A spectral index for the detection of algal blooms using Sentinel-2 Multispectral Instrument (MSI) imagery: a case study of Hulun Lake, China. Issue 12 (18th June 2021)

• Record Type: Journal Article
• Authors: Cao, Mengmeng; Qing, Song; Jin, Eerdemutu; Hao, Yanling; Zhao, Wenjing
• Is Part Of: International journal of remote sensing. Volume 42:Issue 12(2021)
• Page Start: 4510
• Page End: 4531
• Publication Date: 2021-06-18
• Subjects: Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2021.1897186
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: Lakes at a global level have increasingly experienced algal blooms in recent decades, and it has become a key challenge facing the aquatic ecological environment. Remote sensing technology is considered an effective means of algal bloom detection. This study proposed a novel algal bloom detection index (ABDI) based on Sentinel-2 Multispectral Instrument (MSI) data. The ABDI was evaluated by application to Hulun Lake, China. Areas of algal bloom detected by the ABDI were consistent with those identified from visual interpretation maps [the coefficient of determination = 0.87; root-mean-square error = 0.67 km 2 ; overall accuracy >98%; Kappa coefficient >0.88]. The ABDI was less sensitive to thin cloud and turbid water compared to the floating algae index (FAI), adjusted floating algae index (AFAI), normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI). Algal bloom dynamics in relation to meteorological factors in Hulun Lake were analysed using time-series MSI data, which indicated that algal blooms occurred mainly in summer and were distributed in the near-shore waters. Temperature, precipitation, sunshine duration, and wind speed as well as human activities were found to influence spatio-temporal patterns of algal blooms. The results indicate that ABDI is applicable to the detection of algal blooms under a variety of environmental conditions occurring in other regions, such as in the Taihu, Dianchi, and Chaohu lakes and the Yellow Sea. The results of this study can provide an operational algorithm for the detection of algal blooms and environmental management.