Articles | Volume 15, issue 2
https://doi.org/10.5194/tc-15-835-2021
© Author(s) 2021. 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-15-835-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
18 Feb 2021
Research article |
| 18 Feb 2021
| 18 Feb 2021
Estimating fractional snow cover from passive microwave brightness temperature data using MODIS snow cover product over North America
Xiongxin Xiao
School of Remote Sensing and Information Engineering, Wuhan
University, Wuhan 430079, China
Shunlin Liang
Department of Geographical Sciences, University of Maryland, College
Park, MD 20742, USA
School of Remote Sensing and Information Engineering, Wuhan
University, Wuhan 430079, China
Daiqiang Wu
School of Remote Sensing and Information Engineering, Wuhan
University, Wuhan 430079, China
Congyuan Pei
School of Remote Sensing and Information Engineering, Wuhan
University, Wuhan 430079, China
Jianya Gong
School of Remote Sensing and Information Engineering, Wuhan
University, Wuhan 430079, China
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Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hessd-10-3927-2013, https://doi.org/10.5194/hessd-10-3927-2013, 2013
Preprint withdrawn
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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
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.
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
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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.
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.
Cited articles
Amante, C. and Eakins, B. W.: ETOPO1 1 Arc-Minute Global Relief Model:
Procedures, Data Sources and Analysis, National Geophysical Data Center,
NOAA, https://doi.org/10.7289/V5C8276M, 2009.
Arsenault, K. R., Houser, P. R., and De Lannoy, G. J. M.: Evaluation of the
MODIS snow cover fraction product, Hydrol. Process., 28, 980–998,
https://doi.org/10.1002/hyp.9636, 2014.
Barnett, T. P., Adam, J. C., and Lettenmaier, D. P.: Potential impacts of a
warming climate on water availability in snow-dominated regions, Nature,
438, 303–309, https://doi.org/10.1038/nature04141, 2005.
Belgiu, M. and Drăgu, L.: Random forest in remote sensing: A review of
applications and future directions, ISPRS J. Photogramm., 114, 24–31, https://doi.org/10.1016/j.isprsjprs.2016.01.011, 2016.
Berman, E. E., Bolton, D. K., Coops, N. C., Mityok, Z. K., Stenhouse, G. B.,
and Moore, R. D.: Daily estimates of Landsat fractional snow cover driven by
MODIS and dynamic time-warping, Remote Sens. Environ., 216, 635–646,
https://doi.org/10.1016/j.rse.2018.07.029, 2018.
Bormann, K. J., Brown, R. D., Derksen, C., and Painter, T. H.: Estimating
snow-cover trends from space, Nat. Clim. Change, 8, 924–928,
https://doi.org/10.1038/s41558-018-0318-3, 2018.
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32,
https://doi.org/10.1023/a:1010933404324, 2001.
Brodzik, M., Long, D., and Hardman, M.: Best Practices in Crafting the
Calibrated, Enhanced-Resolution Passive-Microwave EASE-Grid 2.0 Brightness
Temperature Earth System Data Record, Remote Sens.-Basel, 10, 1793, https://doi.org/10.3390/rs10111793, 2018.
Brown, R., Derksen, C., and Wang, L.: A multi-data set analysis of
variability and change in Arctic spring snow cover extent, 1967–2008,
J. Geophys. Res., 115, 751–763, https://doi.org/10.1029/2010jd013975, 2010.
Brown, R. D. and Derksen, C.: Is Eurasian October snow cover extent
increasing?, Environ. Res. Lett., 8, 024006, https://doi.org/10.1088/1748-9326/8/2/024006, 2013.
Chang, A., Foster, J., and Hall, D.: Nimbus-7 SMMR derived global snow cover
parameters, Ann. Glaciol, 9, 39–44, 1987.
Che, T., Xin, L., Jin, R., Armstrong, R., and Zhang, T.: Snow depth derived
from passive microwave remote-sensing data in China, Ann. Glaciol.,
49, 145–154, 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, 2016.
Chen, X., Long, D., Liang, S., He, L., Zeng, C., Hao, X., and Hong, Y.:
Developing a composite daily snow cover extent record over the Tibetan
Plateau from 1981 to 2016 using multisource data, Remote Sens. Environ., 215, 284–299, https://doi.org/10.1016/j.rse.2018.06.021, 2018.
Cheng, Z., Guo, Z., Tan, Z., Yang, J., and Wang, Q.: Waste heat recovery
from high-temperature solid granular materials: Energy challenges and
opportunities, Renew. Sust. Energ. Rev., 116, 024006, https://doi.org/10.1016/j.rser.2019.109428, 2019.
Cohen, J., Lemmetyinen, J., Pulliainen, J., Heinila, K., Montomoli, F.,
Seppanen, J., and Hallikainen, M. T.: The Effect of Boreal Forest Canopy in
Satellite Snow Mapping – A Multisensor Analysis, IEEE T. Geosci. Remote, 53, 6593–6607, https://doi.org/10.1109/tgrs.2015.2444422,
2015.
Colditz, R.: An Evaluation of Different Training Sample Allocation Schemes
for Discrete and Continuous Land Cover Classification Using Decision
Tree-Based Algorithms, Remote Sens.-Basel, 7, 9655–9681, https://doi.org/10.3390/rs70809655,
2015.
Coll, J., and Li, X.: Comprehensive accuracy assessment of MODIS daily snow
cover products and gap filling methods, ISPRS J. Photogramm., 144, 435–452, https://doi.org/10.1016/j.isprsjprs.2018.08.004, 2018.
Czyzowska-Wisniewski, E. H., van Leeuwen, W. J. D., Hirschboeck, K. K.,
Marsh, S. E., and Wisniewski, W. T.: Fractional snow cover estimation in
complex alpine-forested environments using an artificial neural network,
Remote Sens. Environ., 156, 403–417, https://doi.org/10.1016/j.rse.2014.09.026,
2015.
Dai, L., Che, T., Wang, J., and Zhang, P.: Snow depth and snow water
equivalent estimation from AMSR-E data based on a priori snow
characteristics in Xinjiang, China, Remote Sens. Environ., 127,
14–29, 2012.
Dai, L., Che, T., Ding, Y., and Hao, X.: Evaluation of snow cover and snow depth on the Qinghai–Tibetan Plateau derived from passive microwave remote sensing, The Cryosphere, 11, 1933–1948, https://doi.org/10.5194/tc-11-1933-2017, 2017.
De Lannoy, G. J. M., Reichle, R. H., Arsenault, K. R., Houser, P. R., Kumar, S., Verhoest, N. E. C., and Pauwels, V. R. N.: Multiscale assimilation of
Advanced Microwave Scanning Radiometer-EOS snow water equivalent and
Moderate Resolution Imaging Spectroradiometer snow cover fraction
observations in northern Colorado, Water Resour. Res., 48, W01522, https://doi.org/10.1029/2011wr010588, 2012.
Derksen, C., LeDrew, E., Walker, A., and Goodison, B.: Influence of sensor
overpass time on passive microwave-derived snow cover parameters, Remote
Sens. Environ., 71, 297–308, 2000.
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, 2011.
Dobreva, I. D. and Klein, A. G.: Fractional snow cover mapping through
artificial neural network analysis of MODIS surface reflectance, Remote
Sens. Environ., 115, 3355–3366, https://doi.org/10.1016/j.rse.2011.07.018, 2011.
Dong, C. and Menzel, L.: Producing cloud-free MODIS snow cover products
with conditional probability interpolation and meteorological data, Remote
Sens. Environ., 186, 439–451, https://doi.org/10.1016/j.rse.2016.09.019, 2016.
Dong, J., Ek, M., Hall, D., Peters-Lidard, C., Cosgrove, B., Miller, J.,
Riggs, G., and Xia, Y.: Using Air Temperature to Quantitatively Predict the
MODIS Fractional Snow Cover Retrieval Errors over the Continental United
States, J. Hydrometeorol., 15, 551–562, https://doi.org/10.1175/jhm-d-13-060.1,
2014.
Du, P., Samat, A., Waske, B., Liu, S., and Li, Z.: Random Forest and
Rotation Forest for fully polarized SAR image classification using
polarimetric and spatial features, ISPRS J. Photogramm., 105, 38–53, https://doi.org/10.1016/j.isprsjprs.2015.03.002, 2015.
Flanner, M. G., Shell, K. M., Barlage, M., Perovich, D. K., and Tschudi, M. A.: Radiative forcing and albedo feedback from the Northern Hemisphere
cryosphere between 1979 and 2008, Nat. Geosci., 4, 151–155,
https://doi.org/10.1038/ngeo1062, 2011.
Foody, G. M.: Explaining the unsuitability of the kappa coefficient in the
assessment and comparison of the accuracy of thematic maps obtained by image
classification, Remote Sens. Environ., 239, 111630, https://doi.org/10.1016/j.rse.2019.111630, 2020.
Foster, J. L., Hall, D. K., Eylander, J. B., Riggs, G. A., Nghiem, S. V.,
Tedesco, M., Kim, E., Montesano, P. M., Kelly, R. E. J., Casey, K. A., and
Choudhury, B.: A blended global snow product using visible, passive
microwave and scatterometer satellite data, Int. J. Remote
Sens., 32, 1371–1395, https://doi.org/10.1080/01431160903548013, 2011.
Frank, E., Hall, M., Trigg, L., Holmes, G., and Witten, I. H.: Data mining
in bioinformatics using Weka, Bioinformatics, 20, 2479–2481, 2004.
Friedman, J. H.: Multivariate Adaptive Regression Splines, Ann. Stat., 19, 1–67, 1991.
Gafurov, A. and Bárdossy, A.: Cloud removal methodology from MODIS snow cover product, Hydrol. Earth Syst. Sci., 13, 1361–1373, https://doi.org/10.5194/hess-13-1361-2009, 2009.
Gao, J., Yao, T., Masson-Delmotte, V., Steen-Larsen, H. C., and Wang, W.:
Collapsing glaciers threaten Asia's water supplies, Nature, 19–21, https://doi.org/10.1038/d41586-018-07838-4, 2019.
Gascoin, S., Hagolle, O., Huc, M., Jarlan, L., Dejoux, J.-F., Szczypta, C., Marti, R., and Sánchez, R.: A snow cover climatology for the Pyrenees from MODIS snow products, Hydrol. Earth Syst. Sci., 19, 2337–2351, https://doi.org/10.5194/hess-19-2337-2015, 2015.
Gascoin, S., Grizonnet, M., Bouchet, M., Salgues, G., and Hagolle, O.: Theia Snow collection: high-resolution operational snow cover maps from Sentinel-2 and Landsat-8 data, Earth Syst. Sci. Data, 11, 493–514, https://doi.org/10.5194/essd-11-493-2019, 2019.
Grippa, M., Mognard, N., Le Toan, T., and Josberger, E.: Siberia snow depth
climatology derived from SSM/I data using a combined dynamic and static
algorithm, Remote Sens. Environ., 93, 30–41, 2004.
Grody, N. C. and Basist, A. N.: Global identification of snowcover using
SSM/I measurements, IEEE T. Geosci. Remote, 34,
237–249, 1996.
Hall, D. K. and Riggs, G. A.: Accuracy assessment of the MODIS snow
products, Hydrol. Process., 21, 1534–1547, https://doi.org/10.1002/hyp.6715, 2007.
Hall, D. K. and Riggs, G. A.: MODIS/Terra Snow Cover Daily L3 Global 500 m
SIN Grid, Version 6, NASA National Snow and Ice Data Center Distributed
Active Archive Center, Boulder, Colorado USA, https://doi.org/https://doi.org/10.5067/MODIS/MOD10A1.006, 2016a.
Hall, D. K. and Riggs, G. A.: MODIS/Aqua Snow Cover Daily L3 Global 500 m
SIN Grid, Version 6., NASA National Snow and Ice Data Center Distributed
Active Archive Center, Boulder, Colorado USA, https://doi.org/10.5067/MODIS/MYD10A1.006, 2016b.
Hall, D. K., Riggs, G. A., and Salomonson, V. V.: Development of methods for
mapping global snow cover using moderate resolution imaging
spectroradiometer data, Remote Sens. Environ., 54, 127–140, 1995.
Hall, D. K., Foster, J. L., Verbyla, D. L., Klein, A. G., and Benson, C. S.:
Assessment of Snow-Cover Mapping Accuracy in a Variety of Vegetation-Cover
Densities in Central Alaska, Remote Sens. Environ., 66, 129–137,
https://doi.org/10.1016/S0034-4257(98)00051-0, 1998.
Hall, D. K., Riggs, G. A., and Salomonson, V. V.: Algorithm Theoretical
Basis Document (ATBD) for the MODIS Snow and Sea Ice-Mapping Algorithms, NASA Goddard Space Flight Center, Greenbelt, MD, USA,
2001.
Han, M., Yang, K., Qin, J., Jin, R., Ma, Y., Wen, J., Chen, Y., Zhao, L.,
Lazhu, and Tang, W.: An Algorithm Based on the Standard Deviation of Passive
Microwave Brightness Temperatures for Monitoring Soil Surface Freeze/Thaw
State on the Tibetan Plateau, IEEE T. Geosci. Remote, 53, 2775–2783, https://doi.org/10.1109/tgrs.2014.2364823, 2015.
Hao, S., Jiang, L., Shi, J., Wang, G., and Liu, X.: Assessment of
MODIS-Based Fractional Snow Cover Products Over the Tibetan Plateau, IEEE
J. Sel. Top. Appl.,
12, 533–548, https://doi.org/10.1109/jstars.2018.2879666, 2019.
Hao, X., Luo, S., Che, T., Wang, J., Li, H., Dai, L., Huang, X., and Feng, Q.: Accuracy assessment of four cloud-free snow cover products over the
Qinghai-Tibetan Plateau, Int. J. Digit. Earth, 12,
375–393, https://doi.org/10.1080/17538947.2017.1421721, 2018.
Haykin, S. O.: Neural Networks and Learning Machines, 3rd Edn., Prentice Hall, Pearson Education, New Jersey,
2009.
He, T., Liang, S., and Song, D.-X.: Analysis of global land surface albedo
climatology and spatial-temporal variation during 1981–2010 from multiple
satellite products, J. Geophys. Res.-Atmos., 119,
10281–210298, https://doi.org/10.1002/2014jd021667, 2014.
Hecht-Nielsen, R.: Theory of the backpropagation neural network, in: Neural
networks for perception, Elsevier, IJCNN, International Joint Conference, 65–93, 1992.
Henderson, G. R., Peings, Y., Furtado, J. C., and Kushner, P. J.:
Snow–atmosphere coupling in the Northern Hemisphere, Nat. Clim. Change,
8, 954–963, https://doi.org/10.1038/s41558-018-0295-6, 2018.
Hori, M., Sugiura, K., Kobayashi, K., Aoki, T., Tanikawa, T., Kuchiki, K.,
Niwano, M., and Enomoto, H.: A 38-year (1978–2015) Northern Hemisphere
daily snow cover extent product derived using consistent objective criteria
from satellite-borne optical sensors, Remote Sens. Environ., 191,
402–418, https://doi.org/10.1016/j.rse.2017.01.023, 2017.
Huang, X., Deng, J., Ma, X., Wang, Y., Feng, Q., Hao, X., and Liang, T.: Spatiotemporal dynamics of snow cover based on multi-source remote sensing data in China, The Cryosphere, 10, 2453–2463, https://doi.org/10.5194/tc-10-2453-2016, 2016.
Huang, Y., Liu, H., Yu, B., Wu, J., Kang, E. L., Xu, M., Wang, S., Klein, A., and Chen, Y.: Improving MODIS snow products with a HMRF-based
spatio-temporal modeling technique in the Upper Rio Grande Basin, Remote
Sens. Environ., 204, 568–582, https://doi.org/10.1016/j.rse.2017.10.001, 2018.
Jin, H., Stehman, S. V., and Mountrakis, G.: Assessing the impact of
training sample selection on accuracy of an urban classification: a case
study in Denver, Colorado, Int. J. Remote Sens., 35,
2067–2081, 2014.
Josberger, E. G. and Mognard, N. M.: A passive microwave snow depth
algorithm with a proxy for snow metamorphism, Hydrol. Process., 16,
1557–1568, 2002.
Kattenborn, T., Lopatin, J., Förster, M., Braun, A. C., and Fassnacht, F. E.: UAV data as alternative to field sampling to map woody invasive
species based on combined Sentinel-1 and Sentinel-2 data, Remote Sens. Environ., 227, 61–73, https://doi.org/10.1016/j.rse.2019.03.025, 2019.
Kelly, R.: The AMSR-E snow depth algorithm: Description and initial results,
Journal of The Remote Sensing Society of Japan, 29, 307–317,
2009.
Kim, R. S., Durand, M., and Liu, D.: Spectral analysis of airborne passive
microwave measurements of alpine snowpack: Colorado, USA, Remote Sens.
Environ., 205, 469–484, https://doi.org/10.1016/j.rse.2017.07.025, 2018.
Kim, R. S., Durand, M., Li, D., Baldo, E., Margulis, S. A., Dumont, M., and
Morin, S.: Estimating alpine snow depth by combining multifrequency passive
radiance observations with ensemble snowpack modeling, Remote Sens.
Environ., 226, 1–15, https://doi.org/10.1016/j.rse.2019.03.016, 2019.
Kostadinov, T. S. and Lookingbill, T. R.: Snow cover variability in a
forest ecotone of the Oregon Cascades via MODIS Terra products, Remote
Sens. Environ., 164, 155–169, https://doi.org/10.1016/j.rse.2015.04.002, 2015.
Kuter, S., Weber, G.-W., Akyürek, Z., and Özmen, A.: Inversion of
top of atmospheric reflectance values by conic multivariate adaptive
regression splines, Inverse Probl. Sci. En., 23,
651–669, https://doi.org/10.1080/17415977.2014.933828, 2015.
Kuter, S., Akyurek, Z., and Weber, G.-W.: Retrieval of fractional snow
covered area from MODIS data by multivariate adaptive regression splines,
Remote Sens. Environ., 205, 236–252, https://doi.org/10.1016/j.rse.2017.11.021,
2018.
Lemmetyinen, J., Derksen, C., Rott, H., Macelloni, G., King, J., Schneebeli, M., Wiesmann, A., Leppänen, L., Kontu, A., and Pulliainen, J.: Retrieval
of Effective Correlation Length and Snow Water Equivalent from Radar and
Passive Microwave Measurements, Remote Sens.-Basel, 10, 170, https://doi.org/10.3390/rs10020170,
2018.
Li, X., Liu, Y., Zhu, X., Zheng, Z., and Chen, A.: Snow Cover Identification
with SSM/I Data in China, J. Appl. Meteorol. Sci., 18,
12–20, 2007.
Liang, H., Huang, X., Sun, Y., Wang, Y., and Liang, T.: Fractional
Snow-Cover Mapping Based on MODIS and UAV Data over the Tibetan Plateau,
Remote Sens.-Basel, 9, 1332, https://doi.org/10.3390/rs9121332, 2017.
Liu, X., Jiang, L., Wu, S., Hao, S., Wang, G., and Yang, J.: Assessment of
Methods for Passive Microwave Snow Cover Mapping Using FY-3C/MWRI Data in
China, Remote Sens.-Basel, 10, 524, https://doi.org/10.3390/rs10040524, 2018.
Long, D. G. and Brodzik, M. J.: Optimum Image Formation for Spaceborne
Microwave Radiometer Products, IEEE T. Geosci. Remote, 54, 2763–2779, https://doi.org/10.1109/TGRS.2015.2505677, 2016.
Luojus, K., Cohen, J., Ikonen, J., Pulliainen, J., Takala, M., Veijola, K.,
Lemmetyinen, J., Nagler, T., Derksen, C., and Brown, R.: Assessment of
Seasonal snow Cover Mass in Northern Hemisphere During the Satellite-ERA,
IGARSS 2018 – 2018 IEEE International Geoscience and Remote Sensing
Symposium, 2018, Valencia, 6255–6258, https://doi.org/10.1109/IGARSS.2018.8517494, 2018.
Lyons, M. B., Keith, D. A., Phinn, S. R., Mason, T. J., and Elith, J.: A
comparison of resampling methods for remote sensing classification and
accuracy assessment, Remote Sens. Environ., 208, 145–153,
https://doi.org/10.1016/j.rse.2018.02.026, 2018.
Marchane, A., Jarlan, L., Hanich, L., Boudhar, A., Gascoin, S., Tavernier, A., Filali, N., Le Page, M., Hagolle, O., and Berjamy, B.: Assessment of daily MODIS snow cover products to monitor snow cover dynamics over the Moroccan Atlas mountain range, Remote Sens. Environ., 160, 72–86, 2015.
Masson, T., Dumont, M., Mura, M., Sirguey, P., Gascoin, S., Dedieu, J.-P.,
and Chanussot, J.: An Assessment of Existing Methodologies to Retrieve Snow
Cover Fraction from MODIS Data, Remote Sens.-Basel, 10, 619, https://doi.org/10.3390/rs10040619,
2018.
Menne, M. J., Durre, I., Korzeniewski, B., McNeal, S., Thomas, K., Yin, X.,
Anthony, S., Ray, R., Vose, R. S., E.Gleason, B., and Houston, T. G.: Global
Historical Climatology Network – Daily (GHCN-Daily), Version 3, NOAA
National Climatic Data Center, https://doi.org/10.7289/V5D21VHZ, 2012a.
Menne, M. J., Durre, I., Vose, R. S., Gleason, B. E., and Houston, T. G.: An
Overview of the Global Historical Climatology Network-Daily Database,
J. Atmos. Ocean. Tech., 29, 897–910,
https://doi.org/10.1175/jtech-d-11-00103.1, 2012b.
Metsämäki, S. J., Anttila, S. T., Markus, H. J., and
Vepsäläinen, J. M.: A feasible method for fractional snow cover
mapping in boreal zone based on a reflectance model, Remote Sens.
Environ., 95, 77–95, https://doi.org/10.1016/j.rse.2004.11.013, 2005.
Millard, K. and Richardson, M.: On the Importance of Training Data Sample
Selection in Random Forest Image Classification: A Case Study in Peatland
Ecosystem Mapping, Remote Sens.-Basel, 7, 8489–8515, https://doi.org/10.3390/rs70708489, 2015.
Moosavi, V., Malekinezhad, H., and Shirmohammadi, B.: Fractional snow cover
mapping from MODIS data using wavelet-artificial intelligence hybrid models,
J. Hydrol., 511, 160–170, https://doi.org/10.1016/j.jhydrol.2014.01.015, 2014.
Mutanga, O., Adam, E., and Cho, M. A.: High density biomass estimation for
wetland vegetation using WorldView-2 imagery and random forest regression
algorithm, Int. J. Appl. Earth Obs., 18, 399–406, https://doi.org/10.1016/j.jag.2012.03.012, 2012.
Neale, C. M. U., McFarland, M. J., and Chang, K.: Land-surface-type
classification using microwave brightness temperatures from the Special
Sensor Microwave/Imager, IEEE T. Geosci. Remote,
28, 829–838, https://doi.org/10.1109/36.58970, 1990.
Nguyen, L. H., Joshi, D. R., Clay, D. E., and Henebry, G. M.: Characterizing
land cover/land use from multiple years of Landsat and MODIS time series: A
novel approach using land surface phenology modeling and random forest
classifier, Remote Sens. Environ., 238, 111017, https://doi.org/10.1016/j.rse.2018.12.016,
2018.
Pan, J., Jiang, L., and Zhang, L.: Wet snow detection in the south of China
by passive microwave remote sensing, 2012 IEEE Int. Geosci. Remote, 2012, 4863–4866, 2012.
Pan, M., Sahoo, A. K., and Wood, E. F.: Improving soil moisture retrievals
from a physically-based radiative transfer model, Remote Sens. Environ., 140, 130–140, https://doi.org/10.1016/j.rse.2013.08.020, 2014.
Parajka, J. and Blöschl, G.: Validation of MODIS snow cover images over Austria, Hydrol. Earth Syst. Sci., 10, 679–689, https://doi.org/10.5194/hess-10-679-2006, 2006.
Parajka, J. and Blöschl, G.: Spatio-temporal combination of MODIS
images – potential for snow cover mapping, Water Resour. Res., 44, 72–84, https://doi.org/10.1029/2007wr006204, 2008.
Parajka, J., Holko, L., Kostka, Z., and Blöschl, G.: MODIS snow cover mapping
accuracy in a small mountain catchment – comparison between open and forest
sites, Hydrol. Earth Syst. Sci., 16, 2365–2377,
https://doi.org/10.5194/hess-16-2365-2012, 2012.
Peng, G., Meier, W. N., Scott, D. J., and Savoie, M. H.: A long-term and reproducible passive microwave sea ice concentration data record for climate studies and monitoring, Earth Syst. Sci. Data, 5, 311–318, https://doi.org/10.5194/essd-5-311-2013, 2013.
Qu, Y., Zhu, Z., Chai, L., Liu, S., Montzka, C., Liu, J., Yang, X., Lu, Z.,
Jin, R., Li, X., Guo, Z., and Zheng, J.: Rebuilding a Microwave Soil
Moisture Product Using Random Forest Adopting AMSR-E/AMSR2 Brightness
Temperature and SMAP over the Qinghai–Tibet Plateau, China, Remote Sens.-Basel,
11, 683, https://doi.org/10.3390/rs11060683, 2019.
Quirós, E., Felicísimo, Á., and Cuartero, A.: Testing
Multivariate Adaptive Regression Splines (MARS) as a Method of Land Cover
Classification of TERRA-ASTER Satellite Images, Sensors, 9, 9011–9028,
https://doi.org/10.3390/s91109011, 2009.
Revuelto, J., López-Moreno, J. I., Azorin-Molina, C., and Vicente-Serrano, S. M.: Topographic control of snowpack distribution in a small catchment in the central Spanish Pyrenees: intra- and inter-annual persistence, The Cryosphere, 8, 1989–2006, https://doi.org/10.5194/tc-8-1989-2014, 2014.
Riggs, G. A. and Hall, D. K.: MODIS Snow Products Collection 6 User Guide, available at: https://modis-snow-ice.gsfc.nasa.gov/uploads/C6_MODIS Snow_User_Guide.pdf (last access: 10 February 2021),
2016.
Riggs, G. A., Hall, D. K., and Román, M. O.: Overview of NASA's MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS) snow-cover Earth System Data Records, Earth Syst. Sci. Data, 9, 765–777, https://doi.org/10.5194/essd-9-765-2017, 2017.
Rodriguez-Galiano, V. F., Ghimire, B., Rogan, J., Chica-Olmo, M., and
Rigol-Sanchez, J. P.: An assessment of the effectiveness of a random forest
classifier for land-cover classification, ISPRS J. Photogramm., 67, 93–104, https://doi.org/10.1016/j.isprsjprs.2011.11.002, 2012.
Romanov, P. and Tarpley, D.: Enhanced algorithm for estimating snow depth
from geostationary satellites, Remote Sens. Environ., 108, 97–110,
2007.
Rosenthal, W. and Dozier, J.: Automated Mapping of Montane Snow Cover at
Subpixel Resolution from the Landsat Thematic Mapper, Water Resour.
Res., 32, 115–130, https://doi.org/10.1029/95WR02718, 1996.
Saberi, N.: Snow Properties Retrieval Using Passive Microwave Observations,
Doctor of Philosophy, Geography Environmental Management, University of
Waterloo, Waterloo, Ontario, Canada, 156 pp., 2019.
Salomonson, V. V. and Appel, I.: Estimating fractional snow cover from
MODIS using the normalized difference snow index, Remote Sens. Environ., 89, 351–360, https://doi.org/10.1016/j.rse.2003.10.016, 2004.
Salomonson, V. V. and Appel, I.: Development of the Aqua MODIS NDSI
fractional snow cover algorithm and validation results, IEEE T. Geosci. Remote, 44, 1747–1756, https://doi.org/10.1109/TGRS.2006.876029,
2006.
Savoie, M. H., Armstrong, R. L., Brodzik, M. J., and Wang, J. R.:
Atmospheric corrections for improved satellite passive microwave snow cover
retrievals over the Tibet Plateau, Remote Sens. Environ., 113,
2661–2669, 2009.
Singh, P. R. and Gan, T. Y.: Retrieval of snow water equivalent using
passive microwave brightness temperature data, Remote Sens. Environ., 74, 275–286, 2000.
Smith, T. and Bookhagen, B.: Assessing uncertainty and sensor biases in
passive microwave data across High Mountain Asia, Remote Sens. Environ., 181, 174–185, 2016.
Sturm, M., Taras, B., Liston, G. E., Derksen, C., Jonas, T., and Lea, J.:
Estimating snow water equivalent using snow depth data and climate classes,
J. Hydrometeorol., 11, 1380–1394, 2010.
Sturm, M.: White water: Fifty years of snow research in WRR and the outlook
for the future, Water Resour. Res., 51, 4948–4965,
https://doi.org/10.1002/2015wr017242, 2015.
Sulla-Menashe, D. and Friedl, M. A.: Use Guide to collection 6 MODIS land
cover (MCD12Q1 and MCD12C1) Product, USGS, Reston, VA, USA, 2018.
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, 2011.
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, 2004.
Tedesco, M. and Jeyaratnam, J.: A New Operational Snow Retrieval Algorithm
Applied to Historical AMSR-E Brightness Temperatures, Remote Sens.-Basel, 8, 1037, https://doi.org/10.3390/rs8121037, 2016.
Tran, H., Nguyen, P., Ombadi, M., Hsu, K. L., Sorooshian, S., and Qing, X.:
A cloud-free MODIS snow cover dataset for the contiguous United States from
2000 to 2017, Sci. Data, 6, 180300, https://doi.org/10.1038/sdata.2018.300, 2019.
Tsai, Y., Dietz, A., Oppelt, N., and Kuenzer, C.: Wet and Dry Snow Detection Using
Sentinel-1 SAR Data for Mountainous Areas with a Machine Learning Technique,
Remote Sens.-Basel, 11, 895, https://doi.org/10.3390/rs11080895, 2019.
Wang, G., Jiang, L., Wu, S., Shi, J., Hao, S., and Liu, X.: Fractional Snow
Cover Mapping from FY-2 VISSR Imagery of China, Remote Sens.-Basel, 9, 983, https://doi.org/10.3390/rs9100983, 2017.
Wang, X., Xie, H., and Liang, T.: Evaluation of MODIS snow cover and cloud
mask and its application in Northern Xinjiang, China, Remote Sens. Environ., 112, 1497–1513, https://doi.org/10.1016/j.rse.2007.05.016, 2008.
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.
Wei, J., Huang, W., Li, Z., Xue, W., Peng, Y., Sun, L., and Cribb, M.:
Estimating 1-km-resolution PM2.5 concentrations across China using the
space-time random forest approach, Remote Sens. Environ., 231, 111221, https://doi.org/10.1016/j.rse.2019.111221, 2019.
Wiesmann, A. and Mätzler, C.: Microwave Emission Model of Layered
Snowpacks, Remote Sens. Environ., 70, 307–316, 1999.
Witten, I., Frank, E., Hall, M., and Pal, C.: Data Mining: Practical Machine Learning Tools and Techniques, 4th. Edn., Morgan Kaufmann, https://doi.org/10.1016/c2009-0-19715-5, 2016.
Wolfe, R. E., Nishihama, M., Fleig, A. J., Kuyper, J. A., Roy, D. P.,
Storey, J. C., and Patt, F. S.: Achieving sub-pixel geolocation accuracy in
support of MODIS land science, Remote Sens. Environ., 83, 31–49,
https://doi.org/10.1016/S0034-4257(02)00085-8, 2002.
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, 2018.
Xiao, X., Zhang, T., Zhong, X., and Li, X.: Spatiotemporal variation of snow
depth in the Northern Hemisphere from 1992 to 2016, Remote Sens.-Basel, 12,
2728, https://doi.org/10.3390/rs12172728, 2020.
Xin, Q., Woodcock, C. E., Liu, J., Tan, B., Melloh, R. A., and Davis, R. E.:
View angle effects on MODIS snow mapping in forests, Remote Sens.
Environ., 118, 50–59, https://doi.org/10.1016/j.rse.2011.10.029, 2012.
Xu, X., Liu, X., Li, X., Xin, Q., Chen, Y., Shi, Q., and Ai, B.: Global snow
cover estimation with Microwave Brightness Temperature measurements and
one-class in situ observations, Remote Sens. Environ., 182, 227–251,
2016.
Yu, J., Zhang, G., Yao, T., Xie, H., Zhang, H., Ke, C., and Yao, R.:
Developing Daily Cloud-Free Snow Composite Products From MODIS Terra–Aqua
and IMS for the Tibetan Plateau, IEEE T. Geosci. Remote, 54, 2171–2180, https://doi.org/10.1109/tgrs.2015.2496950, 2016.
Zhang, H., Zhang, F., Zhang, G., Che, T., Yan, W., Ye, M., and Ma, N.:
Ground-based evaluation of MODIS snow cover product V6 across China:
Implications for the selection of NDSI threshold, Sci. Total
Environ., 651, 2712–2726, https://doi.org/10.1016/j.scitotenv.2018.10.128, 2019.
Zhang, T.: Influence of the seasonal snow cover on the ground thermal
regime: An overview, Rev. Geophys., 43, 589–590, 2005.
Zhang, W., and Goh, A. T. C.: Multivariate adaptive regression splines and
neural network models for prediction of pile drivability, Geosci.
Front., 7, 45–52, https://doi.org/10.1016/j.gsf.2014.10.003, 2016.
Zhao, W., Wu, H., Yin, G., and Duan, S.-B.: Normalization of the temporal
effect on the MODIS land surface temperature product using random forest
regression, ISPRS J. Photogramm., 152,
109–118, https://doi.org/10.1016/j.isprsjprs.2019.04.008, 2019.
Zhong, L., Hu, L., and Zhou, H.: Deep learning based multi-temporal crop
classification, Remote Sens. Environ., 221, 430–443,
https://doi.org/10.1016/j.rse.2018.11.032, 2019.
Zona, D., Gioli, B., Commane, R., Lindaas, J., Wofsy, S. C., Miller, C. E.,
Dinardo, S. J., Dengel, S., Sweeney, C., Karion, A., Chang, R. Y. W.,
Henderson, J. M., Murphy, P. C., Goodrich, J. P., Moreaux, V., Liljedahl, A., Watts, J. D., Kimball, J. S., Lipson, D. A., and Oechel, W. C.: Cold
season emissions dominate the Arctic tundra methane budget,
P. Natl. Acad. Sci. USA, 113, 40–45, https://doi.org/10.1073/pnas.1516017113, 2016.
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.
Daily time series and full space-covered sub-pixel snow cover area data are urgently needed for...
