SCIENTIFIC DECLENSION OF THE …1998-2008) 10 Years of Operational Global VEGETATION Monitoring (Brussels Dec. 2008) SCIENTIFIC DECLENSION OF THE SPOT/VEGETATION TIME SERIES Philippe

(1998-2008) 10 Years of Operational Global VEGETATION Monitoring (Brussels Dec. 2008)

SCIENTIFIC DECLENSION OF THE SPOT/VEGETATION TIME SERIES

Philippe Maisongrande

with Agustin Lobo and Benoît Duchemin


10 years of monitoring to serve various thematic issues

• Some Thematic Aspects : – NDVI as a proxy of LAI

– SWVI as a proxy of Water Stress

– Evapotranspiration and water budget

– Snow

– Land Use Land Cover

Resolution, VGT+other mission


Precipitation climatology-Australian Bureau of Meteo.-

1998->2006 average NDVI-Spot/VEGETATION data-

NDVI makes sense


NDVI & SWVI

Maisongrande, Kuhlmann et al.

MODSIM 2007NDVI

SWVI

10 years

ENSO


La Niña y El Niño


MODSIM 2007

1998/1999

1999/2000

2000/2001

2004/2005

2006

2002/2003


NDVI & SWVIvs

Southern Oscillation Index


MODSIM 2007NDVI

SWVI


1.5*∆-0.75

0.15+SOI/100


MODSIM 2007

∆∆∆∆=NDVI-SWVIvs

Southern Oscillation Index


Rational

VWC VWC ~ ~ 11--(EC(EC’’/A/A’’CC’’) ) -->A*NDVI>A*NDVI--B*SWVI+CB*SWVI+C--> NDVI> NDVI--SWVISWVI(∆)

SWVI


61 92 122 153 183 214 245 2750.02

0.11

0.2

0.29

NDVI & SWVI

SW

VI

Date61 92 122 153 183 214 245 275

0.41

0.49

0.57

0.65

ND

VI

61 92 122 153 183 214 245 2750.3

0.35

0.4

0.45

In-Situ vs Indice-Spectral

Hum

Sol

In-S

itu (

%)

Date61 92 122 153 183 214 245 275

0.17

0.25

0.33

0.41

0.49

ND

VI-

SW

VI

61 92 122 153 183 214 245 275-0.03

0.07

0.17

0.27

0.37NDVI & SWVI

SW

VI

Date61 92 122 153 183 214 245 275

0.38

0.47

0.56

0.65

0.74

ND

VI

61 92 122 153 183 214 245 2750.3

0.35

0.4

0.45

In-Situ vs Indice-SpectralH

umS

ol In

-Situ

(%

)

Date61 92 122 153 183 214 245 275

0.29

0.31

0.33

0.35

0.37

ND

VI-

SW

VI

61 92 122 153 183 214 245 275-0.02

0.04

0.1

0.16

NDVI & SWVI

SW

VI

Date61 92 122 153 183 214 245 275

0.33

0.4

0.47

0.54

0.61

ND

VI

61 92 122 153 183 214 245 2750.19

0.34

0.49

0.64

In-Situ vs Indice-Spectral

Hum

Sol

In-S

itu (

%)

Date61 92 122 153 183 214 245 275

0.14

0.25

0.36

0.47

ND

VI-

SW

VI

Auradé 2004 Auradé 2005 Lamasquère 2005

NDVI, SWVI and Soil Water Content


Photosynthesis

NPP=Ec.PAR.Ei.Eb.Ks(θ)

Soil Water Content: θ

Evapotranspiration

ETR= Ks(θ).Kc. ETO

Simple SVAT approach

F(swvi)

F(swvi)


Kc as a function of NDVI

Kc=(a x NDVI) + b


Photosynthesis

NPP=Ec.PAR.Ei.Eb.Ks(θ)

Soil Water Content: θ

Evapotranspiration

ETR= Ks(θ).Kc. ETO

Simple SVAT approach

F(ndvi)

F(ndvi)


SNOW Monitoring


Snow index

MIRBB

MIRBB

NDSI++

−+

=

220

220


10 years average snow coverfrom December to February

[-1,2 ; -1[

[-1 ; -0,6[

[-0,6 ; -0,3[

[-0,3 ; -0,1[

[-0,1 ; 0,1[

[0,1 ; 0,3[

[0,3 ; 0,52[

[0,52 ; 0,7[

LEG ENDE Intervalles du NDSI :


Interannual Snow dynamic in the Atlas montains

Boudhar et al. 2007


LANDSAT vs VGT Snow Cover

High-Atlas mountains (Morocco)

Proportion of snow

cover


MNDSI

Altitude 2600-4200m

0

200

400

Sno

w s

urfa

ce (

km²)

North South

Space time dynamics of snow covered areas

0

0,5

1

Sno

w c

over

pro

port

ion

1000-1400m1400-1800m1800-2200m2200-2600m2600-3000m3000-3400m3400-3800m3800-4200m

Boudhar et al. Sècheresse 2007. Chaponnière et al. I nternational Journal of Remote Sensing. 2005


5 0 1 0 0 1 5 0 2 0 0 2 5 0 3 0 0 3 5 0 4 0 0 4 5 0 5 0 0

20

40

60

80

1 00

1 20

1 40 -0 .6

-0 .4

-0 .2

0

0 .2

0 .4

0 .6

0 5 0 0 1 0 0 0 1 5 0 0 2 0 0 0 2 5 0 0 3 0 0 0 3 5 0 0-0 . 8

-0 . 6

-0 . 4

-0 . 2

0

0 . 2

0 . 4

0 . 6

0 . 8

A l t i t u d e e n m è t re s

ND

SI

moy

en

P y ré n é e s

10 years average NDSI in the Pyrenees

SNOW INDEX

ALTITUDE


10 years averageappearance dates for snow cover

50 100 150 200 250 300 350 400 450 500

20

40

60

80

100

120

140

0

2

4

6

8

10

12

14

16

18

: 11 sept

: 21 feb

: 1er october

: 11 nov

: 21 october

: 1er dec

: 11 jan

: 21 dec.

: 1str feb


(1998-2007) interannual NDSI variability

19 98 19 9 9 2 00 0 2 00 1 20 02 20 0 3 20 0 4 20 0 5 2 0 06 2 0 070

0. 52 6 1

1. 05 2 2

1. 57 8 3

2. 10 4 4x 1 0

4

Nom

bre

de p

ixel

s où

ND

SI

> 0

,25

1999 2000 2001 2002 2003 2004 2005 2006 2007-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1


0 0.2 0.4 0.6 0.8 1 1.2 1.44

5

6

7

8

9

10x 10

4

Nb

pixe

ls >

0.25

(Ja

nvie

r -

Fév

rier)

Ecart type NAO (Août - Octobre)

Corrélation entre le NAO et le NDSI - PYRENEES

98-99

99-00 00-01

01-02

02-03

03-04 04-05

05-06 R² = 0,818

NAO impact on the snow cover

NAO std (Aug.-Oct)

Nb pixels>0.2


Snow cover map f(NOA)

50 100 150 200 250 300 350 400 450 500

20

40

60

80

100

120

140

Pas de neige

σ(NAO de août à octobre) = 1,35



0 0.2 0.4 0.6 0.8 1 1.2 1.44

5

6

7

8

9

10x 10

4

Nb

pixe

ls >

0.25

(Ja

nvie

r -

Fév

rier)

Ecart type NAO (Août - Octobre)

Corrélation entre le NAO et le NDSI - PYRENEES

98-99

99-00 00-01

01-02

02-03

03-04 04-05

05-06


Land Use Land Cover


Pixel Heterogeneity

1 km 1 km


Estimating land use fractions πij in a Pixel

( ) ( ) )(3

1

ttytY ij

jiji επ +×=∑=

NDVI Profile knownfrom VGT Data

kownendmembersUnkown land

use fractions

( )[ ]∑=

=T

tii t

TRMSE

1

21 ε

land use fractions are estimated by minimising the RMS E pixel by pixel

0≥ijπ 13

1

=∑=j

ijπWith and

.


Benhadj et al. 2008( ) ( ) )(3

1

ttytY ij

jiji επ +×=∑=


Land use fractions estimates

Ann

ualc

rop

Bar

eso

ilor

char

d

VGT MODIS reference

Benhadj et al. 2008



Conclusions•Thank to its qualities (calibration &processing ), the10year archive is a mine for time series analysis withregard to interannual variability: NAO, ENSO, climatechange,…

•SWIR is useful in LULC studies but it does also have interesting applications in snow and water stress monitoring ->VGT3.

•The km2 resolution (VGT1&2) can be a problem for cogeoregistration with other images (TM, HRVIR, MODIS,…) 300m would reduce this problem and makemore efficient the pixel unmixing methodologies thatappear .


remarks –PROBA-V

•Interdate and interspectral staking: 300m uncertaintyrepresent 100% of the pixel

•Existing strategies for the processing of Directional andatmospheric effects do already exit, they could be takein intoaccount in the next ground segment.

•Synergie VGT+ something (S2, S3, ….other)


Thank you

[email protected]


0.740.810.5570.8100.8980.687R²

0.1270.1280.1410.1050.1210.113RMSE

% AnnualVeg.

%B. Soil%Ochard% AnnualVeg.

%B. Soil%Ochard

VEGETATIONMODIS

AND THE WINNER IS….

Documents

SCIENTIFIC DECLENSION OF THE …1998-2008) 10 Years of Operational Global VEGETATION Monitoring (Brussels Dec. 2008) SCIENTIFIC DECLENSION OF THE SPOT/VEGETATION TIME SERIES Philippe