ORIGINAL RESEARCH article
Front. Remote Sens.
Sec. Terrestrial Water Cycle
Volume 6 - 2025 | doi: 10.3389/frsen.2025.1631403
Characterising the Spatio-temporal Patterns of Water Quality Parameters in the Cradle of Humankind World Heritage Site Using Sentinel-2 and Random Forest Regressor
Provisionally accepted
- 1University of Johannesburg, Johannesburg, South Africa
- 2South African National Space Agency, Pretoria, South Africa
- 3Council for Scientific and Industrial Research, Pretoria, South Africa
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Water quality assessment is essential for monitoring and managing freshwater resources, particularly in ecologically and culturally significant areas like the Cradle of Humankind World Heritage Site (COHWHS). This study aimed to predict and map the spatio-temporal patterns of both optically and non-optically active water quality parameters within small inland water bodies located in the COHWHS using high-resolution Sentinel-2 Multispectral Instrument (MSI) satellite data and two random forest models (Model 1 and Model 2). In-situ measurements of chlorophyll-a, suspended solids, dissolved oxygen (DO), pH, Temperature, and electrical conductivity (EC) were integrated to establish empirical relationships and assess spatial variability across high-flow and low-flow conditions. The results indicated that DO had the highest prediction accuracy under low-flow conditions, followed by EC. Specifically, Model 2 for DO achieved an R² of 0.88 and an RMSE of 1.37, while Model 1 for EC achieved an R² of 0.63 and an RMSE of 291.48. For optically active parameters, suspended solids showed the highest prediction accuracy under high-flow conditions with Model 2 (R² = 0.55; RMSE = 118.19). Due to the over-pixelation of other smaller water bodies within the COHWHS in Sentinel-2 imagery, Cradlemoon Lake was selected to show distinct seasonal (highand low-flow) and spatial variations in optically and non-optically active water quality parameters. These variations were influenced by runoff dynamics and upstream pollution: lower Temperatures and suspended solids under low-flow conditions increased DO concentrations, whereas higher suspended solid concentrations under high-flow conditions resulted in decreased spectral reflectance and chlorophyll-a levels. These findings highlight the potential of Sentinel-2 MSI data and machine learning models for monitoring dynamic water quality variations in freshwater ecosystems.
Keywords: Sentinel-2 MSI, optically active water quality parameters, non-optically active water quality parameters, Spatio-temporal patterns, random forest Chlorophyll-a 0.23 17.05 RGI, B12, B11, B4
Received: 19 May 2025; Accepted: 03 Jul 2025.
Copyright: © 2025 Ngamile, Kganyago, Madonsela and Mvandaba. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Mahlatse Kganyago, University of Johannesburg, Johannesburg, South Africa
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