Remote sensing for monitoring and managing Eichhornia crassipes: a review of methods, applications, and...

Eichhornia crassipes (water hyacinth) is among the world's most aggressive aquatic invasive plants, with severe ecological and socioeconomic impacts on freshwater ecosystems. Over the last two decades, remote sensing has emerged as a critical tool for monitoring its spread and supporting management decisions, offering scalable, repeatable, and increasingly precise assessments. This review synthesizes 56 studies published between 2004 and 2025, examining how different platforms (satellites, UAVs, airborne, proximal), sensor types (multispectral, hyperspectral, SAR), and analytical methods (index thresholding, object-based classification, machine learning, deep learning) have been applied to detect, classify, and quantify E. crassipes. We highlight the evolution from early single-platform approaches to recent multimodal frameworks that integrate optical, radar, and UAV data, enabling both large-scale surveillance and fine-resolution diagnostics. Evidence demonstrates that species-level discrimination, phenological tracking, and biomass estimation are now feasible, with promising developments in linking spectral responses to pollutants and stress indicators. Despite these advances, key challenges remain in harmonizing data across platforms, reducing misclassification in mixed vegetation assemblages, and translating remote sensing outputs into decision-relevant information for adaptive management. We propose a decision-support framework built on five strategic pillars: multi-scale sensor fusion, ecologically interpretable classification schemes, scalable analytical ladders, early-warning indicators, and feedback loops linking monitoring to management actions. Together, these directions show how remote sensing can move beyond detection toward more responsive and decision-oriented approaches that support the sustainable management of E. crassipes invasions across diverse aquatic environments.