Remote sensing of environment and disaster laboratory

Institute of Industrial Science, the University of Tokyo, Japan'/%&((*<=>?*@9*ABCDCE*!"#$%&'(")*+,-.,,)*/0"+%0*&'!,*$%112*3',01*#%$$')/*%)1*#,-4%),*3056*1%-%*3&"#*/"(%-4'&"#'*7")%'*89:*.%-%&5*-%;,5!4'*
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Abstract : Methane is one of the main souse of global warming and its emission from rice paddy field is a big issue. To fully investigate the spatial and temporal distribution of methane emission from rice paddy, rice crop
calendar mapping and ground water condition data from satellite observation were integrated to IPCC 2006 guidelines. Then, global methane emission inventory from rice paddy was made. Comparison between
atmospheric methane concentration from GOSAT and another methane emission inventory was done, and estimation in this study was in good agreement.

Introduction
Methane gas is one of the main sources of global warming [1], and rice cropping accounts for about 20 percent of them [2]. Country level or point based research of methane emission from rice paddy field has been done[3] [5], but spatial distribution or timing of rice cropping and methane emission from that is not fully investigated due to lack of parameters such as climate condition, water regime, and soil condition [4].
The objective of of this study is to create rice paddy field distribution and crop calendar [6] and use satellite derived water indices including V-S-W index [7] from MODIS and LSWC [8] from AMSR-E to estimate rice
plant phenology and the water regime of rice paddy. Then, these data are integrated to 2006 IPCC guidelines for national greenhouse gas inventories to estimate the methane emission from rice paddy field globally.
Finally, estimated methane emission from the rice paddy was compared with methane concentration in the atmosphere observed by GOSAT and SCIAMACHY and methane emission inventory, EDGAR [10].

Methodology

! Preparing the dataset from MODIS, AMSR-E, and GOSAT data.
! Creating the new water index with LSWC and V-S-W by unmixing method.
! Assuming the sprouting season of rice by applying Fourier Transform to NDVI time series.
! Detecting rice cropping phenology where NDVI comes to its peak 60 days after the new water index rise andmaking the global rice paddy field mapping.
! Estimating methane emission from each rice crops by utilizing the map, the new water index.


MODIS band1,2

AMSR-E 18,23GHz 

MODIS band31, 32

MODIS band1,2

GOSAT TANSO-FTS

LSWC (10km)(10km)

LST(1km)
LST(1km)

NDVI (1km) Methane concentration
concentration

7-day composite
V-S-W index (250m)


Un-mixing method

EFi = EFc * SFw * SFp * SFo * SFs,rFourier analysis

New water index

Sprouting season

Global rice paddy field mapping

IPCC 2006 Guidelines

Global methane emission inventory

Comparison

EFiEFcSFw

Adjusted daily emission factor for a particular harvested area (kg CH4 / ha * day)
Baseline emission factor for continuously flooded fields without organic amendments (1.3 kg CH4 / ha * day) SFp

Scaling factor to account for the differences in water regime in the pre-season before the cultivation period

SFoScaling factor should vary for both type and amount of organic amendment appliedSFs,rScaling factor for soil type, rice cultivar, etc. (Neglected in this study)

Global methane concentration

Statistics

Figure 2. Equation to estimate the daily methane emission per hectare.

Figure 1. Flow chart of this research.
Water condition during thecultivation affects theestimated methane emission.

Result and Discussion

Satellite

Scaling factor to account for the differences in water regime during the cultivation period

Cultivating duration

Cultivating duration

60days30days

60days30days

2.5)CU*U8ICL*@9:CV*)1W'*0(-*#CIO89C*CX@GG@J9*

Peak of NDVIPeak of NDVI2

Extracted sprouting seasons as
the peaks of NDVI by FourierTransform.

1.5

The new water index
developed in this study rises
sharply 60 days before theextracted sprouting season.

0.5

Figure 3. Time series of NDVI and the new water index. ( 15.1N, 103.2E, Thailand) 1

0

Figure 4. Methane emission modeling from rice cultivation. ( 35.0N, 100.7E, China)

N50°, E60°



Crop intensity per year***
0123

Figure 5. Rice crop intensity map in Asia (2010).
Methane concentration (ppb)

1880

Methane emission (Kt)



Figure 6. Monthly estimated methane emission in Asia (2010). S10°, E150°

Methane concentration (ppb)

250
Emission

1905

GOSAT

Methane emission (Kt)

500
EmissionGOSAT

400

200

1880

1830

150

1855

300

1805

100

1830

200

1780

50

1805

100

1755

0

1780

0

1855

SCIAMACHY

Jan

Table 1. Top 20 methane emitting country from rice paddy and the world total (2010). Sichuan ()Dec Month

SCIAMACHYJanAnhui ()Dec Month

Figure 7. Comparison between estimated methane emission and methane concentration at prefecture level in China. Figure 7 shows the result of comparison between atmospheric methane concentration and the estimated methane emission. In both prefecture, estimated methane emission comes to its peak from May to August, andmethane concentration observed by GOSAT and SCIAMACHY comes to its peak from June to September. From the result, we can conclude that positive relationship between two of them was found, and it takes one or
two months for the emitted methane to go up to the atmosphere above from the ground rice paddy.
In Table 1, the estimation in this study is similar to EDGAR in most country except China and India, and the total of methane emission is 31,750Gg 3,300Gg smaller than EDGAR.

Conclusion
A global rice paddy field distribution and crop calendar are mapped. Along with the mapping and satellite based ground water observation, methane emission from rice paddy is estimated globally. From this result, whenand where methane is emitted from rice paddy can be known. Then, comparison between methane concentration from satellite observation and the result in a regional scale shows a positive relationship. Further study
comparing the methane emission inventory and the methane concentration will help us understand the air transport of methane gas. Reference[1] IPCC (Intergovernmental Panel on Climate Change). IPCC Third Assessment Report : The scientific basis, Atmospheric chemistry and greenhouse gases.
[2] Fung, et al, 1991. Three-dimensional model synthesis of the global methane cycle. Journal of geophysical research, vol. 96, No. D7, PAGES 13,033-13065, July 20, 1991. [3] Kazuyuki Yagi, Haruo Tsuruta, Ken-ichi Kanda, and Katsuyuki Minami, 1996. Effect of water management on methane emission from a Japanese rice paddy field: Automated methane monitoring. Global biogeochemical cycles, vol. 10, NO. 2, pages 255-267, June 1996
[4] Intergovernmental Panel on Climate Change (IPCC) (2007), 2006 IPCC Guidelines for National Greenhouse Gas Inventories, Inst. for Global Environ. Strategies, Hayama, Japan.
[5] Yan, Xiaoyuan,Akiyama, Hiroko,Yagi, Kazuyuki,Akimoto, Hajime, 2009. Global estimations of the inventory and mitigation potential of methane emissions from rice cultivation conducted using the 2006 Intergovernmental Panel on Climate Change Guidelines, Global Biogeochemical Cycles, vol. 23.[6] Hiromi Jonai, Wataru Takeuchi, and Kei Oyoshi, 2013. Development of new water index with MODIS and AMSR-E for global rice paddy field mapping. 34th Asian conference on remote sensing (ACRS): Bali, Indonesia, Oct. 23, 2013.
[7] Yoshiki Yamagata, Mikio Sugita, Yoshifumi Yasuoka. Development of Vegetation Soil Water Index algorithms and applications. Journal of the Remote Sensing Society of Japan; ISSN:0289-7911; VOL.17; NO.1; PAGE.54-65; (1997)
[8] Wataru Takeuchi and Louis Gonzalez, 2009. Blending MODIS and AMSR-E to predict daily land surface water coverage. International Remote Sensing Symposium (ISRS), Busan, South Korea, 2009 Oct. 29.[9] S. Hayashida, A. Ono, C. Frankenberg, W. Takeuchi, X. Yan, Methane concentrations over Monsoon Asia as observed by SCIAMACHY: Signals of methane emission from rice cultivation. Remote Sensing of Environment, vol. 139 (2013) 246-256.
[10] Emission Database for Global Atmospheric Research (EDGAR), 2014, Methane emissions from rice cultivation, http://edgar.jrc.ec.europa.eu/part_CH4.php

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