TITLE:
Mapping Water Quality Using Remote Sensing Technology: A Case Study of Lake Victoria
AUTHORS:
Gideon Wafula Simiyu, Josphat Mwatelah
KEYWORDS:
Water Quality, Water Quality Index (WQI), Satellite Imagery, Water Indices, Lake Victoria
JOURNAL NAME:
Journal of Geoscience and Environment Protection,
Vol.13 No.8,
August
25,
2025
ABSTRACT: Lake Victoria, the largest freshwater lake in Africa and a vital resource for over 45 million people across Kenya, Uganda, and Tanzania, is experiencing escalating environmental degradation due to agricultural runoff, untreated sewage, industrial effluents, and solid waste. These pollutants are driving eutrophication, biodiversity loss, and a rise in waterborne diseases, posing serious ecological and public health threats. This study utilized satellite-based remote sensing and geospatial analytics to assess water quality changes in Lake Victoria over a 20-year period. Cloud-free imagery from the Landsat Collection 2 Tier 1 Surface Reflectance dataset—specifically Landsat 7 ETM+ (2005), Landsat 5 TM (2010), Landsat 8 OLI/TIRS (2013, 2018), and Landsat 9 OLI-2/TIRS-2 (2023)—was analyzed using Google Earth Engine to calculate key spectral indices: Normalized Difference Water Index (NDWI), Chlorophyll Index (CI), Turbidity Index (TI), and Normalized Difference Chlorophyll Index (NDCI). These indices served as proxies for chlorophyll-a, turbidity, and suspended sediment load. Water Quality Index (WQI) values were derived through Python-based scripts, weighted by parameter importance, and classified into four categories: Good, Moderate, Unhealthy, and Very Unhealthy. Results revealed a clear decline in water quality across the lake, particularly near urban centers such as Kisumu, Bukoba, and Entebbe. Notably, 2013 showed an extreme reduction in WQI values, ranging from −8.66 to −330.64, indicating significant pollution levels. The 2023 imagery continued this trend, with WQI values ranging from +69.79 to −130.17, reflecting very high pollution concentrations, especially in eutrophic zones and sediment-laden estuarine regions. The study demonstrates the effectiveness of remote sensing and Python-driven spatial analytics as a scalable, cost-efficient alternative to traditional water monitoring approaches. It recommends institutional adoption of such technologies along with integration of satellite data with machine learning models, in-situ measurements, and community-based monitoring frameworks. Ultimately, informing policy, and promoting sustainable, cooperative management of Lake Victoria’s shared water resources.