Over the past few years, water quality has been threatened and is vulnerable to various pollutants and climate variables. The deteriorating state of water resources/bodies has been further exacerbated by the impacts of climate change patterns in Southern Africa. Therefore, modelling and predicting the quality of water in sub-basins has become important in controlling water pollution. Remote sensing techniques gained popularity over the past few years as these techniques have been used to monitor water quality parameters such as suspended sediments, chlorophyll, temperature and other parameters in surface water bodies. Furthermore, optical and thermal sensors on aircrafts and satellites provide both spatial and temporal information needed to monitor changes in water quality parameters, for the development of management practices which seek to improve the quality of water, at sub-basin level. Thus, the integration of remotely sensed data, geographical information system (GIS), machine learning technologies and in-situ measurements provide valuable tools to monitor the impacts of climate change on water quality. According to literature cited in this paper, measurements and collection of water samples for subsequent laboratory analyses are currently used to evaluate water quality, not only in the South African context but in other developing countries as well. While such measurements are accurate for a point in time and space, they do not give either the spatial or temporal view of water quality needed for accurate assessments and management of water bodies. Hence, the need for and purpose of this study, to explore and review current methodologies and algorithms used to identify microbial and other pollutants that have increased above standard thresholds in sub-basins.
Key words: Water quality, remote sensing, modeling, algorithms, pollutants.
DO, Dissolved oxygen; ETM, enhanced thematic mapper; SDG, sustainable development goal; UTM, universal transverse mercator; AVHRR, advanced very high-resolution radiometer.
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