scholarly journals Verification of Chlorophyll a Concentration Estimated from Ocean Color Data in Omura Bay.

2002 ◽  
Vol 11 (2) ◽  
pp. 235-241
Author(s):  
Joji Ishizaka ◽  
Kiyofumi Tashima ◽  
Motoaki Kishino
Keyword(s):  
Chlorophyll A ◽  
Ocean Color ◽  
Chlorophyll A Concentration ◽  
Color Data

Estimation of chlorophyll-a concentration from satellite ocean color data in upper Gulf of Thailand

2006 ◽  
Author(s):  
Mitsuhiro Toratani ◽  
Hiroshi Kobayashi ◽  
Satsuki Matsumura ◽  
Absonsuda Siripong ◽  
Thaithaworn Lerdwithayaprasith
Keyword(s):  
Chlorophyll A ◽  
Ocean Color ◽  
Gulf Of Thailand ◽  
Chlorophyll A Concentration ◽  
Color Data ◽  
Satellite Ocean Color ◽  
Upper Gulf Of Thailand

Assessment and analysis of the chlorophyll-a concentration variability over the Vietnamese coastal waters from the MERIS ocean color sensor (2002–2012)

2017 ◽  
Vol 190 ◽  
pp. 217-232 ◽  
Author(s):  
Hubert Loisel ◽  
Vincent Vantrepotte ◽  
Sylvain Ouillon ◽  
Dat Dinh Ngoc ◽  
Marine Herrmann ◽  
...  
Keyword(s):  
Chlorophyll A ◽  
Coastal Waters ◽  
Ocean Color ◽  
Chlorophyll A Concentration ◽  
Color Sensor

Regional empirical algorithm for an improved retrieval of chlorophyll a concentrations in the Black Sea using Sentinel 3 ocean color data

2021 ◽  
Author(s):  
Violeta Slabakova ◽  
Snejana Moncheva ◽  
Nataliya Slabakova ◽  
Nina Dzembekova
Keyword(s):  
Remote Sensing ◽  
Chlorophyll A ◽  
Black Sea ◽  
Ocean Color ◽  
The Black Sea ◽  
Ocean Color Remote Sensing ◽  
Color Data ◽  
Cubic Polynomial ◽  
Polynomial Formulation

<p>The Black Sea is an extraordinarily complex water body for ocean color remote sensing, as it belong to Case 2 waters, which are characterized by relatively high absorption by Colored Dissolved Organic Matter (CDOM) while the concentration of non-pigmented particulate matter does not co-vary in a predictable manner with chlorophyll <em>a</em> . The optical complexity of the Black Sea is the reason why the standard bio-optical algorithms developed for Case 1 waters, are the source of large uncertainties (of the order of hundreds of percent) of chlorophyll <em>a</em> concentration in the coastal and shelf regions. In the framework of ESA contract “BIO-OPTICS FOR OCEAN COLOR REMOTE SENSING OF THE BLACK SEA - Black Sea Color” we developed empirical ocean color algorithm for chlorophyll<em> a </em>retrieval from Sentinel 3A/OLCI primary ocean color products using the <em>in situ </em>reference bio-optical datasets collected in the Black Sea in the period 2012-2019. Results obtained from the assessment of operational S3A/OLCI chlorophyll products, highlighted and confirmed that the specific regional algorithm is essential for the Black Sea. The coefficients of the regional algorithm were derived from the regression of log-transformed pigment concentrations and remote sensing reflectance ratio at 490nm and 560 nm with determination coefficient R<sup>2</sup> =0.88 and number of samples N=186. The algorithm predicts chlorophyll a values using a cubic polynomial formulation. The result of assessment of the regional chlorophyll <em>a</em> product against independent in situ measurements from the data utilized for algorithm development, showed relatively high accuracy (31.7%), fewer underestimations (MPD=-9.2%) and a good agreement (R<sup>2</sup>=0.66) between datasets indicating that the regional algorithm is more effective in reproducing the  pigment concentration in the Black Sea waters in comparison to the standard Sentinel 3A/OLCI algorithms. Our analysis revealed the importance of providing regional algorithms strictly required to suit the peculiar bio-optical properties featuring this basin. However, this requires collection of accurate<em> in situ </em>measurements in the different parts of the Black Sea. The validity of the reported empirical algorithm obviously depends on the size of the dataset used for its development. The Black Sea waters vary at a basin level due to the sub-regional features, environmental factors and seasonal variability, consequently the presented regional algorithm might have a limited generalization capability. Clearly, more<em> in situ</em> data with improved spatial and temporal coverage are critically needed for further calibration and validation of the ocean color products in the Black Sea.</p>

Role of Remote Sensing in Potential Fishing Zone Forecast

2019 ◽  
pp. 199-210
Author(s):  
Abhisek Santra ◽  
Debashis Mitra
Keyword(s):  
Remote Sensing ◽  
Chlorophyll A ◽  
Environmental Conditions ◽  
Satellite Remote Sensing ◽  
Thermal Condition ◽  
Ocean Color ◽  
Chlorophyll A Concentration ◽  
Potential Fishing Zone ◽  
Indian Scenario

Forecasting of Potential Fishing Zone (PFZ) is considered as economically and environmentally significant towards ensuring profitable base of economy and planning for sustenance of existing fishing pool. Changes in environmental conditions affect the distribution, abundance and availability of fish. The traditional sampling approaches for PFZ identification using boats and vessels are not only costly and time consuming but practically absurd considering the vastness of seas and oceans. In this chapter importance of alternative but effective methods of airborne and satellite remote sensing has been given. The chapter elucidates the factors for PFZ identification like thermal condition in sea controlling its thermal circulation, chlorophyll-a concentration estimated from ocean color dynamics, etc. Tools/system to prepare PFZ advisories and also the platforms for dissemination of the same, have been illustrated based on Indian scenario.

Role of Remote Sensing in Potential Fishing Zone Forecast

2017 ◽  
pp. 243-257
Author(s):  
Abhisek Santra ◽  
Debashis Mitra
Keyword(s):  
Remote Sensing ◽  
Chlorophyll A ◽  
Environmental Conditions ◽  
Satellite Remote Sensing ◽  
Thermal Condition ◽  
Ocean Color ◽  
Chlorophyll A Concentration ◽  
Potential Fishing Zone ◽  
Indian Scenario

Forecasting of Potential Fishing Zone (PFZ) is considered as economically and environmentally significant towards ensuring profitable base of economy and planning for sustenance of existing fishing pool. Changes in environmental conditions affect the distribution, abundance and availability of fish. The traditional sampling approaches for PFZ identification using boats and vessels are not only costly and time consuming but practically absurd considering the vastness of seas and oceans. In this chapter importance of alternative but effective methods of airborne and satellite remote sensing has been given. The chapter elucidates the factors for PFZ identification like thermal condition in sea controlling its thermal circulation, chlorophyll-a concentration estimated from ocean color dynamics, etc. Tools/system to prepare PFZ advisories and also the platforms for dissemination of the same, have been illustrated based on Indian scenario.

Improved Chlorophyll-a Algorithm for the Satellite Ocean Color Data in the Northern Bering Sea and Southern Chukchi Sea

2018 ◽  
Vol 53 (3) ◽  
pp. 475-485 ◽  
Author(s):  
Sang Heon Lee ◽  
Jongseong Ryu ◽  
Jung-woo Park ◽  
Dabin Lee ◽  
Jae-Il Kwon ◽  
...  
Keyword(s):  
Chlorophyll A ◽  
Bering Sea ◽  
Chukchi Sea ◽  
Ocean Color ◽  
A Algorithm ◽  
Northern Bering Sea ◽  
Color Data ◽  
Satellite Ocean Color

Role of Remote Sensing in Potential Fishing Zone Forecast

2019 ◽  
pp. 361-372
Author(s):  
Abhisek Santra ◽  
Debashis Mitra
Keyword(s):  
Remote Sensing ◽  
Chlorophyll A ◽  
Environmental Conditions ◽  
Satellite Remote Sensing ◽  
Thermal Condition ◽  
Ocean Color ◽  
Chlorophyll A Concentration ◽  
Potential Fishing Zone ◽  
Indian Scenario

Forecasting of Potential Fishing Zone (PFZ) is considered as economically and environmentally significant towards ensuring profitable base of economy and planning for sustenance of existing fishing pool. Changes in environmental conditions affect the distribution, abundance and availability of fish. The traditional sampling approaches for PFZ identification using boats and vessels are not only costly and time consuming but practically absurd considering the vastness of seas and oceans. In this chapter importance of alternative but effective methods of airborne and satellite remote sensing has been given. The chapter elucidates the factors for PFZ identification like thermal condition in sea controlling its thermal circulation, chlorophyll-a concentration estimated from ocean color dynamics, etc. Tools/system to prepare PFZ advisories and also the platforms for dissemination of the same, have been illustrated based on Indian scenario.

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