Snow water equivalent (SWE) distribution at fine spatial scales (≤ 10 m) is difficult to estimate due to modeling and observational constraints. However, the distribution of SWE throughout the spring snowmelt season is often correlated to the timing of snow disappearance. Here, we show that snow cover maps generated from PlanetScope’s constellation of Dove Satellites can resolve the 3 m date of snow disappearance across seven alpine domains in California and Colorado. Across a 5-year period (2019 – 2023), the average uncertainty in the date of snow disappearance, or the period of time between the last date of observed snow cover and the first date of observed snow absence, was 3 days. Using a simple shortwave-based snowmelt model calibrated at nearby snow pillows, the PlanetScope date of snow disappearance could be used to reconstruct spring snow water equivalent (SWE). Relative to lidar SWE estimates, the SWE reconstruction had a spatial coefficient of correlation of 0.75, and SWE spatial variability that was biased by 9%, on average. SWE reconstruction biases were then improved to within 0.04 m, on average, by calibrating snowmelt rates to track the spring temporal evolution of fractional snow cover observed by PlanetScope, including fractional snow cover over the full modeling domain, and across domain subsections where snowmelt rates may differ. This study demonstrates the utility of fine-scale and high-frequency optical observations of snow cover, and the simple and annually repeatable connections between snow cover and spring snow water resources in regions with seasonal snowpack.

Recent studies show that the Ice, Clouds, and Land Elevation Satellite-2 (ICESat-2) can achieve decimeter-level accuracy over forested and mountainous sites in the western United States, as well as over the glaciers of Alaska. However, there has yet to be an assessment on ICESat-2 snow depths over the boreal forests and tundra of Alaska, both of which are significant reservoirs of snow during the winter season. We present two case studies of retrieving snow depth using ICESat-2 over Alaska. We focus on two field sites used by the NASA SnowEx 2022/2023 campaigns: Farmer’s Loop/Creamer’s Field near Fairbanks, AK (forest) and Upper Kuparuk/Toolik on the Arctic North Slope (tundra). When validated against airborne lidar flown by the University of Alaska, Fairbanks (UAF), we find median biases of -6.3 to +2.1 cm among three ICESat-2 data products in the tundra region. Biases over the the boreal forest are somewhat higher at 7.5-13 cm. Utilizing the open source tool SlideRule, we observe little change in results when filtering by the ICESat-2 signal photon confidence scheme or by the vegetation filter. However, uncertainties in snow depth decrease with coarser Sliderule-derived snow depths. The number of signal photons (i.e., signal strength) has an influence on retrievals, with a large number of photons per ICESat-2 return providing more accurate snow depths. The initial results are promising, and we expect to expand this effort to other ICESat-2 overpasses over the SnowEx field sites.

Extensive efforts are made to observe the accumulation and melting of seasonal snow. However, making accurate observations of snow water equivalent (SWE) at the global scale remains elusive. Active radar systems show promise, provided the dielectric properties of the snowpack are accurately understood. The dielectric relative permittivity (k) determines the velocity of the radar wave through snow. 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 is to further understand the dielectric permittivity of seasonal snow under dry and wet conditions. We utilize extensive in situ observations of k with snow density and liquid water content (LWC) observations to: (1) Test current permittivity equations for dry snow conditions, (2) Test current permittivity equations for wet snow conditions, and (3) Determine if any improvements to current permittivity equations are necessary. 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 will present empirical relationships based on 146 snow pits for dry snow conditions and 92 LWC observations in naturally melting snowpacks. Regression results have 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 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, though further testing is necessary. When considering wet snow, large differences were found between commonly applied equations and our in situ testing. Many previous equations assume a background (dry) k that we found to be inaccurate, as previously stated, that is the primary driver of resulting uncertainty. Our results suggest large errors in SWE or LWC estimates based on current equations. The work presented here could prove useful for making accurate observations of changes in SWE using future remote sensing opportunities such as NISAR and ROSE-L.

The Snow Ensemble Uncertainty Project (SEUP) is an effort to establish a baseline characterization of snow water equivalent (SWE) uncertainty across North America with the goal of informing global snow observational needs. An ensemble-based modeling approach, encompassing a suite of current operational models, is used to assess the uncertainty in SWE and total snow storage (SWS) estimation during the 2009-2017 period. The highest modeled SWE uncertainty is observed in mountainous regions, likely due to the relatively deep snow, forcing uncertainties, and variability between the different models in resolving the snow processes over complex terrain. This highlights a need for high-resolution observations in mountains to capture the high spatial SWE variability. The greatest SWS is found in Tundra regions where, even though the spatiotemporal variability in modeled SWE is low, there is considerable uncertainty in the SWS estimates due to the large areal extent over which those estimates are spread. This highlights the need for high accuracy in snow estimations across the Tundra. In mid-latitude boreal forests, large uncertainties in both SWE and SWS indicate that vegetation-snow impacts are a critical area where focused improvements to modeled snow estimation efforts need to be made. Finally, the SEUP results indicate that SWE uncertainty is driving runoff uncertainty and measurements may be beneficial in reducing uncertainty in SWE and runoff, during the melt season at high latitudes (e.g., Tundra and Taiga regions) and in the Western mountain regions, whereas observations at (or near) peak SWE accumulation are more helpful over the mid-latitudes.

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) mission has collected global surface elevation measurements for over three years. ICESat-2 carries the Advanced Topographic Laser Altimeter (ATLAS) instrument, which emits laser light at 532 nm, and ice and snow absorb weakly at this wavelength. Previous modeling studies found that melting snow could induce significant bias to altimetry signals, but there is no formal assessment on ICESat-2 acquisitions during the Northern Hemisphere melting season. In this work, we performed two case studies over the Greenland Ice Sheet to quantify volumetric scattering in ICESat-2 signals over snow. Elevation data from ICESat-2 was compared to Airborne Topographic Mapper (ATM) data to quantify bias. We used snow optical grain sizes derived from ATM and the Next Generation Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-NG) to attribute altimetry bias to snowpack properties. For the first case study, the mean optical grain sizes were 340±65 µm (AVIRIS-NG) and 670±420 µm (ATM), which corresponded with a mean altimetry bias of 4.81±1.76 cm in ATM. We observed larger grain sizes for the second case study, with a mean grain size of 910±381 µm and biases of 6.42±1.77 cm (ICESat-2) and 9.82±0.97 cm (ATM). Although these altimetry biases are within the accuracy requirements of the ICESat-2 mission, we cannot rule out more significant errors over coarse-grained snow, particularly during the Northern Hemisphere melting season.