A straightforward approach for the rapid detection of red Noctiluca scintillans blooms from satellite imagery

Abstract

Red Noctiluca scintillans (RNS), a prominent species of dinoflagellate known for its conspicuous size and ability to form blooms, exhibits heterotrophic behavior and functions as a microzooplankton grazer within the marine food web. In this study, a straightforward technique referred to as the blue-green index (BGI) has been introduced for the purpose of distinguishing and discerning RNS from neighboring waters, owing to its pronounced absorption in the blue-green spectral range. This method has been applied across a range of satellite imagery, encompassing both multi-spectral and hyperspectral sensors. The study delved into three instances of bloom occurrences caused by RNS: firstly, in November 2014 and April 2022 off the western coast of Guangdong, and secondly, in February 2021 within the Beibu Gulf. The notable bloom event in the Beibu Gulf during February 2021 extended across an expansive area totaling 6933.5 km². The motion speed and direction of the RNS bloom patches were also derived from successive satellite images. The recently introduced BGI method demonstrates insensitivity to suspended sediment, though its successful application necessitates accurate atmospheric correction. Subsequent efforts will involve the quantification of RNS blooms in a more precise manner, utilizing hyperspectral satellite data grounded in optimized band configurations.

Keywords: Algal bloom; Blue green index (BGI); Multispectral and hyperspectral; Red Noctiluca scintillans; Remote sensing.