A novel surface water index using local background information for long term and large-scale Landsat images

Abstract

Surface water plays a vital role in natural environment and human development. The research of water extraction method using remote sensing image is a hot topic, which has been widely developed in water index, classification, subpixel, and other aspects. Compared with other methods, a water-index based method has the advantages of fast speed and convenience. The characteristics of surface water, such as wide coverage and instability, make the water index stand out in monitoring large area of surface water. However, land surface in water environment is complex, and the main factors that reduce water extraction accuracy are also different, such as shadow in urban areas and water leakage in unshaded areas. The current index is bound to weaken the information in the water body when suppressing shadows, and vice versa. To address these issues, contrast difference water index (CDWI) and shadow difference water index (SDWI) are proposed in this paper by improving the modified normalized difference water index (MNDWI). CDWI is used to enhance water information, which is suitable for areas without building shadows. SDWI is used to eliminate the shadow of buildings, which is suitable for urban areas. Moreover, background difference water index (BDWI) was proposed by combining the advantages of CDWI and SDWI through a background regularizer B, which is used to extract surface water under complex background. The regularizer B represents the similarity between local background features of the image and the reference urban area, which is used to locally weight SDWI and CDWI, so that BDWI can automatically enhance the water body in the shadowless area and eliminate the shadow of buildings. The water extraction results of BDWI, MNDWI, the tasseled cap wetness index (TCW), the automatic water extraction index (AWEInsh, AWEIsh), and the water index 2015 (WI2015) were used for comparison. Other methods tend to perform well only in built-up areas or non-built-up areas, while the BDWI can extract surface water under various backgrounds with high accuracy and stability. The overall accuracy produced by the BDWI was 91.58–97.57%, CDWI was 84.85–97.09%, SDWI was 81.63–94.40%, MNDWI was 80.19–95.64%, TCW was 82.33–95.98%, AWEIsh was 87.50–96.37%, AWEInsh was 80.59–98.78%, and WI2015 was 78.24–98.38%. Combining water index with image local information is helpful to improve the accuracy of water extraction in large and complex environment. Finally, surface water in Jiangsu Province, China was extracted through BDWI and the changes in 1985, 2000, and 2015 were analyzed.