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New Insights in Remote Sensing of Snow and Glaciers

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Biogeosciences Remote Sensing".

Deadline for manuscript submissions: 15 April 2025 | Viewed by 12179

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Special Issue Editors

Dr. Roberto Salzano

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Guest Editor

Institute for Atmospheric Pollution Research, National Research Council of Italy (CNR), 50019 Florence, Italy
Interests: remote sensing; snow cover; reflectance; hyperspectral sensor; artificial intelligence
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Dr. Rosamaria Salvatori

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Guest Editor

Institute of Polar Sciences, National Research Council of Italy (CNR), 00010 Rome, Italy
Interests: remote Sensing of snow cover; snow; ice optical properties

Prof. Dr. Angelika Humbert

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Guest Editor

Helmholtz Centre for Polar and Marine Research, Alfred Wegener Institute, 27570 Bremerhaven, Germany
Interests: remote sensing of ice sheets; modeling of ice sheets; airborne observations

Special Issue Information

Dear Colleagues,

Advancing remote sensing methods for snow and glaciers are required to improve the capabilities of observing the rapidly changing Earth system. The transition to Digital Earth concept requires novel knowledge and capabilities aimed at describing processes occurring in the cryosphere. The dynamics of snow-covered and glaciated areas, in terms of spatial distribution and time evolution, is a key component of surface processes occurring at different latitudes, with special reference to polar regions.

The combination between different platforms (satellite, seaborne, airborne, and ground-based), different spatial and time scales, as well as different sensors (optical, microwave, etc.) is the ideal strategy for observing the cryosphere. New technologies are an additional critical issue, and the collection of outcomes provided by observing programs, novel sensors or platforms is a high-impact tool. Data value is therefore a critical concept, since the transition from observations and measurements to data products and services is the best strategy for sharing knowledge between communities and for transferring constraints to policy makers.

The scope of this Special Issue is to collect research articles focused on, but not limited to, applications of remote-sensing data/techniques combined with other approaches to better monitor and/or understand processes occurring on snow-covered and glaciated areas, in different environmental frameworks. Manuscripts using novel approaches based on data integration and on multimission products are particularly welcome.

Dr. Roberto Salzano
Dr. Rosamaria Salvatori
Prof. Dr. Angelika Humbert
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 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

snow cover
glaciers
optical remote sensing
radar remote sensing
data integration
novel methodologies

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Published Papers (7 papers)

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Research

22 pages, 14255 KiB

Open AccessArticle

Recent Patterns and Trends of Snow Cover (2000–2023) in the Cantabrian Mountains (Spain) from Satellite Imagery Using Google Earth Engine

by Adrián Melón-Nava

Remote Sens. 2024, 16(19), 3592; https://doi.org/10.3390/rs16193592 - 26 Sep 2024

Viewed by 1017

Abstract

Snow cover is a relevant component of the Earth’s climate system, influencing water supply, ecosystem health, and natural hazard management. This study aims to monitor daily snow cover in the Cantabrian Mountains using Sentinel-2, Landsat (5–8), and MODIS data processed in Google Earth Engine (GEE). The main purpose is to extract metrics on snow cover extent, duration, frequency, and trends. Key findings reveal significant spatial and temporal variability in Snow-Cover Days (SCDs) across the region. Over the past 23 years, there has been a notable overall decrease in snow-cover days (−0.26 days per year, and −0.92 days per year in areas with a significant trend). Altitudes between 1000–2000 m a.s.l. showed marked decreases. The analysis of Snow-Cover Fraction (SCF) indicates high interannual variability and records the highest values at the end of January and the beginning of February. The effectiveness of satellite data and GEE is highlighted in providing detailed, long-term snow cover analysis, despite some limitations in steep slopes, forests, and prolonged cloud-cover areas. These results underscore the capacity for continuous monitoring with satellite imagery, especially in areas with sparse snow observation networks, where studies could be enhanced with more localized studies or additional ground-based observations. Full article

(This article belongs to the Special Issue New Insights in Remote Sensing of Snow and Glaciers)

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12 pages, 3145 KiB

Open AccessCommunication

Comparison of the NASA Standard MODerate-Resolution Imaging Spectroradiometer and Visible Infrared Imaging Radiometer Suite Snow-Cover Products for Creation of a Climate Data Record: A Case Study in the Great Basin of the Western United States

by Dorothy K. Hall, George A. Riggs and Nicolo E. DiGirolamo

Remote Sens. 2024, 16(16), 3029; https://doi.org/10.3390/rs16163029 - 18 Aug 2024

Viewed by 751

Abstract

A nearly continuous daily, global Environmental Science Data Record of NASA Standard MODerate-resolution Imaging Spectroradiometer (MODIS) snow-cover extent (SCE) data products has been available since 2000. When the MODIS record ends, the ‘moderate resolution’ SCE record will continue with NASA Standard Visible Infrared Imaging Radiometer Suite (VIIRS) SCE data products. The objective of this work is to evaluate and quantify the continuity between the MODIS and VIIRS SCE data products to enable the merging of the data product records. A climate data record (CDR) could be developed when 30 years of daily global moderate-resolution SCE become available if the continuity of the MODIS and VIIRS records can be established. Here, we focus on the daily cloud-gap-filled MODIS and VIIRS SCE NASA standard data products, MOD10A1F and VNP10A1F, respectively, for a case study in the Great Basin of the western United States during a period of sensor overlap. Using the methodologies described herein (daily percent of snow cover, duration of snow cover, average monthly number of days (Ndays) of snow cover, and trends in Ndays of snow cover, we show that the snow maps display excellent agreement. For example, the average monthly number of days of snow cover in the Great Basin calculated using MOD10A1F and VNP10A1F agrees with a Pearson’s correlation coefficient of r = 0.99 for our 11-year study period from WY 2013 to 2023. Additionally, the SCE derived from each data product agrees very well with meteorological station data, with a Pearson’s correlation coefficient of r = 0.91 and r = 0.92 for MOD10A1F and VNP10A1F, respectively. Our results support the eventual creation of a CDR. Full article

(This article belongs to the Special Issue New Insights in Remote Sensing of Snow and Glaciers)

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21 pages, 11741 KiB

Open AccessArticle

A Google Earth Engine Platform to Integrate Multi-Satellite and Citizen Science Data for the Monitoring of River Ice Dynamics

by Mohamed Abdelkader, Jorge Humberto Bravo Mendez, Marouane Temimi, Dana R. N. Brown, Katie V. Spellman, Christopher D. Arp, Allen Bondurant and Holli Kohl

Remote Sens. 2024, 16(8), 1368; https://doi.org/10.3390/rs16081368 - 12 Apr 2024

Cited by 6 | Viewed by 2885

Abstract

This study introduces a new automated system that blends multi-satellite information and citizen science data for reliable and timely observations of lake and river ice in under-observed northern regions. The system leverages the Google Earth Engine resources to facilitate the analysis and visualization of ice conditions. The adopted approach utilizes a combination of moderate and high-resolution optical data, along with radar observations. The results demonstrate the system’s capability to accurately detect and monitor river ice, particularly during key periods, such as the freeze-up and the breakup. The integration citizen science data showed added values in the validation of remote sensing products, as well as filling gaps whenever satellite observations cannot be collected due to cloud obstruction. Moreover, it was shown that citizen science data can be converted to valuable quantitative information, such as the case of ice thickness, which is very useful when combined with ice extent derived from remote sensing. In this study, citizen science data were employed for the quantitative assessment of the remote sensing product. Obtained results showed a good agreement between the product and observed river status, with a Critical Success Index of 0.82. Notably, the system has shown effectiveness in capturing the spatial and temporal evolution of snow and ice conditions, as evidenced by its application in analyzing specific ice jam events in 2023. The study concludes that the developed system marks a significant advancement in river ice monitoring, combining technological innovation with community engagement. Full article

(This article belongs to the Special Issue New Insights in Remote Sensing of Snow and Glaciers)

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18 pages, 7307 KiB

Open AccessArticle

Changes in the Antarctic’s Summer Surface Albedo, Observed by Satellite since 1982 and Associated with Sea Ice Anomalies

by Yuqi Sun, Yetang Wang, Zhaosheng Zhai and Min Zhou

Remote Sens. 2023, 15(20), 4940; https://doi.org/10.3390/rs15204940 - 12 Oct 2023

Viewed by 1407

Abstract

In polar regions, positive feedback of snow and ice albedo can intensify global warming. While recent significant decreases in Arctic surface ice albedo have drawn considerable attention, Antarctic surface albedo variability remains underexplored. Here, satellite albedo product CLARA-A2.1-SAL is first validated and then used to investigate spatial and temporal trends in the summer albedo over the Antarctic from 1982 to 2018, along with their association with Antarctic sea ice changes. The SAL product matches well surface albedo observations from eight stations, suggesting its robust performance in Antarctica. Summer surface albedo averaged over the entire ice sheet shows a downward trend since 1982, albeit not statistically significant. In contrast, a significant upward trend is observed in the sea ice region. Spatially, for ice sheet surface albedo, positive trends occur in the eastern Antarctica Peninsula and the margins of East Antarctica, whereas other regions exhibit negative trends, most prominently in the Ross and Ronne ice shelves. For sea ice albedo, positive trends are observed in the Ross Sea and the Weddell Sea, but negative trends are observed in the Bellingshausen and the Amundsen Seas. Between 2016 and 2018, an unusual decrease in the sea ice extent significantly affected both sea ice and Antarctic ice sheet (AIS) surface albedo changes. However, for the 1982–2015 period, while the effect of sea ice on its own albedo is significant, its impact on ice sheet albedo is less apparent. Air temperature and snow depth also contribute much to sea ice albedo changes. However, on ice sheet surface albedo, the influence of temperature and snow accumulation appears limited. Full article

(This article belongs to the Special Issue New Insights in Remote Sensing of Snow and Glaciers)

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17 pages, 5859 KiB

Open AccessArticle

What Is the Threshold Elevation at Which Climatic Factors Determine Snow Cover Variability? A Case Study of the Keriya River Basin

by Wei Yan, Yifan Wang, Xiaofei Ma, Yaogeng Tan, Junhui Yan, Minghua Liu and Sutao Liu

Remote Sens. 2023, 15(19), 4725; https://doi.org/10.3390/rs15194725 - 27 Sep 2023

Cited by 1 | Viewed by 1239

Abstract

Climate and topography are pivotal factors influencing snow cover variation, highlighting the significance of investigating the altitudinal response of snow cover to climate change. This study adopted a new MODIS snow cover extent product over China, reanalysis climate data, and digital elevation model (DEM) data to analyze the variation characteristics of snow cover frequency (SCF) and climatic factors with elevation in the Keriya River Basin (KRB) during the hydrological years from 2000 to 2020. The Partial Least Squares Regression (PLSR) method was utilized to explore the elevation-based relationships between SCF and climatic factors. Our findings can be summarized as follows: (1) The SCF exhibited an “increasing–decreasing–increasing–decreasing” pattern intra-annually, with insignificant monthly inter-annual variations. Only November, January, April, and May demonstrated upward trends, whereas October and December remained relatively stable, and other months exhibited declines. (2) Vertical variations in SCF and climatic factors revealed fluctuating upward trends in SCF and wind speed. On the other hand, the air temperature consistently decreased at a lapse rate ranging from 0.60 to 0.85 °C/100 m. Precipitation demonstrated “rising–falling” or “rapidly rising–slowly rising” patterns, bounded by 3821 m (range 3474–4576 m). (3) A new decision scheme, which took into account the alteration of the primary SCF controlling factors and shifts between positive and negative impacts caused by these factors, was used to determine five threshold elevation zones: 2585 m (range 2426–2723 m), 3447 m (range 3125–3774 m), 4251 m (range 4126–4375 m), 5256 m (range 4975–5524 m), and 5992 m (range 5874–6425 m). These threshold elevation zones were evident in spring, with four of these appearing in autumn (excluding 4251 m) and summer (excluding 2585 m). Only two threshold elevation zones were observed in winter with elevation values of 3447 m and 5992 m, respectively. Our findings are crucial for a deeper understanding of snow cover variation patterns at different elevations and offer essential insights for the responsible management of regional water resources. Full article

(This article belongs to the Special Issue New Insights in Remote Sensing of Snow and Glaciers)

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19 pages, 6129 KiB

Open AccessArticle

Detection of Winter Heat Wave Impact on Surface Runoff in a Periglacial Environment (Ny-Ålesund, Svalbard)

by Roberto Salzano, Riccardo Cerrato, Federico Scoto, Andrea Spolaor, Emiliana Valentini, Marco Salvadore, Giulio Esposito, Serena Sapio, Andrea Taramelli and Rosamaria Salvatori

Remote Sens. 2023, 15(18), 4435; https://doi.org/10.3390/rs15184435 - 9 Sep 2023

Cited by 2 | Viewed by 1507

Abstract

The occurrence of extreme warm events in the Arctic has been increasing in recent years in terms of their frequency and intensity. The assessment of the impact of these episodes on the snow season requires further observation capabilities, where spatial and temporal resolutions are key constraints. This study targeted the snow season of 2022 when a winter rain-on-snow event occurred at Ny-Ålesund in mid-March. The selected methodology was based on a multi-scale and multi-platform approach, combining ground-based observations with satellite remote sensing. The ground-based observation portfolio included meteorological measurements, nivological information, and the optical description of the surface in terms of spectral reflectance and snow-cover extent. The satellite data were obtained by the Sentinel-2 platforms, which provided ten multi-spectral acquisitions from March to July. The proposed strategy supported the impact assessment of heat waves in a periglacial environment, describing the relation and the timing between rain-on-snow events and the surface water drainage system. The integration between a wide range of spectral, time, and spatial resolutions enhanced the capacity to monitor the evolution of the surface water drainage system, detecting two water discharge pulsations, different in terms of duration and effects. This preliminary study aims to improve the description of the snow dynamics during those extreme events and to assess the impact of the produced break during the snow accumulation period. Full article

(This article belongs to the Special Issue New Insights in Remote Sensing of Snow and Glaciers)

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17 pages, 3199 KiB

Open AccessEditor’s ChoiceArticle

NDVI Analysis for Monitoring Land-Cover Evolution on Selected Deglaciated Areas in the Gran Paradiso Group (Italian Western Alps)

by Simona Gennaro, Riccardo Cerrato, Maria Cristina Salvatore, Roberto Salzano, Rosamaria Salvatori and Carlo Baroni

Remote Sens. 2023, 15(15), 3847; https://doi.org/10.3390/rs15153847 - 2 Aug 2023

Cited by 2 | Viewed by 2079

Abstract

The ongoing climate warming is affecting high-elevation areas, reducing the extent and the duration of glacier and snow covers, driving a widespread greening effect on the Alpine region. The impact assessment requires therefore the integration of the geomorphological context with altitudinal and ecological features of the study areas. The proposed approach introduces chronologically-constrained zones as geomorphological evidence for selecting deglaciated areas in the alpine and non-alpine belts. In the present study, the protected and low-anthropic-impacted areas of the Gran Paradiso Group (Italian Western Alps) were analysed using Landsat NDVI time series (1984–2022 CE). The obtained results highlighted a progressive greening even at a higher altitude, albeit not ubiquitous. The detected NDVI trends showed, moreover, how the local factors trigger the greening in low-elevation areas. Spectral reflectance showed a general decrease over time, evidencing the progressive colonisation of recently deglaciated surfaces. The results improved the discrimination between different greening rates in the deglaciated areas of the Alpine regions. The geomorphological-driven approach showed significant potential to support the comprehension of these processes, especially for fast-changing areas such as the high mountain regions. Full article

(This article belongs to the Special Issue New Insights in Remote Sensing of Snow and Glaciers)

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