Abstract:
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.
Keywords:
Arctic, MODIS, melt pond, remote sensing
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
| Access Constraints: | No restrictions apply. |
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| Use Constraints: | Data supplied under Open Government Licence v3.0 http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/. |
| Creation Date: | 2023-11-06 |
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| Dataset Progress: | Complete |
| Dataset Language: | English |
| ISO Topic Categories: |
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| Parameters: |
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| Personnel: | |
| Name | UK Polar Data Centre |
| Role(s) | Metadata Author |
| Organisation | British Antarctic Survey |
| Name | Sanggyun Lee |
| Role(s) | Investigator, Technical Contact |
| Organisation | University College London |
| Name | Julienne C Stroeve |
| Role(s) | Investigator |
| Organisation | University College London |
| Parent Dataset: | N/A |
| Reference: | Lee, S., Stroeve, J., Tsamados, M., & Khan, A. L. (2020). Machine learning approaches to retrieve pan-Arctic melt ponds from visible satellite imagery. Remote Sensing of Environment, 247, 111919. https://doi.org/10.1016/j.rse.2020.111919 | |
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| 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. | |
| Lineage: | 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. |
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| Temporal Coverage: | |
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| Start Date | 2021-05-01 |
| End Date | 2022-08-31 |
| Spatial Coverage: | |
| Latitude | |
| Southernmost | 57.8 |
| Northernmost | 90 |
| Longitude | |
| Westernmost | -180 |
| Easternmost | 180 |
| Altitude | |
| Min Altitude | N/A |
| Max Altitude | N/A |
| Depth | |
| Min Depth | N/A |
| Max Depth | N/A |
| Data Resolution: | |
| Latitude Resolution | N/A |
| Longitude Resolution | N/A |
| Horizontal Resolution Range | 1 km - < 10 km or approximately .01 degree - < .09 degree |
| Vertical Resolution | N/A |
| Vertical Resolution Range | N/A |
| Temporal Resolution | N/A |
| Temporal Resolution Range | N/A |
| Location: | |
| Location | Arctic Ocean |
| Detailed Location | N/A |
| Sensor(s): |
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| Source(s): |
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| Data Collection: | Matlab R2019a ENVI 5.5 ArcGIS 10.0 Python 3 |
| Distribution: | |
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| Distribution Media | Online Internet (HTTP) |
| Distribution Size | 4.3 GB |
| Distribution Format | netCDF |
| Fees | N/A |
| Data Storage: | A collection of daily, 8-day, and monthly netCDF files, along with a grid coordinates netCDF file. |