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.

地表水在自然环境和人类发展中起着至关重要的作用。遥感图像水提取方法的研究是一个热门课题，在水指标，分类，亚像素等方面已经得到了广泛的发展。与其他方法相比，基于水指数的方法具有快速，方便的优点。地表水的特性（如覆盖范围广，不稳定）使水指数在监测大面积地表水方面脱颖而出。但是，水环境中的地表复杂，影响水提取精度的主要因素也有所不同，例如城市地区的阴影和无阴影地区的漏水。当抑制阴影时，当前指数势必削弱水体中的信息，反之亦然。为了解决这些问题，通过改进修正的归一化差异水指数（MNDWI），提出了对比度差异水指数（CDWI）和阴影差异水指数（SDWI）。CDWI用于增强水信息，适用于没有建筑物阴影的区域。SDWI用于消除建筑物的阴影，适用于城市地区。此外，通过背景正则化器B结合CDWI和SDWI的优点，提出了背景差异水指数（BDWI），用于在复杂背景下提取地表水。正则化器B表示图像的局部背景特征与参考市区之间的相似度，用于局部加权SDWI和CDWI，以便BDWI可以自动增强无阴影区域中的水体并消除建筑物的阴影。比较BDWI，MNDWI，抽穗帽湿度指数（TCW），自动抽水指数（AWEInsh，AWEIsh）和水指数2015（WI2015）的水提取结果进行比较。其他方法往往仅在建筑区域或非建筑区域表现良好，而BDWI可以在各种背景下以高精度和高稳定性提取地表水。BDWI产生的总体准确性为91.58–97.57％，CDWI为84.85–97.09％，SDWI为81.63–94.40％，MNDWI为80.19–95.64％，TCW为82.33–95.98％，AWEIsh为87.50–96.37％，AWEInsh为80.59–98.78％，而WI2015为78.24–98.38％。将水指数与图像局部信息相结合有助于提高大型复杂环境中水的提取精度。最后，通过BDWI和1985年的变化，提取了江苏省的地表水，