UAV-based remote sensing of chlorophyll-a concentrations in inland water bodies: a systematic review

Monitoring chlorophyll-a content is crucial for irrigation water quality, as excessive levels can harm water bodies and reduce their volumetric capacity due to algal growth. While satellite data enhances monitoring, its coarse resolution limits application in small water bodies. Unmanned Aerial Vehicles (UAVs) offer high-resolution, near-real-time data, bridging this gap. This review explores global progress, gaps, and recommendations on UAV-based chlorophyll-a monitoring in small inland water bodies, focusing on sensor characteristics, platforms, validation data and retrieval algorithms, using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) approach. Multispectral sensors onboard DJI UAVs are the most widely used and, machine learning methods like random forest dominate chlorophyll-a inversion models. However, gaps remain in Africa due to high UAV costs, limited expertise and stringent regulations. Additionally, a universal chlorophyll-a retrieval method is also lacking. This review serves as a reference for future studies, highlighting UAVs' potential in water quality monitoring.