Abstract
The continuous availability of spatial and temporal distributed data from satellite sensors provides more accurate and timely information regarding surface water quality parameters. Remote sensing data has the potential to serve as an alternative to traditional on-site measurements, which can be resource-intensive due to the time and labor involved. This present study aims in exploring the possibility and comparison of hyperspectral and multispectral imageries (PRISMA) for accurate prediction of surface water quality parameters. Muthupet estuary, situated on the south side of the Cauvery River delta on the Bay of Bengal, is selected as the study area. The remote sensing data is acquired from the PRISMA hyperspectral satellite and the Sentinel-2 multispectral instrument (MSI) satellite. The in situ sampling from the study area is performed, and the testing procedures are carried out for analyzing different water quality parameters. The correlations between the water sample results and the reflectance values of satellites are analyzed to generate appropriate algorithmic models. The study utilized data from both the PRISMA and Sentinel satellites to develop models for assessing water quality parameters such as total dissolved solids, chlorophyll, pH, and chlorides. The developed models demonstrated strong correlations with R2 values above 0.80 in the validation phase. PRISMA-based models for pH and chlorophyll displayed higher accuracy levels than Sentinel-based models with R2 > 0.90.
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The authors are willing to share data and material according to the relevant needs. This project was carried out using PRISMA products, © of the Italian Space Agency (ASI), delivered under an ASI License to use.
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All authors (Rahul T. S. and J. Brema) equally contributed to problem formulation, literature review, research, and paper drafting. Both authors read and approved the final version of the manuscript.
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Rahul, T.S., Brema, J. Assessment of water quality parameters in Muthupet estuary using hyperspectral PRISMA satellite and multispectral images. Environ Monit Assess 195, 880 (2023). https://doi.org/10.1007/s10661-023-11497-y
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DOI: https://doi.org/10.1007/s10661-023-11497-y