Consistent ice and open water classification combining historical synthetic aperture radar satellite images from ERS-1/2, Envisat ASAR, RADARSAT-2 and Sentinel-1A/B

Johansson, A. Malin; Malnes, Eirik; Gerland, Sebastian; Cristea, Anca; Doulgeris, Anthony P.; Divine, Dmitry; Pavlova, Olga; Lauknes, Tom Rune

doi:10.1017/aog.2019.52

Cited by 20 publications

(19 citation statements)

References 45 publications

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“…Hong et al [33] used the statistical coefficient, incidence angle, environment temperature, and speed of wind to improve the sea ice and water classification. Johansson et al, [34] used a statistical mixture model to isolate open water from sea ice. Their method is based on semi-automatic segmentation technique.…”

Section: A Probabilistic Methods For Sea Ice Classificationmentioning

confidence: 99%

Deep Semisupervised Teacher–Student Model Based on Label Propagation for Sea Ice Classification

Khaleghian¹,

Ullah²,

Kræmer³

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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In this paper, we propose a novel deep semisupervised learning (SSL) method namely Teacher-Student based label propagation method (TSLP-SSL) for sea ice classification based on Sentinel-1 synthetic aperture radar (SAR) data. For sea ice classification, labeling the data precisely is very time consuming and requires expert knowledge. Our method efficiently learns sea ice characteristics from limited number of labeled samples and relatively large number of unlabeled samples. Therefore, our method addresses the key challenge of using limited number of precisely labeled samples to achieve generalization capability by discovering the underlying sea-ice characteristics also from unlabeled data. We perform experimental analysis considering a standard dataset consisting of properly labeled sea ice data spanning over different time slots of the year. Both qualitative and quantitative results obtained on this dataset show that our proposed TSLP-SSL method outperforms deep supervised and semi-supervised reference methods.

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Section: A Probabilistic Methods For Sea Ice Classificationmentioning

confidence: 99%

Deep Semisupervised Teacher–Student Model Based on Label Propagation for Sea Ice Classification

Khaleghian¹,

Ullah²,

Kræmer³

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…taking into account the sky view factor SVF. As we applied the model to vertical rock walls, we assumed an SVF of 0.5 for all locations (Kastendeuch, 2013). Therefore, the amount of diffuse shortwave radiation S diff can be expressed as…”

Section: Preprocessingmentioning

confidence: 99%

Surface temperatures and their influence on the permafrost thermal regime in high-Arctic rock walls on Svalbard

et al. 2021

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Abstract. Permafrost degradation in steep rock walls and associated slope destabilization have been studied increasingly in recent years. While most studies focus on mountainous and sub-Arctic regions, the occurring thermo-mechanical processes also play an important role in the high Arctic. A more precise understanding is required to assess the risk of natural hazards enhanced by permafrost warming in high-Arctic rock walls. This study presents one of the first comprehensive datasets of rock surface temperature measurements of steep rock walls in the high Arctic, comparing coastal and near-coastal settings. We applied the surface energy balance model CryoGrid 3 for evaluation, including adjusted radiative forcing to account for vertical rock walls. Our measurements comprise 4 years of rock surface temperature data from summer 2016 to summer 2020. Mean annual rock surface temperatures ranged from −0.6 in a coastal rock wall in 2017/18 to −4.3 ∘C in a near-coastal rock wall in 2019/20. Our measurements and model results indicate that rock surface temperatures at coastal cliffs are up to 1.5 ∘C higher than at near-coastal rock walls when the fjord is ice-free in winter, resulting from additional energy input due to higher air temperatures at the coast and radiative warming by relatively warm seawater. An ice layer on the fjord counteracts this effect, leading to similar rock surface temperatures to those in near-coastal settings. Our results include a simulated surface energy balance with shortwave radiation as the dominant energy source during spring and summer with net average seasonal values of up to 100 W m−2 and longwave radiation being the main energy loss with net seasonal averages between 16 and 39 W m−2. While sensible heat fluxes can both warm and cool the surface, latent heat fluxes are mostly insignificant. Simulations for future climate conditions result in a warming of rock surface temperatures and a deepening of active layer thickness for both coastal and near-coastal rock walls. Our field data present a unique dataset of rock surface temperatures in steep high-Arctic rock walls, while our model can contribute towards the understanding of factors influencing coastal and near-coastal settings and the associated surface energy balance.

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“…While an ice cover in the bay leads to locally decreased RST in the bay, a widespread sea ice extent results in lower RST for all settings. Maximum sea ice coverage is significantly reduced since 2006 and a shorter sea ice season is observed since 2002 (Johansson et al, 2020). Hence, it can be assumed that RST at the coastal cliff has increased since then in winter and spring https://doi.org/10.5194/tc-2020-340 Preprint.…”

Section: The Influence Of Open Water and Sea Ice On Rstmentioning

confidence: 99%

“…According to observations since 1997, Kongsfjorden was characterized by sea ice cover during winter season (Gerland and Hall, 2006). Since 2006, the sea ice extent has been reduced significantly and the ice thickness and snow cover on ice have become thinner (Johansson et al, 2020). This could also affect coastal erosion as sea ice and development of an ice foot protect the cliffs by absorbing ocean wave energy and control the removal of weathered material from the base of the cliff (Ødegård and Sollid, 1993).…”

Section: Introductionmentioning

confidence: 99%

Surface temperatures and their influence on the permafrost thermal regime in high Arctic rock walls on Svalbard

Aga¹,

Etzelmüller²,

Schuler³

et al. 2020

Preprint

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Abstract. Permafrost degradation in steep rock walls and associated slope destabilization have been studied increasingly in recent years. While most studies focus on mountainous and sub-Arctic regions, the occurring thermo-mechanical processes play an important role also in the high Arctic. A more precise understanding is required to assess the risk of natural hazards enhanced by permafrost warming in high Arctic rock walls. This study presents rock surface temperature measurements of coastal and non-coastal rock walls in a high Arctic setting on Svalbard. We applied the surface energy balance model CryoGrid 3 for evaluation, including adjusted radiative forcing to account for vertical rock walls. Our measurements and model results show that rock surface temperatures at coastal cliffs are up to 1.5 °C higher than non-coastal rock walls when the fjord is ice-free in the winter season, resulting from additional energy input due to higher air temperatures at the coast and radiative warming by relatively warm seawater. An ice layer on the fjord counteracts this effect, leading to similar rock surface temperatures as in non-coastal settings. Our results include a simulated surface energy balance with short-wave radiation as the dominant energy source during spring and summer, and long-wave radiation being the main energy loss. While sensible heat fluxes can both warm and cool the surface, latent heat fluxes are mostly insignificant. Simulations for future climate conditions result in a warming of rock surface temperatures and a deepening of active layer thickness for both coastal and non-coastal rock walls. Our field data present a unique data set of rock surface temperatures in steep high Arctic rock walls, while our model can contribute towards the understanding of factors influencing coastal and non-coastal settings and the associated surface energy balance.

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Consistent ice and open water classification combining historical synthetic aperture radar satellite images from ERS-1/2, Envisat ASAR, RADARSAT-2 and Sentinel-1A/B

Cited by 20 publications

References 45 publications

Deep Semisupervised Teacher–Student Model Based on Label Propagation for Sea Ice Classification

Deep Semisupervised Teacher–Student Model Based on Label Propagation for Sea Ice Classification

Surface temperatures and their influence on the permafrost thermal regime in high-Arctic rock walls on Svalbard

Surface temperatures and their influence on the permafrost thermal regime in high Arctic rock walls on Svalbard

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