Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Browser

Emerging Remote Sensing Techniques for Monitoring Glaciers and Snow

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

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 December 2023) | Viewed by 20440

Share This Special Issue

Special Issue Editor

Prof. Dr. Joan Ramage Macdonald

E-Mail Website
Guest Editor

Earth & Environmental Sciences, 1 West Packer Avenue (Room 584), Lehigh University, Bethlehem, PA 18015, USA
Interests: cryosphere; glaciers; microwave remote sensing; SAR; snow; hydrology

Special Issue Information

Dear Colleagues,

Seasonal and perennial snow, glaciers and ice sheets, are of critical importance in atmospheric studies, understanding of climate change, hydrological processes, and their variations have major societal implications on regional to global spatial scales. Remote sensing is a key way to access and monitor process over large space and time scales with consistency and relative efficiency. Remote sensing approaches are continuously evolving, and practitioners and communities have access to both better technology and a lengthening record that can improve monitoring and contribute to communities.

This special issue seeks papers advancing novel and emerging techniques for monitoring snow and ice extent, snow water equivalent, surface properties, and melt status using remote sensing. We seek contributions that include new sensors and tools as well as new approaches and synergies with historical datasets or citizen science. We hope to include approaches for a range of terrestrial environments, regions with differing needs, sensors, sensor combinations, and models. Submissions with visions for how remote sensing of ice and snow may be improved in the future are welcome. We encourage new interdisciplinary and community collaborators and early career researchers to submit manuscripts.

Prof. Dr. Joan Ramage Macdonald
Guest Editor

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 submissions that pass pre-check are 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. Remote Sensing is an international peer-reviewed open access semimonthly 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 2700 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
Cryosphere
glacier
snow
ice
monitoring

Published Papers (10 papers)

Download All Papers

Research

29 pages, 8367 KiB

Open AccessEditor’s ChoiceArticle

X- and Ku-Band SAR Backscattering Signatures of Snow-Covered Lake Ice and Sea Ice

by Katriina Veijola, Juval Cohen, Marko Mäkynen, Juha Lemmetyinen, Jaan Praks and Bin Cheng

Remote Sens. 2024, 16(2), 369; https://doi.org/10.3390/rs16020369 - 16 Jan 2024

Viewed by 952

Abstract

In this work, backscattering signatures of snow-covered lake ice and sea ice from X- and Ku-band synthetic aperture radar (SAR) data are investigated. The SAR data were acquired with the ESA airborne SnowSAR sensor in winter 2012 over Lake Orajärvi in northern Finland and over landfast ice in the Bay of Bothnia of the Baltic Sea. Co-incident with the SnowSAR acquisitions, in situ snow and ice data were measured. In addition, time series of TerraSAR-X images and ice mass balance buoy data were acquired for Lake Orajärvi in 2011–2012. The main objective of our study was to investigate relationships between SAR backscattering signatures and snow depth over lake and sea ice, with the ultimate objective of assessing the feasibility of retrieval of snow characteristics using X- and Ku-band dual-polarization (VV and VH) SAR over freshwater or sea ice. This study constitutes the first comprehensive survey of snow backscattering signatures at these two combined frequencies over both lake and sea ice. For lake ice, we show that X-band VH-polarized backscattering coefficient (

σ^{o}

) and the Ku-band VV/VH-ratio exhibited the highest sensitivity to the snow depth. For sea ice, the highest sensitivity to the snow depth was found from the Ku-band VV-polarized

σ^{o}

and the Ku-band VV/VH-ratio. However, the observed relations were relatively weak, indicating that at least for the prevailing snow conditions, obtaining reliable estimates of snow depth over lake and sea ice would be challenging using only X- and Ku-band backscattering information. Full article

(This article belongs to the Special Issue Emerging Remote Sensing Techniques for Monitoring Glaciers and Snow)

► Show Figures

Figure 1

32 pages, 16283 KiB

Open AccessArticle

Snow Persistence and Snow Line Elevation Trends in a Snowmelt-Driven Basin in the Central Andes and Their Correlations with Hydroclimatic Variables

by Felipe Aranda, Diego Medina, Lina Castro, Álvaro Ossandón, Ramón Ovalle, Raúl P. Flores and Tomás R. Bolaño-Ortiz

Remote Sens. 2023, 15(23), 5556; https://doi.org/10.3390/rs15235556 - 29 Nov 2023

Viewed by 1465

Abstract

The mountain cryosphere is crucial for socio-economic processes, especially during the dry seasons. However, anthropogenic climate change has had a detrimental impact on the cryosphere due to its sensitivity. Over the past two decades, there has been a decline in precipitation and a temperature rise, leading to a substantial reduction in the timing and extent of snow cover. This increase in temperature also elevates the snow line elevation (SLE), further diminishing the volume of available freshwater in the snow-driven basins of the Andes. In this study, we use 22 years (2000–2021) of 8-day snow product (MOD10A2) from the Moderate Resolution Imaging Spectroradiometer (MODIS) to analyze the annual and seasonal variability of snow cover area, SLE, and snow persistence (SP, an indicator of the duration of snow) in the Yeso River basin in Central Chile and the correlation of SP and SLE with hydrometeorological variables and climatic indices. We introduce a new approach called the Maximum Dissimilarity Method to obtain the SLE even on cloudy days. The results are as follows: (1) Snow cover area reductions of 34.0 km² at low elevations in spring and 86.5 km² at mid elevations in summer were found when comparing the period 2016–2021 to 2000–2004; (2) SP trends at the annual scale revealed a significant decrease in 89% of its area and an average of 3.6 fewer days of snow cover per year; (3) an upward and significant trend of 21 m‧year⁻¹ in the annual SLE was found; and (4) annual SP and SLE were highly correlated with annual hydrometeorological variables, and spring and summer snow variables were significantly correlated with dry streamflow. This methodology can potentially serve as a valuable tool for detecting trends in snow-covered surfaces, and thereby associate these changes with climate change or other anthropogenic effects in future research. Full article

(This article belongs to the Special Issue Emerging Remote Sensing Techniques for Monitoring Glaciers and Snow)

► Show Figures

Figure 1

14 pages, 19171 KiB

Open AccessCommunication

Across-Track and Multi-Aperture InSAR for 3-D Glacier Velocity Estimation of the Siachen Glacier

by Vijay Kumar, Kjell Arild Høgda and Yngvar Larsen

Remote Sens. 2023, 15(19), 4794; https://doi.org/10.3390/rs15194794 - 01 Oct 2023

Viewed by 850

Abstract

Interferometric Synthetic Aperture Radar (InSAR) remote sensing generally lacks deformation sensitivity in the along-track direction. In this proposed approach, across-track observations from conventional InSAR, using both ascending and descending passes, were superimposed with the along-track movement derived from multi-aperture InSAR (MAI) to determine the full three-dimensional (3-D) velocity of the Siachen Glacier in the Karakoram range of the Himalayas. The along-track velocity signal is essential for estimating the movement component in the north/south direction, which is needed for a complete delineation of 3-D deformation. The velocity observed was improved using the MAI technique in comparison to the conventional ascending/descending 3-D velocity estimation approach, and substantial differences were noticed between these two methods, particularly in the lower part of the glacier, which is moving almost in an along-track (north/south) direction. Glacier velocity varied from 0.3 md⁻¹ in the accumulation zone to 0.60 md⁻¹ in the terminus zone of the Siachen Glacier using this newly proposed approach. This study presents a 3-D velocity estimation without any preconceived assumptions regarding the flow conditions of glaciers and without any azimuth ambiguity. Full article

(This article belongs to the Special Issue Emerging Remote Sensing Techniques for Monitoring Glaciers and Snow)

► Show Figures

Figure 1

25 pages, 18065 KiB

Open AccessArticle

Research on the Response Characteristics and Identification of Infrasound Signals in the Jialongcuo Ice Avalanche, Tibet

by Yifang Zhang, Qiao Chen, Pengcheng Su, Dunlong Liu, Jianzhao Cui, Jilong Chen, Jianrong Ma, Qiao Xing, Fenglin Xu, Yuanchao Fan and Fangqiang Wei

Remote Sens. 2023, 15(18), 4482; https://doi.org/10.3390/rs15184482 - 12 Sep 2023

Viewed by 733

Abstract

Due to the inability of remote sensing satellites to monitor avalanches in real time, this study focuses on the glaciers in the rear edge of Jialongcuo, Tibet, and uses infrasound sensors to conduct real-time monitoring of ice avalanches. The following conclusions are drawn: (1) In terms of waveform, compared to background noise, ice avalanche events have a slight left deviation and a slightly steep shape; compared to wind, rain, and floods events, ice avalanche events have less obvious kurtosis and skewness. (2) In terms of frequency distribution, the infrasound frequency generated by ice avalanche events is mainly distributed in the range of 1.5 Hz to 9.5 Hz; compared to other events, ice avalanche events differ some in frequency characteristics. (3) The model based on information entropy and marginal spectral frequency distribution characteristics of infrasound have higher accuracy in signal classification and recognition, as they can better represent the differences between infrasound signals of different events than other features. (4) Compared with the K-nearest neighbor algorithm and classification tree algorithm, the support vector machine and BP (Back Propagation) neural network algorithm are more suitable for identifying infrasound signals in the Jialongcuo ice avalanche. The research results can provide theoretical support for the application of infrasound-based ice avalanche monitoring technology. Full article

(This article belongs to the Special Issue Emerging Remote Sensing Techniques for Monitoring Glaciers and Snow)

► Show Figures

Figure 1

23 pages, 12578 KiB

Open AccessArticle

Mapping Surface Features of an Alpine Glacier through Multispectral and Thermal Drone Surveys

by Micol Rossini, Roberto Garzonio, Cinzia Panigada, Giulia Tagliabue, Gabriele Bramati, Giovanni Vezzoli, Sergio Cogliati, Roberto Colombo and Biagio Di Mauro

Remote Sens. 2023, 15(13), 3429; https://doi.org/10.3390/rs15133429 - 06 Jul 2023

Cited by 1 | Viewed by 2107

Abstract

Glacier surfaces are highly heterogeneous mixtures of ice, snow, light-absorbing impurities and debris material. The spatial and temporal variability of these components affects ice surface characteristics and strongly influences glacier energy and mass balance. Remote sensing offers a unique opportunity to characterize glacier optical and thermal properties, enabling a better understanding of different processes occurring at the glacial surface. In this study, we evaluate the potential of optical and thermal data collected from field and drone platforms to map the abundances of predominant glacier surfaces (i.e., snow, clean ice, melting ice, dark ice, cryoconite, dusty snow and debris cover) on the Zebrù glacier in the Italian Alps. The drone surveys were conducted on the ablation zone of the glacier on 29 and 30 July 2020, corresponding to the middle of the ablation season. We identified very high heterogeneity of surface types dominated by melting ice (30% of the investigated area), dark ice (24%), clean ice (19%) and debris cover (17%). The surface temperature of debris cover was inversely related to debris-cover thickness. This relation is influenced by the petrology of debris cover, suggesting the importance of lithology when considering the role of debris over glaciers. Multispectral and thermal drone surveys can thus provide accurate high-resolution maps of different snow and ice types and their temperature, which are critical elements to better understand the glacier’s energy budget and melt rates. Full article

(This article belongs to the Special Issue Emerging Remote Sensing Techniques for Monitoring Glaciers and Snow)

► Show Figures

Figure 1

22 pages, 30361 KiB

Open AccessArticle

Multi-Branch Deep Neural Network for Bed Topography of Antarctica Super-Resolution: Reasonable Integration of Multiple Remote Sensing Data

by Yiheng Cai, Fuxing Wan, Shinan Lang, Xiangbin Cui and Zijun Yao

Remote Sens. 2023, 15(5), 1359; https://doi.org/10.3390/rs15051359 - 28 Feb 2023

Cited by 1 | Viewed by 1301

Abstract

Bed topography and roughness play important roles in numerous ice-sheet analyses. Although the coverage of ice-penetrating radar measurements has vastly increased over recent decades, significant data gaps remain in certain areas of subglacial topography and need interpolation. However, the bed topography generated by interpolation such as kriging and mass conservation is generally smooth at small scales, lacking topographic features important for sub-kilometer roughness. DeepBedMap, a deep learning method combined with multiple surface observation inputs, can generate high-resolution (250 m) bed topography with realistic bed roughness but produces some unrealistic artifacts and higher bed elevation values in certain regions, which could bias ice-sheet models. To address these issues, we present MB_DeepBedMap, a multi-branch deep learning method to generate more realistic bed topography. The model improves upon DeepBedMap by separating inputs into two groups using a multi-branch network structure according to their characteristics, rather than fusing all inputs at an early stage, to reduce artifacts in the generated topography caused by earlier fusion of inputs. A direct upsampling branch preserves large-scale subglacial landforms while generating high-resolution bed topography. We use MB_DeepBedMap to generate a high-resolution (250 m) bed elevation grid product of Antarctica, MB_DeepBedMap_DEM, which can be used in high-resolution ice-sheet modeling studies. Moreover, we test the performance of MB_DeepBedMap model in Thwaites Glacier, Gamburtsev Subglacial Mountains, and several other regions, by comparing the qualitative topographic features and quantitative errors of MB_DeepBedMap, BEDMAP2, BedMachine Antarctica, and DeepBedMap. The results show that MB_DeepBedMap can provide more realistic small-scale topographic features and roughness compared to BEDMAP2, BedMachine Antarctica, and DeepBedMap. Full article

(This article belongs to the Special Issue Emerging Remote Sensing Techniques for Monitoring Glaciers and Snow)

► Show Figures

Figure 1

24 pages, 7217 KiB

Open AccessArticle

Comparison of Three Different Random Forest Approaches to Retrieve Daily High-Resolution Snow Cover Maps from MODIS and Sentinel-2 in a Mountain Area, Gran Paradiso National Park (NW Alps)

by Chiara Richiardi, Consolata Siniscalco and Maria Adamo

Remote Sens. 2023, 15(2), 343; https://doi.org/10.3390/rs15020343 - 06 Jan 2023

Cited by 3 | Viewed by 2917

Abstract

In the Alpine environment, snow plays a key role in many processes involving ecosystems, biogeochemical cycles, and human wellbeing. Due to the inaccessibility of mountain areas and the high spatial and temporal heterogeneity of the snowpack, satellite spatio-temporal data without gaps offer a unique opportunity to monitor snow on a fine scale. In this study, we present a random forest approach within three different workflows to combine MODIS and Sentinel-2 snow products to retrieve daily gap-free snow cover maps at 20 m resolution. The three workflows differ in terms of the type of ingested snow products and, consequently, in the type of random forest used. The required inputs are the MODIS/Terra Snow Cover Daily L3 Global dataset at 500 m and the Sentinel-2 snow dataset at 20 m, automatically retrieved through the recently developed revised-Let It Snow workflow, from which the selected inputs are, alternatively, the Snow Cover Extent (SCE) map or the Normalized Difference Snow Index (NDSI) map, and a Digital Elevation Model (DEM) of consistent resolution with Sentinel-2 imagery. The algorithm is based on two steps, the first to fill the gaps of the MODIS snow dataset and the second to downscale the data and obtain the high resolution daily snow time series. The workflow is applied to a case study in Gran Paradiso National Park. The proposed study represents a first attempt to use the revised-Let It Snow with the purpose of extracting temporal parameters of snow. The validation was achieved by comparison with both an independent dataset of Sentinel-2 to assess the spatial accuracy, including the snowline elevation prediction, and the algorithm’s performance through the different topographic conditions, and with in-situ data collected by meteorological stations, to assess temporal accuracy, with a focus on seasonal snow phenology parameters. Results show that all of the approaches provide robust time series (overall accuracies of A1 = 93.4%, and A2 and A3 = 92.6% against Sentinel-2, and A1 = 93.1%, A2 = 93.7%, and A3 = 93.6% against weather stations), but the first approach requires about one fifth of the computational resources needed for the other two. The proposed workflow is fully automatic and requires input data that are readily and globally available, and promises to be easily reproducible in other study areas to obtain high-resolution daily time series, which is crucial for understanding snow-driven processes at a fine scale, such as vegetation dynamics after snowmelt. Full article

(This article belongs to the Special Issue Emerging Remote Sensing Techniques for Monitoring Glaciers and Snow)

► Show Figures

Figure 1

20 pages, 11158 KiB

Open AccessArticle

High-Resolution Inversion Method for the Snow Water Equivalent Based on the GF-3 Satellite and Optimized EQeau Model

by Yichen Yang, Shifeng Fang, Hua Wu, Jiaqiang Du, Xiaohu Wang, Rensheng Chen, Yongqiang Liu and Hao Wang

Remote Sens. 2022, 14(19), 4931; https://doi.org/10.3390/rs14194931 - 02 Oct 2022

Viewed by 1683

Abstract

High-resolution snow water equivalent studies are important for obtaining a clear picture of the potential of water resources in arid areas, and SAR-based sensors can achieve meter-level snow water equivalent inversion. The advanced C-band SAR satellite Gaofen-3 (GF-3) can now achieve meter-level observations of the same area within one day and has great potential for the inversion of the snow water equivalent. The EQeau model is an empirical method for snow water equivalent inversion using C-band SAR satellites, but the model has major accuracy problems. In this paper, the EQeau model is improved by using classification of underlying surface types and polarization decomposition, and the inversion of the snow water equivalent was also completed using the new data source GF-3 input model. The results found that: (1) the classification of underlying surface types can significantly improve the fit between the snow thermal resistance and the backscattering coefficient ratio; (2) the accuracy of the snow density extracted by the GF-3 satellite using the Singh–Cloude Three-Component Hybrid (S3H) decomposition is better than IDW spatial interpolation, and the overall RMSE can reach 0.005 g/cm³; (3) the accuracy of the optimized EQeau model is significantly improved, and the overall MRE is reduced from 27.4% to 10.3%. Compared with the original model, the optimized model is superior both in terms of verification accuracy and image detail. In the future, with the combination of advanced technologies such as the Internet of Things (IoT), long, gapless, all-weather, and high-resolution snow water equivalent inversion can be achieved, which is conducive to the realization of all-weather monitoring of the regional snow water equivalent. Full article

(This article belongs to the Special Issue Emerging Remote Sensing Techniques for Monitoring Glaciers and Snow)

► Show Figures

Figure 1

17 pages, 10661 KiB

Open AccessArticle

Drone-Borne Ground-Penetrating Radar for Snow Cover Mapping

by Andrea Vergnano, Diego Franco and Alberto Godio

Remote Sens. 2022, 14(7), 1763; https://doi.org/10.3390/rs14071763 - 06 Apr 2022

Cited by 18 | Viewed by 4510

Abstract

Ground-penetrating radar (GPR) is one of the most commonly used instruments to map the Snow Water Equivalent (SWE) in mountainous regions. However, some areas may be difficult or dangerous to access; besides, some surveys can be quite time-consuming. We test a new system to fulfill the need to speed up the acquisition process for the analysis of the SWE and to access remote or dangerous areas. A GPR antenna (900 MHz) is mounted on a drone prototype designed to carry heavy instruments, fly safely at high altitudes, and avoid interference of the GPR signal. A survey of two test sites of the Alpine region during winter 2020–2021 is presented, to check the prototype performance for mapping the snow thickness at the catchment scale. We process the data according to a standard flow-chart of radar processing and we pick both the travel times of the air–snow interface and the snow–ground interface to compute the travel time difference and to estimate the snow depth. The calibration of the radar snow depth is performed by comparing the radar travel times with snow depth measurements at preselected stations. The main results show fairly good reliability and performance in terms of data quality, accuracy, and spatial resolution in snow depth monitoring. We tested the device in the condition of low snow density (<200 kg/m³) and this limits the detectability of the air–snow interface. This is mainly caused by low values of the electrical permittivity of the dry soft snow, providing a weak reflectivity of the snow surface. To overcome this critical aspect, we use the data of the rangefinder to properly detect the travel time of the snow–air interface. This sensor is already installed in our prototype and in most commercial drones for flight purposes. Based on our experience with the prototype, various improvement strategies and limitations of drone-borne GPR acquisition are discussed. In conclusion, the drone technology is found to be ready to support GPR-based snow depth mapping applications at high altitudes, provided that the operators acquire adequate knowledge of the devices, in order to effectively build, tune, use and maintain a reliable acquisition system. Full article

(This article belongs to the Special Issue Emerging Remote Sensing Techniques for Monitoring Glaciers and Snow)

► Show Figures

Figure 1

15 pages, 4925 KiB

Open AccessArticle

GNSS-IR Snow Depth Retrieval Based on the Fusion of Multi-Satellite SNR Data by the BP Neural Network

by Junyu Zhan, Rui Zhang, Jinsheng Tu, Jichao Lv, Xin Bao, Lingxiao Xie, Song Li and Runqing Zhan

Remote Sens. 2022, 14(6), 1395; https://doi.org/10.3390/rs14061395 - 14 Mar 2022

Cited by 6 | Viewed by 2495

Abstract

Compared with previous snow depth monitoring methods, global navigation satellite system-interferometric reflectometry (GNSS-IR) technology has the advantage of obtaining continuous daily observation data, and has great application potential. However, since GNSS satellites are in motion, their position in the sky is constantly varying, and the Fresnel reflection areas about the receiver in different periods alter accordingly. As a result, the retrieving results obtained from different GNSS satellites, and data sets collected in different periods, fluctuate considerably, making the traditional single-satellite-based GNSS-IR retrieving method have limitations in accuracy and reliability. Therefore, this paper proposed a novel GNSS-IR signal-to-noise ratio (SNR) retrieving snow depth method for fusing the available GNSS-IR observations to obtain an accurate and reliable result. We established the retrieval model based on the backpropagation algorithm, which makes full use of the back propagation (BP) neural network’s self-learning and self-adaptive capability to exploit the degree of contribution of different satellites to the final results. Then, the SNR observations of the global positioning system (GPS) L1 carrier from the Plate Boundary Observation (PBO) site P351 were collected to experiment for validation purposes. For all available GPS L1 carrier data, the snow depth values retrieved for each satellite were first obtained by the existing single-satellite-based GNSS-IR retrieval method. Then, four groups of comparison results were acquired, based on the multiple linear regression model, random forest model, mean fusion model, and the proposed BP neural network model, respectively. Taking the snow depth in-situ data provided by snow telemetry (SNOTEL) as a reference, the root mean squared error (RMSE) and mean absolute error (MAE) of the proposed solution are 0.0297 m and 0.0219 m, respectively. Furthermore, the retrieving results are highly consistent with the measured data, and the correlation coefficient is 0.9407. Full article

(This article belongs to the Special Issue Emerging Remote Sensing Techniques for Monitoring Glaciers and Snow)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Emerging Remote Sensing Techniques for Monitoring Glaciers and Snow

Share This Special Issue

Special Issue Editor

Special Issue Information

Keywords

Published Papers (10 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI