A machine learning approach to estimate surface ocean pCO<SUB>2</SUB> from satellite measurements

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    W[2019AGUFM.A43R3135C]
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ADS bibcode
2019AGUFM.A43R3135C
year
2019
Listed Authors
Chen, S.
Hu, C.
Barnes, B. B.
Wanninkhof, R. H.
Cai, W. J.
Barbero, L.
Pierrot, D.
Listed Institutions
Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou, China
College of Marine Science, University of South Florida, St. Petersburg, FL, United States
University of South Florida, St. Petersburg, FL, United States
Ocean Chemistry and Ecosystems Division, NOAA Atlantic Oceanographic and Meteorological Laboratory, Miami, FL, United States
School of Marine Science and Policy, University of Delaware, Newark, DE, United States
Ocean Chemistry and Ecosystems Division, NOAA Atlantic Oceanographic and Meteorological Laboratory, Miami, FL, United States
Cooperative Institute for Marine and Atmospheric Studies Miami, Miami, FL, United StatesAF: Ocean Chemistry and Ecosystems Division, NOAA Atlantic Oceanographic and Meteorological Laboratory, Miami, FL, United States

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