834dc665-1ae6-464d-8c29-265a9be5229a eng utf8 dataset dataset NERC EDS UK Polar Data Centre +44 (0)1223 221400 British Antarctic Survey, High Cross, Madingley Road Cambridge Cambridgeshire CB3 0ET United Kingdom polardatacentre@bas.ac.uk https://www.bas.ac.uk/team/business-teams/information-services/uk-polar-data-centre/ information pointOfContact 2023-11-06 ISO 19115 Geographic Information - Metadata ISO 19115:2003(E) European Petroleum Survey Group (EPSG) Geodetic Parameter Registry 2008-11-12 publication European Petroleum Survey Group EPSGadministrator@iogp.org https://www.epsg-registry.org/ EPSG Geodetic Parameter Dataset The EPSG Geodetic Parameter Dataset is a structured dataset of Coordinate Reference Systems and Coordinate Transformations, accessible through this online registry. information publisher urn:ogc:def:crs:EPSG::3031 6.18.3 Arctic Melt Pond Fraction and Binary Classification, 2021-2022 2023-11-06 creation 2023-11-06 revision 2023-11-06 publication 2023-11-06 released 1.0 https://doi.org/10.5285/834dc665-1ae6-464d-8c29-265a9be5229a doi GB/NERC/BAS/PDC/01786 https://data.bas.ac.uk/ NE/R017123/1 award Please cite this item as: Lee, S., & Stroeve, J. (2023). Arctic Melt Pond Fraction and Binary Classification, 2021-2022 (Version 1.0) [Data set]. NERC EDS UK Polar Data Centre. https://doi.org/10.5285/834dc665-1ae6-464d-8c29-265a9be5229a This dataset provides daily, 8-day, and monthly Arctic melt pond fractions and binary classification, from 2021-05-01 to 2022-08-31. Level-2 MODerate resolution Imaging Spectroradiometer (MODIS) top-of-the-atmosphere (TOA) reflectances for bands 1-4 were obtained, to which two machine learning algorithms such as multi-layer neural networks and logistic regression were applied to map melt pond fraction and binary melt pond/ice classification. This work was funded by NERC standard grant NE/R017123/1. No credit. completed Lee, Sanggyun University College London Researcher author Stroeve, Julienne University College London Researcher author NERC EDS UK Polar Data Centre +44 (0)1223 221400 British Antarctic Survey, High Cross, Madingley Road Cambridge Cambridgeshire CB3 0ET United Kingdom PDCServiceDesk@bas.ac.uk https://www.bas.ac.uk/team/business-teams/information-services/uk-polar-data-centre/ information pointOfContact asNeeded completed EARTH SCIENCE > Cryosphere > Sea Ice > Ice Growth/Melt EARTH SCIENCE > Oceans > Sea Ice > Ice Growth/Melt EARTH SCIENCE > Spectral/Engineering > Infrared Wavelengths > Infrared Imagery EARTH SCIENCE > Spectral/Engineering > Visible Wavelengths > Visible Imagery EARTH SCIENCE > Cryosphere > Sea Ice EARTH SCIENCE > Oceans > Sea Ice theme Global Change Master Directory (GCMD) Science Keywords 2026-01-27 publication 23 Global Change Data Center, Science and Exploration Directorate, Goddard Space Flight Center (GSFC) National Aeronautics and Space Administration (NASA) Greenbelt MD United States of America https://earthdata.nasa.gov/about/gcmd/global-change-master-directory-gcmd-keywords Global Change Master Directory (GCMD) Keywords The information provided on this page seeks to define how the GCMD Keywords are structured, used and accessed. It also provides information on how users can participate in the further development of the keywords. information publisher Arctic MODIS melt pond remote sensing theme Arctic Ocean place Oceanographic geographical features theme GEMET - INSPIRE themes, version 1.0 2021-01-06 revision otherRestrictions no limitations to public access otherRestrictions no limitations license Open Government Licence v3.0 otherRestrictions Data supplied under Open Government Licence v3.0 otherRestrictions No restrictions apply. doi doi crossReference doi doi crossReference url url crossReference textTable eng utf8 imageryBaseMapsEarthCover oceans -180.0 180.0 57.8 90.0 2021-05-01 2022-08-31 It is recommended that careful attention be paid to the contents of any data, and that the author be contacted with any questions regarding appropriate use. If you find any errors or omissions, please report them to polardatacentre@bas.ac.uk. NERC EDS UK Polar Data Centre +44 (0)1223 221400 British Antarctic Survey, High Cross, Madingley Road Cambridge Cambridgeshire CB3 0ET United Kingdom PDCServiceDesk@bas.ac.uk https://www.bas.ac.uk/team/business-teams/information-services/uk-polar-data-centre/ information distributor application/x-hdf application/netcdf bytes 4617089843 http://ramadda.data.bas.ac.uk/repository/entry/show?entryid=834dc665-1ae6-464d-8c29-265a9be5229a WWW:LINK-1.0-http--link Get Data Download data download bytes 4617089843 http://ramadda.data.bas.ac.uk/repository/entry/show?entryid=834dc665-1ae6-464d-8c29-265a9be5229a WWW:LINK-1.0-http--link Get Data Download data download dataset Methodology: Level-2 MODIS top-of-the-atmosphere (TOA) reflectances for bands 1-5 were used for the melt pond fraction and binary classification. Additionally, MODIS bands 5, 13, 16 and 19 were used to remove cloud shadows. The MOD35 data product was also used for cloud masking and MOD29 ice surface temperature product was used to flag refrozen melt ponds. Two machine learning algorithms were applied to the TOA band reflectances to map melt pond fraction and binary melt pond/ice/ocean classification. These included a Multi-Neural Network (MNN) and Multinomial Logistic Regression (MLR). Results were validated against high-resolution WorldView imagery, ship observations and other high resolution unclassified spy satellite data. Data collection: Matlab R2019a ENVI 5.5 ArcGIS 10.0 Python 3 Data quality: The accuracy assessment for melt pond binary classification and fraction is further evaluated against WV imagery, showing mean overall accuracy (85.5%), average mean difference (0.09), and mean RMSE (0.18). In addition to cross-validation with WV, retrieved melt pond data are validated against melt pond fractions from satellite and ship-based observations, showing mean correlation coefficients, root-mean-square-error (RMSE) and mean differences of 0.41, 0.12, and 0.05, respectively. asNeeded completed