Remote Sensing of Water Hyacinth and Water Quality

Water hyacinth (Eichhornia crassipes), native to South America, is one of the world’s most invasive aquatic plants and poses serious threats to freshwater ecosystems. Its rapid growth and spread degrade water quality, reduce biodiversity, obstruct navigation, and disrupt ecosystem services. Effective monitoring and management are therefore essential for controlling its expansion and minimizing ecological and socio-economic impacts.

Remote sensing has emerged as a powerful tool for monitoring aquatic environments due to its ability to provide wide spatial coverage, frequent observations, and increasingly high-resolution imagery. This systematic review examines the application of remote sensing technologies for monitoring water hyacinth and assessing water quality in studies published between 2014 and 2024. A total of 78 peer-reviewed articles were selected from the Web of Science, Scopus, and Google Scholar databases using strict inclusion criteria.

The reviewed studies represent research conducted in 25 countries across five continents, highlighting the global relevance of water hyacinth monitoring. Most studies focused on lakes (61.5%), followed by rivers (21%) and wetlands (10.3%). Among the selected publications, approximately 49% addressed water quality monitoring, 42% focused specifically on detecting and mapping water hyacinth, and 9% examined both topics simultaneously.

Regarding satellite data sources, the Sentinel-2 Multispectral Instrument (MSI) was the most widely used sensor (35%), followed by the Landsat 8 Operational Land Imager (OLI) (26%). Many studies applied multi-sensor approaches, frequently combining Sentinel-2 imagery with Unmanned Aerial Vehicle (UAV) data to enhance spatial detail and monitoring accuracy. Analytical methods included statistical approaches, machine learning, and deep learning techniques, achieving detection accuracies ranging from 74% to 98%.

Despite these advances, several challenges remain, including limited availability of ground-truth data and insufficient atmospheric correction in some analyses. Overall, integrating high-resolution sensors with advanced analytical techniques shows strong potential for improving the monitoring and management of water hyacinth and inland water quality worldwide.