sebs balance system for estimation of - earth online - esa · ‐surface energy balance system for...

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23/07/2013 1

SEBS ‐ Surface Energy Balance System for estimation of turbulent heat fluxes and evapotranspiration

Z. (Bob) SuITC, University of TwenteEnschede, The Netherlands

[email protected]/wrs

Earth Observation of Water Cycle

Precipitation (27%)

Evaporation (100%, 413 k km3/yr)

River & groundwater discharge(10%)

Radiation

Vapour Transport (10%)

Evaporation/Transpiration

(17%)

Condensation (90%)

Water ResourcesManagement

2

Ocean water storage(1,335,040 k km3)

(2627 m)

Ocean ice storage(26350 k km3)(51.659 m)

Atmosphere water vapor storage(12.7 k km3)(0.025 m)

Groundwater storage(15300 k km3)(29.996 m)

Rivers & lakes storage(178 k km3)(0.349 m)

Soil moisture storage(122 k km3)(0.239 m)

permafrost storage(23 k km3)(0.043 m)

Fig 1.

2

Learning Objectives

1. To understand basic ideas of advanced thermal remote sensing

2. To understand the most critical processes in surface energy balance

3. To familiarize with the processing steps used in SEBS for the

derivation of different flux terms

4. To familiarize with the applications of SEBS

Wind

Land-Atmosphere Interactions - Terrestrial Water, Energy and Carbon Cycles

Lat

ent H

eat

(Pha

se c

hang

e)

Pre

cipi

tati

on

Biochemical Processes

Advection

3

Structure of the

atmospheric boundary

layer considered in

SEBS

Viscous sublayerTransition layer

Inertial sublayer

Atmospheric Surface Layer (ASL)

Planetary (Convective) Boundary Layer (PBL)

Roughness sublayer

~ 10 1~2 m

~ 10 -1~1 m

~ 10 -3 m

~ 10 2-3 m

Free Atmosphere

Wind profile

Blending height

PBL height

Interfacial sublayer

Corn

Bare Soil

Water Body

Garlic

Green Grass

Vineyard

Reforestation

Barrax Test Site, Spain

‐ Situated in the area of La Mancha, in the west of the province of Albacete, 28 km from Albacete‐ Geographic coordinates: 39º 3’ N; 2º 6’ W‐ Altitude (above sea level): 700 m

4

Solar R

adiation

Atm

ospheric thermal

radiation

Reflected solar radiation

Surface thermal

radiation

eTemperaturSurfaceT

emissivity

albedo

:

:

:

0

Surface Radiation Balance

401 TRRR lwdswdn

Data from EAGLE/SPARC Campaign 2004, Barrax, Spain

Barrax 2004 Radiation @ Vineyard

-200

0

200

400

600

800

1000

1200

196 197 198 199 200 201 202 203

SW_inc

SW_out

LW_inc

LW_out

5

Wind

Surface Energy Balance Terms

LE

LEHGRn 0

scccn ffRG 10

cfT ,,, 0

Scintillometer Data from EAGLE/SPARC Campaign 2004, Barrax, Spain

Barrax 2004 Fluxes @ Vineyard

-400

-200

0

200

400

600

800

196 197 198 199 200 201 202 203

Rnet

G_avg

H-LAS

LE_Rest.

6

(Radiometric observables) (r0, T0),(RS observables or estimates) (fc, LAI, hc),

(Surface observation, radiosonds, NWP fields) (TPBL, VPBL, PPBL, qPBL, Rs, Rld)

Schematic representation of SEBS

Preprocessing to derive radiation balance terms

Submodel to derive roughness length for heat transfer(Su, Schmugge, Kustas, Massman, 2001)

Energy balance terms (Rn, G0, H, E, )

Energy balance at limiting cases (Menenti, Choudhury,1993)

Submodel to derive stability parameters (ASL scaling or PBL scaling, Brutsaert, 1999)

Remote sensing of heat fluxes and evaporation - SEBS Single source, SEBS Parallel-source

Hs

rahv rahs

Hv

Tv Ts fc

Wind

T0

7

SEBS Basic Equations, Su, 2002, HESS, 6(1),85‐99

wetdry

wetr HH

HH

1

GR

E

GR

E

n

wetr

n

10

ee

r

CGRH s

ew

pnwet

0

0 ,0

GRH

orHGRE

ndry

dryndry

wetnwet

wetnwet

EGRH

orHGRE

0

0 ,

wet

wet

wetr E

EE

E

E

1

EHGRn 0

GRE

GRH

n

n

1

Wind, air temperature, humidity(aerodynamic roughness,

thermal dynamic roughness)

LE

L

z

L

dz

z

dz

k

uu m

mmm

00

0

0* ln

1

00

0

00* ln

L

z

L

dz

z

dzCkuH h

hhh

ap

kgH

uCL vp 3

*

?,, 000 hm zdz

Turbulence parameterisation – wind, stability and sensible heat flux

?,, quTa14

8

The scalar roughness height for heat transfer

The within-canopy wind speed profile extinction coefficient,

22*2 huu

LAICn d

ec

21

*2

2*

10*

214

sst

hz

huu

sccn

t

d fkBC

kfff

ehu

uC

kCkB

m

ec

4.7lnRe46.241

*1

skB

100 exp/ kBzz mh

Graphical representation of the dry and wet limits concept

16

wetdry

wetr HH

HH

1

Dry limits

Wet limits

9

SEBS - The Surface Energy Balance System

SEBS Core Modules

Boundary Layer Similarity Theory

Roughness for Heat Transfer

Surface Energy Balance Index

Meteorological Data

Boundary Layer Variables

Remote Sensing Data

VIS

NIR TIR

Input Output

EvaporativeFraction

Turbulence Heat Fluxes

Actual Evaporation

Software implementation

18

10

General Methodology

Atmospheric models RS

DEM

SYNOPS,Climatology

SEBS(Surface Energy Balance System)Preprocessing Postprocessing

Regional parameters Ta, , L

RS

DEM

Cloud cover

Evaporative Fraction, soil moisture

RSDEM

Preprocessing

RS

DEM

Regional Atmospheric State

Numerical Weather Prediction models

Atmospheric sounding,In-situ meteorological

Observation

NOAA/AVHRR, LANDSAT, ASTER,METEOSAT, AATSR, MODIS,

DN Radiance/Reflectance at sensor

Radiance/Reflectance at surface

Albedo, temperature, vegetation cover, emissivity, incoming radiation, roughness

11

VIS

UV

NIRTIR

MIR

SOLAR TERRESTRIAL

SOLAR AND TERRESTRIAL SPECTRUM

Wavelength (m)

THERMAL INFRARED RADIATION is a form of electromagnetic radiation with a wavelength between 3 to 14 micrometers (µm). Its flux is much lower than visible flux.

Source: J.-L. Casanova

Thermodynamic (kinetic) temperature

Brightness temperature

Directional radiometric surface temperature & Directional emissivity

Hemispheric broadband radiometric surface temperature & Hemispherical broadband emissivity

Terminology in Thermal Remote Sensing

Source: J. Norman, and F. Becker, 1995

12

Thermodynamic (kinetic) temperature (T) is a macroscopic measure to quantify the thermal dynamic state of a system in thermodynamic equilibrium with its environment (no heat transfer) and can be measured with a infinitesimal thermometer in good contact with it.By maximizing the total entropy (S) with respect to the energy (E) of the system, T is defined as

Thermodynamic (kinetic) temperature

TdE

dS 1

which is consistent with the definition of the absolute temperature by the ideal gas law

RTPV

where P is pressure, V volume, R the gas constant (R=kN0, k is Boltzmann constant, N0 number of molecules in a mole).

c1 = 1.19104·108 W µm4 m-2 sr-1

c2 = 1.43877·104 µm K, are the first radiation constant (for spectral radiance) and second radiation constant, respectively, B (λ, Ts) is given in W m-2 sr-1 μm-1 if λ the wavelength is given in µm.

The theory to express the thermal emission is based on the black body concept: a black body is an object that absorbs all electromagnetic radiation at each wavelength that falls onto it. A black body however emits radiation as well, its magnitude depends on its temperature, and is expressed by the Planck’s Law:

1exp

),(2

51

S

S

T

c

cTB

Thermal emission - the Planck’s Law:

13

Brightness temperature (Tb,i) is a directional temperature obtained by equating the measured radiance (Rb,i) with the integral over wavelength of the Planck’s black body function multiplied by the sensor response fi.

Brightness temperature

2

1

1,

exp

,,

,

25

1,,

d

TC

CfTRR

ib

iibib

12

1

df i is the detector relative response.

Algorithms

Land surface temperature derived by using a theoretical split-window algorithm

References: Sorino et al.(2004)

Surface temperature

14

The at-sensor radiance for a given wavelength (λ) s:

where

– ελθ is the surface emissivity,

– Bθ(λ, Ts) is the radiance emitted by a blackbody at temperature Ts of the surface,

is the downwelling radiance,

– λθ is the total transmission of the atmosphere (transmittance) and

is the upwelling atmospheric radiance. All these magnitudes also depend on the observation angle θ.

The radiative transfer equation for LST retrieval

atmatmS

sen LLTBL )1(),(

atmL

atmL

According to Sobrino et al. (1996), the upwelling and downwelling atmospheric radiance can be substituted, respectively, by:

)()1( aiiatm TBL

)()1( 53 aiiatm TBL

where Ta is the effective mean atmospheric temperature andi53 is the total atmospheric path transmittance at 53 degrees.

15

Surface

Atmosphere

THERMAL INFRARED ‐ TIR (8‐14 m)

( ) (1 )sen atm atmsL B T L L

B: Planck’s lawBrightness temperature: LsenB(Tsen)LST: Ts

(→ i)

THEORETICAL BASIS – Radiative Transfer Equation

sTB atmL 1

atmL

atmL1atmL

sTB

(Sobrino&Jiménez‐Muñoz)

MID‐INFRARED ‐MIR (3‐5 m)

, ,( ) (1 )TOAsol solsen atm atm

s

IL B T L L


Nightime acquisitions:

(same equation as for TIR)


(LSE): allows a better surface characterization in the MIRLST: retrievals are less sensitive to errors in LSE determination

sT


16


Atmospheric Correction

Atmospheric Correction:The signal measured by the sensor is the radiance coming from the surface attenuated the atmosphere and the atmospheric contribution


atms

sur LTBL 1

atmsursen LLL

atmsensur LL

L


PROBLEMS TO BE SOLVED FOR LST‐LSE RETRIEVAL:

‐Coupling between LST & LSE‐Coupling between LSE and Latm

COUPLING BETWEEN LST & LSE

The equation system has one more unknown than the equations.Sensor with N bands:

1 1 1 1 1 1 1

2 2 2 2 2 2 2

( ) (1 )

( ) (1 )

...

( ) (1 )

sen atm atms

sen atm atms

sen atm atmN N N s N N N N

L B T L L

L B T L L

L B T L L

N Equations(N+1) Unknowns: (N emissivities + Ts)

(ill‐posed problem)


17

LST retrieval: requires correction of atmospheric and emissivity effects

How to quantify the contribution of these effects:

RADIATIVE TRANSFER EQUATION “fed” with values of atmospheric parameters and surface emissivities.

ATMOSPHERIC PARAMETERS: radiative transfer codes (e.g. MODTRAN)

EMISSIVITY: spectra measured in the laboratory (spectral libraries)

THEORETICAL BASIS – Simulations

ASTER Spectral Library

It includes more than 2,000 spectra (natural and manmade samples), collected from threeexisting libraries:

Johns Hopkins University (JHU)Jet Propulsion Laboratory (JPL)United States Geological Survey (USGS‐Reston).

Very useful for simulation purposes, analysis and validation of emissivity and reflectancespectra of different samples

http://speclib.jpl.nasa.gov


18

Emissivity spectra of Rocks

0.80

0.82

0.84

0.86

0.88

0.90

0.92

0.94

0.96

0.98

1.00

7 8 9 10 11 12 13 14 15

longitud de onda (m)

emis

ivid

ad

basalto

diorita

cuarzo

mármol



Wavelength (m)

Emissivity

BasaltDioriteQuartzMarble


Vegetation and Water

0.80

0.82

0.84

0.86

0.88

0.90

0.92

0.94

0.96

0.98

1.00

7 8 9 10 11 12 13 14 15

Aguahierba secahierba verde



Wavelength (m)

Emissivity

WaterDry grassGreen grass


19

0

1

2

3

4

5

6

7

8

9

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

2 3 4 5 6 7 8 9 10 11 12 13 14 15

Wavelength (m)

Transmissivity

SURF EMISTransm

issivity

Surface Emission (W/m

2∙sr∙m

)

3.5‐4.1 4.6‐5.1 8.1‐9.4 10‐12.5

Thermal remote sensing: atmospheric windows


0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

2 3 4 5 6 7 8 9 10 11 12 13 14

Atmo

sphe

ric Tr

ansm

issivi

ty

Wavelength (m)

MIR

TIR

AHS Bands: 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81

AHS Band

Atm

ospheri

c Tra

nsm

issi

vity

Flight PBN (975 m AGL)

Fligth PMN (1370 m AGL)

Night: Band 66 (3.91 m) Night: Band 75 (10.07 m)Example: AHS bands


20

0

0,2

0,4

0,6

0,8

1

0 2 4 6 8 10 12 14

Longitud de Onda (m)

Tra

nsm

isiv

idad

Atm

osf

éric

aTotalH2OO3H2O cont.CO2

(m)

Absorption by the different atmospheric constituents

Wavelenght (m)

Atm

ospheric Tran

smissivity


0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

7 8 9 10 11 12 13 14 15


tra

ns

mis

ivid

ad

LMV

LMI

SAV

SAI

TRO

Wet atmospheres (high water vapor content) absorbs more TIR radiation than dry atmospheres

Wavelenght (m)

Atm

ospheric Tran

smissivity

MLSMLWSASSAWTRO


21

0

1

2

3

4

5

6

7

8

9

10

6 7 8 9 10 11 12 13 14 15 16

Rad

ian

ce (

W m

-2sr

-1

m-1

)

Wavelength (m)

B(Ts) Lsen

∙∙B(Ts)

L

(1‐)∙∙L

The different terms of the Radiative Transfer Equation

atmatmS

sen LLTBL )1()(


Contribution of the different corrections in terms of temperature

It depends on:‐ Characteristics of the atmosphere‐ Emissivity of the surface‐ Wavelength of the sensor band

Example:MLS atmosphere, water vapor ~ 2 g/cm2

Emissivity: igneous rock (0.86‐0.97)Atmospheric window: 10‐12 m

DIFFERENCE BETWEEN Ts and Tsensor : > 5 K

After atmospheric correction:Atmospheric Effect: 2‐3 KReflection term: < 1 KEmissivity + reflection: 2‐3 K



22

LST retrievals from remote sensing data require an atmospheric correction and also a previous estimation of the surface emissivity (to solve the coupling between LST & )

Rigorous algorithms are based in the Radiative Transfer Equation. Algorithms depend mainly on the spectral characteristics of the TIR sensor

METHODS FOR LST RETRIEVAL:

1 BAND: Single‐channel (mono‐window)2 BANDS: Two‐channel or split‐window1 BAND and 2 VIEW ANGLES: Bi‐angular (dual‐angle)3 BANDS (multispectral): TES algorithm

Other Methods: TISI, day/night combinations, tri‐channel, etc…

THEORETICAL BASIS – Algorithms


SINGLE‐CHANNEL ALGORITHMS (1 BAND)

1

2 1

5

ln 11

s sen

c cT

L LL

Inversion of the radiative transfer equation:

Different single‐channel algorithms have been developed in order to simplifythe inversion procedure, and also to avoid the a priori knowledge of atmospheric parameters:

‐Qin et al. (2001)‐Jiménez‐Muñoz & Sobrino (2003;2009)

Input data:, , L, L

sen atmatmi i i

i s ii i i

L L 1B (T ) L

THEORETICAL BASIS – Algorithms (LST retrieval)



23

SINGLE‐CHANNEL ALGORITHM: QIN ET AL. (2001) Qin, Z., Karnieli, A., y Berliner, P. (2001). A mono‐window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel‐Egypt border region, International Journal of Remote Sensing, 22(18), pp. 3719‐3746.Developed for Landsat5/TM

6 6 6 6 6 6 6 6 6 66

11 1s aT a C D b C D C D T D T

C

(Integrated) Atmospheric temperature and transmissivity are fitted versus the surface air temperature (T0) and water vapor (w)

Input data:‐Emissivity‐Atmospheric parameters

( and Ta) or (T0 and w)

6 6 6

6 6 6 6(1 ) 1 (1 )

C

D


USA 1976: Ta= 25.9396+0.88045T0Tropical : Ta= 17.9769+0.91715T0Mid latitude summer: Ta= 16.0110+0.92621T0Mid latitude winter: Ta= 19.2704+0.91118T0

For high air temperature:τ6=0.974290‐0.08007w, for w=[0.4~1.6] g/cm

2

τ6=1.031412‐0.11536w, for w=[1.6~3.0] g/cm2

For low air temperature:τ6=0.982007‐0.09611w, for w=[0.4~1.6] g/cm

2

τ6=1.053710‐0.14142w, for w=[1.6~3.0] g/cm2


SINGLE‐CHANNEL ALGORITHMS: JM&S (JGR‐2003; IEEE‐TGRS 2009)

1 2 3

1s senT L

(input data: only w)

L4: b = 1290 KL5: b = 1256 KL7: b = 1277 K

1 2 3

1 LL L

Atmospheric functions

Landsat5/TM

),(),(),(),(),( 03211

0 TwwLwTT sens

Application to Landsat series



24

TWO‐CHANNEL ALGORITHMS (2 BANDS)

Based on the “differential absorption” concept, also known as “split‐window”

Differential absorption Atmospheric correction (Ti – Tj)

LST = Ti + c1 (Ti‐Tj) + c2 (Ti‐Tj)2 + c0 + (c3+c4W)(1‐) + (c5+c6W)

= 0.5 (i ‐ j) = i – jTi,Tj in KW in g/cm2

Mathematical structure of the algorithm (Sobrino et al. 1996; Sobrino & Raissouni, 2000)

DUAL‐ANGLE ALGORITHMS

Same as split‐window, but using two different view angles:

Ti Tn (nadir) Tj Tf (forward)



Emissivities are obtained from VNIR data, and not from TIR data! This avoids the under‐determined problem.

Simplified approachEmissivity as an average of soil and vegetation emissivities according to the Fractional Vegetation Cover (FVC).

NDVI Thresholds Method (Sobrino & Raissouni, IJRS, 2000)Pixels are classified into bare soil pixels, mixed pixels and fully‐vegetated pixels. Cavity effect is estimated from a geometric model.

, ,(1 )s vFVC FVC

, ,(1 )

0.985 0.005

red

s v

a b NDVI NDVIs

FVC FVC C NDVIs NDVI NDVIv

NDVIv

(1 ) '(1 )i si vi vC F P 2

' 1 1H H

FS S

LOW SPECTRAL RESOLUTION: SYNERGY BETWEEN VNIR & TIR

THEORETICAL BASIS – Algorithms (Emissivity retrieval)


S

H

2

sv

s

NDVINDVI

NDVINDVIFVC

25

LSE from FVCFVC estimations can be improved by using other retrieval techniques.

Vegetation Indices: NDVI, GVI (VARI)

Spectral Mixture Analysis: requires extraction of end‐members(e.g., land use map, PPI, AMEE)

Case study of CHRIS/PROBA (Sobrino et al., IEEE‐TGRS 2008; Jiménez‐Muñoz et al., Sensors, 2009)

THEORETICAL BASIS – Algorithms (Emissivity retrieval)


THEORETICAL BASIS – Algorithms (TES)

• Nominal accuracy

– ±1.5 K

– ±0.015

TES: Temperature and Emissivity Separation(Gillespie et al., 1998)

Three modules: NEM, RAT & MMD

Retrieves simultaneously LST and LSE! (from TIR data)

Gillespie, et al., 1998, A temperature and emissivity separation algorithm for Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images, IEEE TGRS, Vol. 36, no. 4, 1113-1126.


26

Iterative procedure

NEM (Normalized Emissivity Method)


atmsur

s

LLTB

1'

'max ss TT

atm

s

atmsur

LTB

LL


β‐spectrum (normalized emissivities)

The shape of the β‐spectrum is the same as the ε‐spectrum

β‐spectrum less sensitive to noise

i

i

N

N

ii

1

‐spectrum

RAT (Ratio)


0.88

0.90

0.92

0.94

0.96

0.98

1.00

1.02

1.04

8.0 8.5 9.0 9.5 10.0 10.5 11.0 11.5

emis

ivid

ad &

bet

a


emisividad

beta

diorite


27

MMD (Maximum Minimum Difference)

MMD = max(i) – min(i)Spectral contrast (MMD)

min = 0.994 – 0.687 MMD0.737

(only valid for ASTER data)

Relationship betweenmin and MMD

min

min( )i ii

Final emissivities

1*

12* * *5

ln 1i

i i i

ccT

R

Final LST




28

Examples of split‐window and dual‐angle algorithms for AATSR channelswith values of the coefficients calculated by a minimization process

Some application examples

56

29

Barrax Test Site, Spain

- Situated in the area of La Mancha, in the west of the province of Albacete, 28 km from Albacete- Geographic coordinates: 39º 3’ N; 2º 6’ W- Altitude (above sea level): 700 m

Some examples

57

EAGLE2006(8 June – 2 July 2006)

EAGLE Netherlands Multi-purpose, Multi-Angle and Multi-sensor,

In-situ, Airborne and Space Borne Campaigns over Grassland and Forest

ESA, Italy ITC, The Netherlands

University of Valencia, Spain INTA, Spain

ITRES, Canada DLR, Germany

WUR / ALTERRA, The Netherlands LSIIT, France

NLR, The NetherlandsKNMI, The Netherlands

WUR / Meteorology Group, The NetherlandsRIVM, The Netherlands

WH Stichtse Rijnlanden, The NetherlandsMIRAMAP, The Netherlands

University of Washington, USA University of South Carolina, USA

ISAFoM, Italy Utrecht University, The Netherlands Staatsbosbeheer, The Netherlands

Fugro, The Netherlands

58

30

Results from SEBSSensible HeatAHS 15 July 2004(A. Gieske)

Sensible HeatASTER 18 July 2004

WM^-2

Validation of Atmospheric Models at Regional Scales

60

50

100

150

200

250

300

350

50 150 250 350

H measured

H E

stim

ated

Tomelloso, RMSD=16.1 W m-2

Lleida, RMSD=40.4 W m-2

Badajoz, RMSD=18.3 W m-2

RMSD = 25.6 W m-2

Scintillometer measurements, retrievals by SEBS using ATSR data and RACMO PBL fields, and simulation by RACMO (Jia, Su, vd

Hurk, Moene, Menenti, de Briun, 2002),

31

Application to the Urumqi River Basin, NW China

Spatial Distribution of Annual Evaporation over the Urumqi River Basin

32

Surface energy fluxes over different land cover on the Tibetan plateau(ex. Sensible heat fluxes at QOMS)

(Chen et al., 2012, JAMC)

Scatter plot of sensible heat flux between the 17 measurements of Eddy Covariance and outputs of SEBS (su02, left; SY02, right) sensible heat flux data in 2007 at QOMS/CAS (a), Nam co (b), linzhi (c), maqu station (d). The thick line is 1:1 line. The linear fitting line is Dashed line. Root mean square error (RMSE). Correlation coefficient (R220 ). mean bias (MB).

(Chen et al., 2012, JAMC)

33

Net Radiation Soil heat flux

Sensible heat flux Latent heat flux

Satellite retrieved Diurnal Surface Fluxes on the Tibetan plateau

(Jul. 11, 1998) GMS + ERA40 (Oku, Ishikawa, Su, 2005, JAMC)

Earth Observation of Water Cycle

Clouds (Precipitation, 100%)

Water Storage in Ice and Snow

Groundwater Storage & Flow

Discrharge to ocean(35%)

Soil Moisture

Radiation

Water vapour (transport to land 35%, condensation 65%)

Evaporation (land 65%, ocean 100%)

Water ResourcesManagement

34

Evapotranspiration ‐ Technical specification: SEBS data flow

(Su, 2002, HESS; Oku et al., 2005, JAMC; Su et al., 2005, JHM; Su et al., 2007, JMSJ)

WACMOS Data processing status and validation

35

The largest evapotranspiration rates occur in May, Jun and Jul, around the Mediterranean. The lowest values occur Dec and Jan, at high latitudes, but also in Spain during the summerTiles visible in the product originate from the resolution of the ECMWF meteorological variablesOcean evapotranspiration in August did not work due to erroneous input data. This month is currently rerun

WACMOS ET Product: Monthly average of daily ET

Country Location Lat Lon IGBP class

Belgium Lonzee 50.5522 4.7449 Crops

Germany Selhausen 50.8706 6.4497 Crops

Germany Tharandt 50.9636 13.5669 Forest

Italy Bonis 39.4778 16.5347 Forest

Italy La Mandria 45.5813 7.1546 Grassland

Italy Lecceto 43.3046 11.2706 Forest

Netherlands Cabauw 51.971 4.927 Grassland

Netherlands Dijkgraaf 51.9921 5.6459 Crops

Netherlands Loobos 52.1679 5.744 Forest

Netherlands Speulderbos 52.25 5.6833 Mixed Forest

Spain El Saler e Sueca 39.2755 -0.3152 Crops

Spain Las Majadas del Tietar 39.9415 -5.7734 Savanna

Spain Vall d'Alinya 42.1522 1.4485 Grassland

Sweden Flakaliden 64.1128 19.4569 Forest

Sweden Knottåsen 60.9984 16.2171 Forest

UK Lambourne 51.4425 1.2608 Mixed Forest

Validation sites and preliminary results

• Land surface– CarboEurope

– Classes

• Grass

• Agricultural crops

• Tall vegetation

• Ocean (comparison)– Community Land Model

(CLM)

– ECMWF

36

GPOD – Grid Processing on Demand

WACMOS.org

37

REFLEX2012Hyperspectral thermal radiation measurements and simulation

(J. Timmermans)

Hot blackbody

Cold blackbody

Object

Gold reference plate

10 m 20 m2.5 m 5 m3.3 m

(8 ~ 13 m)

Wavenumber (cm-1) = number of wavelengths per cm = 10,000/ (m) i.e. (m) = 10,000/ wavenumer (cm-1)e.g. 1 m ~ 10,000 cm-1; 10 m ~ 1,000 cm-1…

The objective of this model is to

– Use Remote sensing data to estimate the complex biophysical/biochemical processes (exchange of heat, water vapour and carbon fluxes) from homogeneous land surface with a heterogeneous soil/vegetation distribution

– Simulate directional spectrum of outgoing radiation

• visible & thermal & fluorescence

– Estimate exchanges of fluxes

• Evaporation, transpiration, soil heat flux

photosynthesis

SCOPE ‐Soil Canopy Observation of Photochemistry and Energy Fluxes

(J. Timmermans)

38

75

Coupled model

– Radiative transfer

– Biological chemistry

– Fluorescence

Outputs

– Heat fluxes

– 4 component temperature

– E(s, o)

SCOPE: dataflow

RTMoTransfer of solar and sky radiation

RTMtTransfer of radiation emitted by vegetation and soil

Energy balance moduleNet radiation

Leaf biochemistry

Aerodynamic resistances

Leaf and canopy level fluxes

4 component temperatures

RTMfTransfer of Fluorescence

Inc. TOC spectraProspect par,

R, lE, H, G, A, T4c

Qa, u, TaLeaf and soil par.

L(solar and sky)

L(thermal)

L(fluor)

(J. Timmermans)

Leaf: Prospect/Fluspect

Canopy: RTMo: Original SAIL

RTMt: ThermoSAIL but allowing for heterogeneous leaf and soil temperatures

RTMf: FluorSAIL but allowing for heterogeneous fluorescence

Radiative Transfer Part

(J. Timmermans)

39

‐Distribution of absorbed photons over fluorescence, photochemistry and heat dissipation

‐Feedback from the supply side of CO2 on this distribution

‐Photochemistry with the model of Farquhar

Biochemical model

PSII Mesophyll GPP

Fluorescence

Photochemistry

Heat dissipation

aPAR

CO2

Stomatal conductance

(J. Timmermans)

Aerodynamic scheme:

– many‐sources EB model

• Sunlit/shaded soil

• All leaf orientations

– Surface layer, boundary layer, within vegetation canopy layer, roughness sublayer and inertial sublayer.

Biophysical model

cwr

sbr

scr

cbr

ccr

swr

Ds Dc

D0

Dr

car

Rar

Iar

(J. Timmermans)

40

60 layers

homogeneous leaf distribution

-50 0 50 100 150 200-1

-0.8

-0.6

-0.4

-0.2

0

Rn (W / m2)

Rel

ativ

e he

ight

-100 -50 0 50 100 150 200-1

-0.8

-0.6

-0.4

-0.2

0

H (W / m2)

0 5 10 15 20 25 30-1

-0.8

-0.6

-0.4

-0.2

0

lE (W / m2)

Rel

ativ

e he

ight

-2 0 2 4 6 8-1

-0.8

-0.6

-0.4

-0.2

0

CO2 flux (mol/(m2 s))

15 20 25 30 35-1

-0.8

-0.6

-0.4

-0.2

0

Tsunlit leaves

(oC)

Rel

ativ

e he

ight

15 20 25 30-1

-0.8

-0.6

-0.4

-0.2

0

Tshaded leaves

(oC)

15 20 25 30-1

-0.8

-0.6

-0.4

-0.2

0

Tall leaves

(oC)

(J. Timmermans)

SCOPE – Output ‐ Vertical profile

SCOPE aggregates the distribution of sensible and latent heat

Output example: energy balance

0 6 12 18 24-4

-2

0

2

4

6

8

10

12

time (hours)

Tb-T

a (o C

)

Tb mod

-Ta

Tb meas

-Ta

0 6 12 18 24-100

0

100

200

300

400

500

time (hours)

E (

W m

-2)

Emod

Hmod

Emeas

Hmeas

Simulations for a field experiment in Barrax, Spain (2004)

(J. Timmermans)

41

Hemispherical top‐of‐canopy reflectance (left), brightness temperature (middle) and chlorophyll fluorescence radiance (right) as a function of viewing zenith angle and viewing azimuth angle (relative to the solar azimuth). Zenith angle varies with the radius, the azimuth angle (in italic) increases while rotating anticlockwise from north. The solar zenith angle was 48.

(Van der Tol, Verhoef, Timmermans, Verhoef, Su, 2009, Biogeosciences)

–The Surface Energy Balance System (SEBS) is briefly introduced.

–SEBS is scale invariant, so that it can be applied easily to different scales. Data

of high or low spatial resolution from all sensors in the visible, near-

infrared and thermal infrared frequency ranges can be used in the

system.

–Based on a set of case studies, SEBS has proven to be capable to estimate

turbulent heat fluxes and evaporation from point to continental (global) scale

with acceptable accuracy.

–Some recent experiments are introduced – the data are accessible.

–The SCOPE model is introduced.

Conclusions

42

•Su, Z., 2002, The Surface Energy Balance System (SEBS) for estimation of turbulent heat fluxes, Hydrology and Earth System Sciences, 6(1), 85‐99.

•Su, Z., T. Schmugge, W.P. Kustas, W.J. Massman, 2001, An evaluation of two models for estimation of the roughness height for heat transfer between the land surface and the atmosphere, Journal of Applied Meteorology, 40(11), 1933‐1951.

•Jia, L., Z. Su, B. van den Hurk, M. Menenti, A. Moene, H.A.R. De Bruin, J.J.B.Yrisarry, M. Ibanez, A. Cuesta, 2003, Estimation of sensible heat flux using the Surface Energy Balance System (SEBS) and ATSR measurements, Physics and Chemistry of the Earth, 28(1‐3), 75‐88.

•Su, Z., 2005, Estimation of the surface energy balance. In: Encyclopedia of hydrological sciences : 5 Volumes. / ed. by M.G. Anderson and J.J.McDonnell. Chichester etc., Wiley & Sons, 2005. 3145 p. ISBN: 0‐471‐49103‐9. Vol. 2 pp. 731‐752.

•Oku, Y., H. Ishikawa, Z. Su, 2007, Estimation of Land Surface Heat Fluxes over the Tibetan Plateau Using GMS Data, Journal of Applied Meteorology and Climatology, 46(2): 183‐195.

•Su, Z., Timmermans, W., Gieske, A., Jia, L., Elbers, J. A., Olioso, A., Timmermans, J., Van Der Velde, R., Jin, X., Van Der Kwast, H., Nerry, F., Sabol, D., Sobrino, J. A., Moreno, J. and Bianchi, R., 2008, Quantification of land‐atmosphere exchanges of water, energy and carbon dioxide in space and time over the heterogeneous Barrax site, International Journal of Remote Sensing, 29:17,5215‐5235.

•Su, Z., W. J. Timmermans, C. van der Tol, R. J. J. Dost, R. Bianchi, J. A. Gómez, A. House, I. Hajnsek, M. Menenti, V. Magliulo, M. Esposito, R. Haarbrink, F. C. Bosveld, R. Rothe, H. K. Baltink, Z. Vekerdy, J. A. Sobrino, J. Timmermans, P. van Laake, S. Salama, H. van der Kwast, E. Claassen, A. Stolk, L. Jia, E. Moors, O. Hartogensis, and A. Gillespie, 2009, EAGLE 2006 ‐multi‐purpose, multi‐angle and multi‐sensor in‐situ, airborne and space borne campaigns over grassland and forest, Hydrology and Earth System Sciences, 13, 833–845.

•Su et al., 2011, Earth observation Water Cycle Multi‐Mission Observation Strategy (WACMOS), Hydrol. Earth Syst. Sci. Discuss., 7, 7899–7956.

•Timmermans, J., et al, 2011, Quantifying the Uncertainty in Estimates of Surface Atmosphere Fluxes by evaluation of SEBS and SCOPE models, Hydrol. Earth Syst. Sci. Discuss., 8, 2861‐2893.

•Verhoef, W., Jia, L., Xiao, Q., Su, Z., 2007, Unified optical‐thermal four‐stream radiative transfer theory for homogeneous vegetation canopies, IEEE Transactions on Geoscience and Remote Sensing, 45(6), 1808‐1822.

•Van der Tol, C., Verhoef, W., Timmermans, J., Verhoef, A., and Su, Z., 2009, An integrated model of soil‐canopy spectral radiances, photosynthesis, fluorescence, temperature and energy balance, Biogeosciences, 6(12): 3109‐3129.

•Chen, X., Su, Z., Ma, Y., Yang, K, Wen, J., 2012, An improvement of the parameterization of roughness height for heat transfer in Surface Energy Balance System (SEBS) over the Tibetan Plateau, Journal of Applied Meteorology and Climatology, 52 (2013)3, pp. 607‐622.

Referances/Further Readings

sebs balance system for estimation of - earth online - esa · ‐surface energy balance system for...

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