Priyadarshini, R.Sudhakara, B.Kamath S․, S.Bhattacharjee, S.Umesh, P.Gangadharan, K.V.2026-02-062023Lecture Notes in Networks and Systems, 2023, Vol.586 LNNS, , p. 577-58923673370https://doi.org/10.1007/978-981-19-7867-8_46https://idr.nitk.ac.in/handle/123456789/29541In the past few years, the problem of growing salinity in river estuaries has directly impacted living and health conditions, as well as agricultural activities globally, especially for those rivers which are the sources of daily water consumption for the surrounding community. Key contributing factors include hazardous industrial wastes, residential and urban wastewater, fish hatchery, hospital sewage, and high tidal levels. Conventional survey and sampling-based approaches for water quality assessment are often difficult to undertake on a large-scale basis and are also labor and cost-intensive. On the other hand, remote sensing-based techniques can be a good alternative to cost-prohibitive traditional practices. In this article, an attempt is made to comprehensively assess various approaches, datasets, and models for determining water salinity using remote sensing-based approaches and in situ observations. Our work revealed that remote sensing techniques coupled with other techniques for estimating the salinity of water offer a clear advantage over traditional practices and also is very cost-effective. We also highlight several observations and gaps that can be beneficial for the research community to contribute further in this significant research domain. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Data analyticsMachine learningRegressionRemote sensingWater salinity