In arid regions, pans form a critical water source that supports local communities, livestock, and wildlife, with readily available water resources. However, these waterbodies are unevenly distributed across the landscape and highly ephemeral and this influences their ability to provide water, especially in remote and water scarce areas. In addition, it remains difficult, impractical, and costly to monitor their variability using traditional hydrometric networks. In this regard, their contribution as water sources remains uncertain. The availability of spatially explicit data, with improved sensor design characteristics such as Landsat-8 enables the monitoring of their surface water extent over space and time. This study therefore, for the first time investigated the potential of using Landsat-8 in detecting and monitoring the spatial and temporal dynamics of pan inundation in the water scarce region of Kgalagadi in Southern Africa between 2016 and 2018. This was achieved by testing the performance of multiple indices, namely; the Normalised Difference Water Index (NDWI), Modified Normalised Difference Water Index (MNDWI), Automated Water Extraction Index for Shadow (AWEIsh), Water Ratio Index (WRI) and Land Surface Water Index (LSWI). Overall, the results have shown the potential of remote sensing data to monitor pan inundation. The MNDWI produced the highest overall classification accuracy of 84.91% comparatively. The MNDWI was then used for monitoring and assessing pan inundation dynamics over different seasons between 2016 and 2018. During the study period, pan inundation varied significantly (α = 0.05) for different seasons. Nevertheless, 2017 had the largest surface water extent covering 23 195.8 m², during the wet season and 17 913.3 m² in the dry season. On the other hand, 2018 showed the smallest spatial coverage of 13 076 m² for the wet season and 6032.587 m² during the dry season. The observed spatial variability in pan inundation was attributed to rainfall and temperature variability. The study thus revealed the utility of remotely sensed data sets in providing a more robust approach for monitoring seasonal and inter-annual pan inundation variations in semi-arid environments of Southern Africa. This information is important for water management decision making, specifically for water-limited areas, to conserve these water sources to ensure their sustainability in supporting local communities, livestock and wildlife population.

在干旱地区，平底锅形成了至关重要的水源，可为当地社区，牲畜和野生生物提供可用的水资源。但是，这些水体在整个景观中分布不均且高度短暂，这影响了它们的供水能力，尤其是在偏远和缺水地区。另外，使用传统的水文测量网络来监测它们的可变性仍然困难，不切实际且成本高昂。在这方面，它们作为水的贡献仍然不确定。空间明确数据的可用性以及改进的传感器设计特性（例如Landsat-8）可以监视其随时间和空间变化的地表水范围。因此，这项研究 首次调查了在2016年至2018年之间，使用Landsat-8探测和监测南部非洲Kgalagadi缺水地区泛泛的时空动态的潜力。这是通过测试多个指标的性能来实现的，即 归一化差异水指数（NDWI），修改后的归一化差异水指数（MNDWI），阴影自动提取水指数（AWEIsh），水比指数（WRI）和地表水指数（LSWI）。总的来说，结果表明了遥感数据监测泛滥的潜力。MNDWI产生的最高总分类准确度为84.91％。然后，MNDWI被用于监测和评估2016年至2018年不同季节的泛滥泛滥动态。在研究期间，潘泛滥在不同季节有显着差异（α= 0.05）。尽管如此，2017年的最大地表水覆盖面积为23 195.8 m²在雨季，而17 913.3 m ²在旱季。在另一方面，2018显示了13076米最小空间覆盖²的潮湿季节和6032.587米²在旱季。在泛滥中观测到的空间变异性归因于降雨和温度变异性。因此，这项研究揭示了遥感数据集的实用性，它为监测南部非洲半干旱环境中的季节和年际泛滥泛滥变化提供了更为可靠的方法。这些信息对于水资源管理决策，特别是对于水资源有限的地区，对于保护这些水源以确保其在支持当地社区，牲畜和野生动植物种群方面的可持续性至关重要。