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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 Laboratory Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor, MI, USA.

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slide1

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 Laboratory

Department of Electrical Engineering and Computer Science

University of Michigan

Ann Arbor, MI, USA

introduction 1 3
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)

Courtesy of ESA

introduction 2 3
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.

Courtesy of ESA

Courtesy of JPL

introduction 3 3
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.
outline
Outline
  • Forward Scattering Model for Forested Area
  • Inverse Model
  • Inversion of Model Parameters
    • Forested Area (Synthetic Data)
    • Forested Area (CanEx-SM10 Data)
  • Conclusion
outline1
Outline
  • 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
Forward Model: Introduction

Frequency, incidence angle

Soil & forest parameters

Forward

Model

Scattering coefficients

forward model forest geometry
Forward Model: Forest Geometry
  • Forward Model: A general discrete scatterer radar model
  • by Durden et al.*

Forest Geometry

* S. L. Durden, J. J. van Zyl, and H. A. Zebker, "Modeling and observation of the radar polarization

signature of forested areas," IEEE Trans. Geosci. Remote Sens., May 1989.

forward model scattering mechanisms 1 2
Forward Model: Scattering Mechanisms (1/2)

bg

  • Forward Model: A general discrete scatterer radar model
  • by Durden et al.*

tg

g

b

The model identifies

4 distinct scattering mechanisms:

b: branch

bg: branch-ground

tg: trunk-ground

g: ground

Canopy Layer

Trunk Layer

Ground

* S. L. Durden, J. J. van Zyl, and H. A. Zebker, "Modeling and observation of the radar polarization

signature of forested areas," IEEE Trans. Geosci. Remote Sens., May 1989.

forward model scattering mechanisms 2 2
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.

branch

contribution

branch-ground

contribution

trunk-ground

contribution

ground

contribution

* S. L. Durden, J. J. van Zyl, and H. A. Zebker, "Modeling and observation of the radar polarization

signature of forested areas," IEEE Trans. Geosci. Remote Sens., May 1989.

forward model parameters
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
Forward Model: Sensitivity
  • 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.
outline2
Outline
  • Forward Scattering Model for Forested Area
  • Inverse Model
  • Inversion of Model Parameters
    • Forested Area (Synthetic Data)
    • Forested Area (CanEx-SM10 Data)
  • Conclusion
inverse model allometric relations
Inverse Model: Allometric relations
  • The forward model has too many parameters to allow inversion
  • 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
  • Allometric relations can be based on actual measurements, for example
inverse model simulated annealing 1 2
Inverse Model: Simulated Annealing (1/2)
  • 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
Inverse Model: Simulated Annealing (2/2)

Temperature

Current State

Last accepted point of the chain

Best state so far

inverse model cost function
Inverse Model: Cost Function
  • Cost Function L
  • where
  • X = state
  • pq= polarization
  • f = frequency
  • θ= incidence angle
  • σ= calculated backscattering coefficients
  • d= measured backscattering coefficients
  • HH and VV polarizations components are used in the inversion
outline3
Outline
  • 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
Inversion of Model Parameters: Synthetic Data (1/4)
  • Sample inversion for a sample forest using synthetic dataand
  • hypothetical allometric relationships at L-band for four unknowns
  • 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
Inversion of Model Parameters: Synthetic Data (2/4)
  • Sample inversion for a sample forest using synthetic dataand
  • hypothetical allometric relationships at L-band for four unknowns
  • 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
Inversion of Model Parameters: Synthetic Data (3/4)
  • Sample inversion for a sample forest using synthetic dataand
  • hypothetical allometric relationships at L-band for four unknowns
  • 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
Inversion of Model Parameters: Synthetic Data (4/4)
  • Sample inversion for a sample forest using synthetic dataand
  • hypothetical allometric relationships at L-band for four unknowns
  • 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 h = 0.2 cm

outline4
Outline
  • 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
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
inversion of model parameters three forests
Inversion of Model Parameters: Three forests

Old Jack Pine (OJP), Young Jack Pine (YJP), Old Black Spruce (OBS) forests

Old Black Spruce:

Columnar coniferous trees,

wet loam ground complicated by

a non-uniform moss and organic

layer, water puddles, and bushy

understory

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

inversion of model parameters measurement transects
Inversion of Model Parameters: Measurement transects

Ground measurements included a transect of 100 m along which several measurements were taken in ~10-m intervals.

inversion of model parameters results for ojp

σ0i

Inversion of Model Parameters: Results for OJP

Σ σ0i = σ0

mυi

  • Inversion of soil moisture at L-band
  • Average error (bias) is -0.01
  • RMS error is 0.043 (6m12m)
  • Average error (bias) is -0.008
  • RMS error is 0.03 (18m36m)
inversion of model parameters results for yjp

σ0i

Inversion of Model Parameters: Results for YJP

Σ σ0i = σ0

mυi

  • Inversion of soil moisture at L-band
  • Average error (bias) is 0.014
  • RMS error is 0.02 (6m12m)
  • Average error (bias) is 0.015
  • RMS error is 0.022 (18m36m)
inversion of model parameters results for obs

σ0i

Inversion of Model Parameters: Results for OBS

σ0i

Σ σ0i = σ0

Σ mυi = mυ (*)

mυ (□)

mυi

  • Inversion of soil moisture at L-band
  • Average error (bias) is 0.14
  • RMS error is 0.24 (6m12m)
  • Average error (bias) is 0.92
  • RMS error is 0.16 (18m36m)
  • Average error (bias) is 0.93
  • RMS error is 0.11 (18m36m)
inversion of model parameters adding more unknowns
Inversion of Model Parameters: Adding more unknowns
  • 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
Summary and Conclusion (1/2)
  • 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
Summary and Conclusion (2/2)
  • 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
Questions

Thank you for your interest.

Do you have any questions?

Further questions:

Alireza Tabatabaeenejad

[email protected]

Mariko Burgin

[email protected]

Mahta Moghaddam

[email protected]

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