Water index formulation and its effectiveness research on the complicated surface water surroundings

Abstract

Surface water includes irreplaceable and nonrenewable resources for terrestrial life. However, the rapid urbanization is causing diverse changes in size, amount, and quality of surface water. Accurately extracting surface water from remote sensing images is important for water environmental conservation and water resource management. This study aims to formulate a Multi-Band Water Index (MBWI) that consistently improves surface water extraction accuracy in the presence of various environmental noises. A new MBWI is designed to improve the accuracy of surface water extraction by increasing spectral separability between water and non-water surfaces. The method uses the average reflectance of the pure pixel of the seven land cover types, namely, surface water, forest, mountainous shadow, high-reflectance building, low-reflectance building, farmland, and bare soil, obtained from a Landsat 7 image of the Qinhuai River basin acquired on October 12, 2015. In general, the common threshold method is a popular approach to obtaining the results of surface water extraction from remote sensing imagery. However, determining the optimal threshold is an iterative, complicated, and challenging process. The K-means clustering method is applied to automatically extract surface water to avoid the artificial errors in determining the optimum threshold from the MBWI map. Surface water mapping outputs derived from water index methods that are based on K-means cluster are used to analyze the extraction accuracy of water indexes under complicated land cover types. To validate the availability of MBWI, twelve existing water indexes are collected from 1985 to 2016 as the comparable methods of surface water extraction. Furthermore, six test sites with various impact aspects for extracting surface water, e.g., mountainous shadows, high building shadows, and dark built-up areas that are usually sources of surface water extraction errors, are selected from three images (one Landsat 7 image and two Landsat 8 images) from Nanjing, Nanning, and Yantai. Compared with the existing water indexes, our proposed MBWI yields acceptable surface water mapping outputs. The assessment factors, namely, average overall accuracy (98.62%), Kappa coefficients (0.95), commission errors (3.46%), and omission errors (3.74%), are better than those of the existing surface water extraction methods. Results show that certain water indexes are weak in identifying surface water from land cover types. The Tasseled Cap Wetness (TCW) index is not effective in eliminating mountainous shadows. TCW and Automated Water Extraction Index with no shadow (AWEInsh) inaccurately identify white high-reflectance building noises with surface water. The accuracy of surface water extraction is usually constrained by land cover types that display similar reflectance to surface water. Therefore, low-reflectance non-water surfaces exist more or less in surface water mapping outputs. The maximum reflectance of surface water is presented in visible light bands, whereas that of the non-water surfaces is in infrared bands. Moreover, surface water reveals similarly decreasing trends from green to infrared bands. A new water index, named MBWI, is formulated according to the band difference of water and non-water surfaces. This difference has important practical significance for water resource studies and applications.