Time-series snowmelt detection over the Antarctic using Sentinel-1 SAR images on Google Earth Engine

Liang, Dong; Guo, Huadong; Zhang, Lu; Cheng, Yun; Zhu, Qi; Liu, Xuting

doi:10.1016/j.rse.2021.112318

Cited by 52 publications

(36 citation statements)

References 64 publications

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“…Until recently, the occurrence of surface meltwater was considered spatially limited. Kingslake et al (2017), however, demonstrated that surface meltwater is widespread across the Antarctic continent, and subsequently, we now have a reasonable understanding of the spatial distribution of Antarctic surface meltwater (Stokes et al, 2019;Liang et al, 2021). The majority of surface melting occurs at lower latitudes and elevations of the ice-sheet periphery (Kingslake et al, 2017), with ponding of surface meltwater particularly abundant on relatively flat ice shelf surfaces (Alley et al, 2018;Stokes et al, 2019).…”

Section: Introductionmentioning

confidence: 98%

Automated mapping of the seasonal evolution of surface meltwater and its links to climate on the Amery Ice Shelf, Antarctica

Tuckett

Ely

Sole

et al. 2021

Preprint

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Abstract. Surface meltwater is widespread around the margin of the Antarctic Ice Sheet and has the potential to influence ice-shelf stability, ice-dynamic processes and ice-albedo feedbacks. Whilst the general spatial distribution of surface meltwater across the Antarctic continent is now relatively well known, our understanding of the seasonal and multi-year evolution of surface meltwater is limited. Attempts to generate robust time series of melt cover have largely been constrained by computational expense or limited ice surface visibility associated with mapping from optical satellite imagery. Here, we implement an existing meltwater detection method alongside a novel method for calculating visibility metrics within Google Earth Engine. This enables us to quantify uncertainty induced by cloud cover and variable image data coverage, allowing us to automatically generate time series of surface melt area over large spatial and temporal scales. We demonstrate our method on the Amery Ice Shelf region of East Antarctica, analysing 4,164 Landsat 7 and 8 optical images between 2005 and 2020. Results show high interannual variability in surface meltwater cover, with mapped cumulative lake area totals ranging from 384 km2 to 3898 km2 per melt season. However, by incorporating image visibility assessments into our results, we estimate that cumulative total lake areas are on average 42 % higher than minimum mapped values, highlighting the importance of accounting for variations in image visibility when mapping lake areas. In a typical melt season, total lake area remains low throughout November and early December, before increasing, on average, by an order of magnitude during the second half of December. Peak lake area most commonly occurs during January, before decreasing during February as lakes freeze over. We show that modelled melt predictions from a regional climate model provides a good indication of lake cover in the Amery region, and that annual lake coverage is strongly associated with phases of the Southern Annular Mode (SAM); surface melt is typically highest in years with a negative austral summer SAM index. Furthermore, we suggest that melt-albedo feedbacks modulate the spatial distribution of meltwater in the region, with the exposure of blue ice from persistent katabatic wind scouring influencing the susceptibility of melt ponding. Results demonstrate how our method could be scaled up to generate a multi-year time series record of surface water extent from optical imagery at a continent-wide scale.

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Section: Introductionmentioning

confidence: 98%

Automated mapping of the seasonal evolution of surface meltwater and its links to climate on the Amery Ice Shelf, Antarctica

Tuckett

Ely

Sole

et al. 2021

Preprint

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“…Similarly, another method was developed to produce spatiotemporal monitoring products for glaciers and to forecast Arctic sea ice changes (Huang, Li, Zhou, & Zhang, 2021). Big Earth Data can also contribute to climate change research through time-series analysis for Antarctic ice sheet snowmelt and mass balance (Liang et al, 2021a(Liang et al, , 2021b. These products provide strong information support to all countries to develop mitigation strategies and endeavor to reach the world's emission-cutting and temperature-control targets.…”

Section: Efforts Towards Utilizing Big Earth Data For Sdgsmentioning

confidence: 99%

Innovative approaches to the Sustainable Development Goals using Big Earth Data

et al. 2021

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A persistent challenge for the Sustainable Development Goals (SDGs) has been a lack of data for indicators to assess progress towards each goal and varying capacities among nations to conduct these assessments. Rapid developments in big data, however, are facilitating a global approach to the SDGs. Tools and data products are emerging that can be extended to and leveraged by nations that do not yet have the capacity to measure SDG indicators. Big Earth Data, a special class of big data, integrates multisource data within a geographic context, utilizing the principles and methodologies of the established literature on big data science, applied specifically to Earth system science. This paper discusses the research challenges related to Big Earth Data and the concerted efforts and investments required to make and measure progress towards the SDGs. As an example, the Big Earth Data Science Engineering Program (CASEarth) of the Chinese Academy of Sciences is presented along with other case studies on Big Earth Data in support of the SDGs. Lastly, the paper proposes future priorities for developments in Big Earth Data, such as human resource capacity, digital infrastructure, interoperability, and environmental considerations.

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“…It is, however, recommended to validate the dataset with the available station observations when using it for studies at a local scale. A recent study demonstrated that the dataset has the best performance in snowmelt detection over Antarctica (Liang, Guo, Zhang, Cheng, et al, 2021). This dataset can also be updated frequently with new Sentinel-1 data following new image acquisitions and their accessibility and availability.…”

Section: Usage Notesmentioning

confidence: 99%

“…The two-satellite constellation has an effective return period of six days at the equator but a higher image acquisition frequency for Antarctica due to their polar orbits. All these features make Sentinel-1 SAR data ideal for observing Antarctica (Liang, Guo, Zhang, Cheng, et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Sentinel-1 EW mode dataset for Antarctica from 2014–2020 produced by the CASEarth Cloud Service Platform

et al. 2021

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Antarctica plays an important role in research on global change, and its unique geography, ocean, climate, and environment provide an ideal place for humankind to understand Earth's evolution. Remote sensing provides an effective means to monitor and observe large-scale processes on the continent. Synthetic aperture radar (SAR) in particular provides the capability for all-weather Earth observation. The Sentinel-1A and Sentinel-1B SAR satellites have ideal ground coverage and imaging frequency for observing Antarctica. This study developed a dataset of 69,586 Sentinel-1 EW mode satellite images of the Antarctic ice sheet from October 2014 to December 2020. The dataset was processed with the European Space Agency Sentinel Application Platform (SNAP) and a Python batch scheduling tool on the Big Earth Data Cloud Service Platform of the Chinese Academy of Sciences Big Earth Data Science Engineering Program (CASEarth). Several data processing operations were implemented to process the raw dataset, including radiometric calibration, invalid edge removal, geocoding, data reprojection to an Antarctic projection, data compression to TIFF format, and construction of image pyramids. The dataset is available at http://www.doi.org/10.11922/sciencedb.j00076.00085.

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Time-series snowmelt detection over the Antarctic using Sentinel-1 SAR images on Google Earth Engine

Cited by 52 publications

References 64 publications

Automated mapping of the seasonal evolution of surface meltwater and its links to climate on the Amery Ice Shelf, Antarctica

Automated mapping of the seasonal evolution of surface meltwater and its links to climate on the Amery Ice Shelf, Antarctica

Innovative approaches to the Sustainable Development Goals using Big Earth Data

Sentinel-1 EW mode dataset for Antarctica from 2014–2020 produced by the CASEarth Cloud Service Platform

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