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Water Body Extraction from Sentinel-3 Image with Multiscale Spatiotemporal Super-Resolution Mapping
Water ( IF 3.0 ) Pub Date : 2020-09-17 , DOI: 10.3390/w12092605 Xiaohong Yang , Yue Li , Yu Wei , Zhanlong Chen , Peng Xie
Water ( IF 3.0 ) Pub Date : 2020-09-17 , DOI: 10.3390/w12092605 Xiaohong Yang , Yue Li , Yu Wei , Zhanlong Chen , Peng Xie
Water body mapping is significant for water resource management. In the view of 21 spectral bands and a short revisit time of no more than two days, a Sentinel-3 OLCI (Ocean and Land Colour Instrument) image could be the optimum data source in the near-real-time mapping of water bodies. However, the image is often limited by its low spatial resolution in practice. Super-resolution mapping (SRM) is a good solution to generate finer spatial resolution maps than the input data allows. In this paper, a multiscale spatiotemporal super-resolution mapping (MSST_SRM) method for water bodies is proposed, particularly for Sentinel-3 OLCI images. The proposed MSST_SRM method employs the integrated Normalized Difference Water Index (NDWI) images calculated from four near-infrared (NIR) bands and Green Band 6 of the Sentinel-3 OLCI image as input data and combined the spectral, multispatial, and temporal terms into one objective function to generate a fine water body map. Two experiments in the Tibet Plate and Daye lakes were employed to test the effectiveness of the MSST_SRM method. Results revealed that by using multiscale spatial dependence under the framework of spatiotemporal super-resolution Mapping, MSST_SRM could generate finer water body maps than the hard classification method and the other three SRM-based methods. Therefore, the proposed MSST_SRM method shows marked efficiency and potential in water body mapping using Sentinel-3 OLCI images.
更新日期：2020-09-17
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