Articles | Volume 17, issue 5
https://doi.org/10.5194/tc-17-1997-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-17-1997-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
12 May 2023
Research article |
| 12 May 2023
| 12 May 2023
Estimating snow accumulation and ablation with L-band interferometric synthetic aperture radar (InSAR)
Graduate Program of Hydrologic Sciences, University of Nevada, Reno, Reno, NV, USA
Department of Geography, University of Nevada, Reno, Reno, NV, USA
Ryan W. Webb
Department of Civil and Architectural Engineering and Construction Management, University of Wyoming, Laramie, WY, USA
Hans-Peter Marshall
Department of Geosciences, Boise State University, Boise, ID, USA
Anne W. Nolin
Department of Geography, University of Nevada, Reno, Reno, NV, USA
Graduate Program of Hydrologic Sciences, University of Nevada, Reno, Reno, NV, USA
Franz J. Meyer
Geophysical Institute, University of Alaska Fairbanks, Fairbanks, AK, USA
Related authors
Zachary Hoppinen, Ross T. Palomaki, George Brencher, Devon Dunmire, Eric Gagliano, Adrian Marziliano, Jack Tarricone, and Hans-Peter Marshall
EGUsphere, https://doi.org/10.5194/egusphere-2024-1018, https://doi.org/10.5194/egusphere-2024-1018, 2024
Short summary
Short summary
This study uses radar imagery from the Sentinel-1 satellite to derive snow depth from increases in the returning energy. These retrieved depths are then compared to nine lidar derived snow depths across the western United State to assess the ability of this technique to be used to monitor global snow distributions. We also qualitatively compare the changes in underlying Sentinel-1 amplitudes against both the total lidar snow depths and 9 automated snow monitoring stations.
Randall Bonnell, Daniel McGrath, Jack Tarricone, Hans-Peter Marshall, Ella Bump, Caroline Duncan, Stephanie Kampf, Yunling Lou, Alex Olsen-Mikitowicz, Megan Sears, Keith Williams, Lucas Zeller, and Yang Zheng
EGUsphere, https://doi.org/10.5194/egusphere-2024-236, https://doi.org/10.5194/egusphere-2024-236, 2024
Short summary
Short summary
Snow provides water for billions of people, but the amount of snow is difficult to detect remotely. During the 2020 and 2021 winters, a radar was flown over mountains in Colorado, USA to measure the amount of snow on the ground, while our team collected ground observations to test the radar technique’s capabilities. The technique yielded accurate measurements of the snowpack that had good correlation with ground measurements, making it a promising application for the upcoming NISAR satellite.
Ian E. McDowell, Kaitlin M. Keegan, S. McKenzie Skiles, Christopher P. Donahue, Erich C. Osterberg, Robert L. Hawley, and Hans-Peter Marshall
The Cryosphere, 18, 1925–1946, https://doi.org/10.5194/tc-18-1925-2024, https://doi.org/10.5194/tc-18-1925-2024, 2024
Short summary
Short summary
Accurate knowledge of firn grain size is crucial for many ice sheet research applications. Unfortunately, collecting detailed measurements of firn grain size is difficult. We demonstrate that scanning firn cores with a near-infrared imager can quickly produce high-resolution maps of both grain size and ice layer distributions. We map grain size and ice layer stratigraphy in 14 firn cores from Greenland and document changes to grain size and ice layer content from the extreme melt summer of 2012.
Zachary Hoppinen, Ross T. Palomaki, George Brencher, Devon Dunmire, Eric Gagliano, Adrian Marziliano, Jack Tarricone, and Hans-Peter Marshall
EGUsphere, https://doi.org/10.5194/egusphere-2024-1018, https://doi.org/10.5194/egusphere-2024-1018, 2024
Short summary
Short summary
This study uses radar imagery from the Sentinel-1 satellite to derive snow depth from increases in the returning energy. These retrieved depths are then compared to nine lidar derived snow depths across the western United State to assess the ability of this technique to be used to monitor global snow distributions. We also qualitatively compare the changes in underlying Sentinel-1 amplitudes against both the total lidar snow depths and 9 automated snow monitoring stations.
Rainey Aberle, Ellyn Enderlin, Shad O'Neel, Caitlyn Florentine, Louis Sass, Adam Dickson, Hans-Peter Marshall, and Alejandro Flores
EGUsphere, https://doi.org/10.5194/egusphere-2024-548, https://doi.org/10.5194/egusphere-2024-548, 2024
Short summary
Short summary
Tracking seasonal snow on glaciers is critical for understanding glacier health. However, current snow detection methods struggle to distinguish seasonal snow from glacier ice. To address this, we developed a new automated workflow for tracking seasonal snow on glaciers using satellite imagery and machine learning. Applying this method can help provide insights into glacier health, water resources, and the effects of climate change on snow cover over broad spatial scales.
Shadi Oveisgharan, Robert Zinke, Zachary Hoppinen, and Hans Peter Marshall
The Cryosphere, 18, 559–574, https://doi.org/10.5194/tc-18-559-2024, https://doi.org/10.5194/tc-18-559-2024, 2024
Short summary
Short summary
The seasonal snowpack provides water resources to billions of people worldwide. Large-scale mapping of snow water equivalent (SWE) with high resolution is critical for many scientific and economics fields. In this work we used the radar remote sensing interferometric synthetic aperture radar (InSAR) to estimate the SWE change between 2 d. The error in the estimated SWE change is less than 2 cm for in situ stations. Additionally, the retrieved SWE using InSAR is correlated with lidar snow depth.
Zachary Hoppinen, Shadi Oveisgharan, Hans-Peter Marshall, Ross Mower, Kelly Elder, and Carrie Vuyovich
The Cryosphere, 18, 575–592, https://doi.org/10.5194/tc-18-575-2024, https://doi.org/10.5194/tc-18-575-2024, 2024
Short summary
Short summary
We used changes in radar echo travel time from multiple airborne flights to estimate changes in snow depths across Idaho for two winters. We compared our radar-derived retrievals to snow pits, weather stations, and a 100 m resolution numerical snow model. We had a strong Pearson correlation and root mean squared error of 10 cm relative to in situ measurements. Our retrievals also correlated well with our model, especially in regions of dry snow and low tree coverage.
Randall Bonnell, Daniel McGrath, Jack Tarricone, Hans-Peter Marshall, Ella Bump, Caroline Duncan, Stephanie Kampf, Yunling Lou, Alex Olsen-Mikitowicz, Megan Sears, Keith Williams, Lucas Zeller, and Yang Zheng
EGUsphere, https://doi.org/10.5194/egusphere-2024-236, https://doi.org/10.5194/egusphere-2024-236, 2024
Short summary
Short summary
Snow provides water for billions of people, but the amount of snow is difficult to detect remotely. During the 2020 and 2021 winters, a radar was flown over mountains in Colorado, USA to measure the amount of snow on the ground, while our team collected ground observations to test the radar technique’s capabilities. The technique yielded accurate measurements of the snowpack that had good correlation with ground measurements, making it a promising application for the upcoming NISAR satellite.
Isis Brangers, Hans-Peter Marshall, Gabrielle De Lannoy, Devon Dunmire, Christian Matzler, and Hans Lievens
EGUsphere, https://doi.org/10.5194/egusphere-2023-2927, https://doi.org/10.5194/egusphere-2023-2927, 2023
Short summary
Short summary
To better understand the interactions between C-band radar waves and snow, a tower-based experiment was set up in the Idaho Rocky Mountains. The reflections were collected in the time domain to measure the backscatter profile from the various snowpack and ground surface layers. The results demonstrate that C-band radar is sensitive to seasonal patterns in snow accumulation, but that changes in microstructure, stratigraphy and snow wetness may complicate satellite-based snow depth retrievals.
Taha Sadeghi Chorsi, Franz J. Meyer, and Timothy H. Dixon
EGUsphere, https://doi.org/10.5194/egusphere-2023-2605, https://doi.org/10.5194/egusphere-2023-2605, 2023
Short summary
Short summary
Active layer, the soil or sediment layer above permafrost, thaws and freezes seasonally. The annual freeze-thaw cycle of the active layer causes significant surface height change. We estimate subsidence rate and active layer thickness (ALT) for part of northern Alaska’s permafrost zone for summer 2017 to 2022 using satellite radar interferometry and LiDAR. ALT estimates range from ~20 cm to larger than 150 cm. Subsidence rate varies ranging from ~3–20 cm/year during the thaw season.
Tate G. Meehan, Ahmad Hojatimalekshah, Hans-Peter Marshall, Elias J. Deeb, Shad O'Neel, Daniel McGrath, Ryan W. Webb, Randall Bonnell, Mark S. Raleigh, Christopher Hiemstra, and Kelly Elder
The Cryosphere Discuss., https://doi.org/10.5194/tc-2023-141, https://doi.org/10.5194/tc-2023-141, 2023
Revised manuscript accepted for TC
Short summary
Short summary
Snow water equivalent (SWE) is a critical parameter for yearly water supply forecasting and can be calculated by multiplying the snow depth by the snow density. We combined high-spatial resolution snow depth information with ground-based radar measurements to solve for snow density. Extrapolated density estimates over our study area resolved detailed patterns that agree with the known interactions of snow with wind, terrain, and vegetation and were utilized in the calculation of SWE.
Leung Tsang, Michael Durand, Chris Derksen, Ana P. Barros, Do-Hyuk Kang, Hans Lievens, Hans-Peter Marshall, Jiyue Zhu, Joel Johnson, Joshua King, Juha Lemmetyinen, Melody Sandells, Nick Rutter, Paul Siqueira, Anne Nolin, Batu Osmanoglu, Carrie Vuyovich, Edward Kim, Drew Taylor, Ioanna Merkouriadi, Ludovic Brucker, Mahdi Navari, Marie Dumont, Richard Kelly, Rhae Sung Kim, Tien-Hao Liao, Firoz Borah, and Xiaolan Xu
The Cryosphere, 16, 3531–3573, https://doi.org/10.5194/tc-16-3531-2022, https://doi.org/10.5194/tc-16-3531-2022, 2022
Short summary
Short summary
Snow water equivalent (SWE) is of fundamental importance to water, energy, and geochemical cycles but is poorly observed globally. Synthetic aperture radar (SAR) measurements at X- and Ku-band can address this gap. This review serves to inform the broad snow research, monitoring, and application communities about the progress made in recent decades to move towards a new satellite mission capable of addressing the needs of the geoscience researchers and users.
Juha Lemmetyinen, Juval Cohen, Anna Kontu, Juho Vehviläinen, Henna-Reetta Hannula, Ioanna Merkouriadi, Stefan Scheiblauer, Helmut Rott, Thomas Nagler, Elisabeth Ripper, Kelly Elder, Hans-Peter Marshall, Reinhard Fromm, Marc Adams, Chris Derksen, Joshua King, Adriano Meta, Alex Coccia, Nick Rutter, Melody Sandells, Giovanni Macelloni, Emanuele Santi, Marion Leduc-Leballeur, Richard Essery, Cecile Menard, and Michael Kern
Earth Syst. Sci. Data, 14, 3915–3945, https://doi.org/10.5194/essd-14-3915-2022, https://doi.org/10.5194/essd-14-3915-2022, 2022
Short summary
Short summary
The manuscript describes airborne, dual-polarised X and Ku band synthetic aperture radar (SAR) data collected over several campaigns over snow-covered terrain in Finland, Austria and Canada. Colocated snow and meteorological observations are also presented. The data are meant for science users interested in investigating X/Ku band radar signatures from natural environments in winter conditions.
Hans Lievens, Isis Brangers, Hans-Peter Marshall, Tobias Jonas, Marc Olefs, and Gabriëlle De Lannoy
The Cryosphere, 16, 159–177, https://doi.org/10.5194/tc-16-159-2022, https://doi.org/10.5194/tc-16-159-2022, 2022
Short summary
Short summary
Snow depth observations at high spatial resolution from the Sentinel-1 satellite mission are presented over the European Alps. The novel observations can improve our knowledge of seasonal snow mass in areas with complex topography, where satellite-based estimates are currently lacking, and benefit a number of applications including water resource management, flood forecasting, and numerical weather prediction.
Ahmad Hojatimalekshah, Zachary Uhlmann, Nancy F. Glenn, Christopher A. Hiemstra, Christopher J. Tennant, Jake D. Graham, Lucas Spaete, Arthur Gelvin, Hans-Peter Marshall, James P. McNamara, and Josh Enterkine
The Cryosphere, 15, 2187–2209, https://doi.org/10.5194/tc-15-2187-2021, https://doi.org/10.5194/tc-15-2187-2021, 2021
Short summary
Short summary
We describe the relationships between snow depth, vegetation canopy, and local-scale processes during the snow accumulation period using terrestrial laser scanning (TLS). In addition to topography and wind, our findings suggest the importance of fine-scale tree structure, species type, and distributions on snow depth. Snow depth increases from the canopy edge toward the open areas, but wind and topographic controls may affect this trend. TLS data are complementary to wide-area lidar surveys.
Simon Zwieback and Franz J. Meyer
The Cryosphere, 15, 2041–2055, https://doi.org/10.5194/tc-15-2041-2021, https://doi.org/10.5194/tc-15-2041-2021, 2021
Short summary
Short summary
Thawing of ice-rich permafrost leads to subsidence and slumping, which can compromise Arctic infrastructure. However, we lack fine-scale maps of permafrost ground ice, chiefly because it is usually invisible at the surface. We show that subsidence at the end of summer serves as a
fingerprintwith which near-surface permafrost ground ice can be identified. As this can be done with satellite data, this method may help improve ground ice maps and thus sustainably steward the Arctic.
Ryan W. Webb, Keith Jennings, Stefan Finsterle, and Steven R. Fassnacht
The Cryosphere, 15, 1423–1434, https://doi.org/10.5194/tc-15-1423-2021, https://doi.org/10.5194/tc-15-1423-2021, 2021
Short summary
Short summary
We simulate the flow of liquid water through snow and compare results to field experiments. This process is important because it controls how much and how quickly water will reach our streams and rivers in snowy regions. We found that water can flow large distances downslope through the snow even after the snow has stopped melting. Improved modeling of snowmelt processes will allow us to more accurately estimate available water resources, especially under changing climate conditions.
Rhae Sung Kim, Sujay Kumar, Carrie Vuyovich, Paul Houser, Jessica Lundquist, Lawrence Mudryk, Michael Durand, Ana Barros, Edward J. Kim, Barton A. Forman, Ethan D. Gutmann, Melissa L. Wrzesien, Camille Garnaud, Melody Sandells, Hans-Peter Marshall, Nicoleta Cristea, Justin M. Pflug, Jeremy Johnston, Yueqian Cao, David Mocko, and Shugong Wang
The Cryosphere, 15, 771–791, https://doi.org/10.5194/tc-15-771-2021, https://doi.org/10.5194/tc-15-771-2021, 2021
Short summary
Short summary
High SWE uncertainty is observed in mountainous and forested regions, highlighting the need for high-resolution snow observations in these regions. Substantial uncertainty in snow water storage in Tundra regions and the dominance of water storage in these regions points to the need for high-accuracy snow estimation. Finally, snow measurements during the melt season are most needed at high latitudes, whereas observations at near peak snow accumulations are most beneficial over the midlatitudes.
Miguel A. Aguayo, Alejandro N. Flores, James P. McNamara, Hans-Peter Marshall, and Jodi Mead
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2020-451, https://doi.org/10.5194/hess-2020-451, 2020
Manuscript not accepted for further review
Gabriel Lewis, Erich Osterberg, Robert Hawley, Hans Peter Marshall, Tate Meehan, Karina Graeter, Forrest McCarthy, Thomas Overly, Zayta Thundercloud, and David Ferris
The Cryosphere, 13, 2797–2815, https://doi.org/10.5194/tc-13-2797-2019, https://doi.org/10.5194/tc-13-2797-2019, 2019
Short summary
Short summary
We present accumulation records from sixteen 22–32 m long firn cores and 4436 km of ground-penetrating radar, covering the past 20–60 years of accumulation, collected across the western Greenland Ice Sheet percolation zone. Trends from both radar and firn cores, as well as commonly used regional climate models, show decreasing accumulation over the 1996–2016 period.
Dyre O. Dammann, Leif E. B. Eriksson, Son V. Nghiem, Erin C. Pettit, Nathan T. Kurtz, John G. Sonntag, Thomas E. Busche, Franz J. Meyer, and Andrew R. Mahoney
The Cryosphere, 13, 1861–1875, https://doi.org/10.5194/tc-13-1861-2019, https://doi.org/10.5194/tc-13-1861-2019, 2019
Short summary
Short summary
We validate TanDEM-X interferometry as a tool for deriving iceberg subaerial morphology using Operation IceBridge data. This approach enables a volumetric classification of icebergs, according to volume relevant to iceberg drift and decay, freshwater contribution, and potential impact on structures. We find iceberg volumes to generally match within 7 %. These results suggest that TanDEM-X could pave the way for future interferometric systems of scientific and operational iceberg classification.
Dyre Oliver Dammann, Leif E. B. Eriksson, Joshua M. Jones, Andrew R. Mahoney, Roland Romeiser, Franz J. Meyer, Hajo Eicken, and Yasushi Fukamachi
The Cryosphere, 13, 1395–1408, https://doi.org/10.5194/tc-13-1395-2019, https://doi.org/10.5194/tc-13-1395-2019, 2019
Short summary
Short summary
We evaluate single-pass synthetic aperture radar interferometry (InSAR) as a tool to assess sea ice drift and deformation. Initial validation shows that TanDEM-X phase-derived drift speed corresponds well with ground-based radar-derived motion. We further show that InSAR enables the identification of potentially important short-lived dynamic processes otherwise difficult to observe, with possible implication for engineering and sea ice modeling.
Dyre O. Dammann, Leif E. B. Eriksson, Andrew R. Mahoney, Hajo Eicken, and Franz J. Meyer
The Cryosphere, 13, 557–577, https://doi.org/10.5194/tc-13-557-2019, https://doi.org/10.5194/tc-13-557-2019, 2019
Short summary
Short summary
We present an approach for mapping bottomfast sea ice and landfast sea ice stability using Synthetic Aperture Radar Interferometry. This is the first comprehensive assessment of Arctic bottomfast sea ice extent with implications for subsea permafrost and marine habitats. Our pan-Arctic analysis also provides a new understanding of sea ice dynamics in five marginal seas of the Arctic Ocean relevant for strategic planning and tactical decision-making for different uses of coastal ice.
Daniel McGrath, Louis Sass, Shad O'Neel, Chris McNeil, Salvatore G. Candela, Emily H. Baker, and Hans-Peter Marshall
The Cryosphere, 12, 3617–3633, https://doi.org/10.5194/tc-12-3617-2018, https://doi.org/10.5194/tc-12-3617-2018, 2018
Short summary
Short summary
Measuring the amount and spatial pattern of snow on glaciers is essential for monitoring glacier mass balance and quantifying the water budget of glacierized basins. Using repeat radar surveys for 5 consecutive years, we found that the spatial pattern in snow distribution is stable over the majority of the glacier and scales with the glacier-wide average. Our findings support the use of sparse stake networks for effectively measuring interannual variability in winter balance on glaciers.
Ryan W. Webb, Steven R. Fassnacht, and Michael N. Gooseff
The Cryosphere, 12, 287–300, https://doi.org/10.5194/tc-12-287-2018, https://doi.org/10.5194/tc-12-287-2018, 2018
Short summary
Short summary
We observed how snowmelt is transported on a hillslope through multiple measurements of snow and soil moisture across a small headwater catchment. We found that snowmelt flows through the snow with less infiltration on north-facing slopes and infiltrates the ground on south-facing slopes. This causes an increase in snow water equivalent at the base of the north-facing slope by as much as 170 %. We present a conceptualization of flow path development to improve future investigations.
Gabriel Lewis, Erich Osterberg, Robert Hawley, Brian Whitmore, Hans Peter Marshall, and Jason Box
The Cryosphere, 11, 773–788, https://doi.org/10.5194/tc-11-773-2017, https://doi.org/10.5194/tc-11-773-2017, 2017
Short summary
Short summary
We analyze 25 flight lines from NASA's Operation IceBridge Accumulation Radar totaling to determine snow accumulation throughout the dry snow and percolation zone of the Greenland Ice Sheet. Our results indicate that regional differences between IceBridge and model accumulation are large enough to significantly alter the Greenland Ice Sheet surface mass balance, with implications for future global sea-level rise.
P. R. Lindgren, G. Grosse, K. M. Walter Anthony, and F. J. Meyer
Biogeosciences, 13, 27–44, https://doi.org/10.5194/bg-13-27-2016, https://doi.org/10.5194/bg-13-27-2016, 2016
Short summary
Short summary
We mapped and characterized methane ebullition bubbles trapped in lake ice, and estimated whole-lake methane emission using high-resolution aerial images of a lake acquired following freeze-up. We identified the location and relative sizes of high- and low-flux seepage zones within the lake. A large number of seeps showed spatiotemporal stability over our study period. Our approach is applicable to other regions to improve the estimation of methane emission from lakes at the regional scale.
F. Alshawaf, B. Fersch, S. Hinz, H. Kunstmann, M. Mayer, and F. J. Meyer
Hydrol. Earth Syst. Sci., 19, 4747–4764, https://doi.org/10.5194/hess-19-4747-2015, https://doi.org/10.5194/hess-19-4747-2015, 2015
Short summary
Short summary
This work aims at deriving high spatially resolved maps of atmospheric water vapor by the fusion data from Interferometric Synthetic Aperture Radar (InSAR), Global Navigation Satellite Systems (GNSS), and the Weather Research and Forecasting (WRF) model. The data fusion approach exploits the redundant and complementary spatial properties of all data sets to provide more accurate and high-resolution maps of water vapor. The comparison with maps from MERIS shows rms values of less than 1 mm.
Related subject area
Discipline: Snow | Subject: Remote Sensing
Temperature-dominated spatiotemporal variability in snow phenology on the Tibetan Plateau from 2002 to 2022
Snow water equivalent retrieved from X- and dual Ku-band scatterometer measurements at Sodankylä using the Markov Chain Monte Carlo method
Bayesian physical–statistical retrieval of snow water equivalent and snow depth from X- and Ku-band synthetic aperture radar – demonstration using airborne SnowSAr in SnowEx'17
Snow water equivalent retrieval over Idaho – Part 1: Using Sentinel-1 repeat-pass interferometry
Passive microwave remote-sensing-based high-resolution snow depth mapping for Western Himalayan zones using multifactor modeling approach
Mapping surface hoar from near-infrared texture in a laboratory
Retrieval of snow water equivalent from dual-frequency radar measurements: using time series to overcome the need for accurate a priori information
Snow accumulation, albedo and melt patterns following road construction on permafrost, Inuvik–Tuktoyaktuk Highway, Canada
Measuring the spatiotemporal variability in snow depth in subarctic environments using UASs – Part 1: Measurements, processing, and accuracy assessment
Measuring the spatiotemporal variability in snow depth in subarctic environments using UASs – Part 2: Snow processes and snow–canopy interactions
Evaluating Snow Microwave Radiative Transfer (SMRT) model emissivities with 89 to 243 GHz observations of Arctic tundra snow
Thermal infrared shadow-hiding in GOES-R ABI imagery: snow and forest temperature observations from the SnowEx 2020 Grand Mesa field campaign
Evaluating the utility of active microwave observations as a snow mission concept using observing system simulation experiments
Evaluation of snow depth retrievals from ICESat-2 using airborne laser-scanning data
How do tradeoffs in satellite spatial and temporal resolution impact snow water equivalent reconstruction?
Exploring the use of multi-source high-resolution satellite data for snow water equivalent reconstruction over mountainous catchments
Snowmelt characterization from optical and synthetic-aperture radar observations in the La Joie Basin, British Columbia
Topographic and vegetation controls of the spatial distribution of snow depth in agro-forested environments by UAV lidar
Temporal stability of long-term satellite and reanalysis products to monitor snow cover trends
Towards long-term records of rain-on-snow events across the Arctic from satellite data
Implementing spatially and temporally varying snow densities into the GlobSnow snow water equivalent retrieval
Evaluation of E3SM land model snow simulations over the western United States
Landsat, MODIS, and VIIRS snow cover mapping algorithm performance as validated by airborne lidar datasets
Snow stratigraphy observations from Operation IceBridge surveys in Alaska using S and C band airborne ultra-wideband FMCW (frequency-modulated continuous wave) radar
Brief communication: A continuous formulation of microwave scattering from fresh snow to bubbly ice from first principles
Review article: Global monitoring of snow water equivalent using high-frequency radar remote sensing
Automated avalanche mapping from SPOT 6/7 satellite imagery with deep learning: results, evaluation, potential and limitations
Potential of X-band polarimetric synthetic aperture radar co-polar phase difference for arctic snow depth estimation
Snow water equivalent change mapping from slope-correlated synthetic aperture radar interferometry (InSAR) phase variations
Sentinel-1 time series for mapping snow cover depletion and timing of snowmelt in Arctic periglacial environments: case study from Zackenberg and Kobbefjord, Greenland
Sentinel-1 snow depth retrieval at sub-kilometer resolution over the European Alps
Characterizing tundra snow sub-pixel variability to improve brightness temperature estimation in satellite SWE retrievals
Mapping liquid water content in snow at the millimeter scale: an intercomparison of mixed-phase optical property models using hyperspectral imaging and in situ measurements
Brief communication: Evaluation of the snow cover detection in the Copernicus High Resolution Snow & Ice Monitoring Service
Evaluation of snow extent time series derived from Advanced Very High Resolution Radiometer global area coverage data (1982–2018) in the Hindu Kush Himalayas
Deriving Arctic 2 m air temperatures over snow and ice from satellite surface temperature measurements
Impact of dynamic snow density on GlobSnow snow water equivalent retrieval accuracy
The retrieval of snow properties from SLSTR Sentinel-3 – Part 1: Method description and sensitivity study
The retrieval of snow properties from SLSTR Sentinel-3 – Part 2: Results and validation
Tree canopy and snow depth relationships at fine scales with terrestrial laser scanning
Snow depth mapping with unpiloted aerial system lidar observations: a case study in Durham, New Hampshire, United States
Mapping avalanches with satellites – evaluation of performance and completeness
Estimating fractional snow cover from passive microwave brightness temperature data using MODIS snow cover product over North America
Snow depth time series retrieval by time-lapse photography: Finnish and Italian case studies
Intercomparison of photogrammetric platforms for spatially continuous snow depth mapping
Simulating optical top-of-atmosphere radiance satellite images over snow-covered rugged terrain
Parameterizing anisotropic reflectance of snow surfaces from airborne digital camera observations in Antarctica
Snow depth mapping from stereo satellite imagery in mountainous terrain: evaluation using airborne laser-scanning data
Improving sub-canopy snow depth mapping with unmanned aerial vehicles: lidar versus structure-from-motion techniques
Use of Sentinel-1 radar observations to evaluate snowmelt dynamics in alpine regions
Jiahui Xu, Yao Tang, Linxin Dong, Shujie Wang, Bailang Yu, Jianping Wu, Zhaojun Zheng, and Yan Huang
The Cryosphere, 18, 1817–1834, https://doi.org/10.5194/tc-18-1817-2024, https://doi.org/10.5194/tc-18-1817-2024, 2024
Short summary
Short summary
Understanding snow phenology (SP) and its possible feedback are important. We reveal spatiotemporal heterogeneous SP on the Tibetan Plateau (TP) and the mediating effects from meteorological, topographic, and environmental factors on it. The direct effects of meteorology on SP are much greater than the indirect effects. Topography indirectly effects SP, while vegetation directly effects SP. This study contributes to understanding past global warming and predicting future trends on the TP.
Jinmei Pan, Michael Durand, Juha Lemmetyinen, Desheng Liu, and Jiancheng Shi
The Cryosphere, 18, 1561–1578, https://doi.org/10.5194/tc-18-1561-2024, https://doi.org/10.5194/tc-18-1561-2024, 2024
Short summary
Short summary
We developed an algorithm to estimate snow mass using X- and dual Ku-band radar, and tested it in a ground-based experiment. The algorithm, the Bayesian-based Algorithm for SWE Estimation (BASE) using active microwaves, achieved an RMSE of 30 mm for snow water equivalent. These results demonstrate the potential of radar, a highly promising sensor, to map snow mass at high spatial resolution.
Siddharth Singh, Michael Durand, Edward Kim, and Ana P. Barros
The Cryosphere, 18, 747–773, https://doi.org/10.5194/tc-18-747-2024, https://doi.org/10.5194/tc-18-747-2024, 2024
Short summary
Short summary
Seasonal snowfall accumulation plays a critical role in climate. The water stored in it is measured by the snow water equivalent (SWE), the amount of water released after completely melting. We demonstrate a Bayesian physical–statistical framework to estimate SWE from airborne X- and Ku-band synthetic aperture radar backscatter measurements constrained by physical snow hydrology and radar models. We explored spatial resolutions and vertical structures that agree well with ground observations.
Shadi Oveisgharan, Robert Zinke, Zachary Hoppinen, and Hans Peter Marshall
The Cryosphere, 18, 559–574, https://doi.org/10.5194/tc-18-559-2024, https://doi.org/10.5194/tc-18-559-2024, 2024
Short summary
Short summary
The seasonal snowpack provides water resources to billions of people worldwide. Large-scale mapping of snow water equivalent (SWE) with high resolution is critical for many scientific and economics fields. In this work we used the radar remote sensing interferometric synthetic aperture radar (InSAR) to estimate the SWE change between 2 d. The error in the estimated SWE change is less than 2 cm for in situ stations. Additionally, the retrieved SWE using InSAR is correlated with lidar snow depth.
Dhiraj Kumar Singh, Srinivasarao Tanniru, Kamal Kant Singh, Harendra Singh Negi, and RAAJ Ramsankaran
The Cryosphere, 18, 451–474, https://doi.org/10.5194/tc-18-451-2024, https://doi.org/10.5194/tc-18-451-2024, 2024
Short summary
Short summary
In situ techniques for snow depth (SD) measurement are not adequate to represent the spatiotemporal variability in SD in the Western Himalayan region. Therefore, this study focuses on the high-resolution mapping of daily snow depth in the Indian Western Himalayan region using passive microwave remote-sensing-based algorithms. Overall, the proposed multifactor SD models demonstrated substantial improvement compared to the operational products. However, there is a scope for further improvement.
James Dillon, Christopher Donahue, Evan Schehrer, Karl Birkeland, and Kevin Hammonds
EGUsphere, https://doi.org/10.5194/egusphere-2023-3133, https://doi.org/10.5194/egusphere-2023-3133, 2024
Short summary
Short summary
Surface hoar crystals are snow grains that form when vapor deposits on the snow surface. They create a weak layer in the snowpack that can cause large avalanches to occur. Thus, determining when and where surface hoar forms is a lifesaving matter. Here, we developed a means of mapping surface hoar using remote sensing technologies. We found that surface hoar displayed heightened texture, hence the variability of brightness. Using this, we created surface hoar maps with accuracy upwards of 95 %.
Michael Durand, Joel T. Johnson, Jack Dechow, Leung Tsang, Firoz Borah, and Edward J. Kim
The Cryosphere, 18, 139–152, https://doi.org/10.5194/tc-18-139-2024, https://doi.org/10.5194/tc-18-139-2024, 2024
Short summary
Short summary
Seasonal snow accumulates each winter, storing water to release later in the year and modulating both water and energy cycles, but the amount of seasonal snow is one of the most poorly measured components of the global water cycle. Satellite concepts to monitor snow accumulation have been proposed but not selected. This paper shows that snow accumulation can be measured using radar, and that (contrary to previous studies) does not require highly accurate information about snow microstructure.
Jennika Hammar, Inge Grünberg, Steven V. Kokelj, Jurjen van der Sluijs, and Julia Boike
The Cryosphere, 17, 5357–5372, https://doi.org/10.5194/tc-17-5357-2023, https://doi.org/10.5194/tc-17-5357-2023, 2023
Short summary
Short summary
Roads on permafrost have significant environmental effects. This study assessed the Inuvik to Tuktoyaktuk Highway (ITH) in Canada and its impact on snow accumulation, albedo and snowmelt timing. Our findings revealed that snow accumulation increased by up to 36 m from the road, 12-day earlier snowmelt within 100 m due to reduced albedo, and altered snowmelt patterns in seemingly undisturbed areas. Remote sensing aids in understanding road impacts on permafrost.
Anssi Rauhala, Leo-Juhani Meriö, Anton Kuzmin, Pasi Korpelainen, Pertti Ala-aho, Timo Kumpula, Bjørn Kløve, and Hannu Marttila
The Cryosphere, 17, 4343–4362, https://doi.org/10.5194/tc-17-4343-2023, https://doi.org/10.5194/tc-17-4343-2023, 2023
Short summary
Short summary
Snow conditions in the Northern Hemisphere are rapidly changing, and information on snow depth is important for decision-making. We present snow depth measurements using different drones throughout the winter at a subarctic site. Generally, all drones produced good estimates of snow depth in open areas. However, differences were observed in the accuracies produced by the different drones, and a reduction in accuracy was observed when moving from an open mire area to forest-covered areas.
Leo-Juhani Meriö, Anssi Rauhala, Pertti Ala-aho, Anton Kuzmin, Pasi Korpelainen, Timo Kumpula, Bjørn Kløve, and Hannu Marttila
The Cryosphere, 17, 4363–4380, https://doi.org/10.5194/tc-17-4363-2023, https://doi.org/10.5194/tc-17-4363-2023, 2023
Short summary
Short summary
Information on seasonal snow cover is essential in understanding snow processes and operational forecasting. We study the spatiotemporal variability in snow depth and snow processes in a subarctic, boreal landscape using drones. We identified multiple theoretically known snow processes and interactions between snow and vegetation. The results highlight the applicability of the drones to be used for a detailed study of snow depth in multiple land cover types and snow–vegetation interactions.
Kirsty Wivell, Stuart Fox, Melody Sandells, Chawn Harlow, Richard Essery, and Nick Rutter
The Cryosphere, 17, 4325–4341, https://doi.org/10.5194/tc-17-4325-2023, https://doi.org/10.5194/tc-17-4325-2023, 2023
Short summary
Short summary
Satellite microwave observations improve weather forecasts, but to use these observations in the Arctic, snow emission must be known. This study uses airborne and in situ snow observations to validate emissivity simulations for two- and three-layer snowpacks at key frequencies for weather prediction. We assess the impact of thickness, grain size and density in key snow layers, which will help inform development of physical snow models that provide snow profile input to emissivity simulations.
Steven J. Pestana, C. Chris Chickadel, and Jessica D. Lundquist
EGUsphere, https://doi.org/10.5194/egusphere-2023-1784, https://doi.org/10.5194/egusphere-2023-1784, 2023
Short summary
Short summary
We compared infrared images taken by GOES-R satellites of an area with snow and forests against surface temperature measurements taken on the ground, from an aircraft, and by another satellite. We found that GOES-R measured warmer temperatures than the other measurements, especially in areas with more forest, and when the sun was behind the satellite. From this work, we’ve learned that the position of the sun and surface features such as trees that can cast shadows impact GOES-R infrared images.
Eunsang Cho, Carrie M. Vuyovich, Sujay V. Kumar, Melissa L. Wrzesien, and Rhae Sung Kim
The Cryosphere, 17, 3915–3931, https://doi.org/10.5194/tc-17-3915-2023, https://doi.org/10.5194/tc-17-3915-2023, 2023
Short summary
Short summary
As a future snow mission concept, active microwave sensors have the potential to measure snow water equivalent (SWE) in deep snowpack and forested environments. We used a modeling and data assimilation approach (a so-called observing system simulation experiment) to quantify the usefulness of active microwave-based SWE retrievals over western Colorado. We found that active microwave sensors with a mature retrieval algorithm can improve SWE simulations by about 20 % in the mountainous domain.
César Deschamps-Berger, Simon Gascoin, David Shean, Hannah Besso, Ambroise Guiot, and Juan Ignacio López-Moreno
The Cryosphere, 17, 2779–2792, https://doi.org/10.5194/tc-17-2779-2023, https://doi.org/10.5194/tc-17-2779-2023, 2023
Short summary
Short summary
The estimation of the snow depth in mountains is hard, despite the importance of the snowpack for human societies and ecosystems. We measured the snow depth in mountains by comparing the elevation of points measured with snow from the high-precision altimetric satellite ICESat-2 to the elevation without snow from various sources. Snow depths derived only from ICESat-2 were too sparse, but using external airborne/satellite products results in spatially richer and sufficiently precise snow depths.
Edward H. Bair, Jeff Dozier, Karl Rittger, Timbo Stillinger, William Kleiber, and Robert E. Davis
The Cryosphere, 17, 2629–2643, https://doi.org/10.5194/tc-17-2629-2023, https://doi.org/10.5194/tc-17-2629-2023, 2023
Short summary
Short summary
To test the title question, three snow cover products were used in a snow model. Contrary to previous work, higher-spatial-resolution snow cover products only improved the model accuracy marginally. Conclusions are as follows: (1) snow cover and albedo from moderate-resolution sensors continue to provide accurate forcings and (2) finer spatial and temporal resolutions are the future for Earth observations, but existing moderate-resolution sensors still offer value.
Valentina Premier, Carlo Marin, Giacomo Bertoldi, Riccardo Barella, Claudia Notarnicola, and Lorenzo Bruzzone
The Cryosphere, 17, 2387–2407, https://doi.org/10.5194/tc-17-2387-2023, https://doi.org/10.5194/tc-17-2387-2023, 2023
Short summary
Short summary
The large amount of information regularly acquired by satellites can provide important information about SWE. We explore the use of multi-source satellite data, in situ observations, and a degree-day model to reconstruct daily SWE at 25 m. The results show spatial patterns that are consistent with the topographical features as well as with a reference product. Being able to also reproduce interannual variability, the method has great potential for hydrological and ecological applications.
Sara E. Darychuk, Joseph M. Shea, Brian Menounos, Anna Chesnokova, Georg Jost, and Frank Weber
The Cryosphere, 17, 1457–1473, https://doi.org/10.5194/tc-17-1457-2023, https://doi.org/10.5194/tc-17-1457-2023, 2023
Short summary
Short summary
We use synthetic-aperture radar (SAR) and optical observations to map snowmelt timing and duration on the watershed scale. We found that Sentinel-1 SAR time series can be used to approximate snowmelt onset over diverse terrain and land cover types, and we present a low-cost workflow for SAR processing over large, mountainous regions. Our approach provides spatially distributed observations of the snowpack necessary for model calibration and can be used to monitor snowmelt in ungauged basins.
Vasana Dharmadasa, Christophe Kinnard, and Michel Baraër
The Cryosphere, 17, 1225–1246, https://doi.org/10.5194/tc-17-1225-2023, https://doi.org/10.5194/tc-17-1225-2023, 2023
Short summary
Short summary
This study highlights the successful usage of UAV lidar to monitor small-scale snow depth distribution. Our results show that underlying topography and wind redistribution of snow along forest edges govern the snow depth variability at agro-forested sites, while forest structure variability dominates snow depth variability in the coniferous environment. This emphasizes the importance of including and better representing these processes in physically based models for accurate snowpack estimates.
Ruben Urraca and Nadine Gobron
The Cryosphere, 17, 1023–1052, https://doi.org/10.5194/tc-17-1023-2023, https://doi.org/10.5194/tc-17-1023-2023, 2023
Short summary
Short summary
We evaluate the fitness of some of the longest satellite (NOAA CDR, 1966–2020) and reanalysis (ERA5, 1950–2020; ERA5-Land, 1950–2020) products currently available to monitor the Northern Hemisphere snow cover trends using 527 stations as the reference. We found different artificial trends and stepwise discontinuities in all the products that hinder the accurate monitoring of snow trends, at least without bias correction. The study also provides updates on the snow cover trends during 1950–2020.
Annett Bartsch, Helena Bergstedt, Georg Pointner, Xaver Muri, Kimmo Rautiainen, Leena Leppänen, Kyle Joly, Aleksandr Sokolov, Pavel Orekhov, Dorothee Ehrich, and Eeva Mariatta Soininen
The Cryosphere, 17, 889–915, https://doi.org/10.5194/tc-17-889-2023, https://doi.org/10.5194/tc-17-889-2023, 2023
Short summary
Short summary
Rain-on-snow (ROS) events occur across many regions of the terrestrial Arctic in mid-winter. In extreme cases ice layers form which affect wildlife, vegetation and soils beyond the duration of the event. The fusion of multiple types of microwave satellite observations is suggested for the creation of a climate data record. Retrieval is most robust in the tundra biome, where records can be used to identify extremes and the results can be applied to impact studies at regional scale.
Pinja Venäläinen, Kari Luojus, Colleen Mortimer, Juha Lemmetyinen, Jouni Pulliainen, Matias Takala, Mikko Moisander, and Lina Zschenderlein
The Cryosphere, 17, 719–736, https://doi.org/10.5194/tc-17-719-2023, https://doi.org/10.5194/tc-17-719-2023, 2023
Short summary
Short summary
Snow water equivalent (SWE) is a valuable characteristic of snow cover. In this research, we improve the radiometer-based GlobSnow SWE retrieval methodology by implementing spatially and temporally varying snow densities into the retrieval procedure. In addition to improving the accuracy of SWE retrieval, varying snow densities were found to improve the magnitude and seasonal evolution of the Northern Hemisphere snow mass estimate compared to the baseline product.
Dalei Hao, Gautam Bisht, Karl Rittger, Timbo Stillinger, Edward Bair, Yu Gu, and L. Ruby Leung
The Cryosphere, 17, 673–697, https://doi.org/10.5194/tc-17-673-2023, https://doi.org/10.5194/tc-17-673-2023, 2023
Short summary
Short summary
We comprehensively evaluated the snow simulations in E3SM land model over the western United States in terms of spatial patterns, temporal correlations, interannual variabilities, elevation gradients, and change with forest cover of snow properties and snow phenology. Our study underscores the need for diagnosing model biases and improving the model representations of snow properties and snow phenology in mountainous areas for more credible simulation and future projection of mountain snowpack.
Timbo Stillinger, Karl Rittger, Mark S. Raleigh, Alex Michell, Robert E. Davis, and Edward H. Bair
The Cryosphere, 17, 567–590, https://doi.org/10.5194/tc-17-567-2023, https://doi.org/10.5194/tc-17-567-2023, 2023
Short summary
Short summary
Understanding global snow cover is critical for comprehending climate change and its impacts on the lives of billions of people. Satellites are the best way to monitor global snow cover, yet snow varies at a finer spatial resolution than most satellite images. We assessed subpixel snow mapping methods across a spectrum of conditions using airborne lidar. Spectral-unmixing methods outperformed older operational methods and are ready to to advance snow cover mapping at the global scale.
Jilu Li, Fernando Rodriguez-Morales, Xavier Fettweis, Oluwanisola Ibikunle, Carl Leuschen, John Paden, Daniel Gomez-Garcia, and Emily Arnold
The Cryosphere, 17, 175–193, https://doi.org/10.5194/tc-17-175-2023, https://doi.org/10.5194/tc-17-175-2023, 2023
Short summary
Short summary
Alaskan glaciers' loss of ice mass contributes significantly to ocean surface rise. It is important to know how deeply and how much snow accumulates on these glaciers to comprehend and analyze the glacial mass loss process. We reported the observed seasonal snow depth distribution from our radar data taken in Alaska in 2018 and 2021, developed a method to estimate the annual snow accumulation rate at Mt. Wrangell caldera, and identified transition zones from wet-snow zones to ablation zones.
Ghislain Picard, Henning Löwe, and Christian Mätzler
The Cryosphere, 16, 3861–3866, https://doi.org/10.5194/tc-16-3861-2022, https://doi.org/10.5194/tc-16-3861-2022, 2022
Short summary
Short summary
Microwave satellite observations used to monitor the cryosphere require radiative transfer models for their interpretation. These models represent how microwaves are scattered by snow and ice. However no existing theory is suitable for all types of snow and ice found on Earth. We adapted a recently published generic scattering theory to snow and show how it may improve the representation of snows with intermediate densities (~500 kg/m3) and/or with coarse grains at high microwave frequencies.
Leung Tsang, Michael Durand, Chris Derksen, Ana P. Barros, Do-Hyuk Kang, Hans Lievens, Hans-Peter Marshall, Jiyue Zhu, Joel Johnson, Joshua King, Juha Lemmetyinen, Melody Sandells, Nick Rutter, Paul Siqueira, Anne Nolin, Batu Osmanoglu, Carrie Vuyovich, Edward Kim, Drew Taylor, Ioanna Merkouriadi, Ludovic Brucker, Mahdi Navari, Marie Dumont, Richard Kelly, Rhae Sung Kim, Tien-Hao Liao, Firoz Borah, and Xiaolan Xu
The Cryosphere, 16, 3531–3573, https://doi.org/10.5194/tc-16-3531-2022, https://doi.org/10.5194/tc-16-3531-2022, 2022
Short summary
Short summary
Snow water equivalent (SWE) is of fundamental importance to water, energy, and geochemical cycles but is poorly observed globally. Synthetic aperture radar (SAR) measurements at X- and Ku-band can address this gap. This review serves to inform the broad snow research, monitoring, and application communities about the progress made in recent decades to move towards a new satellite mission capable of addressing the needs of the geoscience researchers and users.
Elisabeth D. Hafner, Patrick Barton, Rodrigo Caye Daudt, Jan Dirk Wegner, Konrad Schindler, and Yves Bühler
The Cryosphere, 16, 3517–3530, https://doi.org/10.5194/tc-16-3517-2022, https://doi.org/10.5194/tc-16-3517-2022, 2022
Short summary
Short summary
Knowing where avalanches occur is very important information for several disciplines, for example avalanche warning, hazard zonation and risk management. Satellite imagery can provide such data systematically over large regions. In our work we propose a machine learning model to automate the time-consuming manual mapping. Additionally, we investigate expert agreement for manual avalanche mapping, showing that our network is equally as good as the experts in identifying avalanches.
Joëlle Voglimacci-Stephanopoli, Anna Wendleder, Hugues Lantuit, Alexandre Langlois, Samuel Stettner, Andreas Schmitt, Jean-Pierre Dedieu, Achim Roth, and Alain Royer
The Cryosphere, 16, 2163–2181, https://doi.org/10.5194/tc-16-2163-2022, https://doi.org/10.5194/tc-16-2163-2022, 2022
Short summary
Short summary
Changes in the state of the snowpack in the context of observed global warming must be considered to improve our understanding of the processes within the cryosphere. This study aims to characterize an arctic snowpack using the TerraSAR-X satellite. Using a high-spatial-resolution vegetation classification, we were able to quantify the variability in snow depth, as well as the topographic soil wetness index, which provided a better understanding of the electromagnetic wave–ground interaction.
Jayson Eppler, Bernhard Rabus, and Peter Morse
The Cryosphere, 16, 1497–1521, https://doi.org/10.5194/tc-16-1497-2022, https://doi.org/10.5194/tc-16-1497-2022, 2022
Short summary
Short summary
We introduce a new method for mapping changes in the snow water equivalent (SWE) of dry snow based on differences between time-repeated synthetic aperture radar (SAR) images. It correlates phase differences with variations in the topographic slope which allows the method to work without any "reference" targets within the imaged area and without having to numerically unwrap the spatial phase maps. This overcomes the key challenges faced in using SAR interferometry for SWE change mapping.
Sebastian Buchelt, Kirstine Skov, Kerstin Krøier Rasmussen, and Tobias Ullmann
The Cryosphere, 16, 625–646, https://doi.org/10.5194/tc-16-625-2022, https://doi.org/10.5194/tc-16-625-2022, 2022
Short summary
Short summary
In this paper, we present a threshold and a derivative approach using Sentinel-1 synthetic aperture radar time series to capture the small-scale heterogeneity of snow cover (SC) and snowmelt. Thereby, we can identify start of runoff and end of SC as well as perennial snow and SC extent during melt with high spatiotemporal resolution. Hence, our approach could support monitoring of distribution patterns and hydrological cascading effects of SC from the catchment scale to pan-Arctic observations.
Hans Lievens, Isis Brangers, Hans-Peter Marshall, Tobias Jonas, Marc Olefs, and Gabriëlle De Lannoy
The Cryosphere, 16, 159–177, https://doi.org/10.5194/tc-16-159-2022, https://doi.org/10.5194/tc-16-159-2022, 2022
Short summary
Short summary
Snow depth observations at high spatial resolution from the Sentinel-1 satellite mission are presented over the European Alps. The novel observations can improve our knowledge of seasonal snow mass in areas with complex topography, where satellite-based estimates are currently lacking, and benefit a number of applications including water resource management, flood forecasting, and numerical weather prediction.
Julien Meloche, Alexandre Langlois, Nick Rutter, Alain Royer, Josh King, Branden Walker, Philip Marsh, and Evan J. Wilcox
The Cryosphere, 16, 87–101, https://doi.org/10.5194/tc-16-87-2022, https://doi.org/10.5194/tc-16-87-2022, 2022
Short summary
Short summary
To estimate snow water equivalent from space, model predictions of the satellite measurement (brightness temperature in our case) have to be used. These models allow us to estimate snow properties from the brightness temperature by inverting the model. To improve SWE estimate, we proposed incorporating the variability of snow in these model as it has not been taken into account yet. A new parameter (coefficient of variation) is proposed because it improved simulation of brightness temperature.
Christopher Donahue, S. McKenzie Skiles, and Kevin Hammonds
The Cryosphere, 16, 43–59, https://doi.org/10.5194/tc-16-43-2022, https://doi.org/10.5194/tc-16-43-2022, 2022
Short summary
Short summary
The amount of water within a snowpack is important information for predicting snowmelt and wet-snow avalanches. From within a controlled laboratory, the optimal method for measuring liquid water content (LWC) at the snow surface or along a snow pit profile using near-infrared imagery was determined. As snow samples melted, multiple models to represent wet-snow reflectance were assessed against a more established LWC instrument. The best model represents snow as separate spheres of ice and water.
Zacharie Barrou Dumont, Simon Gascoin, Olivier Hagolle, Michaël Ablain, Rémi Jugier, Germain Salgues, Florence Marti, Aurore Dupuis, Marie Dumont, and Samuel Morin
The Cryosphere, 15, 4975–4980, https://doi.org/10.5194/tc-15-4975-2021, https://doi.org/10.5194/tc-15-4975-2021, 2021
Short summary
Short summary
Since 2020, the Copernicus High Resolution Snow & Ice Monitoring Service has distributed snow cover maps at 20 m resolution over Europe in near-real time. These products are derived from the Sentinel-2 Earth observation mission, with a revisit time of 5 d or less (cloud-permitting). Here we show the good accuracy of the snow detection over a wide range of regions in Europe, except in dense forest regions where the snow cover is hidden by the trees.
Xiaodan Wu, Kathrin Naegeli, Valentina Premier, Carlo Marin, Dujuan Ma, Jingping Wang, and Stefan Wunderle
The Cryosphere, 15, 4261–4279, https://doi.org/10.5194/tc-15-4261-2021, https://doi.org/10.5194/tc-15-4261-2021, 2021
Short summary
Short summary
We performed a comprehensive accuracy assessment of an Advanced Very High Resolution Radiometer global area coverage snow-cover extent time series dataset for the Hindu Kush Himalayan (HKH) region. The sensor-to-sensor consistency, the accuracy related to snow depth, elevations, land-cover types, slope, and aspects, and topographical variability were also explored. Our analysis shows an overall accuracy of 94 % in comparison with in situ station data, which is the same with MOD10A1 V006.
Pia Nielsen-Englyst, Jacob L. Høyer, Kristine S. Madsen, Rasmus T. Tonboe, Gorm Dybkjær, and Sotirios Skarpalezos
The Cryosphere, 15, 3035–3057, https://doi.org/10.5194/tc-15-3035-2021, https://doi.org/10.5194/tc-15-3035-2021, 2021
Short summary
Short summary
The Arctic region is responding heavily to climate change, and yet, the air temperature of Arctic ice-covered areas is heavily under-sampled when it comes to in situ measurements. This paper presents a method for estimating daily mean 2 m air temperatures (T2m) in the Arctic from satellite observations of skin temperature, providing spatially detailed observations of the Arctic. The satellite-derived T2m product covers clear-sky snow and ice surfaces in the Arctic for the period 2000–2009.
Pinja Venäläinen, Kari Luojus, Juha Lemmetyinen, Jouni Pulliainen, Mikko Moisander, and Matias Takala
The Cryosphere, 15, 2969–2981, https://doi.org/10.5194/tc-15-2969-2021, https://doi.org/10.5194/tc-15-2969-2021, 2021
Short summary
Short summary
Information about snow water equivalent (SWE) is needed in many applications, including climate model evaluation and forecasting fresh water availability. Space-borne radiometer observations combined with ground snow depth measurements can be used to make global estimates of SWE. In this study, we investigate the possibility of using sparse snow density measurement in satellite-based SWE retrieval and show that using the snow density information in post-processing improves SWE estimations.
Linlu Mei, Vladimir Rozanov, Christine Pohl, Marco Vountas, and John P. Burrows
The Cryosphere, 15, 2757–2780, https://doi.org/10.5194/tc-15-2757-2021, https://doi.org/10.5194/tc-15-2757-2021, 2021
Short summary
Short summary
This paper presents a new snow property retrieval algorithm from satellite observations. This is Part 1 of two companion papers and shows the method description and sensitivity study. The paper investigates the major factors, including the assumptions of snow optical properties, snow particle distribution and atmospheric conditions (cloud and aerosol), impacting snow property retrievals from satellite observation.
Linlu Mei, Vladimir Rozanov, Evelyn Jäkel, Xiao Cheng, Marco Vountas, and John P. Burrows
The Cryosphere, 15, 2781–2802, https://doi.org/10.5194/tc-15-2781-2021, https://doi.org/10.5194/tc-15-2781-2021, 2021
Short summary
Short summary
This paper presents a new snow property retrieval algorithm from satellite observations. This is Part 2 of two companion papers and shows the results and validation. The paper performs the new retrieval algorithm on the Sea and Land
Surface Temperature Radiometer (SLSTR) instrument and compares the retrieved snow properties with ground-based measurements, aircraft measurements and other satellite products.
Ahmad Hojatimalekshah, Zachary Uhlmann, Nancy F. Glenn, Christopher A. Hiemstra, Christopher J. Tennant, Jake D. Graham, Lucas Spaete, Arthur Gelvin, Hans-Peter Marshall, James P. McNamara, and Josh Enterkine
The Cryosphere, 15, 2187–2209, https://doi.org/10.5194/tc-15-2187-2021, https://doi.org/10.5194/tc-15-2187-2021, 2021
Short summary
Short summary
We describe the relationships between snow depth, vegetation canopy, and local-scale processes during the snow accumulation period using terrestrial laser scanning (TLS). In addition to topography and wind, our findings suggest the importance of fine-scale tree structure, species type, and distributions on snow depth. Snow depth increases from the canopy edge toward the open areas, but wind and topographic controls may affect this trend. TLS data are complementary to wide-area lidar surveys.
Jennifer M. Jacobs, Adam G. Hunsaker, Franklin B. Sullivan, Michael Palace, Elizabeth A. Burakowski, Christina Herrick, and Eunsang Cho
The Cryosphere, 15, 1485–1500, https://doi.org/10.5194/tc-15-1485-2021, https://doi.org/10.5194/tc-15-1485-2021, 2021
Short summary
Short summary
This pilot study describes a proof of concept for using lidar on an unpiloted aerial vehicle to map shallow snowpack (< 20 cm) depth in open terrain and forests. The 1 m2 resolution snow depth map, generated by subtracting snow-off from snow-on lidar-derived digital terrain models, consistently had 0.5 to 1 cm precision in the field, with a considerable reduction in accuracy in the forest. Performance depends on the point cloud density and the ground surface variability and vegetation.
Elisabeth D. Hafner, Frank Techel, Silvan Leinss, and Yves Bühler
The Cryosphere, 15, 983–1004, https://doi.org/10.5194/tc-15-983-2021, https://doi.org/10.5194/tc-15-983-2021, 2021
Short summary
Short summary
Satellites prove to be very valuable for documentation of large-scale avalanche periods. To test reliability and completeness, which has not been satisfactorily verified before, we attempt a full validation of avalanches mapped from two optical sensors and one radar sensor. Our results demonstrate the reliability of high-spatial-resolution optical data for avalanche mapping, the suitability of radar for mapping of larger avalanches and the unsuitability of medium-spatial-resolution optical data.
Xiongxin Xiao, Shunlin Liang, Tao He, Daiqiang Wu, Congyuan Pei, and Jianya Gong
The Cryosphere, 15, 835–861, https://doi.org/10.5194/tc-15-835-2021, https://doi.org/10.5194/tc-15-835-2021, 2021
Short summary
Short summary
Daily time series and full space-covered sub-pixel snow cover area data are urgently needed for climate and reanalysis studies. Due to the fact that observations from optical satellite sensors are affected by clouds, this study attempts to capture dynamic characteristics of snow cover at a fine spatiotemporal resolution (daily; 6.25 km) accurately by using passive microwave data. We demonstrate the potential to use the passive microwave and the MODIS data to map the fractional snow cover area.
Marco Bongio, Ali Nadir Arslan, Cemal Melih Tanis, and Carlo De Michele
The Cryosphere, 15, 369–387, https://doi.org/10.5194/tc-15-369-2021, https://doi.org/10.5194/tc-15-369-2021, 2021
Short summary
Short summary
The capability of time-lapse photography to retrieve snow depth time series was tested. We demonstrated that this method can be efficiently used in three different case studies: two in the Italian Alps and one in a forested region of Finland, with an accuracy comparable to the most common methods such as ultrasonic sensors or manual measurements. We hope that this simple method based only on a camera and a graduated stake can enable snow depth measurements in dangerous and inaccessible sites.
Lucie A. Eberhard, Pascal Sirguey, Aubrey Miller, Mauro Marty, Konrad Schindler, Andreas Stoffel, and Yves Bühler
The Cryosphere, 15, 69–94, https://doi.org/10.5194/tc-15-69-2021, https://doi.org/10.5194/tc-15-69-2021, 2021
Short summary
Short summary
In spring 2018 in the alpine Dischma valley (Switzerland), we tested different industrial photogrammetric platforms for snow depth mapping. These platforms were high-resolution satellites, an airplane, unmanned aerial systems and a terrestrial system. Therefore, this study gives a general overview of the accuracy and precision of the different photogrammetric platforms available in space and on earth and their use for snow depth mapping.
Maxim Lamare, Marie Dumont, Ghislain Picard, Fanny Larue, François Tuzet, Clément Delcourt, and Laurent Arnaud
The Cryosphere, 14, 3995–4020, https://doi.org/10.5194/tc-14-3995-2020, https://doi.org/10.5194/tc-14-3995-2020, 2020
Short summary
Short summary
Terrain features found in mountainous regions introduce large errors into the calculation of the physical properties of snow using optical satellite images. We present a new model performing rapid calculations of solar radiation over snow-covered rugged terrain that we tested over a site in the French Alps. The results of the study show that all the interactions between sunlight and the terrain should be accounted for over snow-covered surfaces to correctly estimate snow properties from space.
Tim Carlsen, Gerit Birnbaum, André Ehrlich, Veit Helm, Evelyn Jäkel, Michael Schäfer, and Manfred Wendisch
The Cryosphere, 14, 3959–3978, https://doi.org/10.5194/tc-14-3959-2020, https://doi.org/10.5194/tc-14-3959-2020, 2020
Short summary
Short summary
The angular reflection of solar radiation by snow surfaces is particularly anisotropic and highly variable. We measured the angular reflection from an aircraft using a digital camera in Antarctica in 2013/14 and studied its variability: the anisotropy increases with a lower Sun but decreases for rougher surfaces and larger snow grains. The applied methodology allows for a direct comparison with satellite observations, which generally underestimated the anisotropy measured within this study.
César Deschamps-Berger, Simon Gascoin, Etienne Berthier, Jeffrey Deems, Ethan Gutmann, Amaury Dehecq, David Shean, and Marie Dumont
The Cryosphere, 14, 2925–2940, https://doi.org/10.5194/tc-14-2925-2020, https://doi.org/10.5194/tc-14-2925-2020, 2020
Short summary
Short summary
We evaluate a recent method to map snow depth based on satellite photogrammetry. We compare it with accurate airborne laser-scanning measurements in the Sierra Nevada, USA. We find that satellite data capture the relationship between snow depth and elevation at the catchment scale and also small-scale features like snow drifts and avalanche deposits. We conclude that satellite photogrammetry stands out as a convenient method to estimate the spatial distribution of snow depth in high mountains.
Phillip Harder, John W. Pomeroy, and Warren D. Helgason
The Cryosphere, 14, 1919–1935, https://doi.org/10.5194/tc-14-1919-2020, https://doi.org/10.5194/tc-14-1919-2020, 2020
Short summary
Short summary
Unmanned-aerial-vehicle-based (UAV) structure-from-motion (SfM) techniques have the ability to map snow depths in open areas. Here UAV lidar and SfM are compared to map sub-canopy snowpacks. Snow depth accuracy was assessed with data from sites in western Canada collected in 2019. It is demonstrated that UAV lidar can measure the sub-canopy snow depth at a high accuracy, while UAV-SfM cannot. UAV lidar promises to quantify snow–vegetation interactions at unprecedented accuracy and resolution.
Carlo Marin, Giacomo Bertoldi, Valentina Premier, Mattia Callegari, Christian Brida, Kerstin Hürkamp, Jochen Tschiersch, Marc Zebisch, and Claudia Notarnicola
The Cryosphere, 14, 935–956, https://doi.org/10.5194/tc-14-935-2020, https://doi.org/10.5194/tc-14-935-2020, 2020
Short summary
Short summary
In this paper, we use for the first time the synthetic aperture radar (SAR) time series acquired by Sentinel-1 to monitor snowmelt dynamics in alpine regions. We found that the multitemporal SAR allows the identification of the three phases that characterize the melting process, i.e., moistening, ripening and runoff, in a spatial distributed way. We believe that the presented investigation could have relevant applications for monitoring and predicting the snowmelt progress over large regions.
Cited articles
A2 Photonic WISe,
https://a2photonicsensors.com/wise-sensor-liquid-water-content-snow/ (last access: 15 October 2022),
2021. a
Bales, R. C., Molotch, N. P., Painter, T. H., Dettinger, M. D., Rice, R., and
Dozier, J.: Mountain Hydrology of the Western United States, Water
Resour. Res., 42, W08432, https://doi.org/10.1029/2005WR004387, 2006. a
Balzter, H.: Forest Mapping and Monitoring with Interferometric Synthetic
Aperture Radar (InSAR), Prog. Phys. Geogr., 25, 159–177, https://doi.org/10.1177/030913330102500201, 2001. a
Bekaert, D., Walters, R., Wright, T., Hooper, A., and Parker, D.: Statistical
Comparison of InSAR Tropospheric Correction Techniques, Remote Sens.
Environ., 170, 40–47, https://doi.org/10.1016/j.rse.2015.08.035, 2015. a
Bekaert, D. P., Jones, C. E., An, K., and Huang, M.-H.: Exploiting UAVSAR
for a Comprehensive Analysis of Subsidence in the Sacramento Delta,
Remote Sensing of Environment, 220, 124–134,
https://doi.org/10.1016/j.rse.2018.10.023, 2018. a
Bonnell, R., McGrath, D., Williams, K., Webb, R., Fassnacht, S. R., and
Marshall, H.-P.: Spatiotemporal Variations in Liquid Water Content in
a Seasonal Snowpack: Implications for Radar Remote Sensing,
Remote Sensing, 13, 4223, https://doi.org/10.3390/rs13214223, 2021. a, b
Bradford, J. H., Clement, W. P., and Barrash, W.: Estimating Porosity with
Ground-Penetrating Radar Reflection Tomography: A Controlled 3-D
Experiment at the Boise Hydrogeophysical Research Site, Water Resour.
Res., 45, W00D26, https://doi.org/10.1029/2008WR006960, 2009. a, b
Colesanti, C., Ferretti, A., Prati, C., and Rocca, F.: Monitoring Landslides
and Tectonic Motions with the Permanent Scatterers Technique, Eng.
Geol., 68, 3–14, https://doi.org/10.1016/S0013-7952(02)00195-3, 2003. a
Conde, V., Nico, G., Mateus, P., Catalão, J., Kontu, A., and Gritsevich,
M.: On The Estimation of Temporal Changes of Snow Water
Equivalent by Spaceborne Sar Interferometry: A New Application for
the Sentinel-1 Mission, J. Hydrol. Hydromech., 67,
93–100, https://doi.org/10.2478/johh-2018-0003, 2019. a
Conger, S. M. and McClung, D. M.: Comparison of Density Cutters for Snow
Profile Observations, J. Glaciol., 55, 163–169,
https://doi.org/10.3189/002214309788609038, 2009. a
Currier, W. R., Pflug, J., Mazzotti, G., Jonas, T., Deems, J. S., Bormann,
K. J., Painter, T. H., Hiemstra, C. A., Gelvin, A., Uhlmann, Z., Spaete, L.,
Glenn, N. F., and Lundquist, J. D.: Comparing Aerial Lidar Observations
With Terrestrial Lidar and Snow-Probe Transects From NASA's 2017
SnowEx Campaign, Water Resour. Res., 55, 6285–6294,
https://doi.org/10.1029/2018WR024533, 2019. a
Danklmayer, A., Doring, B., Schwerdt, M., and Chandra, M.: Assessment of
Atmospheric Propagation Effects in SAR Images, IEEE T.
Geosci. Remote, 47, 3507–3518,
https://doi.org/10.1109/TGRS.2009.2022271, 2009. a
Deeb, E. J., Forster, R. R., and Kane, D. L.: Monitoring Snowpack Evolution
Using Interferometric Synthetic Aperture Radar on the North Slope of
Alaska, USA, Int. J. Remote Sens., 32, 3985–4003,
https://doi.org/10.1080/01431161003801351, 2011. a, b, c
Deems, J. S., Fassnacht, S. R., and Elder, K. J.: Fractal Distribution of
Snow Depth from Lidar Data, J. Hydrometeorol., 7, 285–297,
https://doi.org/10.1175/JHM487.1, 2006. a
Derksen, C., Walker, A., LeDrew, E., and Goodison, B.: Time-Series Analysis of
Passive-Microwave-Derived Central North American Snow Water Equivalent
Imagery, Ann. Glaciol., 34, 1–7, https://doi.org/10.3189/172756402781817815,
2002. a
Dozier, J., Painter, T. H., Rittger, K., and Frew, J. E.: Time–Space
Continuity of Daily Maps of Fractional Snow Cover and Albedo from MODIS,
Adv. Water Resour., 31, 1515–1526,
https://doi.org/10.1016/j.advwatres.2008.08.011, 2008. a
Durand, M., Barros, A., Dozier, J., Adler, R., Cooley, S., Entekhabi, D.,
Forman, B. A., Konings, A. G., Kustas, W. P., Lundquist, J. D., Pavelsky,
T. M., Rodell, M., and Steele-Dunne, S.: Achieving Breakthroughs in
Global Hydrologic Science by Unlocking the Power of
Multisensor, Multidisciplinary Earth Observations, AGU Adv., 2,
e2021AV000455, https://doi.org/10.1029/2021AV000455, 2021. a
Eiriksson, D., Whitson, M., Luce, C. H., Marshall, H. P., Bradford, J., Benner,
S. G., Black, T., Hetrick, H., and McNamara, J. P.: An Evaluation of the
Hydrologic Relevance of Lateral Flow in Snow at Hillslope and Catchment
Scales: LATERAL FLOW IN SNOW, Hydrol. Process., 27, 640–654,
https://doi.org/10.1002/hyp.9666, 2013. a
Eppler, J., Rabus, B., and Morse, P.: Snow Water Equivalent Change Mapping from
Slope-Correlated Synthetic Aperture Radar Interferometry (InSAR) Phase
Variations, The Cryosphere, 16, 1497–1521, https://doi.org/10.5194/tc-16-1497-2022,
2022. a, b, c
Evans, S. L., Flores, A. N., Heilig, A., Kohn, M. J., Marshall, H.-P., and
McNamara, J. P.: Isotopic Evidence for Lateral Flow and Diffusive Transport,
but Not Sublimation, in a Sloped Seasonal Snowpack, Idaho, USA,
Geophys. Res. Lett., 43, 3298–3306, https://doi.org/10.1002/2015GL067605,
2016. a
Ferretti, A., Prati, C., and Rocca, F.: Permanent Scatterers in SAR
Interferometry, IEEE T. Geosci. Remote, 39,
8–20, https://doi.org/10.1109/36.898661, 2001. a
Foster, J. L., Sun, C., Walker, J. P., Kelly, R., Chang, A., Dong, J., and
Powell, H.: Quantifying the Uncertainty in Passive Microwave Snow Water
Equivalent Observations, Remote Sens. Environ., 94, 187–203,
https://doi.org/10.1016/j.rse.2004.09.012, 2005. a
Funning, G. J., Parsons, B., Wright, T. J., Jackson, J. A., and Fielding,
E. J.: Surface Displacements and Source Parameters of the 2003 Bam
(Iran) Earthquake from Envisat Advanced Synthetic Aperture Radar
Imagery, J. Geophys. Res.-Sol. Ea., 110, B09406,
https://doi.org/10.1029/2004JB003338, 2005. a
Goldstein, R. M. and Zebker, H. A.: Interferometric Radar Measurement of Ocean
Surface Currents, Nature, 328, 707–709, https://doi.org/10.1038/328707a0, 1987. a
Goldstein, R. M., Zebker, H. A., and Werner, C. L.: Satellite Radar
Interferometry: Two-dimensional Phase Unwrapping, Radio Sci., 23,
713–720, https://doi.org/10.1029/RS023i004p00713, 1988. a
Gubler, H. and Hiller, M.: The Use of Microwave FMCW Radar in Snow and
Avalanche Research, Cold Reg. Sci. Technol., 9, 109–119,
https://doi.org/10.1016/0165-232X(84)90003-X, 1984. a
Guneriussen, T., Hogda, K., Johnsen, H., and Lauknes, I.: InSAR for
Estimation of Changes in Snow Water Equivalent of Dry Snow, IEEE T.
Geosci. Remote, 39, 2101–2108, https://doi.org/10.1109/36.957273,
2001. a, b, c, d
Harpold, A., Molotch, N. P., Musselman, K. N., Bales, R. C., Kirchner, P. B.,
Litvak, M., and Brooks, P. D.: Soil Moisture Response to Snowmelt Timing in
Mixed-Conifer Subalpine Forests, Hydrol. Process., 29, 2782–2798,
https://doi.org/10.1002/hyp.10400, 2015. a
Heilig, A., Mitterer, C., Schmid, L., Wever, N., Schweizer, J., Marshall,
H.-P., and Eisen, O.: Seasonal and Diurnal Cycles of Liquid Water in
Snow – Measurements and Modeling, J. Geophys.
Res.-Earth, 120, 2139–2154, https://doi.org/10.1002/2015JF003593, 2015. a
Hensley, S., Wheeler, K., Sadowy, G., Jones, C., Shaffer, S., Zebker, H.,
Miller, T., Heavey, B., Chuang, E., Chao, R., Vines, K., Nishimoto, K.,
Prater, J., Carrico, B., Chamberlain, N., Shimada, J., Simard, M., Chapman,
B., Muellerschoen, R., Le, C., Michel, T., Hamilton, G., Robison, D.,
Neumann, G., Meyer, R., Smith, P., Granger, J., Rosen, P., Flower, D., and
Smith, R.: The UAVSAR Instrument: Description and First Results, in:
2008 IEEE Radar Conference,IEEE, Rome, Italy, 1–6,
https://doi.org/10.1109/RADAR.2008.4720722, 2008. a, b
Holbrook, W. S., Miller, S. N., and Provart, M. A.: Estimating Snow Water
Equivalent over Long Mountain Transects Using Snowmobile-Mounted
Ground-Penetrating Radar, Geophysics, 81, WA183–WA193,
https://doi.org/10.1190/geo2015-0121.1, 2016. a
Johnson, M. and Sandusky, M.: SnowEx/snowex_db: SnowEx Hackweek 2022 release (Version hackweek2022), Zenodo [code], https://doi.org/10.5281/zenodo.7618107, 2023. a, b
Keskinen, Z., Tarricone, J., Adebisi, N., and Marshall, H. P.: SnowEx/uavsar_pytools: Slant Range Image Conversion (v0.7.0), Zenodo [code and data set], https://doi.org/10.5281/zenodo.6578192, 2022. a, b
King, J., Derksen, C., Toose, P., Langlois, A., Larsen, C., Lemmetyinen, J.,
Marsh, P., Montpetit, B., Roy, A., Rutter, N., and Sturm, M.: The Influence
of Snow Microstructure on Dual-Frequency Radar Measurements in a Tundra
Environment, Remote Sens. Enviro., 215, 242–254,
https://doi.org/10.1016/j.rse.2018.05.028, 2018. a
Lettenmaier, D. P., Alsdorf, D., Dozier, J., Huffman, G. J., Pan, M., and Wood,
E. F.: Inroads of Remote Sensing into Hydrologic Science during the WRR
Era, Water Resour. Res., 51, 7309–7342, https://doi.org/10.1002/2015WR017616,
2015. a
Lewis, G., Osterberg, E., Hawley, R., Whitmore, B., Marshall, H. P., and Box, J.: Regional Greenland accumulation variability from Operation IceBridge airborne accumulation radar, The Cryosphere, 11, 773–788, https://doi.org/10.5194/tc-11-773-2017, 2017. a
Li, D., Wrzesien, M. L., Durand, M., Adam, J., and Lettenmaier, D. P.: How Much
Runoff Originates as Snow in the Western United States, and How Will That
Change in the Future?: Western U.S. Snowmelt-Derived Runoff,
Geophys. Res. Lett., 44, 6163–6172, https://doi.org/10.1002/2017GL073551,
2017. a
Li, H., Wang, Z., He, G., and Man, W.: Estimating Snow Depth and Snow
Water Equivalence Using Repeat-Pass Interferometric SAR in the Northern
Piedmont Region of the Tianshan Mountains, J. Sensors, 2017,
1–17, https://doi.org/10.1155/2017/8739598, 2017. a
Li, Z., Fielding, E. J., Cross, P., and Muller, J.-P.: Interferometric
Synthetic Aperture Radar Atmospheric Correction: GPS Topography-Dependent
Turbulence Model: INTEGRATION OF GPS AND INSAR, J. Geophys.
Res.-Sol. Ea., 111, B02404, https://doi.org/10.1029/2005JB003711, 2006. a
Li, Z., Fielding, E. J., Cross, P., and Preusker, R.: Advanced InSAR
Atmospheric Correction: MERIS/MODIS Combination and Stacked Water
Vapour Models, Int. J. Remote Sens., 30, 3343–3363,
https://doi.org/10.1080/01431160802562172, 2009. a
Lievens, H., Demuzere, M., Marshall, H.-P., Reichle, R. H., Brucker, L.,
Brangers, I., de Rosnay, P., Dumont, M., Girotto, M., Immerzeel, W. W.,
Jonas, T., Kim, E. J., Koch, I., Marty, C., Saloranta, T., Schöber, J.,
and De Lannoy, G. J. M.: Snow Depth Variability in the Northern
Hemisphere Mountains Observed from Space, Nat. Commun., 10, 4629,
https://doi.org/10.1038/s41467-019-12566-y, 2019. a
Lievens, H., Brangers, I., Marshall, H.-P., Jonas, T., Olefs, M., and De Lannoy, G.: Sentinel-1 snow depth retrieval at sub-kilometer resolution over the European Alps, The Cryosphere, 16, 159–177, https://doi.org/10.5194/tc-16-159-2022, 2022. a
Liston, G. E. and Elder, K.: A Distributed Snow-Evolution Modeling System
(SnowModel), J. Hydrometeorol., 7, 1259–1276,
https://doi.org/10.1175/JHM548.1, 2006. a
Liu, S., Hanssen, R., and Mika, Á.: On the Value of High-Resolution Weather
Models for Atmospheric Mitigation in SAR Interferometry, in: 2009 IEEE
International Geoscience and Remote Sensing Symposium, 2,
II–749–II–752, https://doi.org/10.1109/IGARSS.2009.5418199, 2009. a
Lund, J., Forster, R. R., Rupper, S. B., Deeb, E. J., Marshall, H. P., Hashmi,
M. Z., and Burgess, E.: Mapping Snowmelt Progression in the Upper Indus
Basin With Synthetic Aperture Radar, Front. Earth Sci., 7, 318,
https://doi.org/10.3389/feart.2019.00318, 2020. a
Marks, D., Domingo, J., Susong, D., Link, T., and Garen, D.: A Spatially
Distributed Energy Balance Snowmelt Model for Application in Mountain Basins,
Hydrol. Process., 13, 1935–1959,
https://doi.org/10.1002/(SICI)1099-1085(199909)13:12/13<1935::AID-HYP868>3.0.CO;2-C,
1999. a
Marshall, H., Vuyovich, C., Hiemstra, C., Brucker, L., Elder, K., Deems, J.,
and Newlin, J.: NASA SnowEx 2020 Experiment Plan, Tech. rep.,
https://snow.nasa.gov/sites/default/files/NASA_SnowEx_Experiment_Plan_v15_draft.pdf (last access: 5 February 2023),
2019. a
Marshall, H., Deeb, E., Forster, R., Vuyovich, C., Elder, K., Hiemstra, C., and
Lund, J.: L-Band InSAR Depth Retrieval During the NASA SnowEx 2020
Campaign: Grand Mesa, Colorado, in: 2021 IEEE International
Geoscience and Remote Sensing Symposium IGARSS, 625–627,
https://doi.org/10.1109/IGARSS47720.2021.9553852, 2021. a, b, c
Marshall, H.-P. and Koh, G.: FMCW Radars for Snow Research, Cold Reg.
Sci. Technol., 52, 118–131,
https://doi.org/10.1016/j.coldregions.2007.04.008, 2008. a
Marshall, H.-P., Koh, G., and Forster, R. R.: Estimating Alpine Snowpack
Properties Using FMCW Radar, Ann. Glaciol., 40, 157–162,
https://doi.org/10.3189/172756405781813500, 2005. a, b, c
McGrath, D., Sass, L., O'Neel, S., McNeil, C., Candela, S. G., Baker, E. H., and Marshall, H.-P.: Interannual snow accumulation variability on glaciers derived from repeat, spatially extensive ground-penetrating radar surveys, The Cryosphere, 12, 3617–3633, https://doi.org/10.5194/tc-12-3617-2018, 2018. a
McGrath, D., Webb, R., Shean, D., Bonnell, R., Marshall, H.-P., Painter, T. H.,
Molotch, N. P., Elder, K., Hiemstra, C., and Brucker, L.: Spatially
Extensive Ground-Penetrating Radar Snow Depth Observations During NASA's
2017 SnowEx Campaign: Comparison With In Situ, Airborne, and
Satellite Observations, Water Resour. Res., 55, 10026–10036,
https://doi.org/10.1029/2019WR024907, 2019. a, b
Meyer, F. J.: Performance Requirements for Ionospheric Correction of
Low-Frequency SAR Data, IEEE T. Geosci. Remote, 49, 3694–3702, https://doi.org/10.1109/TGRS.2011.2146786, 2011. a
Michaelides, R. J., Chen, R. H., Zhao, Y., Schaefer, K., Parsekian, A. D.,
Sullivan, T., Moghaddam, M., Zebker, H. A., Liu, L., Xu, X., and Chen, J.:
Permafrost Dynamics Observatory – Part I: Postprocessing
and Calibration Methods of UAVSAR L-Band InSAR Data for
Seasonal Subsidence Estimation, Earth Space Sci., 8, e2020EA001630,
https://doi.org/10.1029/2020EA001630, 2021. a
Milly, P. C. D., Betancourt, J., Falkenmark, M., Hirsch, R. M., Kundzewicz,
Z. W., Lettenmaier, D. P., and Stouffer, R. J.: Stationarity Is Dead:
Whither Water Management?, Science, 319, 573–574,
https://doi.org/10.1126/science.1151915, 2008. a
Molotch, N. P., Brooks, P. D., Burns, S. P., Litvak, M., Monson, R. K.,
McConnell, J. R., and Musselman, K.: Ecohydrological Controls on Snowmelt
Partitioning in Mixed-Conifer Sub-Alpine Forests, Ecohydrology, 2, 129–142,
https://doi.org/10.1002/eco.48, 2009. a
Mote, P. W., Li, S., Lettenmaier, D. P., Xiao, M., and Engel, R.: Dramatic
Declines in Snowpack in the Western US, npj Climate and Atmospheric
Science, 1, 2, https://doi.org/10.1038/s41612-018-0012-1, 2018. a
Mouginot, J., Scheuchl, B., and Rignot, E.: Mapping of Ice Motion in Antarctica Using Synthetic-Aperture Radar Data, Remote Sens., 4, 2753–2767, https://doi.org/10.3390/rs4092753, 2012. a
Musselman, K. N., Molotch, N. P., and Brooks, P. D.: Effects of Vegetation on
Snow Accumulation and Ablation in a Mid-Latitude Sub-Alpine Forest,
Hydrol. Process., 22, 2767–2776, https://doi.org/10.1002/hyp.7050, 2008. a, b
Nagler, T. and Rott, H.: Retrieval of Wet Snow by Means of Multitemporal
SAR Data, IEEE T. Geosci. Remote, 38,
754–765, https://doi.org/10.1109/36.842004, 2000. a
Nagler, T., Rott, H., Ripper, E., Bippus, G., and Hetzenecker, M.: Advancements
for Snowmelt Monitoring by Means of Sentinel-1 SAR, Remote
Sensing, 8, 348, https://doi.org/10.3390/rs8040348, 2016. a
Nagler, T., Rott, H., Scheiblauer, S., Libert, L., Mölg, N., Horn, R.,
Fischer, J., Keller, M., Moreira, A., and Kubanek, J.: Airborne
Experiment on Insar Snow Mass Retrieval in Alpine Environment,
in: IGARSS 2022 – 2022 IEEE International Geoscience and Remote
Sensing Symposium, 4549–4552, https://doi.org/10.1109/IGARSS46834.2022.9883809,
2022. a
NASA/JPL-Caltech: UAVSAR Data Search, NASA/JPL-Caltech [data set], https://uavsar.jpl.nasa.gov/cgi-bin/data.pl, last access: 6 March 2023. a
Nolin, A., Dozier, J., and Mertes, L.: Mapping Alpine Snow Using a Spectral
Mixture Modeling Technique, Ann. Glaciol., 17, 121–124,
https://doi.org/10.3189/S0260305500012702, 1993. a
OpenTopography: Jemez River Basin Snow-off LiDAR Survey, OpenTopography [data set],
https://doi.org/10.5069/G9RB72JV, 2012. a, b
Painter, T. H., Berisford, D. F., Boardman, J. W., Bormann, K. J., Deems,
J. S., Gehrke, F., Hedrick, A., Joyce, M., Laidlaw, R., Marks, D., Mattmann,
C., McGurk, B., Ramirez, P., Richardson, M., Skiles, S. M., Seidel, F. C.,
and Winstral, A.: The Airborne Snow Observatory: Fusion of Scanning
Lidar, Imaging Spectrometer, and Physically-Based Modeling for Mapping Snow
Water Equivalent and Snow Albedo, Remote Sens. Environ., 184,
139–152, https://doi.org/10.1016/j.rse.2016.06.018, 2016. a
Poland, M. P. and Zebker, H. A.: Volcano Geodesy Using InSAR in 2020: The
Past and next Decades, B. Volcanol., 84, 27,
https://doi.org/10.1007/s00445-022-01531-1, 2022. a
Proksch, M., Rutter, N., Fierz, C., and Schneebeli, M.: Intercomparison of snow density measurements: bias, precision, and vertical resolution, The Cryosphere, 10, 371–384, https://doi.org/10.5194/tc-10-371-2016, 2016. a
Raleigh, M. S. and Small, E. E.: Snowpack Density Modeling Is the Primary
Source of Uncertainty When Mapping Basin-Wide SWE with Lidar:
Uncertainties in SWE Mapping With Lidar, Geophys. Res.
Lett., 44, 3700–3709, https://doi.org/10.1002/2016GL071999, 2017. a
Rango, A., Chang, A. T. C., and Foster, J. L.: The Utilization of
Spaceborne Microwave Radiometers for Monitoring Snowpack Properties,
Hydrol. Res., 10, 25–40, https://doi.org/10.2166/nh.1979.0003, 1979. a
Rittger, K., Krock, M., Kleiber, W., Bair, E. H., Brodzik, M. J., Stephenson,
T. R., Rajagopalan, B., Bormann, K. J., and Painter, T. H.: Multi-Sensor
Fusion Using Random Forests for Daily Fractional Snow Cover at 30 m, Remote
Sens. Environ., 264, 112608, https://doi.org/10.1016/j.rse.2021.112608, 2021. a
Rizzoli, P., Martone, M., Gonzalez, C., Wecklich, C., Borla Tridon, D.,
Bräutigam, B., Bachmann, M., Schulze, D., Fritz, T., Huber, M., Wessel,
B., Krieger, G., Zink, M., and Moreira, A.: Generation and Performance
Assessment of the Global TanDEM-X Digital Elevation Model, ISPRS J. Photogramm. Remote, 132, 119–139,
https://doi.org/10.1016/j.isprsjprs.2017.08.008, 2017. a
Rodríguez, E., Morris, C. S., and Belz, J. E.: A Global Assessment of
the SRTM Performance, Photogramm. Eng. Remote Sens., 72,
249–260, https://doi.org/10.14358/PERS.72.3.249, 2006. a
Rosen, P., Hensley, S., Joughin, I., Li, F., Madsen, S., Rodriguez, E., and
Goldstein, R.: Synthetic Aperture Radar Interferometry, P.
IEEE, 88, 333–382, https://doi.org/10.1109/5.838084, 2000. a
Rosen, P., Hensley, S., Wheeler, K., Sadowy, G., Miller, T., Shaffer, S.,
Muellerschoen, R., Jones, C., Zebker, H., and Madsen, S.: UAVSAR: A New
NASA Airborne SAR System for Science and Technology Research, in:
2006 IEEE Conference on Radar, 22–29, IEEE, Syracuse, NY,
USA, https://doi.org/10.1109/RADAR.2006.1631770, 2006. a
Rosen, P., Hensley, S., Shaffer, S., Edelstein, W., Kim, Y., Kumar, R., Misra,
T., Bhan, R., and Sagi, R.: The NASA-ISRO SAR (NISAR) Mission
Dual-Band Radar Instrument Preliminary Design, in: 2017 IEEE International
Geoscience and Remote Sensing Symposium (IGARSS), 3832–3835,
https://doi.org/10.1109/IGARSS.2017.8127836, 2017. a
Rott, H., Yueh, S. H., Cline, D. W., Duguay, C., Essery, R., Haas, C.,
Hélière, F., Kern, M., Macelloni, G., Malnes, E., Nagler, T.,
Pulliainen, J., Rebhan, H., and Thompson, A.: Cold Regions Hydrology
High-Resolution Observatory for Snow and Cold Land Processes,
Proc. IEEE, 98, 752–765, https://doi.org/10.1109/JPROC.2009.2038947,
2010. a
Rutter, N., Sandells, M. J., Derksen, C., King, J., Toose, P., Wake, L., Watts, T., Essery, R., Roy, A., Royer, A., Marsh, P., Larsen, C., and Sturm, M.: Effect of snow microstructure variability on Ku-band radar snow water equivalent retrievals, The Cryosphere, 13, 3045–3059, https://doi.org/10.5194/tc-13-3045-2019, 2019. a
Ryan, W. A., Doesken, N. J., and Fassnacht, S. R.: Evaluation of Ultrasonic
Snow Depth Sensors for U.S. Snow Measurements, J.
Atmos. Ocean. Tech., 25, 667–684,
https://doi.org/10.1175/2007JTECHA947.1, 2008. a
Sandmeier, K.-J.: REFLEXW,
https://www.sandmeier-geo.de/reflexw.html (last access: 5 March 2022), 2022. a
Selkowitz, D. J., Painter, T. H., Rittger, K. E., Schmidt, G., and Forster, R.: The USGS Landsat Snow Covered Area Products: Methods and Preliminary Validation.” Automated Approaches for Snow and Ice Cover Monitoring Using Optical Remote Sensing, edited by: Selkowitz, D., Salt Lake City, UT, The University of Utah, 76–119, https://www.researchgate.net/publication/331024289_The_USGS_Lands at_Snow_Covered_Area_Products_Methods_and_Preliminary_Validation_ (last access: 16 March 2020), 2017. a, b
Shi, J. and Dozier, J.: Estimation of Snow Water Equivalence Using
SIR-C/X-SAR. II. Inferring Snow Depth and Particle Size, IEEE
T. Geosci. Remote, 38, 2475–2488,
https://doi.org/10.1109/36.885196, 2000. a
Siirila-Woodburn, E. R., Rhoades, A. M., Hatchett, B. J., Huning, L. S.,
Szinai, J., Tague, C., Nico, P. S., Feldman, D. R., Jones, A. D., Collins,
W. D., and Kaatz, L.: A Low-to-No Snow Future and Its Impacts on Water
Resources in the Western United States, Nat. Rev. Earth
Environ., 2, 800–819, https://doi.org/10.1038/s43017-021-00219-y, 2021. a
Stewart, I. T., Cayan, D. R., and Dettinger, M. D.: Changes in Snowmelt
Runoff Timing in Western North America under a “Business as
Usual” Climate Change Scenario, Clim. Change, 62, 217–232,
https://doi.org/10.1023/B:CLIM.0000013702.22656.e8, 2004. a
Stillinger, T., Rittger, K., Raleigh, M. S., Michell, A., Davis, R. E., and Bair, E. H.: Landsat, MODIS, and VIIRS snow cover mapping algorithm performance as validated by airborne lidar datasets, The Cryosphere, 17, 567–590, https://doi.org/10.5194/tc-17-567-2023, 2023. a
Sun, G., Ranson, K., Kharuk, V., and Kovacs, K.: Validation of Surface Height
from Shuttle Radar Topography Mission Using Shuttle Laser Altimeter, Remote
Sens. Environ., 88, 401–411, https://doi.org/10.1016/j.rse.2003.09.001, 2003. a
Tarricone, J.: Estimating snow accumulation and ablation with L-band InSAR: Code and data for analysis
and figure creation (0.0.4), Zenodo [code and data set], https://doi.org/10.5281/zenodo.7754560, 2023. a, b
Trujillo, E., Ramírez, J. A., and Elder, K. J.: Topographic, Meteorologic,
and Canopy Controls on the Scaling Characteristics of the Spatial
Distribution of Snow Depth Fields, Water Resour. Res., 43, W07409,
https://doi.org/10.1029/2006WR005317, 2007. a
Tsai, Y.-L. S., Dietz, A., Oppelt, N., and Kuenzer, C.: Remote Sensing of
Snow Cover Using Spaceborne SAR: A Review, Remote Sens., 11, 1456,
https://doi.org/10.3390/rs11121456, 2019. a
Tsang, L., Durand, M., Derksen, C., Barros, A. P., Kang, D.-H., Lievens, H., Marshall, H.-P., Zhu, J., Johnson, J., King, J., Lemmetyinen, J., Sandells, M., Rutter, N., Siqueira, P., Nolin, A., Osmanoglu, B., Vuyovich, C., Kim, E., Taylor, D., Merkouriadi, I., Brucker, L., Navari, M., Dumont, M., Kelly, R., Kim, R. S., Liao, T.-H., Borah, F., and Xu, X.: Review article: Global monitoring of snow water equivalent using high-frequency radar remote sensing, The Cryosphere, 16, 3531–3573, https://doi.org/10.5194/tc-16-3531-2022, 2022. a
Ulaby, F. T., Stiles, W. H., and Abdelrazik, M.: Snowcover Influence on
Backscattering from Terrain, IEEE T. Geosci.
Remote, GE-22, 126–133, https://doi.org/10.1109/TGRS.1984.350604, 1984. a
U.S. Geological Survey, E. R. O. and Center, S.: Collection-1 Landsat Level-3
Fractional Snow Covered Area (FSCA) Science Product, U.S. Geological Survey [data set],
https://doi.org/10.5066/F7XK8DS5, 2018. a, b
Vuyovich, C. M., Jacobs, J. M., and Daly, S. F.: Comparison of Passive
Microwave and Modeled Estimates of Total Watershed SWE in the Continental
United States, Water Resour. Re., 50, 9088–9102,
https://doi.org/10.1002/2013WR014734, 2014. a
Webb, R.: SnowEx20 Jemez UNM 800 MHz MALA GPR, Version 1, Boulder, Colorado USA, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/H38Q5FTBPZ8K, 2021. a, b
Webb, R. W.: Using Ground Penetrating Radar to Assess the Variability of Snow
Water Equivalent and Melt in a Mixed Canopy Forest, Northern Colorado,
Front. Earth Sci., 11, 482–495, https://doi.org/10.1007/s11707-017-0645-0,
2017. a
Webb, R. W., Jennings, K. S., Fend, M., and Molotch, N. P.: Combining
Ground-Penetrating Radar With Terrestrial LiDAR Scanning to
Estimate the Spatial Distribution of Liquid Water Content in
Seasonal Snowpacks, Water Resour. Res., 54, 10339–10349,
https://doi.org/10.1029/2018WR022680, 2018. a, b
Webb, R. W., Jennings, K., Finsterle, S., and Fassnacht, S. R.: Two-dimensional liquid water flow through snow at the plot scale in continental snowpacks: simulations and field data comparisons, The Cryosphere, 15, 1423–1434, https://doi.org/10.5194/tc-15-1423-2021, 2021a. a
Webb, R. W., Marziliano, A., McGrath, D., Bonnell, R., Meehan, T. G., Vuyovich,
C., and Marshall, H.-P.: In Situ Determination of Dry and Wet Snow
Permittivity: Improving Equations for Low Frequency Radar
Applications, Remote Sens., 13, 4617, https://doi.org/10.3390/rs13224617,
2021b.
a, b, c
Western Regional Climate Center (WRCC): Valles Caldera National Preserve Climate Stations, WRCC [data set], https://wrcc.dri.edu/vallescaldera/, last access: 8 July 2022. a
Yu, C., Li, Z., and Penna, N. T.: Interferometric Synthetic Aperture Radar
Atmospheric Correction Using a GPS-based Iterative Tropospheric
Decomposition Model, Remote Sens. Environ., 204, 109–121,
https://doi.org/10.1016/j.rse.2017.10.038, 2018. a
Yueh, S. H., Dinardo, S. J., Akgiray, A., West, R., Cline, D. W., and Elder,
K.: Airborne Ku-Band Polarimetric Radar Remote Sensing of Terrestrial
Snow Cover, IEEE T. Geosci. Remote, 47,
3347–3364, https://doi.org/10.1109/TGRS.2009.2022945, 2009. a
Yueh, S. H., Shah, R., Xu, X., Stiles, B., and Bosch-Lluis, X.: A Satellite
Synthetic Aperture Radar Concept Using P-Band Signals of Opportunity,
IEEE J. Sel. Top. Appl. Earth Obs., 14, 2796–2816, https://doi.org/10.1109/JSTARS.2021.3059242, 2021. a
Zebker, H. A. and Goldstein, R. M.: Topographic Mapping from Interferometric
Synthetic Aperture Radar Observations, J. Geophys. Res.-Sol.
Ea., 91, 4993–4999, https://doi.org/10.1029/JB091iB05p04993, 1986. a
Zebker, H. A., Rosen, P. A., and Hensley, S.: Atmospheric Effects in
Interferometric Synthetic Aperture Radar Surface Deformation and Topographic
Maps, J. Geophys. Res.-Sol. Ea., 102, 7547–7563,
https://doi.org/10.1029/96JB03804, 1997. a, b
Zhu, J., Tan, S., Tsang, L., Kang, D.-H., and Kim, E.: Snow Water Equivalent
Retrieval Using Active and Passive Microwave Observations, Water
Resour. Res., 57, e2020WR027563, https://doi.org/10.1029/2020WR027563, 2021. a
Short summary
Mountain snowmelt provides water for billions of people across the globe. Despite its importance, we cannot currently measure the amount of water in mountain snowpacks from satellites. In this research, we test the ability of an experimental snow remote sensing technique from an airplane in preparation for the same sensor being launched on a future NASA satellite. We found that the method worked better than expected for estimating important snowpack properties.
Mountain snowmelt provides water for billions of people across the globe. Despite its...
