high spatiotemporal et mapping using multi-sensor data fusion · high spatiotemporal et mapping...

High Spatiotemporal ET Mapping Using Multi-Sensor Data Fusion

M.C. Anderson, W.P. KustasUSDA-ARS, Hydrology and Remote SensingLaboratory

C. Hain, J.R. MecikalskiU Alabama-Huntsville, Atmospheric Science

Presenter

Presentation Notes

5:55

Satellite Thermal Imaging Systems

PixelScale

Spatial Resolution

Temporal Resolution

CurrentSources

FutureSources

Coarse 5-20 km 15 min AIRSGOESMSG

CrISGOESMSG

Moderate 1 km 2-4 times daily MODISAVHRRATSR

VIIRSAVHRRATSR

Fine 90–120 m Once every 8-16 days

ASTERLandsat

LDCMHyspIRI?

Table from S. Hook

Atmosphere-Land Exchange Inverse (ALEXI)

TRAD (φ), fcTRAD (φ), fc

Rsoil

TcTac

Hs

Ts

Ra H = Hc + Hs

Rx

Hc

Ta

ABL

Ta

ALEXI DisALEXI5 km

30 m

Tw

o-S

ou

rce M

od

el

TRAD,i(φi), fc,i

i

Ra,i

Blending height

Regional scale∆TRAD - GOESLAI from MODIS

Landscape scaleTRAD - TM, MODIS, HyspIRILAI = f(NDVI)

Surface temp:Vegetation:

ET = (RNET - G) - HAvailable energy

MODIS (1km) Landsat (~100m)

(hourly) (daily) (monthly)

FORT PECK, MONTANAGOES (10km)

(Wm-2)

Evapotranspiration100

200

300

400

30 J

un 2

002

18 A

ug 2

002

2 S

ep 2

002



SAN PEDRO RIVER, ARIZONAGOES (10km)

(Wm-2)

Evapotranspiration100

200

300

400

11 J

un 2

004

30 A

ug 2

004

HIGH-RESOLUTION INTERPOLATIONLA

ND

SA

T(D

isA

LEXI

)

DOY 328 336

Landsat 5 Landsat 7

329 330 331 332 333 334 335

Conserve ET/PET

Daily Evapotranspiration – Orlando, FL, 2002

MODIS-LANDSAT DATA FUSIONLA

ND

SA

T(D

isA

LEXI

)

DOY 328 336

Landsat 5 Landsat 7

Daily Evapotranspiration – Orlando, FL, 2002

Spatial Temporal Adaptive Reflectance Fusion Model (STARFM)

329 330 331 332 333 334 335

MO

DIS

(Dis

ALE

XI)

POSSIBLE STARFM APPROACHES

Apply STARFM to TRAD?

Acquisition time discrepancy(strong diurnal cycle to LST)

View angle discrepancy

TC

TRAD

θ

TSTRAD(θ) ~ fC(θ)TC + [1-fC(θ)]TS





TC

TRAD

θ

Apply STARFM to ETday

Minimizes dependence on acquisition time

EF = LEi

RNi - GiLEday = EF * (RNday – Gday)

TSTRAD(θ) ~ fC(θ)TC + [1-fC(θ)]TS





TS

TC

TRAD

θ

Apply STARFM to ETday

Minimizes dependence on acquisition timeALEXI/DisALEXI account for view angle

TA

H

HC

HS

TRAD(θ) ~ fC(θ)TC + [1-fC(θ)]TS

Daily ET(November 28, 2002)

GO

ES

(ALE

XI)

MO

DIS

(Dis

ALE

XI)

LAN

DS

AT

(Dis

ALE

XI)

CO

NU

SFlorida

Orlando

Google MapsDaily ET

(December 2, 2002)

GOES/MODIS/Landsat FUSIONG

OE

S(A

LEXI

)M

OD

IS(D

isA

LEXI

)

DOY 328 329 330 331 332 333 334 335 336

LAN

DS

AT

(Dis

ALE

XI/S

TAR

FM)

Landsat 5 Landsat 7

(Gao et al, 2006)Spatial Temporal Adaptive Reflectance Fusion Model

(STARFM)

Daily Evapotranspiration – Reedy Lake, FL, 2002

LEAF AREA INDEX2002328 2002336 2003019 2003043 2003083

MO

DIS

(1k

m)

Land

sat

(30m

)

k2 - NDVITMLAITM = -k1 * lnk2 – k3

Land

sat

(1km

)

k1, k2, k3 fit to MODIS LAI

LANDSURFACE TEMPERATURE

Landsat 5 Landsat 7Landsat 7 Landsat 7 Landsat 5

2002328 2002336 2003019 2003043 2003083

MO

DIS

MO

DIS

(sh

arpe

ned)

Land

sat

EVALUATION OF PREDICTED FIELDS2002328 2002336 2003019 2003043 2003083

MO

DIS

(Dis

ALE

XI)

Land

sat

(Dis

ALE

XI)

Land

sat

(STA

RFM

)O

bservedP

redicted

Landsat 50.7710%

Landsat 70.6312%

Landsat 70.839%

Landsat 70.8111%

Landsat 50.6712%

R2:% Err:

0

5

10

15



BUSHLAND, TEXASGOES (10km)

(MJ m-2 d-1)

Evapotranspiration

21 J

ul 2

008

6 A

ug 2

008

2 A

ug 2

008

JULY AUGUST SEPTEMBER OCTOBER DECEMBERNOVEMBER

2008Midday Latent Heat Flux

(Wm-2)

LandsatMODIS

Met

eosa

t(A

LEXI

)

MODIS-Landsat daily ET fusion appears feasible

CONCLUSIONS

STILL NEED > TWO LANDSATS!

[email protected]

Fusion results are optimized for MODIS-Landsat in formation

Consistent MODIS-Landsat LAI products will be valuable

high spatiotemporal et mapping using multi-sensor data fusion · high spatiotemporal et mapping...

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