Hydrological monitoring networks do not adequately cover non-perennial rivers in most parts of Africa partly because river flows do not frequently occur on these rivers. However, in the arid and semi-arid regions flows occurring on these rivers are an important resource that have to be managed judiciously. The lack of data on magnitudes, timing and frequency of non-perennial river flows constrain sustainable management of their water resources. The availability of moderate resolution satellite data (e.g. Sentinel-2 Multispectral Instrument and Landsat 8 Operational Land Imager) offers an opportunity to monitor the surface water availability specifically changes in surface areas covered with water in pools along these rivers. In this study, we investigated the use of remote sensing data to detect and monitor changes of water surface areas of pools along three non-perennial rivers and one perennial river in the Western Cape, South Africa, from 2016 to 2017, using Landsat 8 and Sentinel-2 datasets. We assessed a range of image pre-processing and image classification techniques to determine the most suitable method for surface water identification and pool area estimation along the selected rivers. The following indices were used; Normalised Difference Water Index (NDWI), Modified NDWI (MNDWI), Normalised Difference Vegetation Index (NDVI), Automated Water Extraction Index for shadowed (AWEI_sh), non-shadowed regions (AWEI_nsh), and the Multi-Band Water Index (MBWI). The NDWI computed from Sentinel-2 TOA reflectance datasets was found to be the most suitable index for mapping pools, with an overall accuracy ranging between 60 and 86%. This index was used successfully to establish the variations of surface areas of pools covered with water along the Breede, Nuwejaars, Tankwa and Touws Rivers during the 2016–2017 period. The temporal variations of pool areas could be explained as expected by changes of rainfall and potential evapotranspiration. Overall, the study demonstrated the feasibility of using moderate resolution datasets to monitor pool changes of areas covered with water in non-perennial rivers, which contributes to improved management of water resources particularly in arid and semi-arid environments.

水文监测网络无法充分覆盖非洲大部分地区的非常年河流，部分原因是这些河流上的河流流量不经常出现。但是，在干旱和半干旱地区，这些河流上发生的水流是重要的资源，必须谨慎管理。缺乏非常年河流流量的大小，时间和频率的数据限制了对其水资源的可持续管理。中分辨率卫星数据（例如Sentinel-2多光谱仪和Landsat 8 Operational Land Imager）的可用性为监测地表水的可用性提供了机会，特别是这些河流池中被水覆盖的表层区域的变化。在这个研究中，我们使用Landsat 8和Sentinel-2数据集，研究了2016年至2017年使用遥感数据检测和监测南非西开普省的3条非常年河流和1条常年河流沿河池水表面积变化的情况。 。我们评估了一系列图像预处理和图像分类技术，以确定沿选定河流进行地表水识别和池面积估计的最合适方法。使用以下索引；归一化差异水指数（NDWI），修改后的NDWI（MNDWI），归一化植被指数（NDVI），遮荫下的自动水提取指数（AWEI 我们评估了一系列图像预处理和图像分类技术，以确定沿选定河流进行地表水识别和池面积估计的最合适方法。使用以下索引；归一化差异水指数（NDWI），修改后的NDWI（MNDWI），归一化植被指数（NDVI），遮荫下的自动水提取指数（AWEI 我们评估了一系列图像预处理和图像分类技术，以确定最合适的方法，用于沿选定河流进行地表水识别和池面积估算。使用以下索引；归一化差异水指数（NDWI），修改后的NDWI（MNDWI），归一化植被指数（NDVI），遮荫下的自动水提取指数（AWEI_sh），非阴影区域（AWEI _nsh）和多波段水指数（MBWI）。从Sentinel-2 TOA反射率数据集计算出的NDWI被认为是最适合映射池的指标，总体准确度在60％到86％之间。该指数已成功用于确定2016–2017年期间沿Breede，Nuwejaars，Tankwa和Touws河的水覆盖池的表面积变化。可以通过降雨和潜在蒸散量的变化来解释池区的时间变化。总体而言，研究表明使用中等分辨率的数据集来监测非常年河流中被水覆盖的区域的池变化的可行性，这有助于改善水资源管理，特别是在干旱和半干旱环境中。