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Detection of macroalgae blooms by complex SAR imagery
Institution:1. Institute of Oceanology, Chinese Academy of Sciences, 7 Nanhai Road, Qingdao 266071, China;2. Fisheries and Oceans Canada, Bedford Institute of Oceanography, 1 Challenger Drive, Dartmouth B2Y 4A2, Canada;3. Key Laboratory of Ocean Circulation and Waves, Chinese Academy of Sciences, 7 Nanhai Road, Qingdao 266071, China;4. North China Sea Branch of the State Oceanic Administration, 27 Yuanling Road, Qingdao 266061, China;5. Nanjing University of Information Science and Technology, 4 Longshanbei Road, Nanjing 210044, China
Abstract:Increased frequency and enhanced damage to the marine environment and to human society caused by green macroalgae blooms demand improved high-resolution early detection methods. Conventional satellite remote sensing methods via spectra radiometers do not work in cloud-covered areas, and therefore cannot meet these demands for operational applications. We present a methodology for green macroalgae bloom detection based on RADARSAT-2 synthetic aperture radar (SAR) images. Green macroalgae patches exhibit different polarimetric characteristics compared to the open ocean surface, in both the amplitude and phase domains of SAR-measured complex radar backscatter returns. In this study, new index factors are defined which have opposite signs in green macroalgae-covered areas, compared to the open water surface. These index factors enable unsupervised detection from SAR images, providing a high-resolution new tool for detection of green macroalgae blooms, which can potentially contribute to a better understanding of the mechanisms related to outbreaks of green macroalgae blooms in coastal areas throughout the world ocean.
Keywords:Green macroalgae blooms  Synthetic aperture radar (SAR)  High resolution early detection  Unsupervised SAR index factor
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