Inland Water Body Mapping Using Multitemporal Sentinel-1 SAR Data
Climate change studies require increasingly detailed information on land cover and land use, to precisely model and predict climate based on their status and changes. A fundamental land cover type that needs to be constantly monitored by the climate change community is water, but currently there is...
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2021
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oai:doaj.org-article:f55757aa890b4c45874b748bb046fc452021-12-03T00:00:12ZInland Water Body Mapping Using Multitemporal Sentinel-1 SAR Data2151-153510.1109/JSTARS.2021.3127748https://doaj.org/article/f55757aa890b4c45874b748bb046fc452021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9613744/https://doaj.org/toc/2151-1535Climate change studies require increasingly detailed information on land cover and land use, to precisely model and predict climate based on their status and changes. A fundamental land cover type that needs to be constantly monitored by the climate change community is water, but currently there is a lack of high-resolution water body maps at the global scale. In this article, we present a fully automated procedure for the extraction of fine spatial resolution (10 m) inland water land cover maps for any region of the Earth by means of a relatively simple <italic>k</italic>-means clustering model applied to multitemporal features extracted from Sentinel-1 SAR sequences. Indeed, due to heavy cloud coverage conditions in many locations, multispectral sensors are not suitable for global water body mapping. For this reason, in this work, we deal only with SAR data, and specifically with multitemporal Sentinel-1 data sequences. The experimental results, obtained for three geographical areas selected because of their wide diversity in terms of geomorphology and climate, show an almost complete consistency with existing datasets, and improve them thanks to their finer spatial details.David MarziPaolo GambaIEEEarticleClimate change<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">k</italic>-meansSentinel-1synthetic aperture radar (SAR)time series analysiswater mappingOcean engineeringTC1501-1800Geophysics. Cosmic physicsQC801-809ENIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 11789-11799 (2021) |
| institution |
DOAJ |
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DOAJ |
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EN |
| topic |
Climate change <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">k</italic>-means Sentinel-1 synthetic aperture radar (SAR) time series analysis water mapping Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809 |
| spellingShingle |
Climate change <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">k</italic>-means Sentinel-1 synthetic aperture radar (SAR) time series analysis water mapping Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809 David Marzi Paolo Gamba Inland Water Body Mapping Using Multitemporal Sentinel-1 SAR Data |
| description |
Climate change studies require increasingly detailed information on land cover and land use, to precisely model and predict climate based on their status and changes. A fundamental land cover type that needs to be constantly monitored by the climate change community is water, but currently there is a lack of high-resolution water body maps at the global scale. In this article, we present a fully automated procedure for the extraction of fine spatial resolution (10 m) inland water land cover maps for any region of the Earth by means of a relatively simple <italic>k</italic>-means clustering model applied to multitemporal features extracted from Sentinel-1 SAR sequences. Indeed, due to heavy cloud coverage conditions in many locations, multispectral sensors are not suitable for global water body mapping. For this reason, in this work, we deal only with SAR data, and specifically with multitemporal Sentinel-1 data sequences. The experimental results, obtained for three geographical areas selected because of their wide diversity in terms of geomorphology and climate, show an almost complete consistency with existing datasets, and improve them thanks to their finer spatial details. |
| format |
article |
| author |
David Marzi Paolo Gamba |
| author_facet |
David Marzi Paolo Gamba |
| author_sort |
David Marzi |
| title |
Inland Water Body Mapping Using Multitemporal Sentinel-1 SAR Data |
| title_short |
Inland Water Body Mapping Using Multitemporal Sentinel-1 SAR Data |
| title_full |
Inland Water Body Mapping Using Multitemporal Sentinel-1 SAR Data |
| title_fullStr |
Inland Water Body Mapping Using Multitemporal Sentinel-1 SAR Data |
| title_full_unstemmed |
Inland Water Body Mapping Using Multitemporal Sentinel-1 SAR Data |
| title_sort |
inland water body mapping using multitemporal sentinel-1 sar data |
| publisher |
IEEE |
| publishDate |
2021 |
| url |
https://doaj.org/article/f55757aa890b4c45874b748bb046fc45 |
| work_keys_str_mv |
AT davidmarzi inlandwaterbodymappingusingmultitemporalsentinel1sardata AT paologamba inlandwaterbodymappingusingmultitemporalsentinel1sardata |
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1718374025815654400 |