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A Study on Algae Bloom Pigment in the Eutrophic Lake Using Bio-Optical Modelling: Hyperspectral Remote Sensing

B R Vishnu Prasanth¹, R Sivakumar², M Ramaraj¹

¹Department of Civil Engineering, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu, Tamil Nadu, 603203, India.

Bulletin of Environmental Contamination and Toxicology

|April 2, 2022

Summary

This study developed a bio-optical algorithm using hyperspectral remote sensing to accurately estimate chlorophyll-a (Chl-a) concentration in inland lakes. The algorithm effectively monitors algae biomass and eutrophic status, crucial for freshwater ecosystem health.

Keywords:

Algae Pigment Bio-optical model Chlorophyll-a Eutrophication Hyperspectral Remote sensing Inland Lake

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Area of Science:

Environmental Science
Remote Sensing
Limnology

Background:

Inland lakes are vital freshwater ecosystems and indicators of aquatic biodiversity.
Chlorophyll-a (Chl-a) is a key biological indicator for assessing lake water eutrophication and algae biomass.

Purpose of the Study:

To develop and validate bio-optical algorithms for estimating Chl-a concentration in inland lake waters.
To assess the potential of hyperspectral remote sensing for monitoring lake eutrophication.

Main Methods:

Developed semi-empirical bio-optical algorithms using spectral wavelengths from 400 to 800 nm.
Utilized hyperspectral remote sensing measurements and compared with Sentinel-2 MSI imagery.
Validated algorithm performance using statistical metrics like R², RMSE, and MAPE.

Main Results:

The developed bio-optical algorithm accurately estimated Chl-a concentration with R² of 0.8958.
Achieved a root mean squared error (RMSE) of 13.028 and a mean absolute percentage error (MAPE) of 8.44%.
Demonstrated the algorithm's capability for predicting algae pigment concentration in eutrophic lakes.

Conclusions:

The developed bio-optical algorithm is accurate and effective for estimating Chl-a in inland lakes.
This approach shows significant potential for monitoring algae spatial dynamics and assessing eutrophic conditions.
The study highlights the utility of hyperspectral remote sensing in freshwater ecosystem management.