Post on 07-Feb-2021
The MRV system of greenhouse gaseous emission
in the world high-carbon reservoirs
Hironori AraiMasayoshi ManoNaoko SaitohKazuyuki Inubushi
Dr. Takeuchi Lab. IIS Tokyo Univ. xx
Development of MRV system of Methane emissions from
Rice paddies in the Mekong delta
Hironori Arai1,3), Wataru Takeuchi1),Kei Oyoshi2), Lam Dao Nguyen4),Towa Tachibana5), Ryuta Uozumi,
Koji Terasaki3), Takemasa Miyoshi3),Hisashi Yashiro3), Kazuyuki Inubushi5)
Modified from Yasuoka 2015
Cycle from Observation to Countermeasure
To target To solution To target
Observ.Survey
ModellingSimulation
Forcast・eval.Solutiondesign
optimization
management
Evaluation
DATAinformation
Estimation
Strategy Policy
EffectObservation of the effect
GOSAT K-computer,Fugaku
Each country must submit INDC (Intended Nationally Determined Contributions) to UNFCCC before 2020
3
Yan et al.2009 Xuan and Matsui, 1998
alluvial soil
1.1 Mha, 28%
acid sulfate soil
1.1 Mha, 28%potentially acid sulfate
0.5 Mha, 13%
431st / Oct / 2011
Rice farmers participatory field observation
6
Characteristics of the Mekong delta
Arai et al., 2018
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100% そのまま圃場等に放置
家畜飼料
その他作物の栽培
キノコ栽培(自身が栽
培)
知人に譲渡
売却(売却先では主にキ
ノコ栽培に用いられる)
焼却Winter-Spring
*n=50Straw use in TL2, Can Tho
(ha ha-1)
Spring-Summer
Summer-Autumn Burned
Sold to mushroom farmers
Free transfer to mushroom farmers
Compost for Mushroom cultivation
Compost forvegetable cropping
Feedstuffs forlivestocks
Left on soil
n=50
Greenhouse gas emission derived from rice straw use
8
0
1
2
3
4
04
69
21
38
18
42
30
27
63
22
36
84
14
46
0
CO flux
CO2 flux
Times after ignition (minutes)
Em
issi
on r
ate
(gC
/ h)
CO
CO2
Straw 40kg Moisture 20%
・CH4・N2O・VOC・O2・LEL
40kg 10kg Inlet
Outlet
Greenhouse gas emission derived from straw burning - Comparison among different straw size and moisture -
0
4
8
12
16
20
土壌還元 焼却 持ち出し
0
30
60
90
120
- CF AWD CF AWD CF AWD
0
700
1400
2100
2800
3500
土壌還元 焼却 持ち出し
■Conventional ■ Straw-use■AWD
- Reduction of irrigation rate & GHGs (2012-2016)- Increase of rice grains and its quality
GH
Gs
em
issi
on
(t- C
O2
eq.h
a-1
year
-1)
60%reduction
30%reduction
Irrigation r
ate
(mm
ha-
1ye
ar-1
)
Gra
in y
ield
(t- 1
4%
mo
istu
re h
a-1
Year
-1)
C/N
ratio o
f gra
ins
0
7
14
21
28
3510%increase
ImprovedRice quality
Strawincorporation
Strawburning
StrawTaken out
Strawincorporation
Strawburning
StrawTaken out
Strawincorporation
Strawburning
StrawTaken out
Strawincorporation
Strawburning
StrawTaken out
Philippines CF
Philippines AWD
Arai et al., 2018
PALSAR-2 Lv.1.1
quadruple6m
Volumediffusion
Coherency matrix [T3] generation
Speckle filtering
Lee-refined (7×7)
Field water level & cropping calendar
CH4 flux data & Soil map
Sampling (25-30 pixel)from each ROI
・Statistical analysis
CH4 emission estimation
model
Inundated / non-
inundated paddies
characterization
Freeman-Durden
decomposition
Single scattering
Doublebounce
Field data collection
Hierarchical bayesian modelling
MODIS (MOD13Q1)Normalized Vegetation/Water index,
Days after sowing, 1km
AMSR-218.7 & 23.8 GHz (V), 10km
Normalized frequencyIndex
Local Maximum Fitting-Kalman filter
Validation&
Data Fusion
HH, HV σ0Enhanced Lee filter (3×3)
PALSAR-2 Lv.1.1
SCAN-SAR25-100m
Multi-looking
Co-registration
De-Grandimulti-temporalspeckle filtering
Geocoding,Radiometric calibration
HH, HV σ0
Local Incidence
angles
Inundated / non-
inundated paddies
classification
Flow chart
CH4 emission map
ValidationGOSAT
(Retrieval)10km
NICAM-TM(LETKF)
Field observation & flux modeling
Remote sensing on field water & vegetation
Validation & integration
with daily EO data
Verification with GOSAT
Straw incorporation timeand amount
Water regime prior to rice cultivation
Water regime duringrice cultivation
A. ①
②
B.
③
CROPflood
CROPNon-flood
CROPNon-flood
>30 days
180 days
IPCC guideline (Tier1)[Emission factor × Scaling factor in IPCC guideline]
Sow
ing
day
s (d
ays
afte
r 2
00
1/0
1/0
8)
The ad
jacent fallo
w’s len
gth (d
ays)Cropping calendar evaluation with
MODIS-NDVI (LMF-KF) for GCOM-C
Samples of paddies
Paddy A (x:184, y:229)Double cropping → Triple cropping
Paddy B (x:185, y:220):Triple cropping
Arai et al., 2018
DAS←days after sowing
β ←acid sulfate・coastal sandy soil coefficient
CH4 emission on a specific date= γ * carbon_management / non-inundated_fallow / inundated_fallow * water_management *α*β
carbon_management (Michaelis-Menten KINETICS)=[exp(-DAS*δ)-exp(-DAS*(δ+ω))+κ]
non-inundated_fallow (OXYDATION CAPACITY)
= [1+EXP(-1* ζ *(DAS – ι* days of nonflooding days of the former fallow))]
inundated_fallow= EXP(ε * days of flooding days of the former fallow)
water_management= EXP(η * inundated days during the last 10days)
γ, η, δ, ε , ω , ζ , ι, κ←constant (>0)
α ←straw incorporation coefficient
Semi-empirical daily CH4 flux (mg C m-2 hr-1) Model
0 days non-flooding fallow 5 days non-flooding fallow
30 days non-flooding fallow 60 days non-flooding fallow
Ob
serv
ed 100
100101
10
1
0.1
0.01
0.001
0.01
Estimated
Da
ily C
H4
fluxe
s(k
g C
km
-2h
-1)
0
50
100
0 20 40 60 80100Days after sowing
CH
4fl
ux
Days after sowingCH
4fl
ux
Arai et al., 2018
ALOS-2/PALSAR-2– Lband-Synthetic Aperture Radar –
Xuan and Matsui, 199830paddies × 1village
5paddies × 4villages
Alluvial soils
1.1 Mha, 28%
Acid sulfate soils
1.1 Mha, 28%potential acid sulfate soils
0.5 Mha, 13%
Strip map
SCAN SAR
Coherency matrix [T3] generation
Polarimetric decomposition
Speckle filteringLEE refined
(7×7)
Sampling (25-30pixel)from each ROI
&Statistical analysis
Modified from Avtar et al. 2012
Freeman-Durden
Cloud-Pottier
PALSAR-2 Lv.1.1(quad. CEOS)
23 scenes
PALSAR-2 Lv.1.1(SCANSAR CEOS)
105 scenes
Multilooking
Co-registration
De Grandimulti-temporal
filtering
Geocoding&
Radiometriccalibration
HH HVIncidence
angle
Rice paddy masking&
Statistical analysis
Classification of inundated paddies and non-inundated paddieswhich is covered by rice plants
Inundated(fallow)
Inundated(cropping)
Non-inundated (cropping)
Non-inundated (fallow)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Single scattering(%)1009080706050403020100
10
20
30
40
50
60
70
80
90
100
100
90
80
70
60
50
40
30
20
10
0
Dry fallow (+rice stumps)
Fallow after plowing or flooding fallow
0-20 days after sowing
21-40 days after sowing
Inundated Non-inundated
41-60 days after sowing
61-100 days after sowing
-Freeman-Durden decomposition-
0
Full-polarimetry (3m)SCAN-SAR (25m)
Fallow paddies
0-20 days after sowing
21-40 days after sowing
41-60 days after sowing
61-100 days after sowing
Inundated Non-inundated
HV
(d
B)
HH (dB)
-40
-30
-20
-10
-40 -30 -20 -10 0
(a) Quadruple-polarimetry
Arai et al., 2018
HV
+H
H t
hre
shold
(d
B*1000)
HH
thre
shold
(d
B*1000)
cosine(incidence angle)
cosine(incidence angle)
HH threshold (dB) = 0.550*HV +12.9*cosine(IA) -11.2
y = 242983x2 - 360742x + 77749R² = 0.7936
-60000
-57000
-54000
-51000
-48000
0.6 0.7 0.8 0.9 1
R² = 0.9321
-20000
-15000
-10000
-5000
0
0.6 0.7 0.8 0.9 1
HV -25dB
Arai et al., 2018
SCAN-SAR (25m)
Truth (Unknown)
Prediction
Analy-sis
Anal.
Pred.
Estimate actual irrigation practice
Irrigation/field water-level
Model simulation
Our data integration scheme
Simulation scheme with 25m-spatial resolution- Hysteresis of soil matric potential energy-
flood flood
Fie
ld w
ate
r le
vel (
cm)
0-10
-20
10
20
30
40
50
https://slideplayer.com/slide/5038747/
Irrigation, potential energy >> Side flow, ground water flow
Implicit RK4 integration model
Irrigation (init./boundary)
inundated
Not-inundated
Matric-potential at irrigation index (Di) = Σ(soil inundation rate before the irrigation, days after sowing, clay content)・αi
t = days after irrigation
𝑾𝑳 = field water level
Gravitational-potential at irrigation index (G) = field water level after irrigation *β
𝑑𝑾𝑳
𝑑𝑡= 𝜸∗exp δ ∗ 1 − log exp Di ∗ t − G + 2 + exp −Di ∗ t − G ∗ Di ∗ t − G
−δ ∗ exp Di ∗ t − G − exp −Di ∗ t − G ∗ Di ∗ t − G
exp Di ∗ t − G + 2 + exp −Di ∗ t − G
+ Di ∗ 1 − log exp Di ∗ t − G + 2 + exp −Di ∗ t − G + rain-fall
Irrigation functionif 𝑾𝑳 < threshold :
irrigate (i.e., 𝑾𝑳 += X)
Irrigation (init./boundary)
Parameter update by the analysis with EO data
https://slideplayer.com/slide/5038747/
Irrigationthreshold
Irrigationthreshold
constraint
Modelstructure
Estimated (cm)
Ob
serv
ed (
cm)
-20
-15
-10
-5
0
5
10
15
-20 -10 0 10
cal
NICAM-TM(Chem)-LETKFwith AMSU, PREPBUFR and GOSAT/Sentinel-5P
Direct comparison between GOSAT and emission data is meaningless…
→GOSAT data assimilation with NICAM-TM!
Nonhydrostatic ICosahedral
Atmospheric Model-TM(Chem)
Local Ensemble Transform
Kalman Filter
Miyoshi., 2005
Terasaki et al., 2014
Observation operator
development
for GOSAT
Implementation of variable localization scheme in NICAM-TM-LETKF (PREPBUFR&GOSAT)
Back ground covariance matrices
Kang et al., 2012 → Flux parameter estimation!2014051718-1803 Glevel 6, Inflation with RTPS=1
Increment of XCH4 (ppb, 950 hpa) w/ VL
Increment of XCH4 (ppb, 950 hpa) w/o VL
Economic assessment of GHG mitigation under various uncertainties
Kalnay et al. 2017
0
50,000
100,000
150,000
200,000
250,000
300,000温室効果ガス(キノコ栽培)
温室効果ガス(野焼き)
温室効果ガス(水田)
菌種購入費
灌漑費
レベリング
脱穀
種(OM4218)
種(Jasmine 85)
Kali肥料
NPK肥料
DAP肥料
尿素肥料
0
50,000
100,000
150,000
200,000
250,000
300,000
常時湛水
常時湛水
常時湛水
常時湛水
稲わ 稲わ 持出 持出
きのこ
稲わら
米
# State and trends of the carbon market 2012,
# Spot and Secondary offset market 2011, 1,822MtCO2
= 23,250million USD
Arai., 2015
CF
AW
D
AW
D
AW
D
AW
D
CF
CF
CF
GHG from mushroom
GHG from field burning
GHG from paddy soil
Mushroom seeds
Irrigation cost
Land preparation
Grain threshing
Seed (OM4218)
Seed (Jasmine 85)
KCl
NPK
DAP
Urea
Rice
Straw
Mushroom
Expenditure
(JP
Y/y
ear)
Incom
e -
Expenditure
(J
PY/y
ear)
Incorp.Burn sell Mush-room
STRAWMANAGEMENT
Transparent MRV system on baselines/mitigation-effects with
satellite data is the key !