• Journal of Korean Society for Geospatial Information Science
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• v.22 no.2
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• pp.11-16
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• 2014

GOSAT satellite $CO_2$ signal in years (June, 2009-December 2012) were compared with ground-based measurement at Anmyeon-do Korea Global Atmosphere Watch Center (KGAWC), located on the west coast of the Korean Peninsula. The result reveals that GOSAT signature is closely associated with ground-based measurement($R^2=0.49$). Strong correlation occurred between summer and fall ($R^2=0.62$) while weak relationship between satellite and ground-based measurement were identified in winter and spring ($R^2=0.37$). Average $CO_2$ concentration of GOSAT were 6.31 ppm lower than the corresponding values obtained from ground-based measurements on the same date. It is anticipated that this research output could be used as a valuable reference in introducing GOSAT to confirm data quality assurance for area-wide carbon monitoring process in relation to Anmyeon-do KGAWC $CO_2$ Concentration data.

• Korean Journal of Remote Sensing
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• v.29 no.6
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• pp.657-662
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• 2013

The comparison between $CO_2$ volume mixing ratios observed by GOSAT and TCCON from September 2009 through November 2012 was performed at Tsukuba and Saga, two downwind sites in East Asia. The temporal trends of $CO_2$ values obtained from GOSAT show good agreement with those observed by TCCON at these two by the TCCON, showing a coefficient of determination ($R^2$) of 0.65. The regression slop we obtained was 0.92, showing a small bias of GOSAT $CO_2$ values compared to those observed by TCCON. However, we found the higher correlation in fall and winter than that in spring and summer. The $CO_2$ volume mixing ratios observ sites. The $CO_2$ volume mixing ratios observed by GOSAT are also in good agreement with those measured ed by GOSAT are in good agreement with those measured by the TCCON at those two sites in fall and winter, showing a coefficient of determination ($R^2$) of 0.66 where as the correlation of determination obtained between GOSAT and TCCON was only 0.27 in spring and summer.

• Journal of Environmental Impact Assessment
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• v.20 no.6
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• pp.879-890
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• 2011

The GOSAT (Greenhouse gases Observing SATellite) data provide new opportunities the most regionally complete and up-to-date assessment of $CO_2$. However, in practice, GOSAT records often suffer from missing data values mainly due to unfavorable meteorological condition in specific time periods of data acquisition. The aim of this research was to identify optimal spatial interpolation techniques to ensure the continuity of $CO_2$ from samples taken in the North East Asia. The accuracy among ordinary kriging (OK), universal kriging (UK) and simple kriging (SK) was compared based on the combined consideration of $R^2$ values, Root Mean Square Error (RMSE), Mean Error (ME) for variogram models. Cross validation for 1312 random sampling points indicate that the (UK) kriging is the best geostatistical method for spatial predictions of $CO_2$ in the East Asia region. The results from this study can be useful for selecting optimal kriging algorithm to produce $CO_2$ map of various landscapes. Also, data users may benefit from a statistical approach that would allow them to better understand the uncertainty and limitations of the GOSAT sample data.

• Korean Journal of Remote Sensing
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• v.31 no.6
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• pp.549-560
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• 2015

With the global scale impact of atmospheric $CO_2$ in global warming and climate system, it is necessary to monitor the $CO_2$ concentration continuously on a global scale, where satellite remote sensing has played a significant role recently. In this study, global monthly $CO_2$ concentrations obtained by satellite remote sensing were compared with ground-based measurements at Anmyeon-do and Gosan Korean Global Atmosphere Watch Center. Atmospheric $CO_2$ concentration has increased from 371.87 ppm in January 1999 to 405.50 ppm in December 2013 at Anmyeon-do station (KMA, 2013). Comparison of the continuous measurements by flask air sampling at Anmyeon-do shows the same trend and seasonal variations with those of global monthly mean dataset. Nevertheless, the trends of $CO_2$ over Northeast Asia showed the higher than those of global and the trends also changes with different slope. $CO_2$ products derived from Greenhouse Gases Observing Satellite (GOSAT) and Atmospheric Infrared Sounder (AIRS) were compared with ground-based measurement at Anmyeon-do. The monthly mean values of GOSAT and AIRS data are systemically lower than those obtained at Anmyeon-do, however, the seasonal cycle of satellite products present the similar trend with values of global and Anmyeon-do. The accuracy of $CO_2$ products from GOSAT and AIRS were evaluated statistically for two years from January 2011 to December 2012. GOSAT showed good correlation with the correlation coefficient, RMSD and bias of 0.947, 5.610 and -5.280 to ground-based measurements respectively, while AIRS showed reasonable comparison with 0.737, 8.574 and -7.316 at Anmyeon-do station, respectively.

• Spatial Information Research
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• v.21 no.5
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• pp.15-22
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• 2013

The purpose of this work is to investigate correlation between $CO_2$ concentration and NDVI (Normalized Difference Vegetation Index) in North East Asia. Geographically weighted regression techniques were used to evaluate the spatial relationships between GOSAT (Greenhouse Observing SATellite) $CO_2$ measurement and MODIS (Moderate Resolution Imaging Spectroradiometer) vegetation index. The results reveals that $CO_2$ concentration to be negatively associated with NDVI. The analysis of Global Morans' I index and Anselin Local Morasn's I showed spatial autocorrelation between the overall spatial pattern of $CO_2$ and NDVI. Ultimately, there were clustered patterns in both data sets. The results show that carbon dioxide concentration shows non-random distribution patterns in relation to NDVI clusters, which proves that intense development activities such as deforestation are influencing carbon dioxide emission across the area of analysis. However, as the concentration of carbon dioxide varies depending on a variety of factors such as artificial sources, plant respiration, and the absorption and discharge of the ocean, follow-up studies are required to evaluate the correlations among more related variables.

• Journal of Korean Society for Geospatial Information Science
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• v.21 no.2
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• pp.85-92
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• 2013

This study determined the spatial distribution characteristics of carbon dioxide in Northeast Asia, connecting land coverage and vegetation index that have influence on concentration and distribution of carbon dioxide measured by GOSAT with GIS spatial analysis method. The results visibly showed that the spatial distribution of carbon dioxide had different patterns in dependent on the present status of land use in its surrounding area. Such high concentration of carbon dioxide was formed in developed sites like cities while forest areas showed low concentration of it. We also found that there were relatively high negative(-) correlations between carbon dioxide and vegetation, in statistically significant level. It is expected to be used as a basic data for establishing measures to reduce greenhouse gas in the future.

• Spatial Information Research
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• v.20 no.5
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• pp.1-13
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• 2012

This study intends to evaluate the seasonal flow direction of carbon dioxide in Northeast Asia by using GOSAT, the first Greenhouse Observing SATellite, in an attempt to overcome costly, laborious and time consuming ground observation which has been frequently pointed out in existing studies. For this purpose, missing values were supplemented by applying the Kriging interpolation and the overall flow direction of carbon dioxide was determined through anisotoropy semi-variogram. As a result, it was found that the overall spatial distribution of carbon dioxide in Northeast Asia varies depending on the latitude, and that carbon dioxide mainly flows southeast or east in spring, autumn and winter, but northeast or north in summer. Similar to the flow of monsoons in Northeast Asia, these results show that carbon dioxide flows mainly from the west to the east, which proves that carbon dioxide discharged from China is influencing even the Korean Peninsula and Japan. However, as the flow of carbon dioxide varies depending on a variety of factors such as artificial sources, plant respiration, and the absorption and discharge of the ocean, follow-up studies are requested to evaluate such variables and the correlations.

• Asian Journal of Atmospheric Environment
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• v.5 no.4
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• pp.263-277
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• 2011

Global atmospheric $CO_2$ distributions were simulated with a chemical transport model (GEOS-Chem) and compared with space-borne observations of $CO_2$ column density by GOSAT from April 2009 to January 2010. The GEOS-Chem model simulated 3-D global atmospheric $CO_2$ at $2^{\circ}{\times}2.5^{\circ}$ horizontal resolution using global $CO_2$ surface sources/sinks as well as 3-D emissions from aviation and the atmospheric oxidation of other carbon species. The seasonal cycle and spatial distribution of GEOS-Chem $CO_2$ columns were generally comparable with GOSAT columns over each continent with a systematic positive bias of ~1.0%. Data from the World Data Center for Greenhouse Gases (WDCGG) from twelve ground stations spanning $90^{\circ}S-82^{\circ}N$ were also compared with the modeled data for the period of 2004-2009 inclusive. The ground-based data show high correlations with the GEOS-Chem simulation ($0.66{\leq}R^2{\leq}0.99$) but the model data have a negative bias of ~1.0%, which is primarily due to the model initial conditions. Together these two comparisons can be used to infer that GOSAT $CO_2$ retrievals underestimate $CO_2$ column concentration by ~2.0%, as demonstrated in recent validation work using other methods. We further estimated individual source/sink contributions to the global atmospheric $CO_2$ budget and trends through 7 tagged $CO_2$ tracers (fossil fuels, ocean exchanges, biomass burning, biofuel burning, net terrestrial exchange, shipping, aviation, and CO oxidation) over 2004-2009. The global $CO_2$ trend over this period (2.1 ppmv/year) has been mainly driven by fossil fuel combustion and cement production (3.2 ppmv/year), reinforcing the fact that rigorous $CO_2$ reductions from human activities are necessary in order to stabilize atmospheric $CO_2$ levels.

• Proceedings of the KSRS Conference
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• v.1
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• pp.7-10
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• 2006

Four programs, i.e. TRMM, ADEOS2, ASTER, and ALOS are going on in Japanese Earth Observation programs. TRMM and ASTER are operating well, and TRMM operation will be continued to 2009. ADEOS2 was failed, but AMSR-E on Aqua is operating. ALOS (Advanced Land Observing Satellite) was successfully launched on $24^{th}$ Jan. 2006. ALOS carries three instruments, i.e., PRISM (Panchromatic Remote Sensing Instrument for Stereo Mapping), AVNIR-2 (Advanced Visible and Near Infrared Radiometer), and PALSAR (Phased Array L band Synthetic Aperture Radar). PRISM is a 3 line panchromatic push broom scanner with 2.5m IFOV. AVNIR-2 is a 4 channel multi spectral scanner with 10m IFOV. PALSAR is a full polarimetric active phased array SAR. PALSAR has many observation modes including full polarimetric mode and scan SAR mode. After the unfortunate accident of ADEOS2, JAXA still have plans of Earth observation programs. Next generation satellites will be launched in 2008-2012 timeframe. They are GOSAT (Greenhouse Gas Observation Satellite), GCOM-W and GCOM-C (ADEOS-2 follow on), and GPM (Global Precipitation Mission) core satellite. GOSAT will carry 2 instruments, i.e. a green house gas sensor and a cloud/aerosol imager. The main sensor is a Fourier transform spectrometer (FTS) and covers 0.76 to 15 ${\mu}m$ region with 0.2 to 0.5 $cm^{-1}$ resolution. GPM is a joint project with NASA and will carry two instruments. JAXA will develop DPR (Dual frequency Precipitation Radar) which is a follow on of PR on TRMM. Another project is EarthCare. It is a joint project with ESA and JAXA is going to provide CPR (Cloud Profiling Radar). Discussions on future Earth Observation programs have been started including discussions on ALOS F/O.

• Korean Journal of Remote Sensing
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• v.31 no.5
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• pp.409-419
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• 2015

The Atmospheric Emitted Radiance Interferometer (AERI) which is the Fourier Transform InfraRed (FTIR) spectrometer has been operated by the National Institute of Meteorological Research (NIMR) in Anmyeon island, South Korea since June 2010. The ground-based AERI with similar hyper-spectral infrared sensor to satellite could be an alternative way to validate satellite-based remote sensing. In this regard, the NIMR has focused on the improvement of Cloud data Filtering Method (CFM) which employed only one reference spectrum of clear sky in winter season. This study suggests Seasonal-Cloud data Filtering Method (S-CFM) which applied seasonal AERI reference spectra. For the comparison of applied S-CFM and CFM, the methane retrievals (surface volume mixing ratio) from AERI spectra are used. The quality of AERI methane retrieval applied S-CFM was significantly more improved than that of CFM. The positive result of S-CFM is similar pattern with the seasonal variation of methane from ground-based in-situ measurement, even if the summer season's methane is retrieved over-estimation. In addition, the comparison of vertical total column of methane from AERI and GOSAT shows good result except for the summer season.