Characterising the spatio-temporal patterns of water quality parameters in the cradle of humankind world heritage site using Sentinel-2 and random forest regressor

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Introduction 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. Methods High-resolution Sentinel-2 Multispectral Instrument (MSI) satellite data and two random forest models (Model 1 [consisting of sensitive spectral bands] and Model 2 [consisting of spectral bands + indices]) were used alongside 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. Results The results indicated that DO could be predicted with the highest accuracy under low-flow conditions, followed by EC. Specifically, Model 2 achieved an R2 of 0.88 and an RMSE of 1.37 for DO, while Model 1 achieved an R2 of 0.63 and an RMSE of 291.48 for EC. For optically active parameters, suspended solids showed the highest prediction accuracy under high-flow conditions using Model 2 (R2p = 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 (high- and low-flow) and spatial variations in optically and non-optically active water quality parameters. Discussion Variations in the results 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 likely reduced light penetration, resulting in lower 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.

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