Meta-analysis: efficiency of using remote sensing to monitor algal and cyanobacterial blooms in continental aquatic environments

Abstract

Algae, including both eukaryotes and prokaryotes (cyanobacteria), play a crucial role in aquatic ecosystems. However, eutrophication caused by human activities has led to excessive algal blooms, degrading water quality. Traditional phytoplankton monitoring methods are limited in scale and cost, whereas remote sensing emerges as an efficient alternative, enabling broad and continuous analysis. Chlorophyll-a, a pigment found in algae and cyanobacteria, is a key indicator of phytoplankton biomass and can be quantified using remote sensing techniques. This study conducted a meta-analysis of 267 articles (1983-2021) to assess the effectiveness of remote sensing models in estimating chlorophyll-a in inland waters. The results demonstrated that these models are effective, with a mean effect size of 0.6776. High heterogeneity among studies was observed, primarily influenced by atmospheric correction methods. Group 4 corrections (simultaneous retrieval of atmospheric and water components) helped explain part of the model's heterogeneity. Hyperspectral imagery and semi-analytical algorithms showed higher accuracy, though they were less frequently used. Lotic environments (rivers) exhibited larger effect sizes than lentic ones (lakes), reflecting ecological differences. Arid and temperate climates yielded better results than tropical and cold climates. The study concludes that remote sensing is a viable tool for monitoring algal blooms, with the potential to enhance water quality management.