Retrieval of Soil Moisture and Vegetation Canopy Parameters With L-
band Radar for a Range of Boreal Forests
Alireza Tabatabaeenejad, Mariko Burgin, and Mahta Moghaddam
Radiation LaboratoryDepartment of Electrical Engineering and Computer Science
University of MichiganAnn Arbor, MI, USA
Introduction (1/3)
Soil Moisture is of fundamental
importance to the study and
understanding of
Cycling of Water & Energy,
Runoff Potential, Flood Control
Weather and Climate
Geotechnical Engineering,
Soil Erosion
Agricultural Productivity,
Drought Monitoring
Human Health
(mosquito-transmitted diseases
in wet areas)
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Courtesy of ESA
Introduction (2/3)
The need to monitor soil moisture on a global scale has motivated
the European Space Agency (ESA)'s Soil Moisture and Ocean
Salinity (SMOS) mission and the National Aeronautics and Space
Administration (NASA)'s Soil Moisture Active and Passive (SMAP)
mission.
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Courtesy of ESACourtesy of JPL
Introduction (3/3)
In this work,
We study the radar retrieval of soil moisture, as well as
canopy parameters, in a range of boreal forests.
The forward model is a discrete scatterer radar model.
The retrieval is formulated as an optimization problem.
The optimization algorithm is a global optimization scheme
known as simulated annealing.
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Outline
Forward Scattering Model for Forested Area
Inverse Model
Inversion of Model Parameters
Forested Area (Synthetic Data)
Forested Area (CanEx-SM10 Data)
Conclusion
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Outline
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Forward Scattering Model for Forested Area
Inverse Model
Inversion of Model Parameters
Forested Area (Synthetic Data)
Forested Area (CanEx-SM10 Data)
Conclusion
Forward Model: Introduction
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Soil & forest parameters
Scattering coefficients
Frequency, incidence angle
ForwardModel
;f(X p)X
p
Forward Model: Forest Geometry
Forest Geometry
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* S. L. Durden, J. J. van Zyl, and H. A. Zebker, "Modeling and observation of the radar polarizationsignature of forested areas," IEEE Trans. Geosci. Remote Sens., May 1989.
Forward Model: A general discrete scatterer radar model
by Durden et al.*
Forward Model: Scattering Mechanisms (1/2)
Canopy Layer
Trunk Layer
Ground
bg
bg
tgThe model identifies
4 distinct scattering
mechanisms:
b: branch
bg: branch-ground
tg: trunk-ground
g: ground
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* S. L. Durden, J. J. van Zyl, and H. A. Zebker, "Modeling and observation of the radar polarizationsignature of forested areas," IEEE Trans. Geosci. Remote Sens., May 1989.
Forward Model: A general discrete scatterer radar model
by Durden et al.*
Forward Model: Scattering Mechanisms (2/2)
Forward Model: A general discrete scatterer radar model
by Durden et al.*
The total backscattered power, represented by the Stokes matrix, is the
sum of the powers from all contributing scatterers.
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* S. L. Durden, J. J. van Zyl, and H. A. Zebker, "Modeling and observation of the radar polarizationsignature of forested areas," IEEE Trans. Geosci. Remote Sens., May 1989.
𝑀𝑡𝑜𝑡 = 𝑀𝑏 +𝑇𝑏𝑇𝑡𝑀𝑏𝑔𝑇𝑡𝑇𝑏 +𝑇𝑏𝑇𝑡𝑀𝑡𝑔𝑇𝑡𝑇𝑏 + 𝑇𝑏𝑇𝑡𝑀𝑔𝑇𝑡𝑇𝑏
branchcontribution
branch-groundcontribution
trunk-groundcontribution
groundcontribution
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Forward Model: Parameters
The forest floor is modeled as a rough dielectric surface with a layer of
nearly vertical dielectric cylinders (representing tree trunks) on top of it.
• The soil dielectric constant is related to the soil moisture via the soil type*
Branches are represented by a layer of randomly oriented cylinders.
The forward model uses properties of
• large and small branches (dielectric constant, length, radius, density,
orientation)
• leaves (dielectric constant, length, radius, density)
• trunks (dielectric constant, length, radius, density)
• soil (volumetric moisture content, roughness RMS height)
• canopy height
to characterize a forested area.
* N.R. Peplinski, F.T. Ulaby, and M.C. Dobson, “Dielectric properties of soils in the 0.3-1.3 GHz range,” IEEE Trans. Geosci. Remote Sens., vol. 33, no. 3, pp. 803-807, 1995.
Forward Model: Sensitivity
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Sensitivity of several dBs to soil
moisture at L-band in the
presence of large amount of
vegetation
Less sensitivity to soil moisture
as soil moisture increases
Preserved dynamic range while
canopy height increases
Increase in trunk-ground
double bounce counterbalanced
by an increase in attenuation by
trunk layer as trunk density
increases.
Dielectric constants correspond to OJP trees
(CanEx-SM10) and allometric relationships
are hypothetical.
Outline
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Forward Scattering Model for Forested Area
Inverse Model
Inversion of Model Parameters
Forested Area (Synthetic Data)
Forested Area (CanEx-SM10 Data)
Conclusion
The forward model has too many parameters to allow inversion
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Inverse Model: Allometric relations
Allometric relations can be based on actual measurements, for example
Allometric relations are used to
relate unknown parameters to each
other and reduce the overall number
of unknowns
Ideally, one or two stand parameters
can be used as kernels to describe
the entire forest stand
Inverse Model: Simulated Annealing (1/2)
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Simulated annealing uses an analogy between the unknown parameters
and particles in the annealing process of solids.
A small randomly-generated perturbation is applied to the current model
parameters.
If ΔL<0, the new state is accepted, otherwise it is accepted with probability
exp(-ΔL /T) → Metropolis criterion
This process is repeated at a sequence of decreasing temperatures.
Inverse Model: Simulated Annealing (2/2)
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Temperature
Current State
Last accepted point of the chain
Best state so far
Inverse Model: Cost Function
Cost Function L
where X = state pq = polarizationf = frequencyθ = incidence angle
σ = calculated backscattering coefficients
d = measured backscattering coefficients
HH and VV polarizations components are used in the inversion
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Outline
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Forward Scattering Model for Forested Area
Inverse Model
Inversion of Model Parameters
Forested Area (Synthetic Data)
Forested Area (CanEx-SM10 Data)
Conclusion
Inversion of Model Parameters: Synthetic Data (1/4)
Sample inversion for a sample forest using synthetic data and
hypothetical allometric relationships at L-band for four unknowns
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d=2.5 m, ρtr=0.72 #/m2, mv=0.25,
h=2 cm
Dielectric constants are from
CanEx-SM10 (for an OBS forest)
and allometric relationships are
hypothetical.
Accurate retrieval for all
unknowns (soil moisture,
trunk density, canopy height,
roughness RMS height)
Inversion of Model Parameters: Synthetic Data (2/4)
Sample inversion for a sample forest using synthetic data and
hypothetical allometric relationships at L-band for four unknowns
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d=2.5 m, ρtr=0.72 #/m2, mv=0.25,
h=2 cm
Dielectric constants are from
CanEx-SM10 (for an OBS forest)
and allometric relationships are
hypothetical.
Accurate retrieval for all
unknowns (soil moisture,
trunk density, canopy height,
roughness RMS height)
Inversion of Model Parameters: Synthetic Data (3/4)
Sample inversion for a sample forest using synthetic data and
hypothetical allometric relationships at L-band for four unknowns
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d=2.5 m, ρtr=0.72 #/m2, mv=0.25,
h=2 cm
Dielectric constants are from
CanEx-SM10 (for an OBS forest)
and allometric relationships are
hypothetical.
Accurate retrieval for all
unknowns (soil moisture,
trunk density, canopy height,
roughness RMS height)
Absolute error in d = 0 m
Inversion of Model Parameters: Synthetic Data (4/4)
Sample inversion for a sample forest using synthetic data and
hypothetical allometric relationships at L-band for four unknowns
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Absolute error in h = 0.2 cm
d=2.5 m, ρtr=0.72 #/m2, mv=0.25,
h=2 cm
Dielectric constants are from
CanEx-SM10 (for an OBS forest)
and allometric relationships are
hypothetical.
Accurate retrieval for all
unknowns (soil moisture,
trunk density, canopy height,
roughness RMS height)
Outline
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Forward Scattering Model for Forested Area
Inverse Model
Inversion of Model Parameters
Forested Area (Synthetic Data)
Forested Area (CanEx-SM10 Data)
Conclusion
Inversion of Model Parameters: Overview
The data are from CanEx-SM10 in June 2010.
Data acquisition included Old Jack Pine, Young Jack Pine, and Old
Black Spruce forests, located in Saskatchewan, Canada.
NASA/JPL UAVSAR flown on a Gulfstream III aircraft acquired large
swaths of fully polarimetric L-band measurements.
Soil moistures and roughness RMS height are unknowns.
The other forest parameters are assumed known from ground
measurement
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Inversion of Model Parameters: Three forests
Old Jack Pine (OJP), Young Jack Pine (YJP), Old Black Spruce (OBS) forests
Old Jack Pine:
Columnar trees, dry and flat sandy
loam ground, densely covered
with dry lichen, which is
transparent at L-band
Young Jack Pine:
Pyramidally-shaped trees, very
dry and flat sandy ground with
short and sparse ground cover
Old Black Spruce:
Columnar coniferous trees,
wet loam ground complicated by
a non-uniform moss and organic
layer, water puddles, and bushy
understory
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Inversion of Model Parameters: Measurement transects
Ground measurements included a transect of 100 m along which several
measurements were taken in ~10-m intervals.
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Inversion of Model Parameters: Results for OJP
Inversion of soil moisture at L-band
Average error (bias) is -0.01
RMS error is 0.043 (6m12m)
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Average error (bias) is -0.008
RMS error is 0.03 (18m36m)
mυi
σ0i
Σ σ0i = σ0
mυ
Inversion of Model Parameters: Results for YJP
Inversion of soil moisture at L-band
Average error (bias) is 0.014
RMS error is 0.02 (6m12m)
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Average error (bias) is 0.015
RMS error is 0.022 (18m36m)
mυi
σ0i
Σ σ0i = σ0
mυ
Inversion of Model Parameters: Results for OBS
Inversion of soil moisture at L-band
Average error (bias) is 0.14
RMS error is 0.24 (6m12m)
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Average error (bias) is 0.92
RMS error is 0.16 (18m36m)
Average error (bias) is 0.93
RMS error is 0.11 (18m36m)
Σ σ0i = σ0
mυ (□)mυi
σ0i σ0
i
Σ mυi = mυ (*)
Inversion of Model Parameters: Adding more unknowns
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Adding canopy height and trunk density to the
unknowns (four unknowns) and cross-pol
backscattering coefficient to the measured data points
(three data points), the error in soil moisture would
be large (0.085 cm3/cm3 for OJP) due to
Unreliability of the cross-pol radar measurements
Adding only canopy height to the unknowns (three
unknowns) and using only co-pol data (two data points),
results in an RMS error of 0.025 cm3/cm3 in soil
moisture.
Summary and Conclusion (1/2)
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L-band retrieval of under-canopy soil moisture as well as
other canopy parameters using radar data was
investigated.
Simulated annealing accurately retrieved soil moisture
from only a few data points. (synthesize data, four
unknowns, allometric relationships)
Inversion was successful for the OJP and YJP sites.
(CanEx-SM10 data, two unknowns)
Summary and Conclusion (2/2)
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Error was large for the OBS forest mostly due to
small sensitivity of the forward model to soil moisture for
larger moisture values
possible inaccuracies in the forest parameterization
complex nature of the forest floor
L-band radar is capable of retrieving surface soil
moisture in high-biomass forests (such as OJP) where
the soil moisture information is mainly carried by the
trunk-ground scattering mechanism.
Questions
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Thank you for your interest.
Do you have any questions?
Further questions:
Alireza Tabatabaeenejad [email protected]
Mariko [email protected]
Mahta [email protected]