1. N. Pierdicca 1, L. Guerriero 2, R. Giusto 1, M. Brogioni 3, A. Egido 4, N. Floury 5 1 DIET - Sapienza Univ. of Rome, Rome, Italy 2 DISP - University of Tor Vergata, Rome, Italy 3 CNR/IFAC, Sesto Fiorentino. Italy 4 Starlab, Barcelona, Spain 5 ESA/ESTEC, Noordwyik, The Netherland GNSS Reflection from Bare and Vegetated Soils: Experimental Validation of an End-to-end Simulator

2. 2 Content  Introduction: the Leimon project  Simulator description  Simulator validation during Leimon  Conclusions

3. 3 Land MOnitoring with Navigation signals  Evaluating the potential of GNSS signals for remote sensing soil moisture and vegetation biomass, through a ground based experimental campaign LEiMON Project  Developing a simulator to theoretically explain experimental data and predict the capability of a GNSS-R aerospace systems The SAM dual pol GNSS-R instrument (by Starlab) LEiMON set up

4. 4 The LEIMON experiment FDR and TDR soil moisture probes Meteo station Soil roughness profilometer Bare (different roughness) and vegetated fields Plant height and water content

5. 5 Time serie overview West field with developed plants

6. 6 |Y | 2 Processed signal power at the receiver vs. delay  and frequency f. P T The transmitted power of the GPS satellite. G T, G R The antenna gains of the transmitting and the receiving instrument. R R, R T The distance from target on the surface to receiving and transmitting antennas. T i The coherent integration time used in signal processing.  Bistatic scattering coefficient  2 The GPS correlation (triangle) function S 2 The attenuation sinc function due to Doppler misalignment dA Differential area within scattering surface area A (the glistening zone). The mean power of received signal vs. delay  and frequency f is modeled by integral Bistatic Radar Equation which includes time delay domain response    ’  and Doppler domain response S 2 ( f’-f ) of the system (Zavorotny and Voronovich, 2000). The Bistatic Radar Equation

7. 7 The BRE integration  In order to solve the integral we have to introduce the equations relating all those variables to the coordinates over the surface. function of point scattering delay function of point looking direction wrt boresight function of incidence direction function of point wrt RX and TX position function of point bistatic angle (zenith and azimuth) function of point scattering Doppler Integrand variables are the coordinates defined on the surface

8. 8 Local vs global frames  Elliptic Earth approximation  x’y’z’ local frame centered in the specular point  z’ axes along the geodetic vertical  x’z’ plane coincident with the bistatic scattering plane z’ x’ y’ TX RX SP=[x s,y s,z s ] ECEFF Earth ellipsoide ii  s =  i Earth surface x z y  Given RX and TX trajectory/velocity & epoch, integral is computed in the local frame, where soil/vegetation parms are defined

9. 9  ° electromagnetic modelling Indefinite mean surface plane with roughness at wavelength scale. Bistatic scattering of locally incident plane waves by AIEM Homogeneous vegetation cover. Attenuation and multiple scattering by a discrete medium (Tor Vergata model) INCOHERENT component COHERENT component Scattering of spherical wave by Kirchoff approxi. (Eom & Fung, 1988) attenuated by vegetation

10. 10 Polarization synthesis for real antennas  Nominal polarization unit vector are p RHCP =(  0 -j  0 )/√2 and p LHCP =(  0 +j  0 )/√2  Real antenna polariz. unit vector  SCS of “real” antennas and channel cross talk x y z P=r, ,  2 orthogonal electric dipoles  /2 phase shifted

11. 11 The signal simulator structure Geometric module: GPS TX orbit SP position E.M. bare soil bistatic  ° model Simulator core E.M. vegetation bistatic  ° model

12. 12 AIEM vs scattering direction  s,  s  i =31° m v =20 %  z =1.5 cm l=5 cm H RX =10 km HPBW=120°  Incoherent component RR & LR more spread  Coherent component RR & LR focused around specular  s =31° Azimuth  Zenith 

13. 13 DDM output example DDM’s (delay on the horizontal axes, frequency on the vertical axes) for incoherent (top) and coherent (bottom) component at RHCP (left) and LHCP (right) Spaceborne incoherent airbone incoherent

14. 14 Peak power output example m v =20 %  z =1.5 cm l=5 cm H RX =10 km V RX =180 m/s Head RX =45° HPBW=120°  Coherent and incoherent RR & LR peak power as the satellite moves along the orbit

15. 15  The incoherent signal fluctuates (speckle) with long correlation time (15 sec) so only incoherent summation was feasible  Different soil/vegetation conditions investigated Leimon data  In the Leimon experiment the instrument records temporal series of the direct and reflected signals (I&Q of the correlator output peak)  The mean reflected power (both LR and RR pol) normalized to the mean direct power (at RR pol.) is studied vs. the incidence angle reflected direct

16. 16 August 26th SMC=10% East  Z =0.6 cm West  Z =1cm April 8th SMC=30% East field  Z =3cm West field  Z =1.75cm Simulator reproduces quite well LR signal versus  at incidence angles  ≤45°. Validation: angular trend

17. 17 August 26th SMC=10% East  Z =0.6 cm April 8th SMC=30% East  Z =3cm Theoretical simulations show that incoherent component strongly contributes to total signal when soil is rough. Coherent vs. incoherent: soil coherent total

18. 18  General overestimation of the DOWN/UP ratio but good correlation  Cross talk and/or different down and up antenna gains could explain the bias RMSE=1.6 dB Bias=-1.4 dB Bare soil overall comparison LR East Side, West Side Bare Soils 10%<SMC<30% 0.6<  z <3cm

19. 19 RMSE=7.25 dB Bias=6.1 dB  The model underestimates RR polarization, especially at smaller angles  The RR measurements seem to saturate at -18 dB  A deficiency of the mode could occur Bare soil overall comparison RR East Side, West Side

20. 20 20 RR underestimation RR August 26th SMC=10%  Z =0.6cm  At large incidence angle, the RR coherent component is the dominant one  Theoretical incoherent RR scattering (rough case) may be underestimated RR April 8th SMC=30%  Z =3cm coherent total

21. 21 Vegetation overall comparison Fair correspondence between model and vegetation data (except for largest angle =55°) June 28, July 10,28 22<SMC<18% PWC=1.2, 3.6, 6.7 kg/m 2

22. 22 Sensitivity to SMC Black: Leimon data (May) and regression line Blue/Red: Theoretical simulations, two roughnesses  Model predicts  2.5 dB for a 10% SMC difference  Leimon shows  2.8 dB

23. 23 Sensitivity to vegetation Magenta: Leimon data Green: Theoretical simulations vs. PWC  Model sensitivity reproduces the measured one  Sensitivity to vegetation is quite low: about 2 dB for the whole PWC range

24. 24 Coherent vs Incoherent: vegetation At 35°, the coherent component is attenuated by about 1 dB each 1kg/m2, which would predict a large sensitivity to PWC  The Simulator predicts a quite large incoherent component which explains the saturation effect with PWC in the data.  The model reproduces the measured trend thanks to the consideration of both component

25. 25Conclusions  A simulator has been developed which provides DDM’s and waveforms of a GNSS-R system looking at land  It takes as input arbitrary receiver position/velocity, in view GPS satellite PRN code, surface properties (soil moisture, roughness, vegetation parameters).  It singles out coherent and incoherent signal components.  A fair agreement has been found at LR polarization and angles <45° (the antenna beamwidth)  Incoherent component may be high in a ground based configuration, and antenna properties must be considered  Sensitivity to SMC is significant and well reproduced  Sensitivity to vegetation is reproduced and it is quite low because of the coherent and incoherent combination.  A few discrepancies could be due to measurement or model problems (of course) and are under investigation

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