Cargando…

Assessment of Global Carbon Dioxide Concentration Using MODIS and GOSAT Data

Carbon dioxide (CO(2)) is the most important greenhouse gas (GHG) in the atmosphere and is the greatest contributor to global warming. CO(2) concentration data are usually obtained from ground observation stations or from a small number of satellites. Because of the limited number of observations an...

Descripción completa

Detalles Bibliográficos
Autores principales: Guo, Meng, Wang, Xiufeng, Li, Jing, Yi, Kunpeng, Zhong, Guosheng, Tani, Hiroshi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Molecular Diversity Preservation International (MDPI) 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3571787/
https://www.ncbi.nlm.nih.gov/pubmed/23443383
http://dx.doi.org/10.3390/s121216368
_version_ 1782259204449370112
author Guo, Meng
Wang, Xiufeng
Li, Jing
Yi, Kunpeng
Zhong, Guosheng
Tani, Hiroshi
author_facet Guo, Meng
Wang, Xiufeng
Li, Jing
Yi, Kunpeng
Zhong, Guosheng
Tani, Hiroshi
author_sort Guo, Meng
collection PubMed
description Carbon dioxide (CO(2)) is the most important greenhouse gas (GHG) in the atmosphere and is the greatest contributor to global warming. CO(2) concentration data are usually obtained from ground observation stations or from a small number of satellites. Because of the limited number of observations and the short time series of satellite data, it is difficult to monitor CO(2) concentrations on regional or global scales for a long time. The use of the remote sensing data such as the Advanced Very High Resolution Radiometer (AVHRR) or Moderate Resolution Imaging Spectroradiometer (MODIS) data can overcome these problems, particularly in areas with low densities of CO(2) concentration watch stations. A model based on temperature (MOD11C3), vegetation cover (MOD13C2 and MOD15A2) and productivity (MOD17A2) of MODIS (which we have named the TVP model) was developed in the current study to assess CO(2) concentrations on a global scale. We assumed that CO(2) concentration from the Thermal And Near infrared Sensor for carbon Observation (TANSO) aboard the Greenhouse gases Observing SATellite (GOSAT) are the true values and we used these values to check the TVP model accuracy. The results indicate that the accuracy of the TVP model is different in different continents: the greatest Pearson’s correlation coefficient (R(2)) was 0.75 in Eurasia (RMSE = 1.16) and South America (RMSE = 1.17); the lowest R(2) was 0.57 in Australia (RMSE = 0.73). Compared with the TANSO-observed CO(2) concentration (XCO(2)), we found that the accuracy throughout the World is between −2.56∼3.14 ppm. Potential sources of TVP model uncertainties were also analyzed and identified.
format Online
Article
Text
id pubmed-3571787
institution National Center for Biotechnology Information
language English
publishDate 2012
publisher Molecular Diversity Preservation International (MDPI)
record_format MEDLINE/PubMed
spelling pubmed-35717872013-02-19 Assessment of Global Carbon Dioxide Concentration Using MODIS and GOSAT Data Guo, Meng Wang, Xiufeng Li, Jing Yi, Kunpeng Zhong, Guosheng Tani, Hiroshi Sensors (Basel) Article Carbon dioxide (CO(2)) is the most important greenhouse gas (GHG) in the atmosphere and is the greatest contributor to global warming. CO(2) concentration data are usually obtained from ground observation stations or from a small number of satellites. Because of the limited number of observations and the short time series of satellite data, it is difficult to monitor CO(2) concentrations on regional or global scales for a long time. The use of the remote sensing data such as the Advanced Very High Resolution Radiometer (AVHRR) or Moderate Resolution Imaging Spectroradiometer (MODIS) data can overcome these problems, particularly in areas with low densities of CO(2) concentration watch stations. A model based on temperature (MOD11C3), vegetation cover (MOD13C2 and MOD15A2) and productivity (MOD17A2) of MODIS (which we have named the TVP model) was developed in the current study to assess CO(2) concentrations on a global scale. We assumed that CO(2) concentration from the Thermal And Near infrared Sensor for carbon Observation (TANSO) aboard the Greenhouse gases Observing SATellite (GOSAT) are the true values and we used these values to check the TVP model accuracy. The results indicate that the accuracy of the TVP model is different in different continents: the greatest Pearson’s correlation coefficient (R(2)) was 0.75 in Eurasia (RMSE = 1.16) and South America (RMSE = 1.17); the lowest R(2) was 0.57 in Australia (RMSE = 0.73). Compared with the TANSO-observed CO(2) concentration (XCO(2)), we found that the accuracy throughout the World is between −2.56∼3.14 ppm. Potential sources of TVP model uncertainties were also analyzed and identified. Molecular Diversity Preservation International (MDPI) 2012-11-26 /pmc/articles/PMC3571787/ /pubmed/23443383 http://dx.doi.org/10.3390/s121216368 Text en © 2012 by the authors; licensee MDPI, Basel, Switzerland This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
spellingShingle Article
Guo, Meng
Wang, Xiufeng
Li, Jing
Yi, Kunpeng
Zhong, Guosheng
Tani, Hiroshi
Assessment of Global Carbon Dioxide Concentration Using MODIS and GOSAT Data
title Assessment of Global Carbon Dioxide Concentration Using MODIS and GOSAT Data
title_full Assessment of Global Carbon Dioxide Concentration Using MODIS and GOSAT Data
title_fullStr Assessment of Global Carbon Dioxide Concentration Using MODIS and GOSAT Data
title_full_unstemmed Assessment of Global Carbon Dioxide Concentration Using MODIS and GOSAT Data
title_short Assessment of Global Carbon Dioxide Concentration Using MODIS and GOSAT Data
title_sort assessment of global carbon dioxide concentration using modis and gosat data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3571787/
https://www.ncbi.nlm.nih.gov/pubmed/23443383
http://dx.doi.org/10.3390/s121216368
work_keys_str_mv AT guomeng assessmentofglobalcarbondioxideconcentrationusingmodisandgosatdata
AT wangxiufeng assessmentofglobalcarbondioxideconcentrationusingmodisandgosatdata
AT lijing assessmentofglobalcarbondioxideconcentrationusingmodisandgosatdata
AT yikunpeng assessmentofglobalcarbondioxideconcentrationusingmodisandgosatdata
AT zhongguosheng assessmentofglobalcarbondioxideconcentrationusingmodisandgosatdata
AT tanihiroshi assessmentofglobalcarbondioxideconcentrationusingmodisandgosatdata