Background: Water is undoubtedly a very precious resource that helps life thrive on this
planet, and its pollution is a problem, which is potent in erasing almost all forms of life on the earth.
Hence, considering the magnitude of this problem, studies and experiments have been focused (from
the day water pollution was recognised as an eminent issue) on monitoring and finding possible solutions
for water pollution. While the latter deals with treating and purifying water using a variety of
concepts, monitoring deals with the continuous assessment of water quality of a waterbody.
Methodology: There are several methods for monitoring purposes, and remote sensing is a popular
choice, thanks to its wide applicability and flexibility in implementation. Remote sensing deals with
collecting data about a place (which is to be monitored) and sending the data to another ‘remote’ location
for analysis. This article provides a description of some methods employed in recent times for
remote sensing and a short section which deals with the analysis of the remotely sensed data using
machine learning / deep learning models, hence making the reader aware of the concept of remote
sensing and its scope for monitoring water pollution (or any form of pollution) in the future.
Conclusion: The detailed comparative analysis of these methods showed that sensor-based water
quality monitoring with Geographical Information System (GIS) would be more efficient for the detection
of water pollutants. Further research in this field may introduce many advancements to enable
efficient water pollution detection techniques.