Spatial-temporal variability analysis of water quality using remote sensing data: A case study of Lake Manyame

Abstract

Worldwide, the quality of freshwater in inland water bodies has been a major issue of concern due to the negative impact of human activities. With the increase in global population, it is projected that the quality of the water resources will deteriorate. Quantitative information on the state of water quality is quite crucial in water resources planning and conservation. Conventional or ground-based measuring tools are more time demanding, expensive for monitoring water quality parameters of inland water bodies, resulting in incomprehensive coverage in time and space. Due to the paucity of images with fine spatial and temporal resolution like Sentinel 2, provides invaluable information at a fine spatial scale for water quality monitoring to supporting progress towards achieving Sustainable Developments Goals (SDGs). This study quantified the spatial and temporal variations of water quality parameters of Total Nitrogen (TN), Turbidity, Chlorophyll-a (Chl-a) and Total Suspended Matter (TSS) derived from cloud free and remotely sensed Sentinel 2 satellite data for a period from 2017 to 2022 for Lake Manyame in Zimbabwe. Furthermore, the research developed empirical models based on the linear regression between in-situ water sample data and water quality indicators of Sentinel 2. The results showed that between 2017 and 2022, the water quality in Lake Manyame significantly fluctuated. The regression coefficients (R²) between remote sensed water quality parameters and field or sample water data ranged from R² = 0.63 to R² = 0.95, providing a promising possibility for operational use of freely available remote sensing data in water quality monitoring in data constrained countries.The study demonstrated the importance and capability of using freely available Sentinel 2 data, with fine spatial and temporal resolution in providing invaluable information and evaluating on the state and indicators of water quality in inland water bodies in space and time. Such information is crucial in informing resource managers and decision makers in conserving water resources.