Journal of Remote Sensing & GIS

Application of Remote Sensing in Surface Water Quality Assessment

Harry Williams^* Department of Geographical Sciences, University of Southampton, Southampton, United Kingdom
^*Correspondence: Harry Williams, Department of Geographical Sciences, University of Southampton, Southampton, United Kingdom, Email:

Received: 05-Sep-2025, Manuscript No. JGRS-25-29362; Editor assigned: 08-Sep-2025, Pre QC No. JGRS-25-29362(PQ); Reviewed: 23-Sep-2025, QC No. JGRS-25-29362; Revised: 30-Sep-2025, Manuscript No. JGRS-25-29362(R); Published: 07-Oct-2025, DOI: 10.35248/2469-4134.24.14.389

Description

Surface water bodies such as rivers, lakes and reservoirs are vital for drinking water supply, agriculture, aquaculture, recreation and ecosystem support. Their quality is influenced by a variety of factors including land use, urbanization, industrial discharge, agricultural runoff and climate variability. Traditional methods for water quality assessment involve periodic field sampling and laboratory testing, which, although accurate, can be timeconsuming, labor-intensive and limited in spatial coverage. Remote sensing provides a complementary approach that enables broader and more frequent monitoring of water quality parameters across large geographic areas.

Satellites equipped with optical sensors detect light reflected from the Earth's surface in different wavelengths. These measurements are particularly sensitive to the physical and biological characteristics of water bodies. Parameters such as turbidity, chlorophyll concentration, total suspended solids, colored dissolved organic matter and surface temperature influence the spectral reflectance of water. By analyzing changes in reflectance values in visible and near-infrared bands, remote sensing allows estimation of these water quality indicators.

Turbidity and total suspended solids are among the most commonly assessed parameters using satellite imagery. Increased turbidity often results from sediment runoff due to rainfall, deforestation, construction, or erosion. It can also indicate industrial or sewage discharge. High turbidity reduces light penetration in water, affecting aquatic life and plant growth. Sensors such as those on Landsat, Sentinel-2 and MODIS satellites provide time-series data that help track sediment dynamics over time, especially after storm events or human disturbances.

Chlorophyll-a concentration is a key indicator of phytoplankton biomass and is used to monitor algal blooms. Excessive nutrient input, especially nitrogen and phosphorus from fertilizers or wastewater, can lead to eutrophication and harmful algal blooms. These blooms deplete oxygen, kill fish and pose risks to human health. Chlorophyll absorbs red and blue wavelengths and reflects green, which can be detected by satellite sensors. Algorithms have been developed to estimate chlorophyll-a concentration from these spectral signatures. Monitoring seasonal and spatial variation in chlorophyll levels helps water managers identify pollution sources and assess ecosystem health.

Surface water temperature, another important parameter, can also be monitored using thermal infrared sensors. Changes in water temperature affect oxygen solubility, chemical reactions and species distribution. Remote sensing of thermal emissions allows detection of heat pollution from industrial discharges, climate-related temperature changes and stratification in lakes. For example, the Landsat series includes a thermal band that has been widely used to monitor lake surface temperatures around the world.

Color variations in water bodies, which result from suspended sediments, algae and dissolved matter, are easily captured in satellite imagery. These color changes are used as proxies to assess general water quality status. The Normalized Difference Water Index (NDWI) and Floating Algae Index (FAI) are examples of spectral indices derived from remote sensing to monitor water extent and floating vegetation or algal matter.

Remote sensing also enables the delineation of water boundaries and estimation of surface area. This is important for water balance studies, flood monitoring and reservoir management. During droughts or dry seasons, shrinking water bodies can be identified early, allowing for proactive water conservation efforts. Similarly, sudden changes in water extent may indicate flooding, dam failure, or illegal sand mining.

One of the strengths of remote sensing is its ability to detect spatial patterns and changes that would be missed by point-based field sampling. Satellite imagery provides full spatial coverage, allowing the identification of pollution hotspots, upstreamdownstream gradients and influence zones of tributaries or industrial outfalls. Time-series analysis reveals trends and seasonal variations, supporting better understanding of the drivers behind water quality changes.

Integration of remote sensing data with GIS and field observations enhances the accuracy and interpretation of results. Ground-based data is used to calibrate and validate remote sensing-derived estimates. Machine learning and statistical models can combine satellite reflectance with field measurements to improve parameter estimation. Furthermore, combining water quality data with land use, population density, or climate variables allows for a comprehensive assessment of environmental pressures.

There are limitations to remote sensing in water quality monitoring. Optical sensors are less effective in small, narrow, or heavily shaded water bodies. Atmospheric interference, cloud cover and sun glint can distort measurements. Accuracy varies depending on water depth, substrate type and presence of mixed constituents. Therefore, remote sensing is best used in combination with in-situ measurements and modeling for robust assessment.

With the increasing availability of high-resolution and frequent satellite data, platforms such as Sentinel-2 and PlanetScope are improving the temporal and spatial granularity of monitoring. Open-access tools and cloud computing environments, such as Google Earth Engine, make it easier for researchers and water managers to access and process large volumes of imagery. Automated alert systems based on satellite data are now being used to detect sudden changes in water quality and support early response.

In conclusion, remote sensing offers a valuable and efficient method for assessing and monitoring surface water quality. By capturing spatial and temporal patterns of turbidity, chlorophyll, temperature and other indicators, satellite imagery enhances our ability to understand and respond to water quality issues. It supports decision-making for water resource management, pollution control, ecosystem conservation and public health protection. As technology advances and data become more accessible, remote sensing will play an increasingly important role in ensuring the sustainable management of freshwater ecosystems.