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Combining Different Data Sources for Environmental and Operational Satellite Monitoring of Sea Ice Conditions

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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 (30 September 2019) | Viewed by 46166

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Special Issue Editors

Prof. Dr. Wolfgang Dierking

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Guest Editor

1. Center for Integrated Remote Sensing and Forecasting for Arctic Operations (CIRFA), UiT The Arctic University of Norway, Tromsø, Norway
2. Alfred Wegener Institute Helmholtz Center for Polar and Marine Research, Bremerhaven, Germany
Interests: remote sensing of the Polar Regions; sensor technologies; field and airborne measurement techniques; image processing methods; parameter retrieval algorithms

Dr. Marko Makynen

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Finnish Meteorological Institute, Erik Palmenin aukio 1, FI-00560 Helsinki, Finland
Interests: sea ice; Arctic environments; ship-based observations
Special Issues, Collections and Topics in MDPI journals

Mr. Markku Similä

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Guest Editor

Marine Research Unit, Finnish Meteorological Institute, Helsinki, Finland
Interests: Imaging and non-imaging microwave remote sensing of sea ice focusing on the Baltic and Arctic; statistical analysis of single and combined data sets; the development of operational marine services

Special Issue Information

Dear Colleagues,

Satellite remote sensing is an important tool for monitoring the state of and changes in the sea ice cover in the Arctic, Antarctic, and other regions such as, for example, the Baltic and the Bohai Sea. Information on daily and weekly changes—provided by operational ice services—is essential for marine traffic and operations in ice-infested waters, and improves the understanding and forecasting of short-term interactions between atmosphere, ice, and ocean. When focusing on regional and local sea ice conditions, the synthetic aperture radar (SAR) is one of the most useful sensors. However, the interpretation and analysis of SAR images may be prone to ambiguities. Since we are dependent on operational or scientific applications, it is therefore beneficial to combine SAR images with data obtained from other types of satellite sensors (e.g., optical and thermal spectrometers, microwave radiometers, altimeters, scatterometers) and/or to link them with results from airborne and ground measurements when available. Examples for applications are ice type mapping, ice thickness retrieval, detection of ice drift and deformation, studies of lead or polynya dynamics, monitoring of sea ice thermodynamic state (e.g. melting conditions), or detection of ice areas most suitable for navigation. The retrieval of sea ice conditions and parameters does not only benefit from the combination of different data sources but also from linking such retrievals with results from modeling sea ice thermodynamics and dynamics, or interpreting remote sensing data based on simulations of the interaction between electromagnetic radiation and sea ice.

This planned issue of Remote Sensing shall specifically address the potential of combining SAR with different complementary data sources (satellite, airborne, field, modeling) in science studies and for operational applications, considering the most advanced technologies, for enhancing the sea ice monitoring capabilities and reducing ambiguities in data analysis. Also, studies of suitable methods for analyzing merged data sets are welcome.

Examples are:

multi-polarization and multi-frequency SAR for ice classification;
different combinations of SAR, laser and radar altimeter, and radiometer and spectrometer for ice thickness retrieval (both thin (<0.5 m) and thick ice);
mixing of image sequences obtained at different SAR frequencies, polarizations, and/or imaging modes, or from SAR and complementary sensor types, for improving temporal resolution of ice drift/deformation retrievals;
using remotely sensed data from different sensor types (including SAR) as input or for validation of models simulating, e.g., evolution of polynyas or sea ice deformation;
sea ice thermodynamic stages (e.g., melt ponding and its evolution) determined from combinations of SAR and complementary sensor data and thermodynamic modeling;
comparison between observed radar signatures and calculations using scattering models with realistic ranges of input parameters;
examples of applications of interferometric SAR and complementary data for sea ice studies;

Other topics in line with the general idea of the special issue are, of course, also very welcome.

In the hope of receiving many exciting contributions.

Prof. Dr. Wolfgang Dierking
Adjunct Prof. Dr. Marko Mäkynen
Mr. Markku Similä
Guest Editors

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Published Papers (11 papers)

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Editorial

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4 pages, 186 KiB

Open AccessEditorial

Editorial for the Special Issue “Combining Different Data Sources for Environmental and Operational Satellite Monitoring of Sea Ice Conditions”

by Wolfgang Dierking, Marko Mäkynen and Markku Similä

Remote Sens. 2020, 12(4), 606; https://doi.org/10.3390/rs12040606 - 12 Feb 2020

Cited by 2 | Viewed by 1768

Abstract

Satellite remote sensing is an important tool for continuous monitoring of sea ice covered ocean regions and spatial and temporal variations of their geophysical characteristics [...] Full article

(This article belongs to the Special Issue Combining Different Data Sources for Environmental and Operational Satellite Monitoring of Sea Ice Conditions)

Research

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17 pages, 5488 KiB

Open AccessArticle

Sensitivity of Radar Altimeter Waveform to Changes in Sea Ice Type at Resolution of Synthetic Aperture Radar

by Wiebke Aldenhoff, Céline Heuzé and Leif E. B. Eriksson

Remote Sens. 2019, 11(22), 2602; https://doi.org/10.3390/rs11222602 - 6 Nov 2019

Cited by 10 | Viewed by 3599

Abstract

Radar altimetry in the context of sea ice has mostly been exploited to retrieve basin-scale information about sea ice thickness. In this paper, we investigate the sensitivity of altimetric waveforms to small-scale changes (a few hundred meters to about 10 km) of the sea ice surface. Near-coincidental synthetic aperture radar (SAR) imagery and CryoSat-2 altimetric data in the Beaufort Sea are used to identify and study the spatial evolution of altimeter waveforms over these features. Open water and thin ice features are easily identified because of their high peak power waveforms. Thicker ice features such as ridges and multiyear ice floes of a few hundred meters cause a response in the waveform. However, these changes are not reflected in freeboard estimates. Retrieval of robust freeboard estimates requires homogeneous floes in the order of 10 km along-track and a few kilometers to both sides across-track. We conclude that the combination of SAR imagery and altimeter data could improve the local sea ice picture by extending spatially scarce freeboard estimates to regions of similar SAR signature. Full article

(This article belongs to the Special Issue Combining Different Data Sources for Environmental and Operational Satellite Monitoring of Sea Ice Conditions)

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31 pages, 23899 KiB

Open AccessArticle

Comparison of Arctic Sea Ice Concentrations from the NASA Team, ASI, and VASIA2 Algorithms with Summer and Winter Ship Data

by Tatiana Alekseeva, Vasiliy Tikhonov, Sergei Frolov, Irina Repina, Mikhael Raev, Julia Sokolova, Evgeniy Sharkov, Ekaterina Afanasieva and Sergei Serovetnikov

Remote Sens. 2019, 11(21), 2481; https://doi.org/10.3390/rs11212481 - 24 Oct 2019

Cited by 25 | Viewed by 4224

Abstract

The paper presents a comparison of sea ice concentration (SIC) derived from satellite microwave radiometry data and dedicated ship observations. For the purpose, the NASA Team (NT), Arctic Radiation and Turbulence Interaction Study (ARTIST) Sea Ice (ASI), and Variation Arctic/Antarctic Sea Ice Algorithm 2 (VASIA2) algorithms were used as well as the database of visual ice observations accumulated in the course of 15 Arctic expeditions. The comparison was performed in line with the SIC gradation (in tenths) into very open (1–3), open (4–6), close (7–8), very close and compact (9–10,10) ice, separately for summer and winter seasons. On average, in summer NT underestimates SIC by 0.4 tenth as compared to ship observations, while ASI and VASIA2 by 0.3 tenth. All three algorithms overestimate total SIC in regions of very open ice and underestimate it in regions of close, very close, and compact ice. The maximum average errors are typical of open ice regions that are most common in marginal ice zones. In winter, NT and ASI also underestimate SIC on average by 0.4 and 0.8 tenths, respectively, while VASIA2, on the contrary, overestimates by 0.2 tenth against the ship data, however, for open and close ice the average errors are significantly higher than in summer. In the paper, we also estimate the impact of ice melt stage and presence of new ice and nilas on SIC derived from NT, ASI, and VASIA2. Full article

(This article belongs to the Special Issue Combining Different Data Sources for Environmental and Operational Satellite Monitoring of Sea Ice Conditions)

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27 pages, 12037 KiB

Open AccessArticle

Remote Sensing of Ice Phenology and Dynamics of Europe’s Largest Coastal Lagoon (The Curonian Lagoon)

by Rasa Idzelytė, Igor E. Kozlov and Georg Umgiesser

Remote Sens. 2019, 11(17), 2059; https://doi.org/10.3390/rs11172059 - 2 Sep 2019

Cited by 11 | Viewed by 3771

Abstract

A first-ever spatially detailed record of ice cover conditions in the Curonian Lagoon (CL), Europe’s largest coastal lagoon located in the southeastern Baltic Sea, is presented. The multi-mission synthetic aperture radar (SAR) measurements acquired in 2002–2017 by Envisat ASAR, RADARSAT-2, Sentinel-1 A/B, and supplemented by the cloud-free moderate imaging spectroradiometer (MODIS) data, are used to document the ice cover properties in the CL. As shown, satellite observations reveal a better performance over in situ records in defining the key stages of ice formation and decay in the CL. Using advantages of both data sources, an updated ice season duration (ISD) record is obtained to adequately describe the ice cover season in the CL. High-resolution ISD maps provide important spatial details of ice growth and decay in the CL. As found, ice cover resides longest in the south-eastern CL and along the eastern coast, including the Nemunas Delta, while the shortest ice season is observed in the northern CL. During the melting season, the ice melt pattern is clearly shaped by the direction of prevailing winds, and ice drift velocities obtained from a limited number of observations range within 0.03–0.14 m/s. The pronounced shortening of the ice season duration in the CL is observed at a rate of 1.6–2.3 days year^‒1 during 2002–2017, which is much higher than reported for the nearby Baltic Sea regions. While the timing of the freeze onset and full freezing has not changed much, the dates of the final melt onset and last observation of ice have a clear decreasing pattern toward an earlier ice break-up and complete melt-off due to an increase of air temperature strongly linked to the North Atlantic Oscillation (NAO). Notably, the correlation between the ISD, air temperature, and winter NAO index is substantially higher when considering the lagoon-averaged ISD values derived from satellite observations compared to those derived from coastal records. The latter clearly demonstrated the richness of the satellite observations that should definitely be exploited in regional ice monitoring programs. Full article

(This article belongs to the Special Issue Combining Different Data Sources for Environmental and Operational Satellite Monitoring of Sea Ice Conditions)

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15 pages, 6497 KiB

Open AccessArticle

An Optimal Decision-Tree Design Strategy and Its Application to Sea Ice Classification from SAR Imagery

by Johannes Lohse, Anthony P. Doulgeris and Wolfgang Dierking

Remote Sens. 2019, 11(13), 1574; https://doi.org/10.3390/rs11131574 - 3 Jul 2019

Cited by 33 | Viewed by 4545

Abstract

We introduce the fully automatic design of a numerically optimized decision-tree algorithm and demonstrate its application to sea ice classification from SAR data. In the decision tree, an initial multi-class classification problem is split up into a sequence of binary problems. Each branch of the tree separates one single class from all other remaining classes, using a class-specific selected feature set. We optimize the order of classification steps and the feature sets by combining classification accuracy and sequential search algorithms, looping over all remaining features in each branch. The proposed strategy can be adapted to different types of classifiers and measures for the class separability. In this study, we use a Bayesian classifier with non-parametric kernel density estimation of the probability density functions. We test our algorithm on simulated data as well as airborne and spaceborne SAR data over sea ice. For the simulated cases, average per-class classification accuracy is improved between 0.5% and 4% compared to traditional all-at-once classification. Classification accuracy for the airborne and spaceborne SAR datasets was improved by 2.5% and 1%, respectively. In all cases, individual classes can show larger improvements up to 8%. Furthermore, the selection of individual feature sets for each single class can provide additional insights into physical interpretation of different features. The improvement in classification results comes at the cost of longer computation time, in particular during the design and training stage. The final choice of the optimal algorithm therefore depends on time constraints and application purpose. Full article

(This article belongs to the Special Issue Combining Different Data Sources for Environmental and Operational Satellite Monitoring of Sea Ice Conditions)

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20 pages, 2733 KiB

Open AccessArticle

A New Retracking Algorithm for Retrieving Sea Ice Freeboard from CryoSat-2 Radar Altimeter Data during Winter–Spring Transition

by Xiaoyi Shen, Markku Similä, Wolfgang Dierking, Xi Zhang, Changqing Ke, Meijie Liu and Manman Wang

Remote Sens. 2019, 11(10), 1194; https://doi.org/10.3390/rs11101194 - 20 May 2019

Cited by 9 | Viewed by 4471

Abstract

A new method called Bézier curve fitting (BCF) for approximating CryoSat-2 (CS-2) SAR-mode waveform is developed to optimize the retrieval of surface elevation of both sea ice and leads for the period of late winter/early spring. We found that the best results are achieved when the retracking points are fixed on positions at which the rise of the fitted Bézier curve reaches 70% of its peak in case of leads, and 50% in case of sea ice. In order to evaluate the proposed retracking algorithm, we compare it to other empirically-based methods currently reported in the literature, namely the threshold first-maximum retracker algorithm (TFMRA) and the European Space Agency (ESA) CS-2 in-depth Level-2 algorithm (L2I). The results of the retracking procedure for the different algorithms are validated using data of the Operation Ice Bridge (OIB) airborne mission. For two OIB campaign periods in March 2015 and April 2016, the mean absolute differences between freeboard values retrieved from CS-2 and OIB data were 9.22 and 7.79 cm when using the BCF method, 10.41 cm and 8.16 cm for TFMRA, and 10.01 cm and 8.42 cm for L2I. This suggests that the sea ice freeboard data can be obtained with a higher accuracy when using the proposed BCF method instead of the TFMRA or the CS-2 L2I algorithm. Full article

(This article belongs to the Special Issue Combining Different Data Sources for Environmental and Operational Satellite Monitoring of Sea Ice Conditions)

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24 pages, 14835 KiB

Open AccessArticle

Automatic Detection of Small Icebergs in Fast Ice Using Satellite Wide-Swath SAR Images

by Ingri Halland Soldal, Wolfgang Dierking, Anton Korosov and Armando Marino

Remote Sens. 2019, 11(7), 806; https://doi.org/10.3390/rs11070806 - 3 Apr 2019

Cited by 26 | Viewed by 5828

Abstract

Automatic detection of icebergs in satellite images is regarded a useful tool to provide information necessary for safety in Arctic shipping or operations over large ocean areas in near-real time. In this work, we investigated the feasibility of automatic iceberg detection in Sentinel-1 Extra Wide Swath (EWS) SAR images which follow the preferred image mode in operational ice charting. As test region, we selected the Barents Sea where the size of many icebergs is on the order of the spatial resolution of the EWS-mode. We tested a new approach for a detection scheme. It is based on a combination of a filter for enhancing the contrast between icebergs and background, subsequent blob detection, and final application of a Constant False Alarm Rate (CFAR) algorithm. The filter relies mainly on the HV-polarized intensity which often reveals a larger difference between icebergs and sea ice or open water. The blob detector identifies locations of potential icebergs and thus shortens computation time. The final detection is performed on the identified blobs using the CFAR algorithm. About 2000 icebergs captured in fast ice were visually identified in Sentinel-2 Multi Spectral Imager (MSI) data and exploited for an assessment of the detection scheme performance using confusion matrices. For our performance tests, we used four Sentinel-1 EWS images. For judging the effect of spatial resolution, we carried out an additional test with one Sentinel-1 Interferometric Wide Swath (IWS) mode image. Our results show that only 8–22 percent of the icebergs could be detected in the EWS images, and over 90 percent of all detections were false alarms. In IWS mode, the number of correctly identified icebergs increased to 38 percent. However, we obtained a larger number of false alarms in the IWS image than in the corresponding EWS image. We identified two problems for iceberg detection: 1) with the given frequency–polarization combination, not all icebergs are strong scatterers at HV-polarization, and (2) icebergs and deformation structures present on fast ice can often not be distinguished since both may reveal equally strong responses at HV-polarization. Full article

(This article belongs to the Special Issue Combining Different Data Sources for Environmental and Operational Satellite Monitoring of Sea Ice Conditions)

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17 pages, 6039 KiB

Open AccessArticle

Snow Thickness Estimation on First-Year Sea Ice from Late Winter Spaceborne Scatterometer Backscatter Variance

by John Yackel, Torsten Geldsetzer, Mallik Mahmud, Vishnu Nandan, Stephen E. L. Howell, Randall K. Scharien and Hoi Ming Lam

Remote Sens. 2019, 11(4), 417; https://doi.org/10.3390/rs11040417 - 18 Feb 2019

Cited by 15 | Viewed by 5042

Abstract

Ku- and C-band spaceborne scatterometer sigma nought (σ°) backscatter data of snow covered landfast first-year sea ice from the Canadian Arctic Archipelago are acquired during the winter season with coincident in situ snow-thickness observations. Our objective is to describe a methodological framework for estimating relative snow thickness on first-year sea ice based on the variance in σ° from daily time series ASCAT and QuikSCAT scatterometer measurements during the late winter season prior to melt onset. We first describe our theoretical basis for this approach, including assumptions and conditions under which the method is ideally suited and then present observational evidence from four independent case studies to support our hypothesis. Results suggest that the approach can provide a relative measure of snow thickness prior to σ° detected melt onset at both Ku- and C-band frequencies. We observe that, during the late winter season, a thinner snow cover displays a larger variance in daily σ° compared to a thicker snow cover on first-year sea ice. This is because for a given increase in air temperature, a thinner snow cover manifests a larger increase in basal snow layer brine volume owing to its higher thermal conductivity, a larger increase in the dielectric constant and a larger increase in σ° at both Ku- and C bands. The approach does not apply when snow thickness distributions on first-year sea ice being compared are statistically similar, indicating that similar late winter σ° variances likely indicate regions of similar snow thickness. Full article

(This article belongs to the Special Issue Combining Different Data Sources for Environmental and Operational Satellite Monitoring of Sea Ice Conditions)

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16 pages, 531 KiB

Open AccessArticle

Numerical Analysis of Microwave Scattering from Layered Sea Ice Based on the Finite Element Method

by Xu Xu, Camilla Brekke, Anthony P. Doulgeris and Frank Melandsø

Remote Sens. 2018, 10(9), 1332; https://doi.org/10.3390/rs10091332 - 21 Aug 2018

Cited by 9 | Viewed by 3874

Abstract

A two-dimensional scattering model based on the Finite Element Method (FEM) is built for simulating the microwave scattering of sea ice, which is a layered medium. The scattering problem solved by the FEM is formulated following a total- and scattered-field decomposition strategy. The model set-up is first validated with good agreements by comparing the results of the FEM with those of the small perturbation method and the method of moment. Subsequently, the model is applied to two cases of layered sea ice to study the effect of subsurface scattering. The first case is newly formed sea ice which has scattering from both air–ice and ice–water interfaces. It is found that the backscattering has a strong oscillation with the variation of sea ice thickness. The found oscillation effects can increase the difficulty of retrieving the thickness of newly formed sea ice from the backscattering data. The second case is first-year sea ice with C-shaped salinity profiles. The scattering model accounts for the variations in the salinity profile by approximating the profile as consisting of a number of homogeneous layers. It is found that the salinity profile variations have very little influence on the backscattering for both C- and L-bands. The results show that the sea ice can be considered to be homogeneous with a constant salinity value in modelling the backscattering and it is difficult to sense the salinity profile of sea ice from the backscattering data, because the backscattering is insensitive to the salinity profile. Full article

(This article belongs to the Special Issue Combining Different Data Sources for Environmental and Operational Satellite Monitoring of Sea Ice Conditions)

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23 pages, 1309 KiB

Open AccessArticle

Estimating the Speed of Ice-Going Ships by Integrating SAR Imagery and Ship Data from an Automatic Identification System

by Markku Similä and Mikko Lensu

Remote Sens. 2018, 10(7), 1132; https://doi.org/10.3390/rs10071132 - 18 Jul 2018

Cited by 20 | Viewed by 4190

Abstract

The automatic identification system (AIS) was developed to support the safety of marine traffic. In ice-covered seas, the ship speeds extracted from AIS data vary with ice conditions that are simultaneously reflected by features in synthetic aperture radar (SAR) images. In this study, the speed variation was related to the SAR features and the results were applied to generate a chart of expected speeds from the SAR image. The study was done in the Gulf of Bothnia in March 2013 for ships with ice class IA Super that are able to navigate without icebreaker assistance. The speeds were normalized to dimensionless units ranging from 0 to 10 for each ship. As the matching between AIS and SAR was complicated by ice drift during the time gap (from hours to two days), we calculated a set of local statistical SAR features over several scales. Random forest tree regression was used to estimate the speed. The accuracy was quantified by mean squared error and by the fraction of estimates close to the actual speeds. These depended strongly on the route and the day. The error varied from 0.4 to 2.7

{units}^{2}

for daily routes. Sixty-five percent of the estimates deviated by less than one speed unit and 82% by less than 1.5 speed units from the AIS speeds. The estimated daily mean speeds were close to the observations. The largest speed decreases were provided by the estimator in a dampened form or not at all. This improved when the ice chart thickness was included as a predictor. Full article

(This article belongs to the Special Issue Combining Different Data Sources for Environmental and Operational Satellite Monitoring of Sea Ice Conditions)

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Other

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11 pages, 4796 KiB

Open AccessLetter

SAR Pancake Ice Thickness Retrieval in the Terra Nova Bay (Antarctica) during the PIPERS Expedition in Winter 2017

by Giuseppe Aulicino, Peter Wadhams and Flavio Parmiggiani

Remote Sens. 2019, 11(21), 2510; https://doi.org/10.3390/rs11212510 - 26 Oct 2019

Cited by 15 | Viewed by 3868

Abstract

Pancake and frazil ice represent an important component of the Arctic and Antarctic cryosphere, especially in marginal ice zones. The retrieval of their thickness by remote sensing is, in general, a difficult task. A processing system was developed and refined by the present authors in the framework of the EU SPICES project; it is meant for routinely deriving ice thickness in frazil-pancake regions using the spectral changes in wave spectra from imagery provided by space-borne Synthetic Aperture Radar (SAR) systems. This methodology was successfully tested in the Beaufort Sea through comparison with ground truth collected during the cruise of the “Sikuliaq” in the fall of 2015. In the present study, this technique has been adapted and applied to Antarctic frazil/pancake icefields using COSMO-SkyMed satellite images. Our retrievals were analyzed and validated through a comparison with co-located in situ observations collected during the 2017 PIPERS cruise in Terra Nova Bay polynya. A broad agreement was found between measured thicknesses and those retrieved from the SAR analysis. Results and statistics presented and discussed in detail in this study represent a step towards the autonomous measurement of pancake icefields in remote areas such as Antarctic coastal polynyas and marginal ice zones. Full article

(This article belongs to the Special Issue Combining Different Data Sources for Environmental and Operational Satellite Monitoring of Sea Ice Conditions)

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