Asian dust: Spatial and optical properties as seen by satellite ocean color sensors

2002 ◽  
Author(s):  
H. Fukushima ◽  
M. Schmidt ◽  
B.J. Sohn ◽  
W. Takahashi ◽  
M. Toratani ◽  
...  
Keyword(s):  
Optical Properties ◽  
Ocean Color ◽  
Asian Dust ◽  
Color Sensors ◽  
Satellite Ocean Color

scholarly journals Optical properties of Forel-Ule water types deduced from 15 years of global satellite ocean color observations

2019 ◽  
Vol 231 ◽  
pp. 111249 ◽  
Author(s):  
Jaime Pitarch ◽  
Hendrik J. van der Woerd ◽  
Robert J.W. Brewin ◽  
Oliver Zielinski
Keyword(s):  
Optical Properties ◽  
Ocean Color ◽  
Water Types ◽  
Satellite Ocean Color

scholarly journals Dynamic range and sensitivity requirements of satellite ocean color sensors: learning from the past

2012 ◽  
Vol 51 (25) ◽  
pp. 6045 ◽  
Author(s):  
Chuanmin Hu ◽  
Lian Feng ◽  
Zhongping Lee ◽  
Curtiss O. Davis ◽  
Antonio Mannino ◽  
...  
Keyword(s):  
Dynamic Range ◽  
Ocean Color ◽  
The Past ◽  
Color Sensors ◽  
Satellite Ocean Color

scholarly journals ESTIMATION OF OCEANIC PARTICULATE ORGANIC CARBON WITH MACHINE LEARNING

2020 ◽  
Vol V-2-2020 ◽  
pp. 949-956
Author(s):  
R. Sauzède ◽  
J. E. Johnson ◽  
H. Claustre ◽  
G. Camps-Valls ◽  
A. B. Ruescas
Keyword(s):  
Machine Learning ◽  
Optical Properties ◽  
Organic Carbon ◽  
Vertical Distribution ◽  
Particulate Organic Carbon ◽  
Ocean Color ◽  
Global Scale ◽  
Vertical Profiles ◽  
The Vertical Distribution ◽  
Satellite Ocean Color

Abstract. Understanding and quantifying ocean carbon sinks of the planet is of paramount relevance in the current scenario of global change. Particulate organic carbon (POC) is a key biogeochemical parameter that helps us characterize export processes of the ocean. Ocean color observations enable the estimation of bio-optical proxies of POC (i.e. particulate backscattering coefficient, bbp) in the surface layer of the ocean quasi-synoptically. In parallel, the Argo program distributes vertical profiles of the physical properties with a global coverage and a high spatio-temporal resolution. Merging satellite ocean color and Argo data using a neural networkbased method has already shown strong potential to infer the vertical distribution of bio-optical properties at global scale with high space-time resolution. This method is trained and validated using a database of concurrent vertical profiles of temperature, salinity, and bio-optical properties, i.e. bbp, collected by Biogeochemical-Argo (BGC-Argo) floats, matched up with satellite ocean color products. The present study aims at improving this method by 1) using a larger dataset from BGC-Argo network since 2016 for training, 2) using additional inputs such as altimetry data, which provide significant information on mesoscale processes impacting the vertical distribution of bbp, 3) improving the vertical resolution of estimation, and 4) examining the potential of alternative machine learning-based techniques. As a first attempt with the new data, we used some feature-specific preprocessing routines followed by a Multi-Output Random Forest algorithm on two regions with different ocean dynamics: North Atlantic and Subtropical Gyres. The statistics and the bbp profiles obtained from the validation floats show promising results and suggest this direction is worth investigating even further at global scale.

Out-of-band effects of satellite ocean color sensors

2016 ◽  
Vol 55 (9) ◽  
pp. 2312 ◽  
Author(s):  
Menghua Wang ◽  
Puneeta Naik ◽  
SeungHyun Son
Keyword(s):  
Ocean Color ◽  
Color Sensors ◽  
Satellite Ocean Color

Vicarious calibration of satellite ocean color sensors at two coastal sites

2010 ◽  
Vol 49 (5) ◽  
pp. 798 ◽  
Author(s):  
Frédéric Mélin ◽  
Giuseppe Zibordi
Keyword(s):  
Ocean Color ◽  
Vicarious Calibration ◽  
Color Sensors ◽  
Satellite Ocean Color

Shallow water bathymetry with multi-spectral satellite ocean color sensors: Leveraging temporal variation in image data

2020 ◽  
Vol 250 ◽  
pp. 112035
Author(s):  
Jianwei Wei ◽  
Menghua Wang ◽  
Zhongping Lee ◽  
Henry O. Briceño ◽  
Xiaolong Yu ◽  
...  
Keyword(s):  
Temporal Variation ◽  
Shallow Water ◽  
Ocean Color ◽  
Image Data ◽  
Color Sensors ◽  
Satellite Ocean Color
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