Dictionary of Intermediate Japanese Grammar - By Makino, Tsutsui
Hui Lu ( Tsinghua University, China ) Toshio Koike & Hiroyuki Tsutsui ( The University of Tokyo )
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Transcript of Hui Lu ( Tsinghua University, China ) Toshio Koike & Hiroyuki Tsutsui ( The University of Tokyo )
IGARSS 2011, Jul. 27, Vancouver 1
Monitoring Vegetation Water Content by Using Optical Vegetation Index and Microwave
Vegetation Index: Field Experiment and Application
Hui Lu ( Tsinghua University, China)Toshio Koike & Hiroyuki Tsutsui (The University of Tokyo)
Hedeyuki Fujii (JAXA)
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Outline
• Background and motivation• Microwave vegetation index• Field Experiment
– Setting and instruments– Observed Results
• Application – Mongolia site
• Remark
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Background• Global vegetation information is closely related to
– Food productivity, famine, ……– Environment, ecological system, ….
• In land surface modeling and remote sensing retrieval, vegetation is– A key variable of land surface remote sensing
• Soil moisture, soil temperature, vegetation water content– A key parameter in GCM, hydrology and land surface
scheme• LAI, fPAR, ET, precipitation interception
– A key parameter in terrestrial ecosystem model• Carbon cycle
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Motivation• Vegetation parameters observed by satellites:
– VIS/IR: fractional coverage, NDVI, LAI, NDWI, EVI– MW: Vegetation water content (VWC), Microwave
vegetation index (MVI)• MW RS has daily global coverage and deeper
penetration depth– Complement vegetation information to VIS/IR
• What the relationship between these parameters? • Accurate VWC is useful in
– Improving soil moisture retrieval algorithm– Improving LDAS
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VWC, MVI, NDVI, NDWI
• Microwave vegetation index by Shi
• NDVI: VIS (620 - 670nm) & NIR (841 - 876 nm)
• NDWI:SWIR in band 5 (1230-1250 nm) or band 6 (1628-1652 nm)
),2(),2(
),1(),1()2,1(
HfTBVfTB
HfTBVfTBffMVI
VISNIR
VISNIRNDVI
SWIRNIR
SWIRNIRNDWI
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Field Experiment--Instruments and setting
0
2000
4000
6000
8000
10000
12000
0 500 1000 1500 2000 2500 3000wavelength(nm)
Refl
ectiv
ity
Brightness temperature observed by Ground Based Microwave Radiometer, at 6.925, 10.65, 18.7, 23.8, 36.5, 89 GHz
VIS/IR reflectance measured by ASD FieldSpec Pro in a spectral range of 350nm – 2500nm
Time Series of TB (Horizontal Polarization)
180
200
220
240
260
280
300
11/28 12/28 01/27 02/26 03/28 04/27 05/27Time
TB
(K)
18h-6ch18h-7ch
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Experiment design
123
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Experiment design• Observing winter wheat
– One kind of main crops– VWC is not so big, C-band could penetrate.
Vegetation Samples
y = 0.0009x3 + 0.0685x2 - 9.3659x
R2 = 0.9805
y = 0.0002x3 + 0.1648x2 - 12.557x
R2 = 0.9685
y = 0.0007x3 - 0.0963x2 + 3.1906x
R2 = 0.9948
0
1000
2000
3000
4000
5000
6000
7000
8000
0 20 40 60 80 100 120 140 160 180 200Days
(g/m
^2)
Wet Biomass Dry Biomass Water Content
Poly. (Wet Biomass) Poly. (Water Content) Poly. (Dry Biomass)
Winter wheat development VWC was measured by sampling
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Vegetation
01-16 01-19 01-24 02-07
11-29 12-08 12-13 12-20
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Observed ResultsVWC ~ NDVI
y = 0.0226x + 0.7694R2 = 0.1352
0.0
0.2
0.4
0.6
0.8
1.0
0 1 2 3 4 5VWC(Kg/ m̂ 2)
NDVI
NDVI
NDVI shows a poor correlation to the VWC, with an R-square less than 0.2.
It is not good to estimate VWC from NDVI observation!
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Observed ResultsVWC ~ NDWI
NDWI has a good correlation to VWC, while band 5 has bigger R value
VWC information maybe can be estimated by NDWI 5, for vwc in [0,4]
y = 0.053x + 0.2916R2 = 0.5772
y = 0.0373x + 0.0289R2 = 0.6203
-0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
0 1 2 3 4 5VWC (kg/ m̂ 2)
NDW
I
NDWI5 NDWI6
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Observed ResultsVWC ~ MWI
y = -0.0483x + 0.5351R2 = 0.7453
y = -0.1463x + 0.9393R2 = 0.6891
0
0.2
0.4
0.6
0.8
1
1.2
0.0 1.0 2.0 3.0 4.0 5.0VWC(kg/ m̂ 2)
MVI
MVI(10,6)MVI(18,10)
VWC = linear regression function of MVI
y = -15.445x + 8.8809R2 = 0.7453
y = -4.7118x + 5.1782R2 = 0.6891
0.00.51.01.52.02.53.03.54.04.5
0.0 0.2 0.4 0.6 0.8 1.0 1.2MVI(kg/ m̂ 2)
VW
C
MVI(10,6)MVI(18,10)
High R for X-C band
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Application
Domain
• AMPEX– Mongolia;
– Relative homogenous
– VWC survey at 2003 Jul
and Aug;
– 160*120km;0
1
2
3
4
5
6
7
8
A B C D E F G H I J
AWS
ASSH
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Application: VWC retrieved from JAXA algorithm Vs. in situ
Jul 2003 averaged WC
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.18
F2 A3 E4 G6 GUS H7 D0 D8 A6 C2 C4 D7
Stations
Wa
ter
Co
nte
nt
Observed WC
Estimated WC byJAXA
Aug 2003 averaged WC
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.18
F2 A3 E4 G6 GUS H7 D0 D8 A6 C2 C4
Stations
Wa
ter
Co
nte
nt
Observed WCEstiamted WC by JAXA
VWC provided by JAXA algorithm is comparable to the in situ observed VWC
Using as reference data to check the performance of MVI-based method
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Results: VWC from MVI-based method
(b) H7 station
y = 3.4726x - 2.8608R2 = 0.7897
-0.02
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.81 0.82 0.83 0.84 0.85 0.86VWC-MVI(10,6) (kg/ m̂ 2)
VW
C-J
AXA (kg
/m̂2)
(a) A3 station
y = 2.8419x - 2.3393R2 = 0.7719
-0.02
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.81 0.82 0.83 0.84 0.85 0.86VWC-MVI(10,6) (kg/ m̂ 2)
VW
C-J
AXA (kg
/m̂2)
(d) H7 station
y = 5.0309x - 2.5245R2 = 0.3065
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.5 0.505 0.51 0.515 0.52VWC-MVI(18,10) (kg/ m̂ 2)
VW
C-J
AXA (kg
/m̂2)
(C) A3 stationy = 7.821x - 3.9288
R2 = 0.449
-0.02
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.5 0.505 0.51 0.515 0.52VWC-MVI(18,10) (kg/ m̂ 2)
VW
C-J
AXA (kg
/m̂2)
A3
H7
MVI(10,6) MVI (18,10)
High R for X-C band
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Remark• Field experiment which observing winter wheat development by
using microwave radiometer and VIS/IF spectroradiometer simultaneously.
• Comparing to in situ observed VWC– NDVI show poor correlation– NDWI show good correlation– MVI show strong correlation
• MVI-based linear equation could provide VWC information, but the absolute values should be scaled– Can be used to monitor the vegetation temporal variation– The coefficient of linear equation should be related to (vfc, vegetation type)
• Future work: – Quantify the coefficient by each vegetation type (LSM classification, or real
type)– Test for more observation sites (US site, MDB site, China)
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Thank you for your attention!