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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)

IGARSS 2011, Jul. 27, Vancouver 1 Monitoring Vegetation Water Content by Using Optical Vegetation Index and Microwave Vegetation Index: Field Experiment

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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)

IGARSS 2011, Jul. 27, Vancouver 2

Outline

• Background and motivation• Microwave vegetation index• Field Experiment

– Setting and instruments– Observed Results

• Application – Mongolia site

• Remark

IGARSS 2011, Jul. 27, Vancouver 3

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

IGARSS 2011, Jul. 27, Vancouver 4

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

IGARSS 2011, Jul. 27, Vancouver 5

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

IGARSS 2011, Jul. 27, Vancouver 6

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

IGARSS 2011, Jul. 27, Vancouver 7

Experiment design

123

IGARSS 2011, Jul. 27, Vancouver 8

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

IGARSS 2011, Jul. 27, Vancouver 9

Vegetation

01-16 01-19 01-24 02-07

11-29 12-08 12-13 12-20

IGARSS 2011, Jul. 27, Vancouver 10

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!

IGARSS 2011, Jul. 27, Vancouver 11

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

IGARSS 2011, Jul. 27, Vancouver 12

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

IGARSS 2011, Jul. 27, Vancouver 13

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

IGARSS 2011, Jul. 27, Vancouver 14

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

IGARSS 2011, Jul. 27, Vancouver 15

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

IGARSS 2011, Jul. 27, Vancouver 16

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)

IGARSS 2011, Jul. 27, Vancouver 17

Thank you for your attention!