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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Molecular Diversity Preservation International (MDPI)
2012
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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 |
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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 |
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