King's College London

Water quality is a critical indicator of ecosystem health, yet many lakes in East Africa face growing pressures from climate change, land use changes, and human activities. To support the sustainable management of these vital ecosystems, researchers at King’s College London, in collaboration with the African Center for Aquatic Research and Education (ACARE), have developed the KCL-ACARE Water Quality Monitoring Tool.

This interactive, web-based platform harnesses satellite data to provide near real-time and historical insights into lake water quality. Users can access maps and time series data for key water quality indicators, including:

• Total Suspended Solids (TSS) – measures sediment and particulate matter
• Secchi Disk Depth (SDD) – indicates water clarity
• Chlorophyll-a (Chl-a) – estimates algal biomass

The tool also integrates climate datasets, offering information on rainfall and surface temperature trends.

Advancing Water Quality Monitoring with Earth Observation

The KCL-ACARE Water Quality Monitoring Tool was developed as part of a larger research initiative to improve environmental monitoring using Earth observation technologies. By combining satellite data with ground-based observations, the project enhances our ability to track changes in lake ecosystems over time and across vast regions. The tool has been trained and validated using data from multiple East African lakes.

Who Can Use This Tool?

The tool is designed for:

• Researchers studying aquatic ecosystems
• Policymakers & government institutions making data-driven decisions on lake management
• Local communities & NGOs interested in tracking water quality trends

Try the Tool

The latest version of the tool offers improved accuracy and now includes Sentinel-2 data for higher-resolution monitoring. Explore the tool below and start analysing water quality trends today!

Access the KCL-ACARE Water Quality Monitoring Tool

For more details on the research behind this tool, see our project report. If you have feedback or in situ data that could improve the tool, please contact emma.tebbs@kcl.ac.uk.