Spatio-temporal analysis of surface water quality on urban tanks in Coimbatore - Ramachandran, R; Kannan, B; Sivasubramanian, K; Arunadevi, K; Patil, S G (Plant Science Today, 2025)
Effective monitoring and predicting surface water quality are vital for sustainable water resource control. Traditional in-situ techniques are regularly constrained by their time-consuming nature and restrained spatial coverage. This study seeks to develop a predictive version that combines physio-chemical water quality parameters with remote sensing indices derived from the Sentinel-2A dataset to enhance accuracy and spatial attainment. The research focuses on four urban tanks in Coimbatore namely Krishnampathy, Selvampathy, Kumaraswamy and Ukkadam Periyakulam. The physio-chemical parameters for assessing the water quality which include pH, Electrical Conductivity (EC), Total Dissolved Solids (TDS), Dissolved oxygen (DO), Calcium (Ca), Magnesium (Mg), Total hardness, Chloride (Cl-), Carbonate (Co3-) and Bicarbonate (HCo3-) have been measured, additionally for the detection of surface water extent using remote sensing indices namely Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Water Ratio Index, Normalized Difference Chlorophyll Index (NDCI) indices which were extracted from sentinel-2A datasets. The parameters such as EC, TDS, Ca2+, Cl- and Total hardness show a high coefficient of determination (R2). Correlation and regression techniques have been employed to integrate these datasets, resulting in the development of a robust predictive model. By combining these two information sources, the model is constructed using stepwise regression analysis. The model's accuracy turned into established towards ground-truth records, showing large improvements whilst far away Remote sensing indices had been covered.