Special Issue "Remote Sensing of Snow and Its Applications"

A special issue of Geosciences (ISSN 2076-3263). This special issue belongs to the section "Geophysics".

Deadline for manuscript submissions: closed (31 March 2019)

Special Issue Editors

Guest Editor
Dr. Ali Nadir Arslan

Finnish Meteorological Institute, Helsinki, Finland
Website | E-Mail
Interests: remote sensing; electromagnetic theory & modeling; methods & applications for cryosphere; snow cover; phenology
Guest Editor
Dr. Zuhal Akyurek

Middle East Technical University, Ankara, Turkey
Website | E-Mail
Interests: snow hydrology; modelling; optical remote sensing; spatial variability of snow

Special Issue Information

Dear Colleagues,

Snow cover is an essential climate variable directly affecting the Earth’s energy balance. Snow cover has a number of important physical properties that exert an influence on global and regional energy, water and carbon cycles. Surface temperature is highly dependent on the presence or absence of snow cover, and temperature trends have been shown to be related to changes in snow cover. Its quantification in a changing climate is thus important for various environmental and economic impact assessments. Identification of snowmelt processes could significantly support water management, flood prediction and prevention.

Remote sensing provides a good understanding of snow cover and enable snow cover information to be assimilated into hydrological, land surface, meteorological and climate models for predicting snowmelt runoff, snow water resources and to warn about snow-related natural hazards.

This Special Issue invites and encourages to submit covering all instrumentation/sensors and methodologies/models/algorithms in remote sensing of snow parameters (snow extent, snow depth, snow wetness, snow density, snow water equivalent, etc.) and applications where remotely-sensed snow information are used for, including, but not limited to:

  • Remote sensing techniques and methods for snow
  • Modelling, retrieval algorithms and in-situ measurements of snow parameters
  • Multi-source and multi-sensor remote sensing of snow
  • Remote sensing and model integrated approaches of snow
  • Applications where remotely sensed snow information used for such as weather forecasting, flooding, avalanche, water management, traffic, health and sport, agriculture and forestry, climate scenarios, etc.
  • Copernicus Sentinels, etc.

Dr. Ali Nadir Arslan
Dr. Zuhal Akyurek
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Geosciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 850 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • remote sensing
  • snow parameters
  • spatial and temporal variability of snow
  • snow hydrology
  • integration of remote sensing with models (hydrological, land surface, meteorological and climate)

Published Papers (9 papers)

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Research

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Open AccessArticle
Cross-Country Assessment of H-SAF Snow Products by Sentinel-2 Imagery Validated against In-Situ Observations and Webcam Photography
Geosciences 2019, 9(3), 129; https://doi.org/10.3390/geosciences9030129
Received: 2 December 2018 / Revised: 8 March 2019 / Accepted: 11 March 2019 / Published: 15 March 2019
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Abstract
Information on snow properties is of critical relevance for a wide range of scientific studies and operational applications, mainly for hydrological purposes. However, the ground-based monitoring of snow dynamics is a challenging task, especially over complex topography and under harsh environmental conditions. Remote [...] Read more.
Information on snow properties is of critical relevance for a wide range of scientific studies and operational applications, mainly for hydrological purposes. However, the ground-based monitoring of snow dynamics is a challenging task, especially over complex topography and under harsh environmental conditions. Remote sensing is a powerful resource providing snow observations at a large scale. This study addresses the potential of using Sentinel-2 high-resolution imagery to assess moderate-resolution snow products, namely H10—Snow detection (SN-OBS-1) and H12—Effective snow cover (SN-OBS-3) supplied by the Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF) project of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT). With the aim of investigating the reliability of reference data, the consistency of Sentinel-2 observations is evaluated against both in-situ snow measurements and webcam digital imagery. The study area encompasses three different regions, located in Finland, the Italian Alps and Turkey, to comprehensively analyze the selected satellite products over both mountainous and flat areas having different snow seasonality. The results over the winter seasons 2016/17 and 2017/18 show a satisfying agreement between Sentinel-2 data and ground-based observations, both in terms of snow extent and fractional snow cover. H-SAF products prove to be consistent with the high-resolution imagery, especially over flat areas. Indeed, while vegetation only slightly affects the detection of snow cover, the complex topography more strongly impacts product performances. Full article
(This article belongs to the Special Issue Remote Sensing of Snow and Its Applications)
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Open AccessArticle
Automated Classification of Terrestrial Images: The Contribution to the Remote Sensing of Snow Cover
Geosciences 2019, 9(2), 97; https://doi.org/10.3390/geosciences9020097
Received: 11 December 2018 / Revised: 11 February 2019 / Accepted: 13 February 2019 / Published: 19 February 2019
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Abstract
The relation between the fraction of snow cover and the spectral behavior of the surface is a critical issue that must be approached in order to retrieve the snow cover extent from remotely sensed data. Ground-based cameras are an important source of datasets [...] Read more.
The relation between the fraction of snow cover and the spectral behavior of the surface is a critical issue that must be approached in order to retrieve the snow cover extent from remotely sensed data. Ground-based cameras are an important source of datasets for the preparation of long time series concerning the snow cover. This study investigates the support provided by terrestrial photography for the estimation of a site-specific threshold to discriminate the snow cover. The case study is located in the Italian Alps (Falcade, Italy). The images taken over a ten-year period were analyzed using an automated snow-not-snow detection algorithm based on Spectral Similarity. The performance of the Spectral Similarity approach was initially investigated comparing the results with different supervised methods on a training dataset, and subsequently through automated procedures on the entire dataset. Finally, the integration with satellite snow products explored the opportunity offered by terrestrial photography for calibrating and validating satellite-based data over a decade. Full article
(This article belongs to the Special Issue Remote Sensing of Snow and Its Applications)
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Open AccessArticle
Discriminating Wet Snow and Firn for Alpine Glaciers Using Sentinel-1 Data: A Case Study at Rofental, Austria
Geosciences 2019, 9(2), 69; https://doi.org/10.3390/geosciences9020069
Received: 10 December 2018 / Revised: 14 January 2019 / Accepted: 22 January 2019 / Published: 30 January 2019
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Abstract
Continuous monitoring of glacier changes supports our understanding of climate related glacier behavior. Remote sensing data offer the unique opportunity to observe individual glaciers as well as entire mountain ranges. In this study, we used synthetic aperture radar (SAR) data to monitor the [...] Read more.
Continuous monitoring of glacier changes supports our understanding of climate related glacier behavior. Remote sensing data offer the unique opportunity to observe individual glaciers as well as entire mountain ranges. In this study, we used synthetic aperture radar (SAR) data to monitor the recession of wet snow area extent per season for three different glacier areas of the Rofental, Austria. For four glaciological years (GYs, 2014/2015–2017/2018), Sentinel-1 (S1) SAR data were acquired and processed. For all four GYs, the seasonal snow retreated above the elevation range of perennial firn. The described processing routine is capable of discriminating wet snow from firn areas for all GYs with sufficient accuracy. For a short in situ transect of the snow—firn boundary, SAR derived wet snow extent agreed within an accuracy of three to four pixels or 30–40 m. For entire glaciers, we used optical remote sensing imagery and field data to assess reliability of derived wet snow covered area extent. Differences in determination of snow covered area between optical data and SAR analysis did not exceed 10% on average. Offsets of SAR data to results of annual field assessments are below 10% as well. The introduced workflow for S1 data will contribute to monitoring accumulation area extent for remote and hazardous glacier areas and thus improve the data basis for such locations. Full article
(This article belongs to the Special Issue Remote Sensing of Snow and Its Applications)
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Open AccessArticle
Multi-Source Based Spatio-Temporal Distribution of Snow in a Semi-Arid Headwater Catchment of Northern Mongolia
Geosciences 2019, 9(1), 53; https://doi.org/10.3390/geosciences9010053
Received: 6 December 2018 / Revised: 14 January 2019 / Accepted: 14 January 2019 / Published: 19 January 2019
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Abstract
Knowledge of the duration and distribution of seasonal snow cover is important for understanding the hydrologic regime in mountainous regions within semi-arid climates. In the headwater of the semi-arid Sugnugur catchment (in the Khentii Mountains, northern Mongolia), a spatial analysis of seasonal snow [...] Read more.
Knowledge of the duration and distribution of seasonal snow cover is important for understanding the hydrologic regime in mountainous regions within semi-arid climates. In the headwater of the semi-arid Sugnugur catchment (in the Khentii Mountains, northern Mongolia), a spatial analysis of seasonal snow cover duration (SCD) was performed on a 30 m spatial resolution by integrating the spatial resolution of Landsat-7, Landsat-8, and Sentinel-2A images with the daily temporal resolution of Moderate Resolution Imaging Spectroradiometer (MODIS) snow products (2000–2017). Validation was achieved using in situ time series measurements from winter field campaigns and distributed surface temperature loggers. We found a mean increase of SCD with altitude at approximately +6 days/100 m. However, we found no altitude-dependent changes in snow depth during field campaigns. The southern exposed valley slopes are either snow free or covered by intermittent snow throughout the winter months due to high sublimation rates and prevailing wind. The estimated mean SCD ranges from 124 days in the lower parts of the catchment to 226 days on the mountain peaks, with a mean underestimation of 12–13 days. Snow onset and melt dates exhibited large inter-annual variability, but no significant trend in the seasonal SCD was evident. This method can be applied to high-resolution snow mapping in similar mountainous regions. Full article
(This article belongs to the Special Issue Remote Sensing of Snow and Its Applications)
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Open AccessArticle
Advances in Snow Hydrology Using a Combined Approach of GNSS In Situ Stations, Hydrological Modelling and Earth Observation—A Case Study in Canada
Geosciences 2019, 9(1), 44; https://doi.org/10.3390/geosciences9010044
Received: 8 January 2019 / Revised: 8 January 2019 / Accepted: 10 January 2019 / Published: 15 January 2019
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Abstract
The availability of in situ snow water equivalent (SWE), snowmelt and run-off measurements is still very limited especially in remote areas as the density of operational stations and field observations is often scarce and usually costly, labour-intense and/or risky. With remote sensing products, [...] Read more.
The availability of in situ snow water equivalent (SWE), snowmelt and run-off measurements is still very limited especially in remote areas as the density of operational stations and field observations is often scarce and usually costly, labour-intense and/or risky. With remote sensing products, spatially distributed information on snow is potentially available, but often lacks the required spatial or temporal requirements for hydrological applications. For the assurance of a high spatial and temporal resolution, however, it is often necessary to combine several methods like Earth Observation (EO), modelling and in situ approaches. Such a combination was targeted within the business applications demonstration project SnowSense (2015–2018), co-funded by the European Space Agency (ESA), where we designed, developed and demonstrated an operational snow hydrological service. During the run-time of the project, the entire service was demonstrated for the island of Newfoundland, Canada. The SnowSense service, developed during the demonstration project, is based on three pillars, including (i) newly developed in situ snow monitoring stations based on signals of the Global Navigation Satellite System (GNSS); (ii) EO snow cover products on the snow cover extent and on information whether the snow is dry or wet; and (iii) an integrated physically based hydrological model. The key element of the service is the novel GNSS based in situ sensor, using two static low-cost antennas with one being mounted on the ground and the other one above the snow cover. This sensor setup enables retrieving the snow parameters SWE and liquid water content (LWC) in the snowpack in parallel, using GNSS carrier phase measurements and signal strength information. With the combined approach of the SnowSense service, it is possible to provide spatially distributed SWE to assess run-off and to provide relevant information for hydropower plant management in a high spatial and temporal resolution. This is particularly needed for so far non, or only sparsely equipped catchments in remote areas. We present the results and validation of (i) the GNSS in situ sensor setup for SWE and LWC measurements at the well-equipped study site Forêt Montmorency near Quebec, Canada and (ii) the entire combined in situ, EO and modelling SnowSense service resulting in assimilated SWE maps and run-off information for two different large catchments in Newfoundland, Canada. Full article
(This article belongs to the Special Issue Remote Sensing of Snow and Its Applications)
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Open AccessArticle
Generating Observation-Based Snow Depletion Curves for Use in Snow Cover Data Assimilation
Geosciences 2018, 8(12), 484; https://doi.org/10.3390/geosciences8120484
Received: 1 November 2018 / Revised: 6 December 2018 / Accepted: 10 December 2018 / Published: 14 December 2018
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Abstract
Snow depletion curves (SDC) are functions that are used to show the relationship between snow covered area and snow depth or water equivalent. Previous snow cover data assimilation (DA) studies have used theoretical SDC models as observation operators to map snow depth to [...] Read more.
Snow depletion curves (SDC) are functions that are used to show the relationship between snow covered area and snow depth or water equivalent. Previous snow cover data assimilation (DA) studies have used theoretical SDC models as observation operators to map snow depth to snow cover fraction (SCF). In this study, a new approach is introduced that uses snow water equivalent (SWE) observations and satellite-based SCF retrievals to derive SDC relationships for use in an Ensemble Kalman filter (EnKF) to assimilate snow cover estimates. A histogram analysis is used to bin the SWE observations, which the corresponding SCF observations are then averaged within, helping to constrain the amount of data dispersion across different temporal and regional conditions. Logarithmic functions are linearly regressed with the binned average values, for two U.S. mountainous states: Colorado and Washington. The SDC-based logarithmic functions are used as EnKF observation operators, and the satellite-based SCF estimates are assimilated into a land surface model. Assimilating satellite-based SCF estimates with the observation-based SDC shows a reduction in SWE-related RMSE values compared to the model-based SDC functions. In addition, observation-based SDC functions were derived for different intra-annual and physiographic conditions, and landcover and elevation bands. Lower SWE-based RMSE values are also found with many of these categorical observation-based SDC EnKF experiments. All assimilation experiments perform better than the open-loop runs, except for the Washington region’s 2004–2005 snow season, which was a major drought year that was difficult to capture with the ensembles and observations. Full article
(This article belongs to the Special Issue Remote Sensing of Snow and Its Applications)
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Open AccessArticle
Geometric Versus Anemometric Surface Roughness for a Shallow Accumulating Snowpack
Geosciences 2018, 8(12), 463; https://doi.org/10.3390/geosciences8120463
Received: 2 November 2018 / Revised: 28 November 2018 / Accepted: 1 December 2018 / Published: 6 December 2018
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Abstract
When applied to a snow-covered surface, aerodynamic roughness length, z0, is typically considered as a static parameter within energy balance equations. However, field observations show that z0 changes spatially and temporally, and thus z0 incorporated as a dynamic parameter [...] Read more.
When applied to a snow-covered surface, aerodynamic roughness length, z0, is typically considered as a static parameter within energy balance equations. However, field observations show that z0 changes spatially and temporally, and thus z0 incorporated as a dynamic parameter may greatly improve models. To evaluate methods for characterizing snow surface roughness, we compared concurrent estimates of z0 based on (1) terrestrial light detection and ranging derived surface geometry of the snowpack surface (geometric, z0G) and (2) vertical wind profile measurements (anemometric, z0A). The value of z0G was computed from Lettau’s equation and underestimated z0A but compared well when scaled by a factor of 2.34. The Counihan method for computing z0G was found to be unsuitable for estimating z0 on a snow surface. During snowpack accumulation in early winter, z0 varied as a function of the snow-covered area (SCA). Our results show that as the SCA increases, z0 decreases, indicating there is a topographic influence on this relation. Full article
(This article belongs to the Special Issue Remote Sensing of Snow and Its Applications)
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Open AccessArticle
Analysis of QualitySpec Trek Reflectance from Vertical Profiles of Taiga Snowpack
Geosciences 2018, 8(11), 404; https://doi.org/10.3390/geosciences8110404
Received: 20 September 2018 / Revised: 26 October 2018 / Accepted: 1 November 2018 / Published: 6 November 2018
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Abstract
Snow microstructure is an important factor for microwave and optical remote sensing of snow. One parameter used to describe it is the specific surface area (SSA), which is defined as the surface-area-to-mass ratio of snow grains. Reflectance at near infrared (NIR) and short-wave [...] Read more.
Snow microstructure is an important factor for microwave and optical remote sensing of snow. One parameter used to describe it is the specific surface area (SSA), which is defined as the surface-area-to-mass ratio of snow grains. Reflectance at near infrared (NIR) and short-wave infrared (SWIR) wavelengths is sensitive to grain size and therefore also to SSA through the theoretical relationship between SSA and optical equivalent grain size. To observe SSA, the IceCube measures the hemispherical reflectance of a 1310 nm laser diode from the snow sample surface. The recently developed hand-held QualitySpec Trek (QST) instrument measures the almost bidirectional spectral reflectance in the range of 350–2500 nm with direct contact to the object. The geometry is similar to the Contact Probe, which was previously used successfully for snow measurements. The collected data set includes five snow pit measurements made using both IceCube and QST in a taiga snowpack in spring 2017 in Sodankylä, Finland. In this study, the correlation between SSA and a ratio of 1260 nm reflectance to differentiate between 1260 nm and 1160 nm reflectances is researched. The correlation coefficient varied between 0.85 and 0.98, which demonstrates an empirical linear relationship between SSA and reflectance observations of QST. Full article
(This article belongs to the Special Issue Remote Sensing of Snow and Its Applications)
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Review

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Open AccessReview
Review of Snow Data Assimilation Methods for Hydrological, Land Surface, Meteorological and Climate Models: Results from a COST HarmoSnow Survey
Geosciences 2018, 8(12), 489; https://doi.org/10.3390/geosciences8120489
Received: 28 September 2018 / Revised: 30 November 2018 / Accepted: 7 December 2018 / Published: 14 December 2018
Cited by 2 | PDF Full-text (2556 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The European Cooperation in Science and Technology (COST) Action ES1404 “HarmoSnow”, entitled, “A European network for a harmonized monitoring of snow for the benefit of climate change scenarios, hydrology and numerical weather prediction” (2014-2018) aims to coordinate efforts in Europe to harmonize approaches [...] Read more.
The European Cooperation in Science and Technology (COST) Action ES1404 “HarmoSnow”, entitled, “A European network for a harmonized monitoring of snow for the benefit of climate change scenarios, hydrology and numerical weather prediction” (2014-2018) aims to coordinate efforts in Europe to harmonize approaches to validation, and methodologies of snow measurement practices, instrumentation, algorithms and data assimilation (DA) techniques. One of the key objectives of the action was “Advance the application of snow DA in numerical weather prediction (NWP) and hydrological models and show its benefit for weather and hydrological forecasting as well as other applications.” This paper reviews approaches used for assimilation of snow measurements such as remotely sensed and in situ observations into hydrological, land surface, meteorological and climate models based on a COST HarmoSnow survey exploring the common practices on the use of snow observation data in different modeling environments. The aim is to assess the current situation and understand the diversity of usage of snow observations in DA, forcing, monitoring, validation, or verification within NWP, hydrology, snow and climate models. Based on the responses from the community to the questionnaire and on literature review the status and requirements for the future evolution of conventional snow observations from national networks and satellite products, for data assimilation and model validation are derived and suggestions are formulated towards standardized and improved usage of snow observation data in snow DA. Results of the conducted survey showed that there is a fit between the snow macro-physical variables required for snow DA and those provided by the measurement networks, instruments, and techniques. Data availability and resources to integrate the data in the model environment are identified as the current barriers and limitations for the use of new or upcoming snow data sources. Broadening resources to integrate enhanced snow data would promote the future plans to make use of them in all model environments. Full article
(This article belongs to the Special Issue Remote Sensing of Snow and Its Applications)
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