Water Quality Monitoring for Lake Constance with a Physically Based Algorithm for MERIS Data

Area of Science:

• Earth and Marine Sciences
• Environmental Monitoring
• Remote Sensing Technology

Background:

• The Medium Resolution Imaging Spectrometer (MERIS) provides valuable data for aquatic environments.
• Processing MERIS level 1B data requires algorithms adaptable to varying atmospheric and aquatic conditions.
• Existing algorithms need specific parameterization for operational use in distinct aquatic regions like lakes.

Purpose of the Study:

• To adapt and apply a physically based algorithm for automatic processing of MERIS level 1B full resolution data for lake environments.
• To develop a lake-specific parameterization for the algorithm to account for spatio-temporal variations.
• To validate the algorithm's performance using in-situ measurements.

Main Methods:

• Utilized a physically based algorithm for atmospheric correction and inversion of water quality parameters.
• Employed a Look-Up Table (LUT) for at-sensor radiance and downhill simplex inversion for chlorophyll-a (chl-a), suspended matter (sm), and yellow substance (y).
• Applied a selective filter using retrieval residuals and validated outputs with regular chl-a sampling data.

Main Results:

• Successfully implemented and parameterized the algorithm for MERIS data processing in a lake environment.
• The algorithm demonstrated effective atmospheric correction and retrieval of key hydro-optical parameters.
• Validation using in-situ chl-a measurements confirmed the reliability of the algorithm's outputs.

Conclusions:

• The developed lake-specific algorithm provides an accurate and automated method for processing MERIS data.
• This approach enhances the monitoring of water quality parameters in lakes using satellite remote sensing.
• The methodology is adaptable for different sensors and aquatic conditions, improving operational remote sensing applications.