Snow is a critical water resource for the western United States and many regions across the globe. However, our ability to accurately measure and monitor changes in snow mass from satellite remote sensing, specifically its water equivalent, remains a challenge. To confront these challenges, NASA initiated the SnowEx program, a multiyear effort to address knowledge gaps in snow remote sensing. During SnowEx 2020, the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) team acquired an L-band interferometric synthetic aperture radar (InSAR) data time series to evaluate the capabilities and limitations of repeat-pass L-band InSAR for tracking changes in snow water equivalent (SWE). The goal was to develop a more comprehensive understanding of where and when L-band InSAR can provide SWE change estimates, allowing the snow community to leverage the upcoming NASA–ISRO (NASA–Indian Space Research Organization) SAR (NISAR) mission. Our study analyzed three InSAR image pairs from the Jemez Mountains, NM, between 12 and 26 February 2020. We developed a snow-focused multi-sensor method that uses UAVSAR InSAR data synergistically with optical fractional snow-covered area (fSCA) information. Combining these two remote sensing datasets allows for atmospheric correction and delineation of snow-covered pixels within the radar swath. For all InSAR pairs, we converted phase change values to SWE change estimates between the three acquisition dates. We then evaluated InSAR-derived retrievals using a combination of fSCA, snow pits, meteorological station data, in situ snow depth sensors, and ground-penetrating radar (GPR). The results of this study show that repeat-pass L-band InSAR is effective for estimating both snow accumulation and ablation with the proper measurement timing, reference phase, and snowpack conditions.

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.

Snow provides critical water resources for billions of people, making the remote sensing of snow water equivalent (SWE) a highly prioritized endeavor, particularly given ongoing climate change impacts. Synthetic aperture radar (SAR) is a promising method for remote sensing of SWE because radar penetrates snow, and SAR interferometry (InSAR) can be used to estimate changes in SWE (ΔSWE) between SAR acquisitions. We calculated ΔSWE retrievals from 10 NASA L-band (1–2 GHz, ∼25 cm wavelength) uninhabited aerial vehicle SAR (UAVSAR) acquisitions covering a ∼640 km² swath in northern Colorado during the winters of 2020 and 2021. UAVSAR acquisitions coincided with ∼117 mm of accumulation in 2020 and ∼282 mm of accumulation in 2021. ΔSWE retrievals were evaluated against measurements of SWE from repeat ground-penetrating radar (GPR) and terrestrial lidar scans (TLSs) collected during the NASA SnowEx time series campaigns at two field sites (total area $=\sim \mathrm{0.2}$ km²) as well as SWE measurements from seven automated stations distributed throughout the UAVSAR swath. For single InSAR pairs, UAVSAR ΔSWE retrievals yielded an overall r of 0.72–0.79 and an RMSE of 19–22 mm when compared with TLS and GPR ΔSWE retrievals. UAVSAR ΔSWE showed some scatter with ΔSWE measured at automated stations for both study years, but cumulative UAVSAR SWE yielded a r of 0.92 and an RMSE of 42 mm when compared to total SWE measured by the stations. Further, UAVSAR ΔSWE RMSEs differed by <10 mm for coherences (i.e., the complex interferometric coherence) of 0.10 to 0.90, suggesting that coherence has only a small influence on the ΔSWE retrieval accuracy. Given the evaluations presented here and in other recent studies, the upcoming NASA-ISRO SAR (NISAR) satellite mission, with a 12 d revisit period, offers an exciting opportunity to apply this methodology globally.

Forest thinning and prescribed fire are expected to improve the climate resilience and water security of forests in the western U.S., but few studies have directly modeled the hydrological effects of multi‐decadal landscape‐scale forest disturbance. By updating a distributed process‐based hydrological model (DHSVM) with vegetation maps from a distributed forest ecosystem model (LANDIS‐II), we simulate the water resource impacts of forest management scenarios targeting partial or full restoration of the pre‐colonial disturbance return interval in the central Sierra Nevada mountains. In a fully restored disturbance regime that includes fire, thinning, and insect mortality, reservoir inflow increases by 4%–9% total and 8%–14% in dry years. At sub‐watershed scales (10–100 km²), thinning dense forests can increase streamflow by >20% in dry years. In a thinner forest, increased understory transpiration compensates for decreased overstory transpiration. Consequentially, 73% of streamflow gains are attributable to decreased overstory rain and snow interception loss. Thinner forests can increase headwater peak flows, but reservoir‐scale peak flows are almost exclusively influenced by climate. Uncertainty in future precipitation causes high uncertainty in future water yield, but the additional water yield attributable to forest disturbance is about five times less sensitive to annual precipitation uncertainty. This partial decoupling of the streamflow disturbance response from annual precipitation makes disturbance especially valuable for water supply during dry years. Our study can increase confidence in the water resource benefits of restoring historic forest disturbance frequencies in the central Sierra Nevada mountains, and our modeling framework is widely applicable to other forested mountain landscapes.

No single remote sensing technique can accurately measure snow water equivalent (SWE) from space for mountain hydrology applications. To address this challenge in SWE monitoring, we evaluated a multisensor approach that leverages the strengths of both optical and radar sensors. Our study aims to understand how differences between optically derived snow cover data products propagate variability and uncertainty into interferometric synthetic aperture radar (InSAR) SWE change retrievals. We analyzed 4 airborne InSAR pairs acquired using the Uninhabited Aerial Vehicle Synthetic Aperture Radar flown over the Sierra Nevada, CA, mountains, during the National Aeronautics and Space Administration’s SnowEx 2020 campaign. We computed InSAR-based SWE changes, in combination with 6 different optically derived satellite-based fractional snow-covered area (fSCA) products used to differentiate snow-free and snow-covered terrain. We quantified the volumetric SWE change variability due to the different fSCA products using a moving window analysis and evaluated the results using the Kolmogorov–Smirnov test. Results show that the moderate-resolution (~375- to 500-m) normalized-difference-snow-index-based fSCA products provide SWE change results statistically similar to those from using more complex spectral unmixing and machine learning retrieval methods. This indicates that the readily available snow cover products from the Moderate Resolution Imaging Spectroradiometer and the Visible Infrared Imaging Radiometer Suite are adequate for an optical-radar SWE monitoring approach when there is limited cloud cover. Additionally, we found statistically significant differences between the SWE change results from the Landsat fSCA and all other fSCA data due to canopy cover correction differences. Lastly, we identified potential sources of uncertainty in L-band InSAR SWE retrievals using a western US SWE reanalysis. Future work should focus on understanding how subcanopy snow in forested regions affects the accuracy and variability of snow cover products. Furthermore, near-real-time, high-resolution cloud- and gap-filled snow cover data will be important for supporting water resource decision-making.

Earth's snow cover strongly influences the climate system and represents an important resource for agricultural, industrial, and domestic water use. The last decade of snow‐focused research has improved our understanding of snow across scales. These efforts have culminated in new snow measurement instruments and methods, operational models for tracking snowpack evolution and forecasting snowmelt, multi‐year and international snow and remote sensing field campaigns, and satellite mission proposals to measure snowpack water resources from space, with two submitted to NASA's Earth Explorer AO and the Environment and Climate Change Canada Terrestrial Snow Mass Mission moving closer to a launch opportunity. Yet, shortcomings in each snowpack observation system still exist, including uncertainty in product performance, mission proposal advancement, and synergies across methods. The snow community aims to navigate next actionable steps toward improved and global‐scale snow monitoring for climate and human purposes. Building from recent advances in snow research and operations and carrying momentum from the conclusion of the NASA SnowEx field campaigns, NASA's Terrestrial Hydrology Program (THP) sponsored a Community Snow Meeting in August 2024 in Boulder, Colorado, USA, with 200 total in‐person and virtual attendees. Meeting objectives were to outline existing and ongoing snowpack monitoring techniques and identify knowledge gaps and recommended next steps for the snow community. We broadly summarize the state of numerous snow science sub‐disciplines and share the insights and takeaways from the Community Snow Meeting, focused largely but not exclusively on NASA opportunities, and intended to support ongoing and future pathways toward the next decade of snow research and development.