Manually categorized initial training data for open-water–sea-ice–cloud discrimination
This data set contains labeled training data for supervised classification of open-water/thin-ice, sea-ice, and cloud pixels from MODIS thermal-infrared satellite data and is created from manual categorization of dimensional reduced and unsupervised clustered co-located Sentinel-1 SAR and MODIS MOD0...
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| Format: | Dataset |
| Language: | English |
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Zenodo
2021
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| Online Access: | https://dx.doi.org/10.5281/zenodo.4596406 https://zenodo.org/record/4596406 |
| Summary: | This data set contains labeled training data for supervised classification of open-water/thin-ice, sea-ice, and cloud pixels from MODIS thermal-infrared satellite data and is created from manual categorization of dimensional reduced and unsupervised clustered co-located Sentinel-1 SAR and MODIS MOD021KM/MYD021KM swaths. All data originates from the Brunt Ice Shelf area in the Antarctic Southeastern Weddell Sea [34degW to 18degW; 73degS to 77degS] resampled to an equi-rectangular grid [445 (rows) x 460 (columns)]. The data is organized as tab-delimited tables per Sentinel-1 reference swath with geolocation (lon/lat) and the compiled predictors for different MODIS swath combinations. This data can be used to retrace the classifier training as described in the reference publication [DOI: https://doi.org/10.5194/tc-15-1551-2021] or used as a basis to create your own classification scheme. Additional information can be found in the provided meta data file. : Improved machine-learning-based open-water–sea-ice–cloud discrimination over wintertime Antarctic sea ice using MODIS thermal-infrared imagery [https://doi.org/10.5194/tc-15-1551-2021] |
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