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Earth Observation (EO), Remote Sensing (RS), and Geoinformation (GI) Applications in Svalbard

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing and Geo-Spatial Science".

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 68846

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Special Issue Editors


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Guest Editor
Svalbard Integrated Arctic Earth Observing System (SIOS), SIOS Knowledge Centre, Svalbard Science Centre, P.O. Box 156, N-9171 Longyearbyen, Svalbard, Norway
Interests: cryospheric GIScience and remote sensing; glaciological image analysis; machine learning and deep learning; Earth observation applications in Arctic, Antarctic, and Himalayas; airborne remote sensing
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Geosciences, University of Oslo, P.O Box 1047, Blindern, 0316 Oslo, Norway
Interests: Earth Observation technology; glaciology; cryosphere remote sensing; geoinformatics

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Guest Editor
Department of Earth Sciences, Uppsala University, Geocentrum, Villavägen 16, 752 36 Uppsala, Sweden
Interests: glaciology; mass balance and ice dynamics in Svalbar; firn aquifers and snow distribution

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Guest Editor
National Institute of Polar Research, 10-3, Midori-cho, Tachikawa-shi, Tokyo 190-8518, Japan
Interests: glaciology; climatology; remote sensing engineering

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Guest Editor
Norwegian Polar Institute, Fram Centre, P.O. Box 6606 Langnes, N-9296 Tromsø, Norway
Interests: remote sensing of the cryosphere glacier and ice-sheet changes
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Guest Editor
NORCE Technology, NORCE Norwegian Research Center AS, Sykehusvn 21, 9019 Tromsø, Norway
Interests: InSAR; glaciers; airborne data; drones; permafrost; Svalbard vegetation and growing season

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Guest Editor
Faculty of Natural Sciences, University of Silesia in Katowice, Bankowa 12, 40-007 Katowice, Poland
Interests: remote sensing of polar areas; glaciology

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Guest Editor
Norwegian Space Agency (NoSA) and Svalbard Integrated Arctic Earth Observing System - Knowledge Centre (SIOS-KC), Svalbard Forskningspark, P.O. Box 156, N-9171 Longyearbyen, Svalbard, Norway
Interests: satellite cal/val; Earth Observation and remote sensing applications in Svalbard

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Guest Editor
Atmosphere and Climate Department, Norwegian Institute for Air Research (NILU), P.O box 100, 2027 Kjeller, Norway
Interests: Satellite cal/val; Sentinel-5p; Pandora; atmosphere remote sensing applications

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Guest Editor
Department of Polar and Marine Research, Institute of Geophysics Polish Academy of Sciences, 01-452 Warszawa, Poland
Interests: snow climatology; snow remote sensing; snow hydrology; glaciology
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Institute for Atmospheric Pollution Research (IIA), National Research Council of Italy (CNR), Via Madonna del Piano 10, 50019 Sesto Fiorentino, Italy
Interests: remote sensing of cold regions
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Guest Editor
Remote Sensing and Data Management, Norwegian Meteorological Institute (MET Norway), PO Box 6314 Langnes, 9293 Tromsø, Norway
Interests: sea ice remote sensing; SAR; passive microwave radiometers

Special Issue Information

Dear Colleagues,

The Svalbard Integrated Arctic Earth Observing System (SIOS) is an international observing system for long-term in situ and remotely sensed measurements in and around Svalbard addressing Earth System Science (ESS) questions. SIOS research infrastructures (RI) are distributed all over Svalbard for collection of long-term in situ measurements. These in situ measurements are useful for various current and future satellite missions for calibration and validation (cal/val) activities. Eventually, integration of in situ and satellite-based measurements would benefit the entire ESS community to address broader scientific questions. Over the past three decades, tremendous developments in Earth Observation (EO) satellites have made significant contributions to the spatial–spectral–temporal sampling and subsequent extraction of geoinformation (GI) from the Arctic. Svalbard is probably the region in the Arctic with the most in situ measurements; still, there are massive gaps. Such data gaps can be filled using frequent satellite-based acquisitions, new product generation using remote sensing (RS), and integration of in situ data with satellite-based information. This Special Issue will provide a broad platform to various regional and Svalbard-wide studies that are being conducted using EO/RS/GI. For this Special Issue, we seek submissions focusing on:

  • EO/RS/GI techniques relevant for field campaigns, modelling, and long-term monitoring programs;
  • Optical (e.g., Sentinel-2-3), Microwave (e.g., scatterometers, SAR) and Lidar (e.g., ICESat) applications in Svalbard;
  • Terrestrial, marine, atmospheric, and cryospheric applications of RS/EO/GI in Svalbard and associated waters;
  • Remote sensing of the marine cryosphere and its interactions with ocean, land, and atmosphere;
  • Ground-, space-, and airborne platform-based studies in Svalbard;
  • Integration of remote sensing, in situ and previously published geoinformation to gain new knowledge about Svalbard;
  • Cal/val activities for satellite missions that are being conducted in Svalbard, e.g., Pandora installation in Ny Ålesund, cal/val of snow parameters from satellite, cal/val activities using moorings;
  • Machine learning, deep learning, neural networks and cloud computing (e.g., Google Earth Engine) based applications in Svalbard;
  • Broader review papers on EO/RS/GI driven research activities in Svalbard (e.g., review on monitoring calving events in Svalbard);
  • Svalbard wide GI extraction/product generation and operationalization using EO/RS;
  • Derivation of geophysical and biophysical parameters using satellites (e.g., sea ice drift and type, chlorophyll concentration, phytoplankton blooms);
  • Remote sensing applications in glaciological studies in Svalbard (geodetic mass balance, snow cover and snow properties, surface elevation changes, etc.;
  • Remote sensing of sea ice, icebergs, snow/firn/ice, ground ice, snow on sea ice, avalanche activities, permafrost subsidence studies using InSAR
  • Methods for characterizing the terrestrial vegetation, mapping abundance and extent, growing season, primary productivity, and time series analysis;
  • Applications of new technologies such as AUVs, robots, drones, mapping using Surface from Motion, terrestrial LiDAR;
  • Very high resolution (VHR) satellite remote sensing in Svalbard including applications using airborne imagery and hyperspectral data acquired by SIOS-NORCE research aircraft and drones;
  • Relevant research studies supported by the SIOS-ACCESS, SIOS-SESS, and SIOS-InfraNor initiative.

We especially encourage contributors to provide access of data and products generated as a part of study via the SIOS data management system (SDMS).

Dr. Shridhar D. Jawak
Prof. Dr. Veijo Pohjola
Prof. Dr. Andreas Kääb
Prof. Hiroyuki Enomoto
Dr. Geir Moholdt
Dr. Kjell Arild Høgda
Dr. Malgorzata Blaszczyk
Dr. Bo N. Andersen
Ms. Ann Mari Fjæraa
Dr. Bartłomiej Luks
Dr. Roberto Salzano
Dr. Frode Dinessen
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

  • Svalbard
  • Arctic
  • SIOS
  • research infrastructures
  • new sensors
  • cal/val
  • geospatial product generation
  • UAV/drones
  • research aircraft
  • permafrost
  • arctic vegetation
  • glaciology
  • Infranor

Published Papers (20 papers)

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Editorial

Jump to: Research, Other

11 pages, 249 KiB  
Editorial
Status of Earth Observation and Remote Sensing Applications in Svalbard
by Shridhar D. Jawak, Veijo Pohjola, Andreas Kääb, Bo N. Andersen, Małgorzata Błaszczyk, Roberto Salzano, Bartłomiej Luks, Hiroyuki Enomoto, Kjell Arild Høgda, Geir Moholdt, Frode Dinessen and Ann Mari Fjæraa
Remote Sens. 2023, 15(2), 513; https://doi.org/10.3390/rs15020513 - 15 Jan 2023
Cited by 1 | Viewed by 2329
Abstract
Remarkable developments in the fields of earth observation (EO) satellites and remote sensing (RS) technology over the past four decades have substantially contributed to spatial, spectral, and temporal sampling [...] Full article

Research

Jump to: Editorial, Other

38 pages, 11284 KiB  
Article
Multispectral Characteristics of Glacier Surface Facies (Chandra-Bhaga Basin, Himalaya, and Ny-Ålesund, Svalbard) through Investigations of Pixel and Object-Based Mapping Using Variable Processing Routines
by Shridhar D. Jawak, Sagar F. Wankhede, Alvarinho J. Luis and Keshava Balakrishna
Remote Sens. 2022, 14(24), 6311; https://doi.org/10.3390/rs14246311 - 13 Dec 2022
Cited by 8 | Viewed by 2237
Abstract
Fundamental image processing methods, such as atmospheric corrections and pansharpening, influence the signal of the pixel. This morphs the spectral signature of target features causing a change in both the final spectra and the way different mapping methods may assign thematic classes. In [...] Read more.
Fundamental image processing methods, such as atmospheric corrections and pansharpening, influence the signal of the pixel. This morphs the spectral signature of target features causing a change in both the final spectra and the way different mapping methods may assign thematic classes. In the current study, we aim to identify the variations induced by popular image processing methods in the spectral reflectance and final thematic maps of facies. To this end, we have tested three different atmospheric corrections: (a) Quick Atmospheric Correction (QUAC), (b) Dark Object Subtraction (DOS), and (c) Fast Line-of-Sight Atmospheric Analysis of Hypercubes (FLAASH), and two pansharpening methods: (a) Hyperspherical Color Sharpening (HCS) and (b) Gram–Schmidt (GS). WorldView-2 and WorldView-3 satellite images over Chandra-Bhaga Basin, Himalaya, and Ny-Ålesund, Svalbard are tested via spectral subsets in traditional (BGRN1), unconventional (CYRN2), visible to near-infrared (VNIR), and the complete available spectrum (VNIR_SWIR). Thematic mapping was comparatively performed using 12 pixel-based (PBIA) algorithms and 3 object-based (GEOBIA) rule sets. Thus, we test the impact of varying image processing routines, effectiveness of specific spectral bands, utility of PBIA, and versatility of GEOBIA for mapping facies. Our findings suggest that the image processing routines exert an extreme impact on the end spectral reflectance. DOS delivers the most reliable performance (overall accuracy = 0.64) averaged across all processing schemes. GEOBIA delivers much higher accuracy when the QUAC correction is employed and if the image is enhanced by GS pansharpening (overall accuracy = 0.79). SWIR bands have not enhanced the classification results and VNIR band combination yields superior performance (overall accuracy = 0.59). The maximum likelihood classifier (PBIA) delivers consistent and reliable performance (overall accuracy = 0.61) across all processing schemes and can be used after DOS correction without pansharpening, as it deteriorates spectral information. GEOBIA appears to be robust against modulations in atmospheric corrections but is enhanced by pansharpening. When utilizing GEOBIA, we find that a combination of spatial and spectral object features (rule set 3) delivers the best performance (overall accuracy = 0.86), rather than relying only on spectral (rule set 1) or spatial (rule set 2) object features. The multiresolution segmentation parameters used here may be transferable to other very high resolution (VHR) VNIR mapping of facies as it yielded consistent objects across all processing schemes. Full article
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29 pages, 6109 KiB  
Article
Effect of Image-Processing Routines on Geographic Object-Based Image Analysis for Mapping Glacier Surface Facies from Svalbard and the Himalayas
by Shridhar D. Jawak, Sagar F. Wankhede, Alvarinho J. Luis and Keshava Balakrishna
Remote Sens. 2022, 14(17), 4403; https://doi.org/10.3390/rs14174403 - 4 Sep 2022
Cited by 7 | Viewed by 2005
Abstract
Advancements in remote sensing have led to the development of Geographic Object-Based Image Analysis (GEOBIA). This method of information extraction focuses on segregating correlated pixels into groups for easier classification. This is of excellent use in analyzing very-high-resolution (VHR) data. The application of [...] Read more.
Advancements in remote sensing have led to the development of Geographic Object-Based Image Analysis (GEOBIA). This method of information extraction focuses on segregating correlated pixels into groups for easier classification. This is of excellent use in analyzing very-high-resolution (VHR) data. The application of GEOBIA for glacier surface mapping, however, necessitates multiple scales of segmentation and input of supportive ancillary data. The mapping of glacier surface facies presents a unique problem to GEOBIA on account of its separable but closely matching spectral characteristics and often disheveled surface. Debris cover can induce challenges and requires additions of slope, temperature, and short-wave infrared data as supplements to enable efficient mapping. Moreover, as the influence of atmospheric corrections and image sharpening can derive variations in the apparent surface reflectance, a robust analysis of the effects of these processing routines in a GEOBIA environment is lacking. The current study aims to investigate the impact of three atmospheric corrections, Dark Object Subtraction (DOS), Quick Atmospheric Correction (QUAC), and Fast Line-of-Sight Atmospheric Analysis of Hypercubes (FLAASH), and two pansharpening methods, viz., Gram–Schmidt (GS) and Hyperspherical Color Sharpening (HCS), on the classification of surface facies using GEOBIA. This analysis is performed on VHR WorldView-2 imagery of selected glaciers in Ny-Ålesund, Svalbard, and Chandra–Bhaga basin, Himalaya. The image subsets are segmented using multiresolution segmentation with constant parameters. Three rule sets are defined: rule set 1 utilizes only spectral information, rule set 2 contains only spatial and contextual features, and rule set 3 combines both spatial and spectral attributes. Rule set 3 performs the best across all processing schemes with the highest overall accuracy, followed by rule set 1 and lastly rule set 2. This trend is observed for every image subset. Among the atmospheric corrections, DOS displays consistent performance and is the most reliable, followed by QUAC and FLAASH. Pansharpening improved overall accuracy and GS performed better than HCS. The study reports robust segmentation parameters that may be transferable to other VHR-based glacier surface facies mapping applications. The rule sets are adjusted across the processing schemes to adjust to the change in spectral characteristics introduced by the varying routines. The results indicate that GEOBIA for glacier surface facies mapping may be less prone to the differences in spectral signatures introduced by different atmospheric corrections but may respond well to increasing spatial resolution. The study highlighted the role of spatial attributes for mapping fine features, and in combination with appropriate spectral features may enhance thematic classification. Full article
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22 pages, 3691 KiB  
Article
Spatio-Temporal Variability of Suspended Particulate Matter in a High-Arctic Estuary (Adventfjorden, Svalbard) Using Sentinel-2 Time-Series
by Daniela M. R. Walch, Rakesh K. Singh, Janne E. Søreide, Hugues Lantuit and Amanda Poste
Remote Sens. 2022, 14(13), 3123; https://doi.org/10.3390/rs14133123 - 29 Jun 2022
Cited by 5 | Viewed by 2608
Abstract
Arctic coasts, which feature land-ocean transport of freshwater, sediments, and other terrestrial material, are impacted by climate change, including increased temperatures, melting glaciers, changes in precipitation and runoff. These trends are assumed to affect productivity in fjordic estuaries. However, the spatial extent and [...] Read more.
Arctic coasts, which feature land-ocean transport of freshwater, sediments, and other terrestrial material, are impacted by climate change, including increased temperatures, melting glaciers, changes in precipitation and runoff. These trends are assumed to affect productivity in fjordic estuaries. However, the spatial extent and temporal variation of the freshwater-driven darkening of fjords remain unresolved. The present study illustrates the spatio-temporal variability of suspended particulate matter (SPM) in the Adventfjorden estuary, Svalbard, using in-situ field campaigns and ocean colour remote sensing (OCRS) via high-resolution Sentinel-2 imagery. To compute SPM concentration (CSPMsat), a semi-analytical algorithm was regionally calibrated using local in-situ data, which improved the accuracy of satellite-derived SPM concentration by ~20% (MRD). Analysis of SPM concentration for two consecutive years (2019, 2020) revealed strong seasonality of SPM in Adventfjorden. Highest estimated SPM concentrations and river plume extent (% of fjord with CSPMsat > 30 mg L1) occurred during June, July, and August. Concurrently, we observed a strong relationship between river plume extent and average air temperature over the 24 h prior to the observation (R2 = 0.69). Considering predicted changes to environmental conditions in the Arctic region, this study highlights the importance of the rapidly changing environmental parameters and the significance of remote sensing in analysing fluxes in light attenuating particles, especially in the coastal Arctic Ocean. Full article
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14 pages, 5351 KiB  
Article
The Collection of Hyperspectral Measurements on Snow and Ice Covers in Polar Regions (SISpec 2.0)
by Rosamaria Salvatori, Roberto Salzano, Mauro Valt, Riccardo Cerrato and Stefano Ghergo
Remote Sens. 2022, 14(9), 2213; https://doi.org/10.3390/rs14092213 - 5 May 2022
Cited by 5 | Viewed by 2076
Abstract
The data value of hyperspectral measurements on ice and snow cover is strongly impacted by the availability of data services, where spectral libraries are integrated to detailed descriptions of the observed surface cover. For snow and ice cover, we present an updated version [...] Read more.
The data value of hyperspectral measurements on ice and snow cover is strongly impacted by the availability of data services, where spectral libraries are integrated to detailed descriptions of the observed surface cover. For snow and ice cover, we present an updated version of the Snow/Ice Spectral Archive (SISpec 2.0), which has been integrated into a web portal characterized by different functionalities. The adopted metadata scheme features basic geographic data, information about the acquisition setup, and parameters describing the different surface types. While the implementation of the IACS Classification of Seasonal Snow on the Ground is the core component for snow cover, ice cover is approached using different parameters associated with its surface roughness and location. The web portal is not only a visualization tool, but also supports interoperability functionalities, providing data in the NetCDF file format. The availability of these functionalities sets the foundation for sharing a novel platform with the community and is an interesting tool for calibrating and validating data and models. Full article
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23 pages, 43109 KiB  
Article
Multi-Sensor Analysis of Snow Seasonality and a Preliminary Assessment of SAR Backscatter Sensitivity to Arctic Vegetation: Limits and Capabilities
by Laura Stendardi, Stein Rune Karlsen, Eirik Malnes, Lennart Nilsen, Hans Tømmervik, Elisabeth J. Cooper and Claudia Notarnicola
Remote Sens. 2022, 14(8), 1866; https://doi.org/10.3390/rs14081866 - 13 Apr 2022
Cited by 2 | Viewed by 2113
Abstract
Snow melt timing and the last day of snow cover have a significant impact on vegetation phenology in the Svalbard archipelago. The aim of this study is to assess the seasonal variations of the snow using a multi-sensor approach and to analyze the [...] Read more.
Snow melt timing and the last day of snow cover have a significant impact on vegetation phenology in the Svalbard archipelago. The aim of this study is to assess the seasonal variations of the snow using a multi-sensor approach and to analyze the sensitivity of the Synthetic Aperture Radar (SAR) backscatter to vegetation growth and soil moisture in an arctic environment. A combined approach using time series data from active remote sensing sensors such as SAR and passive optical sensors is a known technique in snow monitoring, while there is little knowledge of the radar C-band’s response pattern to vegetation dynamics in the arctic. First, we created multi-sensor masks using the HV backscatter coefficients from Sentinel-1 and the Normalized Difference Snow Index (NDSI) time series from Sentinel-2, monitoring the snow dynamics in Adventdalen (Svalbard) for the season from 2017 to 2018. Second, radar sensitivity analysis was performed using the HV polarized channel responses to vegetation growth and soil moisture dynamics. (1) Our results showed that the C-band radar data are capable of monitoring the seasonal variability in timing of snow melting in Adventdalen, revealing an earlier start by approximately 20 days in 2018 compared to 2017. (2) From the sensitivity analyses, the HV channel showed a major response to the vegetation component in areas with drier graminoid dominated vegetation without water-saturated soil (R = 0.69). However, the temperature was strongly correlated with the HV channel (R = 0.74) during the years with delayed snow melting. Areas of frozen tundra with drier vegetation dominated by graminoids had delayed soil thawing processes and therefore this may limit the ability of the radar to follow the vegetation growth pattern and soil moisture. Full article
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46 pages, 8890 KiB  
Article
Impact of Image-Processing Routines on Mapping Glacier Surface Facies from Svalbard and the Himalayas Using Pixel-Based Methods
by Shridhar D. Jawak, Sagar F. Wankhede, Alvarinho J. Luis and Keshava Balakrishna
Remote Sens. 2022, 14(6), 1414; https://doi.org/10.3390/rs14061414 - 15 Mar 2022
Cited by 13 | Viewed by 3441
Abstract
Glacier surface facies are valuable indicators of changes experienced by a glacial system. The interplay of accumulation and ablation facies, followed by intermixing with dust and debris, as well as the local climate, all induce observable and mappable changes on the supraglacial terrain. [...] Read more.
Glacier surface facies are valuable indicators of changes experienced by a glacial system. The interplay of accumulation and ablation facies, followed by intermixing with dust and debris, as well as the local climate, all induce observable and mappable changes on the supraglacial terrain. In the absence or lag of continuous field monitoring, remote sensing observations become vital for maintaining a constant supply of measurable data. However, remote satellite observations suffer from atmospheric effects, resolution disparity, and use of a multitude of mapping methods. Efficient image-processing routines are, hence, necessary to prepare and test the derivable data for mapping applications. The existing literature provides an application-centric view for selection of image processing schemes. This can create confusion, as it is not clear which method of atmospheric correction would be ideal for retrieving facies spectral reflectance, nor are the effects of pansharpening examined on facies. Moreover, with a variety of supervised classifiers and target detection methods now available, it is prudent to test the impact of variations in processing schemes on the resultant thematic classifications. In this context, the current study set its experimental goals. Using very-high-resolution (VHR) WorldView-2 data, we aimed to test the effects of three common atmospheric correction methods, viz. Dark Object Subtraction (DOS), Quick Atmospheric Correction (QUAC), and Fast Line-of-Sight Atmospheric Analysis of Hypercubes (FLAASH); and two pansharpening methods, viz. Gram–Schmidt (GS) and Hyperspherical Color Sharpening (HCS), on thematic classification of facies using 12 supervised classifiers. The conventional classifiers included: Mahalanobis Distance (MHD), Maximum Likelihood (MXL), Minimum Distance to Mean (MD), Spectral Angle Mapper (SAM), and Winner Takes All (WTA). The advanced/target detection classifiers consisted of: Adaptive Coherence Estimator (ACE), Constrained Energy Minimization (CEM), Matched Filtering (MF), Mixture-Tuned Matched Filtering (MTMF), Mixture-Tuned Target-Constrained Interference-Minimized Filter (MTTCIMF), Orthogonal Space Projection (OSP), and Target-Constrained Interference-Minimized Filter (TCIMF). This experiment was performed on glaciers at two test sites, Ny-Ålesund, Svalbard, Norway; and Chandra–Bhaga basin, Himalaya, India. The overall performance suggested that the FLAASH correction delivered realistic reflectance spectra, while DOS delivered the least realistic. Spectra derived from HCS sharpened subsets seemed to match the average reflectance trends, whereas GS reduced the overall reflectance. WTA classification of the DOS subsets achieved the highest overall accuracy (0.81). MTTCIMF classification of the FLAASH subsets yielded the lowest overall accuracy of 0.01. However, FLAASH consistently provided better performance (less variable and generally accurate) than DOS and QUAC, making it the more reliable and hence recommended algorithm. While HCS-pansharpened classification achieved a lower error rate (0.71) in comparison to GS pansharpening (0.76), neither significantly improved accuracy nor efficiency. The Ny-Ålesund glacier facies were best classified using MXL (error rate = 0.49) and WTA classifiers (error rate = 0.53), whereas the Himalayan glacier facies were best classified using MD (error rate = 0.61) and WTA (error rate = 0.45). The final comparative analysis of classifiers based on the total error rate across all atmospheric corrections and pansharpening methods yielded the following reliability order: MXL > WTA > MHD > ACE > MD > CEM = MF > SAM > MTMF = TCIMF > OSP > MTTCIMF. The findings of the current study suggested that for VHR visible near-infrared (VNIR) mapping of facies, FLAASH was the best atmospheric correction, while MXL may deliver reliable thematic classification. Moreover, an extensive account of the varying exertions of each processing scheme is discussed, and could be transferable when compared against other VHR VNIR mapping methods. Full article
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20 pages, 3711 KiB  
Article
Evaluation of Satellite-Derived Estimates of Lake Ice Cover Timing on Linnévatnet, Kapp Linné, Svalbard Using In-Situ Data
by Samuel E. Tuttle, Steven R. Roof, Michael J. Retelle, Alan Werner, Grant E. Gunn and Erin L. Bunting
Remote Sens. 2022, 14(6), 1311; https://doi.org/10.3390/rs14061311 - 9 Mar 2022
Cited by 6 | Viewed by 2616
Abstract
Arctic lakes are sensitive to climate change, and the timing and duration of ice presence and absence (i.e., ice phenology) on the lake surface can be used as a climate indicator. In this study of Linnévatnet, one of the largest lakes on Svalbard, [...] Read more.
Arctic lakes are sensitive to climate change, and the timing and duration of ice presence and absence (i.e., ice phenology) on the lake surface can be used as a climate indicator. In this study of Linnévatnet, one of the largest lakes on Svalbard, we compare inferences of lake ice duration from satellite data with continuously monitored lake water temperature and photographs from automatic cameras. Visible surface reflectance data from the moderate-resolution imaging spectroradiometer (MODIS) were used to observe the change in the lake-wide mean surface reflectance of Linnévatnet from 2003–2019, and smoothing splines were applied to the to determine the date of summer ice-off (also called “break-up end”—BUE). Similarly, BUE and fall ice-on (or “freeze-up end”—FUE) were determined from lake-wide mean time series of Sentinel-1 microwave backscatter from 2014–2019. Overall, the ice timing dates identified from the satellite observations agree well with the in-situ observations (RMSE values of approximately 2–7 days for BUE and FUE, depending on the method and in-situ dataset), lending confidence to the accuracy of remote sensing of lake ice phenology in remote Arctic regions. Our observations of Linnévatnet indicate that BUE dates do not have a significant trend, while FUE dates have been occurring approximately 1.5 days later per year during the study period. These results support an overall decrease in annual duration of lake ice cover in this part of Svalbard. Full article
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39 pages, 79946 KiB  
Article
Airborne Validation of ICESat-2 ATLAS Data over Crevassed Surfaces and Other Complex Glacial Environments: Results from Experiments of Laser Altimeter and Kinematic GPS Data Collection from a Helicopter over a Surging Arctic Glacier (Negribreen, Svalbard)
by Ute C. Herzfeld, Matthew Lawson, Thomas Trantow and Thomas Nylen
Remote Sens. 2022, 14(5), 1185; https://doi.org/10.3390/rs14051185 - 27 Feb 2022
Cited by 11 | Viewed by 2904
Abstract
The topic of this paper is the airborne evaluation of ICESat-2 Advanced Topographic Laser Altimeter System (ATLAS) measurement capabilities and surface-height-determination over crevassed glacial terrain, with a focus on the geodetical accuracy of geophysical data collected from a helicopter. To obtain surface heights [...] Read more.
The topic of this paper is the airborne evaluation of ICESat-2 Advanced Topographic Laser Altimeter System (ATLAS) measurement capabilities and surface-height-determination over crevassed glacial terrain, with a focus on the geodetical accuracy of geophysical data collected from a helicopter. To obtain surface heights over crevassed and otherwise complex ice surface, ICESat-2 data are analyzed using the density-dimension algorithm for ice surfaces (DDA-ice), which yields surface heights at the nominal 0.7 m along-track spacing of ATLAS data. As the result of an ongoing surge, Negribreen, Svalbard, provided an ideal situation for the validation objectives in 2018 and 2019, because many different crevasse types and morphologically complex ice surfaces existed in close proximity. Airborne geophysical data, including laser altimeter data (profilometer data at 905 nm frequency), differential Global Positioning System (GPS), Inertial Measurement Unit (IMU) data, on-board-time-lapse imagery and photographs, were collected during two campaigns in summers of 2018 and 2019. Airborne experiment setup, geodetical correction and data processing steps are described here. To date, there is relatively little knowledge of the geodetical accuracy that can be obtained from kinematic data collection from a helicopter. Our study finds that (1) Kinematic GPS data collection with correction in post-processing yields higher accuracies than Real-Time-Kinematic (RTK) data collection. (2) Processing of only the rover data using the Natural Resources Canada Spatial Reference System Precise Point Positioning (CSRS-PPP) software is sufficiently accurate for the sub-satellite validation purpose. (3) Distances between ICESat-2 ground tracks and airborne ground tracks were generally better than 25 m, while distance between predicted and actual ICESat-2 ground track was on the order of 9 m, which allows direct comparison of ice-surface heights and spatial statistical characteristics of crevasses from the satellite and airborne measurements. (4) The Lasertech Universal Laser System (ULS), operated at up to 300 m above ground level, yields full return frequency (400 Hz) and 0.06–0.08 m on-ice along-track spacing of height measurements. (5) Cross-over differences of airborne laser altimeter data are −0.172 ± 2.564 m along straight paths, which implies a precision of approximately 2.6 m for ICESat-2 validation experiments in crevassed terrain. (6) In summary, the comparatively light-weight experiment setup of a suite of small survey equipment mounted on a Eurocopter (Helicopter AS-350) and kinematic GPS data analyzed in post-processing using CSRS-PPP leads to high accuracy repeats of the ICESat-2 tracks. The technical results (1)–(6) indicate that direct comparison of ice-surface heights and crevasse depths from the ICESat-2 and airborne laser altimeter data is warranted. Numerical evaluation of height comparisons utilizes spatial surface roughness measures. The final result of the validation is that ICESat-2 ATLAS data, analyzed with the DDA-ice, facilitate surface-height determination over crevassed terrain, in good agreement with airborne data, including spatial characteristics, such as surface roughness, crevasse spacing and depth, which are key informants on the deformation and dynamics of a glacier during surge. Full article
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17 pages, 3862 KiB  
Article
Combined Use of Aerial Photogrammetry and Terrestrial Laser Scanning for Detecting Geomorphological Changes in Hornsund, Svalbard
by Małgorzata Błaszczyk, Michał Laska, Agnar Sivertsen and Shridhar D. Jawak
Remote Sens. 2022, 14(3), 601; https://doi.org/10.3390/rs14030601 - 26 Jan 2022
Cited by 15 | Viewed by 3473
Abstract
The Arctic is a region undergoing continuous and significant changes in land relief due to different glaciological, geomorphological and hydrogeological processes. To study those phenomena, digital elevation models (DEMs) and highly accurate maps with high spatial resolution are of prime importance. In this [...] Read more.
The Arctic is a region undergoing continuous and significant changes in land relief due to different glaciological, geomorphological and hydrogeological processes. To study those phenomena, digital elevation models (DEMs) and highly accurate maps with high spatial resolution are of prime importance. In this work, we assess the accuracy of high-resolution photogrammetric DEMs and orthomosaics derived from aerial images captured in 2020 over Hornsund, Svalbard. Further, we demonstrate the accuracy of DEMs generated using point clouds acquired in 2021 with a Riegl VZ®-6000 terrestrial laser scanner (TLS). Aerial and terrestrial data were georeferenced and registered based on very reliable ground control points measured in the field. Both DEMs, however, had some data gaps due to insufficient overlaps in aerial images and limited sensing range of the TLS. Therefore, we compared and integrated the two techniques to create a continuous and gapless DEM for the scientific community in Svalbard. This approach also made it possible to identify geomorphological activity over a one-year period, such as the melting of ice cores at the periglacial zone, changes along the shoreline or snow thickness in gullies. The study highlights the potential for combining other techniques to represent the active processes in this region. Full article
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16 pages, 5665 KiB  
Article
Fifty Years of Tidewater Glacier Surface Elevation and Retreat Dynamics along the South-East Coast of Spitsbergen (Svalbard Archipelago)
by Jan Kavan, Guy D. Tallentire, Mihail Demidionov, Justyna Dudek and Mateusz C. Strzelecki
Remote Sens. 2022, 14(2), 354; https://doi.org/10.3390/rs14020354 - 13 Jan 2022
Cited by 11 | Viewed by 2408
Abstract
Tidewater glaciers on the east coast of Svalbard were examined for surface elevation changes and retreat rate. An archival digital elevation model (DEM) from 1970 (generated from aerial images by the Norwegian Polar Institute) in combination with recent ArcticDEM were used to compare [...] Read more.
Tidewater glaciers on the east coast of Svalbard were examined for surface elevation changes and retreat rate. An archival digital elevation model (DEM) from 1970 (generated from aerial images by the Norwegian Polar Institute) in combination with recent ArcticDEM were used to compare the surface elevation changes of eleven glaciers. This approach was complemented by a retreat rate estimation based on the analysis of Landsat and Sentinel-2 images. In total, four of the 11 tidewater glaciers became land-based due to the retreat of their termini. The remaining tidewater glaciers retreated at an average annual retreat rate of 48 m year−1, and with range between 10–150 m year−1. All the glaciers studied experienced thinning in their frontal zones with maximum surface elevation loss exceeding 100 m in the ablation areas of three glaciers. In contrast to the massive retreat and thinning of the frontal zones, a minor increase in ice thickness was recorded in some accumulation areas of the glaciers, exceeding 10 m on three glaciers. The change in glacier geometry suggests an important shift in glacier dynamics over the last 50 years, which very likely reflects the overall trend of increasing air temperatures. Such changes in glacier geometry are common at surging glaciers in their quiescent phase. Surging was detected on two glaciers studied, and was documented by the glacier front readvance and massive surface thinning in high elevated areas. Full article
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19 pages, 34592 KiB  
Article
Eddies in the Marginal Ice Zone of Fram Strait and Svalbard from Spaceborne SAR Observations in Winter
by Igor E. Kozlov and Oksana A. Atadzhanova
Remote Sens. 2022, 14(1), 134; https://doi.org/10.3390/rs14010134 - 29 Dec 2021
Cited by 13 | Viewed by 2897
Abstract
Here we investigate the intensity of eddy generation and their properties in the marginal ice zone (MIZ) regions of Fram Strait and around Svalbard using spaceborne synthetic aperture radar (SAR) data from Envisat ASAR and Sentinel-1 in winter 2007 and 2018. Analysis of [...] Read more.
Here we investigate the intensity of eddy generation and their properties in the marginal ice zone (MIZ) regions of Fram Strait and around Svalbard using spaceborne synthetic aperture radar (SAR) data from Envisat ASAR and Sentinel-1 in winter 2007 and 2018. Analysis of 2039 SAR images allowed identifying 4619 eddy signatures. The number of eddies detected per image per kilometer of MIZ length is similar for both years. Submesoscale and small mesoscale eddies dominate with cyclones detected twice more frequently than anticyclones. Eddy diameters range from 1 to 68 km with mean values of 6 km and 12 km over shallow and deep water, respectively. Mean eddy size grows with increasing ice concentration in the MIZ, yet most eddies are detected at the ice edge and where the ice concentration is below 20%. The fraction of sea ice trapped in cyclones (53%) is slightly higher than that in anticyclones (48%). The amount of sea ice trapped by a single ‘mean’ eddy is about 40 km2, while the average horizontal retreat of the ice edge due to eddy-induced ice melt is about 0.2–0.5 km·d–1 ± 0.02 km·d–1. Relation of eddy occurrence to background currents and winds is also discussed. Full article
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19 pages, 3565 KiB  
Article
Properties of Cirrus Clouds over the European Arctic (Ny-Ålesund, Svalbard)
by Konstantina Nakoudi, Christoph Ritter and Iwona S. Stachlewska
Remote Sens. 2021, 13(22), 4555; https://doi.org/10.3390/rs13224555 - 12 Nov 2021
Cited by 6 | Viewed by 2063
Abstract
Cirrus is the only cloud type capable of inducing daytime cooling or heating at the top of the atmosphere (TOA) and the sign of its radiative effect highly depends on its optical depth. However, the investigation of its geometrical and optical properties over [...] Read more.
Cirrus is the only cloud type capable of inducing daytime cooling or heating at the top of the atmosphere (TOA) and the sign of its radiative effect highly depends on its optical depth. However, the investigation of its geometrical and optical properties over the Arctic is limited. In this work the long-term properties of cirrus clouds are explored for the first time over an Arctic site (Ny-Ålesund, Svalbard) using lidar and radiosonde measurements from 2011 to 2020. The optical properties were quality assured, taking into account the effects of specular reflections and multiple-scattering. Cirrus clouds were generally associated with colder and calmer wind conditions compared to the 2011–2020 climatology. However, the dependence of cirrus properties on temperature and wind speed was not strong. Even though the seasonal cycle was not pronounced, the winter-time cirrus appeared under lower temperatures and stronger wind conditions. Moreover, in winter, geometrically- and optically-thicker cirrus were found and their ice particles tended to be more spherical. The majority of cirrus was associated with westerly flow and westerly cirrus tended to be geometrically-thicker. Overall, optically-thinner layers tended to comprise smaller and less spherical ice crystals, most likely due to reduced water vapor deposition on the particle surface. Compared to lower latitudes, the cirrus layers over Ny-Ålesund were more absorbing in the visible spectral region and they consisted of more spherical ice particles. Full article
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25 pages, 118279 KiB  
Article
Disturbance Mapping in Arctic Tundra Improved by a Planning Workflow for Drone Studies: Advancing Tools for Future Ecosystem Monitoring
by Isabell Eischeid, Eeva M. Soininen, Jakob J. Assmann, Rolf A. Ims, Jesper Madsen, Åshild Ø. Pedersen, Francesco Pirotti, Nigel G. Yoccoz and Virve T. Ravolainen
Remote Sens. 2021, 13(21), 4466; https://doi.org/10.3390/rs13214466 - 6 Nov 2021
Cited by 12 | Viewed by 4269
Abstract
The Arctic is under great pressure due to climate change. Drones are increasingly used as a tool in ecology and may be especially valuable in rapidly changing and remote landscapes, as can be found in the Arctic. For effective applications of drones, decisions [...] Read more.
The Arctic is under great pressure due to climate change. Drones are increasingly used as a tool in ecology and may be especially valuable in rapidly changing and remote landscapes, as can be found in the Arctic. For effective applications of drones, decisions of both ecological and technical character are needed. Here, we provide our method planning workflow for generating ground-cover maps with drones for ecological monitoring purposes. The workflow includes the selection of variables, layer resolutions, ground-cover classes and the development and validation of models. We implemented this workflow in a case study of the Arctic tundra to develop vegetation maps, including disturbed vegetation, at three study sites in Svalbard. For each site, we generated a high-resolution map of tundra vegetation using supervised random forest (RF) classifiers based on four spectral bands, the normalized difference vegetation index (NDVI) and three types of terrain variables—all derived from drone imagery. Our classifiers distinguished up to 15 different ground-cover classes, including two classes that identify vegetation state changes due to disturbance caused by herbivory (i.e., goose grubbing) and winter damage (i.e., ‘rain-on-snow’ and thaw-freeze). Areas classified as goose grubbing or winter damage had lower NDVI values than their undisturbed counterparts. The predictive ability of site-specific RF models was good (macro-F1 scores between 83% and 85%), but the area of the grubbing class was overestimated in parts of the moss tundra. A direct transfer of the models between study sites was not possible (macro-F1 scores under 50%). We show that drone image analysis can be an asset for studying future vegetation state changes on local scales in Arctic tundra ecosystems and encourage ecologists to use our tailored workflow to integrate drone mapping into long-term monitoring programs. Full article
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14 pages, 11142 KiB  
Article
Time-Series of Cloud-Free Sentinel-2 NDVI Data Used in Mapping the Onset of Growth of Central Spitsbergen, Svalbard
by Stein Rune Karlsen, Laura Stendardi, Hans Tømmervik, Lennart Nilsen, Ingar Arntzen and Elisabeth J. Cooper
Remote Sens. 2021, 13(15), 3031; https://doi.org/10.3390/rs13153031 - 2 Aug 2021
Cited by 21 | Viewed by 3859
Abstract
The Arctic is a region that is expected to experience a high increase in temperature. Changes in the timing of phenological phases, such as the onset of growth (as observed by remote sensing), is a sensitive bio-indicator of climate change. In this paper, [...] Read more.
The Arctic is a region that is expected to experience a high increase in temperature. Changes in the timing of phenological phases, such as the onset of growth (as observed by remote sensing), is a sensitive bio-indicator of climate change. In this paper, the study area was the central part of Spitsbergen, Svalbard, located between 77.28°N and 78.44°N. The goals of this study were: (1) to prepare, analyze and present a cloud-free time-series of daily Sentinel-2 NDVI datasets for the 2016 to 2019 seasons, and (2) to demonstrate the use of the dataset in mapping the onset of growth. Due to a short and intense period with greening-up and frequent cloud cover, all the cloud-free Sentinel-2 data were used. The onset of growth was then mapped by a NDVI threshold method, which showed significant correlation (r2 = 0.47, n = 38, p < 0.0001) with ground-based phenocam observation of the onset of growth in seven vegetation types. However, large bias was found between the Sentinel-2 NDVI-based mapped onset of growth and the phenocam-based onset of growth in a moss tundra, which indicates that the data in these vegetation types must be interpreted with care. In 2018, the onset of growth was about 10 days earlier compared to 2017. Full article
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30 pages, 10664 KiB  
Article
Seasonal InSAR Displacements Documenting the Active Layer Freeze and Thaw Progression in Central-Western Spitsbergen, Svalbard
by Line Rouyet, Lin Liu, Sarah Marie Strand, Hanne Hvidtfeldt Christiansen, Tom Rune Lauknes and Yngvar Larsen
Remote Sens. 2021, 13(15), 2977; https://doi.org/10.3390/rs13152977 - 28 Jul 2021
Cited by 12 | Viewed by 4134
Abstract
In permafrost areas, the active layer undergoes seasonal frost heave and thaw subsidence caused by ice formation and melting. The amplitude and timing of the ground displacement cycles depend on the climatic and ground conditions. Here we used Sentinel-1 Synthetic Aperture Radar Interferometry [...] Read more.
In permafrost areas, the active layer undergoes seasonal frost heave and thaw subsidence caused by ice formation and melting. The amplitude and timing of the ground displacement cycles depend on the climatic and ground conditions. Here we used Sentinel-1 Synthetic Aperture Radar Interferometry (InSAR) to document the seasonal displacement progression in three regions of Svalbard. We retrieved June–November 2017 time series and identified thaw subsidence maxima and their timing. InSAR measurements were compared with a composite index model based on ground surface temperature. Cyclic seasonal patterns are identified in all areas, but the timing of the displacement progression varies. The subsidence maxima occurred later on the warm western coast (Kapp Linné and Ny-Ålesund) compared to the colder interior (Adventdalen). The composite index model is generally able to explain the observed patterns. In Adventdalen, the model matches the InSAR time series at the location of the borehole. In Kapp Linné and Ny-Ålesund, larger deviations are found at the pixel-scale, but km or regional averaging improves the fit. The study highlights the potential for further development of regional InSAR products to represent the cyclic displacements in permafrost areas and infer the active layer thermal dynamics. Full article
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23 pages, 7410 KiB  
Article
A Compilation of Snow Cover Datasets for Svalbard: A Multi-Sensor, Multi-Model Study
by Hannah Vickers, Eirik Malnes, Ward J. J. van Pelt, Veijo A. Pohjola, Mari Anne Killie, Tuomo Saloranta and Stein Rune Karlsen
Remote Sens. 2021, 13(10), 2002; https://doi.org/10.3390/rs13102002 - 20 May 2021
Cited by 4 | Viewed by 3704
Abstract
Reliable and accurate mapping of snow cover are essential in applications such as water resource management, hazard forecasting, calibration and validation of hydrological models and climate impact assessments. Optical remote sensing has been utilized as a tool for snow cover monitoring over the [...] Read more.
Reliable and accurate mapping of snow cover are essential in applications such as water resource management, hazard forecasting, calibration and validation of hydrological models and climate impact assessments. Optical remote sensing has been utilized as a tool for snow cover monitoring over the last several decades. However, consistent long-term monitoring of snow cover can be challenging due to differences in spatial resolution and retrieval algorithms of the different generations of satellite-based sensors. Snow models represent a complementary tool to remote sensing for snow cover monitoring, being able to fill in temporal and spatial data gaps where a lack of observations exist. This study utilized three optical remote sensing datasets and two snow models with overlapping periods of data coverage to investigate the similarities and discrepancies in snow cover estimates over Nordenskiöld Land in central Svalbard. High-resolution Sentinel-2 observations were utilized to calibrate a 20-year MODIS snow cover dataset that was subsequently used to correct snow cover fraction estimates made by the lower resolution AVHRR instrument and snow model datasets. A consistent overestimation of snow cover fraction by the lower resolution datasets was found, as well as estimates of the first snow-free day (FSFD) that were, on average, 10–15 days later when compared with the baseline MODIS estimates. Correction of the AVHRR time series produced a significantly slower decadal change in the land-averaged FSFD, indicating that caution should be exercised when interpreting climate-related trends from earlier lower resolution observations. Substantial differences in the dynamic characteristics of snow cover in early autumn were also present between the remote sensing and snow model datasets, which need to be investigated separately. This work demonstrates that the consistency of earlier low spatial resolution snow cover datasets can be improved by using current-day higher resolution datasets. Full article
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24 pages, 7574 KiB  
Article
Does the Intra-Arctic Modification of Long-Range Transported Aerosol Affect the Local Radiative Budget? (A Case Study)
by Konstantina Nakoudi, Christoph Ritter, Christine Böckmann, Daniel Kunkel, Oliver Eppers, Vladimir Rozanov, Linlu Mei, Vasileios Pefanis, Evelyn Jäkel, Andreas Herber, Marion Maturilli and Roland Neuber
Remote Sens. 2020, 12(13), 2112; https://doi.org/10.3390/rs12132112 - 1 Jul 2020
Cited by 12 | Viewed by 4654
Abstract
The impact of aerosol spatio-temporal variability on the Arctic radiative budget is not fully constrained. This case study focuses on the intra-Arctic modification of long-range transported aerosol and its direct aerosol radiative effect (ARE). Different types of air-borne and ground-based remote sensing observations [...] Read more.
The impact of aerosol spatio-temporal variability on the Arctic radiative budget is not fully constrained. This case study focuses on the intra-Arctic modification of long-range transported aerosol and its direct aerosol radiative effect (ARE). Different types of air-borne and ground-based remote sensing observations (from Lidar and sun-photometer) revealed a high tropospheric aerosol transport episode over two parts of the European Arctic in April 2018. By incorporating the derived aerosol optical and microphysical properties into a radiative transfer model, we assessed the ARE over the two locations. Our study displayed that even in neighboring Arctic upper tropospheric levels, aged aerosol was transformed due to the interplay of removal processes (nucleation scavenging and dry deposition) and alteration of the aerosol source regions (northeast Asia and north Europe). Along the intra-Arctic transport, the coarse aerosol mode was depleted and the visible wavelength Lidar ratio (LR) increased significantly (from 15 to 64–82 sr). However, the aerosol modifications were not reflected on the ARE. More specifically, the short-wave (SW) atmospheric column ARE amounted to +4.4 - +4.9 W m−2 over the ice-covered Fram Strait and +4.5 W m−2 over the snow-covered Ny-Ålesund. Over both locations, top-of-atmosphere (TOA) warming was accompanied by surface cooling. These similarities can be attributed to the predominant accumulation mode, which drives the SW radiative budget, as well as to the similar layer altitude, solar geometry, and surface albedo conditions over both locations. However, in the context of retreating sea ice, the ARE may change even along individual transport episodes due to the ice albedo feedback. Full article
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29 pages, 23726 KiB  
Article
A 20-Year MODIS-Based Snow Cover Dataset for Svalbard and Its Link to Phenological Timing and Sea Ice Variability
by Hannah Vickers, Stein Rune Karlsen and Eirik Malnes
Remote Sens. 2020, 12(7), 1123; https://doi.org/10.3390/rs12071123 - 1 Apr 2020
Cited by 19 | Viewed by 4744
Abstract
The climate in Svalbard has been warming dramatically compared with the global average for the last few decades. Seasonal snow cover, which is sensitive to temperature and precipitation changes, is therefore expected to undergo both spatial and temporal changes in response to the [...] Read more.
The climate in Svalbard has been warming dramatically compared with the global average for the last few decades. Seasonal snow cover, which is sensitive to temperature and precipitation changes, is therefore expected to undergo both spatial and temporal changes in response to the changing climate in Svalbard. This will in turn have implications for timing of terrestrial productivity, which is closely linked to the disappearance of seasonal snow. We have produced a 20-year snow cover fraction time series for the Svalbard archipelago, derived from MODIS (Moderate Resolution Imaging Spectroradiometer) Terra data to map and identify changes in the timing of the first snow-free day (FSFD) for the period 2000–2019. Moreover, we investigate the influence of sea ice concentration (SIC) variations on FSFD and how FSFD is related to the start of the phenological growing season in Svalbard. Our results revealed clear patterns of earlier FSFD in the southern and central parts of the archipelago, while the northernmost parts exhibit little change or trend toward later FSFD, resulting in weaker trends in summer and winter duration. We found that FSFD preceded the onset of the phenological growing season with an average difference of 12.4 days for the entire archipelago, but with large regional variations that are indicative of temperature dependence. Lastly, we found a significant correlation between variations of time-integrated SIC and variations in FSFD, which maximizes when correlating SIC northeast of Svalbard with FSFD averaged over Nordaustlandet. Prolonged sea ice cover in the spring was correlated with late snow disappearance, while lower-than-average sea ice cover correlated with early snow disappearance, indicating that proximity to sea ice plays an important role in regulating the timing of snow disappearance on land through influencing the regional air temperature and therefore rate of spring snowmelt. Full article
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30 pages, 3000 KiB  
Perspective
SIOS’s Earth Observation (EO), Remote Sensing (RS), and Operational Activities in Response to COVID-19
by Shridhar D. Jawak, Bo N. Andersen, Veijo A. Pohjola, Øystein Godøy, Christiane Hübner, Inger Jennings, Dariusz Ignatiuk, Kim Holmén, Agnar Sivertsen, Richard Hann, Hans Tømmervik, Andreas Kääb, Małgorzata Błaszczyk, Roberto Salzano, Bartłomiej Luks, Kjell Arild Høgda, Rune Storvold, Lennart Nilsen, Rosamaria Salvatori, Kottekkatu Padinchati Krishnan, Sourav Chatterjee, Dag A. Lorentzen, Rasmus Erlandsson, Tom Rune Lauknes, Eirik Malnes, Stein Rune Karlsen, Hiroyuki Enomoto, Ann Mari Fjæraa, Jie Zhang, Sabine Marty, Knut Ove Nygård and Heikki Lihavainenadd Show full author list remove Hide full author list
Remote Sens. 2021, 13(4), 712; https://doi.org/10.3390/rs13040712 - 15 Feb 2021
Cited by 10 | Viewed by 6073
Abstract
Svalbard Integrated Arctic Earth Observing System (SIOS) is an international partnership of research institutions studying the environment and climate in and around Svalbard. SIOS is developing an efficient observing system, where researchers share technology, experience, and data, work together to close knowledge gaps, [...] Read more.
Svalbard Integrated Arctic Earth Observing System (SIOS) is an international partnership of research institutions studying the environment and climate in and around Svalbard. SIOS is developing an efficient observing system, where researchers share technology, experience, and data, work together to close knowledge gaps, and decrease the environmental footprint of science. SIOS maintains and facilitates various scientific activities such as the State of the Environmental Science in Svalbard (SESS) report, international access to research infrastructure in Svalbard, Earth observation and remote sensing services, training courses for the Arctic science community, and open access to data. This perspective paper highlights the activities of SIOS Knowledge Centre, the central hub of SIOS, and the SIOS Remote Sensing Working Group (RSWG) in response to the unprecedented situation imposed by the global pandemic coronavirus (SARS-CoV-2) disease 2019 (COVID-19). The pandemic has affected Svalbard research in several ways. When Norway declared a nationwide lockdown to decrease the rate of spread of the COVID-19 in the community, even more strict measures were taken to protect the Svalbard community from the potential spread of the disease. Due to the lockdown, travel restrictions, and quarantine regulations declared by many nations, most physical meetings, training courses, conferences, and workshops worldwide were cancelled by the first week of March 2020. The resumption of physical scientific meetings is still uncertain in the foreseeable future. Additionally, field campaigns to polar regions, including Svalbard, were and remain severely affected. In response to this changing situation, SIOS initiated several operational activities suitable to mitigate the new challenges resulting from the pandemic. This article provides an extensive overview of SIOS’s Earth observation (EO), remote sensing (RS) and other operational activities strengthened and developed in response to COVID-19 to support the Svalbard scientific community in times of cancelled/postponed field campaigns in Svalbard. These include (1) an initiative to patch up field data (in situ) with RS observations, (2) a logistics sharing notice board for effective coordinating field activities in the pandemic times, (3) a monthly webinar series and panel discussion on EO talks, (4) an online conference on EO and RS, (5) the SIOS’s special issue in the Remote Sensing (MDPI) journal, (6) the conversion of a terrestrial remote sensing training course into an online edition, and (7) the announcement of opportunity (AO) in airborne remote sensing for filling the data gaps using aerial imagery and hyperspectral data. As SIOS is a consortium of 24 research institutions from 9 nations, this paper also presents an extensive overview of the activities from a few research institutes in pandemic times and highlights our upcoming activities for the next year 2021. Finally, we provide a critical perspective on our overall response, possible broader impacts, relevance to other observing systems, and future directions. We hope that our practical services, experiences, and activities implemented in these difficult times will motivate other similar monitoring programs and observing systems when responding to future challenging situations. With a broad scientific audience in mind, we present our perspective paper on activities in Svalbard as a case study. Full article
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