23.2.29 Algal Blooms, Analysis, Detection

Chapter Contents (Back)
Classification. Algal Blooms.
See also Cyanobacteria, Analysis, Detection.
See also Plankton Analysis, Extraction, Features, Small Scale and Large Scale.

Zavalas, R.[Richard], Ierodiaconou, D.[Daniel], Ryan, D.[David], Rattray, A.[Alex], Monk, J.[Jacquomo],
Habitat Classification of Temperate Marine Macroalgal Communities Using Bathymetric LiDAR,
RS(6), No. 3, 2014, pp. 2154-2175.
DOI Link 1404
BibRef

Song, W.L.[Wei-Long], Dolan, J.M.[John M.], Cline, D.[Danelle], Xiong, G.M.[Guang-Ming],
Learning-Based Algal Bloom Event Recognition for Oceanographic Decision Support System Using Remote Sensing Data,
RS(7), No. 10, 2015, pp. 13564.
DOI Link 1511
BibRef

Xing, Q.G.[Qian-Guo], Hu, C.M.[Chuan-Min], Tang, D.L.[Dan-Ling], Tian, L.Q.[Li-Qiao], Tang, S.L.[Shi-Lin], Wang, X.H.[Xiao Hua], Lou, M.J.[Ming-Jing], Gao, X.L.[Xue-Lu],
World's Largest Macroalgal Blooms Altered Phytoplankton Biomass in Summer in the Yellow Sea: Satellite Observations,
RS(7), No. 9, 2015, pp. 12297.
DOI Link 1511
BibRef

Zhang, Y.C.[Yu-Chao], Ma, R.H.[Rong-Hua], Zhang, M.[Min], Duan, H.T.[Hong-Tao], Loiselle, S.[Steven], Xu, J.D.[Jin-Duo],
Fourteen-Year Record (2000-2013) of the Spatial and Temporal Dynamics of Floating Algae Blooms in Lake Chaohu, Observed from Time Series of MODIS Images,
RS(7), No. 8, 2015, pp. 10523.
DOI Link 1509
BibRef

Kamerosky, A.[Andrew], Cho, H.J.[Hyun Jung], Morris, L.[Lori],
Monitoring of the 2011 Super Algal Bloom in Indian River Lagoon, FL, USA, Using MERIS,
RS(7), No. 2, 2015, pp. 1441-1460.
DOI Link 1503
BibRef

Zhao, J.[Jun], Temimi, M.[Marouane], Ghedira, H.[Hosni],
Characterization of harmful algal blooms (HABs) in the Arabian Gulf and the Sea of Oman using MERIS fluorescence data,
PandRS(101), No. 1, 2015, pp. 125-136.
Elsevier DOI 1503
HAB BibRef

El-Habashi, A.[Ahmed], Ioannou, I.[Ioannis], Tomlinson, M.C.[Michelle C.], Stumpf, R.P.[Richard P.], Ahmed, S.[Sam],
Satellite Retrievals of Karenia brevis Harmful Algal Blooms in the West Florida Shelf Using Neural Networks and Comparisons with Other Techniques,
RS(8), No. 5, 2016, pp. 377.
DOI Link 1606
BibRef

Ouyang, Z.T.[Zu-Tao], Shao, C.L.[Chang-Liang], Chu, H.[Housen], Becker, R.[Richard], Bridgeman, T.[Thomas], Stepien, C.A.[Carol A.], John, R.[Ranjeet], Chen, J.[Jiquan],
The Effect of Algal Blooms on Carbon Emissions in Western Lake Erie: An Integration of Remote Sensing and Eddy Covariance Measurements,
RS(9), No. 1, 2017, pp. xx-yy.
DOI Link 1702
BibRef

Zeng, C.[Chen], Zeng, T.[Tao], Fischer, A.M.[Andrew M.], Xu, H.P.[Hui-Ping],
Fluorescence-Based Approach to Estimate the Chlorophyll-A Concentration of a Phytoplankton Bloom in Ardley Cove (Antarctica),
RS(9), No. 3, 2017, pp. xx-yy.
DOI Link 1704
BibRef

Shehhi, M.R.A.[Maryam R. Al], Gherboudj, I.[Imen], Zhao, J.[Jun], Ghedira, H.[Hosni],
Improved atmospheric correction and chlorophyll-a remote sensing models for turbid waters in a dusty environment,
PandRS(133), No. Supplement C, 2017, pp. 46-60.
Elsevier DOI 1711
Harmful algal blooms, Chlorophyll a, Atmospheric correction, Arabian Gulf, Sea of Oman, Arabian Sea, Dusty climate, Shallow water, Turbid, water BibRef

Tan, W.X.[Wen-Xia], Liu, P.C.[Peng-Cheng], Liu, Y.[Yi], Yang, S.[Shao], Feng, S.[Shunan],
A 30-Year Assessment of Phytoplankton Blooms in Erhai Lake Using Landsat Imagery: 1987 to 2016,
RS(9), No. 12, 2017, pp. xx-yy.
DOI Link 1802
BibRef

Wu, L.[Lin], Wang, L.[Le], Min, L.[Lin], Hou, W.[Wei], Guo, Z.W.[Zheng-Wei], Zhao, J.H.[Jian-Hui], Li, N.[Ning],
Discrimination of Algal-Bloom Using Spaceborne SAR Observations of Great Lakes in China,
RS(10), No. 5, 2018, pp. xx-yy.
DOI Link 1806
BibRef

Cui, T.W., Liang, X.J., Gong, J.L., Tong, C., Xiao, Y.F., Liu, R.J., Zhang, X., Zhang, J.,
Assessing and refining the satellite-derived massive green macro-algal coverage in the Yellow Sea with high resolution images,
PandRS(144), 2018, pp. 315-324.
Elsevier DOI 1809
Green macro-algal bloom, Coverage, MODIS, SAR, Pixel un-mixing, Mixed pixel effect BibRef

Harun-Al-Rashid, A.[Ahmed], Yang, C.S.[Chan-Su],
Improved Detection of Tiny Macroalgae Patches in Korea Bay and Gyeonggi Bay by Modification of Floating Algae Index,
RS(10), No. 9, 2018, pp. xx-yy.
DOI Link 1810
BibRef

Gao, B.C.[Bo-Cai], Li, R.R.[Rong-Rong],
FVI: A Floating Vegetation Index Formed with Three Near-IR Channels in the 1.0-1.24 µm Spectral Range for the Detection of Vegetation Floating over Water Surfaces,
RS(10), No. 9, 2018, pp. xx-yy.
DOI Link 1810
BibRef

Karki, S.[Sita], Sultan, M.[Mohamed], Elkadiri, R.[Racha], Elbayoumi, T.[Tamer],
Mapping and Forecasting Onsets of Harmful Algal Blooms Using MODIS Data over Coastal Waters Surrounding Charlotte County, Florida,
RS(10), No. 10, 2018, pp. xx-yy.
DOI Link 1811
BibRef

Li, J.[Jing], Ma, R.H.[Rong-Hua], Xue, K.[Kun], Zhang, Y.C.[Yu-Chao], Loiselle, S.[Steven],
A Remote Sensing Algorithm of Column-Integrated Algal Biomass Covering Algal Bloom Conditions in a Shallow Eutrophic Lake,
IJGI(7), No. 12, 2018, pp. xx-yy.
DOI Link 1901
BibRef

Shin, J.[Jisun], Kim, K.[Keunyong], Son, Y.B.[Young Baek], Ryu, J.H.[Joo-Hyung],
Synergistic Effect of Multi-Sensor Data on the Detection of Margalefidinium polykrikoides in the South Sea of Korea,
RS(11), No. 1, 2018, pp. xx-yy.
DOI Link 1901
BibRef

Mcilwaine, B.[Ben], Casado, M.R.[Monica Rivas], Leinster, P.[Paul],
Using 1st Derivative Reflectance Signatures within a Remote Sensing Framework to Identify Macroalgae in Marine Environments,
RS(11), No. 6, 2019, pp. xx-yy.
DOI Link 1903
BibRef

Marmorino, G.O.[George O.], Chen, W.[Wei],
Use of WorldView-2 Along-Track Stereo Imagery to Probe a Baltic Sea Algal Spiral,
RS(11), No. 7, 2019, pp. xx-yy.
DOI Link 1904
BibRef

El-Alem, A., Chokmani, K., Laurion, I., El-Adlouni, S.E., Raymond, S., Ratté-Fortin, C.,
Ensemble-Based Systems to Monitor Algal Bloom With Remote Sensing,
GeoRS(57), No. 10, October 2019, pp. 7955-7971.
IEEE DOI 1910
calibration, hydrological techniques, lakes, remote sensing, water pollution, water quality, monitor algal bloom, remote sensing BibRef

Bi, S., Li, Y., Lyu, H., Mu, M., Xu, J., Lei, S., Miao, S., Hong, T., Zhou, L.,
Quantifying Spatiotemporal Dynamics of the Column-Integrated Algal Biomass in Nonbloom Conditions Based on OLCI Data: A Case Study of Lake Dianchi, China,
GeoRS(57), No. 10, October 2019, pp. 7447-7459.
IEEE DOI 1910
lakes, oceanographic regions, remote sensing, water quality, nonbloom areas, nonbloom regions, water surface biomass, Ocean and Land Color Instrument (OLCI) BibRef

Jing, Y.Y.[Yuan-Yuan], Zhang, Y.C.[Yu-Chao], Hu, M.Q.[Min-Qi], Chu, Q.[Qiao], Ma, R.H.[Rong-Hua],
MODIS-Satellite-Based Analysis of Long-Term Temporal-Spatial Dynamics and Drivers of Algal Blooms in a Plateau Lake Dianchi, China,
RS(11), No. 21, 2019, pp. xx-yy.
DOI Link 1911
BibRef

Malthus, T.J.[Tim J.], Lehmann, E.[Eric], Ho, X.[Xavier], Botha, E.[Elizabeth], Anstee, J.[Janet],
Implementation of a Satellite Based Inland Water Algal Bloom Alerting System Using Analysis Ready Data,
RS(11), No. 24, 2019, pp. xx-yy.
DOI Link 1912
BibRef

Mora-Soto, A.[Alejandra], Palacios, M.[Mauricio], Macaya, E.C.[Erasmo C.], Gómez, I.[Iván], Huovinen, P.[Pirjo], Pérez-Matus, A.[Alejandro], Young, M.[Mary], Golding, N.[Neil], Toro, M.[Martin], Yaqub, M.[Mohammad], Macias-Fauria, M.[Marc],
A High-Resolution Global Map of Giant Kelp (Macrocystis pyrifera) Forests and Intertidal Green Algae (Ulvophyceae) with Sentinel-2 Imagery,
RS(12), No. 4, 2020, pp. xx-yy.
DOI Link 2003
BibRef

Schimel, A.C.G.[Alexandre C. G.], Brown, C.J.[Craig J.], Ierodiaconou, D.[Daniel],
Automated Filtering of Multibeam Water-Column Data to Detect Relative Abundance of Giant Kelp (Macrocystis pyrifera),
RS(12), No. 9, 2020, pp. xx-yy.
DOI Link 2005
BibRef

Lőugas, L.[Laura], Kutser, T.[Tiit], Kotta, J.[Jonne], Vahtmäe, E.[Ele],
Detecting Long Time Changes in Benthic Macroalgal Cover Using Landsat Image Archive,
RS(12), No. 11, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Ni, T.N.K.[Tran Ngoc Khanh], Tin, H.C.[Hoang Cong], Thach, V.T.[Vo Trong], Jamet, C.[Cédric], Saizen, I.[Izuru],
Mapping Submerged Aquatic Vegetation along the Central Vietnamese Coast Using Multi-Source Remote Sensing,
IJGI(9), No. 6, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Shin, J.[Jisun], Kim, S.M.[Soo Mee], Kim, K.[Keunyong], Ryu, J.H.[Joo-Hyung],
Quantification of Margalefidinium polykrikoides Blooms along the South Coast of Korea Using Airborne Hyperspectral Imagery,
RS(12), No. 15, 2020, pp. xx-yy.
DOI Link 2008
BibRef

Kislik, C.[Chippie], Genzoli, L.[Laurel], Lyons, A.[Andy], Kelly, M.[Maggi],
Application of UAV Imagery to Detect and Quantify Submerged Filamentous Algae and Rooted Macrophytes in a Non-Wadeable River,
RS(12), No. 20, 2020, pp. xx-yy.
DOI Link 2010
BibRef

Cao, M.M.[Meng-Meng], Mao, K.B.[Ke-Biao], Shen, X.Y.[Xin-Yi], Xu, T.R.[Tong-Ren], Yan, Y.[Yibo], Yuan, Z.J.[Zi-Jin],
Monitoring the Spatial and Temporal Variations in The Water Surface and Floating Algal Bloom Areas in Dongting Lake Using a Long-Term MODIS Image Time Series,
RS(12), No. 21, 2020, pp. xx-yy.
DOI Link 2011
BibRef

Lee, M.S.[Min-Sun], Park, K.A.[Kyung-Ae], Micheli, F.[Fiorenza],
Derivation of Red Tide Index and Density Using Geostationary Ocean Color Imager (GOCI) Data,
RS(13), No. 2, 2021, pp. xx-yy.
DOI Link 2101
BibRef

Ma, J.Y.[Jie-Ying], Jin, S.G.[Shuang-Gen], Li, J.[Jian], He, Y.[Yang], Shang, W.[Wei],
Spatio-Temporal Variations and Driving Forces of Harmful Algal Blooms in Chaohu Lake: A Multi-Source Remote Sensing Approach,
RS(13), No. 3, 2021, pp. xx-yy.
DOI Link 2102
BibRef

Zhang, Y.C.[Yu-Chao], Loiselle, S.[Steven], Shi, K.[Kun], Han, T.[Tao], Zhang, M.[Min], Hu, M.Q.[Min-Qi], Jing, Y.Y.[Yuan-Yuan], Lai, L.[Lai], Zhan, P.F.[Peng-Fei],
Wind Effects for Floating Algae Dynamics in Eutrophic Lakes,
RS(13), No. 4, 2021, pp. xx-yy.
DOI Link 2103
BibRef

Schreyers, L.[Louise], van Emmerik, T.[Tim], Biermann, L.[Lauren], Le Lay, Y.F.[Yves-François],
Spotting Green Tides over Brittany from Space: Three Decades of Monitoring with Landsat Imagery,
RS(13), No. 8, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Aláez, F.M.B.[Francisco M. Bellas], Palenzuela, J.M.T.[Jesus M. Torres], Spyrakos, E.[Evangelos], Vilas, L.G.[Luis González],
Machine Learning Methods Applied to the Prediction of Pseudo-nitzschia spp. Blooms in the Galician Rias Baixas (NW Spain),
IJGI(10), No. 4, 2021, pp. xx-yy.
DOI Link 2104
Refers to Oceanography paper. BibRef

de Lucia Lobo, F.[Felipe], Nagel, G.W.[Gustavo Willy], Maciel, D.A.[Daniel Andrade], de Carvalho, L.A.S.[Lino Augusto Sander], Martins, V.S.[Vitor Souza], Barbosa, C.C.F.[Cláudio Clemente Faria], de Moraes Novo, E.M.L.[Evlyn Márcia Leăo ],
AlgaeMAp: Algae Bloom Monitoring Application for Inland Waters in Latin America,
RS(13), No. 15, 2021, pp. xx-yy.
DOI Link 2108
BibRef

Brando, V.E.[Vittorio E.], Sammartino, M.[Michela], Colella, S.[Simone], Bracaglia, M.[Marco], di Cicco, A.[Annalisa], d'Alimonte, D.[Davide], Kajiyama, T.[Tamito], Kaitala, S.[Seppo], Attila, J.[Jenni],
Phytoplankton Bloom Dynamics in the Baltic Sea Using a Consistently Reprocessed Time Series of Multi-Sensor Reflectance and Novel Chlorophyll-a Retrievals,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Zhang, G.Z.[Guang-Zong], Wu, M.Q.[Meng-Quan], Wei, J.[Juan], He, Y.F.[Yu-Fang], Niu, L.F.[Li-Feng], Li, H.Y.[Han-Yu], Xu, G.C.[Guo-Chang],
Adaptive Threshold Model in Google Earth Engine: A Case Study of Ulva prolifera Extraction in the South Yellow Sea, China,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Zeng, C.Q.[Chui-Qing], Binding, C.E.[Caren E.],
Consistent Multi-Mission Measures of Inland Water Algal Bloom Spatial Extent Using MERIS, MODIS and OLCI,
RS(13), No. 17, 2021, pp. xx-yy.
DOI Link 2109
BibRef

An, D.Y.[De-Yu], Yu, D.F.[Ding-Feng], Zheng, X.Y.[Xiang-Yang], Zhou, Y.[Yan], Meng, L.[Ling], Xing, Q.G.[Qian-Guo],
Monitoring the Dissipation of the Floating Green Macroalgae Blooms in the Yellow Sea (2007-2020) on the Basis of Satellite Remote Sensing,
RS(13), No. 19, 2021, pp. xx-yy.
DOI Link 2110
BibRef

Izadi, M.[Moein], Sultan, M.[Mohamed], El Kadiri, R.[Racha], Ghannadi, A.[Amin], Abdelmohsen, K.[Karem],
A Remote Sensing and Machine Learning-Based Approach to Forecast the Onset of Harmful Algal Bloom,
RS(13), No. 19, 2021, pp. xx-yy.
DOI Link 2110
BibRef

Park, J.[Jinku], Lee, S.J.[Sung-Jae], Jo, Y.H.[Young-Heon], Kim, H.C.[Hyun-Cheol],
Phytoplankton Bloom Changes under Extreme Geophysical Conditions in the Northern Bering Sea and the Southern Chukchi Sea,
RS(13), No. 20, 2021, pp. xx-yy.
DOI Link 2110
BibRef

Pan, Y.F.[Yu-Feng], Ding, D.[Dong], Li, G.X.[Guang-Xue], Liu, X.[Xue], Liang, J.[Jun], Wang, X.D.[Xiang-Dong], Liu, S.D.[Shi-Dong], Shi, J.[Jinghao],
Potential Temporal and Spatial Trends of Oceanographic Conditions with the Bloom of Ulva Prolifera in the West of the Southern Yellow Sea,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Li, Y.R.[Yuan-Rui], Zhou, Q.C.[Qi-Chao], Zhang, Y.[Yun], Li, J.Y.[Jing-Yi], Shi, K.[Kun],
Research Trends in the Remote Sensing of Phytoplankton Blooms: Results from Bibliometrics,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Liu, M.[Miao], Ling, H.[Hong], Wu, D.[Dan], Su, X.M.[Xiao-Mei], Cao, Z.G.[Zhi-Gang],
Sentinel-2 and Landsat-8 Observations for Harmful Algae Blooms in a Small Eutrophic Lake,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Khan, R.M.[Rabia Munsaf], Salehi, B.[Bahram], Mahdianpari, M.[Masoud], Mohammadimanesh, F.[Fariba], Mountrakis, G.[Giorgos], Quackenbush, L.J.[Lindi J.],
A Meta-Analysis on Harmful Algal Bloom (HAB) Detection and Monitoring: A Remote Sensing Perspective,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Li, X.[Xue], Shang, S.L.[Shao-Ling], Lee, Z.P.[Zhong-Ping], Lin, G.[Gong], Zhang, Y.N.[Yong-Nian], Wu, J.Y.[Jing-Yu], Kang, Z.J.[Zhen-Jun], Liu, X.X.[Xiang-Xu], Yin, C.[Cheng], Gao, Y.[Yue],
Detection and Biomass Estimation of Phaeocystis globosa Blooms off Southern China From UAV-Based Hyperspectral Measurements,
GeoRS(60), 2022, pp. 1-13.
IEEE DOI 2112
Sea measurements, Unmanned aerial vehicles, Time measurement, Sensors, Hyperspectral imaging, Spatial resolution, Satellites, unmanned aerial vehicle (UAV) BibRef

Ciancia, E.[Emanuele], Lacava, T.[Teodosio], Pergola, N.[Nicola], Vellucci, V.[Vincenzo], Antoine, D.[David], Satriano, V.[Valeria], Tramutoli, V.[Valerio],
Quantifying the Variability of Phytoplankton Blooms in the NW Mediterranean Sea with the Robust Satellite Techniques (RST),
RS(13), No. 24, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Zhao, X.[Xin], Liu, R.J.[Rong-Jie], Ma, Y.[Yi], Xiao, Y.F.[Yan-Fang], Ding, J.[Jing], Liu, J.Q.[Jian-Qiang], Wang, Q.B.[Quan-Bin],
Red Tide Detection Method for HY-1D Coastal Zone Imager Based on U-Net Convolutional Neural Network,
RS(14), No. 1, 2022, pp. xx-yy.
DOI Link 2201
BibRef

Laneve, G.[Giovanni], Bruno, M.[Milena], Mukherjee, A.[Arghya], Messineo, V.[Valentina], Giuseppetti, R.[Roberto], de Pace, R.[Rita], Magurano, F.[Fabio], d'Ugo, E.[Emilio],
Remote Sensing Detection of Algal Blooms in a Lake Impacted by Petroleum Hydrocarbons,
RS(14), No. 1, 2022, pp. xx-yy.
DOI Link 2201
BibRef

Liu, R.J.[Rong-Jie], Xiao, Y.F.[Yan-Fang], Ma, Y.[Yi], Cui, T.W.[Ting-Wei], An, J.[Jubai],
Red tide detection based on high spatial resolution broad band optical satellite data,
PandRS(184), 2022, pp. 131-147.
Elsevier DOI 2202
Pseudo hue angle, Red tide, Remote sensing, Detection method, Broad band sensor BibRef

Cui, B.[Binge], Zhang, H.Q.[Hao-Qing], Jing, W.[Wei], Liu, H.F.[Hui-Fang], Cui, J.M.[Jian-Ming],
SRSe-Net: Super-Resolution-Based Semantic Segmentation Network for Green Tide Extraction,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202
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Duan, H.Y.[Hong-Yu], Yao, X.J.[Xiao-Jun], Zhang, D.[Dahong], Jin, H.[Huian], Wei, Q.X.[Qi-Xin],
Long-Term Temporal and Spatial Monitoring of Cladophora Blooms in Qinghai Lake Based on Multi-Source Remote Sensing Images,
RS(14), No. 4, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Pu, J.[Jing], Song, K.[Kaishan], Lv, Y.F.[Yun-Feng], Liu, G.[Ge], Fang, C.[Chong], Hou, J.B.[Jun-Bin], Wen, Z.D.[Zhi-Dan],
Distinguishing Algal Blooms from Aquatic Vegetation in Chinese Lakes Using Sentinel 2 Image,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Li, C.P.[Chang-Peng], Tao, B.Y.[Bang-Yi], Liu, Y.L.[Ya-Lin], Zhang, S.[Shugang], Zhang, Z.[Zhao], Song, Q.J.[Qing-Jun], Jiang, Z.B.[Zhi-Bing], He, S.Y.[Shuang-Yan], Huang, H.Q.[Hai-Qing], Mao, Z.H.[Zhi-Hua],
Assessment of VIIRS on the Identification of Harmful Algal Bloom Types in the Coasts of the East China Sea,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link 2205
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Al-Shehhi, M.R.[Maryam R.], Samad, Y.A.[Yarjan Abdul],
Identifying Algal Bloom 'Hotspots' in Marginal Productive Seas: A Review and Geospatial Analysis,
RS(14), No. 10, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Chen, S.L.[Shuang-Ling], Meng, Y.[Yu],
Phytoplankton Blooms Expanding Further Than Previously Thought in the Ross Sea: A Remote Sensing Perspective,
RS(14), No. 14, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Lu, W.F.[Wen-Fang], Gao, X.Y.[Xin-Yu], Wu, Z.[Zelun], Wang, T.H.[Tian-Hao], Lin, S.[Shaowen], Xiao, C.[Canbo], Lai, Z.G.[Zhi-Gang],
Framework to Extract Extreme Phytoplankton Bloom Events with Remote Sensing Datasets: A Case Study,
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Shin, J.[Jisun], Khim, B.K.[Boo-Keun], Jang, L.H.[Lee-Hyun], Lim, J.[Jinwook], Jo, Y.H.[Young-Heon],
Convolutional neural network model for discrimination of harmful algal bloom (HAB) from non-HABs using Sentinel-3 OLCI imagery,
PandRS(191), 2022, pp. 250-262.
Elsevier DOI 2208
Sentinel-3 OLCI, Convolutional neural network BibRef

Zhang, Y.C.[Yu-Chao], Shi, K.[Kun], Cao, Z.[Zhen], Lai, L.[Lai], Geng, J.P.[Jian-Ping], Yu, K.T.[Kui-Ting], Zhan, P.F.[Peng-Fei], Liu, Z.M.[Zhao-Min],
Effects of satellite temporal resolutions on the remote derivation of trends in phytoplankton blooms in inland waters,
PandRS(191), 2022, pp. 188-202.
Elsevier DOI 2208
Temporal resolutions, Phytoplankton blooms, Inland waters, MODIS, Lake Taihu BibRef

Ma, J.[Jinge], He, F.[Feng], Qi, T.[Tianci], Sun, Z.[Zhe], Shen, M.[Ming], Cao, Z.G.[Zhi-Gang], Meng, D.[Di], Duan, H.T.[Hong-Tao], Luo, J.[Juhua],
Thirty-Four-Year Record (1987-2021) of the Spatiotemporal Dynamics of Algal Blooms in Lake Dianchi from Multi-Source Remote Sensing Insights,
RS(14), No. 16, 2022, pp. xx-yy.
DOI Link 2208
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Ye, W.W.[Wei-Wen], Zhang, F.[Feng], Du, Z.H.[Zhen-Hong],
Machine Learning in Extreme Value Analysis, an Approach to Detecting Harmful Algal Blooms with Long-Term Multisource Satellite Data,
RS(14), No. 16, 2022, pp. xx-yy.
DOI Link 2208
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Ananias, P.H.M.[Pedro Henrique M.], Negri, R.G.[Rogério G.], Dias, M.A.[Maurício A.], Silva, E.A.[Erivaldo A.], Casaca, W.[Wallace],
A Fully Unsupervised Machine Learning Framework for Algal Bloom Forecasting in Inland Waters Using MODIS Time Series and Climatic Products,
RS(14), No. 17, 2022, pp. xx-yy.
DOI Link 2209
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Yang, K.Y.[Kai-Yuan], Wang, Z.H.[Zhong-Hao], Yang, Z.[Zheng], Zheng, P.Y.[Pei-Yang], Yao, S.L.[Shan-Liang], Zhu, X.H.[Xiao-Hui], Yue, Y.[Yong], Wang, W.[Wei], Zhang, J.[Jie], Ma, J.[Jieming],
RecepNet: Network with Large Receptive Field for Real-Time Semantic Segmentation and Application for Blue-Green Algae,
RS(14), No. 21, 2022, pp. xx-yy.
DOI Link 2212
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Zou, Y.[Yibo], Wang, X.L.[Xiao-Liang], Wang, L.[Lei], Chen, K.[Ke], Ge, Y.[Yan], Zhao, L.L.[Lin-Lin],
A High-Quality Instance-Segmentation Network for Floating-Algae Detection Using RGB Images,
RS(14), No. 24, 2022, pp. xx-yy.
DOI Link 2212
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Chen, Y.[Ying], Pan, G.[Gang], Mortimer, R.[Robert], Zhao, H.[Hui],
Possible Mechanism of Phytoplankton Blooms at the Sea Surface after Tropical Cyclones,
RS(14), No. 24, 2022, pp. xx-yy.
DOI Link 2212
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Wang, Z.[Zhuyi], Fan, B.[Bowen], Yu, D.F.[Ding-Feng], Fan, Y.G.[Yan-Guo], An, D.Y.[De-Yu], Pan, S.Q.[Shun-Qi],
Monitoring the Spatio-Temporal Distribution of Ulva prolifera in the Yellow Sea (2020-2022) Based on Satellite Remote Sensing,
RS(15), No. 1, 2023, pp. xx-yy.
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Chapter on Remote Sensing General Issue, Land Use, Land Cover continues in
Cyanobacteria, Analysis, Detection .


Last update:Mar 16, 2024 at 20:36:19