Semi-analytical algorithm for estimating particulate absorption coefficient in inland water based on OLCI images.

Area of Science:

• Remote Sensing
• Ocean Optics
• Water Quality Monitoring

Background:

• Particulate matter absorption (ap) is a key inherent optical property influencing underwater light conditions.
• Accurate retrieval of ap is essential for understanding aquatic ecosystems and water quality.
• Existing methods often struggle with the complexity of turbid inland waters.

Purpose of the Study:

• To develop and validate a novel semi-analytical model for estimating particulate matter absorption at 674 nm (ap(674)) in turbid inland waters.
• To utilize Sentinel-3 Ocean and Land Colour Instrument (OLCI) data for improved spatial and temporal monitoring.
• To assess the algorithm's performance against established methods like QAA-750E.

Main Methods:

• A semi-analytical model was developed using three spectral bands from Sentinel-3 OLCI imagery (674, 709, and 779 nm).
• The model employs remote sensing reflectance (Rrs) ratios to estimate optical coefficients, specifically targeting ap(674).
• Algorithm validation was performed using extensive datasets from lakes in China and Belgium.

Main Results:

• The proposed algorithm demonstrated excellent estimation performance in turbid waters, significantly outperforming the QAA-750E algorithm.
• Key performance indicators showed substantial improvements: Median Absolute Percentage Error (MdAPD) decreased from 50.17% to 17.68%, Unbiased Mean Absolute Percentage Error (UMAPD) dropped from 44.09% to 21.82%, and Root Mean Square Error (RMSD) improved from 0.61 m⁻¹ to 0.24 m⁻¹.
• The algorithm was successfully applied to map ap(674) in Eastern Plains lakes from 2016-2023, revealing spatial and temporal trends.

Conclusions:

• The developed semi-analytical model provides a robust and accurate method for retrieving ap(674) in turbid inland waters.
• The algorithm shows strong potential for reliable, long-term monitoring of particulate matter absorption in inland waters globally.
• This advancement supports better understanding and management of aquatic environments through enhanced remote sensing capabilities.