Surface water quality is essential for ecological stability and mortal health, but it faces growing pitfalls from urbanization, industrialization, and husbandry. Traditional in-situ monitoring styles are essential yet limited in their spatial and temporal compass. This paper aims to provide a comparative analysis of different techniques available for surface water quality analysis. We have analysed studies grounded on freely available satellite data from Landsat, Sentinel-2, and MERIS to determine crucial water quality parameters similar to chlorophyll- at attention, turbidity, and dangerous algal blooms. The review demonstrates the effectiveness of various methods to use spectral imaging to predict parameters such as BOD, chlorophyll content in water. Further to this multi-sensor data integration within the pall calculating platform Google Earth Engine aids in dynamic water quality assessments. Results indicate these technologies indeed give scalable low-cost observers of submarine ecosystems and implicit means of filling gaps between in-situ measures and comprehensive water resource operation. The study identifies implicit in the integration of a Civilians approach grounded on remote seeing in climate modelling, monitoring of ecosystem health, and sustainable water governance.