Sled-based side-looking C-band radar profiles were collected around Fairbanks, Alaska, in March 2023 during the NASA SnowEx campaign to improve the conceptual understanding of C-band radar wave interactions with snow in a boreal forest environment. Seven transects with different vegetation and ground conditions were studied. Significant volume scattering from snow was observed in this shallow snowpack, indicating sensitivity at lower snow depths (SDs) which are common in high-latitude snowpacks. Manual removal of the snowpack decreased the backscatter by more than 2 dB in all polarizations, with a larger decrease in the cross-polarization, supporting the potential use of Sentinel-1 to retrieve SD.

During 2020 and 2021, the NASA SnowEx Mission performed a time series with UAVSAR, and L-band InSAR, in the Western U.S. Small field efforts were performed throughout the time series, and in addition, SnowEx carried out an intensive campaign on Grand Mesa, involving five aircraft with seven different airborne instruments, and a large field campaign. Snow properties can vary significantly over distances of 50-200 meters, and therefore rapid techniques for measuring bulk snow properties are valuable for calibration and validation of snow remote sensing efforts. We developed and deployed a ground-based microwave radar from a snowmobile, during the 2020 NASA SnowEx campaign on Grand Mesa. These observations provide information about the spatial distribution of snow depth, snow water equivalent, and stratigraphy, and were performed coincident with many different in-situ and airborne snow remote sensing observations.

In snowpacks the propagation speed of electromagnetic radiation is controlled by the snow depth, density, and the amount of liquid water stored within the pore space. In the case of dry snow, if snow depth is constrained, snow density can be estimated from the travel-time of the radar wave through the snowpack\begin{equation*}{v_s} = 2\frac{{{h_s}}}{\tau },\tag{1}\end{equation*}where vsrepresents the electromagnetic wavespeed, hsis the snow depth, and τ is the two-way travel-time. The Complex Refractive Index Method [1]\begin{equation*}{\rho _s} = {\rho _i}\left({1 - \frac{{{v_a}\left({{v_i} - {v_s}}\right)}}{{{v_s}\left({{v_i} - {v_a}}\right)}}}\right),\tag{2}\end{equation*}where ρ represents density, v represents the propagation speed, and subscripts a,i, and s indicate air, ice or snow respectively, is one of several equations that relate electromagentic wavespeed (or permittivity) to snow density. By working equations 1 and 2 backwards, an estimate of snow density, acquired in-situ by weighing a known volume of snow, can yield snow depth provided a travel-time.Snow depth tends to vary over shorter spatial scales than snow density and can now be accurately observed over large spatial extents (~ 10–100 km2) by repeated airborne lidar surveys [2]. Because snow density is often observed infrequently, its spatial distribution is largely unknown. Snow water equivalent (SWE) is the equivalent height of water stored within a snowpack and can be calculated by the multiplication of snow depth and density. Snow density contributes a greater relative uncertainty in SWE, especially in deeper snowpacks, from either modeled or observational perspectives [3]. To improve the accuracy of SWE estimates at water shed scales, improvements are needed in the spatial observations and understanding of the drivers of spatial density patterns. In this work we present a technique for the spatial estimation and prediction of snow density within a ~ 16 km2sub-alpine study area of the western United States.We combined Ground-Penetrating Radar (GPR) surveys and airborne LiDAR snow depths from SnowEx 2020 at Grand Mesa, Colorado to constrain the radar travel-times and infer the average snow density along ~ 150 km of radar transects. Terrain and vegetation parameters derived from LiDAR acquisitions form the basis of predictive features within a supervised learning framework to extrapolate snow density estimates across the study region. Multiple Linear, Random Forest, and Artificial Neural Network Regression models of snow density were evaluated, but the choice of a best model is difficult to quantitatively assess – as outputs similarly exhibit weak correlation (average R2= 0.04) when compared to sparse validation observations, yet have low error (average RMSE = 10 %). Using Random Field Synthesis [4], a snow density model with mean and variance representative of in-situ measurements and spatial correlation comparable to that measured via variogram analysis, we generated a baseline model for evaluation against the regression model ensemble.This work implores a deepened focus on the meteorological, terrain, and ecological variables controlling the densification of snow at Grand Mesa, Colorado, with the intent of better understanding the representation of physical processes affecting snow densification in empirical models. We evaluated the characterization of wind, terrain, and vegetation interactions with snow density estimated by the distributed models using the maximum upwind slope and wind factor parameters [5]. Without informing the models with wind information, each of the regression models show greatest correlation with these wind exposure parameters in the direction of winds capable of transporting snow [6]. This finding supports that regression model ensembles contain physically meaningful and repeatable spatial structures of snow density. An improved observational comprehension of the influences of snow densification will enable snow scientists to better assess and improve physically modeled snow density.

The Snow Water Equivalent (SWE) is identified as the key element of a snowpack that impacts rivers' streamflow and water cycle. Active and passive microwave remote sensing methods have been used to retrieve SWE. Interferometric Synthetic Aperture Radar (InSAR) has been shown to have the potential to estimate SWE change. In this study, we apply this technique to a large time series of Sentinel-1 data from winter 2021. The retrieved SWE change observations align really well with in situ stations with 0.82 correlation and 0.76cm RSME. The total retrieved SWE also align really well with 16 in situ values in the scene with less than 20cm SWE error. On the other hand, the retrieved SWE using Sentinel-1 data is highly correlated with LIDAR snow depth data with correlation of more than 0.5.

Snow surface albedo is a crucial component to the energy balance of our seasonal snowpack on planet Earth, reflecting most of the incoming solar radiation, and maintaining cool snow surface temperatures. Snow surface albedo, as well as other optical properties (fractional snow cover and specific surface area (SSA)), can be modeled using imaging spectroscopy measurements. The added spectral information, as compared to multispectral remote sensing, enables us to reduce errors by providing information to solve spatially heterogeneous mixed pixels. However, there is a computational burden to solve due to the added complexity of hundreds of spectral bands at 30 meter resolution. To help address these challenges, we present a fast open-source algorithm for computing snow surface properties from imaging spectroscopy, which we refer to as Global Optical Snow properties via High-speed Algorithm With K-means clustering (GOSHAWK). In this brief paper, we present the current algorithm methodology as well as validation to net-radiometers across North America.

Recent research has shown the sensitivity of Sentinel-1 C-band (5.4 GHz) radar data to snow depth. This finding could potentially help fill a long standing gap in remote sensing, but the physical basis behind this sensitivity is not yet sufficiently understood. A field experiment was set-up at two sites in the US Rocky Mountains in Idaho to study the polarimetric radar response, continuously throughout multiple winter seasons. This paper describes the design and properties of the tower-based, fully polarimetric, C-band radar system and presents the first findings. Hourly measurements were made during the winters of 2019–2020 and 2020–2021 at two different sites in Idaho. When studying the time domain responses, the scattering from the snow volume, the ground surface and multiple bounces can be discerned.

The goal of this study is to understand the pattern of snow distribution over mountain ranges and the capability of L-band Synthetic Aperture Radar (SAR) data to retrieve snow depth. Ground-based snow records and Airborne Lidar and SAR data collected as part of NASA's snow expedition over Mores Creek Summit in 2021 were employed for this study. The preliminary result shows that co-polarization particularly VV has better coherence and thus most optimal for snow monitoring. The impact of large temporal baseline, vegetation and elevation on coherence were analyzed. Result shows that decorrelation increases with vegetation and temporal separation as expected but decreases with elevation. A good agreement exists between lidar snow depth and snow depth recorded by SNOTEL. Snow depth retrieved from the UAVSAR data captured snow accumulation and melt pattern between the satellite acquisition dates as confirmed by the snow depth record at SNOTEL study site. Atmospheric correction of the phase change is required to improve the accuracy of InSAR techniques for snow depth estimation. This study will contribute to existing efforts in the snow science community to understand the capability of future satellite missions such as NISAR, a U.S-Indian satellite that is planned to operate on L-band.

Seasonal snow is an essential source of water, especially in mountain regions. However, accurate satellite observations of the amount of snow stored in mountains are still lacking. We provide estimates of snow depth at sub-kilometer resolution over the European Alps for 2017–2019 from Sentinel-1 observations. The retrievals are based on a change detection algorithm that includes the masking of wet snow. For dry snow conditions, 300-m Sentinel-1 retrievals have a spatiotemporal correlation of 0.82 and mean absolute error of 0.19m compared with in situ measurements from 743 sites across the Alps. The results show the potential of Sentinel-1 to provide unprecedented snow estimates in regions with complex topography, where satellite observations of snow mass are currently lacking.

As part of the NASA SnowEx 2020 campaign, we performed a time series experiment with NASA's UAVSAR, an airborne L-band InSAR, over 13 sites across 5 states. Six flights were performed (December-March), capturing a wide range of snow conditions. On Grand Mesa, Colorado, two of the InSAR overflights were well aligned with two airborne lidar surveys. We show that in a 4 km2wind exposed area on the west end of Grand Mesa, the measured change in phase can be used to estimate change in snow depth. In this region we observed both scouring and drifting, with a dynamic range of ± 20 cm. We use the measured surface density, change in phase, and local incidence angle to estimate change in snow depth over approximately a 2 week period, with a RMSD of 4.7cm depth, and 0.9cm SWE. This technique shows promise in open regions in dry snow conditions, and may be a useful component of a global snow satellite concept.

Changes in the calving front position of marine-terminating glaciers strongly influence the mass balance of glaciers, ice caps, and ice sheets. At present, quantification of frontal position change primarily relies on time-consuming and subjective manual mapping techniques, limiting our ability to understand changes to glacier calving fronts. Here we describe a newly developed automated method of mapping glacier calving fronts in satellite imagery using observations from a representative sample of Greenland’s peripheral marine-terminating glaciers. Our method is adapted from the 2-D wavelet transform modulus maxima (WTMM) segmentation method, which has been used previously for image segmentation in biomedical and other applied science fields. The gradient-based method places edge detection lines along regions with the greatest intensity gradient in the image, such as the contrast between glacier ice and water or glacier ice and sea ice. The lines corresponding to the calving front are identified using thresholds for length, average gradient value, and orientation that minimize the misfit with respect to a manual validation data set. We demonstrate that the method is capable of mapping glacier calving fronts over a wide range of image conditions (light to intermediate cloud cover, dim or bright, mélange presence, etc.). With these time series, we are able to resolve subseasonal to multiyear temporal patterns as well as regional patterns in glacier frontal position change.

Seasonal snow cover plays a key role in freshwater resources, water security, natural hazards, and weather and climate. However, accurate estimation of snow-water equivalent (SWE) with remote sensing observations remains a significant challenge. NASA Terrestrial Hydrology Program launched its multi-year SnowEx mission whose primary goal is to develop and test techniques for estimating how much water is stored in some complex Earth's terrestrial snow-covered regions (e.g. forested areas). The first year of the 5-year campaign took place in Colorado during the winter 2016–2017, during which in situ measurements and ground-based remote sensing observations were collected by the scientific community. Throughout February 2017, about 100 people were deployed and over 30 remote sensing instruments were used. This required an exceptional coordination effort, which resulted in collocated in situ measurements from snowpits (e.g. profiles of stratigraphy, density, grain size and type, specific surface area, temperature) and along transects (mainly for snow depth measurements) with ground-based remote sensing observations (microwave radiometers, radar, scatterometers, lidars, etc.). The public release of all these datasets has started (nsidc.org/data/snowex).

NASA SnowEx's goal is estimating how much water is stored in Earth's terrestrial snow-covered regions. To that end, two fundamental questions drive the mission objectives: (a) What is the distribution of snow-water equivalent (SWE), and the snow energy balance, among different canopy and topographic situations?; and (b) What is the sensitivity and accuracy of different SWE sensing techniques among these different areas? In situ, ground-based and airborne remote sensing observations were collected during winter 2016–2017 in Colorado to provide the scientific community with data needed to work on these key questions. An intensive period of observations occurred in February 2017 during which over 30 remote sensing instruments were used. Their observations were coordinated with in situ measurements from snowpits (e.g. profiles of stratigraphy, density, grain size and type, specific surface area, temperature) and along transects (mainly for snow depth measurements). Both remote sensing and in situ data will be archived and publicly distributed by the National Snow and Ice Data Center at nsidc.org/data/snowex.

The objectives of this research are to (1) address remote sensing strategies and requirements for estimating snow depth and snow water equivalent (SWE) using existing L-Band interferometric data sets in coordination with field-based observations and modeling frameworks and, with this information, (2) inform the Next Generation Cold Land Processes Experiment (SnowEx) toward articulating the appropriate science and research questions for a single motivating science plan. As proposed, SnowEx is a multi-year airborne snow campaign with a primary goal of exploring multimodal sensor observations in coordination with field campaigns to inform the next generation snow remote sensing satellite platform. Based on limitations of satellite-based optical and LiDAR instruments operating in regions of the globe with consistent cloud-cover, the fact that many snow-dominated regions are at more northerly latitudes (limited solar illumination in the middle of winter), and these snow-dominated regions often experience periods of prolonged cloud cover (due to synoptic precipitation events), a microwave remote sensing platform may be the most viable path to space for a dedicated snow remote sensing mission. Specifically, L-Band radar interferometry has shown some unique promise with an archive of historical and contemporary satellite collections from JAXA's PALSAR-1 and PALSAR-2 instruments, respectively. Moreover, with the expected NISAR (NASA-ISRO Synthetic Aperture Radar) mission launch in 2020 and the unprecedented availability of dedicated global interferometric L-Band products every 12-days, as well as what is in essence a NISAR airborne simulator in JPL's UAVSAR platform, the L-Band interferometric approach to estimating snow depth and snow water equivalent (SWE) requires further investigation within the context of in-situ observations and modeling frameworks.

SnowEx is a multi-year airborne snow campaign with the primary goal of addressing the question: How much water is stored in Earth's terrestrial snow-covered regions? Year 1 (2016-17) focused on the distribution of snow-water equivalent (SWE) and the snow energy balance in a forested environment. The year 1 primary site was Grand Mesa and the secondary site was the Senator Beck Basin, both in western, Colorado, USA. Nine sensors on five aircraft made observations using a broad range of sensing techniques-active and passive microwave, and active and passive optical/infrared - to determine the sensitivity and accuracy of these potential satellite remote sensing techniques, along with models, to measure snow under a range of forest conditions. SnowEx also included an extensive range of ground truth measurements - in-situ manual samples, snow pits, ground based remote sensing measurements, and sophisticated new techniques. A detailed description of the data collected will be given and some preliminary results will be presented.

Snow microstructure plays an important role in the remote sensing of snow water equivalent (SWE) for both passive and active microwave radars. The accuracy of microwave SWE retrieval algorithms is sensitive to (usually unknown) changes in microstructure. These algorithms could be improved with high-resolution estimates of microstructural properties by using an advanced instrument such as the Snow Micro Penetrometer (SMP), which measures penetration force at the millimeter scale and is sensitive to microstructure. The SMP can also take full micromechanical measurements at much greater speed and resolution and without observer bias than a traditional snow pit. Previous studies have shown that the snowpack stratigraphy and grain type can be accurately classified with one SMP measurement using basic statistics and classification trees (CTs). For this study, we used basic statistical measures of the penetration force and micromechanical estimates from an SMP inversion algorithm to significantly improve the classification accuracy of grain type and layer discrimination. We applied random forest (RF) techniques to classify three snow grain types (new snow, rounds, and facets) from SMP measurements collected in Switzerland and Grand Mesa, Colorado. RFs performed up to 8% better than single CTs, with overall misclassification errors between 17% and 40%. The coefficient of variation of the penetration force proved to be the most important variable, followed by variables that contain information about grain size like microscale strength and the number of ruptures.