A tuned ocean color algorithm for the Arctic Ocean: a solution for waters with high CDM content

Area of Science:

• Oceanography
• Remote Sensing
• Marine Ecology

Background:

• The Arctic Ocean is heavily influenced by rivers and coastal processes.
• Phytoplankton blooms are common in Arctic coastal waters, which are sensitive to climate change.
• Accurate chlorophyll a (Chl) estimation is vital for assessing Arctic marine ecosystem health and climate change impacts.

Purpose of the Study:

• To enhance chlorophyll a (Chl) estimation accuracy in the Arctic Ocean, particularly in coastal areas.
• To optimize the Garver-Siegel-Maritorena (GSM) algorithm for Arctic bio-optical conditions, including high colored detrital material (CDM).

Main Methods:

• An Arctic bio-optical dataset with seven wavelengths was used to tune the GSM algorithm.
• The optimized algorithm, GSMA, was compared against an existing Arctic GSM algorithm (AO.GSM).
• Algorithm performance was validated using satellite imagery and non-Arctic coastal datasets.

Main Results:

• The tuned GSMA algorithm demonstrated improved performance over the AO.GSM algorithm.
• A modest 8% improvement in Chl estimation accuracy was achieved with GSMA.
• GSMA exhibited good robustness when tested on diverse datasets.

Conclusions:

• The optimized GSMA algorithm offers a more accurate method for estimating Chl in the Arctic Ocean.
• Further refinement is needed to fully address the complexities of Arctic coastal waters.
• The GSMA algorithm provides a valuable tool for monitoring Arctic marine ecosystems under climate change.