Literature DB >> 29124415

Spatial and temporal distribution of carbon dioxide gas using GOSAT data over IRAN.

Samereh Falahatkar1, Seyed Mohsen Mousavi2, Manochehr Farajzadeh3.   

Abstract

CO2 concentration (XCO2) shows the spatial and temporal variation in Iran. The major purpose of this investigation is the assessment of the spatial distribution of carbon dioxide concentration in the different seasons of 2013 based on the Thermal And Near Infrared Sensor for Carbon Observation-Fourier Transform Spectrometer (TANSO-FTS) level 2 GOSAT data by implementing the ordinary kriging (OK) method. In this study, the Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI) data from the MODerate resolution Imaging Spectroradiometer (MODIS), and metrological parameters (temperature and precipitation) were used for the analysis of the spatial distribution of CO2 over Iran in 2013. The spatial distribution maps of XCO2 show the highest concentration of this gas in the south and south-east and the lowest concentration in the north and north-west. These results indicate that the concentration of carbon dioxide decreased with the increase of LST and temperature and a decrease of NDVI and humidity in the study area. Therefore, the existence of vegetation has an effective role in capturing carbon from the atmosphere by photosynthesis phenomena, and sustainable land management can be effective for carbon absorption from the atmosphere and mitigation of climate change in arid and semi-arid regions.

Entities:  

Keywords:  Climate change; Interpolation; LST; Land cover; NDVI; Satellite monitoring

Mesh:

Substances:

Year:  2017        PMID: 29124415     DOI: 10.1007/s10661-017-6285-8

Source DB:  PubMed          Journal:  Environ Monit Assess        ISSN: 0167-6369            Impact factor:   2.513


  2 in total

1.  Assessment of global carbon dioxide concentration using MODIS and GOSAT data.

Authors:  Meng Guo; Xiufeng Wang; Jing Li; Kunpeng Yi; Guosheng Zhong; Hiroshi Tani
Journal:  Sensors (Basel)       Date:  2012-11-26       Impact factor: 3.576

2.  Correction: Combining XCO2 Measurements Derived from SCIAMACHY and GOSAT for Potentially Generating Global CO2 Maps with High Spatiotemporal Resolution.

Authors:  Tianxing Wang; Jiancheng Shi; Yingying Jing; Tianjie Zhao; Dabin Ji; Chuan Xiong
Journal:  PLoS One       Date:  2016-01-25       Impact factor: 3.240

  2 in total
  2 in total

1.  Differentiating carbon sinks versus sources on a university campus using synergistic UAV NIR and visible signatures.

Authors:  Seong-Il Park; Jung-Sup Um
Journal:  Environ Monit Assess       Date:  2018-10-18       Impact factor: 2.513

2.  An empirical model for estimating daily atmospheric column-averaged CO2 concentration above São Paulo state, Brazil.

Authors:  Luis Miguel da Costa; Gustavo André de Araújo Santos; Alan Rodrigo Panosso; Glauco de Souza Rolim; Newton La Scala
Journal:  Carbon Balance Manag       Date:  2022-06-11
  2 in total

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