requirements placed for remotely sensed observations of snow depth, with a mean root mean square error (RMSE) of 0.92 m and a mean Pearson correlation coefficient r of 0.46. Algorithm performance improves slightly in deeper snowpacks and at higher elevations. We further investigate the underlying Sentinel-1 data for a snow signal through an exploratory analysis of the cross- to co-backscatter ratio (${\mathit{\sigma }}_{\mathrm{VH}}/{\mathit{\sigma }}_{\mathrm{VV}}$ ; i.e., cross ratio) relative to lidar snow depths. We find the cross ratio increases through the time series for snow depths over ∼ 1.5 m but that the cross ratio decreases for snow depths less than ∼ 1.5 m. We attribute poor algorithm performance to (a) the variable amount of apparent snow depth signal in the S1 cross ratio and (b) an algorithm structure that does not adequately convert S1 backscatter signal to snow depth. Our findings provide an open-source framework for future investigations, along with insight into the applicability of C-band SAR for snow depth retrievals and directions for future C-band snow depth retrieval algorithm development. C-band SAR has the potential to address gaps in radar monitoring of deep snowpacks; however, more research into retrieval algorithms is necessary to better understand the physical mechanisms and uncertainties of C-band volume-scattering-based retrievals.

Mapping volcanic flow deposits can be achieved by considering backscattering characteristics as a metric of surface roughness. In this study, we developed an approach to extract a measure of surface roughness from dual-band airborne Synthetic Aperture Radar (ASAR) backscattering data to characterize and map various volcanic flow deposits—namely, debris avalanches, lahars, lava flows, and pyroclastic density currents. We employed ASAR and Indian Space Research Organization (ISRO) airborne SAR datasets, from a joint project (ASAR-ISRO), acquired in December 2019 at 2 m spatial resolution, to assess the role and importance of incorporating dual-band data, i.e., L-band and S-band, into surface roughness models. Additionally, we derived and analyzed surface roughness from a digital surface model (DSM) generated from unoccupied aircraft systems (UAS) acquisitions using Structure from Motion (SfM) photogrammetry techniques. These UAS-derived surface roughness outputs served as meter-scale calibration products to validate the radar roughness data over targeted areas. Herein, we applied our method to a region in the United States over the Mount St. Helens volcano in the Cascade Range of Washington state. Our results showed that dual-band systems can be utilized to characterize different types of volcanic deposits and range of terrain roughness. Importantly, we found that a combination of radar wavelengths (i.e., 9 and 24 cm), in tandem with high-spatial-resolution backscatter measurements, yields improved surface roughness maps, compared to single-band, satellite-based approaches at coarser resolution. The L-band (24 cm) can effectively differentiate small, medium, and large-scale structures, namely, blocks/boulders from fine-grained lahar deposits and hummocks from debris avalanche deposits. Additionally, variation in the roughness estimates of lahar and debris avalanche deposits can be identified and quantified individually. In contrast, the S-band (9 cm) can distinguish different soil moisture conditions across variable terrain; for example, identify wet active channels. In principle, this dual-band approach can also be employed with time series of various other SAR data of higher coherence (such as satellite SAR), using different wavelengths and polarizations, encompassing a wider range of surface roughness, and ultimately enabling additional applications at other volcanoes worldwide and even beyond volcanology.

While there have been recent advancements in synthetic aperture radar (SAR)‐based snow water equivalent (SWE) retrievals, obtaining accurate estimates of SWE requires knowledge of the amount of liquid water content (LWC) in the snowpack given its strong impact on radar velocity. Recent studies have utilised Sentinel‐1 SAR to identify snowmelt runoff onset in complex, high‐elevation terrain based on a seasonal minimum backscatter time‐series; however, detailed investigations into the snowpack state before and after snowmelt runoff onset are lacking. In this study, we integrated repeat field measurements at five sites, SNOw TELemetry (SNOTEL) station data ( n = 260) from across the Western United States, and paired Sentinel‐1 SAR estimates of snowmelt runoff onset to (1) assess the snowpack state prior to and after Sentinel‐1 SAR‐derived runoff onset estimates, and (2) evaluate Sentinel‐1 SAR estimates of runoff onset with SNOTEL‐derived estimates of melt output via soil moisture ‘pulses’. We found that on the date of minimum backscatter, the snowpack was isothermal at three of the five field sites, and snow pit‐measured LWC was increasing at all field sites relative to previous survey dates. SNOTEL soil moisture pulses preceded Sentinel‐1 SAR estimates of snowmelt runoff onset by a median of 3 days (standard deviation = ±25.3 days) and post‐dated peak SWE by a median of 3 days (standard deviation = ±18.2 days). Snow density and the number of positive degree days on soil moisture pulse date increased with latitude and longitude and decreased with elevation. Although satellite‐based estimates of snowmelt runoff onset provide a promising approach for improving spaceborne retrievals of SWE, local climatological conditions exert significant influence on meltwater runoff onset signal clarity for both in situ and satellite‐based estimates.

Mountain snowpacks provide vital water resources for communities in the western U.S. (WUS), but high spatial variability challenges accurate measurement of snow water equivalent (SWE) from remote sensing platforms. Studies using repeat airborne L‐band (∼25 cm wavelength) Interferometric Synthetic Aperture Radar (InSAR) have demonstrated sensitivity to forest cover fraction (FCF), liquid water content, and incidence angle. We use these factors to map feasibility of L‐band InSAR for SWE change (ΔSWE) retrievals in major mountain ecoregions of WUS. We found feasibility declines from ∼65% on 1 February, to 58% on 1 March, and 30% on 1 April, corresponding to 73%, 70%, and 49% of total SWE volume, respectively. Thus, these feasibility maps provide groundwork for future InSAR snow studies using satellite data from missions such as NISAR (NASA‐ISRO Synthetic Aperture Radar) to refine work based on regional conditions and hence improve ΔSWE retrievals globally.