Development of ocean color algorithms for estimating chlorophyll-a concentrations and inherent optical properties...

Area of Science:

• Oceanography
• Remote Sensing
• Bio-optics

Background:

• Accurate estimation of Chlorophyll-a (Chla) and inherent optical properties (IOPs) is crucial for understanding ocean dynamics.
• Remote sensing reflectance (Rrs) measurements are key inputs for these estimations.
• Existing algorithms have limitations in precision and scope.

Purpose of the Study:

• To develop novel inversion algorithms for estimating Chla and IOPs from Rrs.
• To utilize gene expression programming (GEP) for creating and optimizing these algorithms.
• To validate the performance of GEP-derived algorithms against existing methods and datasets.

Main Methods:

• Development of inversion algorithms using gene expression programming (GEP).
• Training and validation using in situ data from the NASA bio-optical marine algorithm dataset (NOMAD).
• Evaluation through simulated Rrs spectra, closure tests, and comparison with various datasets (in situ, synthetic, satellite match-up).

Main Results:

• GEP-derived algorithms provide accurate Chla and IOPs retrievals comparable to state-of-the-art methods.
• No significant performance differences were observed between GEP, support vector regression, and multilayer perceptron models.
• Application to SeaWiFS imagery revealed enhanced details of algal blooms and water constituent distribution.

Conclusions:

• GEP offers a robust approach for developing oceanographic inversion algorithms.
• The new algorithms improve the mapping of Chla and IOPs, aiding in the study of algal blooms and water masses.
• These methods provide valuable insights into ocean biogeochemistry and ecological processes.