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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (15 February 2018) | Viewed by 76460

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Dr. Gareth Rees

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Scott Polar Research Institute, University of Cambridge, Lensfield Road, Cambridge CB2 1ER, UK
Interests: remote sensing; arctic ecosystems; science diplomacy
Special Issues, Collections and Topics in MDPI journals

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Dear Colleagues,

The cryosphere—the Earth's icy regions—generally embraces sea ice, lake and river ice, ice sheets, ice caps and glaciers, icebergs, snow cover, permafrost and frozen ground. The above-surface part of the cryosphere occupies around one sixth of the Earth's surface, and is located in places that are generally very remote from human habitation and infrastructure, and in challenging climatic conditions. Its study is thus well suited to the use of remote sensing techniques, especially those operated from spaceborne platforms, and snow and ice research was early to adopt remote sensing methods and to develop new algorithms for extracting information from them. Quantitative data on the cryosphere are urgently needed to enhance our understanding of the behaviour of the global climate system, as well as for more locally centred applications, and some of the best known and most telling indications of climatic behaviour have been obtained from cryospheric measurements. In 2013, a Special Issue of Remote Sensing presented a broad view of the state-of-the-art in cryospheric remote sensing. It is now time to revisit the topic, and contributions are invited that present new measurements of any of the components of the cryosphere using data collected from spaceborne or airborne (including UAV) platforms with passive or active remote sensing systems, or new ways of collecting or analyzing remotely sensed data. Review papers are also welcome.

Dr. Gareth Rees
Guest Editor

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Keywords

cryosphere
ice
glaciers
snow
permafrost
frozen ground

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Published Papers (11 papers)

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Research

21 pages, 14843 KiB

Open AccessArticle

Predicting Melt Pond Fraction on Landfast Snow Covered First Year Sea Ice from Winter C-Band SAR Backscatter Utilizing Linear, Polarimetric and Texture Parameters

by Saroat Ramjan, Torsten Geldsetzer, Randall Scharien and John Yackel

Remote Sens. 2018, 10(10), 1603; https://doi.org/10.3390/rs10101603 - 9 Oct 2018

Cited by 6 | Viewed by 3983

Abstract

Early-summer melt pond fraction is predicted using late-winter C-band backscatter of snow-covered first-year sea ice. Aerial photographs were acquired during an early-summer 2012 field campaign in Resolute Passage, Nunavut, Canada, on smooth first-year sea ice to estimate the melt pond fraction. RADARSAT-2 Synthetic Aperture Radar (SAR) data were acquired over the study area in late winter prior to melt onset. Correlations between the melt pond fractions and late-winter linear and polarimetric SAR parameters and texture measures derived from the SAR parameters are utilized to develop multivariate regression models that predict melt pond fractions. The results demonstrate substantial capability of the regression models to predict melt pond fractions for all SAR incidence angle ranges. The combination of the most significant linear, polarimetric and texture parameters provide the best model at far-range incidence angles, with an

R^{2}

of 0.62 and a pond fraction RMSE of 0.09. Near- and mid- range incidence angle models provide

R^{2}

values of 0.57 and 0.61, respectively, with an RMSE of 0.11. The strength of the regression models improves when SAR parameters are combined with texture parameters. These predictions also serve as a proxy to estimate snow thickness distributions during late winter as higher pond fractions evolve from thinner snow cover. Full article

(This article belongs to the Special Issue Cryospheric Remote Sensing II)

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21 pages, 5238 KiB

Open AccessArticle

DInSAR for a Regional Inventory of Active Rock Glaciers in the Dry Andes Mountains of Argentina and Chile with Sentinel-1 Data

by Cristian Daniel Villarroel, Guillermo Tamburini Beliveau, Ana Paula Forte, Oriol Monserrat and Monica Morvillo

Remote Sens. 2018, 10(10), 1588; https://doi.org/10.3390/rs10101588 - 3 Oct 2018

Cited by 53 | Viewed by 7075

Abstract

The Dry Andes region of Argentina and Chile is characterized by a highly developed periglacial environment. In these arid or semi-arid regions, rock glaciers represent one of the main pieces of evidence of mountain creeping permafrost and water reserves in a solid state. However, their distribution, degree of activity, and response to global warming are not yet well understood. In this context, this work aims to show the potential of the Sentinel-1-based interferometric technique (DInSAR) to map active rock glaciers at a regional level. In particular, the paper presents an active rock glacier inventory for the study area, which covers approximately 40,000 km², ranging from latitude 30°21′S to 33°21′S. A total of 2116 active rock glaciers have been detected, and their elevations show a high correlation with the west-east direction. This result was obtained by using only 16 interferometric pairs. Compared to other remote sensing classification techniques, the interferometric technique offers a means to measure surface displacement (active rock glacier). This results in a reliable classification of the degree of activity compared to other methods, based on geomorphological, geomorphometric, and/or ecological criteria. This work presents evidence of this aspect by comparing the obtained results with existing optical data-based inventories. We conclude that the combination of both types of sensors (radar and optical) is an appropriate procedure for active rock glacier inventories, as both mapping methodologies are complementary. Full article

(This article belongs to the Special Issue Cryospheric Remote Sensing II)

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14 pages, 4962 KiB

Open AccessArticle

Sensitivity Analysis of Arctic Sea Ice Extent Trends and Statistical Projections Using Satellite Data

by Ge Peng, Jessica L. Matthews and Jason T. Yu

Remote Sens. 2018, 10(2), 230; https://doi.org/10.3390/rs10020230 - 2 Feb 2018

Cited by 8 | Viewed by 8215

Abstract

An ice-free Arctic summer would have pronounced impacts on global climate, coastal habitats, national security, and the shipping industry. Rapid and accelerated Arctic sea ice loss has placed the reality of an ice-free Arctic summer even closer to the present day. Accurate projection of the first Arctic ice-free summer year is extremely important for business planning and climate change mitigation, but the projection can be affected by many factors. Using an inter-calibrated satellite sea ice product, this article examines the sensitivity of decadal trends of Arctic sea ice extent and statistical projections of the first occurrence of an ice-free Arctic summer. The projection based on the linear trend of the last 20 years of data places the first Arctic ice-free summer year at 2036, 12 years earlier compared to that of the trend over the last 30 years. The results from a sensitivity analysis of six commonly used curve-fitting models show that the projected timings of the first Arctic ice-free summer year tend to be earlier for exponential, Gompertz, quadratic, and linear with lag fittings, and later for linear and log fittings. Projections of the first Arctic ice-free summer year by all six statistical models appear to converge to the 2037 ± 6 timeframe, with a spread of 17 years, and the earliest first ice-free Arctic summer year at 2031. Full article

(This article belongs to the Special Issue Cryospheric Remote Sensing II)

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17 pages, 7007 KiB

Open AccessArticle

An Investigation of Ice Surface Albedo and Its Influence on the High-Altitude Lakes of the Tibetan Plateau

by Jiahe Lang, Shihua Lyu, Zhaoguo Li, Yaoming Ma and Dongsheng Su

Remote Sens. 2018, 10(2), 218; https://doi.org/10.3390/rs10020218 - 1 Feb 2018

Cited by 25 | Viewed by 5837

Abstract

Most high-altitude lakes are more sensitive to global warming than the regional atmosphere. However, most existing climate models produce unrealistic surface temperatures on the Tibetan Plateau (TP) lakes, and few studies have focused on the influence of ice surface albedo on high-altitude lakes. Based on field albedo measurements, moderate resolution imaging spectrometer (MODIS) albedo products and numerical simulation, this study evaluates the ice albedo parameterization schemes in existing lake models and investigates the characteristics of the ice surface albedo in six typical TP lakes, as well as the influence of ice albedo error in the FLake model. Compared with observations, several ice albedo schemes all clearly overestimate the lake ice albedo by 0.26 to 0.66, while the average bias of MODIS albedo products is only 0.07. The MODIS-observed albedo of a snow-covered lake varies with the snow proportion, and the lake surface albedo in a snow-free state is approximately 0.15 during the frozen period. The MODIS-observed ice surface (snow-free) albedos are concentrated within the ranges of 0.14–0.16, 0.08–0.10 and 0.10–0.12 in Aksai Chin Lake, Nam Co Lake and Ngoring Lake, respectively. The simulated lake surface temperature is sensitive to variations in lake ice albedo especially in the spring and winter. Full article

(This article belongs to the Special Issue Cryospheric Remote Sensing II)

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20 pages, 4781 KiB

Open AccessArticle

Dependence of C-Band Backscatter on Ground Temperature, Air Temperature and Snow Depth in Arctic Permafrost Regions

by Helena Bergstedt, Simon Zwieback, Annett Bartsch and Marina Leibman

Remote Sens. 2018, 10(1), 142; https://doi.org/10.3390/rs10010142 - 19 Jan 2018

Cited by 26 | Viewed by 6728

Abstract

Microwave remote sensing has found numerous applications in areas affected by permafrost and seasonally frozen ground. In this study, we focused on data obtained by the Advanced Scatterometer (ASCAT, C-band) during winter periods when the ground is assumed to be frozen. This paper discusses the relationships of ASCAT backscatter with snow depth, air and ground temperature through correlations and the analysis of covariance (ANCOVA) to quantify influences on backscatter values during situations of frozen ground. We studied sites in Alaska, Northern Canada, Scandinavia and Siberia. Air temperature and snow depth data were obtained from 19 World Meteorological Organization (WMO) and 4 Snow Telemetry (SNOTEL) stations. Ground temperature data were obtained from 36 boreholes through the Global Terrestrial Network for Permafrost Database (GTN-P) and additional records from central Yamal. Results suggest distinct differences between sites with and without underlying continuous permafrost. Sites characterized by high freezing indices (>4000 degree-days) have consistently stronger median correlations of ASCAT backscatter with ground temperature for all measurement depths. We show that the dynamics in winter-time backscatter cannot be solely explained through snow processes, but are also highly correlated with ground temperature up to a considerable depth (60 cm). These findings have important implications for both freeze/thaw and snow water equivalent retrieval algorithms based on C-band radar measurements. Full article

(This article belongs to the Special Issue Cryospheric Remote Sensing II)

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18948 KiB

Open AccessArticle

A New Method for Automatically Tracing Englacial Layers from MCoRDS Data in NW Greenland

by Siting Xiong, Jan-Peter Muller and Raquel Caro Carretero

Remote Sens. 2018, 10(1), 43; https://doi.org/10.3390/rs10010043 - 27 Dec 2017

Cited by 16 | Viewed by 6283

Abstract

Englacial layering reflects ice dynamics within the ice bodies, which improves understanding of ice flow variation, past accumulation rates and vertical flows transferring between the surface and the underlying bedrock. The internal layers can be observed by using Radar Echo Sounding (RES), such as the Multi-channel Coherent Radar Depth Sounder (MCoRDS) used in NASA’s Operation IceBridge (OIB) mission. Since the 1960s, the accumulation of the RES data has prompted the development of automated methods to extract the englacial layers. In this study, we propose a new automated method that combines peak detection methods, namely the CWT-based peak detection or the Automatic Phase Picker (APP), with a Hough Transform (HT) to trace boundaries of englacial layers. For CWT-based peak detection, we test it using two different wavelets. The proposed method is tested with twelve MCoRDS radio echograms, which are acquired south of the Northern Greenland Eemian (NEEM) ice drilling site, where the folding of ice layers was observed. The method is evaluated in comparison to the isochrones that were extracted in an independent study. In comparison, the proposed new automated method can restore more than 70% of the englacial layers. This new automated layer-tracing method is publicly available on github. Full article

(This article belongs to the Special Issue Cryospheric Remote Sensing II)

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22049 KiB

Open AccessArticle

Ice Velocity Variations of the Polar Record Glacier (East Antarctica) Using a Rotation-Invariant Feature-Tracking Approach

by Tingting Liu, Muye Niu and Yuande Yang

Remote Sens. 2018, 10(1), 42; https://doi.org/10.3390/rs10010042 - 27 Dec 2017

Cited by 26 | Viewed by 6401

Abstract

In this study, the ice velocity changes from 2004 to 2015 of the Polar Record Glacier (PRG) in East Antarctica were investigated based on a feature-tracking method using Landsat-7 enhanced thematic mapper plus (ETM+) and Landsat-8 operational land imager (OLI) images. The flow field of the PRG curves make it difficult to generate ice velocities in some areas using the traditional normalized cross-correlation (NCC)-based feature-tracking method. Therefore, a rotation-invariant parameter from scale-invariant feature transform (SIFT) is introduced to build a novel rotation-invariant feature-tracking approach. The validation was performed based on multi-source images and the making earth system data records for use in research environments (MEaSUREs) interferometric synthetic aperture radar (InSAR)-based Antarctica ice velocity map data set. The results indicate that the proposed method is able to measure the ice velocity in more areas and performs as well as the traditional NCC-based feature-tracking method. The sequential ice velocities obtained present the variations in the PRG during this period. Although the maximum ice velocity of the frontal margin of the PRG and the frontal iceberg reached about 900 m/a and 1000 m/a, respectively, the trend from 2004 to 2015 showed no significant change. Under the interaction of the Polar Times Glacier and the Polarforschung Glacier, both the direction and the displacement of the PRG were influenced. This impact also led to higher velocities in the western areas of the PRG than in the eastern areas. In addition, elevation changes and frontal iceberg calving also impacted the ice velocity of the PRG. Full article

(This article belongs to the Special Issue Cryospheric Remote Sensing II)

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10677 KiB

Open AccessArticle

MODIS Sea Ice Thickness and Open Water–Sea Ice Charts over the Barents and Kara Seas for Development and Validation of Sea Ice Products from Microwave Sensor Data

by Marko Mäkynen and Juha Karvonen

Remote Sens. 2017, 9(12), 1324; https://doi.org/10.3390/rs9121324 - 16 Dec 2017

Cited by 15 | Viewed by 9159

Abstract

We have developed algorithms and procedures for calculating daily sea ice thickness (SIT) and open water–sea ice (OWSI) charts, based on the Moderate Resolution Imaging Spectroradiometer (MODIS), ice surface temperature (IST) (night-time only), and reflectance (

R

) swath data, respectively. The resolution [...] Read more.

R

) swath data, respectively. The resolution of the SIT chart is 1 km and that of the OWSI chart is 250 m. The charts are targeted to be used in development and validation of sea ice products from microwave sensor data. We improve the original MODIS cloud masks for the IST and

R

data, with a focus on identifying larger cloud-free areas in the data. The SIT estimation from the MODIS IST swath data follows previous studies. The daily SIT chart is composed from available swath charts by assigning daily median SIT to a pixel. The OWSI classification is simply conducted by a fixed threshold for the MODIS band 1

R

. This was based on manually selected

R

data for various ice types in late winter, early melt, and advanced melt conditions. The composition procedures for the daily SIT and OWSI charts somewhat compensates for errors due to the undetected clouds. The SIT and OWSI charts were compared against manual ice charts by Arctic and Antarctic Research Institute in Russia and by Norwegian Meteorological Institute, respectively, and on average, a good relationship between the charts was found. Pixel-wise comparison of the SIT and OWSI charts showed very good agreement in open water vs. sea ice classification, which gives further confidence on the reliability of our algorithms. We also demonstrate usage of the MODIS OWSI and SIT charts for validation of sea ice concentration charts based on the SENTINEL-1 SAR and AMSR2 radiometer data and two different algorithms. Full article

(This article belongs to the Special Issue Cryospheric Remote Sensing II)

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9552 KiB

Open AccessArticle

Satellite and Ground Observations of Snow Cover in Tibet during 2001–2015

by Droma Basang, Knut Barthel and Jan Asle Olseth

Remote Sens. 2017, 9(11), 1201; https://doi.org/10.3390/rs9111201 - 22 Nov 2017

Cited by 27 | Viewed by 6956

Abstract

The seasonal snow cover of the Tibetan Plateau exerts a profound environmental influence both regionally and globally. Daily observations of snow depth at 37 meteorological stations in Tibet and MODIS eight-day snow products (MOD10A2) during the period 2001–2015 are analyzed with respect to the frequency and spatial distribution of snow cover for each season and for various altitude ranges. The results show that the average snow cover percentage was 16%. Snow cover frequency was less than 21% for 70% of the Tibetan area, while it was more than 40% in eastern Tibet and in the Himalayas. We also estimated the variations in the starting times of snow accumulation and ablation. During the 15 years, both datasets revealed a significant trend of earlier onset of ablation, but no evident trend for the start of accumulation. The two datasets differed slightly with respect to the seasonal variation of snow cover. MODIS data showed more snow in winter than in other seasons, but the ground data showed most snow in early spring. For the station locations, the correlation between ground and MODIS snow cover percentage (number of snow-covered stations/number of cloud-free stations) is 0.77. Combining the advantages of remote sensing data and ground observation data is the best way to investigate snow in Tibet. Full article

(This article belongs to the Special Issue Cryospheric Remote Sensing II)

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6634 KiB

Open AccessArticle

Structure-from-Motion Using Historical Aerial Images to Analyse Changes in Glacier Surface Elevation

by Nico Mölg and Tobias Bolch

Remote Sens. 2017, 9(10), 1021; https://doi.org/10.3390/rs9101021 - 3 Oct 2017

Cited by 70 | Viewed by 8474

Abstract

The application of structure-from-motion (SfM) to generate digital terrain models (DTMs) derived from different image sources has strongly increased, the major reason for this being that processing is substantially easier with SfM than with conventional photogrammetry. To test the functionality in a demanding environment, we applied SfM and conventional photogrammetry to archival aerial images from Zmuttgletscher, a mountain glacier in Switzerland, for nine dates between 1946 and 2005 using the most popular software packages, and compared the results regarding bundle adjustment and final DTM quality. The results suggest that by using SfM it is possible to produce DTMs of similar quality as with conventional photogrammetry. Higher point cloud density and less noise allow a higher ground resolution of the final DTM, and the time effort from the user is 3–6 times smaller, while the controls of the commercial software packages Agisoft PhotoScan (Version 1.2; Agisoft, St. Petersburg, Russia) and Pix4Dmapper (Version 3.0; Pix4D, Lausanne, Switzerland) are limited in comparison to ERDAS photogrammetry. SfM performs less reliably when few images with little overlap are processed. Even though SfM facilitates the largely automated production of high quality DTMs, the user is not exempt from a thorough quality check, at best with reference data where available. The resulting DTM time series revealed an average change in surface elevation at the glacier tongue of −67.0 ± 5.3 m. The spatial pattern of changes over time reflects the influence of flow dynamics and the melt of clean ice and that under debris cover. With continued technological advances, we expect to see an increasing use of SfM in glaciology for a variety of purposes, also in processing archival aerial imagery. Full article

(This article belongs to the Special Issue Cryospheric Remote Sensing II)

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3783 KiB

Open AccessArticle

Spatio-Temporal Variability and Model Parameter Sensitivity Analysis of Ice Production in Ross Ice Shelf Polynya from 2003 to 2015

by Zian Cheng, Xiaoping Pang, Xi Zhao and Cheng Tan

Remote Sens. 2017, 9(9), 934; https://doi.org/10.3390/rs9090934 - 10 Sep 2017

Cited by 12 | Viewed by 5883

Abstract

Antarctic sea ice formation is strongly influenced by polynyas occurring in austral winter. The sea ice production of Ross Ice Shelf Polynya (RISP) located in the Ross Sea is the highest among coastal polynyas around the Southern Ocean. In this paper, daily sea ice production distribution of RISP in wintertime is estimated during 2003–2015, and the spatial and temporal trends of ice production are explored. Moreover, the sensitivity of the ice production model to parameterization is tested. To define the extent of RISP, this study uses sea ice concentration (SIC) maps mainly derived from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSRE) and the Advanced Microwave Scanning Radiometer 2 (AMSR2) by ARTIST (Arctic Radiation and Turbulence Interaction Study) sea ice algorithm (ASI) and constrains the ice production estimation to areas with SIC less than 75%. ERA-Interim reanalysis meteorological data are applied to a thermodynamic model to estimate daily ice production distribution between April and October during 2003–2015 for the open water fractions within the polynya. This estimation is conducted under the assumption that the meteorological data represent the reality. We further analyzed the spatial variability, monthly trend, and interannual trend for wintertime of the total RISP sea ice production. The results show that the ocean surface produces ice at a high rate within the distance of 20–30 km from the ice shelf front. In most high production areas, the ice production significantly increases. Some local regions show a contrarily significant decreasing trend as a result of ice shelf expansion and iceberg events. The monthly total RISP ice production ranges from 14 to 76 km³, showing substantial fluctuations in each month during 2003–2015. The seasonal variation of each year also shows substantial fluctuations. The wintertime total ice productions of RISP for 2003–2015 range 164–313 km³ with an average of 219 km³, showing no obvious temporal trend. More importantly, we conducted ten sensitivity tests, aiming to illustrate the sensitivity of the ice production model to parameterization. The output of the ice production model is sensitive to the value of the bulk transfer coefficients (

C_{s}

and

C_{e}

), latent heat of sea ice fusion (

L_{f}

), and the threshold of SIC for RISP extent definition.

C_{s}

and

C_{e}

have the greatest influence, leading to a variation of average wintertime total RISP ice production results as high as 87.1%. A set of optimal local parameter values are recommended, including

C_{s}

and

C_{e}

= 0.002 and

L_{f}

= 2.79 × 10⁵ J·kg⁻¹.

L_{f}

is calculated by the salinity and temperature of sea ice, the value of which may lead to potential influence to the value of

L_{f}

and the following ice production results. Full article

(This article belongs to the Special Issue Cryospheric Remote Sensing II)

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