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

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.