Drone-based hyperspectral remote sensing of cyanobacteria using vertical cumulative pigment concentration in a deep reservoir
文献类型：期刊论文
| 作者 | Kwon, Yong Sung; Pyo, JongCheol; Kwon, Yong-Hwan; Duan, Hongtao; Cho, Kyung Hwa; Park, Yongeun |
| 刊名 | REMOTE SENSING OF ENVIRONMENT
 |
| 出版日期 | 2020 |
| 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 (R-2) 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 R-2 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. |
| 源URL | [http://159.226.73.51/handle/332005/20121]  |
| 专题 | 中国科学院南京地理与湖泊研究所 |
| 推荐引用方式 GB/T 7714 | Kwon, Yong Sung,Pyo, JongCheol,Kwon, Yong-Hwan,et al. Drone-based hyperspectral remote sensing of cyanobacteria using vertical cumulative pigment concentration in a deep reservoir[J]. REMOTE SENSING OF ENVIRONMENT,2020,236. |
| APA | Kwon, Yong Sung,Pyo, JongCheol,Kwon, Yong-Hwan,Duan, Hongtao,Cho, Kyung Hwa,&Park, Yongeun.(2020).Drone-based hyperspectral remote sensing of cyanobacteria using vertical cumulative pigment concentration in a deep reservoir.REMOTE SENSING OF ENVIRONMENT,236. |
| MLA | Kwon, Yong Sung,et al."Drone-based hyperspectral remote sensing of cyanobacteria using vertical cumulative pigment concentration in a deep reservoir".REMOTE SENSING OF ENVIRONMENT 236(2020). |
入库方式： OAI收割
来源：南京地理与湖泊研究所
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