BIOMASS_E2ES_IGARSS2011.ppt

Folie 1

BIOMASS End-to-End Mission PerformanceSimulator

Paco López-Dekker, Francesco De Zan, Thomas Börner, Marwan Younis, Kostas Papathanassiou (DLR); Tomás

Guardabrazo (DEIMOS); Valerie Bourlon, Sophie Ramongassie, Nicolas Taveneau (TAS-F); Lars Ulander, Daniel

Murdin (FOI); Neil Rogers, Shaun Quegan (U. Sheffiled) and Raffaella Franco (ESA)

Microwaves and Radar Institute, German Aerospace Center (DLR)

Paco López-Dekker > 28.07.2011

Slide 2

Project Context and Objectives BEES: BIOMASS End-to-End (mission performance) Simulator

ESA funded project in context of BIOMASS EE-7 Phase-A study

Provide a tool to evaluate the expected End-to-End performance of the mission

• Realistic, distributed scenes • Model system residual errors (noise, ambiguities, instrument stability, channel

unbalances…)• Ionospheric disturbances (Faraday rotation and scintillation)• Processing

- L0, L1, L1b- Ionospheric error correction- L2 retrieval

Focus on including all main effects and disturbances• Not detailed instrument simulator


Slide 3


Slide 4

text text

text

text

ProductGeneration

Module(L1b)

Geometry Module

IonosphericCorrection

Module

SceneGeneration

Module

L2RetrievalModule

ObservingSystem

Simulator

text

text

ProductGeneration

Module(L2)

PerformanceEvaluationModule

(L2)

PerformanceEvaluation

Module(L1b)

IonosphereGeneration

Module


Slide 5

BEES Modules

“Engineering” Modules• Geometry Module: provides common geometry to all modules [DEIMOS]• Observing System Simulator (OSS-A & OSS-B) [A: DLR; B: Thales Alenia Space]• Product Generation Module(s) [DLR]

- PGM-L1a- PGM-L1b

“Scientific” Modules• Scene Generation Module (SGM) [DLR+U. Chalmers]• Ionospheric Modules [U. of Sheffield]

- Ionospheric Generation Module (IGM)- Ionospheric Correction Module (ICM)

• Level-2 retrieval module (L2RM) [FOI]

Performance evaluation modules [DLR]• PEM-L1b• PEM-L2


Slide 6

GMOrbit Init

Iono

sphe

ric M

odul

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orre

ctio

n)

Iono

sphe

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odu

le

PGM


text

text

OSS

Partiallyfocused

data.(Ambiguous

stack)

Partiallyfocused

data.(Ambiguous

stack)

IonosphericPhase & Faraday

RotationScreen

SGM

ComplexScene

Generator(multi-

channel speckle) IRF

&AzimuthDecompr to Iono

IonosphericPhase

&FaradayRotation

IonosphericPhase

&FaradayRotation

AzimuthRecompr.

AzimuthRecompr.

SystemDisturbances

SystemDisturbances

L1bProcessing(multi-look,

ground-range

projection)

L2Processing


RotationScreen

IMSpectrumGenerator

Random Ionosphere(realization) Generator

GMBulk

IRF decomposition

IRF&

AzimuthDecompr.

to Iono

System Errors and Sensitivity

IRF

“RPG“

BEES Block Diagram

OpenSF Simulation control

OpenSF drives the E2ES. This includes: - UI- Execution Monte Carlo runs.- Etc…


Slide 7

GMOrbit Init

Iono

sphe

ric M

odul

e(c

orre

ctio

n)

Iono

sphe

ric M

odul

e

PGM


text

text

OSS

Partiallyfocused

data.(Ambiguous

stack)

Partiallyfocused

data.(Ambiguous

stack)


RotationScreen

SGM

ComplexScene

Generator(multi-



IonosphericPhase

&FaradayRotation

IonosphericPhase

&FaradayRotation

AzimuthRecompr.

AzimuthRecompr.

SystemDisturbances

SystemDisturbances


ground-range

projection)

L2Processing


RotationScreen

IMSpectrumGenerator


GMBulk

IRF decomposition

IRF&

AzimuthDecompr.

to Iono


IRF

“RPG“

BEES diagram: OSS

3 sub-modules• Dummy Radar Parameter Generator (RPG)

• System Errors and Sensititvity Module (SES)

• Impulse Response Function Module

IRF strategy• IRF models SAR system + processing

• This avoids generation of RAW data

SES strategy: model residual errors

Two OSS versions corresponding to the two industry Phase-A studies.


Slide 8

SGM: Scene Definition200t/ha, Clark-Evans Index 1.8 300t/ha, Clark-Evans Index 0.8

1. Forest Type (Out of a Predefined List);

2. Mean Biomass Level (Ha level);

Spatial Distribution of “single” trees each with a individual (top) Height / Biomass tag.

500t/ha0t/ha

100x100 m:

• Biomass (t/ha)

• Tree height (h100)

To forward model


Slide 9

SGM output (ground truth)

Biomass

Tree height (H100)


Slide 10

Input to PGM: PolInSAR covariance matrices

σHH

σHV

σVV


Slide 11

Input to PGM: PolInSAR covariance matrices

ρHH1-HH2

ρHV1-HV2

ρVV1-VV2


Slide 12

BEES Block Diagram: PGM


Slide 13

Review of PGM algorithm

Generation of interferometric/polarimetric channels for the scatter (correlated) and the noise (uncorrelated)

Spectral shift modulation (geometric decorrelation part I)

2-D convolution

Add ionospheric phase screen (scintillations) and Faraday rotation

Spectral shift demodulation (geometric decorrelation part II)

Ambiguity stacking

Additional system disturbances (cross-talk, phase and gain drifts…)

L1b product generation (multilooking)

L1a product generation

SGM, OSS

GM

OSS

GM

IM, GM

OSS

OSS

ICM

GM

inputs macro steps


Slide 14

Multichannel signal simulation

ChannelLinear

Combination

channel #1channel #2

channel #N


channel #N


channel #N

Independent channels(complex)

Correlatedchannels(complex)

Spatial convolutions

Desired spectralproperties foreach complex

channel

[ ] [ ] [ ]Hv LLC ⋅=

[ ] wLv ⋅=

Tree Height Coherence – HH-HH

SLC – HH


Slide 15

Introduction of Ionospheric distorion

Orbit

Target 1

Aperture length

Aperture angle:This is what really matters!

Lower (virtual) orbitEquivalent Aperture

Target 2

Ionosphere(modeled as

a layer)This part of the ionosphere

Modifies this part of the raw data for Target 1

…but this part for Target 2

Ionospheric distortion cannot be applieddirectly to raw data!!!

(the raw data distortion is target dependent)

For an orbit at Ionosphere heightDistortions can be applied directly

to the raw data

2

21

4);(

−−=v

frrf aionoionoa

λλπϕ


Slide 16

BEES Block Diagram

GMOrbit Init

Iono

sphe

ric M

odul

e(c

orre

ctio

n)

Iono

sphe

ric M

odul

e

PGM


text

text

OSS

Partiallyfocused

data.(Ambiguous

stack)

Partiallyfocused

data.(Ambiguous

stack)


RotationScreen

SGM

ComplexScene

Generator(multi-



IonosphericPhase

&FaradayRotation

IonosphericPhase

&FaradayRotation

AzimuthRecompr.

AzimuthRecompr.

SystemDisturbances

SystemDisturbances


ground-range

projection)

L2Processing


RotationScreen

IMSpectrumGenerator


GMBulk

IRF decomposition

IRF&

AzimuthDecompr.

to Iono


IRF

“RPG“

• This block applies the ionospheric correction (Faraday rotation and shifts only).

The simulation of the Ionosphere is divided in two steps. First the spectral coefficients describing the state of the Ionosphere are generated.

For a given spectra random realizations are generated.


Slide 17

Level-2 Retrieval


Slide 18

L2 retrieved heights (H100)

SGM

L2

Range dependent H100 bias

Software bug or realistic feature?


Slide 19

L2 retrieved biomass

SGM

L2


Slide 20

Performance Evaluation (L1b)

L1b performance in terms of element-wise covariance matrix errors

• Bias• Standard deviation

In example• Significant coherence loss,

due to spectral shift


Slide 21

Performance Evaluation (L2)

L2 performance in terms of biomass and tree height errors


Error statistics vs. range and biomass levels

In example• Height error leads to biomass

error?


Slide 22

Performance Evaluation (L2)

L2 performance in terms of biomass and tree height errors


Error statistics vs. range and biomass levels

In example• Height error leads to biomass

error?


Slide 23

Monte Carlo (multiple runs of BEES)

Monte Carlo simulations are implemented by OpenSF• BEES is run repeatedly perturbing (if necessary) some input parameters.

Perturbation approach• Random realizations implemented by modules (OpenSF can provide varying

seed for independent realizations).• This gives the control of the randomization to the module developers in order to

ensure physical correctness.• Most of this randomness is introduced by IGM and PGM


Slide 24


Slide 25

Validation: challenges and strategy

BEES is a complex software tool comprising modules developed by different teams under heterogeneous environments

How do we know that the outputs are correct?• We are developing the tool because we do not know (exactly) what we will get!• We are simulating a random process:

- Speckle- Random noise- Random hardware disturbances- Random realizations of Ionosphere- …

Validating the software requires approaches that resemble the post-launch validation/calibration of a real system

• Homogeneous scenes• Point targets

Validation needs to check if resulting statistics for some canonic cases are in agreement with theory.


Slide 26

Example: NESZ validation

NESZ is range dependent

The threshold is designed for a

failure probability of 10-3

test failure

test success

test failure

The nominal NESZ value


Slide 27

Example: PGM L1b Verification Probabilistic Threshold

Due to random nature of speckle, the estimated covariance matrices will not be identical to the true one (even when all error sources are turned off)

We can however evaluate the likelihood of a certain output given the input in probabilistic terms (e.g. using confidence intervals).

We will do the test using the complex coherences, i.e. the normalized elements of the sample covariance matrix:

Using a probability threshold (th), it is possible to bind the deviation:

The threshold will be a function of the desired error (t), the input coherence (γ) and the number of looks (L).

∑∑∑

=

kk

k

kskskmkm

kskm

)(*)()(*)(

)(*)(γ̂

tthp =>− )ˆ(2γγ


Slide 28

PGM L1b Verification – Caveat!

The assumption that the estimate is unbiased doesn’t hold for high coherences and low number of looks.

For a given coherence one has to make sure that enough looks are taken into account, i.e.:

σγ >>− ||1

105 simulations, gamma=0.5, L=250 105 simulations, gamma=0.95, L=30

histograms from simulations

To validate the simulator we need (to simulate) large, homogeneous scenes!

Sound familiar?


Slide 29

Project Status/Outlook

Software almost completed• Full handling of ambiguities missing• Some ionospheric features/possibilities pending

Validation and debugging on-going• Distinguishing between bugs and features not easy!

Mission Performance Assessment• Once BEES is validated it will be used to assess mission performance for both

Phase-A designs• Hundreds of test cases requiring “N” Monte Carlo repetitions• Weeks of simulation time

BIOMASS_E2ES_IGARSS2011.ppt

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