Surface water plays a fundamental role in supporting life and the functioning of various ecosystems. In this regard, its availability and variability influence the functioning of various ecosystems and most importantly human livelihoods. This becomes critical in drought prone and water-scarce areas of sub-Saharan Africa. The use of satellite remote sensing is increasingly becoming the primary data source for water resources monitoring. However, with emerging and advances in satellite remote sensing data, their ability in monitoring water resources remain uncertain. In addition, the remote sensing of surface water mapping has been limited to specific periods, which undermines the influence of seasonality on their spatial variability. This study therefore assessed the potential of Landsat-8 Operational Land Imager and Sentinel-2 Multi-Spectral Instrument derived Normalised Difference Water Index (NDWI), Modified Normalised Difference Water Index Land Surface Water Index (LSWI+5) and Modified Normalised Difference Index (MNDVI), for monitoring seasonal surface waterbodies in the Western Cape, South Africa. The derived remote sensing estimates were assessed using the Producers', User's, Overall accuracies as well the Kappa coefficient. The spatial variations were also linked to monthly evapotranspiration, rainfall and temperature. These climatic factors all contribute to seasonal surface water availability. Further, the satellite matrixes derived estimates were compared using the analysis of variance (ANOVA), to test whether there were any significant differences (α = 0.05) in detection ability. The results indicated that both sensors managed to detect surface water variations surface waterbodies varied spatially over waterbodies over time. During the wet season, more surface waterbodies were detected than during the dry season. The majority of waterbodies have been detected by both sensors within the south western part of the study area and all the indices showed similar sizes and presence of seasonal surface waterbodies. Normalized Difference Water Index (NDWI) performed the best and yielded the highest accuracy, however, the Land Surface Water Index VI (LSWI+5) results overestimated the size and occurrence of surface waterbodies, when compared to other indices, especially during the wet season. The derived waterbodies followed a similar pattern with the observed climate data and evapotranspiration rates during the study period. Landsat 8 derived Normalised Difference Water Index (NDWI) performed slightly better when compared to other indices, with 92.33% and a Kappa coefficient of 84.67% in detecting and mapping surface waterbodies. On the other hand, Sentinel-2 produced lower classification estimates. Statistical analysis results demonstrated significant differences (α = 0.05) in the detection and mapping ability of different satellite-derived indices. Overall, this work demonstrates the potential of using Landsat 8 and Sentinel 2 data in understanding spatial variations in surface water.

地表水在支持生命和各种生态系统的功能中起着基本作用。在这方面，其可用性和可变性影响各种生态系统的功能，最重要的是影响人类生计。这在撒哈拉以南非洲易干旱和缺水的地区变得至关重要。卫星遥感的使用越来越成为水资源监测的主要数据来源。但是，随着卫星遥感数据的兴起和发展，其监测水资源的能力仍然不确定。此外，地表水制图的遥感仅限于特定时期，这破坏了季节性对其空间变异性的影响。因此，本研究评估了Landsat-8实用陆地成像仪和Sentinel-2多光谱仪得出的归一化水差指数（NDWI），修正后的归一化水指数地面水指数（LSWI + 5）和修正的归一化差水指数（ MNDVI），用于监测南非西开普省的季节性地表水体。使用生产者，用户，总体精度以及Kappa系数评估得出的遥感估计。空间变化还与每月的蒸散量，降雨和温度有关。这些气候因素都有助于季节性地表水的供应。此外，使用方差分析（ANOVA）对卫星矩阵得出的估计值进行比较，以测试是否存在任何显着差异（α= 0。05）在检测能力上。结果表明，两个传感器都能够检测地表水的变化。地表水体随时间的推移在水体上的空间变化也很大。在雨季，与旱季相比，发现了更多的地表水体。研究区域西南部的两个传感器都检测到了大多数水体，所有指标均显示出相似的大小和季节性地表水体的存在。归一化差异水指数（NDWI）表现最好且获得最高的准确性，但是与其他指数相比，土地地表水指数VI（LSWI + 5）结果高估了地表水体的大小和发生，尤其是在雨季。在研究期间，所观测到的气候数据和蒸散速率遵循相似的模式。与其他指数相比，Landsat 8衍生的归一化差异水指数（NDWI）的性能稍好一些，在检测和测绘地表水体中具有92.33％的Kappa系数和84.67％。另一方面，Sentinel-2产生的分类估计较低。统计分析结果表明，不同卫星衍生指标的检测和制图能力存在显着差异（α= 0.05）。总的来说，这项工作证明了使用Landsat 8和Sentinel 2数据来了解地表水的空间变化的潜力。在检测和测绘地表水体中，其33％的Kappa系数为84.67％。另一方面，Sentinel-2产生的分类估计较低。统计分析结果表明，不同卫星衍生指标的检测和制图能力存在显着差异（α= 0.05）。总的来说，这项工作证明了使用Landsat 8和Sentinel 2数据来了解地表水的空间变化的潜力。在检测和测绘地表水体中，其33％的Kappa系数为84.67％。另一方面，Sentinel-2产生的分类估计较低。统计分析结果表明，不同卫星衍生指标的检测和制图能力存在显着差异（α= 0.05）。总的来说，这项工作证明了使用Landsat 8和Sentinel 2数据来了解地表水的空间变化的潜力。