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GNSS Reflection from Bare and Vegetated Soils: Experimental Validation of an End-to-end Simulator

GNSS Reflection from Bare and Vegetated Soils: Experimental Validation of an End-to-end Simulator.

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GNSS Reflection from Bare and Vegetated Soils: Experimental Validation of an End-to-end Simulator

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  1. GNSS Reflection from Bare and Vegetated Soils: Experimental Validation of an End-to-end Simulator N. Pierdicca1, L. Guerriero2 , R. Giusto1, M. Brogioni3, A. Egido4, N. Floury51 DIET - Sapienza Univ. of Rome, Rome, Italy2 DISP - University of Tor Vergata, Rome, Italy3CNR/IFAC, Sesto Fiorentino. Italy4Starlab, Barcelona, Spain5ESA/ESTEC, Noordwyik, The Netherland

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

  3. LEiMON Project 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 • Developing a simulator to theoretically explain experimental data and predict the capability of a GNSS-R aerospace systems LEiMON set up The SAM dual pol GNSS-R instrument (by Starlab)

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

  5. Time serie overview West field with developed plants

  6. The Bistatic Radar Equation The mean power of received signal vs. delay t and frequency fis modeled by integral Bistatic Radar Equation which includes time delay domain response 2(t’-t) and Doppler domain response S2 (f’-f ) of the system (Zavorotny and Voronovich, 2000). |Y |2 Processed signal power at the receiver vs. delay tand frequency f. PT The transmitted power of the GPS satellite. GT , GR The antenna gains of the transmitting and the receiving instrument. RR, RT The distance from target on the surface to receiving and transmitting antennas. Ti The coherent integration time used in signal processing. s° Bistatic scattering coefficient 2The GPS correlation (triangle) function S2 The attenuation sinc function due to Doppler misalignment dA Differential area within scattering surface area A (the glistening zone).

  7. The BRE integration Integrand variables are thecoordinatesdefined on the surface • 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 Doppler function of point scattering delay function of point bistatic angle (zenith and azimuth) function of incidence direction function of point looking direction wrt boresight function of point wrt RX and TX position

  8. Given RX and TX trajectory/velocity & epoch, integral is computed in the local frame, where soil/vegetation parms are defined RX z’ qi qs =qi z y’ TX Earth surface SP=[xs,ys,zs]ECEFF Earth ellipsoide x’ y l x 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

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

  10. Polarization synthesis for real antennas P=r,q, z y x • Nominal polarization unit vector are pRHCP=(q0-jf0)/√2and pLHCP=(q0+jf0)/√2 • Real antenna polariz. unit vector • SCS of “real” antennas and channel cross talk 2 orthogonal electric dipoles p/2 phase shifted

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

  12. AIEM vs scattering direction qs,s qi=31° mv=20 % sz=1.5 cm l=5 cm HRX=10 km HPBW=120° Azimuth f • Incoherent component RR & LR more spread • Coherent component RR & LR focused around specular qs=31° Zenith q Zenith q Azimuth f

  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) airbone incoherent Spaceborne incoherent

  14. Peak power output example mv=20 % sz=1.5 cm l=5 cm HRX=10 km VRX=180 m/s HeadRX=45° HPBW=120° • Coherent and incoherent RR & LR peak power as the satellite moves along the orbit

  15. 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 • The incoherent signal fluctuates (speckle) with long correlation time (15 sec) so only incoherent summation was feasible • Different soil/vegetation conditions investigated direct reflected

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

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

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

  19. Bare soil overall comparison RR East Side, West Side 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

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

  21. Vegetation overall comparison June 28, July 10,28 22<SMC<18% PWC=1.2, 3.6, 6.7 kg/m2 Fair correspondence between model and vegetation data (except for largest angle =55°)

  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. 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. Coherent vs Incoherent: vegetation At 35°,the coherent componentis 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. Conclusions 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

  26. Thank you for your attention

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