Developing Benthic Class Specific, Chlorophyll-a Retrieving Algorithms for Optically-Shallow Water Using SeaWiFS.

Area of Science:

• Oceanography
• Remote Sensing
• Coastal Ecology

Background:

• Accurate chlorophyll-a (chl-a) estimation is crucial for monitoring coastal water quality.
• Optically shallow waters present unique challenges for standard ocean color algorithms.
• Existing algorithms may not adequately account for benthic influences on water reflectance.

Purpose of the Study:

• To assess the effectiveness of benthic class-specific algorithms for improving SeaWiFS chl-a retrieval in optically shallow coastal waters.
• To compare the accuracy of tailored bio-optical algorithms against a unified regional model.
• To identify areas for future refinement in chl-a estimation techniques.

Main Methods:

• Applied Ocean Color (OC) algorithm framework, retaining operational atmospheric correction.
• Classified benthic environments using satellite image analysis.
• Evaluated chl-a retrieval accuracy by comparing algorithm outputs with in situ measurements.
• Developed and tested regionally-tuned models specific to different benthic classes.

Main Results:

• Regionally-tuned models varying by benthic class significantly improved chl-a estimation accuracy.
• Benthic class-specific algorithms achieved a mean absolute percent difference of approximately 70%.
• Residual analysis indicated potential for further accuracy gains with finer benthic characterization.

Conclusions:

• Tailoring bio-optical algorithms to benthic classes enhances chl-a retrieval in optically shallow coastal waters.
• Finer characterization of benthic environments and specialized atmospheric correction hold promise for future improvements.
• This approach offers a more accurate method for monitoring coastal phytoplankton biomass.