Identification of Floating Green Tide in High-Turbidity Water from Sentinel-2 MSI Images Employing NDVI and CIE Hue Angle Thresholds

Author:

Wang Lin1,Meng Qinghui1,Wang Xiang1,Chen Yanlong1,Wang Xinxin1ORCID,Han Jie2,Wang Bingqiang2

Affiliation:

1. National Marine Environmental Monitoring Center, Dalian 116023, China

2. Department of Information Engineering, Cangzhou Technical College, Cangzhou 061001, China

Abstract

Remote sensing technology is widely used to obtain information on floating green tides, and thresholding methods based on indices such as the normalized difference vegetation index (NDVI) and the floating algae index (FAI) play an important role in such studies. However, as the methods are influenced by many factors, the threshold values vary greatly; in particular, the error of data extraction clearly increases in situations of high-turbidity water (HTW) (NDVI > 0). In this study, high spatial resolution, multispectral images from the Sentinel-2 MSI mission were used as the data source. It was found that the International Commission on Illumination (CIE) hue angle calculated using remotely sensed equivalent multispectral reflectance data and the RGB method is extremely effective in distinguishing floating green tides from areas of HTW. Statistical analysis of Sentinel-2 MSI images showed that the threshold value of the hue angle that can effectively eliminate the effect of HTW is 218.94°. A test demonstration of the method for identifying the floating green tide in HTW in a Sentinel-2 MSI image was carried out using the identified threshold values of NDVI > 0 and CIE hue angle < 218.94°. The demonstration showed that the method effectively eliminates misidentification caused by HTW pixels (NDVI > 0), resulting in better consistency of the identification of the floating green tide and its distribution in the true color image. The method enables rapid and accurate extraction of information on floating green tide in HTW, and offers a new solution for the monitoring and tracking of green tides in coastal areas.

Funder

Ministry of Science and Technology of the People’s Republic of China, National Key Research and Development Program of China

Publisher

MDPI AG

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