Summary of the Final Report of Research Results1) Title of the proposed research
A study on the relationship between distributions of CO2 and CH4 near ground and land-use in
Northeast Asian regions
2) Principal Investigator (PI) and Co-Investigators (Co-Is)
PI:
Dr. Xiufeng Wang
Co-I:
Dr. Hiroshi Tani
Dr. Yang Liu
Dr. Guirei Yu
Dr. Kebiao Mao
3) PI’s affiliation
Hokkaido University4) Summary of the Final Report of Research Results
Our research from 2008 to 2013 by using GOSAT data could be divided into 5 parts:
Contents 1:
Northeast Asia is a unique region of various land-use systems. It includes Forest areas ofstock-raising in Mongolian grasslands, Xingan Mountains in Siberia and China, Changbai
Mountains, etc; Densely populated zone of heavy industry area in China, etc; The area of paddy
fields in southern China and Japan, etc. Human activity in these regions have different forms,
the emissions of Methane from pasture and paddy fields area, The absorption and emission ofcarbon dioxide from forest area, The emission of carbon dioxide from heavy industry anddensely populated area, however the balance of CO2 and CH4 are different. In order to analysis
these causes. At first, depending on the availability of L2, L3 data, 1) Analysis theconcentration of CO2, CH4 distribution in narrow range region (100km2-300km2) and seasonalchanges. Land-use and vegetation analysis use AVHRR, SPOT and MODIS data. Moreover, 2)
Depending on the L4 data reception, it will be used to analysis the concentration of CO2, CH4
distribution in wide-ranging region (2.5-degree Mesh), seasonal changes, time series changeand high concentration distribution. Also use MODIS to analysis land-use. In addition, 3)
Extract the special district of abnormal concentration, and examine the relationship between
land-use and concentration of CO2, CH4 distribution.
Contents 2:
With the continuous development of human society and associated increase in industrialisation,

atmospheric concentrations of CO2 and CH4 have also increased. Asia has the third largest
nominal GDP of all continents, after North America and Europe. Rapid growth in large regional
economies such as China and India has elevated human prosperity. Thus, a study of this region's
GHG emissions has great significance for understanding global climate change. Theobservations from the GOSAT are being processed to generate the initial results for XCO2 andXCH4, which are calculated from the GOSAT radiant spectrum data obtained under cloud-freeconditions. Therefore, to integrate land classification and other environmental data with theregional GHG and environmental interaction research, this part combines GOSAT FTS Level 2
data with the Kriging method to analyze the spatial variations and distributions of GHG
throughout the land surface in this region.
Contents 3:In this part, we used the GHGs from GOSAT, meteorological data from ground stations in
China (192) and Japan (133) and the NDVI from the MODerate resolution Imaging
Spectroradiometer (MODIS) in 2010 to analyze the correlations among GHGs, climateparameters and NDVI. After analysis, we found that the XCH4 concentration in the atmosphere
had a strong correlation with climate parameters and NDVI (r=0.672**, 0.709**, 0.733 ** and0.684** to temperature, precipitation, relative humidity and NDVI, respectively). Also, theconcentration of water vapor in the atmosphere (XH2O) has a slight correlation with climateparameters and NDVI, and it has the highest correlation with relative humidity (r=0.732**)
followed by the correlation with NDVI (r=0.638**). Furthermore, the XCO2 has a low
correlation with climate parameters and NDVI and has the highest correlation coefficient withprecipitation (r=0.625**) followed by the correlation with NDVI (r=0.450**). We also
monitored the spatial distribution of the GHGs and found that the higher GHGs are distribute in
the southeast coastal areas of China, Japan and South Korea, and the low GHGs are distributed
in western China and Mongolia.
Contents 4:
Measurements of land cover changes have suggested that such shifts may alter the GHGs in theatmosphere. However, due to the lack of large-scale GHG data, a quantitative description of therelationships between land cover changes and GHG concentrations does not exist on a regional
scale. The Greenhouse Gases Observing SATellite (GOSAT) launched by Japan on January 23,
2009 can be used to investigate this issue. In this part, we first calculated the monthly average
GHG concentrations in East Asia from April 2009 to October 2011 and found that the CO2
concentration displays a seasonal cycle, but the CH4 seasonal trend is unclear. To understandthe relationship between land cover and GHG concentrations, we used the GHG data fromGOSAT, the Normalized Difference Vegetation Index (NDVI) from the MODerate resolution
Imaging Spectroradiometer (MODIS) and the land cover data from EAS-GlobCover (2009) toanalyze the correlation coefficients between land cover and GHG concentrations. We observedthat vegetation may generally be considered as a source but not a sink of CO2 and CH4, either

on a yearly scale or during the growing season. With respect to the relationships between land
cover types and GHG concentrations, we conclude that on a yearly scale, land cover types are
not closely correlated with GHG concentrations. During the growing season, croplands andscrublands are negatively correlated with XCO2, and forest, grasslands and bare areas are
positively correlated with XCO2. Forest and croplands can be viewed as CH4 sources, while
scrublands and grasslands can be thought of as CH4 sinks.

Contents 5:
We used GHG data from the ENVIronment SATellite (ENVISAT) and the Greenhouse gases
Observing Satellite (GOSAT), the Normalized Difference Vegetation Index (NDVI) and Land
Surface Temperature (LST) data from the MODerate resolution Imaging Spectroradiometer(MODIS) and precipitation data from ground stations to analyze the way land degradation
affects GHG concentrations in northern China and Mongolia, which exhibit the most serious
land degradation process in East Asia. Due to the infrequency of sustained periods of carbon
uptake, there is no research on the spatial distribution of GHG concentrations in arid andsemi-arid regions based on remote sensing data for that time frame. To fill this gap, the aims ofthis study were as follows: first, to analyze changes in XCO2 and XCH4 from 2003 to 2011using ENVISAT SCIAMACHY and GOSAT TANSO data; second, to analyze the spatial
distributions of XCO2 and XCH4 in the study area in 2010 based on the TANSO data and using
the Ordinal Kriging method; and finally, using NDVI data from MODIS combined withprecipitation data and LST data to analyze the spatial distribution of GHG concentrations. We
know that human activities are the most important source of GHG. However, here we will only
discuss the relationships between NDVI and GHG concentrations.
5) List of publications relating to the proposed researchMeng Guo, Xiufeng Wang, Jing Li, Hongmei Wang, And Hiroshi Tani: Examining therelationships between land cover and greenhouse gas concentrations using remote sensing datain East Asia. International Journal of Remote Sensing, 34, 4281-4303, 2013.
M. Guo, X. Wang, J. Li, K. Yi, G. Zhong, H.Wang, and H. Tani: Spatial distribution ofgreenhouse gas concentrations in arid and semi-arid regions: A case study in East Asia. Journalof Arid Environments, 91, 119-128, 2013.
Yang Liu, Xiufeng Wang, Meng Guo And Hiroshi Tani: Mapping the FTS SWIR L2
product of XCO2 and XCH4 data from the GOSAT by the Kriging method – a case study in EastAsia. International Journal of Remote Sensing, 33(10), 3004–3025, 2012.
Meng Guo, Xiufeng Wang, Yang Liu, Jing Li, Hongmei Wang, Nobuhiro Matsuoka AndHiroshi Tani: The effects of sand dust storms on greenhouse gases. International Journal ofRemote Sensing, 33(21), 6838–6853, 2012.
Meng Guo, Xiufeng Wang, Jing Li, Kunpeng Yi, Guosheng Zhong and Hiroshi Tani:
Assessment of global carbon dioxide concentration using MODIS and GOSAT data. Sensors,12(12), 16368-16389, 2012.
Meng Guo, Xiufeng Wang, Kunpeng Yi, Guosheng Zhong, and Hiroshi Tani: The
distribution of greenhouse gas concentrations in arid and semi-arid areas. InternationalConference on Agricultural Biodiversity and Sustainability 2012, August 26-31, 2012, Sapporo,Japan.
Meng Guo, Xiufeng Wang, Guosheng Zhong and Hiroshi Tani: The distribution ofgreenhouse gas concentrations in China using GOSAT data. International Symposium on
Agricultural Meteorology 2012, pp.160, March 13-17, 2012, Osaka, Japan.
Y. Liu, X. F. Wang, H. Tani, M. Guo: Analysis of relationship between NDVI and GHG in
Daxing’an Mountain region, China. Proceedings for ISPRS Workshop on Dynamic andMulti-dimensional GIS, pp.40-43, October 17-19, 2011, Shanghai, China.
Yang LIU, Xiufeng WANG, Meng GUO and Hiroshi TANI: Comparison of GOSAT CAIand SPOT VGT NDVI data with different season and land cover in East Asia. ISPRS Workshop
on Geospatial Data Infrastructure: from data acquisition and updating to smarter services,
pp.94-97, October 20-21, 2011, Guilin, China.
Yang Liu, Xiufeng Wang, Meng Guo, Hiroshi Tani and Guosheng Zhong: Application ofthe Kriging method to XCO2 and XCH4 data from GOSAT - a case study in East Asia. The 49thAutumn Conference of the Remote Sensing Society of Japan, pp.77-78, November 13-14, 2010,Kagoshima, Japan.