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Jun WEN
Theory of Top Soil Moisture Retrieval from Microwave Remote Sensing
17-18th Dec 2009, Tokyo, JAPAN
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Introduction1
Parameters in Soil Moisture Retrieval2
Microwave Radiative Transfer Theory3
Methodology for Soil Moisture Retrieval4
Application Cases5
Outline
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1. Introduction
1.1 Remote sensingpolar orbit satellite
Spectral resolution
Spatial resolution
Satellite orbit type
visible
center wavelengthhyperspectral/band/panchromatic
band width
Wave-lengthmicrowave
geostationary satellite
low & moderate resolution
high resolution
Temporal resolution minuets – hourly – daily - several
days-many days overpass
thermal
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θ Volumetric soil water content, unit: m3 of water m3 of soil
Gravity soil water content, unit: kg of water of kg of dry soil
Relative soil water content, ,SM & SMs are soilwater content and saturated soil water content.
Definition:
Measurement method
Drying soil sample and weighing
Measurement by TDR
Retrieval using satellite RS data
thermal inertia method
passive microwave technique
W
active microwave technique
1.2 Soil moisture (SM)
sSM SM
SMR =
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2.1 Relevant parameters
Soil moisture ( soil water content)
Surface soil temperature & brightness temperature
Vegetation water content (water in vegetation, kg/m2)
Vegetation optical thickness (linked to VWC)
Emissivity/Reflectivity (linked to SM)
Dielectric constant (real & imagine parts， linked to SM）
Backscattering coefficient
2. Parameters in SM retrieval
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Top layer soil (0~d, d is penetrating depth)
Soil texture (percent of clay, silt and sand)
Surface roughness (relevant to SD of height)
Incident angle
Polarization (horizontal and vertical)
Image/Scanning
Pixel/Footprint
2.1 Relevant specification and definition
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3. Microwave Radiative Transfer (MRT)
The equation means that radiation received by sensor contains five contributions,
a. Upward radiation emitted from atmosphere layer
b. Upward radiation emitted from soil, and transmitted through vegetation and atmosphere layer
c. Upward radiation emitted from vegetation layer and transmitted through atmosphere
d. Downward radiation emitted from vegetation layer, reflected by soil surface and transmitted through vegetation & atmosphere layers
e. Downward radiation emitted from atmosphere and cosmic, two-way transmitted through atmosphere & vegetation layers and reflected by soil surface.
3.1 Conception of radiative transfer
sky αΤ + Τ
soil
Vegetation
Atmosphereθ
ac bde
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Microwave radiative transfer equation put forward by Kerr and Njoku,1990
3.2 Theory of passive MRT
])1(1)[(1)(1(
)1)((2
vegpvegpvegatmsoilpvegatm
pskyatmadvegatmauBp
LLTLTLL
TLTLLTT
εωε
ε
−+−−++
−++=
where BpT is the satellite brightness temperature at horizontal or vertical polarization. Tau and Tad are the upwelling and downwelling atmospheric temperatures. vegT and soilT are vegetation and soil thermal temperatures. L is vegetation or atmospheric transmittance expressed as
)secexp( τθ ⋅−=L , θ is the incident angle of the observation. τ is the vegetation or atmospheric optical depth, which depends upon the vegetation or atmosphere extinction coefficients.
pω is the vegetation single scattering albedo. pε is the soil emissivity at p polarization, which is related to soil water content and a soil surface roughness parameter. skyT is the cosmic brightness temperature.
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3.3 Conception of active MRT
soil
Vegetation
Atmosphere
0soilσ 0
vegetationσ 0interactionσ
FC 1-FC
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Suppose that bare soil and the soil beneath vegetation layer havesame property characterized by surface roughness and soil moisture, theradiative transfer process can be described as following
3.4 Theory of active MRT
0 2 0 0 0( ) [1 (1 ( ))] ( )soil interaction vegetationFC T FCσ θ θ σ σ σ= − − + +
where )(0 θσ is observed backscattering coefficient, FC isvegetation fractional coverage, T2(θ) is canopy transmittance intwo ways of incoming and outgoing path, 0
soilσ is contribution ofsoil, 0
vegetationσ is vegetation volumetric contribution, 0int eractionσ is the
contribution from land surface-vegetation interaction.
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4.1 Passive microwave algorithm
4. Algorithms for SM retrieval
])1(1)[(1)(1( vegpvegpvegsoilpvegBp LLTTeLT εω −+−−+=
Brightness temperatures measured by microwave radiometers integratecontributions from soil temperature, soil moisture, surface roughness, andvegetation as well as atmospheric layer, the atmospheric contribution can beneglected for C- or L-band. Thus, the radiative transfer can be described asfollows
where TBp is brightness temperature at p (h or v) polarization. Tveg and Tsoilare vegetation and effective soil thermal temperatures. Lveg is vegetationtransmittance expressed as Lveg=exp(-secθ·τ), θ is the incident angle of theobservation. τ is vegetation optical depth. ωp is the vegetation single scatteringalbedo. ep is soil emissivity at p polarization linked to soil water content and asoil surface roughness.
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, , and are related parameters relevant to surface roughness
While, according to Fresnel reflection equation
212 2
12 2
cos ( sin )
cos ( sin )s
H
s
R μ ε μ
μ ε μ
⎛ ⎞− −⎜ ⎟=⎜ ⎟+ −⎝ ⎠
212 2
12 2
sin ( sin )
sin ( sin )s s
V
s s
R ε μ ε μ
ε μ ε μ
⎛ ⎞− −⎜ ⎟=⎜ ⎟+ −⎝ ⎠
εs is complex permittivity, it is a function of soil water content for smoother surface, μ is incident angle of sensor.
Wang and Choudhary(1980) developed a model for estimating rough land surface reflectivity,
( )* 1 exp( cos )Np p pR Q R Q R h μ⎡ ⎤= − + −⎣ ⎦
Q hN
The ep+ Rp= 1.0 and linked to soil εs
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According Dobson(1985) proposed method
Canopy transmittance
is vegetation optical thicknessτis incident angle of sensorμ
proposed by Wang J R and Choudhury B J in 1980exp( / cos )c τ μΓ = −
0 1 2 0 1 2 0 1 2( ) ( ) ( )va a S a C b b S b C m c c S c Cε = + + + + + + + +
εs is complex permittivity, ai, bi and ci are empirical constants, S and Care the percentage of soil clay and sand, Mv is soil volumetric moisture
Parameterization in the complex permittivity
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Owe and De Jeu proposed in 2001,
1 2 3ln( )C C MPDI Cτ = × +
0.78260.0967 1VWC MPDI −= −
(6.6 ) 2( ) / ( )GHz bv bh bv bhMPDI T T T T= − +
is microwave polarization difference index at 6.6GHz.
Wen and Jackson proposed in 2005
representing 37GHz vegetation density information
(37 ) 2( ) / ( )GHz bv bh bv bhMPDI T T T T= − +
(37 )GHzMPDI
(6.6 )GHzMPDI
Paramerization of VWC and Vegetation Optical thickness
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Retrieval algorithm for AMSR developed by Njoku et al
three bands dual polarization retrieval of vegetation opacity, soil moisture and effect temperature
Retrieval algorithm for SMMR developed by Owe et al
two bands dual polarization retrieval of vegetation opacity and soil moisture
Retrieval algorithm for SSM/I developed by Wen et al
single band dual polarization retrieval of soil temperature and soil moisture
Review of the retrieval algorithms
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Radiative Transfer Equation
( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( )(1- )(1- ) (1- )(1- )(1- )b p s p s p p p p c p p p p cT T e T e Tω ω= Γ + Γ + Γ Γ
soil moisturevegetation opacity effect temperature
* 21- exp(- cos )( )e R h us p p=exp(- / cos )uτΓ =
observation of brightness temperature
RMS Error
( , , , ( ), )K f BD Texture T F i Weff=
Fresnel equation( , )R f K u=
262
1
- ( )obsBi i
i i
T j xχσ=
⎡ ⎤= ⎢ ⎥
⎣ ⎦∑
soil information
Three-parameter retrieval algorithm developed by Njoku
s c effT T T= =0.06ω =
simulation of brightness temperature
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simulation of brightness temperature being
equal to the observation
Fresnel equation
Wang-Schmugge(1981)( , )bV bHMPDI f T T=
( , )R f K u=
( , , , , )sK f P WP T F W=
exp(- / cos )uτΓ = * 21- exp(- cos )( )e R h us p p=
variable-step
vegetation opacity
( , , )f k u MPDIτ = (37 )bT Hz
( (37 ))s bT f T GHz=
soil moisture
( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( )(1- )(1- ) (1- )(1- )(1- )b p s p s p p p p c p p p p cT T e T e Tω ω= Γ + Γ + Γ Γs c effT T T= =
0.06ω =
False True
soil information
Two-parameter retrieval algorithm developed by Owe et al
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(Radiative Transfer Equation Mo 1982)
soil moisture (Fresnel equation)simulation of
brightness temperature beingequal to the observation
variable-step
soil temperature
soil emissivity
False True
2( ) ( )(1 ) ( (1 )(1 ))(1 (1 ) )b p au au ad sky ad sp c au sp e c v c sp cT T T T e e T T eω= + Γ + Γ − Γ + Γ Γ + − − Γ + − Γ
vegetation Type
vegetationextinction coefficient ( , )bV bHMPDI f T T=
( , , , )VWC f MPDI uκ λ=
( , )f b VWCτ =
SMEX02 sounding data
(Ulaby,1982), ,au ad atmT T T
(Hiltbrunner,1982)atmΓ
0.045ω=skyT
surfaceroughness
soilinformation
Two-parameter retrieval algorithm developed by Wen et al
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σ0(θ) is observed backscattering coefficient by sensor. FC is vegetation fractional coverage. T2 (θ ) is canopy transmittance in two ways of incoming and outgoing
4.2 Active microwave retrieval method
0 2 0 0 0( ) [1 (1 ( ))] ( )soil interaction vegetationFC T FCσ θ θ σ σ σ= − − + +
Active microwave radiative trandsfer
Beside soil moisture, two unknown variables in aboveequation, vegetation transmittance and fractional coverage. Tosimply the solving process, an effective FC is to be induced, whichmeans FC at a given transmittance.
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Contribution from bare soil
22
20
2 4
tan(0) exp( )2
2 cossoils
s
θ
σθ
Γ −=
where ls /2σ= is the root mean square (RMS) slope of surface height, σis standard deviation of the surface height and l is horizontal distancebetween two different points on the surface. Γ(0) is soil Fresnel reflectivityat normal incidence
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Volumetric contribution from vegetation layer
0vegetationσ
Ks and Ke are scattering and extinction coefficient of vegetationdiscrete elements. ωv is the single scattering albedo of thevegetation scatters.
0 2cos 1[1 exp( 2 ) sec ] cos [1 ( )]2 2
svegetation e v
e
Tκ θσ κ θ ω θ θκ
= − − = −
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Flow chart of the active microwave RS algorithm
000vegetationimagesoil σσσ −=
005.000 ≤− groundsoil σσ
ε Γ 0groundσ
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5.1. Passive microwave remote sensing resultSoil moisture estimates from TRMM Microwave Imager observations over the Southern
United States, Rajat Bindlish and Thomas J. Jackson, 2002
Time series of observed and TMI-
estimated soil moisture during SGP99
and GA2000 for:
(a) Central Facility, Oklahoma
(b) El Reno, OK,
(c) Little Washita Watershed, Oklahoma
(d) Little River Watershed, Georgia.
5. Application cases
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Daily TMI SM over southern US for July 6 –21, 1999
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The SSM/I soil moisture and the SMEX02 regional-averaged SM
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The Iowa statewide soil moisture and the corresponding daily antecedent precipitation (smaller maps) (Wen, 2006).
on 27 June,
and 1, 7,
and 11 July
2002 (large
maps)
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ESA - Wind Scatterometer Scatterometer Database
5.2. Active Microwave RS Data for Volumetric SM
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Relative soil moisture
The temporal relative soil moisture is consistent with the convertedground Vsm in the Tibetan plateau.
Regional Distribution of Rsm
The temporal relative soil moisture
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Active microwave remote sensing result
The comparison between ERS
wind scatterometer
estimated volumetric soil moisture and
ground measurement
The estimation of soil moisture from ERS wind scatterometer data over the Tibetan plateau (Wen and Su， 2003),
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(a) 19 May
(b) 4 June
(c) 1 July
(d) 13August
(e) 9 September
1998 over GAME/Tibet
experiment area
The regional vegetation FC and soil moisture
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Soil moisture retrieval from ENVISAT/ASAR
Landuse of the study area
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Results and discussions
The regional soil moisture distribution in the study area
Pingiang
Max deviation=0.04.Rmse= 2.0%
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45 km
20 soil moisture and soil temperature stations
CAREERI&ITC
Maqu Soil Moisture Network
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Overview of the sites
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WETLANDS
FLAT GRASSLAND
Main landscapes of the sites
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4300 m
4000 m
3700 m
3400 m
3100 m
Top characteristics of the sites
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Topography: elevation 3430-3750 m10 low valley 4 high valley4 steep hill 2 wetlands
Soil texture 0 to about 40 cm:17 silt loam1 sand loam2 high organic matter content
Available information:• Bulk density• pF curves• Field capacity and wilting point
What we measured beside soil moisture
Land surface process data• 2 sites’ meteorological
variables • 1 site near surface tower
Wind, T&RH(5 levels)• 1 site radiation budget
(1.5m height)• 1site latent and sensible
heat flux(2.9m height)• 1 site soil intensive
observation (T,SM,Hs, 0.05-2.0m depth)
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An example :Data at CAS tower
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Organic soilsSandy loam soil
SM of all the sites at 5 cm depth
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Preliminary results
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Others
1) Uncertainty in the retrievalsSatellite observationsAlgorithmsValidations
2) Prospect of the retrievalsPast: SMMR, SSM/IPresent: AMSR-E, TMI, ENVISAT/ASAR,ALOS/PALSAR Future: SMOS, SMAP
3) Potential of applicationsRegional and temporal soil moisture productDrought monitoring and early warningClime change researchMeteorology numerical simulations
