scholarly journals Spectral Classification of the Yellow Sea and Implications for Coastal Ocean Color Remote Sensing

2016 ◽  
Vol 8 (4) ◽  
pp. 321 ◽  
Author(s):  
Huping Ye ◽  
Junsheng Li ◽  
Tongji Li ◽  
Qian Shen ◽  
Jianhua Zhu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Yellow Sea ◽  
Ocean Color ◽  
Coastal Ocean ◽  
The Yellow Sea ◽  
Spectral Classification ◽  
Ocean Color Remote Sensing

scholarly journals Primary production in the Yellow Sea determined by ocean color remote sensing

2005 ◽  
Vol 303 ◽  
pp. 91-103 ◽  
Author(s):  
SH Son ◽  
J Campbell ◽  
M Dowell ◽  
S Yoo ◽  
J Noh
Keyword(s):  
Remote Sensing ◽  
Primary Production ◽  
Yellow Sea ◽  
Ocean Color ◽  
The Yellow Sea ◽  
Ocean Color Remote Sensing

Multi-band optical imaging sensor for coastal ocean color remote sensing

2019 ◽  
Author(s):  
Hiroyuki Tan ◽  
Tomohiko Oishi ◽  
Akihiko Tanaka ◽  
Takaaki Katsumata ◽  
Masato Niki
Keyword(s):  
Remote Sensing ◽  
Optical Imaging ◽  
Ocean Color ◽  
Coastal Ocean ◽  
Ocean Color Remote Sensing ◽  
Imaging Sensor ◽  
Multi Band

Non-supervised classification of aerosol mixtures for ocean color remote sensing

2003 ◽  
Author(s):  
Lydwine Gross-Colzy ◽  
Robert J. Frouin ◽  
Christophe M. Pietras ◽  
Giulietta S. Fargion
Keyword(s):  
Remote Sensing ◽  
Supervised Classification ◽  
Ocean Color ◽  
Ocean Color Remote Sensing

scholarly journals Evaluation of Four Atmospheric Correction Algorithms for GOCI Images over the Yellow Sea

2019 ◽  
Vol 11 (14) ◽  
pp. 1631 ◽  
Author(s):  
Xiaocan Huang ◽  
Jianhua Zhu ◽  
Bing Han ◽  
Cédric Jamet ◽  
Zhen Tian ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Yellow Sea ◽  
Near Infrared ◽  
Ocean Color ◽  
Atmospheric Correction ◽  
The Yellow Sea ◽  
Optical Parameters ◽  
Band Ratio ◽  
Percentage Difference

Atmospheric correction (AC) for coastal waters is an important issue in ocean color remote sensing. AC performance is fundamental in retrieving reliable water-leaving radiances and then bio-optical parameters. Unlike polar-orbiting satellites, geostationary ocean color sensors allow high-frequency (15–60 min) monitoring of ocean color over the same area. The first geostationary ocean color sensor, i.e., the Geostationary Ocean Color Imager (GOCI), was launched in 2010. Using GOCI data acquired over the Yellow Sea in summer 2017 at three principal overpass times (02:16, 03:16, 04:16 UTC) with ±1 and ±3 h match-up times, this study compared four GOCI AC algorithms: (1) the standard near infrared (NIR) algorithm of NASA (NASA-STD), (2) the Korea Ocean Satellite Center (KOSC) standard algorithm for GOCI (KOSC-STD), (3) the diffuse attenuation coefficient at 490 nm Kd (490)-based NIR correction algorithm (Kd-based), and (4) the Management Unit of the North Sea Mathematical Models (MUMM). The GOCI-estimated remote sensing reflectance (Rrs), aerosol parameters [aerosol optical thickness (AOT), Angström Exponent (AE)], and chlorophyll-a (Chla) were validated using in situ data. For Rrs, AOT, AE, and Chla, GOCI-retrieved results performed well within the ±1 h temporal window, but the number of match-ups was extended within the ±3 h match-up window. For ±3 h GOCI-derived Rrs, all algorithms had an absolute percentage difference (APD) at 490 and 555 nm of <40%, while other bands showed larger differences (APD > 60%). Compared with in situ values, the APD of the Rrs(490)/Rrs(555) band ratio was <20% for all ACs. For AOT and AE, the APD was >40% and >200%, respectively. Of the four algorithms, the KOSC-STD algorithm demonstrated satisfactory performance in deriving Rrs for the region of interest (Rrs APD: 22.23%–73.95%) in the visible bands. The Kd-based algorithm worked well obtaining Ocean Color 3 GOCI Chla because Rrs(443) is more accurate than the KOSC-STD. The poorest Rrs retrievals were achieved using the NASA-STD and the MUMM algorithms. Statistical analysis indicated that all methods had optimal performance at 04:16 UTC.

scholarly journals Global land mask for satellite ocean color remote sensing

2021 ◽  
Vol 257 ◽  
pp. 112356
Author(s):  
Karlis Mikelsons ◽  
Menghua Wang ◽  
Xiao-Long Wang ◽  
Lide Jiang
Keyword(s):  
Remote Sensing ◽  
Ocean Color ◽  
Ocean Color Remote Sensing ◽  
Global Land ◽  
Satellite Ocean Color

scholarly journals Assessing Phytoplankton Bloom Phenology in Upwelling-Influenced Regions Using Ocean Color Remote Sensing

2021 ◽  
Vol 13 (4) ◽  
pp. 675
Author(s):  
Afonso Ferreira ◽  
Vanda Brotas ◽  
Carla Palma ◽  
Carlos Borges ◽  
Ana C. Brito
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Phytoplankton Bloom ◽  
Ocean Color ◽  
Coastal Region ◽  
Coastal Areas ◽  
Ocean Color Remote Sensing ◽  
Chl A ◽  
Peak Biomass ◽  
Phytoplankton Communities

Phytoplankton bloom phenology studies are fundamental for the understanding of marine ecosystems. Mismatches between fish spawning and plankton peak biomass will become more frequent with climate change, highlighting the need for thorough phenology studies in coastal areas. This study was the first to assess phytoplankton bloom phenology in the Western Iberian Coast (WIC), a complex coastal region in SW Europe, using a multisensor long-term ocean color remote sensing dataset with daily resolution. Using surface chlorophyll a (chl-a) and biogeophysical datasets, five phenoregions (i.e., areas with coherent phenology patterns) were defined. Oceanic phytoplankton communities were seen to form long, low-biomass spring blooms, mainly influenced by atmospheric phenomena and water column conditions. Blooms in northern waters are more akin to the classical spring bloom, while blooms in southern waters typically initiate in late autumn and terminate in late spring. Coastal phytoplankton are characterized by short, high-biomass, highly heterogeneous blooms, as nutrients, sea surface height, and horizontal water transport are essential in shaping phenology. Wind-driven upwelling and riverine input were major factors influencing bloom phenology in the coastal areas. This work is expected to contribute to the management of the WIC and other upwelling systems, particularly under the threat of climate change.

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