Abstract
Remote sensing was used as a potential solution for monitoring the surface water quality parameters as an alternative to the traditional in situ measurements which are time consuming and labour-intensive. While most of the studies are restricted in just analysing the optical water quality parameters, only few studies have attempted the estimation of non-optical water quality parameters. In this paper, a correlation was developed between various optical and non-optical water quality parameters, thereby establishing an indirect relationship between non-optical parameters and reflectance data based on which the predictive models were developed. The water body chosen for this present study is the Ukkadam Lake situated in Coimbatore, Tamilnadu, India (10.9917° North, 76.9722° East). The correlation between reflectance data obtained from Sentinel-2 and Unmanned aerial vehicle images along with in situ measured data were analysed using stepwise regression method. Algorithms were developed for assessing the water quality parameters like turbidity, Total suspended solids, Total organic carbon, Chemical oxygen demand, Biological oxygen demand and Dissolved oxygen that were based on Sentinel-2 with high coefficient of determination (R2). Unmanned aerial vehicle-based Stepwise regression models were employed for assessing Total suspended solids, Total organic carbon and Chemical oxygen demand. The developed models were validated with 25% of sample data acquired, and the algorithms showed that multispectral data from Sentinel-2 and RGB data from Unmanned aerial vehicles can be effectively used to estimate the concentration of various water quality parameters with reasonable accuracy in case of large water bodies, including the one chosen for this study.
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The authors would like to thank the authorities of Coimbatore Municipal Corporation for their support in the fieldwork activities and data collection.
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Rahul, T.S., Brema, J. & Wessley, G.J.J. Evaluation of surface water quality of Ukkadam lake in Coimbatore using UAV and Sentinel-2 multispectral data. Int. J. Environ. Sci. Technol. 20, 3205–3220 (2023). https://doi.org/10.1007/s13762-022-04029-7
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DOI: https://doi.org/10.1007/s13762-022-04029-7