In this paper we describe the development and motivation behind the development of a NASA-sponsored airborne instrument, SNOWWI (Snow Water-equivalent Wide Swath Interferometer) that is being developed for exploring the volume scattering and penetration depth characteristics of the snowpack at three different frequencies (5.4 GHz, C-band; 13.64 GHz known as Ku-low; 17.24 GHz known as Ku-high). The system, as it is being constructed is able to receive co- and cross-polarized (VV and VH) returns in an interferometric configuration. By implementing these components of the radar signature on the same platform, we will be able to explore the relationship between snow depth, density and snow water equivalent on the overall radar signature. This work is being done in conjunction with a strong modeling component being led by the University of Michigan, a ground campaign component supported by Boise State University and the US Army Corps of Engineers Cold Regions Research and Engineering Laboratory (CRREL), and a spaceborne concept development being led by Capella Space.

This article presents the first results of the newly conceived airborne Synthetic Aperture Radar system, SNOWWI.SNOWWI is a dual Ku- and C-Band interferometric and dualpolarized (VV and VH) system operating at 13.64 GHz, 17.24 GHz, and 5.39 GHz. The system aims to deliver snowpack observations to quantify Snow Depth (SD) and Snow Water Equivalent (SWE), which have been included as Targeted Observables in the National Academies’ 2017 Decadal Strategy for Earth Observation from Space. This manuscript includes results from the system’s first deployment in Grand Mesa, CO, in January and March 2024.

In an agricultural river valley in central Idaho, USA, we conducted a case study to investigate interferometric synthetic aperture (InSAR) coherence and phase response as an indication of soil moisture change. Throughout a 3-day observational campaign, repeated observations were acquired from a mobile vehicle with a multi-polarization L-band (1.6 GHz) InSAR system, first prior to controlled irrigation and then during a dry-out period. Here, we present results that show the time series of coherence and phase in coordination with in-situ soil moisture observations at two depths over the three days of the controlled experiment. During the subsequent dry out period, the time series of interferometric coherence shows an immediate degradation of the signal with a subsequent improvement. The in-situ soil moisture observations highlight this transition of controlled irrigation to subsequent drying out of the soils. We anticipate future work to include investigation of the interferometric times series of phase change as it relates to quantitative changes in soil moisture.

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.

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.

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.

A method is presented for calculating longitudinal glacier strain rates directly from the wrapped phase of an interferometric synthetic aperture radar (InSAR) interferogram assuming the ice flow path is known. This technique enables strain rates to be calculated for scenes lacking any velocity control points or areas within a scene where the phase is not continuously unwrappable from a velocity control point. The contributions to the error in the estimate of the strain rate are evaluated, and recommendations for appropriate SAR and InSAR parameters are presented. An example using Radarsat-1 InSAR data of an East Antarctic ice stream demonstrates the technique for calculating longitudinal strain rate profiles and estimating tensile strength of ice (186-215 kPa) from locations of crevasse initiation. The strain rate error was found to be 17% corresponding to a tensile strength of ice error of 5.3%.