Abdulqader Mahdi Salh

The combination of remote sensing through satellite imagery and artificial intelligence (AI) represents a modern and efficient method for analyzing water quality in rivers and lakes. Satellites such as Sentinel-2, Landsat 8/9, and MODIS provide multispectral and hyperspectral data that can be used to assess key water parameters, including turbidity, chlorophyll-a concentration, suspended solids, phytoplankton levels, and surface water temperature. These data are further analyzed using machine learning and deep learning algorithms, which recognize patterns and changes in aquatic ecosystems, enabling early detection of pollution and prediction of future conditions. The application of AI significantly increases the accuracy and efficiency of processing large volumes of satellite data, with the potential for automating monitoring systems and developing early warning tools. However, despite numerous advantages, it is important to emphasize that reliable interpretation of results requires calibration with in-situ measurements, as well as correction for atmospheric disturbances that may affect image quality. This interdisciplinary integration offers a sustainable approach to the management and protection of water resources, especially under the pressure of accelerated climate change and anthropogenic activities.

Remote sensing measures water quality by detecting the way water bodies reflect and absorb electromagnetic radiation at different wavelengths. Satellite sensors capture multispectral (Sentinel-2, Landsat 8/9) or hyperspectral imagery (MODIS), which provides information on the optical properties of water that are influenced by its physical, chemical, and biological constituents. For example, clear water absorbs most near-infrared radiation, while suspended particles and dissolved organic matter increase reflectance in the visible and near-infrared regions. Similarly, chlorophyll-a, a proxy for algal biomass, shows distinct absorption in the blue and red bands and strong reflectance in the green and red-edge regions. Thermal infrared sensors, on the other hand, measure emitted radiation to estimate surface water temperature. By analyzing these spectral signatures, remote sensing allows indirect but reliable estimation of water quality parameters over large spatial scales and at frequent intervals.

A wide range of water quality parameters can be derived from satellite observations. These include turbidity and total suspended solids (TSS), which are linked to changes in water reflectance; chlorophyll-a concentration, an indicator of eutrophication and algal blooms; dissolved organic matter, which is detectable in the blue and ultraviolet spectrum; and water transparency, often expressed as Secchi disk depth, inferred from optical clarity. Thermal sensors (TIRS, MODIS) provide estimates of surface water temperature, which is critical for assessing aquatic ecosystem health. In addition, advanced hyperspectral sensors (PRISMA, Hyperion) can capture subtle spectral features that allow for the detection of colored dissolved organic matter (CDOM), harmful algal blooms, and even proxies for nutrient levels such as nitrogen and phosphorus when combined with supporting datasets.

The integration of satellite imagery with artificial intelligence (AI) significantly enhances the capacity to study and determine water quality in rivers and lakes. While traditional bio-optical models rely on empirical or semi-analytical relationships, AI techniques such as machine learning and deep learning can capture complex, nonlinear interactions between spectral signatures and in-situ water quality measurements. Supervised learning algorithms like random forests, gradient boosting, and support vector machines are effective in predicting quantitative water quality metrics, while deep learning models such as convolutional neural networks (CNNs) leverage spatial and spectral patterns within imagery for improved accuracy. Furthermore, temporal models like long short-term memory (LSTM) networks can track seasonal or inter-annual changes in water quality. Here are a few real-world case studies :-

1. Lake Victoria, East Africa (Chlorophyll-a and Algal Blooms):

Sentinel-2 multispectral imagery has been used to estimate chlorophyll-a concentration and detect harmful algal blooms. Machine learning algorithms, particularly Random Forest regression, have been applied to spectral band ratios and indices to predict chlorophyll-a with high accuracy, supporting fisheries and water quality management in the region.

2. Ganga River, India (Turbidity and Suspended Sediments):

MODIS and Landsat data have been used to monitor turbidity and total suspended solids (TSS) in the Ganga. AI models trained with in-situ data improved predictions of sediment concentrations, helping track seasonal variations due to monsoon flows and anthropogenic pollution inputs.

Very nicely explained by Mr. Ugrinov. I have to stress here that there is no substitute for field testing and it is necessary to calibrate the results obtained from remote sensing by the actual field and lab test results.