Remote sensing of environment and disaster laboratory

Institute of Industrial Science, The University of Tokyo, Japan

Remote Sensing and Model-Based Methane Emission
Estimation from Paddy Rice Field over Bangladesh

photo

Md Rahedul Islam and Wataru Takeuchi
Institute of Industrial Science, University of Tokyo, Japan

Abstract: Agriculture is estimated to be one of the largest sources of GHG emissions in Bangladesh, estimated at 78 Tera-gram (Tg) carbon di-oxide (CO2)-eq. in2016, to which rice cultivation contributes approximately 30% of total GHG (CO2-eq.) emitted from agriculture (FAOSTAT, 2018). Rice paddy is a well-known
source of methane emission, which accelerated the climate change impacts. Bangladesh as a climate vulnerable country, there is potential to reduce GHG emissions
from agriculture. To reduce greenhouse gas emission from rice cultivation, it’s very important to proper emission estimation from rice paddy field. In this study, weused remote sensing derived seasonal rice and irrigated rice area map with country adjusted IPCC (IPCC, 2006) model for methane emission estimation from rice
paddy field over Bangladesh from 2001 to 2018. There are a numbers of uncertainties to estimate methane emission from rice paddy. In this study we used remotesensing-based rice and irrigated rice area map and IPCC model for methane estimation from rice paddy field in Bangladesh. The result shows that the irrigatedBoro rice is the highest methane emission season (218.4 kg CH4 ha-1) and rainfed Amon rice is the lowest methane emission season (55 kg CH4 ha-1 ). Annually,
Boro rice season is the highest methane emission season (1029.44 Gg) followed by Amon (780.91 Gg) and Aus (111.05 Gg ) rice growing season in 2018. The
methane emission from rice paddy field increased from 1384.97 Gg in 2001 to 1941.21 Gg in 2018. We compare our result with relevant study and found goodagreement.

Objective: The main objective of this study to estimate methane emission from rice paddy field with remote sensing-based seasonal rice and irrigated rice
area map and regional adjusted IPCC model over Bangladesh from 2001 to 2018.

02. Result

01. Data and Methodology
Methane Emission from Boro Rice(2001-2018)
MOD13A2 (Seasonal Rice Area, 2001-2018)

MOD16A2 and GSMaP (Seasonal Irrigated
Rice Area, 2001-2018)

Aus Season

200150Methane Emission from Amon Rice(2001-2018)500
Irrigated

Annual Methane Emission from Rice Paddy(2001-2018)(1)

EFi,j,k = Daily Emission factors under i,j,k conditions, kg CH4 ha-1yr-1
ti,j,k = Cultivation period for i, j, and k conditions, days
A i,j,k = Annual Rice cultivated area under i, j and k conditions, yri,j and k= Represent different ecosystems, water regimes, type and amount of organic
amendments, and other condition under which CH4 emissions from rice may vary.

Amon Season

100

CH4 Rice = Annual Methane emission from rice paddy field, Gg CH4 yr-1

𝑬𝑭𝒊 = 𝑬𝑭𝒄 × S𝑭𝒘 × S𝑭𝒑 × S𝑭𝒐 × SF s , r(b) 250Boro Season

Methane Emission from Aus Rice(2001-2018)

IPCC, 2006 (MethaneEmission Estimation fromRice Paddy)

Figure 1: Research Flowchart
𝑪𝑯𝟒 𝑹𝒊𝒄𝒆 = ∑i,j,k (EFi,j,k× ti,j,k × A i,j,k ×10-6)(a)Supplementary
Irrigated

RainfedRice Irrigation Type

Figure 2: (a) Methane emission from seasonal and annual total rice paddy field of Bangladesh, 2001 to 2018; (b)(3 )SFo = [1 + {(ROAi RS × CFOAi R S ) + (ROAi FM × CFOAi FM)}] 0 . 5 9
ROAi RS = Application rate of rice straw (Bor o= 0.70, Aus/Amon = 0.40)
ROAi FM = Application rate of Farm manure (0.60)
CFOAi R S = C o n v e r s i o n f a c t o r f o r o r g a n i c a m e n d m e n t ( R i c eStraw)=1
CFOAi FM = Conversion factor for incorporated shortly before cultivation = 0.14

(2)

𝑬𝑭𝒊 = Adjusted daily emission factor for cultivated rice area
𝑬𝑭𝒄 = Baseline emission factor without organic amendment (IPCC default value,) 1.30 kgCH4h-1d-1
S𝑭𝒘 = Scaling factor for different water regime (Irrigated =1, Supplementary irrigated = 0.70, rainfed= 0.28)
S𝑭𝒑 = Scaling factor for preseason water regime before cultivation period ( default value = 1)
S𝑭𝒐 = Scaling factor for organic amendment (Boro = 1.40, Aus = 1.31, and Amon = 1.26)

Equation 1, 2 and 3 are based on IPCC,
2006 guideline. The parameters value
are adjusted based on the ricecultivationcharacteristicsofBangladesh. The seasonal emission
rate is used for; Boro, Aus and Amon
season methane emission estimation

Seasonal methane emission rate from irrigated, supplementary irrigated and rainfed rice paddy field (in kg ha-1)▪ The maximum seasonal emission factor in Irrigated Boro rice is 218.4 kg CH4 ha-1 andminimum in rainfed Aus season 195.6 kg CH4 ha-1.
▪ The Annual Boro season estimated methane emission increased 797.49 Gg to 1029.44
Gg from 2001 to 2018 and followed by Amon (481.21 to781.91 Gg) and Aus season(106.26 to111.05 Gg)
(d)

(a)

(b)

(e)

(f)

(g)

(h)

(i)

(j)

(k)

(l)

(c)

(r)

SF s , r = Scaling factor for Soil type, rice cultivar etc. (Not considered in this study)

03. Discussion• The seasonal methane emission
factor for Boro, Aus and Amon
rice from others model and fluxbased estimation showed goodagreement.
(a)• In our study we separately(b)estimated Boro, Aus and Amon
season emission factor with
Irrigated, Supplementary
Irrigated and Rainfed rice.
• The Annual estimated methaneemission increased with thetime.
• Our result slightly underestimated from EDGAR3.2emission inventory and PIK
Figure 4: (a) Comparison our RS-IPCC based seasonal emission rate with CH4MOD2.5,estimation; and over-estimated
DayCent, MODEVAL and field level CH4 flux data.
from FAOSTAT and CIAT(b) Comparison of our RS-IPCC based annual methane emission from rice
inventory estimation.
paddy field with FAOSTAT, EDGAR3.2, CIAT and PIK inventory estimation;

(m)

(n)

(o)

(p)

(q)

Figure 3: Annual methane emission distribution from rice paddy field of Bangladesh; (a) 2001, (b) 2002, (c) 2003, (d)2004, (e) 2005, (f) 2006, (g) 2007, (h) 2008, (i) 2009, (j) 2010, (k) 2011, (l) 2012, (m) 2013, (n) 2014, (o) 2015, (p) 2016,(q) 2017 and (r) 2018.

▪ The Methane emission distribution showed that the central north part of the country ishighest emission region and the central-southern part of the country is the lowest emitted
region due to the rice cropping patten change single to double and triple rice andirrigation mode.

04. Conclusion and Future Work
The highest seasonal CH4 emitted from irrigated Boro (218.4 kg ha-1), and lowest from rainfed Amon rice ( 55.0 kg ha-1). The annual methane emitted from Boro,
Aus and Amon rice are 1029.44 Gg, 780.91Gg and 110.05 Gg respectively. The Annual methane emission was 1384.97 Gg in 2001 and 1941.21 Gg in 2018. The
methane emission from rice paddy field in Bangladesh gradually increasing over the time . The annual methane emission increased from 1384.97 Gg in 2001 to1921.46 Gg in 2018. The rice growing season specific emission factor associated with the different irrigation application used methane emission estimation could be amore reliable tools for emission estimation. In future, we will try to investigated AWD irrigation application-based methane emission estimation.

References:
1. IPCC, “Guidelines for National Greenhouse Gas Inventories”. Agriculture, Forestry and Other Land Use, vol. 4, Chapter 5, 5.1-5.66, 2006.
2. Crippa, M., Oreggioni, G., Guizzardi, D., Muntean, M., Schaaf, E., Lo Vullo, E., Solazzo, E., Monforti-Ferrario, F., Olivier, J.G.J., Vignati, E., Fossil CO2 and GHG emissions of all world countries - 2019 Report, EUR
29849 EN, Publications Office of the European Union, Luxembourg, 2019, ISBN 978-92-76-11100-9
3. CAIT Climate Data Explorer. 2017. CAIT Historical Emission data contains sector-level greenhouse gas (GHG) emissions data for 191 countries and the European Union (EU) for the period 1990-2014, including emissionsof the six major GHGs from most major sources and sinks. It also contains historical country-level carbon dioxide (CO2) emissions data Washington, DC: World Resources Institute. Available online at: http://cait.wri.org
4. Gütschow, Johannes; Jeffery, Louise; Gieseke, Robert (2019): The PRIMAP-hist national historical emissionstime series (1850-2016). V. 2.0. GFZ Data Services. http://doi.org/10.5880/PIK.2019.001

For further details, contact: Islam Md Rahedul, Bw-602, 6-1, Komaba 4-chome, Meguro, Tokyo 153-8505 JAPAN (URL: http://wtlab.iis.u-tokyo.ac.jp/ E-mail: rahe@iis.u-tokyo.ac.jp)