The Caucasus is an important global diversity hotspot and hosts a wide variety of surface water features, including major transboundary wetlands, in addition to large areas with irrigated agriculture and newly developed fishponds. In this study, we aim to establish the best performing methodology to produce surface water maps with a high degree of accuracy in the Caucasus. We evaluate optical data from Landsat 8 in both the dry and wet season for three study areas in the Caucasus. We test the performance of four different optical water indices derived from Landsat data, a method by Zou et al. (2017) also applied to Landsat data, and the European Commission Joint Research Centre (ECJRC) Global Surface Water dataset. We evaluate the performance of each water index using 5744 land cover validation/training points over all three study areas, which we manually classified by evaluating imagery from Google Earth. Using all validation points from all three study areas and both the wet and dry season, we find that the application of a logistic regression model using an optical surface water index (MNDWI) resulted in the most accurate open surface water maps. This approach achieved an overall accuracy of 93.0%, which is better than was found for freely available global surface water products.

高加索地区是全球重要的生物多样性热点，拥有广阔的地表水特征，包括主要的跨界湿地，以及灌溉农业和新开发鱼塘的大面积地区。在这项研究中，我们旨在建立性能最好的方法，以在高加索地区生成高精度的地表水图。我们评估了高加索地区三个研究区在旱季和湿季的Landsat 8光学数据。我们测试了由Landsat数据得出的四种不同光学水指数的性能，这是Zou等人的方法。（2017年）也适用于Landsat数据和欧盟委员会联合研究中心（ECJRC）的全球地表水数据集。我们在所有三个研究区域中使用5744个土地覆被验证/培训点来评估每个水指标的性能，我们通过评估Google Earth的图像对其进行了手动分类。使用来自所有三个研究区域以及湿季和干季的所有验证点，我们发现使用光学地表水指数（MNDWI）的逻辑回归模型的应用产生了最准确的开放地表水图。这种方法的总体精度为93.0％，比可免费获得的全球地表水产品的精度更高。