Articles | Volume 12, issue 5
https://doi.org/10.5194/tc-12-1629-2018
© Author(s) 2018. 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-12-1629-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
04 May 2018
Research article |
| 04 May 2018
| 04 May 2018
On the need for a time- and location-dependent estimation of the NDSI threshold value for reducing existing uncertainties in snow cover maps at different scales
Stefan Härer
Institute of Water Management, Hydrology and Hydraulic Engineering (IWHW), University of Natural Resources and Life Sciences (BOKU), 1190 Vienna, Austria
Matthias Bernhardt
CORRESPONDING AUTHOR
Institute of Water Management, Hydrology and Hydraulic Engineering (IWHW), University of Natural Resources and Life Sciences (BOKU), 1190 Vienna, Austria
Matthias Siebers
Commission for Glaciology, Bavarian Academy of Sciences and Humanities, 80539 Munich, Germany
Karsten Schulz
Institute of Water Management, Hydrology and Hydraulic Engineering (IWHW), University of Natural Resources and Life Sciences (BOKU), 1190 Vienna, Austria
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Geosci. Model Dev., 9, 307–321, https://doi.org/10.5194/gmd-9-307-2016, https://doi.org/10.5194/gmd-9-307-2016, 2016
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This paper describes a new method to produce spatially and temporally calibrated NDSI-based satellite snow cover maps utilizing simultaneously captured terrestrial photographs as in situ information. First results confirm a high quality of the produced satellite snow cover maps and emphasize the need for calibration of the NDSI threshold value to ensure a high accuracy and reproduciblity. The software "PRACTISE V.2.1" was developed to automatically process the photographs and satellite images.
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Benjamin Müller, Matthias Bernhardt, Conrad Jackisch, and Karsten Schulz
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S. Härer, M. Bernhardt, and K. Schulz
Geosci. Model Dev., 9, 307–321, https://doi.org/10.5194/gmd-9-307-2016, https://doi.org/10.5194/gmd-9-307-2016, 2016
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B. Müller, M. Bernhardt, and K. Schulz
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S. Härer, M. Bernhardt, J. G. Corripio, and K. Schulz
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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.
Siddharth Singh, Michael Durand, Edward Kim, and Ana P. Barros
EGUsphere, https://doi.org/10.5194/egusphere-2023-1987, https://doi.org/10.5194/egusphere-2023-1987, 2023
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Seasonal snowfall accumulation (snowpack) 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/Ku-band SAR backscatter measurements constrained by physically based snow hydrology and radar models. We explored different spatial resolutions and vertical structures that agree well with ground observation.
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.
Shadi Oveisgharan, Robert Zinke, Zachary Hoppinen, and Hans Peter Marshall
The Cryosphere Discuss., https://doi.org/10.5194/tc-2023-95, https://doi.org/10.5194/tc-2023-95, 2023
Revised manuscript accepted for TC
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The seasonal snowpack provides water resources to billions of people worldwide. Snow is the primary source of water for river channel discharge. 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 phase change to estimate the SWE change between two measurement. The error in estimated SWE change is less than 2 cm for in situ stations.
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.
Dhiraj Kumar Singh, Srinivasarao Tanniru, Kamal Kant Singh, Harendra Singh Negi, and Raaj Ramsankaran
The Cryosphere Discuss., https://doi.org/10.5194/tc-2023-66, https://doi.org/10.5194/tc-2023-66, 2023
Revised manuscript accepted for TC
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In-situ techniques for snow depth (SD) measurement are not adequate to represent the spatiotemporal variability of 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.
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.
Xiongxin Xiao, Shunlin Liang, Tao He, Daiqiang Wu, Congyuan Pei, and Jianya Gong
The Cryosphere, 15, 835–861, https://doi.org/10.5194/tc-15-835-2021, https://doi.org/10.5194/tc-15-835-2021, 2021
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Daily time series and full space-covered sub-pixel snow cover area data are urgently needed for climate and reanalysis studies. Due to the fact that observations from optical satellite sensors are affected by clouds, this study attempts to capture dynamic characteristics of snow cover at a fine spatiotemporal resolution (daily; 6.25 km) accurately by using passive microwave data. We demonstrate the potential to use the passive microwave and the MODIS data to map the fractional snow cover area.
Marco Bongio, Ali Nadir Arslan, Cemal Melih Tanis, and Carlo De Michele
The Cryosphere, 15, 369–387, https://doi.org/10.5194/tc-15-369-2021, https://doi.org/10.5194/tc-15-369-2021, 2021
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The capability of time-lapse photography to retrieve snow depth time series was tested. We demonstrated that this method can be efficiently used in three different case studies: two in the Italian Alps and one in a forested region of Finland, with an accuracy comparable to the most common methods such as ultrasonic sensors or manual measurements. We hope that this simple method based only on a camera and a graduated stake can enable snow depth measurements in dangerous and inaccessible sites.
Lucie A. Eberhard, Pascal Sirguey, Aubrey Miller, Mauro Marty, Konrad Schindler, Andreas Stoffel, and Yves Bühler
The Cryosphere, 15, 69–94, https://doi.org/10.5194/tc-15-69-2021, https://doi.org/10.5194/tc-15-69-2021, 2021
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In spring 2018 in the alpine Dischma valley (Switzerland), we tested different industrial photogrammetric platforms for snow depth mapping. These platforms were high-resolution satellites, an airplane, unmanned aerial systems and a terrestrial system. Therefore, this study gives a general overview of the accuracy and precision of the different photogrammetric platforms available in space and on earth and their use for snow depth mapping.
Maxim Lamare, Marie Dumont, Ghislain Picard, Fanny Larue, François Tuzet, Clément Delcourt, and Laurent Arnaud
The Cryosphere, 14, 3995–4020, https://doi.org/10.5194/tc-14-3995-2020, https://doi.org/10.5194/tc-14-3995-2020, 2020
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Terrain features found in mountainous regions introduce large errors into the calculation of the physical properties of snow using optical satellite images. We present a new model performing rapid calculations of solar radiation over snow-covered rugged terrain that we tested over a site in the French Alps. The results of the study show that all the interactions between sunlight and the terrain should be accounted for over snow-covered surfaces to correctly estimate snow properties from space.
Tim Carlsen, Gerit Birnbaum, André Ehrlich, Veit Helm, Evelyn Jäkel, Michael Schäfer, and Manfred Wendisch
The Cryosphere, 14, 3959–3978, https://doi.org/10.5194/tc-14-3959-2020, https://doi.org/10.5194/tc-14-3959-2020, 2020
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The angular reflection of solar radiation by snow surfaces is particularly anisotropic and highly variable. We measured the angular reflection from an aircraft using a digital camera in Antarctica in 2013/14 and studied its variability: the anisotropy increases with a lower Sun but decreases for rougher surfaces and larger snow grains. The applied methodology allows for a direct comparison with satellite observations, which generally underestimated the anisotropy measured within this study.
César Deschamps-Berger, Simon Gascoin, Etienne Berthier, Jeffrey Deems, Ethan Gutmann, Amaury Dehecq, David Shean, and Marie Dumont
The Cryosphere, 14, 2925–2940, https://doi.org/10.5194/tc-14-2925-2020, https://doi.org/10.5194/tc-14-2925-2020, 2020
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We evaluate a recent method to map snow depth based on satellite photogrammetry. We compare it with accurate airborne laser-scanning measurements in the Sierra Nevada, USA. We find that satellite data capture the relationship between snow depth and elevation at the catchment scale and also small-scale features like snow drifts and avalanche deposits. We conclude that satellite photogrammetry stands out as a convenient method to estimate the spatial distribution of snow depth in high mountains.
Phillip Harder, John W. Pomeroy, and Warren D. Helgason
The Cryosphere, 14, 1919–1935, https://doi.org/10.5194/tc-14-1919-2020, https://doi.org/10.5194/tc-14-1919-2020, 2020
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Unmanned-aerial-vehicle-based (UAV) structure-from-motion (SfM) techniques have the ability to map snow depths in open areas. Here UAV lidar and SfM are compared to map sub-canopy snowpacks. Snow depth accuracy was assessed with data from sites in western Canada collected in 2019. It is demonstrated that UAV lidar can measure the sub-canopy snow depth at a high accuracy, while UAV-SfM cannot. UAV lidar promises to quantify snow–vegetation interactions at unprecedented accuracy and resolution.
Carlo Marin, Giacomo Bertoldi, Valentina Premier, Mattia Callegari, Christian Brida, Kerstin Hürkamp, Jochen Tschiersch, Marc Zebisch, and Claudia Notarnicola
The Cryosphere, 14, 935–956, https://doi.org/10.5194/tc-14-935-2020, https://doi.org/10.5194/tc-14-935-2020, 2020
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In this paper, we use for the first time the synthetic aperture radar (SAR) time series acquired by Sentinel-1 to monitor snowmelt dynamics in alpine regions. We found that the multitemporal SAR allows the identification of the three phases that characterize the melting process, i.e., moistening, ripening and runoff, in a spatial distributed way. We believe that the presented investigation could have relevant applications for monitoring and predicting the snowmelt progress over large regions.
Edward H. Bair, Karl Rittger, Jawairia A. Ahmad, and Doug Chabot
The Cryosphere, 14, 331–347, https://doi.org/10.5194/tc-14-331-2020, https://doi.org/10.5194/tc-14-331-2020, 2020
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Ice and snowmelt feed the Indus River and Amu Darya, but validation of estimates from satellite sensors has been a problem until recently, when we were given daily snow depth measurements from these basins. Using these measurements, estimates of snow on the ground were created and compared with models. Estimates of water equivalent in the snowpack were mostly in agreement. Stratigraphy was also modeled and showed 1 year with a relatively stable snowpack but another with multiple weak layers.
Nick Rutter, Melody J. Sandells, Chris Derksen, Joshua King, Peter Toose, Leanne Wake, Tom Watts, Richard Essery, Alexandre Roy, Alain Royer, Philip Marsh, Chris Larsen, and Matthew Sturm
The Cryosphere, 13, 3045–3059, https://doi.org/10.5194/tc-13-3045-2019, https://doi.org/10.5194/tc-13-3045-2019, 2019
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Impact of natural variability in Arctic tundra snow microstructural characteristics on the capacity to estimate snow water equivalent (SWE) from Ku-band radar was assessed. Median values of metrics quantifying snow microstructure adequately characterise differences between snowpack layers. Optimal estimates of SWE required microstructural values slightly less than the measured median but tolerated natural variability for accurate estimation of SWE in shallow snowpacks.
Alexandre R. Bevington, Hunter E. Gleason, Vanessa N. Foord, William C. Floyd, and Hardy P. Griesbauer
The Cryosphere, 13, 2693–2712, https://doi.org/10.5194/tc-13-2693-2019, https://doi.org/10.5194/tc-13-2693-2019, 2019
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We investigate the influence of ocean–atmosphere teleconnections on the start, end, and duration of snow cover in British Columbia, Canada. We do this using daily satellite imagery from 2002 to 2018 and assess the accuracy of our methods using reported snow cover at 60 weather stations. We found that there are very strong relationships that vary by region and elevation. This improves our understanding of snow cover distribution and could be used to predict snow cover from ocean–climate indices.
Cited articles
Abermann, J., Kuhn, M., and Fischer, A.: A reconstruction of annual mass balances
of Austria's glaciers from 1969 to 1998, Ann. Glaciol., 52, 127–134,
https://doi.org/10.3189/172756411799096259, 2011.
Agosta, C., Fettweis, X., and Datta, R.: Evaluation of the CMIP5 models in the
aim of regional modelling of the Antarctic surface mass balance, The Cryosphere,
9, 2311–2321, https://doi.org/10.5194/tc-9-2311-2015, 2015.
Aronica, G., Bates, P. D., and Horritt, M. S.: Assessing the uncertainty in
distributed model predictions using observed binary pattern information within
GLUE, Hydrol. Process., 16, 2001–2016, https://doi.org/10.1002/hyp.398, 2002.
Bernhardt, M. and Schulz, K.: SnowSlide: A simple routine for calculating
gravitational snow transport, Geophys. Res. Lett., 37, L11502, https://doi.org/10.1029/2010GL043086, 2010.
Bernhardt, M., Schulz, K., Liston, G. E., and Zängl, G.: The influence of
lateral snow redistribution processes on snow melt and sublimation in alpine
regions, J. Hydrol., 424–425, 196–206, https://doi.org/10.1016/j.jhydrol.2012.01.001, 2012.
Bernhardt, M., Härer, S., Jacobeit, J., Wetzel, K. F., and Schulz, K.: The
Virtual Alpine Observatory – research focus Alpine hydrology, Hydrol. Wasserbewirts.,
58, 241–243, 2014.
Bernhardt, M., Schulz, K., and Pomeroy, J.: The International Network for Alpine
Research Catchment Hydrology. A new GEWEX crosscutting Project, Hydrol.
Wasserbewirts., 59, 190–191, 2015.
Burns, P. and Nolin, A.: Using atmospherically-corrected Landsat imagery to
measure glacier area change in the Cordillera Blanca, Peru from 1987 to 2010,
Remote Sens. Environ., 140, 165–178, https://doi.org/10.1016/j.rse.2013.08.026, 2014.
Butt, M. J.,and Bilal, M.: Application of snowmelt runoff model for water
resource management, Hydrol. Process., 25, 3735–3747, https://doi.org/10.1002/hyp.8099, 2011.
Chaponnière, A., Maisongradne, P., Duchemin, B., Hanich, L., Boulet, G.,
Escadal, R., and Elouaddat, S.: A combined high and low spatial resolution
approach for mapping snow covered areas in the Atlas mountains, Int. J. Remote
Sens., 26, 2755–2777, https://doi.org/10.1080/01431160500117758, 2005.
Deb, D., Butcher, J., and Srinivasan, R.: Projected Hydrologic Changes Under
Mid-21st Century Climatic Conditions in a Sub-arctic Watershed, Water Resour.
Manage., 29, 1467–1487, https://doi.org/10.1007/s11269-014-0887-5, 2015.
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi,
S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars,
A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R.,
Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Holm, E.
V., Isaksen, L., Kallberg, P., Koehler, M., Matricardi, M., McNally, A. P.,
Monge-Sanz, B. M., Morcrette, J.-J., Park, B.-K., Peubey, C., de Rosnay, P.,
Tavolato, C., Thepaut, J.-N., and Vitart, F.: The ERA-Interim reanalysis:
configuration and performance of the data assimilation system, Q. J. Roy. Meteorol.
Soc., 137, 553–597, https://doi.org/10.1002/qj.828, 2011.
Dozier, J.: Snow reflectance from LANDSAT-4 Thematic Mapper, IEEE T. Geosci.
Remote, GE-22, 323–328, https://doi.org/10.1109/TGRS.1984.350628, 1984.
Dozier, J.: Spectral Signature of Alpine Snow Cover from the Landsat Thematic
Mapper, Remote Sens. Environ., 28, 9–22, https://doi.org/10.1016/0034-4257(89)90101-6, 1989.
Drusch, M., Vasiljevic, D., and Viterbo, P.: ECMWF's global snow analysis:
Assessment and revision based on satellite observations, J. Appl. Meteorol.,
43, 1282–1294, https://doi.org/10.1175/1520-0450(2004)043<1282:EGSAAA>2.0.CO;2, 2004.
Dutra, E., Viterbo, P., Miranda, P. M. A., and Balsamo, G.: Complexity of Snow
Schemes in a Climate Model and Its Impact on Surface Energy and Hydrology, J.
Hydrometeorol., 13, 521–538, https://doi.org/10.1175/JHM-D-11-072.1, 2012.
Dyurgerov, M.: Mountain and subpolar glaciers show an increase in sensitivity
to climate warming and intensification of the water cycle, J. Hydrol., 282,
164–176, https://doi.org/10.1016/S0022-1694(03)00254-3, 2003.
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., Foster, J. L., Salomonson, V. V., Klein, A. G., and Chien, J. Y.
L.: Development of a technique to assess snow-cover mapping errors from space,
IEEE T. Geosci. Remote, 39, 432–438, https://doi.org/10.1109/36.905251, 2001.
Hall, D. K., Riggs, G. A., Salomonson, V. V., DiGirolamo, N. E., and Bayr, K.
J.: MODIS snow-cover products, Remote Sens. Environ., 83, 181–194,
https://doi.org/10.1016/S0034-4257(02)00095-0, 2002.
Härer, S., Bernhardt, M., Corripio, J. G., and Schulz, K.: PRACTISE – Photo
Rectification And ClassificaTIon SoftwarE (V.1.0), Geosci. Model Dev., 6, 837–848,
https://doi.org/10.5194/gmd-6-837-2013, 2013.
Härer, S., Bernhardt, M., and Schulz, K.: PRACTISE – Photo Rectification
And ClassificaTIon SoftwarE (V.2.1), Geosci. Model Dev., 9, 307–321,
https://doi.org/10.5194/gmd-9-307-2016, 2016.
Homan, J. W., Luce, C. H., McNamara, J. P., and Glenn, N. F.: Improvement of
distributed snowmelt energy balance modeling with MODIS-based NDSI-derived
fractional snow-covered area data, Hydrol. Process., 25, 650–660, https://doi.org/10.1002/hyp.7857, 2011.
Kyle, H. L., Curran, R. J., Barnes, W. L., and Escoe, D.: A cloud physics
radiometer, in: 3rd Conference on Atmospheric Radiation, American Meteorological
Society, 28–30 June 1978, Davis, California, p. 107, 1978.
Liston, G. E.: Representing subgrid snow cover heterogeneities in regional and
global models, J. Climate, 17, 1381–1397, https://doi.org/10.1175/1520-0442(2004)017<1381:RSSCHI>2.0.CO;2, 2004.
Maher, A. I., Treitz, P. M., and Ferguson, M. A. D.: Can Landsat data detect
variations in snow cover within habitats of arctic ungulates?, Wildlife Biol.,
18, 75–87, https://doi.org/10.2981/11-055, 2012.
Mankin, J. S. and Diffenbaugh, N. S.: Influence of temperature and precipitation
variability on near-term snow trends, Clim. Dynam., 45, 1099-1116, https://doi.org/10.1007/s00382-014-2357-4, 2015.
Maussion, F., Scherer, D., Finkelnburg, R., Richters, J., Yang, W., and Yao, T.:
WRF simulation of a precipitation event over the Tibetan Plateau, China – an
assessment using remote sensing and ground observations, Hydrol. Earth Syst. Sci.,
15, 1795–1817, https://doi.org/10.5194/hess-15-1795-2011, 2011.
Mayr, E., Hagg, W., Mayer, C., and Braun, L.: Calibrating a spatially distributed
conceptual hydrological model using runoff, annual mass balance and winter mass
balance, J. Hydrol., 478, 40–49, https://doi.org/10.1016/j.jhydrol.2012.11.035, 2013.
Otsu, N.: Threshold Selection Method from Gray-Level Histograms, IEEE T. Syst.
Man. Cyb., 9, 62–66, https://doi.org/10.1109/Tsmc.1979.4310076, 1979.
Painter, T. H., Rittger, K., McKenzie, C., Slaughter, P., Davis R. E., and
Dozier, J.: Retrieval of subpixel snow covered area, grain size, and albedo from
MODIS, Remote Sens. Environ., 113, 868–879, https://doi.org/10.1016/j.rse.2009.01.001, 2009.
Pomeroy, J., Bernhardt, M., and Marks, D.: Research network to track alpine
water, Nature, 521, 32–32 https://doi.org/10.1038/521032c, 2015.
Racoviteanu, A. E., Paul, F., Raup, B., Khalsa, S. J. S., and Armstrong, R.:
Challenges and recommendations in mapping of glacier parameters from space:
results of the 2008 Global Land Ice Measurements from Space (GLIMS) workshop,
Boulder, Colorado, USA, Ann. Glaciol., 50, 53–69, https://doi.org/10.3189/172756410790595804, 2009.
Rangwala, I., Miller, J. R., Russell, G. L., and Xu, M.: Using a global climate
model to evaluate the influences of water vapor, snow cover and atmospheric
aerosol on warming in the Tibetan Plateau during the twenty-first century, Clim.
Dynam., 34, 859–872, https://doi.org/10.1007/s00382-009-0564-1, 2010.
Rouse Jr., J., Haas, R. H., Schell, J. A., and Deering, D. W.: Monitoring
vegetation systems in the Great Plains with ERTS, in: 3rd Earth Resources
Technology Satellite-1 Symposium – Vol. I: Technical Presentations, NASA SP-351,
NASA, Washington, USA, 309–317, 1974.
Salvatori, R., Plini, P., Giusto, M., Valt, M., Salzano, R., Montagnoli, M.,
Cagnati, A., Crepaz, G., and Sigismondi, D.: Snow cover monitoring with images
from digital camera systems, Ital. J. Remote Sens., 43, 137–145, https://doi.org/10.5721/ItJRS201143211, 2011.
Sankey, T., Donald, J., Mcvay, J., Ashley, M., O'Donnell, F., Lopez, S. M., and
Springer, A.: Multi-scale analysis of snow dynamics at the southern margin of
the North American continental snow distribution, Remote Sens. Environ., 169,
307–319, https://doi.org/10.1016/j.rse.2015.08.028, 2015.
Santini, M. and di Paola, A.: Changes in the world rivers' discharge projected
from an updated high resolution dataset of current and future climate zones, J.
Hydrol., 531, 768–780, https://doi.org/10.1016/j.jhydrol.2015.10.050, 2015.
Silverio, W. and Jaquet, J. M.: Prototype land-cover mapping of the Huascaran
Biosphere Reserve (Peru) using a digital elevation model, and the NDSI and NDVI
indices, J. Appl. Remote Sens., 3, 033516, https://doi.org/10.1117/1.3106599, 2009.
Sirguey, P., Mathieu, R., and Arnaud, Y.: Subpixel monitoring of the seasonal
snow cover with MODIS at 250 m spatial resolution in the Southern Alps of New
Zealand: Methodology and accuracy assessment, Remote Sens. Environ., 113,
160–181, https://doi.org/10.1016/j.rse.2008.09.008, 2009.
Takata, K., Emori, S., and Watanabe, T.: Development of the minimal advanced
treatments of surface interaction and runoff, Global Planet. Change, 38, 209–222,
https://doi.org/10.1016/S0921-8181(03)00030-4, 2003.
Tekeli, A. E., Akyurek, Z., Sorman, A. A., Sensoy, A., and Sorman, A. U.: Using
MODIS snow cover maps in modeling snowmelt runoff process in the eastern part
of Turkey, Remote Sens. Environ., 97, 216–230, https://doi.org/10.1016/j.rse.2005.03.013, 2005.
Tennant, C. J., Crosby, B. T., and Godsey, S. E.: Elevation-dependent responses
of streamflow to climate warming, Hydrol. Process., 29, 991–1001, https://doi.org/10.1002/hyp.10203, 2015.
Thirel, G., Salamon, P., Burek, P., and Kalas, M.: Assimilation of MODIS Snow
Cover Area Data in a Distributed Hydrological Model Using the Particle Filter,
Remote Sens., 5, 5825–5850, https://doi.org/10.3390/rs5115825, 2013.
Tucker, C. J.: Red and photographic infrared linear combinations for monitoring
vegetation, Remote Sens. Environ., 8, 127–150, https://doi.org/10.1016/0034-4257(79)90013-0, 1979.
Vavrus, S., Philippon-Berthier, G., Kutzbach, J. E., and Ruddiman, W. F.: The
role of GCM resolution in simulating glacial inception, Holocene, 21, 819–830,
https://doi.org/10.1177/0959683610394882, 2011.
Viviroli, D., Archer, D. R., Buytaert, W., Fowler, H. J., Greenwood, G. B.,
Hamlet, A. F., Huang, Y., Koboltschnig, G., Litaor, M. I., Lopez-Moreno, J. I.,
Lorentz, S., Schaedler, B., Schreier, H., Schwaiger, K., Vuille, M., and Woods,
R.: Climate change and mountain water resources: overview and recommendations
for research, management and policy, Hydrol. Earth Syst. Sci., 15, 471–504,
https://doi.org/10.5194/hess-15-471-2011, 2011.
Warscher, M., Strasser, U., Kraller, G., Marke, T., Franz, H., and Kunstmann,
H.: Performance of complex snow cover descriptions in a distributed hydrological
model system: A case study for the high Alpine terrain of the Berchtesgaden Alps,
Water Resour. Res., 49, 2619–2637, https://doi.org/10.1002/wrcr.20219, 2013.
Weber, M., Bernhardt, M., Pomeroy, J. W., Fang, X., Härer, S., and Schulz,
K.: Description of current and future snow processes in a small basin in the
Bavarian Alps, Environ. Earth Sci., 75, 1223, https://doi.org/10.1007/s12665-016-6027-1, 2016.
Yin, D., Gao, X., Chen, X., Shao, Y., and Chen, J.: Comparison of automatic
thresholding methods for snow-cover mapping using Landsat TM imagery, Int. J.
Remote Sens., 34, 6529–6538, https://doi.org/10.1080/01431161.2013.803631, 2013.
Zhu, Z., Wang, S., and Woodcock, C. E.: Improvement and expansion of the Fmask
algorithm: cloud, cloud shadow, and snow detection for Landsats 4–7, 8, and
Sentinel 2 images, Remote Sens. Environ., 159, 269–277, https://doi.org/10.1016/j.rse.2014.12.014, 2015.
Short summary
The paper presents an approach which can be used to process satellite-based snow cover maps with a higher-than-today accuracy at the local scale. Many of the current satellite-based snow maps are using the NDSI with a threshold as a tool for deciding if there is snow on the ground or not. The presented study has shown that, firstly, using the standard threshold of 0.4 can result in significant derivations at the local scale and that, secondly, the deviations become smaller for coarser scales.
The paper presents an approach which can be used to process satellite-based snow cover maps with...
