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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
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
( ) (1 )sen atm atmsL B T L L
Nightime acquisitions:
(same equation as for TIR)
THEORETICAL BASIS – Radiative Transfer Equation
(LSE): allows a better surface characterization in the MIRLST: retrievals are less sensitive to errors in LSE determination
sT
(Sobrino&Jiménez‐Muñoz)
16
( ) (1 )sen atm atmsL B T L L
Atmospheric Correction
Atmospheric Correction:The signal measured by the sensor is the radiance coming from the surface attenuated the atmosphere and the atmospheric contribution
THEORETICAL BASIS – Radiative Transfer Equation
atms
sur LTBL 1
atmsursen LLL
atmsensur LL
L
(Sobrino&Jiménez‐Muñoz)
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)
THEORETICAL BASIS – Radiative Transfer Equation
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
THEORETICAL BASIS – Simulations
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
THEORETICAL BASIS – Simulations
ASTER Spectral Library
Wavelength (m)
Emissivity
BasaltDioriteQuartzMarble
(Sobrino&Jiménez‐Muñoz)
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
THEORETICAL BASIS – Simulations
ASTER Spectral Library
Wavelength (m)
Emissivity
WaterDry grassGreen grass
(Sobrino&Jiménez‐Muñoz)
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
(Sobrino&Jiménez‐Muñoz)
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
(Sobrino&Jiménez‐Muñoz)
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
(Sobrino&Jiménez‐Muñoz)
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
longitud de onda (m)
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
(Sobrino&Jiménez‐Muñoz)
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()(
(Sobrino&Jiménez‐Muñoz)
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
THEORETICAL BASIS – Simulations
(Sobrino&Jiménez‐Muñoz)
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
(Sobrino&Jiménez‐Muñoz)
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)
( ) (1 )sen atm atmsL B T L L
(Sobrino&Jiménez‐Muñoz)
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
THEORETICAL BASIS – Algorithms (LST retrieval)
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
(Sobrino&Jiménez‐Muñoz)
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
THEORETICAL BASIS – Algorithms (LST retrieval)
(Sobrino&Jiménez‐Muñoz)
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)
THEORETICAL BASIS – Algorithms (LST retrieval)
(Sobrino&Jiménez‐Muñoz)
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)
(Sobrino&Jiménez‐Muñoz)
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)
(Sobrino&Jiménez‐Muñoz)
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.
(Sobrino&Jiménez‐Muñoz)
26
Iterative procedure
NEM (Normalized Emissivity Method)
THEORETICAL BASIS – Algorithms (TES)
atmsur
s
LLTB
1'
'max ss TT
atm
s
atmsur
LTB
LL
(Sobrino&Jiménez‐Muñoz)
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)
THEORETICAL BASIS – Algorithms (TES)
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
longitud de onda (m)
emisividad
beta
diorite
(Sobrino&Jiménez‐Muñoz)
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
THEORETICAL BASIS – Algorithms (TES)
(Sobrino&Jiménez‐Muñoz)
(Sobrino&Jiménez‐Muñoz)
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