A Machine Learning Approach to Estimate Surface Ocean PCO2 from Satellite Measurements
Surface seawater partial pressure of CO2 (pCO2) is a critical parameter in the quantification of air-sea CO2 flux, which further plays an important role in quantifying the global carbon budget and understanding ocean acidification. Yet, the remote estimation of pCO2 in coastal waters (under influenc...
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ftusouthflorida:oai:digitalcommons.usf.edu:msc_facpub-2894 2023-07-30T04:06:06+02:00 A Machine Learning Approach to Estimate Surface Ocean PCO2 from Satellite Measurements Chen, Shuangling Hu, Chuanmin Barnes, Brian B. Wanninkhof, Rik Cai, Wei-Jun Barbero, Leticia Pierrot, Denis 2019-01-01T08:00:00Z https://digitalcommons.usf.edu/msc_facpub/2034 https://doi.org/10.1016/j.rse.2019.04.019 unknown Digital Commons @ University of South Florida https://digitalcommons.usf.edu/msc_facpub/2034 doi:10.1016/j.rse.2019.04.019 https://doi.org/10.1016/j.rse.2019.04.019 Marine Science Faculty Publications Surface pCO2 SST SSS Chlorophyll Kd Satellite remote sensing Gulf of Mexico Life Sciences article 2019 ftusouthflorida https://doi.org/10.1016/j.rse.2019.04.019 2023-07-13T21:01:51Z Surface seawater partial pressure of CO2 (pCO2) is a critical parameter in the quantification of air-sea CO2 flux, which further plays an important role in quantifying the global carbon budget and understanding ocean acidification. Yet, the remote estimation of pCO2 in coastal waters (under influences of multiple processes) has been difficult due to complex relationships between environmental variables and surface pCO2. To date there is no unified model to remotely estimate surface pCO2 in oceanic regions that are dominated by different oceanic processes. In our study area, the Gulf of Mexico (GOM), this challenge is addressed through the evaluation of different approaches, including multi-linear regression (MLR), multi-nonlinear regression (MNR), principle component regression (PCR), decision tree, supporting vector machines (SVMs), multilayer perceptron neural network (MPNN), and random forest based regression ensemble (RFRE). After modeling, validation, and extensive tests using independent cruise datasets, the RFRE model proved to be the best approach. The RFRE model was trained using data comprised of extensive pCO2 datasets (collected over 16 years by many groups) and MODIS (Moderate Resolution Imaging Spectroradiometer) estimated sea surface temperature (SST), sea surface salinity (SSS), surface chlorophyll concentration (Chl), and diffuse attenuation of downwelling irradiance (Kd). This RFRE-based pCO2 model allows for the estimation of surface pCO2 from satellites with a spatial resolution of ~1 km. It showed an overall performance of a root mean square difference (RMSD) of 9.1 μatm, with a coefficient of determination (R2) of 0.95, a mean bias (MB) of −0.03 μatm, a mean ratio (MR) of 1.00, an unbiased percentage difference (UPD) of 0.07%, and a mean ratio difference (MRD) of 0.12% for pCO2 ranging between 145 and 550 μatm. The model, with its original parameterization, has been tested with independent datasets collected over the entire GOM, with satisfactory performance in each case (RMSD of ≤~10 μatm ... Article in Journal/Newspaper Ocean acidification University of South Florida St. Petersburg: Digital USFSP Remote Sensing of Environment 228 203 226 |
institution |
Open Polar |
collection |
University of South Florida St. Petersburg: Digital USFSP |
op_collection_id |
ftusouthflorida |
language |
unknown |
topic |
Surface pCO2 SST SSS Chlorophyll Kd Satellite remote sensing Gulf of Mexico Life Sciences |
spellingShingle |
Surface pCO2 SST SSS Chlorophyll Kd Satellite remote sensing Gulf of Mexico Life Sciences Chen, Shuangling Hu, Chuanmin Barnes, Brian B. Wanninkhof, Rik Cai, Wei-Jun Barbero, Leticia Pierrot, Denis A Machine Learning Approach to Estimate Surface Ocean PCO2 from Satellite Measurements |
topic_facet |
Surface pCO2 SST SSS Chlorophyll Kd Satellite remote sensing Gulf of Mexico Life Sciences |
description |
Surface seawater partial pressure of CO2 (pCO2) is a critical parameter in the quantification of air-sea CO2 flux, which further plays an important role in quantifying the global carbon budget and understanding ocean acidification. Yet, the remote estimation of pCO2 in coastal waters (under influences of multiple processes) has been difficult due to complex relationships between environmental variables and surface pCO2. To date there is no unified model to remotely estimate surface pCO2 in oceanic regions that are dominated by different oceanic processes. In our study area, the Gulf of Mexico (GOM), this challenge is addressed through the evaluation of different approaches, including multi-linear regression (MLR), multi-nonlinear regression (MNR), principle component regression (PCR), decision tree, supporting vector machines (SVMs), multilayer perceptron neural network (MPNN), and random forest based regression ensemble (RFRE). After modeling, validation, and extensive tests using independent cruise datasets, the RFRE model proved to be the best approach. The RFRE model was trained using data comprised of extensive pCO2 datasets (collected over 16 years by many groups) and MODIS (Moderate Resolution Imaging Spectroradiometer) estimated sea surface temperature (SST), sea surface salinity (SSS), surface chlorophyll concentration (Chl), and diffuse attenuation of downwelling irradiance (Kd). This RFRE-based pCO2 model allows for the estimation of surface pCO2 from satellites with a spatial resolution of ~1 km. It showed an overall performance of a root mean square difference (RMSD) of 9.1 μatm, with a coefficient of determination (R2) of 0.95, a mean bias (MB) of −0.03 μatm, a mean ratio (MR) of 1.00, an unbiased percentage difference (UPD) of 0.07%, and a mean ratio difference (MRD) of 0.12% for pCO2 ranging between 145 and 550 μatm. The model, with its original parameterization, has been tested with independent datasets collected over the entire GOM, with satisfactory performance in each case (RMSD of ≤~10 μatm ... |
format |
Article in Journal/Newspaper |
author |
Chen, Shuangling Hu, Chuanmin Barnes, Brian B. Wanninkhof, Rik Cai, Wei-Jun Barbero, Leticia Pierrot, Denis |
author_facet |
Chen, Shuangling Hu, Chuanmin Barnes, Brian B. Wanninkhof, Rik Cai, Wei-Jun Barbero, Leticia Pierrot, Denis |
author_sort |
Chen, Shuangling |
title |
A Machine Learning Approach to Estimate Surface Ocean PCO2 from Satellite Measurements |
title_short |
A Machine Learning Approach to Estimate Surface Ocean PCO2 from Satellite Measurements |
title_full |
A Machine Learning Approach to Estimate Surface Ocean PCO2 from Satellite Measurements |
title_fullStr |
A Machine Learning Approach to Estimate Surface Ocean PCO2 from Satellite Measurements |
title_full_unstemmed |
A Machine Learning Approach to Estimate Surface Ocean PCO2 from Satellite Measurements |
title_sort |
machine learning approach to estimate surface ocean pco2 from satellite measurements |
publisher |
Digital Commons @ University of South Florida |
publishDate |
2019 |
url |
https://digitalcommons.usf.edu/msc_facpub/2034 https://doi.org/10.1016/j.rse.2019.04.019 |
genre |
Ocean acidification |
genre_facet |
Ocean acidification |
op_source |
Marine Science Faculty Publications |
op_relation |
https://digitalcommons.usf.edu/msc_facpub/2034 doi:10.1016/j.rse.2019.04.019 https://doi.org/10.1016/j.rse.2019.04.019 |
op_doi |
https://doi.org/10.1016/j.rse.2019.04.019 |
container_title |
Remote Sensing of Environment |
container_volume |
228 |
container_start_page |
203 |
op_container_end_page |
226 |
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1772818512990961664 |