NERC Data Catalogue Service
Arctic Melt Pond Fraction and Binary Classification, 2021-2022
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.
Simple
- Date (Creation)
- 2023-11-06
- Date (Revision)
- 2023-11-06
- Date (Publication)
- 2023-11-06
- Date (released)
- 2023-11-06
- Edition
- 1.0
- Unique resource identifier
- https://doi.org/10.5285/834dc665-1ae6-464d-8c29-265a9be5229a
- Codespace
- doi
- Unique resource identifier
- GB/NERC/BAS/PDC/01786
- Codespace
- https://data.bas.ac.uk/
- Unique resource identifier
- NE/R017123/1
- Codespace
- award
- Other citation details
- 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
- Credit
- No credit.
- Status
- completed Completed
https://www.bas.ac.uk/team/business-teams/information-services/uk-polar-data-centre/
- Maintenance and update frequency
- asNeeded As needed
- Maintenance note
- completed Completed
- Global Change Master Directory (GCMD) Science Keywords
-
- 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
-
- Arctic
- MODIS
- melt pond
- remote sensing
- Place
-
- Arctic Ocean
- GEMET - INSPIRE themes, version 1.0
- Access constraints
- otherRestrictions Other restrictions
- Other constraints
- no limitations to public access
- Access constraints
- otherRestrictions Other restrictions
- Other constraints
- no limitations
- Use constraints
- license License
- Other constraints
- Open Government Licence v3.0
- Use constraints
- otherRestrictions Other restrictions
- Other constraints
- Data supplied under Open Government Licence v3.0
- Use constraints
- otherRestrictions Other restrictions
- Other constraints
- No restrictions apply.
- Unique resource identifier
- doi
- Codespace
- doi
- Association Type
- crossReference Cross reference
- Unique resource identifier
- doi
- Codespace
- doi
- Association Type
- crossReference Cross reference
- Unique resource identifier
- url
- Codespace
- url
- Association Type
- crossReference Cross reference
- Spatial representation type
- textTable Text, table
- Metadata language
- engEnglish
- Character set
- utf8 UTF8
- Topic category
-
- Imagery base maps earth cover
- Oceans
))
- Begin date
- 2021-05-01
- End date
- 2022-08-31
- Supplemental Information
- 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.
Distributor
https://www.bas.ac.uk/team/business-teams/information-services/uk-polar-data-centre/
- Name
- application/x-hdf
- Name
- application/netcdf
- Units of distribution
- bytes
- Transfer size
- 4617089843
- OnLine resource
-
Get Data
(
WWW:LINK-1.0-http--link
)
Download data
- Units of distribution
- bytes
- Transfer size
- 4617089843
- OnLine resource
-
Get Data
(
WWW:LINK-1.0-http--link
)
Download data
- Hierarchy level
- dataset Dataset
- Statement
-
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.
- File identifier
- 834dc665-1ae6-464d-8c29-265a9be5229a XML
- Metadata language
- engEnglish
- Character set
- utf8 UTF8
- Hierarchy level
- dataset Dataset
- Hierarchy level name
- dataset
- Date stamp
- 2023-11-06
- Metadata standard name
- ISO 19115 Geographic Information - Metadata
- Metadata standard version
- ISO 19115:2003(E)
https://www.bas.ac.uk/team/business-teams/information-services/uk-polar-data-centre/
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