alireza tabatabaeenejad, mariko burgin , and mahta moghaddam radiation laboratory

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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Inverse Model




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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by Durden et al.*

Forward Model: Scattering Mechanisms (2/2)


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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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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Inverse Model




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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Inverse Model




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)




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d=2.5 m, ρtr=0.72 #/m2, mv=0.25,

h=2 cm




hypothetical.








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d=2.5 m, ρtr=0.72 #/m2, mv=0.25,

h=2 cm




hypothetical.





Absolute error in d = 0 m




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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




hypothetical.





Outline

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Inverse Model




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


mυi

σ0i

Σ σ0i = σ0

mυ

Inversion of Model Parameters: Results for YJP


Average error (bias) is 0.014


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mυi

σ0i

Σ σ0i = σ0

mυ

Inversion of Model Parameters: Results for OBS




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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]

mailto:[email protected]



alireza tabatabaeenejad, mariko burgin , and mahta moghaddam radiation laboratory

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