22.1.6.53 Cyanobacteria, Analysis, Detection

Chapter Contents (Back)
Classification. Cyanobacteria.

Song, K.[Kaishan], Li, L.[Lin], Tedesco, L.P.[Lenore P.], Li, S.[Shuai], Hall, B.E.[Bob E.], Du, J.[Jia],
Remote quantification of phycocyanin in potable water sources through an adaptive model,
PandRS(95), No. 1, 2014, pp. 68-80.
Elsevier DOI 1408
Cyanobacteria BibRef

Mishra, S., Mishra, D.R., Lee, Z.P.[Zhong-Ping],
Bio-Optical Inversion in Highly Turbid and Cyanobacteria-Dominated Waters,
GeoRS(52), No. 1, January 2014, pp. 375-388.
IEEE DOI 1402
hydrological techniques BibRef

Wozniak, M.[Monika], Bradtke, K.M.[Katarzyna M.], Darecki, M.[Miroslaw], Krezel, A.[Adam],
Empirical Model for Phycocyanin Concentration Estimation as an Indicator of Cyanobacterial Bloom in the Optically Complex Coastal Waters of the Baltic Sea,
RS(8), No. 3, 2016, pp. 212.
DOI Link 1604
BibRef

Liang, Q.[Qichun], Zhang, Y.[Yuchao], Ma, R.[Ronghua], Loiselle, S.[Steven], Li, J.[Jing], Hu, M.[Minqi],
A MODIS-Based Novel Method to Distinguish Surface Cyanobacterial Scums and Aquatic Macrophytes in Lake Taihu,
RS(9), No. 2, 2017, pp. xx-yy.
DOI Link 1703
BibRef

Beck, R.[Richard], Xu, M.[Min], Zhan, S.A.[Sheng-An], Liu, H.X.[Hong-Xing], Johansen, R.A.[Richard A.], Tong, S.[Susanna], Yang, B.[Bo], Shu, S.[Song], Wu, Q.S.[Qiu-Sheng], Wang, S.[Shujie], Berling, K.[Kevin], Murray, A.[Andrew], Emery, E.[Erich], Reif, M.[Molly], Harwood, J.[Joseph], Young, J.[Jade], Martin, M.[Mark], Stillings, G.[Garrett], Stumpf, R.[Richard], Su, H.B.[Hai-Bin], Ye, Z.X.[Zhao-Xia], Huang, Y.[Yan],
Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations,
RS(9), No. 6, 2017, pp. xx-yy.
DOI Link 1706
BibRef

Wang, G.Q.[Guo-Qing], Lee, Z.P.[Zhong-Ping], Mouw, C.[Colleen],
Multi-Spectral Remote Sensing of Phytoplankton Pigment Absorption Properties in Cyanobacteria Bloom Waters: A Regional Example in the Western Basin of Lake Erie,
RS(9), No. 12, 2017, pp. xx-yy.
DOI Link 1802
BibRef
And: Erratum: RS(10), No. 2, 2018, pp. xx-yy.
DOI Link 1804
BibRef

Soja-Wozniak, M.[Monika], Craig, S.E.[Susanne E.], Kratzer, S.[Susanne], Wojtasiewicz, B.[Bozena], Darecki, M.[Miroslaw], Jones, C.T.[Chris T.],
A Novel Statistical Approach for Ocean Colour Estimation of Inherent Optical Properties and Cyanobacteria Abundance in Optically Complex Waters,
RS(9), No. 4, 2017, pp. xx-yy.
DOI Link 1705
BibRef

Zong, J.M.[Jia-Min], Wang, X.X.[Xin-Xin], Zhong, Q.Y.[Qiao-Yan], Xiao, X.M.[Xiang-Ming], Ma, J.[Jun], Zhao, B.[Bin],
Increasing Outbreak of Cyanobacterial Blooms in Large Lakes and Reservoirs under Pressures from Climate Change and Anthropogenic Interferences in the Middle-Lower Yangtze River Basin,
RS(11), No. 15, 2019, pp. xx-yy.
DOI Link 1908
BibRef

Jia, T.[Tianxia], Zhang, X.[Xueqi], Dong, R.[Rencai],
Long-Term Spatial and Temporal Monitoring of Cyanobacteria Blooms Using MODIS on Google Earth Engine: A Case Study in Taihu Lake,
RS(11), No. 19, 2019, pp. xx-yy.
DOI Link 1910
BibRef

Riddick, C.A.L.[Caitlin A.L.], Hunter, P.D.[Peter D.], Gómez, J.A.D.[José Antonio Domínguez], Martinez-Vicente, V.[Victor], Présing, M.[Mátyás], Horváth, H.[Hajnalka], Kovács, A.W.[Attila W.], Vörös, L.[Lajos], Zsigmond, E.[Eszter], Tyler, A.N.[Andrew N.],
Optimal Cyanobacterial Pigment Retrieval from Ocean Colour Sensors in a Highly Turbid, Optically Complex Lake,
RS(11), No. 13, 2019, pp. xx-yy.
DOI Link 1907
BibRef

Pyo, J.C.[Jong-Cheol], Duan, H.T.[Hong-Tao], Ligaray, M.[Mayzonee], Kim, M.[Minjeong], Baek, S.[Sangsoo], Kwon, Y.S.[Yong Sung], Lee, H.[Hyuk], Kang, T.[Taegu], Kim, K.[Kyunghyun], Cha, Y.K.[Yoon-Kyung], Cho, K.H.[Kyung Hwa],
An Integrative Remote Sensing Application of Stacked Autoencoder for Atmospheric Correction and Cyanobacteria Estimation Using Hyperspectral Imagery,
RS(12), No. 7, 2020, pp. xx-yy.
DOI Link 2004
BibRef

Miao, S., Li, Y., Wu, Z., Lyu, H., Li, Y., Bi, S., Xu, J., Lei, S., Mu, M., Wang, Q.,
A Semianalytical Algorithm for Mapping Proportion of Cyanobacterial Biomass in Eutrophic Inland Lakes Based on OLCI Data,
GeoRS(58), No. 7, July 2020, pp. 5148-5161.
IEEE DOI 2006
Lakes, Absorption, Biomass, Indexes, Atmospheric measurements, Remote sensing, Particle measurements, Absorption coefficient, OLCI bands BibRef

Ogashawara, I.[Igor],
Determination of Phycocyanin from Space: A Bibliometric Analysis,
RS(12), No. 3, 2020, pp. xx-yy.
DOI Link 2002
pigment of inland water cyanobacteria. BibRef

Kumar, A.[Abhishek], Mishra, D.R.[Deepak R.], Ilango, N.[Nirav],
Landsat 8 Virtual Orange Band for Mapping Cyanobacterial Blooms,
RS(12), No. 5, 2020, pp. xx-yy.
DOI Link 2003
BibRef


Lin, Y.[Yi], Ye, Z.L.[Zhang-Lin], Zhang, Y.[Yugan], Yu, J.[Jie],
Spectral Feature Analysis For Quantitative Estimation Of Cyanobacteria Chlorophyll-a,
ISPRS16(B7: 91-98).
DOI Link 1610
BibRef

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Last update:Nov 23, 2020 at 10:27:11