Estimating melt onset over Arctic sea ice from time series multi-sensor Sentinel-1 and RADARSAT-2 backscatter

Howell, Stephen E. L.; Small, David; Rohner, Christoph; Mahmud, Mallik; Yackel, John J.; Brady, Michael S.

doi:10.1016/j.rse.2019.04.031

Cited by 24 publications

(19 citation statements)

References 32 publications

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“…During the early melt season (May 18 and June 7), the SFYI values largely overlap each other despite the larger difference in incidence angle, and both the highest and lowest incidence angles here correspond to backscatter values higher than all the winter scenes. Increased backscatter returns at the melt season onset have already been noted in, e.g., [5], [45], and [46]. We attribute this change to physical changes in the snow-sea ice interface, where warmer temperatures likely result in increased brine volume and, hence, increased backscatter values.…”

Section: Classification Resultssupporting

confidence: 54%

Robustness of SAR Sea Ice Type Classification Across Incidence Angles and Seasons at L-Band

Singha

Johansson

Doulgeris

2021

IEEE Trans. Geosci. Remote Sensing

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In recent years, space-borne synthetic aperture radar (SAR) polarimetry has become a valuable tool for sea ice type retrieval. L-band SAR has proven to be sensitive toward deformed sea ice and is complementary compared with operationally used C-band SAR for sea ice type classification during the early and advanced melt seasons. Here, we employ an artificial neural network (ANN)-based sea ice type classification algorithm on a comprehensive data set of ALOS-2 PALSAR-2 fully polarimetric images acquired with a range of incidence angles and during different environmental conditions. The variability within the data set means that it is ideal for making a novel assessment of the robustness of the sea ice classification, investigating the intraclass variability, the seasonal variations, and the incidence angle effect on the sea ice classification results. The images coincide with two different Arctic campaigns in 2015: the Norwegian Young Sea Ice Cruise 2015 (N-ICE2015) and the Polarstern's (PS92) Transitions in the Arctic Seasonal Sea Ice Zone (TRANSSIZ). We find that it is essential to take into account seasonality and intraclass variability when establishing training data for machine learning-based algorithms though moderate differences in incidence angle are possible to accommodate by the classifier during the dry and cold winter season. We also conclude that the incidence angle dependence of backscatter for a given ice type is consistent for different Arctic regions.

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Section: Classification Resultssupporting

confidence: 54%

Robustness of SAR Sea Ice Type Classification Across Incidence Angles and Seasons at L-Band

Singha

Johansson

Doulgeris

2021

IEEE Trans. Geosci. Remote Sensing

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“…Satellite remote sensing has proven to be a useful tool to observe ice velocity change [22], supraglacial lakes [23][24][25][26] and sea ice evolution [19,27,28]. Optical and microwave remote sensing images (e.g., Landsat-8 and Sentinel-1) have been widely applied to extract annual and seasonal ice velocity of outlet glaciers in Greenland [29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Hydrological and Kinematic Precursors of the 2017 Calving Event at the Petermann Glacier in Greenland Observed from Multi-Source Remote Sensing Data

Jiang

Huang

2021

Remote Sensing

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Both a decrease of sea ice and an increase of surface meltwater, which may induce ice-flow speedup and frontal collapse, have a significant impact on the stability of the floating ice shelf in Greenland. However, detailed dynamic precursors and drivers prior to a fast-calving process remain unclear due to sparse remote sensing observations. Here, we present a comprehensive investigation on hydrological and kinematic precursors before the calving event on 26 July 2017 of Petermann Glacier in northern Greenland, by jointly using remote sensing observations at high-temporal resolution and an ice-flow model. Time series of ice-flow velocity fields during July 2017 were retrieved with Sentinel-2 observations with a sub-weekly sampling interval. The ice-flow speed quickly reached 30 m/d on 26 July (the day before the calving), which is roughly 10 times quicker than the mean glacier velocity. Additionally, a significant decrease in the radar backscatter coefficient of Sentinel-1 images suggests a rapid transformation from landfast sea ice into open water, associated with a decrease in sea ice extent. Additionally, the area of melt ponds on the floating ice tongue began to increase in mid-May, quickly reached a peak at the end of June and lasted for nearly one month until the calving occurred. We used the ice sheet system model to model the spatial-temporal damage and stress on the floating ice, thereby finding an abnormal stress distribution in a cracked region. It is inferred that this calving event may relate to a weakening of the sea ice, shearing of the tributary glacier, and meltwater infiltrating crevasses.

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“…However, to provide an integrated and effective mechanism to monitor snow cover and its variability, SAR data will help identify snowmelt processes [21]. In addition to snow mapping, Sentinel-1 analysis-ready data has been shown to be useful for vegetation mapping and dynamics [22], rapid assessment after a storm event [23], and melt-onset mapping using multiple SAR sensors [24].…”

Section: Introductionmentioning

confidence: 99%

Towards Sentinel-1 SAR Analysis-Ready Data: A Best Practices Assessment on Preparing Backscatter Data for the Cube

Truckenbrodt

Freemantle

Williams

et al. 2019

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This study aims at assessing the feasibility of automatically producing analysis-ready radiometrically terrain-corrected (RTC) Synthetic Aperture Radar (SAR) gamma nought backscatter data for ingestion into a data cube for use in a large spatio-temporal data environment. As such, this study investigates the analysis readiness of different openly available digital elevation models (DEMs) and the capability of the software solutions SNAP and GAMMA in terms of overall usability as well as backscatter data quality. To achieve this, the study builds on the Python library pyroSAR for providing the workflow implementation test bed and provides a Jupyter notebook for transparency and future reproducibility of performed analyses. Two test sites were selected, over the Alps and Fiji, to be able to assess regional differences and support the establishment of the Swiss and Common Sensing Open Data cubes respectively.

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Estimating melt onset over Arctic sea ice from time series multi-sensor Sentinel-1 and RADARSAT-2 backscatter

Cited by 24 publications

References 32 publications

Robustness of SAR Sea Ice Type Classification Across Incidence Angles and Seasons at L-Band

Robustness of SAR Sea Ice Type Classification Across Incidence Angles and Seasons at L-Band

Hydrological and Kinematic Precursors of the 2017 Calving Event at the Petermann Glacier in Greenland Observed from Multi-Source Remote Sensing Data

Towards Sentinel-1 SAR Analysis-Ready Data: A Best Practices Assessment on Preparing Backscatter Data for the Cube

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