BIOMASS_E2ES_IGARSS2011.ppt

BIOMASS End-to-End Mission Performance Simulator 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)

Project Context and Objectives ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

BEES Modules ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

BEES Block Diagram OpenSF Simulation control ,[object Object],[object Object],[object Object],[object Object]

BEES diagram: OSS ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

SGM: Scene Definition 200t/ha, Clark-Evans Index 1.8 300t/ha, Clark-Evans Index 0.8 ,[object Object],[object Object],Spatial Distribution of “single” trees each with a individual (top) Height / Biomass tag. 500t/ha 0t/ha ,[object Object],[object Object],[object Object],To forward model

SGM output (ground truth) Biomass Tree height (H 100 )

Input to PGM: PolInSAR covariance matrices σ HH σ HV σ VV

Input to PGM: PolInSAR covariance matrices ρ HH1-HH2 ρ HV1-HV2 ρ VV1-VV2

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

Multichannel signal simulation Channel Linear Combination channel #1 channel #2 channel #N channel #1 channel #2 channel #N channel #1 channel #2 channel #N Independent channels (complex) Correlated channels (complex) Spatial convolutions Desired spectral properties for each complex channel Tree Height Coherence – HH-HH SLC – HH

Introduction of Ionospheric distorion Orbit Target 1 Aperture angle: This is what really matters! Lower (virtual) orbit Equivalent 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 applied directly to raw data!!! (the raw data distortion is target dependent) For an orbit at Ionosphere height Distortions can be applied directly to the raw data Aperture length

BEES Block Diagram ,[object Object],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.

Level-2 Retrieval Discussed in previous talk!

L2 retrieved heights (H 100 ) SGM L2 Range dependent H 100 bias Software bug or realistic feature?

Performance Evaluation (L1b) ,[object Object],[object Object],[object Object],[object Object],[object Object]

Performance Evaluation (L2) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Monte Carlo (multiple runs of BEES) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Validation: challenges and strategy ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Example: NESZ validation ,[object Object],The threshold is designed for a failure probability of 10 -3 test failure test success test failure The nominal NESZ value

Example: PGM L1b Verification Probabilistic Threshold ,[object Object],[object Object],[object Object],[object Object],[object Object]

PGM L1b Verification – Caveat! ,[object Object],[object Object],10 5 simulations, gamma=0.5, L=250 10 5 simulations, gamma=0.95, L=30 histograms from simulations To validate the simulator we need (to simulate) large, homogeneous scenes! Sound familiar?

Project Status/Outlook ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

BIOMASS_E2ES_IGARSS2011.ppt

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BIOMASS_E2ES_IGARSS2011.ppt