Determination of Phycocyanin from Space-A Bibliometric Analysis

被引:27
作者
Ogashawara, Igor [1 ]
机构
[1] Leibniz Inst Freshwater Ecol & Inland Fisheries, D-16775 Stechlin, Ot Neuglobsow, Germany
关键词
phycocyanin; cyanobacteria; water quality; algal blooms; bio-optical modeling; lake color; ocean color; MAPPING CYANOBACTERIAL BLOOMS; INLAND WATERS; CHLOROPHYLL-A; ALGORITHM; PIGMENT; KNOWLEDGE; PATTERNS; CLIMATE;
D O I
10.3390/rs12030567
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Over the past few decades, there has been an increase in the number of studies about the estimation of phycocyanin derived from remote sensing techniques. Since phycocyanin is a unique pigment of inland water cyanobacteria, the quantification of its concentration from earth observation data is important for water quality monitoring - once some species can produce toxins. Because of the growth of this field in the past decade, several reviews and studies comparing algorithms have been published. Thus, instead of focusing on algorithms comparison or description, the goal of the present study is to systematically analyze and visualize the evolution of publications. Using the Web of Science database this study analyzed the existing publications on remote sensing of phycocyanin decade-by-decade for the period 1991-2020. The bibliometric analysis showed how research topics evolved from measuring pigments to the quantification of optical properties and from laboratory experiments to measuring entire temperate and tropical aquatic systems. This study provides the status quo and development trend of the field and points out what could be the direction for future research.
引用
收藏
页数:16
相关论文
共 56 条
[51]   Hyperspectral Remote Sensing of the Pigment C-Phycocyanin in Turbid Inland Waters, Based on Optical Classification [J].
Sun, Deyong ;
Li, Yunmei ;
Wang, Qiao ;
Gao, Jay ;
Le, Chengfeng ;
Huang, Changchun ;
Gong, Shaoqi .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2013, 51 (07) :3871-3884
[52]   Phycocyanin detection from LANDSAT TM data for mapping cyanobacterial blooms in Lake Erie [J].
Vincent, RK ;
Qin, XM ;
McKay, RML ;
Miner, J ;
Czajkowski, K ;
Savino, J ;
Bridgeman, T .
REMOTE SENSING OF ENVIRONMENT, 2004, 89 (03) :381-392
[53]   Mapping cyanobacterial blooms in Lake Champlain's Missisquoi Bay using QuickBird and MERIS satellite data [J].
Wheeler, Sarah M. ;
Morrissey, Leslie A. ;
Levine, Suzanne N. ;
Livingston, Gerald P. ;
Vincent, Warwick F. .
JOURNAL OF GREAT LAKES RESEARCH, 2012, 38 :68-75
[54]   Empirical Model for Phycocyanin Concentration Estimation as an Indicator of Cyanobacterial Bloom in the Optically Complex Coastal Waters of the Baltic Sea [J].
Wozniak, Monika ;
Bradtke, Katarzyna M. ;
Darecki, Miroslaw ;
Krezel, Adam .
REMOTE SENSING, 2016, 8 (03)
[55]   Relating spectral shape to cyanobacterial blooms in the Laurentian Great Lakes [J].
Wynne, T. T. ;
Stumpf, R. P. ;
Tomlinson, M. C. ;
Warner, R. A. ;
Tester, P. A. ;
Dyble, J. ;
Fahnenstiel, G. L. .
INTERNATIONAL JOURNAL OF REMOTE SENSING, 2008, 29 (12) :3665-3672
[56]   Phycocyanin concentration retrieval in inland waters: A comparative review of the remote sensing techniques and algorithms [J].
Yan, Yaner ;
Bao, Zhongjue ;
Shao, Jingan .
JOURNAL OF GREAT LAKES RESEARCH, 2018, 44 (04) :748-755