Lake ice thickness is a sensitive indicator of climate change largely through its dependency on near-surface air temperature and on-ice snow mass (depth and density). Monitoring of the seasonal variations and trends in ice thickness is also important for the operation of winter ice roads that northern communities rely on for the movement of goods as well as for cultural and leisure activities (e.g., snowmobiling). Therefore, consistent measurements of ice thickness over lakes is important; however, field measurements tend to be sparse in both space and time in many northern countries. Here, we present an application of L-band frequency Global Navigation Satellite System (GNSS) Interferometric Reflectometry (GNSS-IR) for the estimation of lake ice thickness. The proof of concept is demonstrated through the analysis of Signal-to-Noise Ratio (SNR) time series extracted from Global Positioning System (GPS) constellation L1 band raw data acquired between 8 and 22 March (2017 and 2019) at 14 lake ice sites located in the Northwest Territories, Canada. Dominant frequencies are extracted using Least Squares Harmonic Estimation (LS-HE) for the retrieval of ice thickness. Estimates compare favorably with in-situ measurements (mean absolute error = 0.05 m, mean bias error = −0.01 m, and root mean square error = 0.07 m). These results point to the potential of GPS/GNSS-IR as a complementary tool to traditional field measurements for obtaining consistent ice thickness estimates at many lake locations, given the relatively low cost of GNSS antennas/receivers.

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.

UWScat, a ground-based Ku- and X-band scatterometer, was used to compare forested and non-forested landscapes in a terrestrial snow accumulation environment as part of the NASA SnowEx17 field campaign. Field observations from Trail Valley Creek, Northwest Territories; Tobermory, Ontario; and the Canadian Snow and Ice Experiment (CASIX) campaign in Churchill, Manitoba, were also included. Limited sensitivity to snow was observed at 9.6 GHz, while the forest canopy attenuated the signal from sub-canopy snow at 17.2 GHz. Forested landscapes were distinguishable using the volume scattering component of the Freeman–Durden three-component decomposition model by applying a threshold in which values ≥50% indicated forested landscape. It is suggested that the volume scattering component of the decomposition can be used in current snow water equivalent (SWE) retrieval algorithms in place of the forest cover fraction (FF), which is an optical surrogate for microwave scattering and relies on ancillary data. The performance of the volume scattering component of the decomposition was similar to that of FF when used in a retrieval scheme. The primary benefit of this method is that it provides a current, real-time estimate of the forest state, it automatically accounts for the incidence angle and canopy structure, and it provides coincident information on the forest canopy without the use of ancillary data or modeling, which is especially important in remote regions. Additionally, it enables the estimation of forest canopy transmissivity without ancillary data. This study also demonstrates the use of these frequencies in a forest canopy application, and the use of the Freeman–Durden three-component decomposition on scatterometer observations in a terrestrial snow accumulation environment.

Radar scatterometer observations at 17.2 GHz and 9.6 GHz were made of the snow cover in mid-latitude agricultural fields, using the University of Waterloo scatterometer, to determine the sensitivity of the frequency-dependent radar response to snow water equivalent. Observations were made in alfalfa fields near Maryhill, Ontario during the 2013–2014 and 2014–2015 winter seasons. Additional observations in the 2014–2015 season were made at Englehart, Ontario. The natural snowpack was scanned using polarimetric observations throughout the season as snow accumulated. Conditions typical of the mid-latitude snow environment, including non-frozen ground, snow melt, freezing rain, and agricultural vegetation were observed to confound the relationship between backscatter and SWE or snow depth. With these special cases removed from the analysis, the strongest relationship is observed at 17.2 GHz where VV-polarized backscatter increased linearly by 0.35 dB/cm SWE. Further, the backscatter increases linearly by 0.10 dB/cm of snow depth. Relationships at 9.6 GHz are weaker. These findings are unique. They highlight the suitability and challenges of radar remote sensing for estimating snow accumulation in a mid-latitude environment and demonstrate the use of polarization signatures for identifying the effects of short vegetation prevalent in this environment.

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).

Mid-latitude snow is understudied compared to snow in the northern high latitudes despite its importance as a source of freshwater to this economically significant region. The mid-latitudes provide opportunity to understand the influence on SWE retrievals of ice and vegetation, conditions which may encroach the north with a warming climate. Successful retrieval of SWE and snow depth was demonstrated using the Microwave Emission Model of Layered Snowpacks, adapted for backscattering (MEMLS3&a) with multi-angle Ku-band UWScat snow observations (VV polarization), of agricultural fields in Maryhill and Englehart, Ontario. A single-layer parameterization provided best results for Maryhill (RMSE of 21.9 mm SWE) while a dual-layer parameterization provided best results for Englehart (RMSE of 24.6 mm). Accounting for soil effects using a soil-subtraction method improved RMSE by up to 6.3 mm SWE. Retrievals were repeated with in situ snow depths improving accuracy at both sites (RMSE = 12.0 and 10.9 mm SWE for Maryhill and Englehart, respectively). Observations containing ice lenses and partially buried vegetation yielded a retrieval accuracy of RMSE = 60.4 mm which improved with in situ snow depths (RMSE = 21.1 mm SWE). These results compared favorably with the often-cited accuracy requirement of RMSE = 30 mm SWE.