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Remote Sensing of the Polar Oceans

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

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 33077

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Special Issue Editors

Dr. Giuseppe Aulicino

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Guest Editor

Department of Science and Technology, University of Naples Parthenope, Centro Direzionale Is C4, Napoli, Italy
Interests: oceanography (operational oceanography; AUV monitoring; waves in ice; ocean-ice-atmosphere exchanges; polynyas dynamics and monitoring; mesoscale dynamics and eddies; seabed methane emissions); remote sensing of sea ice and other applications of satellite data to polar and mediterranean studies (SAR, passive microwaves, altimetry, optical, thermal infrared); Multiplatform monitoring of polar regions (combining satellite, AUV, research vessel and buoys data); teleconnections (ENSO, SAM, ACW, NAO).

Prof. Dr. Peter Wadhams

E-Mail Website
Guest Editor

Department of Applied Mathematics and Theoretical Physics (DAMTP), University of Cambridge, Cambridge CB3 0WB, UK
Interests: polar oceanography; sea ice research; research on climate change processes in polar regions

Special Issue Information

Dear colleagues,

Polar regions have undergone significant changes over the past 40 years impacting the global climate. Sea ice is rapidly changing (in extent, concentration, and even more thickness) both in the Arctic and in the Southern Ocean. This affects climate and biogeochemical cycles, ocean circulation and stratification, as well as ocean-atmosphere exchange of momentum, heat and gases. Light and the nutrient environment of the upper ocean, and hence biological productivity, are also influenced since sea ice variability is a critical component of the polar ecosystem, providing habitats, refuge, and a source of food or nutrients for many dependent species.

The Arctic is warming much more rapidly than Antarctica and other parts of the world, and Northern Hemisphere summer sea ice is thinning and shrinking dramatically, so that an ice-free Arctic is a possible future scenario. Beyond the dramatic impacts on polar biodiversity, in the Arctic these changes have also notable implications for global trade and economies linked to polar shipping corridors, and for coastal communities exposed to multiple climate-related hazards (e.g., permafrost thaw, local pollution, offshore methane release).

On the other hand, the recent dramatic reversal in the changes occurring in the Antarctic sea ice, i.e., from relatively gradual increases to rapid regional decreases that rival those observed in the Arctic, emphasizes the urgency of improving our capability to monitor its formation and evolution. More icebergs are now floating in the region, affecting the growth, structure, and, hence, the identification of the sea ice.

In this context, obtaining detailed information on physical and biogeochemical characteristics of polar oceans, as well as on sea ice formation/deformation, drift, distribution, melting and variability, represents a strategic challenge to improve the quantification of ocean and sea ice feedbacks, the representation of the cryosphere in climate simulations, and the understanding of its coupling with atmospheric, oceanic and ecosystem processes. Vastness of harsh and difficult-to-access marine polar areas, and scarcity of in situ data, make remote sensing (e.g., satellite and aircraft) observations the only tool which can provide this key information on a continuous basis and with better spatio-temporal coverage than traditional field measurements.

This Special Issue invites authors to contribute original research submissions on the past and current physical and biogeochemical status of both polar oceans. Manuscripts focusing on sea ice and snow cover, icebergs, ice shelves and coastlines, mesoscale processes, ocean-atmosphere interactions, biological productivity, are warmly welcome. All submissions should employ satellite or aircraft remote sensing observations, also in combination with numerical simulations and ground or ship-board measurements. The specific themes for the submission cover a range of relevant aspects including, but not limited to:

Potential use of new satellite sensors
Combination of sensors for polar ocean monitoring
Assimilation of remote sensing data in numerical simulations
Machine learning applications to polar remote sensing data
Geophysical processes within ocean-ice-atmosphere system
Physical and biogeochemical mesoscale and sub-mesoscale processes
Retrieval of sea ice parameters (concentration, type, thickness)
Snow on sea ice monitoring
Dynamics and impacts of icebergs and ice shelves
Reconstruction of ocean color data
Impacts of sea ice changes on navigation, marine operations, coastal communities

Dr. Giuseppe Aulicino
Prof. Dr. Peter Wadhams
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.

Published Papers (12 papers)

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Editorial

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6 pages, 224 KiB

Open AccessEditorial

Editorial for the Special Issue “Remote Sensing of the Polar Oceans”

by Giuseppe Aulicino and Peter Wadhams

Remote Sens. 2022, 14(24), 6195; https://doi.org/10.3390/rs14246195 - 7 Dec 2022

Viewed by 1022

Abstract

This Special Issue gathers papers reporting research on various aspects of the use of satellites for monitoring polar oceans. It includes contributions presenting improvements in the retrieval of sea ice concentration, extent and area, and concerning error information; the interannual and decadal variability of sea surface temperature and sea ice concentration in the Barents Sea; validation and comparison of Arctic salinity products; melt pond retrieval applying a Linear Polar algorithm to Landsat data; the characterization of surface layer freshening from sea surface salinity and coloured detrital matter in the Kara and Laptev Seas; multi-sensor estimations of chlorophyll-a concentrations in the Western Antarctic Peninsula; and enhanced techniques for detection and monitoring of glacier dynamics and iceberg paths. Full article

(This article belongs to the Special Issue Remote Sensing of the Polar Oceans)

Research

Jump to: Editorial, Other

26 pages, 9046 KiB

Open AccessArticle

Interannual and Decadal Variability of Sea Surface Temperature and Sea Ice Concentration in the Barents Sea

by Bayoumy Mohamed, Frank Nilsen and Ragnheid Skogseth

Remote Sens. 2022, 14(17), 4413; https://doi.org/10.3390/rs14174413 - 5 Sep 2022

Cited by 8 | Viewed by 4164

Abstract

Sea ice loss and accelerated warming in the Barents Sea have recently been one of the main concerns of climate research. In this study, we investigated the trends and possible relationships between sea surface temperature (SST), sea ice concentration (SIC), and local and large-scale atmospheric parameters over the last 39 years (1982 to 2020). We examined the interannual and long-term spatiotemporal variability of SST and SIC by performing an empirical orthogonal function (EOF) analysis. The SST warming rate from 1982 through 2020 was 0.35 ± 0.04 °C/decade and 0.40 ± 0.04 °C/decade in the ice-covered and ice-free regions, respectively. This climate warming had a significant impact on sea-ice conditions in the Barents Sea, such as a strong decline in the SIC (−6.52 ± 0.78%/decade) and a shortening of the sea-ice season by about −26.1 ± 7.5 days/decade, resulting in a 3.4-month longer summer ice-free period over the last 39 years. On the interannual and longer-term scales, the Barents Sea has shown strong coherent spatiotemporal variability in both SST and SIC. The temporal evolution of SST and SIC are strongly correlated, whereas the Atlantic Multidecadal Oscillation (AMO) influences the spatiotemporal variability of SST and SIC. The highest spatial variability (i.e., the center of action of the first EOF mode) of SST was observed over the region bounded by the northern and southern polar fronts, which are influenced by both warm Atlantic and cold Arctic waters. The largest SIC variability was found over the northeastern Barents Sea and over the Storbanken and Olga Basin. The second EOF mode revealed a dipole structure with out-of-phase variability between the ice-covered and ice-free regions for the SST and between the Svalbard and Novaya Zemlya regions for SIC. In order to investigate the processes that generate these patterns, a correlation analysis was applied to a set of oceanic (SST) and atmospheric parameters (air temperature, zonal, and meridional wind components) and climate indices. This analysis showed that SST and SIC are highly correlated with air temperature and meridional winds and with two climate indices (AMO and East Atlantic Pattern (EAP)) on an interannual time scale. The North Atlantic Oscillation (NAO) only correlated with the second EOF mode of SST on a decadal time scale. Full article

(This article belongs to the Special Issue Remote Sensing of the Polar Oceans)

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24 pages, 6608 KiB

Open AccessArticle

OC4-SO: A New Chlorophyll-a Algorithm for the Western Antarctic Peninsula Using Multi-Sensor Satellite Data

by Afonso Ferreira, Ana C. Brito, Carlos R. B. Mendes, Vanda Brotas, Raul R. Costa, Catarina V. Guerreiro, Carolina Sá and Thomas Jackson

Remote Sens. 2022, 14(5), 1052; https://doi.org/10.3390/rs14051052 - 22 Feb 2022

Cited by 13 | Viewed by 3326

Abstract

Chlorophyll-a (Chl-a) underestimation by global satellite algorithms in the Southern Ocean has long been reported, reducing their accuracy, and limiting the potential for evaluating phytoplankton biomass. As a result, several regional Chl-a algorithms have been proposed. The present work aims at assessing the performance of both global and regional satellite algorithms that are currently available for the Western Antarctic Peninsula (WAP) and investigate which factors are contributing to the underestimation of Chl-a. Our study indicates that a global algorithm, on average, underestimates in-situ Chl-a by ~59%, although underestimation was only observed for waters with Chl-a > 0.5 mg m⁻³. In high Chl-a waters (>1 mg m⁻³), Chl-a underestimation rose to nearly 80%. Contrary to previous studies, no clear link was found between Chl-a underestimation and the pigment packaging effect, nor with the phytoplankton community composition and sea ice contamination. Based on multi-sensor satellite data and the most comprehensive in-situ dataset ever collected from the WAP, a new, more accurate satellite Chl-a algorithm is proposed: the OC4-SO. The OC4-SO has great potential to become an important tool not only for the ocean colour community, but also for an effective monitoring of the phytoplankton communities in a climatically sensitive region where in-situ data are scarce. Full article

(This article belongs to the Special Issue Remote Sensing of the Polar Oceans)

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16 pages, 3544 KiB

Open AccessArticle

Comparison of Hemispheric and Regional Sea Ice Extent and Area Trends from NOAA and NASA Passive Microwave-Derived Climate Records

by Walter N. Meier, J. Scott Stewart, Ann Windnagel and Florence M. Fetterer

Remote Sens. 2022, 14(3), 619; https://doi.org/10.3390/rs14030619 - 27 Jan 2022

Cited by 7 | Viewed by 3357

Abstract

Three passive microwave-based sea ice products archived at the National Snow and Ice Data Center (NSIDC) are compared: (1) the NASA Team (NT) algorithm product, (2) Bootstrap (BT) algorithm product, and (3) a new version (Version 4) of the NOAA/NSIDC Climate Data Record (CDR) product. Most notable for the CDR Version 4 is the addition of the early passive microwave record, 1979 to 1987. The focus of this study is on long-term trends in monthly extent and area. In addition to hemispheric trends, regional analysis is also carried out, including use of a new Northern Hemisphere regional mask. The results indicate overall good consistency between the products, with all three products showing strong statistically significant negative trends in the Arctic and small borderline significant positive trends in the Antarctic. Regionally, the patterns are similar, except for a notable outlier of the NT area having a steeper trend in the Central Arctic, likely related to increasing surface melt. Other differences are due to varied approaches to quality control, e.g., weather filtering and correction of mixed land-ocean grid cells. Another factor, particularly in regards to NT trends with BT or CDR, is the inter-sensor calibration approach, which yields small discontinuities between the products. These varied approaches yield small differences in trends. In the Arctic, such differences are not critical, but in the Antarctic, where overall trends are near zero and borderline statistically significant, the differences are potentially important in the interpretation of trends. Full article

(This article belongs to the Special Issue Remote Sensing of the Polar Oceans)

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17 pages, 4968 KiB

Open AccessArticle

The Distribution of pCO_2W and Air-Sea CO₂ Fluxes Using FFNN at the Continental Shelf Areas of the Arctic Ocean

by Iwona Wrobel-Niedzwiecka, Małgorzata Kitowska, Przemyslaw Makuch and Piotr Markuszewski

Remote Sens. 2022, 14(2), 312; https://doi.org/10.3390/rs14020312 - 11 Jan 2022

Cited by 1 | Viewed by 1823

Abstract

A feed-forward neural network (FFNN) was used to estimate the monthly climatology of partial pressure of CO₂ (pCO_2W) at a spatial resolution of 1° latitude by 1° longitude in the continental shelf of the European Arctic Sector (EAS) of the Arctic Ocean (the Greenland, Norwegian, and Barents seas). The predictors of the network were sea surface temperature (SST), sea surface salinity (SSS), the upper ocean mixed-layer depth (MLD), and chlorophyll-a concentration (Chl-a), and as a target, we used 2 853 pCO_2W data points from the Surface Ocean CO₂ Atlas. We built an FFNN based on three major datasets that differed in the Chl-a concentration data used to choose the best model to reproduce the spatial distribution and temporal variability of pCO_2W. Using all physical–biological components improved estimates of the pCO_2W and decreased the biases, even though Chl-a values in many grid cells were interpolated values. General features of pCO_2W distribution were reproduced with very good accuracy, but the network underestimated pCO_2W in the winter and overestimated pCO_2W values in the summer. The results show that the model that contains interpolating Chl-a concentration, SST, SSS, and MLD as a target to predict the spatiotemporal distribution of pCO_2W in the sea surface gives the best results and best-fitting network to the observational data. The calculation of monthly drivers of the estimated pCO_2W change within continental shelf areas of the EAS confirms the major impact of not only the biological effects to the pCO_2W distribution and Air-Sea CO₂ flux in the EAS, but also the strong impact of the upper ocean mixing. A strong seasonal correlation between predictor and pCO_2W seen earlier in the North Atlantic is clearly a yearly correlation in the EAS. The five-year monthly mean CO₂ flux distribution shows that all continental shelf areas of the Arctic Ocean were net CO₂ sinks. Strong monthly CO₂ influx to the Arctic Ocean through the Greenland and Barents Seas (>12 gC m⁻² day⁻¹) occurred in the fall and winter, when the pCO_2W level at the sea surface was high (>360 µatm) and the strongest wind speed (>12 ms⁻¹) was present. Full article

(This article belongs to the Special Issue Remote Sensing of the Polar Oceans)

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24 pages, 11286 KiB

Open AccessArticle

Spatial Correlation Length Scales of Sea-Ice Concentration Errors for High-Concentration Pack Ice

by Stefan Kern

Remote Sens. 2021, 13(21), 4421; https://doi.org/10.3390/rs13214421 - 3 Nov 2021

Cited by 2 | Viewed by 1775

Abstract

The European Organisation for the Exploitation of Meteorological Satellites-Ocean and Sea Ice Satellite Application Facility–European Space Agency-Climate Change Initiative (EUMETSAT-OSISAF–ESA-CCI) Level-4 sea-ice concentration (SIC) climate data records (CDRs), named SICCI-25km, SICCI-50km and OSI-450, provide gridded SIC error estimates in addition to SIC. These error estimates, called total error henceforth, comprise a random, uncorrelated error contribution from retrieval and sensor noise, aka the algorithm standard error, and a locally-to-regionally correlated contribution from gridding and averaging Level-2 SIC into the Level-4 SIC CDRs, aka the representativity error. However, these CDRs do not yet provide an error covariance matrix. Therefore, correlation scales of these error contributions and the total error in particular are unknown. In addition, larger-scale SIC errors due to, e.g., unaccounted weather influence or mismatch between the actual ice type and the algorithm setup are neither well represented by the total error, nor are their correlation scales known for these CDRs. In this study, I attempt to contribute to filling this knowledge gap by deriving spatial correlation length scales for the total error and the large-scale SIC error for high-concentration pack ice. For every grid cell with >90% SIC, I derive circular one-point correlation maps of 1000 km radius by computing the cross-correlation between the central 31-day time series of the errors and all other 31-day error time series within that circular area (disc) with 1000 km radius. I approximate the observed decrease in the correlation away from the disc’s center with an exponential function that best fits this decrease and thereby obtain the correlation length scale L sought. With this approach, I derive L separately for the total error and the large-scale SIC error for every high-concentration grid cell, and map, present and discuss these for the Arctic and the Southern Ocean for the year 2010 for the above-mentioned products. I find correlation length scales are substantially smaller for the total error, mostly below ~200 km, than the SIC error, ~200 km to ~700 km, in both hemispheres. I observe considerable spatiotemporal variability of the SIC error correlation length scales in both hemispheres and provide first directions to explain these. For SICCI-50km, I present the first evidence of the method’s robustness for other years and time series of L for 2003–2010. Full article

(This article belongs to the Special Issue Remote Sensing of the Polar Oceans)

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29 pages, 12935 KiB

Open AccessArticle

Using Remotely Sensed Sea Surface Salinity and Colored Detrital Matter to Characterize Freshened Surface Layers in the Kara and Laptev Seas during the Ice-Free Season

by Marta Umbert, Carolina Gabarro, Estrella Olmedo, Rafael Gonçalves-Araujo, Sebastien Guimbard and Justino Martinez

Remote Sens. 2021, 13(19), 3828; https://doi.org/10.3390/rs13193828 - 24 Sep 2021

Cited by 6 | Viewed by 2505

Abstract

The overall volume of freshwater entering the Arctic Ocean has been growing as glaciers melt and river runoff increases. Since 1980, a 20% increase in river runoff has been observed in the Arctic system. As the discharges of the Ob, Yenisei, and Lena rivers are an important source of freshwater in the Kara and Laptev Seas, an increase in river discharge might have a significant impact on the upper ocean circulation. The fresh river water mixes with ocean water and forms a large freshened surface layer (FSL), which carries high loads of dissolved organic matter and suspended matter into the Arctic Ocean. Optically active material (e.g., phytoplankton and detrital matter) are spread out into plumes, which are evident in satellite data. Russian river signatures in the Kara and Laptev Seas are also evident in recent SMOS Sea Surface Salinity (SSS) Arctic products. In this study, we compare the new Arctic+ SSS products, produced at the Barcelona Expert Center, with the Ocean Color absorption coefficient of colored detrital matter (CDM) in the Kara and Laptev Seas for the period 2011–2019. The SSS and CDM are found to be strongly negatively correlated in the regions of freshwater influence, with regression coefficients between

- 0.72

and

- 0.91

in the studied period. Exploiting this linear correlation, we estimate the SSS back to 1998 using two techniques: one assuming that the relationship between the CDM and SSS varies regionally in the river-influenced areas, and another assuming that it does not. We use the 22-year time-series of reconstructed SSS to estimate the interannual variability of the extension of the FSL in the Kara and Laptev Seas as well as their freshwater content. For the Kara and Laptev Seas, we use 32 and 28 psu as reference salinities, and 26 and 24 psu isohalines as FSL boundaries, respectively. The average FSL extension in the Kara Sea is 2089–2611 km

^{2}

, with a typical freshwater content of 11.84–14.02 km

^{3}

. The Laptev Sea has a slightly higher mean FSL extension of 2320–2686 km

^{2}

and a freshwater content of 10.15–12.44 km

^{3}

. The yearly mean freshwater content and extension of the FSL, computed from SMOS SSS and Optical data, is (as expected) found to co-vary with in situ measurements of river discharge from the Arctic Great Rivers Observatory database, demonstrating the potential of SMOS SSS to better monitor the river discharge changes in Eurasia and to understand the Arctic freshwater system during the ice-free season. Full article

(This article belongs to the Special Issue Remote Sensing of the Polar Oceans)

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20 pages, 9604 KiB

Open AccessArticle

Retrieval of Summer Sea Ice Concentration in the Pacific Arctic Ocean from AMSR2 Observations and Numerical Weather Data Using Random Forest Regression

by Hyangsun Han, Sungjae Lee, Hyun-Cheol Kim and Miae Kim

Remote Sens. 2021, 13(12), 2283; https://doi.org/10.3390/rs13122283 - 10 Jun 2021

Cited by 9 | Viewed by 2490

Abstract

The Arctic sea ice concentration (SIC) in summer is a key indicator of global climate change and important information for the development of a more economically valuable Northern Sea Route. Passive microwave (PM) sensors have provided information on the SIC since the 1970s by observing the brightness temperature (T_B) of sea ice and open water. However, the SIC in the Arctic estimated by operational algorithms for PM observations is very inaccurate in summer because the T_B values of sea ice and open water become similar due to atmospheric effects. In this study, we developed a summer SIC retrieval model for the Pacific Arctic Ocean using Advanced Microwave Scanning Radiometer 2 (AMSR2) observations and European Reanalysis Agency-5 (ERA-5) reanalysis fields based on Random Forest (RF) regression. SIC values computed from the ice/water maps generated from the Korean Multi-purpose Satellite-5 synthetic aperture radar images from July to September in 2015–2017 were used as a reference dataset. A total of 24 features including the T_B values of AMSR2 channels, the ratios of T_B values (the polarization ratio and the spectral gradient ratio (GR)), total columnar water vapor (TCWV), wind speed, air temperature at 2 m and 925 hPa, and the 30-day average of the air temperatures from the ERA-5 were used as the input variables for the RF model. The RF model showed greatly superior performance in retrieving summer SIC values in the Pacific Arctic Ocean to the Bootstrap (BT) and Arctic Radiation and Turbulence Interaction STudy (ARTIST) Sea Ice (ASI) algorithms under various atmospheric conditions. The root mean square error (RMSE) of the RF SIC values was 7.89% compared to the reference SIC values. The BT and ASI SIC values had three times greater values of RMSE (20.19% and 21.39%, respectively) than the RF SIC values. The air temperatures at 2 m and 925 hPa and their 30-day averages, which indicate the ice surface melting conditions, as well as the GR using the vertically polarized channels at 23 GHz and 18 GHz (GR(23V18V)), TCWV, and GR(36V18V), which accounts for atmospheric water content, were identified as the variables that contributed greatly to the RF model. These important variables allowed the RF model to retrieve unbiased and accurate SIC values by taking into account the changes in T_B values of sea ice and open water caused by atmospheric effects. Full article

(This article belongs to the Special Issue Remote Sensing of the Polar Oceans)

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16 pages, 17251 KiB

Open AccessArticle

Using Saildrones to Validate Arctic Sea-Surface Salinity from the SMAP Satellite and from Ocean Models

by Jorge Vazquez-Cuervo, Chelle Gentemann, Wenqing Tang, Dustin Carroll, Hong Zhang, Dimitris Menemenlis, Jose Gomez-Valdes, Marouan Bouali and Michael Steele

Remote Sens. 2021, 13(5), 831; https://doi.org/10.3390/rs13050831 - 24 Feb 2021

Cited by 19 | Viewed by 4999

Abstract

The Arctic Ocean is one of the most important and challenging regions to observe—it experiences the largest changes from climate warming, and at the same time is one of the most difficult to sample because of sea ice and extreme cold temperatures. Two NASA-sponsored deployments of the Saildrone vehicle provided a unique opportunity for validating sea-surface salinity (SSS) derived from three separate products that use data from the Soil Moisture Active Passive (SMAP) satellite. To examine possible issues in resolving mesoscale-to-submesoscale variability, comparisons were also made with two versions of the Estimating the Circulation and Climate of the Ocean (ECCO) model (Carroll, D; Menmenlis, D; Zhang, H.). The results indicate that the three SMAP products resolve the runoff signal associated with the Yukon River, with high correlation between SMAP products and Saildrone SSS. Spectral slopes, overall, replicate the −2.0 slopes associated with mesoscale-submesoscale variability. Statistically significant spatial coherences exist for all products, with peaks close to 100 km. Based on these encouraging results, future research should focus on improving derivations of satellite-derived SSS in the Arctic Ocean and integrating model results to complement remote sensing observations. Full article

(This article belongs to the Special Issue Remote Sensing of the Polar Oceans)

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Other

Jump to: Editorial, Research

16 pages, 11886 KiB

Open AccessTechnical Note

Multi-Polarisation C-Band SAR Imagery to Estimate the Recent Dynamics of the d’Iberville Glacier

by Mozhgan Zahriban Hesari, Andrea Buono, Ferdinando Nunziata, Giuseppe Aulicino and Maurizio Migliaccio

Remote Sens. 2022, 14(22), 5758; https://doi.org/10.3390/rs14225758 - 14 Nov 2022

Cited by 5 | Viewed by 1617

Abstract

To monitor polar regions is of paramount importance for climatological studies. Climate change due to anthropogenic activities is inducing global warming that, for example, has resulted in glacier melting. This has had a significant impact on sea levels and ocean circulation. In this study, the temporal trend of the marine-terminated d’Iberville glacier (Ellesmere Island, Canada) is analysed using C-band synthetic aperture radar satellite imagery collected by the Radarsat-2 and Sentinel-1 missions. The data set consists of a time series of 10 synthetic aperture radar data collected from 2010 to 2022 in dual-polarimetric imaging mode, where a horizontally polarised electromagnetic wave was transmitted. An automatic approach based on a global threshold constant false alarm rate method is applied to the single- and dual-polarisation features, namely the HH-polarised normalised radar cross-section and a combination of the HH- and HV-polarised scattering amplitudes, with the aim of extracting the ice front of the glacier and, therefore, estimating its behaviour over time. Independent collocated satellite optical imagery from the Sentinel-2 multi-spectral instrument is also considered, where available, to support the experimental outcomes. The experimental results show that (1) the HH-polarised normalised radar cross-section achieved better performance with respect to the dual-polarised feature, especially under the most challenging case of a sea-ice infested sea surface; (2) when the HH-polarised normalised radar cross-section was considered, the ice front extraction methodology provided a satisfactory accuracy, i.e., a root mean square error spanning from about 1.1 pixels to 3.4 pixels, depending on the sea-surface conditions; and (3) the d’Iberville glacier exhibited, during the study period, a significant retreat whose average surface velocity was 160 m per year, resulting in a net ice area loss of 2.2 km² (0.18 km² per year). These outcomes demonstrate that the d’Iberville glacier is behaving as most of the marine-terminated glaciers in the study area while experiencing a larger ice loss. Full article

(This article belongs to the Special Issue Remote Sensing of the Polar Oceans)

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23 pages, 8497 KiB

Open AccessTechnical Note

Melt Pond Retrieval Based on the LinearPolar Algorithm Using Landsat Data

by Yuqing Qin, Jie Su and Mingfeng Wang

Remote Sens. 2021, 13(22), 4674; https://doi.org/10.3390/rs13224674 - 19 Nov 2021

Cited by 3 | Viewed by 1634

Abstract

The formation and distribution of melt ponds have an important influence on the Arctic climate. Therefore, it is necessary to obtain more accurate information on melt ponds on Arctic sea ice by remote sensing. The present large-scale melt pond products, especially the melt pond fraction (MPF), still require verification, and using very high resolution optical satellite remote sensing data is a good way to verify the large-scale retrieval of MPF products. Unlike most MPF algorithms using very high resolution data, the LinearPolar algorithm using Sentinel-2 data considers the albedo of melt ponds unfixed. In this paper, by selecting the best band combination, we applied this algorithm to Landsat 8 (L8) data. Moreover, Sentinel-2 data, as well as support vector machine (SVM) and iterative self-organizing data analysis technique (ISODATA) algorithms, are used as the comparison and verification data. The results show that the recognition accuracy of the LinearPolar algorithm for melt ponds is higher than that of previous algorithms. The overall accuracy and kappa coefficient results achieved by using the LinearPolar algorithm with L8 and Sentinel-2A (S2), the SVM algorithm, and the ISODATA algorithm are 95.38% and 0.88, 94.73% and 0.86, and 92.40%and 0.80, respectively, which are much higher than those of principal component analysis (PCA) and Markus algorithms. The mean MPF (10.0%) obtained from 80 cases from L8 data based on the LinearPolar algorithm is much closer to Sentinel-2 (10.9%) than the Markus (5.0%) and PCA algorithms (4.2%), with a mean MPF difference of only 0.9%, and the correlation coefficients of the two MPFs are as high as 0.95. The overall relative error of the LinearPolar algorithm is 53.5% and 46.4% lower than that of the Markus and PCA algorithms, respectively, and the root mean square error (RMSE) is 30.9% and 27.4% lower than that of the Markus and PCA algorithms, respectively. In the cases without obvious melt ponds, the relative error is reduced more than that of those with obvious melt ponds because the LinearPolar algorithm can identify 100% of dark melt ponds and relatively small melt ponds, and the latter contributes more to the reduction in the relative error of MPF retrieval. With a wider range and longer time series, the MPF from Landsat data are more efficient than those from Sentinel-2 for verifying large-scale MPF products or obtaining long-term monitoring of a fixed area. Full article

(This article belongs to the Special Issue Remote Sensing of the Polar Oceans)

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13 pages, 1609 KiB

Open AccessTechnical Note

On the Detection and Long-Term Path Visualisation of A-68 Iceberg

by Ludwin Lopez-Lopez, Flavio Parmiggiani, Miguel Moctezuma-Flores and Lorenzo Guerrieri

Remote Sens. 2021, 13(3), 460; https://doi.org/10.3390/rs13030460 - 28 Jan 2021

Cited by 6 | Viewed by 2192

Abstract

The article presents a methodology for examining a temporal sequence of synthetic aperture radar (SAR) images, as applied to the detection of the A-68 iceberg and its drifting trajectory. Using an improved image processing scheme, the analysis covers a period of eighteen months and makes use of a set of Sentinel-1 images. A-68 iceberg calved from the Larsen C ice shelf in July 2017 and is one of the largest icebergs observed by remote sensing on record. After the calving, there was only a modest decrease in the area (about 1%) in the first six months. It has been drifting along the east coast of the Antarctic Peninsula, and is expected to continue its path for more than a decade. It is important to track the huge A-68 iceberg to retrieve information on the physics of iceberg dynamics and for maritime security reasons. Two relevant problems are addressed by the image processing scheme presented here: (a) How to achieve quasi-automatic analysis using a fuzzy logic approach to image contrast enhancement, and (b) The use of ferromagnetic concepts to define a stochastic segmentation. The Ising equation is used to model the energy function of the process, and the segmentation is the result of a stochastic minimization. Full article

(This article belongs to the Special Issue Remote Sensing of the Polar Oceans)

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