气候变化领域集成服务门户(CCPortal): Drone-based hyperspectral remote sensing of cyanobacteria using vertical cumulative pigment concentration in a deep reservoir

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DOI	10.1016/j.rse.2019.111517
	Drone-based hyperspectral remote sensing of cyanobacteria using vertical cumulative pigment concentration in a deep reservoir
	Kwon Y.S.; Pyo J.; Kwon Y.-H.; Duan H.; Cho K.H.; Park Y.
发表日期	2020
ISSN	00344257
卷号	236
英文摘要	The remote sensing of algal pigments is essential for understanding the temporal and spatial distribution of harmful algal blooms (HABs). In particular, the vertical distribution of cyanobacterial pigment (e.g., phycocyanin (PC)) is critical factor in remote sensing because the diel vertical migration of cyanobacteria may affect the spectral signals according to observational time. Although numerous studies have been conducted on the remote sensing of algal bloom using pigments, few studies considered the vertical distribution of the pigments for the remote sensing of cyanobacteria in inland waters. In this regard, the objective of this study was to develop an improved bio-optical remote-sensing method using in-situ remote-sensing reflectance (Rrs) at different water depths and cumulative PC and Chlorophyll-a (Chl-a) concentrations, which was cumulated from the surface to a 5-m water depth. The results showed that the bio-optical algorithm using surface Rrs and surface pigment concentration was more accurate than that using the subsurface Rrs and surface pigments. The bio-optical algorithm using subsurface Rrs showed the highest R-squared (R2) values (0.87–0.94) in each regression with the cumulative PC concentration from surface to each depth. The regressions between drone-based surface reflectance and cumulative PC concentration for each depth indicated a better performance than those between the reflectance and surface PC concentration; the highest R2 value of 0.82 was obtained from a bio-optical algorithm using drone-based reflectance and a 1.0-m cumulative PC concentration, which was the best-performing algorithm. The PC maps developed using the best bio-optical algorithm accurately described the spatial and temporal distributions of the PC concentrations in the reservoir. This study demonstrates that the application of vertical cumulative pigment concentration and subsurface Rrs measurement in bio-optical algorithms can improve their performance in estimating pigments, and that drone-based hyperspectral imagery is an efficient tool for the remote sensing of cyanobacterial pigments over a wide area. © 2019 Elsevier Inc.
英文关键词	Bio-optical algorithm; Drone-based hyperspectral imagery; Phycocyanin; Subsurface remote sensing reflectance; Vertical cumulative pigment concentration
语种	英语
scopus关键词	Drones; Image enhancement; Reflection; Reservoirs (water); Spectroscopy; Bio-optical algorithms; Hyper-spectral imageries; Phycocyanin; Pigment concentration; Remote-sensing reflectance; Remote sensing; algal bloom; algorithm; cyanobacterium; pigment; remote sensing; reservoir; spatial distribution; surface reflectance; temporal distribution; vertical distribution; algae; Cyanobacteria
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179574
作者单位	School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan, 44919, South Korea; Electronics and Telecommunication Research Institute (ETRI), 218 Gajeong-ro, Yuseong-gu, Deajeon, 305-700, South Korea; Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 210008, China; School of Civil and Environmental Engineering, Konkuk University, Seoul, 05029, South Korea
推荐引用方式 GB/T 7714	Kwon Y.S.,Pyo J.,Kwon Y.-H.,et al. Drone-based hyperspectral remote sensing of cyanobacteria using vertical cumulative pigment concentration in a deep reservoir[J],2020,236.
APA	Kwon Y.S.,Pyo J.,Kwon Y.-H.,Duan H.,Cho K.H.,&Park Y..(2020).Drone-based hyperspectral remote sensing of cyanobacteria using vertical cumulative pigment concentration in a deep reservoir.Remote Sensing of Environment,236.
MLA	Kwon Y.S.,et al."Drone-based hyperspectral remote sensing of cyanobacteria using vertical cumulative pigment concentration in a deep reservoir".Remote Sensing of Environment 236(2020).