A neural network-based four-band model for estimating the total absorption coefficients from the global oceanic and coastal waters-Reference-Cited by-同舟云学术

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A neural network-based four-band model for estimating the total absorption coefficients from the global oceanic and coastal waters

Published:2015-01 Issue:1 Volume:120 Page:36-49
ISSN:2169-9275
Container-title:Journal of Geophysical Research: Oceans
language:en
Short-container-title:J. Geophys. Res. Oceans

Author:

Chen Jun¹²,Cui Tingwei³,Quan Wenting⁴

Affiliation:

1. School of Ocean Sciences, China University of Geosciences; Beijing China

2. Qingdao Institute of Marine Geology; Qingdao China

3. First Institute of Oceanography, State Oceanic Administration; Qingdao China

4. Shanxi Agricultural Remote Sensing Information; Xian China

Funder

National Natural Science Foundation of China

Publisher

American Geophysical Union (AGU)

Subject

Earth and Planetary Sciences (miscellaneous),Space and Planetary Science,Geochemistry and Petrology,Geophysics,Oceanography

Reference49 articles.

1. A multi-sensor approach for the on-orbit validation of ocean color satellite data products;Bailey;Remote Sens. Environ.,2006

2. Impact of sea ice on the retrieval of water-leaving reflectance, chlorophyll a concentration and inherent optical properties from satellite ocean color data;Bélanger;Remote Sens. Environ.,2007

3. Estimating global chlorophyll changes over the past century;Boyce;Prog. Oceanogr.,2014

4. Atmospheric correction of MERIS data for case-2 waters using a neuro-variational inversion;Brajard;Remote Sens. Environ.,2012

5. A novel technique for detection of the toxic dinoflagellate, Kareniabrevis, in the Gulf of Mexico from remotely sensed ocean color data;Cannizzaro;Cont. Shelf Res.,2008

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2. Deriving colored dissolved organic matter absorption coefficient from ocean color with a neural quasi-analytical algorithm;Journal of Geophysical Research: Oceans;2017-11

3. Estimating sea surface salinity in the northern Gulf of Mexico from satellite ocean color measurements;Remote Sensing of Environment;2017-11

4. A system to measure the data quality of spectral remote sensing reflectance of aquatic environments;Journal of Geophysical Research: Oceans;2016-10

5. A Neural Network Model for K(λ) Retrieval and Application to Global Kpar Monitoring;PLOS ONE;2015-06-17

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