1. Folie 1 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)

2. 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

3. Paco López-Dekker > 28.07.2011 Slide 3

4. Paco López-Dekker > 28.07.2011 Slide 4

5. Paco López-Dekker > 28.07.2011 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

6. Paco López-Dekker > 28.07.2011 Slide 6 BEES Block Diagram OpenSF Simulation control OpenSF drives the E2ES. This includes: - UI - Execution Monte Carlo runs. - Etc…

7. Paco López-Dekker > 28.07.2011 Slide 7 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.

8. Paco López-Dekker > 28.07.2011 Slide 8 SGM: Scene Definition 200t/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/ha 0t/ha 100x100 m: 1.Biomass (t/ha) 2.Tree height (h 100 ) To forward model

9. Paco López-Dekker > 28.07.2011 Slide 9 SGM output (ground truth) Biomass Tree height (H 100 )

10. Paco López-Dekker > 28.07.2011 Slide 10 Input to PGM: PolInSAR covariance matrices σ HH σ HV σ VV

11. Paco López-Dekker > 28.07.2011 Slide 11 Input to PGM: PolInSAR covariance matrices ρ HH1-HH2 ρ HV1-HV2 ρ VV1-VV2

12. Paco López-Dekker > 28.07.2011 Slide 12 BEES Block Diagram: PGM

13. Paco López-Dekker > 28.07.2011 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 ICM GM inputs macro steps

14. Paco López-Dekker > 28.07.2011 Slide 14 Multichannel signal simulation Channel Linear Combination 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

15. Paco López-Dekker > 28.07.2011 Slide 15 Introduction of Ionospheric distorion Orbit Target 1 Aperture length 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

16. Paco López-Dekker > 28.07.2011 Slide 16 BEES Block Diagram 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.

17. Paco López-Dekker > 28.07.2011 Slide 17 Level-2 Retrieval

18. Paco López-Dekker > 28.07.2011 Slide 18 L2 retrieved heights (H 100 ) SGM L2 Range dependent H 100 bias Software bug or realistic feature?

19. Paco López-Dekker > 28.07.2011 Slide 19 L2 retrieved biomass SGM L2

20. Paco López-Dekker > 28.07.2011 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

21. Paco López-Dekker > 28.07.2011 Slide 21 Performance Evaluation (L2)  L2 performance in terms of biomass and tree height errors Bias Standard deviation  Error statistics vs. range and biomass levels  In example Height error leads to biomass error?

22. Paco López-Dekker > 28.07.2011 Slide 22 Performance Evaluation (L2)  L2 performance in terms of biomass and tree height errors Bias Standard deviation  Error statistics vs. range and biomass levels  In example Height error leads to biomass error?

23. Paco López-Dekker > 28.07.2011 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

24. Paco López-Dekker > 28.07.2011 Slide 24

25. Paco López-Dekker > 28.07.2011 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.

26. Paco López-Dekker > 28.07.2011 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

27. Paco López-Dekker > 28.07.2011 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).

28. Paco López-Dekker > 28.07.2011 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.: 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?

29. Paco López-Dekker > 28.07.2011 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