Abstract
Surface water bodies, such as rivers, lakes, and reservoirs, play an irreplaceable role in global ecosystems and climate systems. Sentinel-2 imagery provides new high-resolution satellite remote sensing data. Based on the analysis of the spectral characteristics of the Sentinel-2 satellite, a novel water index called the Sentinel-2 water index (SWI) that is based on the vegetation-sensitive red-edge band (Band 5) and shortwave infrared (Band 11) bands was developed. Four representative water body types, namely, Taihu Lake, Yangtze River, Chaka Salt Lake, and Chain Lake, were selected as study areas to conduct a water body extraction performance comparison with the normalized difference water index (NDWI). We found that (1) the contrast value of the SWI was larger than that of the NDWI in terms of various water body types, including purer water, turbid water, salt water, and floating ice, which suggested that the SWI could achieve better enhancement performance for water bodies. (2) An effective water body extraction method was proposed by integrating the SWI and Otsu algorithm, which could accurately extract various water body types with high overall accuracy. (3) The method effectively extracted large water bodies and wide river channels by suppressing shadow noise in urban areas. Our results suggested that the novel method can achieve efficient water body extraction for rapidly and accurately extracting various water bodies from Sentinel-2 data and the novel method has application potential for larger-scale surface water mapping.
Highlights
The flow contains three steps: (1) 200 random water sample points and 200 random nonwater sample points are generated for each study area; (2) high-resolution images are used as the reference data to verify and adjust the random sample points and the ground reference points are generated; (3) ground reference points are superimposed on the water extraction results to verify the accuracy of each random sample point; (4) four widely used accuracy evaluation indices, including producer accuracy, user accuracy, overall accuracy, and Kappa coefficient, are employed to quantitatively assess the water body extraction accuracy
The enhanced water bodies utilizing the normalized difference water index (NDWI) and Sentinel-2 water index (SWI) are shown in Figure 4b,c, respectively
In order to analyze the accuracy of the NDWI and SWI, the contrast value derived from the mean values of NDWI or SWI is calculated to evaluate the separation between water bodies and nonwater bodies
Summary
In the referenced paper [15], Xu proposed the modified normalized difference water index (MNDWI) based on Landsat Thematic Mapper/Enhanced Thematic Mapper Plus (TM/ETM+) data, which can overcome the shortcomings of NDWI and reveal characteristic information, such as the water environment. In the referenced paper [25], the slope and NIR band are combined to construct the normalized difference vegetation index (NDVI) and NDWI and the index is applied to HuanJing (HJ)-1A/B satellite images for water extraction; the result is consistent with the MNDWI results based on the Landsat TM data. For Gaofen-1 satellite remote sensing imagery, the NDWI and modified two-mode method were combined to extract surface water and the results suggested that the proposed method has higher and stable mapping accuracy [27]. This novel water index can assist the understanding of the dynamic changes in surface waters by utilizing large-scale surface water mapping
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