Extensive efforts have been made to observe the accumulation and melting of seasonal snow. However, making accurate observations of snow water equivalent (SWE) at global scales is challenging. Active radar systems show promise, provided the dielectric properties of the snowpack are accurately constrained. The dielectric constant (k) determines the velocity of a radar wave through snow, which is a critical component of time-of-flight radar techniques such as ground penetrating radar and interferometric synthetic aperture radar (InSAR). However, equations used to estimate k have been validated only for specific conditions with limited in situ validation for seasonal snow applications. The goal of this work was to further understand the dielectric permittivity of seasonal snow under both dry and wet conditions. We utilized extensive direct field observations of k, along with corresponding snow density and liquid water content (LWC) measurements. Data were collected in the Jemez Mountains, NM; Sandia Mountains, NM; Grand Mesa, CO; and Cameron Pass, CO from February 2020 to May 2021. We present empirical relationships based on 146 snow pits for dry snow conditions and 92 independent LWC observations in naturally melting snowpacks. Regression results had r² values of 0.57 and 0.37 for dry and wet snow conditions, respectively. Our results in dry snow showed large differences between our in situ observations and commonly applied equations. We attribute these differences to assumptions in the shape of the snow grains that may not hold true for seasonal snow applications. Different assumptions, and thus different equations, may be necessary for varying snowpack conditions in different climates, suggesting that further testing is necessary. When considering wet snow, large differences were found between commonly applied equations and our in situ measurements. Many previous equations assume a background (dry snow) k that we found to be inaccurate, as previously stated, and is the primary driver of resulting uncertainty. Our results suggest large errors in SWE (10–15%) or LWC (0.05–0.07 volumetric LWC) estimates based on current equations. The work presented here could prove useful for making accurate observations of changes in SWE using future InSAR opportunities such as NISAR and ROSE-L.

Radar instruments have been widely used to measure snow water equivalent (SWE) and Interferometric Synthetic Aperture Radar is a promising approach for doing so from spaceborne platforms. Electromagnetic waves propagate through the snowpack at a velocity determined by its dielectric permittivity. Velocity estimates are a significant source of uncertainty in radar SWE retrievals, especially in wet snow. In dry snow, velocity can be calculated from relations between permittivity and snow density. However, wet snow velocity is a function of both snow density and liquid water content (LWC); the latter exhibits high spatiotemporal variability, there is no standard observation method, and it is not typically measured by automated stations. In this study, we used ground-penetrating radar (GPR), probed snow depths, and measured in situ vertically-averaged density to estimate SWE and bulk LWC for seven survey dates at Cameron Pass, Colorado (~3120 m) from April to June 2019. During this cooler than average season, median LWC for individual survey dates never exceeded 7 vol. %. However, in June, LWC values greater than 10 vol. % were observed in isolated areas where the ground and the base of the snowpack were saturated and therefore inhibited further meltwater output. LWC development was modulated by canopy cover and meltwater drainage was influenced by ground slope. We generated synthetic SWE retrievals that resemble the planned footprint of the NASA-ISRO L-band InSAR satellite (NISAR) from GPR using a dry snow density model. Synthetic SWE retrievals overestimated observed SWE by as much as 40% during the melt season due to the presence of LWC. Our findings emphasize the importance of considering LWC variability in order to fully realize the potential of future spaceborne radar missions for measuring SWE.

Estimating snow mass in the mountains remains a major challenge for remote-sensing methods. Airborne lidar can retrieve snow depth, and some promising results have recently been obtained from spaceborne platforms, yet density estimates are required to convert snow depth to snow water equivalent (SWE). However, the retrieval of snow bulk density remains unsolved, and limited data are available to evaluate model estimates of density in mountainous terrain. Toward the goal of landscape-scale retrievals of snow density, we estimated bulk density and length-scale variability by combining ground-penetrating radar (GPR) two-way travel-time observations and airborne-lidar snow depths collected during the mid-winter NASA SnowEx 2020 campaign at Grand Mesa, Colorado, USA. Key advancements of our approach include an automated layer-picking method that leverages the GPR reflection coherence and the distributed lidar–GPR-retrieved bulk density with machine learning. The root-mean-square error between the distributed estimates and in situ observations is 11 cm for depth, 27 kg m⁻³ for density, and 46 mm for SWE. The median relative uncertainty in distributed SWE is 13 %. Interactions between wind, terrain, and vegetation display corroborated controls on bulk density that show model and observation agreement. Knowledge of the spatial patterns and predictors of density is critical for the accurate assessment of SWE and essential snow research applications. The spatially continuous snow density and SWE estimated over approximately 16 km² may serve as necessary calibration and validation for stepping prospective remote-sensing techniques toward broad-scale SWE retrieval.

Depth‐based and radar‐based remote sensing methods (e.g., lidar, synthetic aperture radar) are promising approaches for remotely measuring snow water equivalent (SWE) at high spatial resolution. These approaches require snow density estimates, obtained from in‐situ measurements or density models, to calculate SWE. However, in‐situ measurements are operationally limited, and few density models have seen extensive evaluation. Here, we combine near‐coincident, lidar‐measured snow depths with ground‐penetrating radar (GPR) two‐way travel times (twt) of snowpack thickness to derive >20 km of relative permittivity estimates from nine dry and two wet snow surveys at Grand Mesa, Cameron Pass, and Ranch Creek, Colorado. We tested three equations for converting dry snow relative permittivity to snow density and found the Kovacs et al. (1995) equation to yield the best comparison with in‐situ measurements (RMSE = 54 kg m⁻³). Variogram analyses revealed a 19 m median correlation length for relative permittivity and snow density in dry snow, which increased to >30 m in wet conditions. We compared derived densities with estimated densities from several empirical models, the Snow Data Assimilation System (SNODAS), and the physically based iSnobal model. Estimated and derived densities were combined with snow depths and twt to evaluate density model performance within SWE remote sensing methods. The Jonas et al. (2009) empirical model yielded the most accurate SWE from lidar snow depths (RMSE = 51 mm), whereas SNODAS yielded the most accurate SWE from GPR twt (RMSE = 41 mm). Densities from both models generated SWE estimates within ±10% of derived SWE when SWE averaged >400 mm, however, model uncertainty increased to >20% when SWE averaged <300 mm. The development and refinement of density models, particularly in lower SWE conditions, is a high priority to fully realize the potential of SWE remote sensing methods.

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.

There is a pressing need for global monitoring of snow water equivalent (SWE) at high spatiotemporal resolution, and L‐band (1–2 GHz) interferometric synthetic aperture radar (InSAR) holds promise. However, the technique has not seen extensive evaluation in forests. We evaluated this technique across varying forest canopy conditions using eight InSAR pairs collected at the Fraser Experimental Forest, Colorado, USA by NASA UAVSAR during the 10‐week NASA SnowEx 2021 Campaign. Compared with in situ measurements, we found root mean squared errors (RMSEs) of 14–17 mm for SWE changes in forest cover fractions (FCF) < 0.40, but RMSEs increased to 33–40 mm at FCF > 0.50. Statistical distributions between normalized lidar snow depths and normalized UAVSAR SWE were similar at FCF < 0.5, but diverged at FCF > 0.50. Thus, the upcoming NISAR L‐band satellite has strong potential for global snowpack monitoring, including below sparse to moderate forest cover.