<p>Some of the sustainable development goals (SDG’s) include attainment of good health and well-being (SDG 3) and the access to safe water and sanitation (SDG 6) by all global human communities by 2030. Although some progress has been made towards realizing these goals, degrading water quality and humans continue to be a problem in low to middle-income countries. This is especially true in informal settlements where provision of both health and water services are limited due to highly contaminated surface water bodies. The surface water bodies in these settlements are often very small thus posing a challenge to the analysis through medium to coarse resolution satellite data. Thus, this study aimed at exploring the capability of high-resolution satellite data for mapping biophysical and chemical parameters for water quality assessment in the informal settlements of South Africa. The four-band PlanetScope (PS) data with 3&#xa0;m spatial resolution and field data were used to map the potential water quality in the selected informal settlements. The correlation analysis (CA), and stepwise multiple linear (including logistic) regression analyses were used to model concentrations of pH, turbidity, total suspended solids (TSS) <i>Escherichia coli</i> (<i>E. coli</i>), total dissolved solids (TDS), total coliforms, salmonella, heavy metals, and chlorophyll-<i>a</i> (chl-<i>a</i>) amongst other indicators. The results have shown that turbidity was the most spectrally correlated variable (<i>r</i> = 0.61) to blue wavelength (0.485 𝜇m) of PS data, while the lowest spectral correlation (<i>r</i> = 0.01) was found between salmonella presence/absence with both blue and red (0.630 𝜇m) bands. The higher concentrations of <i>E. coli</i> were spatially models for Alexandra informal settlement compared to other settlements. The model developed for predicting salmonella yielded the highest correlation (R² = 0.86) while pH model (R² = 0.43) did poorly in predicting the distribution of pH in the informal settlements’ water bodies. The models developed for predicting heavy metals (Fe, Zn, Cu, and Cd) exhibited higher correlation (R² &gt; 0.70) except for the Pb model (R² = 0.41).These results offer a capability promise of high spatial resolution PS data for mapping the biophysical and biochemical parameters of surface water bodies, although in general the correlations obtained were not very strong due to the optically inactive nature of most of the water quality parameters considered in this study.</p>