Remote sensing of environment and disaster laboratory

Institute of Industrial Science, the University of Tokyo, Japan

Comparative analysis of CH4 measured by MODIS andSCIAMACHY from west Siberian lowland
SUDESURIGUGE1), Wataru Takeuchi1) and Sachiko Hayashida2)
1)IIS, The University of Tokyo, Japan.
2)Faculty of Science, Nara Women’s University, Japan.
In this study, our approach was based on MODIS and AMSR-E satellite database, through NDVI and LST (Land Surface Temperature) biophysical inference model to estimate
the methane emission in Siberian lowland and to do comparative analysis with SCIAMACHY measurement data. The wetland coverage was represented by 7 types of land coverfrom MERIS, ENVISAT data. The biophysical inference models have three equations (CH4_lst, CH4_ndvi and CH4_Ndl), which were calculated from regression analysis basedon different vegetation and land surface temperature condition. We found that from 2003 to 2010 more that 12% of year growth rate of methane emission measured by
SCIAMACHY. The models estimation of the year growth rate of CH4_lst, CH4_ndvi and CH4_Ndl from 2003 to 2012 were 0.24%, 4.74% and 0.36% respectively. Our findingsshow the CH4 emission increase significantly in time series with seasonal increases in temperature. Background

Methodology Methane is a particularly effective greenhouse gas and its emission sources compriseanthropogenic activity, fossil fuel combustion, rice agriculture, livestock, landfill and somebiomass burning and natural sources such as wetlands, termites and the ocean (IPCC. 2007).
The concentration of atmospheric CH4 has more than doubled since pre-industrial times, andits radiative forcing is estimated to be the second largest after CO2 (Forster et al. 2007).
Global warming is predicted to be most pronounced at high latitudes (Wagner,2009), andthawing of permafrost could release large quantities of greenhouse gases into theatmosphere, thus further increasing global warming and transforming the Arctic tundra
ecosystems from a carbon sink to a carbon source(Oechel et al. 1993).

Objective•! To estimate methane emission through NDVI and Land Surface Temperature(LST) biophysical inference model by MODIS.
•! To do comparative analysis with SCIAMACHY measurement data.

Results and Discussion

Temperature in April []CH4 Emission Estimation

12108-26

y = 0.137x - 278.03
R! = 0.08

4!$#-4202002!"#0y = 0.1519x - 295.17
R! = 0.22

20032004

2005

2006

2007

2008 2009 2010
AprilOctober

Temperature in October []Where FCH4lst= CH4 emission in LST function (mg/m2day) day)
FCH4ndvi = CH4 emission in NDVI function(mg/m2day)
FCH4Ndl = CH4 emission,
combine LST and NDVI function (mg/m2day)
NDVI = original NDVI * 100

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Fig.2 Mean temperature changing dynamics of April andOctober in 2003 and 2010.

Fig.1 Map of monthly CH4 emission of April and October in 2003 and 2010. Map (a) and (b) are derived from equation (1) and (3)respectively. Left and right rows are the emission maps of April and October in 2003 and 2010 separately. J FMAM J J A SOND J FMAM J J A SOND J FMAM J J A SOND J FMAM J J A SOND J FMAM J J A SOND J FMAM J J A SOND J FMAM J J A SOND J FMAM J J A SOND

2003

2004

2005
CH4Emission-LST2006
2007
2008
SCIAMACHY and CH4Emission 2003-2010
CH4Emission-NDVI

CH4Emission-Ndl2009
SCIAMACHY

2010

2500
2400
2300
2200
2100
2000
1900
1800
1700

Temperature of September[] 

4500
4000
3500
3000
2500
2000
1500
1000
500
0Fig.3 CH4 estimations and SCIAMACHY measurement curves.
SCIAMACHY (ppbv)

CH4Emission (10^6 kg)

Comparison analysis with SCIAMACHY

12-1

11-2

10-3

9-4

8

2002

2003

Mean temperature of Sep. 2001-2012

Mean temperature of Sep.

Mean temperature of Oct.2001-2012

Mean temperature of Oct.

2004

2005

2006

2007

2008

2009

2010

Temperature of October[]•! Fig.1 (a) and (b) clearly indicates the CH4 emitting area in April was larger in •! Fig.2 is a curve of averaged temperature of April and October from 2003 to 2010. There are
obvious rising trends, imply the temperature has increased in the same month of year. It is a2010 (right row) than in 2003 (left row). During summer growing season
probably reason that why CH4 emission spring earlier and fallen time was delayed. It is also
continuous CH4 emissions has been extended till temperature goes minus.
means LST is an important index, which impact CH4 emission.
Whereas, CH4 emission didn’t stop immediately when winter comes.

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Fig.4 Mean annual temperature of September andOctober from 2003 to 2010.

•! Fig. 3 expresses the seasonal characteristics of CH4 emission and SCIAMACHY concentration data. From 2003 to 2010 more that 12% of year growth rate of methane emission wasmeasured by SCIAMACHY and the models estimation of the year growth rate of CH4_lst, CH4_ndvi and CH4_Ndl from 2003 to 2012 were 0.24%, 4.74% and 0.36% respectively.
There is good agreement among three definitions and SCIAMACHY. The CH4_lst (pink) and CH4_Ndl (blue) have quite similar character, but CH4_ndvi has a delay phenomenon
than the others, but the offset time fitted very well. It because vegetation growing usually starts when temperature risen to specified value, but emission will stop when ground frozen
up. It means CH4 emission is also affected by vegetation. For SCIAMACHY data, in 2003 and 2009 the peak value happened in September and at the same time lower temperature
will happen in October.
•! Fig. 4 represents mean annual temperature September and October from 2003 to 2010 to prove this phenomenon. There is a big difference in 2003 and 2009, indicate higher
temperature induce higher emissions.
ReferencesConclusion remarks•! The estimation results have good correlation with SCIAMACHY measurement data, although they
are not in the same scale measurement, at least show the proximity trends and seasonal changes.
•! The wider range of SCIAMACHY than estimation curves probably because of estimation error,
land-cover type and human activity source such as fossil fuel and so on.
•! The amount of emission is quite depending on temperature. The estimation still under calibration
by other referenced publications, these examinations will lead positive effects in near future.

Forster, P., Ramaswamy, V., Artaxo, P., Berntsen, T., Betts, R., Fahey, D.W., Haywood, J., Lean, J., Lowe,D.C., Myhre, G., Nganga, J., Prinn, R., Raga, G., M., S. and Van Dorland, R. Changes in Atmospheric
Constituents and in Radiative Forcing. In: S. Solomon et al. (Editors). Climate Change 2007: The
Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of theIntergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, U.K.
Osterkamp TE (2001). Subsea Permafrost. In: Steele JH, Thorpe SA and Turekian KK (ed) Encyclopediaof Ocean Sciences, Academic Press, pp 2902-2912.
Wataru Takeuchi and Louis Gonzalez (2009). Blending MODIS and AMSR-E to predict daily land
surface water coverage, Proceedings of International Remote Sensing Symposium (ISRS), Busan, SouthKorea, 2009 Oct. 29.

For further details, contact: SUDESURIGUGE, Ce-506, 6-1, Komaba 4-chome, Meguro, Tokyo 153-8505 JAPAN (URL: http://wtlab.iis.u-tokyo.ac.jp/ E-mail: sorgog@iis.u-tokyo.ac.jp)