Manually categorized initial training data for open-water–sea-ice–cloud discrimination

• Main Author: Stephan Paul
• Format: Other/Unknown Material
• Language: English
• Published: Zenodo 2021
• Subjects: MODIS; Sentinel-1; OSCD; sea ice; polynya; clouds; thin ice; Antarc*; Antarctic; Brunt Ice Shelf; Ice Shelf; Sea ice; The Cryosphere; Weddell Sea
• Online Access: https://doi.org/10.5281/zenodo.4596407

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 to18degW; 73degS to77degS]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 referencepublication [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 datafile. 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]