Retrieval of Melt Ponds on Arctic Multiyear Sea Ice in Summer from TerraSAR-X Dual-Polarization Data Using Machine Learning Approaches: A Case Study in the Chukchi Sea with Mid-Incidence Angle Data

Melt ponds, a common feature on Arctic sea ice, absorb most of the incoming solar radiation and have a large effect on the melting rate of sea ice, which significantly influences climate change. Therefore, it is very important to monitor melt ponds in order to better understand the sea ice-climate interaction. In this study, melt pond retrieval models were developed using the TerraSAR-X dual-polarization synthetic aperture radar (SAR) data with mid-incidence angle obtained in a summer multiyear sea ice area in the Chukchi Sea, the Western Arctic, based on two rule-based machine learning approachesdecision trees (DT) and random forest (RF)in order to derive melt pond statistics at high spatial resolution and to identify key polarimetric parameters for melt pond detection. Melt ponds, sea ice and open water were delineated from the airborne SAR images (0.3-m resolution), which were used as a reference dataset. A total of eight polarimetric parameters (HH and VV backscattering coefficients, co-polarization ratio, co-polarization phase difference, co-polarization correlation coefficient, alpha angle, entropy and anisotropy) were derived from the TerraSAR-X dual-polarization data and then used as input variables for the machine learning models. The DT and RF models could not effectively discriminate melt ponds from open water when using only the polarimetric parameters. This is because melt ponds showed similar polarimetric signatures to open water. The average and standard deviation of the polarimetric parameters based on a 15 x 15 pixel window were supplemented to the input variables in order to consider the difference between the spatial texture of melt ponds and open water. Both the DT and RF models using the polarimetric parameters and their texture features produced improved performance for the retrieval of melt ponds, and RF was superior to DT. The HH backscattering coefficient was identified as the variable contributing the most, and its spatial standard deviation was the next most contributing one to the classification of open water, sea ice and melt ponds in the RF model. The average of the co-polarization phase difference and the alpha angle in a mid-incidence angle were also identified as the important variables in the RF model. The melt pond fraction and sea ice concentration retrieved from the RF-derived melt pond map showed root mean square deviations of 2.4% and 4.9%, respectively, compared to those from the reference melt pond maps. This indicates that there is potential to accurately monitor melt ponds on multiyear sea ice in the summer season at a local scale using high-resolution dual-polarization SAR data.open

Simultaneous disintegration of outlet glaciers in Porpoise Bay (Wilkes Land), East Antarctica, driven by sea ice break-up

The floating ice shelves and glacier tongues which fringe the Antarctic continent are important because they help buttress ice flow from the ice sheet interior. Dynamic feedbacks associated with glacier calving have the potential to reduce buttressing and subsequently increase ice flow into the ocean. However, there are few high temporal resolution studies on glacier calving, especially in East Antarctica. Here we use remote sensing to investigate monthly glacier terminus change across six marine-terminating outlet glaciers in Porpoise Bay (−76° S, 128° E), Wilkes Land (East Antarctica), between November 2002 and March 2012. This reveals a large simultaneous calving event in January 2007, resulting in a total of ~ 2900 km2 of ice being removed from glacier tongues. Our observations suggest that sea-ice must be removed from glacier termini for any form of calving to take place, and we link this major calving event to a rapid break-up of the multi-year sea-ice which usually occupies Porpoise Bay. Using sea-ice concentrations as a proxy for glacier calving, and by analysing available satellite imagery stretching back to 1963, we reconstruct the long-term calving activity of the largest glacier in Porpoise Bay: Holmes (West) Glacier. This reveals that its present-day velocity (~ 1450 m a−1) is approximately 50 % faster than between 1963 and 1973 (~ 900 m a−1). We also observed the start of a large calving event in Porpoise Bay in March 2016 that is consistent with our reconstructions of the periodicity of major calving events. These results highlight the importance of sea-ice in modulating outlet glacier calving and velocity in East Antarctica

A new algorithm for sea ice melt pond fraction estimation from high-resolution optical satellite imagery

doi: 10.1029/2019JC015716Abstract Melt ponds occupy a large fraction of the Arctic sea ice surface during spring and summer. The fraction and distribution of melt ponds have considerable impacts on Arctic climate and ecosystem by reducing the albedo. There is an urgency to obtain improved accuracy and a wider coverage of melt pond fraction (MPF) data for studying these processes. MPF information has generally been acquired from optical imagery. Conventional MPF algorithms based on high-resolution optical sensors have treated melt ponds as features with constant reflectance; however, the spectral reflectance of ponds can vary greatly, even at a local scale. Here we use Sentinel-2 imagery to demonstrate those previous algorithms assuming fixed melt pond-reflectance greatly underestimate MPF. We propose a new algorithm (?LinearPolar?) based on the polar coordinate transformation that treats melt ponds as variable-reflectance features and calculates MPF across the vector between melt pond and bare ice axes. The angular coordinate ? of the polar coordinate system, which is only associated with pond fraction rather than reflectance, is used to determinate MPF. By comparing the new algorithm and previous methods with IceBridge optical imagery data, across a variety of Sentinel-2 images with melt ponds at various stages of development, we show that the RMSE value of the LinearPolar algorithm is about 30% lower than for the previous algorithms. Moreover, based on a sensitivity test, the new algorithm is also less sensitive to the subjective threshold for melt pond reflectance than previous algorithms.Peer reviewe

MeltPondNet: A Swin Transformer U-Net for Detection of Melt Ponds on Arctic Sea Ice

High-resolution aerial photographs of Arctic region are a great source for different sea ice feature recognition, which are crucial to validate, tune and improve climate models. Melt ponds on the surface of melting Arctic sea ice are of particular interest as they are sensitive and valuable indicators and are proxy to the processes in the Arctic climate system. Manual analysis of this remote sensing data is extremely difficult and time-consuming due to the complex shapes and unpredictable boundaries of the melt ponds, and that leads to the necessity for automatizing the processes. In this study, we propose a robust and efficient automatic method for melt pond region segmentation and boundary extraction from high-resolution aerial photographs. The proposed algorithm is based on a swin transformer U-Net in which we introduce novel cross-channel attention mechanisms into the decoder design. The framework operates with optical data and allows for classifying imagery into four classes: sea ice/snow, open water, melt pond, and submerged ice. We use aerial photographs collected during the Healy–Oden Trans Arctic Expedition (HO-TRAX) expedition over Arctic sea ice in the summer season of 2005 as test data. The experimental results show that the proposed method is suitable for precise automatic extraction of melt pond geometry and it can also be extended for other optical data sources that involve melt ponds. The approach has a promising potential to be used to analyze melt ponds’ corresponding changes between years

Signature of Arctic first-year ice melt pond fraction in X-band SAR imagery

In this paper we investigate the potential of melt pond fraction retrieval from X-band polarimetric synthetic aperture radar (SAR) on drifting first-year sea ice. Melt pond fractions retrieved from a helicopter-borne camera system were compared to polarimetric features extracted from four dual-polarimetric X-band SAR scenes, revealing significant relationships. The correlations were strongly dependent on wind speed and SAR incidence angle. Co-polarisation ratio was found to be the most promising SAR feature for melt pond fraction estimation at intermediate wind speeds (6. 2 m s−1), with a Spearman's correlation coefficient of 0. 46. At low wind speeds (0. 6 m s−1), this relation disappeared due to low backscatter from the melt ponds, and backscatter VV-polarisation intensity had the strongest relationship to melt pond fraction with a correlation coefficient of −0. 53. To further investigate these relations, regression fits were made both for the intermediate (R2fit = 0. 21) and low (R2fit = 0. 26) wind case, and the fits were tested on the satellite scenes in the study. The regression fits gave good estimates of mean melt pond fraction for the full satellite scenes, with less than 4 % from a similar statistics derived from analysis of low-altitude imagery captured during helicopter ice-survey flights in the study area. A smoothing window of 51 × 51 pixels gave the best reproduction of the width of the melt pond fraction distribution. A considerable part of the backscatter signal was below the noise floor at SAR incidence angles above  ∼  40°, restricting the information gain from polarimetric features above this threshold. Compared to previous studies in C-band, limitations concerning wind speed and noise floor set stricter constraints on melt pond fraction retrieval in X-band. Despite this, our findings suggest new possibilities in melt pond fraction estimation from X-band SAR, opening for expanded monitoring of melt ponds during melt season in the future

The patterns and drivers of recent outlet glacier change in East Antarctica

West Antarctica and Greenland have made substantial contributions to global sea level rise over the past two decades. In contrast, the East Antarctic Ice Sheet (EAIS) has largely been in balance or slightly gaining mass over the past two decades. This is consistent with the long-standing view that the EAIS is relatively immune to global warming. However, several recent reports have highlighted instabilities in the EAIS in the past, and some numerical models now predict near-future sea level contributions from the ice sheet, albeit with large uncertainties surrounding the rates of mass loss. Using primarily remote sensing methods, this thesis aims to determine spatial and temporal patterns of outlet glacier change in the EAIS and assess the drivers and mechanisms of any changes in their dynamics. In doing so, it will also explore the wider debate surrounding the potential vulnerability of the ice sheet in the coming decades to centuries. Pan-ice sheet terminus mapping in 1974, 1990, 2000 and 2012 reveals significant decadal variability in the behaviour of the EAIS. The majority of outlet glaciers retreated between 1974 and 1990, before switching to a dominant advance phase from 1990-2000. This trend of outlet glacier advance largely continued between 2000 and 2012, with the exception of Wilkes Land, where 74% of glaciers retreated. It is hypothesized that this anomalous retreat is linked to a reduction in sea ice and associated impacts on ocean stratification. A more detailed examination of six glaciers in Porpoise Bay, Wilkes Land, reveals that large simultaneous calving events in January 2007 and March 2016, totalling ~2,900 km2 and 2,200 km2, were driven by the break-up of the multi-year landfast sea ice which usually occupies Porpoise Bay. However, these break-up events were driven by contrasting mechanisms. The 2007 break-up event is linked to an exceptionally warm December 2005 weakening the band of multi-year sea ice prior to its eventual break-up in the following summer. Whereas, the 2016 event is linked to the terminus advance of Holmes (West) Glacier pushing the multi-year sea ice further into the open ocean, making it more vulnerable to break-up. In order to examine how changes at the terminus of glaciers might impact on their inland velocity, this thesis then analyses Cook Glacier, which is a major outlet glacier which drains a large proportion of the Wilkes Subglacial Basin. Analysis of ice-front positon change from 1947-2017, glacier velocity from 1973-2017 and ice shelf thickness from 1994-2012, reveals dynamic instability in the recent past. Cook West Ice Shelf retreated to its grounding line between 1973 and 1989, resulting the doubling of its velocity. Cook East Ice Shelf did not show a similar retreat pattern, but its ice shelf thinned rapidly between 1998 and 2002, which coincided with an increase of its velocity of ~10%. This rapid thinning is linked to periodic intrusions of warm mCDW. If these intrusions become more persistent in the future, Cook Glacier has the potential to contribute to sea level rise in the future. In a wider context the results from this thesis highlight some key issues which need to be considered when predicting the response of the EAIS to future climate warming: i) The sensitivity of the EAIS to decadal variations in climate. ii) The potential for future changes in the location and persistency of landfast sea ice to alter outlet glacier dynamics. iii) The potential for rapid thinning of those ice shelves with a low steady-state basal melt rate

TanDEM-X multiparametric data features in sea ice classification over the Baltic sea

In this study, we assess the potential of X-band Interferometric Synthetic Aperture Radar imagery for automated classification of sea ice over the Baltic Sea. A bistatic SAR scene acquired by the TanDEM-X mission over the Bothnian Bay in March of 2012 was used in the analysis. Backscatter intensity, interferometric coherence magnitude, and interferometric phase have been used as informative features in several classification experiments. Various combinations of classification features were evaluated using Maximum likelihood (ML), Random Forests (RF) and Support Vector Machine (SVM) classifiers to achieve the best possible discrimination between open water and several sea ice types (undeformed ice, ridged ice, moderately deformed ice, brash ice, thick level ice, and new ice). Adding interferometric phase and coherence-magnitude to backscatter-intensity resulted in improved overall classification performance compared to using only backscatter-intensity. The RF algorithm appeared to be slightly superior to SVM and ML due to higher overall accuracies, however, at the expense of somewhat longer processing time. The best overall accuracy (OA) for three methodologies were achieved using combination of all tested features were 71.56, 72.93, and 72.91% for ML, RF and SVM classifiers, respectively. Compared to OAs of 62.28, 66.51, and 63.05% using only backscatter intensity, this indicates strong benefit of SAR interferometry in discriminating different types of sea ice. In contrast to several earlier studies, we were particularly able to successfully discriminate open water and new ice classes.Peer reviewe

High Resolution Remote Sensing Observations of Summer Sea Ice

During the Arctic summer melt season, the sea ice transitions from a consolidated ice pack with a highly reflective snow-covered surface to a disintegrating unconsolidated pack with melt ponds spotting the ice surface. The albedo of the Arctic decreases by up to 50%, resulting in increased absorption of solar radiation, triggering the positive sea ice albedo feedback that further enhances melting. Summer melt processes occur at a small scale and are required for melt pond parameterization in models and quantifying albedo change. Arctic-wide observations of melt features were however not available until recently. In this work we develop original techniques for the analysis of high-resolution remote sensing observations of summer sea ice. By applying novel algorithms to data acquired from airborne and satellite sensors onboard IceBridge, Sentinel-2, WorldView and ICESat-2, we derive a set of parameters that describe melt conditions on Arctic sea ice in summer. We present a new, pixel-based classification scheme to identify melt features in high-resolution summer imagery. We apply the classification algorithm to IceBridge Digital Mapping System data and find a greater melt pond fraction (25%) on sea ice in the Beaufort and Chukchi Seas, a region consisting of predominantly first year ice, compared to the Central Arctic, where the melt pond fraction is 14% on predominantly multiyear ice. Expanding the study to observations acquired by the Sentinel-2 Multispectral Instrument, we track the variability in melt pond fraction and sea ice concentration with time, focusing on the anomalously warm summer of 2020. So as to obtain a three-dimensional view of the evolution of summer melt we also exploit ICESat-2 surface elevation measurements. We develop and apply the Melt Pond Algorithm to track ponds in ICESat-2 photon cloud data and derive their depth. Pond depth measurements in conjunction with melt pond fraction and sea ice concentration provide insights into the regional patterns and temporal evolution of melt on summer sea ice. We found mean melt pond fraction increased rapidly in the beginning of the melt season, peaking at 16% on 24 June 2020, while median pond depths increased steadily from 0.4 m at the beginning of the melt season, to peaking at 0.97 m on 16 July, even as melt pond fraction had begun to decrease. Our findings may be used to improve parameterization of melt processes in models, quantify freshwater storage, and study the partitioning of under ice light