Remote Sensing Methods for Chlorophyll-a Concentration Estimation in Water Bodies: A Review
DOI:
https://doi.org/10.54097/pk2tda35Keywords:
Chlorophyll-a, Remote Sensing, Water Quality, Machine Learning, Spectral Indices, UAV, Satellite Ocean ColorAbstract
Chlorophyll-a (Chl-a) concentration estimation in water bodies using remote sensing techniques has become increasingly important for water quality monitoring and aquatic ecosystem assessment. This paper reviews various remote sensing systems and techniques for Chl-a monitoring in inland waters, coastal areas, and oceans. It first introduces the common Chl-a estimation parameters, followed by definitions of remote sensing principles and techniques specifically applicable to Chl-a retrieval. This study systematically analyzes the capabilities and types of spaceborne systems including optical sensors (Sentinel-2 MSI, Landsat-8/9 OLI, MODIS, Sentinel-3 OLCI), unmanned aerial vehicles (UAVs), and ground-based hyperspectral systems. The paper also reviews various spectral indices, semi-empirical algorithms, and machine learning approaches that have been applied to Chl-a concentration estimation. Results indicate that Sentinel-2 MSI has emerged as the leading platform for inland water monitoring due to its optimal spatial-temporal-spectral characteristics. Machine learning methods, particularly Random Forest and deep learning approaches, have demonstrated superior performance over traditional empirical algorithms. Multi-platform integration approaches combining satellite, UAV, and ground-based observations offer the most comprehensive monitoring capability. The paper summarizes the opportunities and limitations of remote sensing data for spatial and temporal estimation of Chl-a, and identifies future research directions.
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