Abstract
Accurate water mapping depends largely on the water index. However, most previously widely-adopted water index methods are developed from 30-m resolution Landsat imagery, with low-albedo commission error (e.g., shadow misclassified as water) and threshold instability being identified as the primary issues. Besides, since the shortwave-infrared (SWIR) spectral band (band 11) on Sentinel-2 is 20 m spatial resolution, current SWIR-included water index methods usually produce water maps at 20 m resolution instead of the highest 10 m resolution of Sentinel-2 bands, which limits the ability of Sentinel-2 to detect surface water at finer scales. This study aims to develop a water index from Sentinel-2 that improves native resolution and accuracy of water mapping at the same time. Support Vector Machine (SVM) is used to exploit the 10-m spectral bands among Sentinel-2 bands of three resolutions (10-m; 20-m; 60-m). The new Multi-Spectral Water Index (MuWI), consisting of the complete version and the revised version (MuWI-C and MuWI-R), is designed as the combination of normalized differences for threshold stability. The proposed method is assessed on coincident Sentinel-2 and sub-meter images covering a variety of water types. When compared to previous water indexes, results show that both versions of MuWI enable to produce native 10-m resolution water maps with higher classification accuracies (p-value < 0.01). Commission and omission errors are also significantly reduced particularly in terms of shadow and sunglint. Consistent accuracy over complex water mapping scenarios is obtained by MuWI due to high threshold stability. Overall, the proposed MuWI method is applicable to accurate water mapping with improved spatial resolution and accuracy, which possibly facilitates water mapping and its related studies and applications on growing Sentinel-2 images.
Highlights
Sentinel-2 mission, which was launched by European Space Agency (ESA) in 2015, provides an alternative source of globally covered, openly accessible optical remote sensing
Spatial resolution is critical to accurate water mapping, likely to be an important source of accuracy improvements for multi-spectral water index (MuWI)
This study proposed a new automated water index method MuWI with the ability to natively produce 10 m water maps on Sentinel-2 MSI imagery
Summary
Sentinel-2 mission, which was launched by European Space Agency (ESA) in 2015, provides an alternative source of globally covered, openly accessible optical remote sensing. The water detection enables the analysis of human alterations to the environment in a more quantitative and visible way, such as the linkage between river engineering and permanent lake loss in Central Asia, and the coupling of water loss with long-term droughts in the United States [8]. Such applications identify the transition of Earth’s surface as one of the four situations, including land into water, water into land, permanent land, or permanent water [9], which supports studies and assessments on flood inundation, land reclamation, and sea-level rise, in environmental and social hotspots [10]. Land surface water detection significantly contributes to remote sensing studies and applications
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