USAP-DC

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{"@context": "https://schema.org/", "@type": "Dataset", "@id": "doi:10.15784/601842", "additionalType": ["geolink:Dataset", "vivo:Dataset"], "name": "Surface melt-related multi-source remote-sensing and climate model data over Larsen C Ice Shelf, Antarctica for segmentation and machine learning applications", "description": "This dataset contains high-resolution satellite-derived snow/ice surface melt-related data on a common 100 m equal area grid (Lambert azimuthal equal area projection; EPSG 9820) over Larsen C Ice Shelf and surrounding areas in Antarctica. The data is prepared to be used as part of a machine learning framework that aims to fill data gaps in computed meltwater fraction on the 100 m grid using a range of methods, results of which will be published separately.\r\n


The data include fraction of a grid cell covered by meltwater derived from Sentinel-1 synthetic aperture radar (SAR) backscatter, satellite-derived passive microwave (PMW) brightness temperatures, snowpack liquid water content within the first meter of snow and atmospheric and radiative variables from the Mod\u00e9le Atmosph\u00e9rique R\u00e8gional (MAR) regional climate model, a static digital elevation model (DEM), and an ice sheet mask. \r\n


A similar dataset has been produced for Helheim Glacier, Greenland and is also available through the US Antarctic Program Data Center.", "citation": "Alexander, P., Antwerpen, R., Cervone, G., Fettweis, X., L\u00fctjens, B., & Tedesco, M. (2024) \"Surface melt-related multi-source remote-sensing and climate model data over Larsen C Ice Shelf, Antarctica for segmentation and machine learning applications\" U.S. Antarctic Program (USAP) Data Center. doi: https://doi.org/10.15784/601842.", "datePublished": "2024-10-04", "keywords": ["Antarctica", "Climate Modeling", "Cryosphere", "Downscaling", "Glaciers/ice Sheet", "Ice Shelf", "Larsen C Ice Shelf", "Machine Learning", "MAR", "Remote Sensing", "Sea Level Rise", "Snow/ice", "Surface Melt"], "creator": [{"@type": "Person", "additionalType": "geolink:Person", "name": "Alexander, Patrick", "email": "pma2107@ldeo.columbia.edu", "affiliation": {"@type": "Organization", "name": null}}, {"@type": "Person", "additionalType": "geolink:Person", "name": "Antwerpen, Raphael", "email": null, "affiliation": {"@type": "Organization", "name": null}}, {"@type": "Person", "additionalType": "geolink:Person", "name": "Cervone, Guido", "email": null, "affiliation": {"@type": "Organization", "name": null}}, {"@type": "Person", "additionalType": "geolink:Person", "name": "Fettweis, Xavier", "email": null, "affiliation": {"@type": "Organization", "name": null}}, {"@type": "Person", "additionalType": "geolink:Person", "name": "L\u00fctjens, Bj\u00f6rn", "email": null, "affiliation": {"@type": "Organization", "name": null}}, {"@type": "Person", "additionalType": "geolink:Person", "name": "Tedesco, Marco", "email": "cryocity@gmail.com", "affiliation": {"@type": "Organization", "name": null}}], "distribution": [{"@type": "DataDownload", "additionalType": "http://www.w3.org/ns/dcat#DataCatalog", "encodingFormat": "text/xml", "name": "ISO Metadata Document", "url": "https://www.usap-dc.org/metadata/isoxml/601842iso.xml", "contentUrl": "/dataset/filename"}, {"@type": "DataDownload", "@id": "http://dx.doi.org/10.15784/601842", "additionalType": "dcat:distribution", "url": "http://dx.doi.org/10.15784/601842", "contentUrl": "/dataset/filename", "encodingFormat": "text/html"}, {"@type": "DataDownload", "@id": "https://www.usap-dc.org/view/dataset/601842", "additionalType": "dcat:distribution", "url": "https://www.usap-dc.org/view/dataset/601842", "contentUrl": "/dataset/filename", "encodingFormat": "text/html"}], "identifier": {"@type": "PropertyValue", "propertyID": "https://registry.identifiers.org/registry/doi", "value": "doi:10.15784/601842", "url": "https://doi.org/10.15784/601842"}, "contributor": [{"@type": "Role", "roleName": "credit", "description": "funderName:NSF:GEO:OPP:Polar Cyberinfrastructure awardNumber:2136938 awardTitle:Collaborative Research: EAGER: Generation of High Resolution Surface Melting Maps over Antarctica Using Regional Climate Models, Remote Sensing and Machine Learning"}], "license": [{"@type": "CreativeWork", "URL": "https://creativecommons.org/licenses/by/4.0/", "name": "MD_Constraints", "description": "useLimitation: Creative Commons Attribution Only v4.0 Generic [CC BY 4.0]."}, {"@type": "CreativeWork", "name": "MD_LegalConstraints", "description": "accessConstraints: license. otherConstraints: Creative Commons Attribution Only v4.0 Generic [CC BY 4.0]."}, {"@type": "CreativeWork", "name": "MD_SecurityConstraints", "description": "classification: "}], "publisher": {"@type": "Organization", "name": "U.S. Antarctic Program (USAP) Data Center"}, "spatialCoverage": [{"@type": "Place", "geo": {"@type": "GeoShape", "box": "-69.27, -68.5, -65.25, -57"}}]}

Surface melt-related multi-source remote-sensing and climate model data over Larsen C Ice Shelf, Antarctica for segmentation and machine learning applications Data DOI: https://doi.org/10.15784/601842 Cite as Alexander, P., Antwerpen, R., Cervone, G., Fettweis, X., Lütjens, B., & Tedesco, M. (2024) "Surface melt-related multi-source remote-sensing and climate model data over Larsen C Ice Shelf, Antarctica for segmentation and machine learning applications" U.S. Antarctic Program (USAP) Data Center. doi: https://doi.org/10.15784/601842. Abstract This dataset contains high-resolution satellite-derived snow/ice surface melt-related data on a common 100 m equal area grid (Lambert azimuthal equal area projection; EPSG 9820) over Larsen C Ice Shelf and surrounding areas in Antarctica. The data is prepared to be used as part of a machine learning framework that aims to fill data gaps in computed meltwater fraction on the 100 m grid using a range of methods, results of which will be published separately. The data include fraction of a grid cell covered by meltwater derived from Sentinel-1 synthetic aperture radar (SAR) backscatter, satellite-derived passive microwave (PMW) brightness temperatures, snowpack liquid water content within the first meter of snow and atmospheric and radiative variables from the Modéle Atmosphérique Règional (MAR) regional climate model, a static digital elevation model (DEM), and an ice sheet mask. A similar dataset has been produced for Helheim Glacier, Greenland and is also available through the US Antarctic Program Data Center. Creator(s): Alexander, Patrick; Antwerpen, Raphael; Cervone, Guido; Fettweis, Xavier; Lütjens, Björn; Tedesco, Marco Date Created: 2024-10-07 Repository: USAP-DC (current) License: Creative Commons Attribution Only v4.0 Generic [CC BY 4.0] Spatial Extent(s) West: -68.5, East: -57, South: -69.27, North: -65.25 Temporal Extent(s) Start: 2016-01-01 - End: 2022-03-31 Award(s) NSF 2136938 Version: 1 Related Project(s) Collaborative Research: EAGER: Generation of high resolution surface melting maps over Antarctica using regional climate models, remote sensing and machine learning Keywords Antarctica Climate Modeling Cryosphere Downscaling Glaciers/ice Sheet Ice Shelf Larsen C Ice Shelf Machine Learning MAR Remote Sensing Sea Level Rise Snow/ice Surface Melt