Detection of macroalgae blooms by complex SAR imagery.

Area of Science:

• Marine Ecology
• Remote Sensing Technology
• Oceanography

Background:

• Green macroalgae blooms pose increasing threats to marine ecosystems and human societies.
• Conventional satellite remote sensing methods are limited by cloud cover, hindering operational early detection.
• There is a critical need for high-resolution, reliable methods for monitoring these blooms.

Purpose of the Study:

• To develop and present a novel methodology for detecting green macroalgae blooms using RADARSAT-2 synthetic aperture radar (SAR) data.
• To leverage the unique polarimetric characteristics of macroalgae in SAR images for improved detection.
• To provide a high-resolution tool for unsupervised detection of green macroalgae blooms.

Main Methods:

• Utilized RADARSAT-2 SAR imagery to analyze green macroalgae blooms.
• Investigated differences in amplitude and phase domains of SAR-measured radar backscatter between macroalgae and open ocean.
• Defined new index factors based on polarimetric characteristics for unsupervised detection.

Main Results:

• Green macroalgae patches show distinct polarimetric signatures compared to open ocean surfaces in SAR data.
• The newly defined index factors exhibit opposite signs in macroalgae-covered areas versus open water.
• These index factors enable effective unsupervised detection of green macroalgae blooms from SAR images.

Conclusions:

• The proposed SAR-based methodology provides a high-resolution tool for early detection of green macroalgae blooms.
• This approach overcomes limitations of optical remote sensing in cloud-covered regions.
• The tool can contribute to a better understanding of macroalgae bloom dynamics in coastal areas globally.