Articles | Volume 18, issue 1
https://doi.org/10.5194/tc-18-451-2024
© Author(s) 2024. 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-18-451-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
31 Jan 2024
Research article |
| 31 Jan 2024
| 31 Jan 2024
Passive microwave remote-sensing-based high-resolution snow depth mapping for Western Himalayan zones using multifactor modeling approach
Dhiraj Kumar Singh
Hydro-Remote Sensing Applications(H-RSA) Group, Department of Civil Engineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India
Kalpana Chawla Centre for Research in Space Science & Technology (KCCRST), Chandigarh University, Mohali 140413, India
Srinivasarao Tanniru
Hydro-Remote Sensing Applications(H-RSA) Group, Department of Civil Engineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India
Kamal Kant Singh
Defence Geoinformatics Research Establishment, Him Parisar, Sector 37A, Chandigarh 160036, India
Harendra Singh Negi
Defence Geoinformatics Research Establishment, Him Parisar, Sector 37A, Chandigarh 160036, India
RAAJ Ramsankaran
CORRESPONDING AUTHOR
Hydro-Remote Sensing Applications(H-RSA) Group, Department of Civil Engineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India
Interdisciplinary Program in Climate Studies, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India
Related authors
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Sanjay Saifi and RAAJ Ramsankaran
Proc. IAHS, 387, 73–77, https://doi.org/10.5194/piahs-387-73-2024, https://doi.org/10.5194/piahs-387-73-2024, 2024
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Visibility assessment is crucial for informed decision-making and disaster preparedness in mountainous regions due to snow-induced disasters. This paper presents an innovative approach using augmented reality (AR) to address this challenge. The Him-Drishti application harnesses the established correlation between snowfall intensity and visibility to create a predictive visibility simulation model. This study demonstrates the capacity of AR in disaster management and risk reduction.
Imtiyaz Ahmad Bhat, Irfan Rashid, RAAJ Ramsankaran, Argha Banerjee, and Saurabh Vijay
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-522, https://doi.org/10.5194/essd-2023-522, 2024
Preprint withdrawn
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A comprehensive rock glacier inventory (n = 5492) has been generated through manual delineation in a GIS environment for the western Himalayan region. The inventory has characterized each rock glacier with 22 attributes following the standard protocols. This inventory shall serve as a baseline for the future research related to rock glacier dynamics, their hydrological contribution and response to climate change.
Vasaw Tripathi, Andreas Groh, Martin Horwath, and Raaj Ramsankaran
Hydrol. Earth Syst. Sci., 26, 4515–4535, https://doi.org/10.5194/hess-26-4515-2022, https://doi.org/10.5194/hess-26-4515-2022, 2022
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GRACE/GRACE-FO provided global observations of water storage change since 2002. Scaling is a common approach to compensate for the spatial filtering inherent to the results. However, for complex hydrological basins, the compatibility of scaling with the characteristics of regional hydrology has been rarely assessed. We assess traditional scaling approaches and a new scaling approach for the Indus Basin. Our results will help users with regional focus understand implications of scaling choices.
P. Verma, S. K. Ghosh, and R. Ramsankaran
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2021, 197–202, https://doi.org/10.5194/isprs-annals-V-3-2021-197-2021, https://doi.org/10.5194/isprs-annals-V-3-2021-197-2021, 2021
Daniel Farinotti, Douglas J. Brinkerhoff, Garry K. C. Clarke, Johannes J. Fürst, Holger Frey, Prateek Gantayat, Fabien Gillet-Chaulet, Claire Girard, Matthias Huss, Paul W. Leclercq, Andreas Linsbauer, Horst Machguth, Carlos Martin, Fabien Maussion, Mathieu Morlighem, Cyrille Mosbeux, Ankur Pandit, Andrea Portmann, Antoine Rabatel, RAAJ Ramsankaran, Thomas J. Reerink, Olivier Sanchez, Peter A. Stentoft, Sangita Singh Kumari, Ward J. J. van Pelt, Brian Anderson, Toby Benham, Daniel Binder, Julian A. Dowdeswell, Andrea Fischer, Kay Helfricht, Stanislav Kutuzov, Ivan Lavrentiev, Robert McNabb, G. Hilmar Gudmundsson, Huilin Li, and Liss M. Andreassen
The Cryosphere, 11, 949–970, https://doi.org/10.5194/tc-11-949-2017, https://doi.org/10.5194/tc-11-949-2017, 2017
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ITMIX – the Ice Thickness Models Intercomparison eXperiment – was the first coordinated performance assessment for models inferring glacier ice thickness from surface characteristics. Considering 17 different models and 21 different test cases, we show that although solutions of individual models can differ considerably, an ensemble average can yield uncertainties in the order of 10 ± 24 % the mean ice thickness. Ways forward for improving such estimates are sketched.
Related subject area
Discipline: Snow | Subject: Remote Sensing
Improved snow property retrievals by solving for topography in the inversion of at-sensor radiance measurements
Simulation of Arctic snow microwave emission in surface-sensitive atmosphere channels
Retrieval of snow and soil properties for forward radiative transfer modeling of airborne Ku-band SAR to estimate snow water equivalent: the Trail Valley Creek 2018/19 snow experiment
Evaluating L-band InSAR snow water equivalent retrievals with repeat ground-penetrating radar and terrestrial lidar surveys in northern Colorado
Reanalyzing the spatial representativeness of snow depth at automated monitoring stations using airborne lidar data
Tower-based C-band radar measurements of an alpine snowpack
Mapping surface hoar from near-infrared texture in a laboratory
Thermal infrared shadow-hiding in GOES-R ABI imagery: snow and forest temperature observations from the SnowEx 2020 Grand Mesa field campaign
Evaluating Snow Depth Retrievals from Sentinel-1 Volume Scattering over NASA SnowEx Sites
Temperature-dominated spatiotemporal variability in snow phenology on the Tibetan Plateau from 2002 to 2022
Temporal stability of a new 40-year daily AVHRR Land Surface Temperature dataset for the Pan-Arctic region
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
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
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
Estimating snow accumulation and ablation with L-band interferometric synthetic aperture radar (InSAR)
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
Brenton A. Wilder, Joachim Meyer, Josh Enterkine, and Nancy F. Glenn
The Cryosphere, 18, 5015–5029, https://doi.org/10.5194/tc-18-5015-2024, https://doi.org/10.5194/tc-18-5015-2024, 2024
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Remotely sensed properties of snow are dependent on accurate terrain information, which for a lot of the cryosphere and seasonal snow zones is often insufficient in accuracy. However, as we show in this paper, we can bypass this issue by optimally solving for the terrain by utilizing the raw radiance data returned to the sensor. This method performed well when compared to validation datasets and has the potential to be used across a variety of different snow climates.
Melody Sandells, Nick Rutter, Kirsty Wivell, Richard Essery, Stuart Fox, Chawn Harlow, Ghislain Picard, Alexandre Roy, Alain Royer, and Peter Toose
The Cryosphere, 18, 3971–3990, https://doi.org/10.5194/tc-18-3971-2024, https://doi.org/10.5194/tc-18-3971-2024, 2024
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Satellite microwave observations are used for weather forecasting. In Arctic regions this is complicated by natural emission from snow. By simulating airborne observations from in situ measurements of snow, this study shows how snow properties affect the signal within the atmosphere. Fresh snowfall between flights changed airborne measurements. Good knowledge of snow layering and structure can be used to account for the effects of snow and could unlock these data to improve forecasts.
Benoit Montpetit, Joshua King, Julien Meloche, Chris Derksen, Paul Siqueira, J. Max Adam, Peter Toose, Mike Brady, Anna Wendleder, Vincent Vionnet, and Nicolas R. Leroux
The Cryosphere, 18, 3857–3874, https://doi.org/10.5194/tc-18-3857-2024, https://doi.org/10.5194/tc-18-3857-2024, 2024
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This paper validates the use of free open-source models to link distributed snow measurements to radar measurements in the Canadian Arctic. Using multiple radar sensors, we can decouple the soil from the snow contribution. We then retrieve the "microwave snow grain size" to characterize the interaction between the snow mass and the radar signal. This work supports future satellite mission development to retrieve snow mass information such as the future Canadian Terrestrial Snow Mass Mission.
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
The Cryosphere, 18, 3765–3785, https://doi.org/10.5194/tc-18-3765-2024, https://doi.org/10.5194/tc-18-3765-2024, 2024
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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.
Jordan N. Herbert, Mark S. Raleigh, and Eric E. Small
The Cryosphere, 18, 3495–3512, https://doi.org/10.5194/tc-18-3495-2024, https://doi.org/10.5194/tc-18-3495-2024, 2024
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Automated stations measure snow properties at a single point but are frequently used to validate data that represent much larger areas. We use lidar snow depth data to see how often the mean snow depth surrounding a snow station is within 10 cm of the snow station depth at different scales. We found snow stations overrepresent the area-mean snow depth in ~ 50 % of cases, but the direction of bias at a site is temporally consistent, suggesting a site could be calibrated to the surrounding area.
Isis Brangers, Hans-Peter Marshall, Gabrielle De Lannoy, Devon Dunmire, Christian Mätzler, and Hans Lievens
The Cryosphere, 18, 3177–3193, https://doi.org/10.5194/tc-18-3177-2024, https://doi.org/10.5194/tc-18-3177-2024, 2024
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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.
James Dillon, Christopher Donahue, Evan Schehrer, Karl Birkeland, and Kevin Hammonds
The Cryosphere, 18, 2557–2582, https://doi.org/10.5194/tc-18-2557-2024, https://doi.org/10.5194/tc-18-2557-2024, 2024
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Surface hoar crystals are snow grains that form when vapor deposits on a 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 an accuracy upwards of 95 %.
Steven J. Pestana, C. Chris Chickadel, and Jessica D. Lundquist
The Cryosphere, 18, 2257–2276, https://doi.org/10.5194/tc-18-2257-2024, https://doi.org/10.5194/tc-18-2257-2024, 2024
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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 learned that the position of the Sun and surface features such as trees that can cast shadows impact GOES-R infrared images.
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
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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.
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
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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.
Sonia Dupuis, Frank-Michael Göttsche, and Stefan Wunderle
EGUsphere, https://doi.org/10.5194/egusphere-2024-857, https://doi.org/10.5194/egusphere-2024-857, 2024
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The Arctic experienced pronounced warming throughout the last decades. This warming threatens ecosystems, vegetation dynamics, snow cover duration, and permafrost. Traditional monitoring methods like stations and climate models lack the detail needed. Land surface temperature (LST) data derived from satellites offers high spatial and temporal coverage, perfect for studying changes in the Arctic. In particular, LST information from AVHRR provides a 40-year record, valuable for analyzing trends.
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
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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
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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
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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.
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
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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
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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
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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
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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
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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.
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
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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
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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
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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
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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.
Jack Tarricone, Ryan W. Webb, Hans-Peter Marshall, Anne W. Nolin, and Franz J. Meyer
The Cryosphere, 17, 1997–2019, https://doi.org/10.5194/tc-17-1997-2023, https://doi.org/10.5194/tc-17-1997-2023, 2023
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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.
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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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.
Cited articles
Ahmad, S.: Impact of climate change on cryosphere-atmosphere-biosphere interaction over the Garhwal Himalaya, India, Disaster Adv., 13, 32–38, 2020.
Amlien, J.,: Remote sensing of snow with passive microwave radiometers: A review of current algorithms. Norsk Regnesentral, Report No. 1019, 1–52, 2008.
Ansari, H., Marofi, S., and Mohamadi, M.: Topography and Land Cover Effects on Snow Water Equivalent Estimation Using AMSR-E and GLDAS Data, Water Resour. Manag., 33, 1699–1715, https://doi.org/10.1007/S11269-019-2200-0, 2019.
Aschbacher, J.: Land surface studies and atmospheric effects by satellite microwave radiometry, PhD thesis, University of Innsbruck, Innsbruck, Austria, https://www.elibrary.ru/item.asp?id=6853827 (last access: 29 January 2024), 1989.
Bernier, P. Y.: Microwave Remote Sensing of Snowpack Properties: Potential and Limitations, Hydrol. Res., 18, 1–20, https://doi.org/10.2166/NH.1987.0001, 1987.
Chandrashekar, G. and Sahin, F.: A survey on feature selection methods, Comput. Electr. Eng., 40, 16–28, https://doi.org/10.1016/j.compeleceng.2013.11.024, 2014.
Chang, A. T. C., Foster, J. L., and Hall, D. K.: Nimbus-7 SMMR Derived Global Snow Cover Parameters, Ann. Glaciol., 9, 39–44, https://doi.org/10.1017/s0260305500000355, 1987.
Chang, A. T. C., Foster, J. L., Hall, D. K., Robinson, D. A., Peiji, L., and Meisheng, C.: The use of microwave radiometer data for characterizing snow storage in western China, Ann. Glaciol., 16, 215–219, https://doi.org/10.3189/1992aog16-1-215-219, 1992.
Chang, A. T. C., Foster, J. L., Hall, D. K., Goodison, B. E., Walker, A. E., Metcalfe, J. R., and Harby, A.: Snow parameters derived from microwave measurements during the BOREAS winter field campaign, J. Geophys. Res.-Atmos., 102, 29663–29671, https://doi.org/10.1029/96JD03327, 1997.
Che, T., Li, X., Jin, R., Armstrong, R., and Zhang, T.: Snow depth derived from passive microwave remote-sensing data in China, Ann. Glaciol., 49, 145–154, https://doi.org/10.3189/172756408787814690, 2008.
Che, T., Dai, L., Zheng, X., Li, X., and Zhao, K.: Estimation of snow depth from passive microwave brightness temperature data in forest regions of northeast China, Remote Sens. Environ., 183, 334–349, https://doi.org/10.1016/j.rse.2016.06.005, 2016.
Dai, L., Che, T., Xie, H., and Wu, X.: Estimation of Snow Depth over the Qinghai-Tibetan Plateau Based on AMSR-E and MODIS Data, Remote Sensing, 10, 1989, https://doi.org/10.3390/RS10121989, 2018.
Das, I. and Sarwade, R. N.: Snow depth estimation over north-western Indian Himalaya using AMSR-E, Int. J. Remote Sens., 29, 4237–4248, https://doi.org/10.1080/01431160701874595, 2008.
Derksen, C.: The contribution of AMSR-E 18.7 and 10.7 GHz measurements to improved boreal forest snow water equivalent retrievals, Remote Sens. Environ., 112, 2701–2710, https://doi.org/10.1016/J.RSE.2008.01.001, 2008.
Dietz, A. J., Kuenzer, C., Gessner, U., and Dech, S.: Remote sensing of snow – a review of available methods, Int. J. Remote Sens., 33, 4094–4134, https://doi.org/10.1080/01431161.2011.640964, 2012.
Dimri, A. P. and Dash, S. K.: Wintertime climatic trends in the western Himalayas, Climatic Change, 111, 775–800, https://doi.org/10.1007/S10584-011-0201-Y, 2012.
Dong, C.: Remote sensing, hydrological modeling and in situ observations in snow cover research: A review, J. Hydrol., 561, 573–583, https://doi.org/10.1016/j.jhydrol.2018.04.027, 2018.
Dong, J., Walker, J. P., and Houser, P. R.: Factors affecting remotely sensed snow water equivalent uncertainty, Remote Sens. Environ., 97, 68–82, https://doi.org/10.1016/J.RSE.2005.04.010, 2005.
Estilow, T. W., Young, A. H., and Robinson, D. A.: A long-term Northern Hemisphere snow cover extent data record for climate studies and monitoring, Earth Syst. Sci. Data, 7, 137–142, https://doi.org/10.5194/essd-7-137-2015, 2015.
Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S., Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., and Alsdorf, D. E.: The Shuttle Radar Topography Mission, Rev. Geophys., 45, RG2004, https://doi.org/10.1029/2005RG000183, 2007.
Ferraro, R. R., Weng, F., Grody, N. C., and Basist, A.: An Eight-Year (1987–1994) Time Series of Rainfall, Clouds, Water Vapor, Snow Cover, and Sea Ice Derived from SSM/I Measurements, B. Am. Meteorol. Soc., 77, 891–905, https://doi.org/10.1175/1520-0477(1996)077<0891:AEYTSO>2.0.CO;2, 1996.
Friedl, M. A., McIver, D. K., Hodges, J. C. F., Zhang, X. Y., Muchoney, D., Strahler, A. H., Woodcock, C. E., Gopal, S., Schneider, A., Cooper, A., Baccini, A., Gao, F., and Schaaf, C.: Global land cover mapping from Modis: Algorithms and early results, Remote Sens. Environ., 83, 287–302, https://doi.org/10.1016/s0034-4257(02)00078-0, 2002.
Foster, J., Chang, A. T. C., and Hall, D. K.: Comparison of snow mass estimates from a prototype passive microwave snow algorithm, a revised algorithm and a snow depth climatology, Remote Sens. Environ., 62, 132–142, https://doi.org/10.1016/S0034-4257(97)00085-0, 1997.
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.
Ganju, A., Thakur, N. K., and Rana, V.: Characteristics of avalanche accidents in western Himalayan region, India, in: Proceedings of the International Snow Science Workshop, Penticton, BC, Canada, 29 September–4 October 2002, 200–207, https://arc.lib.montana.edu/snow-science/objects/issw-2002-200-207.pdf (last access: 24 January 2024), 2002.
Graf, T., Koike, T., Li, X., Hirai, M., and Tsutsui, H.: Assimilating passive microwave brightness temperature data into a land surface model to improve the snow depth predictability, in: International Geoscience and Remote Sensing Symposium (IGARSS), Denver, CO, USA, 31 July–4 August, 2006, 710–713, https://doi.org/10.1109/IGARSS.2006.185, 2006.
Grippa, M., Mognard, N., le Toan, T., and Josberger, E. G.: Siberia snow depth climatology derived from SSM/I data using a combined dynamic and static algorithm, Remote Sens. Environ., 93, 30–41, https://doi.org/10.1016/J.RSE.2004.06.012, 2004.
Grody, N. C. and Basist, A. N.: Global identification of snowcover using ssm/i measurements, IEEE T. Geosci. Remote, 34, 237–249, https://doi.org/10.1109/36.481908, 1996.
Gurung, D. R., Giriraj, A., Aung, K. S., Shrestha, B., and Kulkarni, A. V.: Snow-cover mapping and monitoring in the Hindu Kush-Himalayas, International Centre for Integrated Mountain Development (ICIMOD), Patan, Nepal, https://doi.org/10.53055/ICIMOD.550, 2011.
Gusain, H. S., Mishra, V. D., Arora, M. K., Mamgain, S., and Singh, D. K.: Operational algorithm for generation of snow depth maps from discrete data in Indian Western Himalaya, Cold Reg. Sci. Technol., 126, 22–29, https://doi.org/10.1016/j.coldregions.2016.02.012, 2016.
Imaoka, K., Maeda, T., Kachi, M., Kasahara, M., Ito, N., and Nakagawa, K.: Status of AMSR2 instrument on GCOM-W1, in: Earth observing missions and sensors, Development, implementation, and characterization II, 8528, 201–206, https://doi.org/10.1117/12.977774, 2011.
Jarvis, A., Reuter, H., Nelson, A., and Guevara, E.: Hole-filled SRTM for the globe Version 4, available from the CGIAR-CSI SRTM 90m Database, https://srtm.csi.cgiar.org/ (last access: 24 January 2024), 2008.
Jiang, L. M., Wang, P., Zhang, L. X., Yang, H., and Yang, J. T.: Improvement of snow depth retrieval for FY3B-MWRI in China, Sci. China Earth Sci., 57, 1278–1292, https://doi.org/10.1007/s11430-013-4798-8, 2014.
Joshi, J. C., Tankeshwar, K., and Srivastava, S.: Hidden Markov Model for quantitative prediction of snowfall and analysis of hazardous snowfall events over Indian Himalaya, J. Earth Syst. Sci., 126, 33, https://doi.org/10.1007/s12040-017-0810-6, 2017.
Kelly, R.: The AMSR-E Snow Depth Algorithm: Description and Initial Results, Journal of The Remote Sensing Society of Japan, 29, 307–317, https://doi.org/10.11440/rssj.29.307, 2009.
Kelly, R. E., Chang, A. T., Tsang, L., and Foster, J. L.: A prototype AMSR-E global snow area and snow depth algorithm, IEEE T. Geosci. Remote, 41, 230–242, https://doi.org/10.1109/TGRS.2003.809118, 2003.
Kelly, R. E. J., Chang, A. T. C., Tsang, L., and Chen, C. T.: Parameterization of snowpack grain size for global satellite microwave estimates of snow depth, in: International Geoscience and Remote Sensing Symposium (IGARSS), Toronto, ON, Canada, 24–28 June 2002, 686–688, https://doi.org/10.1109/IGARSS.2002.1025146, 2002.
Kelly, R. E. J., Chang, A. T. C., Foster, J. L., and Hall, D. K.: Using Remote Sensing and Spatial Models to Monitor Snow Depth and Snow Water Equivalent, in: Spatial Modelling of the Terrestrial Environment, edited by: Kelly, R. E. J., Drake, N. A., and Barr, S. L., John Wiley & Sons, Ltd., 35–57, https://doi.org/10.1002/0470094001.ch3, 2005.
Kinar, N. J. and Pomeroy, J. W.: Measurement of the physical properties of the snowpack, Rev. Geophys., 53, 481–544, https://doi.org/10.1002/2015RG000481, 2015.
Kumar, P., Saharwardi, M. S., Banerjee, A., Azam, M. F., Dubey, A. K., and Murtugudde, R.: Snowfall Variability Dictates Glacier Mass Balance Variability in Himalaya-Karakoram, Scientific Reports, 9, 18192, https://doi.org/10.1038/s41598-019-54553-9, 2019.
Kurvonen, L. and Hallikainen, M.: Influence of land-cover category on brightness temperature of snow, IEEE T. Geosci. Remote, 35, 367–377, https://doi.org/10.1109/36.563276, 1997.
Kwon, Y., Yang, Z. L., Hoar, T. J., and Toure, A. M.: Improving the Radiance Assimilation Performance in Estimating Snow Water Storage across Snow and Land-Cover Types in North America, J. Hydrometeorol., 18, 651–668, https://doi.org/10.1175/JHM-D-16-0102.1, 2017.
Liang, J., Liu, X., Huang, K., Li, X., Shi, X., Chen, Y., and Li, J.: Improved snow depth retrieval by integrating microwave brightness temperature and visible/infrared reflectance, Remote Sens. Environ., 156, 500–509, https://doi.org/10.1016/J.RSE.2014.10.016, 2015.
Lemke, P., Ren, J., Alley, R. B., Allison, I., Carrasco, J., Flato, G., Fujii, Y., Kaser, G., Mote, P., Thomas, R. H., and Zhang, T.: AR4 – Changes in snow, ice and frozen ground, Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, https://www.ipcc.ch/report/ar4/wg1/observations-changes-in-snow-ice-and-frozen-ground/ (last access: 27 January 2024), 2007.
Luojus, K., Pulliainen, J., Takala, M., Lemmetyinen, J., Mortimer, C., Derksen, C., Mudryk, L., Moisander, M., Hiltunen, M., Smolander, T., Ikonen, J., Cohen, J., Salminen, M., Norberg, J., Veijola, K., and Venäläinen, P.: Globsnow v3.0 Northern Hemisphere Snow Water Equivalent Dataset, Scientific Data, 8, 163, https://doi.org/10.1038/s41597-021-00939-2, 2021.
McClung, D. M.: Avalanche character and fatalities in the high mountains of Asia, Ann. Glaciol., 57, 114–118, https://doi.org/10.3189/2016AoG71A075, 2016.
Muhammad, S.: Improved daily MODIS TERRA/AQUA Snow and Randolph Glacier Inventory (RGI6.0) data for High Mountain Asia (2002–2019), PANGAEA [data set], https://doi.org/10.1594/PANGAEA.918198, 2020.
Muhammad, S. and Thapa, A.: An improved Terra–Aqua MODIS snow cover and Randolph Glacier Inventory 6.0 combined product (MOYDGL06*) for high-mountain Asia between 2002 and 2018, Earth Syst. Sci. Data, 12, 345–356, https://doi.org/10.5194/essd-12-345-2020, 2020.
Mukherji, A., Sinisalo, A., Nüsser, M., Garrard, R., and Eriksson, M.: Contributions of the cryosphere to mountain communities in the Hindu Kush Himalaya: a review, Reg. Environ. Change, 19, 1311–1326, https://doi.org/10.1007/S10113-019-01484-W, 2019.
Mukul, M., Srivastava, V., Jade, S., and Mukul, M.: Uncertainties in the Shuttle Radar Topography Mission (SRTM) heights: Insights from the Indian himalaya and Peninsula, Scientific Reports, 7, 41672, https://doi.org/10.1038/srep41672, 2017.
Negi, H. S., Kanda, N., and Ganju, A.: Recent wintertime climatic variability over the North West Himalayan cryosphere, Curr. Sci., 114, 760–770, 2018.
Negi, H. S., Ganju, A., Kanda, N., and Gusain, H. S.: Climate Change and Cryospheric Response Over North-West and Central Himalaya, India, in: Himalayan Weather and Climate and their Impact on the Environment, edited by: Dimri, A. P., Bookhagen, B., Stoffel, M., and Yasunari, T., Springer Nature, Switzerland, 309–330, https://doi.org/10.1007/978-3-030-29684-1_16, 2020.
Nüsser, M., Dame, J., Parveen, S., Kraus, B., Baghel, R., and Schmidt, S.: Cryosphere-Fed Irrigation Networks in the Northwestern Himalaya: Precarious Livelihoods and Adaptation Strategies Under the Impact of Climate Change, Mt. Res. Dev., 39, 1–11, https://www.jstor.org/stable/26869916 (last access: 27 January 2024), 2019.
Obaid, H. S., Dheyab, S. A., and Sabry, S. S.: The impact of data pre-processing techniques and dimensionality reduction on the accuracy of machine learning, in: IEMECON 2019 – 9th Annual Information Technology, Electromechanical Engineering and Microelectronics Conference, Jaipur, India, 13–15 March 2019, 279–283, https://doi.org/10.1109/IEMECONX.2019.8877011, 2019.
Rango, A., Chang, A. T. C., and Foster, J. L.: Utilization of Spaceborne Microwave Radiometers for Monitoring Snowpack Properties, Nord. Hydrol., 10, 25–40, https://doi.org/10.2166/NH.1979.0003, 1979.
Saraf, A. K., Tarafdar, S., Foster, J. L., and Singh, P.: Passive microwave data for snow-depth and snow-extent estimations in the Himalayan mountains, Int. J. Remote Sens., 20, 83–95, https://doi.org/10.1080/014311699213613, 1999.
Saydi, M. and Ding, J.: Impacts of topographic factors on regional snow cover characteristics, Water Science and Engineering, 13, 171–180, https://doi.org/10.1016/j.wse.2020.09.002, 2020.
Sharma, S. S. and Ganju, A.: Complexities of avalanche forecasting in Western Himalaya – an overview, Cold Reg. Sci. Technol., 31, 95–102, https://doi.org/10.1016/S0165-232X(99)00034-8, 2000.
Sharma, V., Mishra, V. D., and Joshi, P. K.: Topographic controls on spatio-temporal snow cover distribution in Northwest Himalaya, Int. J. Remote Sens., 35, 3036–3056, https://doi.org/10.1080/01431161.2014.894665, 2014.
Singh, D., Juyal, V., and Sharma, V.: Consistent seasonal snow cover depth and duration variability over the Western Himalayas (WH), J. Earth Syst. Sci., 125, 1451–1461, https://doi.org/10.1007/s12040-016-0737-3, 2016.
Singh, D. K., Singh, K. K., Mishra, V. D., and Sharma, J. K.: Formulation of Snow Depth Algorithms for Different regions of NW Himalaya using Passive Microwave Satellite Data, International Journal of Engineering Research & Technology, 1, 1–9, https://www.ijert.org/research/formulation-of-snow-depth-algorithms-for-different-regions-of-nw-himalaya-using-passive-microwave-satellite-data-IJERTV1IS5164.pdf (last access: 27 January 2024), 2012.
Singh, D. K., Gusain, H. S., Mishra, V., and Gupta, N.: Snow cover variability in North-West Himalaya during last decade, Arab. J. Geosci., 11, 579, https://doi.org/10.1007/S12517-018-3926-3, 2018.
Singh, K. K. and Mishra, V. D.: Snow cover study of northwest Himalaya using passive microwave remote sensing data, in: Proceedings of SPIE Asia pacific remote sensing conference, Microwave Remote Sensing of the Atmosphere and Environment V, Goa, India, 13–17 November 2006, 641014, https://doi.org/10.1117/12.693942, 2006.
Singh, K. K., Mishra, V. D., and Negi, H. S.: Evaluation of snow parameters using passive microwave remote sensing, Def. Sci. J., 57, 271–278, 2007.
Singh, K. K., Kumar, A., Kulkarni, A. V., Datt, P., Dewali, S. K., Kumar, V., and Chauhan, R.: Snow depth estimation in the Indian Himalaya using multi-channel passive microwave radiometer, Curr. Sci., 108, 942–953, http://www.jstor.org/stable/24216523 (last access: 27 January 2024), 2015
Singh, K. K., Kumar, R., Singh, D. K., Negi, H. S., Dewali, S. K., and Kedia, J.: Retrieving snow cover information from AMSR-2 satellite data for North-West Himalaya, India, Geocarto Int., 35, 1783–1799 , https://doi.org/10.1080/10106049.2019.1588394, 2020.
Singh, S. K., Rathore, B. P., Bahuguna, I. M., and Ajai: Snow cover variability in the Himalayan–Tibetan region, Int. J. Climatol., 34, 446–452, https://doi.org/10.1002/JOC.3697, 2014.
Stiles, W. H. and Ulaby, F. T.: The active and passive microwave response to snow parameters: 1. Wetness, J. Geophys. Res.-Oceans, 85, 1037–1044, https://doi.org/10.1029/JC085IC02P01037, 1980.
Takala, M., Luojus, K., Pulliainen, J., Derksen, C., Lemmetyinen, J., Kärnä, J. P., Koskinen, J., and Bojkov, B.: Estimating northern hemisphere snow water equivalent for climate research through assimilation of space-borne radiometer data and ground-based measurements, Remote Sens. Environ., 115, 3517–3529, https://doi.org/10.1016/J.RSE.2011.08.014, 2011.
Tanniru, S. and Ramsankaran, R. A. A. J.: Passive Microwave Remote Sensing of Snow Depth: Techniques, Challenges and Future Directions, Remote Sens., 15, 1052, https://doi.org/10.3390/rs15041052, 2023.
Tedesco, M. and Narvekar, P. S.: Assessment of the NASA AMSR-E SWE Product, IEEE J. Sel. Top. Appl., 3, 141–159, https://doi.org/10.1109/JSTARS.2010.2040462, 2010.
Tedesco, M., Pulliainen, J., Takala, M., Hallikainen, M., and Pampaloni, P.: Artificial neural network-based techniques for the retrieval of SWE and snow depth from SSM/I data, Remote Sens. Environ., 90, 76–85, https://doi.org/10.1016/J.RSE.2003.12.002, 2004.
Tedesco, M., Reichle, R., Löw, A., Markus, T., and Foster, J. L.: Dynamic approaches for snow depth retrieval from spaceborne microwave brightness temperature, IEEE T. Geosci. Remote, 48, 1955–1967, https://doi.org/10.1109/TGRS.2009.2036910, 2010.
Tedesco, M., Derksen, C., Deems, J. S., and Foster, J. L.: Remote sensing of snow depth and snow water equivalent, in: Remote Sensing of the Cryosphere, edited by: Tedesco, M., John Wiley & Sons, Ltd, Chichester, UK, https://doi.org/10.1002/9781118368909.ch5, 2015.
Thakur, P. K., Garg, V., Nikam, B. R., and Aggarwal, S. P.: Cryosphere Studies in Northwest Himalaya, Remote Sensing of Northwest Himalayan Ecosystems, Springer Verlag, Singapore, Singapore., 69–107, https://doi.org/10.1007/978-981-13-2128-3_5, 2019.
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.
Velliangiri, S., Alagumuthukrishnan, S., and Thankumar, J. S. I.: A review of dimensionality reduction techniques for efficient computation, Procedia Comput. Sci., 165, 104–111, https://doi.org/10.1016/j.procs.2020.01.079, 2019.
Vishwakarma, B. D., Ramsankaran, R. A. A. J., Azam, M. F., Bolch,T., Mandal, A., Srivastava, S., Kumar, P., Sahu, R., Navinkumar, P. J., Tanniru, S. R., Javed, A., Soheb, M., Dimri, A. P., Yadav, M., Devaraju, B., Chinnasamy, P., Reddy, M. J., Murugesan, G. P., Arora, M., Jain, S. K., Ojha, C. S. P., Harrison, S., and Bamber, J.: Challenges in Understanding the Variability of the Cryosphere in the Himalaya and Its Impact on Regional Water Resources, Front. Water, 4, 909246, https://doi.org/10.3389/frwa.2022.909246, 2022.
Wang, J., Huang, X., Wang, Y., and Liang, T.: Retrieving Snow Depth Information from AMSR2 Data for Qinghai-Tibet Plateau, IEEE J. Sel. Top. Appl., 13, 752–768, https://doi.org/10.1109/JSTARS.2020.2970738, 2020.
Wang, P., Jiang, L., Zhang, L., and Guo, Y.: Impact of terrain topography on retrieval of snow water equivalence using passive microwave remote sensing, in: International Geoscience and Remote Sensing Symposium (IGARSS), Honolulu, HI, USA, 25–30 July 2010, 1757–1760, https://doi.org/10.1109/IGARSS.2010.5652279, 2010.
Wang, Y., Huang, X., Deng, J., Ma, X., and Liang, T.: Development and validation for daily cloud-free snow products in middle-and-high latitude areas in Eurasia, Remote Sensing Technology and Application, 31, 1013–1021, http://www.rsta.ac.cn/EN/Y2016/V31/I5/1013 (last access: 24 January 2024), 2016.
Wang, Y., Huang, X., Wang, J., Zhou, M., and Liang, T.: AMSR2 snow depth downscaling algorithm based on a multifactor approach over the Tibetan Plateau, China, Remote Sens. Environ., 231, 111268, https://doi.org/10.1016/j.rse.2019.111268, 2019.
Webb, G. I., Sammut, C., Perlich, C., Horváth, T., Wrobel, S., Korb, K. B., Noble, W. S., Leslie, C., Lagoudakis, M. G., Quadrianto, N., Buntine, W. L., Quadrianto, N., Buntine, W. L., Getoor, L., Namata, G., Getoor, L., Han, X. J. J., Ting, J.-A., Vijayakumar, S., Schaal, S., and de Raedt, L.: Leave-One-Out Cross-Validation, in: Encyclopedia of Machine Learning, Springer US, 600–601, https://doi.org/10.1007/978-0-387-30164-8_469, 2011.
Wei, Y., Li, X., Gu, L., Zheng, X., Jiang, T., Li, X., and Wan, X.: A Dynamic Snow Depth Inversion Algorithm Derived from AMSR2 Passive Microwave Brightness Temperature Data and Snow Characteristics in Northeast China, IEEE J. Sel. Top. Appl., 14, 5123–5136, https://doi.org/10.1109/JSTARS.2021.3079703, 2021.
Xiao, L., Che, T., and Dai, L.: Evaluation of Remote Sensing and Reanalysis Snow Depth Datasets over the Northern Hemisphere during 1980–2016, Remote Sensing, 12, 3253, https://doi.org/10.3390/RS12193253, 2020.
Xiao, X., Zhang, T., Zhong, X., Shao, W., and Li, X.: Support vector regression snow-depth retrieval algorithm using passive microwave remote sensing data, Remote Sens. Environ., 210, 48–64, https://doi.org/10.1016/j.rse.2018.03.008, 2018.
Xiao, X., Zhang, T., Zhong, X., and Li, X.: Spatiotemporal Variation of Snow Depth in the Northern Hemisphere from 1992 to 2016, Remote Sensing, 12, 2728, https://doi.org/10.3390/RS12172728, 2020.
Yang, J., Jiang, L., Luojus, K., Pan, J., Lemmetyinen, J., Takala, M., and Wu, S.: Snow depth estimation and historical data reconstruction over China based on a random forest machine learning approach, The Cryosphere, 14, 1763–1778, https://doi.org/10.5194/tc-14-1763-2020, 2020.
Yang, J. W., Jiang, L. M., Lemmetyinen, J., Pan, J. M., Luojus, K., and Takala, M.: Improving snow depth estimation by coupling HUT-optimized effective snow grain size parameters with the random forest approach, Remote Sens. Environ., 264, 112630, https://doi.org/10.1016/J.RSE.2021.112630, 2021.
You, Q., Kang, S., Ren, G., Fraedrich, K., Pepin, N., Yan, Y., and Ma, L.: Observed changes in snow depth and number of snow days in the eastern and central Tibetan Plateau, Clim. Res., 46, 171–183, https://doi.org/10.3354/cr00985, 2011.
Yu, H., Zhang, X., Liang, T., Xie, H., Wang, X., Feng, Q., and Chen, Q.: A new approach of dynamic monitoring of 5‐day snow cover extent and snow depth based on Modis and amsr‐e data from Northern Xinjiang region, Hydrol. Process., 26, 3052–3061, https://doi.org/10.1002/hyp.8253, 2012.
Zhang, R., Liang, T., Feng, Q., Huang, X., Wang, W., Xie, H., and Guo, J.: Evaluation and adjustment of the amsr2 snow depth algorithm for the northern Xinjiang region, China, IEEE J. Sel. Top. Appl., 10, 3892–3903, https://doi.org/10.1109/jstars.2016.2620521, 2017.
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.
In situ techniques for snow depth (SD) measurement are not adequate to represent the...
