Mapping surface water distribution and its dynamics over various environments with robust methods is essential for managing water resources and supporting water-related policy design. Thresholding Single Water Index image (TSWI) with threshold is a common way of using water index (WI) for mapping water for it is easy to use and could obtain acceptable accuracies in many applications. As more and more WIs are available and each has its distinct merits, the real-world application of TSWI, however, often face two practical concerns: (1) selection of an appropriate WI and (2) determination of an appropriate threshold for a given WI. These two issues are problematic for many users who rely either on trial-and-error procedures that are time-consuming or on their personal preferences that are somewhat subjective. To better deal with these two practical concerns, an alternative way of using WIs is suggested here by transforming the current paradigm into a simple but robust ensemble approach called Collaborative Decision-making with Water Indices (CDWI). A total of 145 subsite images (900 × 900 m) from 22 Landsat-8 OLI scenes that covering various water-land environments around the world were used to assess the performance of TSWI and the CDWI. Five benchmark WIs were adopted in five TSWI methods and CDWI method: Normalized Difference Water Index (NDWI), the Modified NDWI (MNDWI), the Automated Water Extraction Indices without considering (AWEI0) and with considering (AWEI1) shadows, and the state-of-the-art 2015 water index (WI2015). Two aspects of performance were analyzed: comparing their accuracies (indicated by both F1-scores and Youden’s Index) over various environments and comparing their accuracy sensitivities to threshold. The results demonstrate that CDWI produced higher accuracies than the other five TSWI methods for most application cases. Particularly, more cases (indicated by percentage) produced higher F1-scores by CDWI than the other five TSWI methods, i.e. 67% (CDWI) vs. 15% (TSWI_NDWI), 54% (CDWI) vs. 22% (TSWI_MNDWI), 42% (CDWI) vs. 12% (TSWI_AWEI0), 57% (CDWI) vs. 17% (TSWI_AWEI1), and 34% (CDWI) vs. 12% (TSWI_WI2015). Moreover, the F1-score of the CDWI is less sensitive to the change of thresholds compared with that of the five TSWI methods. These important benefits of CDWI make it a robust approach for mapping water. The uncertainty of CDWI method was thoroughly discussed and a general guidance (or look-up-table) for determining parameters of CDWI method was also suggested. The underlying framework of CDWI could be readily generalizable and applicable to other satellite images, such as Landsat TM/ETM+, MODIS, and Sentinel-2 images.

使用健壮的方法绘制地表水分布及其在各种环境中的动态图对于管理水资源和支持与水有关的政策设计至关重要。具有阈值的阈值单一水指数图像（TSWI）是使用水指数（WI）绘制水图的常用方法，因为它易于使用并且可以在许多应用中获得可接受的精度。随着可用的WI越来越多，每种都有各自的优点，TSWI在现实世界中的应用常常面临两个实际问题：（1）选择合适的WI和（2）确定给定给定的合适阈值威斯康星州。这两个问题对于许多用户来说都是有问题的，他们要么依赖耗时的反复试验程序，要么依赖于一些主观的个人喜好。为了更好地处理这两个实际问题，在这里提出了一种使用WI的替代方法，即将当前的范式转换为一种简单但强大的集成方法，称为“水指标协作决策”（CDWI）。来自22个Landsat-8 OLI场景的总共145个子站点图像（900×900 m）用于评估TSWI和CDWI的性能，这些场景涵盖了世界范围内的各种水陆环境。在五个TSWI方法和CDWI方法中采用了五个基准WI：归一化差水指数（NDWI），修正NDWI（MNDWI），不考虑（AWEI0）和考虑（AWEI1）阴影的自动水提取指数，以及最新的2015年水指数（WI2015）。分析了两个方面的性能：比较它们在各种环境下的准确性（由F1分数和Youden指数表示），并将其准确性灵敏度与阈值进行比较。结果表明，对于大多数应用案例，CDWI产生的准确性高于其他五种TSWI方法。特别是，与其他五种TSWI方法相比，CDWI产生的F1得分更高（以百分比表示），即67％（CDWI）对15％（TSWI）_NDWI），54％（CDWI）与22％（TSWI _MNDWI），42％（CDWI）与12％（TSWI _AWEI0），57％（CDWI）对17％（TSWI _AWEI1），和34％（CDWI ）和12％（TSWI _WI2015）。而且，与五种TSWI方法相比，CDWI的F1分数对阈值的变化不那么敏感。CDWI的这些重要优点使其成为绘制水图的可靠方法。全面讨论了CDWI方法的不确定性，并提出了确定CDWI方法参数的一般指南（或查询表）。CDWI的基础框架可以很容易地推广并适用于其他卫星图像，例如Landsat TM / ETM +，MODIS和Sentinel-2图像。