Annotated time-series of lake ice C-band synthetic aperture radar backscatter created using Sentinel-1, ERS-1/2, and RADARSAT-1 imagery of Old Crow Flats, Yukon, Canada
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| Natura: | Dataset Open Access |
| Lingua: | en |
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PANGAEA
2022
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| _version_ | 1867168217666945024 |
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| author | Shaposhnikova, Maria Duguay, Claude R Roy-Léveillée, Pascale |
| author_facet | Shaposhnikova, Maria Duguay, Claude R Roy-Léveillée, Pascale |
| collection | Datos científicos de ciencias marinas y ambientales |
| contents | The lake ice backscatter time-series dataset was created for the purpose of developing an automated temporal deep learning method of lake ice regime classification and study of lake ice dynamics in the Old Crow Flats (OCF), Yukon, Canada. The dataset consists of approximately 129,000 labeled backscatter time-series collected using imagery from four C-band synthetic aperture radar (SAR) spaceborne platforms: Sentinel-1 A (VV polarization), ERS-1 and 2 (VV polarization), and RADARSAT-1 (HH polarization), which cover the time period between 1992 to 2021. Labeling was done in Sentinel Application Platform (SNAP) by manually placing pins at locations identified as either floating ice, bedfast ice, or land through visual assessment of the ice regime/land on the last day of the time-series for a given season. Due to variable temporal coverage, the dates of labeling ranged from March 4 to March 22. The labeling date was selected as close as possible to mid-March, and care was taken to ensure that the air temperature was below 0°C. Then, the backscatter values at the locations marked by each pin were extracted for each of the scenes in a SAR stack, creating time-series of labeled backscatter values for each year covering the October to mid-March period. Labels were assigned based on three factors: 1) backscatter values, 2) value of the projected incidence angle of the SAR pulse, and 3) location of the pixel within the scene. Resampling to a daily frequency and linear interpolation were applied to compensate for the temporal irregularity of the data gearing it for the deep learning classification. The final labeled time-series consist of 161 time steps (i.e., one time step per day) covering the time period between October 4 and March 13. In addition, lake ice maps (containing three classes: bedfast ice, floating ice, and land) created using the novel temporal deep learning approach developed based on the time-series dataset are provided in PNG and GeoTIFF formats. |
| format | Dataset Open Access |
| id | pangaea_https___doi_org_10_1594_PANGAEA_947789 |
| institution | PANGAEA |
| language | en |
| publishDate | 2022 |
| publisher | PANGAEA |
| record_format | pangaea |
| spellingShingle | Annotated time-series of lake ice C-band synthetic aperture radar backscatter created using Sentinel-1, ERS-1/2, and RADARSAT-1 imagery of Old Crow Flats, Yukon, Canada Shaposhnikova, Maria Duguay, Claude R Roy-Léveillée, Pascale Binary Object; Binary Object (File Size); Binary Object (Media Type); deep learning; File content; floating ice; ice regime; lake ice; OCF; Old Crow Flats; Old Crow Flats, Yukon, Canada; synthetic aperture radar; temporal convolutional neural network The lake ice backscatter time-series dataset was created for the purpose of developing an automated temporal deep learning method of lake ice regime classification and study of lake ice dynamics in the Old Crow Flats (OCF), Yukon, Canada. The dataset consists of approximately 129,000 labeled backscatter time-series collected using imagery from four C-band synthetic aperture radar (SAR) spaceborne platforms: Sentinel-1 A (VV polarization), ERS-1 and 2 (VV polarization), and RADARSAT-1 (HH polarization), which cover the time period between 1992 to 2021. Labeling was done in Sentinel Application Platform (SNAP) by manually placing pins at locations identified as either floating ice, bedfast ice, or land through visual assessment of the ice regime/land on the last day of the time-series for a given season. Due to variable temporal coverage, the dates of labeling ranged from March 4 to March 22. The labeling date was selected as close as possible to mid-March, and care was taken to ensure that the air temperature was below 0°C. Then, the backscatter values at the locations marked by each pin were extracted for each of the scenes in a SAR stack, creating time-series of labeled backscatter values for each year covering the October to mid-March period. Labels were assigned based on three factors: 1) backscatter values, 2) value of the projected incidence angle of the SAR pulse, and 3) location of the pixel within the scene. Resampling to a daily frequency and linear interpolation were applied to compensate for the temporal irregularity of the data gearing it for the deep learning classification. The final labeled time-series consist of 161 time steps (i.e., one time step per day) covering the time period between October 4 and March 13. In addition, lake ice maps (containing three classes: bedfast ice, floating ice, and land) created using the novel temporal deep learning approach developed based on the time-series dataset are provided in PNG and GeoTIFF formats. |
| title | Annotated time-series of lake ice C-band synthetic aperture radar backscatter created using Sentinel-1, ERS-1/2, and RADARSAT-1 imagery of Old Crow Flats, Yukon, Canada |
| topic | Binary Object; Binary Object (File Size); Binary Object (Media Type); deep learning; File content; floating ice; ice regime; lake ice; OCF; Old Crow Flats; Old Crow Flats, Yukon, Canada; synthetic aperture radar; temporal convolutional neural network |
| url | https://doi.org/10.1594/PANGAEA.947789 |