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Modelling the GNSS Reflectometry Signal over Land: sensitivity to soil moisture and biomass

Modelling the GNSS Reflectometry Signal over Land: sensitivity to soil moisture and biomass.

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Modelling the GNSS Reflectometry Signal over Land: sensitivity to soil moisture and biomass

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  1. Modelling the GNSS Reflectometry Signal over Land: sensitivity to soil moisture and biomass N. Pierdicca1, L. Guerriero2 , E. Santi3, A. Egido41 DIET - Sapienza Univ. of Rome, Rome, Italy2 DISP - University of Tor Vergata, Rome, Italy3CNR/IFAC, Sesto Fiorentino. Italy4Starlab, Barcelona, Spain

  2. Introduction: GNSS-R • GNSS Reflectometry performs bistatic measurements, with most of the signal coming from around the specular direction • Specular reflectionand diffuse scatteringfrom the Earth surface are combined • This is similar to the radar altimeter, which however works in monostatic configuration

  3. Introduction • ESA has funded two projects aiming at • evaluating the potential of GNSS signals for remote sensing of land bio-geophysical parameters (soil moisture and vegetation biomass), through ground based(LEIMON, 2009) and airborne (GRASS, 2011; in progress) experimental campaigns • developing a simulator to theoretically explain experimental data and predict the capability of airborne and spaceborne GNSS-R systems for moisture and vegetationmonitoring • This presentation resumes some issues addressed and some experience gained on land GNSS-R signal modeling

  4. Content • The projects • The problem • Simulator description • Validation results and sensitivity • Conclusions

  5. LeiMonproject The crane • The GNSS receiver antenna • The monitoredfields (West and East side)

  6. GRASS project • The flighttrack and monitoredfields The airplane The antenna

  7. v Moisture & rain Biomass West field Roughness Leimon Overview Left-right & rain Right-Right & rain

  8. Coherent vs. incoherentpower Gpq Incoherentscatteringdiffused in anydirection Eincoh=E-<E> TX Coherentreflection along specular direction Ecoh=<E> • Different dependence on ranges (rt and rr) and resolution (dA) makes the relative magnitude of incoherent and coherent components varies with receiver height, besides dependence on surface roughness, vegetation.

  9. Signal correlation time • Incoherent component fluctuates (speckle) with correlation timeTc dependent on system configuration • In LeiMonground based steady receiver →TC15 s • In GRASS airborne receiver →TC7-8 ms • Long coherent integration (TI>>TC) can reduce incoherent, zero mean, component when possible • Alternatively, long incoherent integration is required to mitigate fading, still preserving the incoherent signal power direct ≈TC reflected • Confirmed by the slow fluctuation of LeiMon signal (red line)

  10. Incoherent component model Absent or homogeneousvegetation cover. RTE solutionconsiderindattenuationand multiple scattering by a discrete medium (Tor Vergata model) Indefinite meansurfaceplane with roughnessatwavelength scale. Bistaticscattering of locallyincidentplanewaves by AIEM (Fung, Chen) • Contributions from single independent surface elements, whose dimension can be assimilated to the roughness correlation length (order of the wavelength) , add incoherently. • Then, the incident wave can be assumed locally plane

  11. Coherent component model Scattering of sphericalwave by Kirchoffapproxi. (Eom & Fung, 1988) • This a canonical problem in electromagnetism (antenna above a dissipative plane, Sommerfeld equality, Exact Image Theory, and so on) • Signal is determined by a large portion of the mean surface, at least the first Fresnel zone, from few meters (ground) to tens of meters (airborne), to kms(spaceborne) • Kirchoff approximation can be useful. The incident wave must be assumed to be spherical (as in Fung & Eom, 1988) • We have removed some constraints of Fung & Eom, i.e., consideration of identical transmitting and receiving antennas at the same distance from the surface, and restriction to backscattering and specular scattering cases.

  12. 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 provided by the electromagnetic model 2The GPS correlation (triangle) function S2 The attenuation sinc function due to Doppler misalignment. The longer the time Tithe narrower the filter in Doppler space dADifferential area within scattering surface area A (the glistening zone).

  13. Model validation: LeiMon angular trend Reflected (down antenna) over direct (up antenna) • April 8th SMC=30% • East field sZ=3cm • West fieldsZ=1.75cm • August 26th SMC=10% • East fieldsZ=0.6 cm • West fieldsZ=1cm • Wetter and smoother fields exhibits higher down/up • Simulator reproduces quite well LR signal versus q at incidence angles q ≤45°. • Higher angle may suffer from poor antenna characterization (pattern, polarization mismatch)

  14. LeiMon coherent & incoherent: soil • August 26th SMC=10% • East sZ=0.6 cm • April 8th SMC=30% • East sZ=3cm total coherent • Comparison of LR theoretical simulations and data shows that incoherent component strongly contributes to total signal when soil is rough.

  15. Model vsLeiMon data: bare soil LR • Overall LR LeiMon model vs data comparison over bare soil • 10%<SMC<30% • 0.6<z<3cm • Slight model overestimation of the DOWN/UP ratio but good correlation • Without considering incoherent component lower values (rough surfaces) would be strongly underestimated

  16. Model vs GRASS data: overall LR RMSE2dB Bias1dB • Overall LR GRASS model vs data (DOWN/UP) comparison over bare soil and forest • Good performance over forest and slight model underestimation over bare soil (but still good correlation) • Note that soil underestimation can be easily reduce by changing correlation length

  17. Model vs data: RR component LeiMon experiment GRAA experiment • The Simulator underestimates the signal at RR polarization especially when it is expected to be low (e.g. rough soil) • Besides the possible model errors (underestimation of cross-polarized incoherent scattering) e limit due to instrumental noise seems to saturate observations below -18 dB (LeiMon) and -22 dB (GRASS)

  18. Sensitivity to SMC • Reflected power at 35° incidence in Left Right (LR) polarization exhibits a good sensitivity÷correlation(0.3 dB/%÷0.76) with SMC of the West field (the one covered by sunflower), whilst the correlation with SMC is poor on the East field (which was always bare, but with change in roughness). • By using the ratio RR/LR, we observed a significant negative sensitivity÷correlation(0.2 dB/%÷0.84) to the SMC on both fields

  19. LeiMon LR data bivariatefitting Best fitting of LR versus sz and mv, for the East field data, by equation: • Data (blue diamonds) and fittingsurface (gray) • Slopes of the surfacemeasuressensitivity Sensitivity to sz: -4 to -2 dB/cm Sensitivity to mv : 1 to 3 dB/10%

  20. LeiMon: LR sensitivity to cropbiomass At 35°,the coherent componentis attenuated by about 1 dB each 1kg/m2, which would predict a large sensitivity to PWC (Ulaby et al., 1983; Jackson et al., 1982; O’Neill,1983) • The Simulator predicts a quite large incoherent component according to the shortcoherent integration time (1 msec), which explains the saturation effect with PWC in the data. • The model reproduces the measured (low) sensitivity (0.3 dB/kg·m-2, about 2 dB for the whole PWC range) thanks to the consideration of both component

  21. GRASS: LR sensitivity to forestbiomass • Coherent component is dominant. • Incoherent component is reduced by the longest coherent integration time (20 msec) which filter a narrower Doppler band • Data and simulations agree in showing a fairly good sensitivity to biomass (1dB every 100 m3/ha) • The observed Highest Tree Volume corresponds to a Dry Biomass of about 110 t/ha (beyond the SAR saturation limit ) • Sensitivity to even highest biomass to be verified

  22. Conclusions A simulator has been developed which provides DDM’s, waveforms and peak power (reflected/direct) of a GNSS-R system looking at bare or vegetated soils (LHCP and RHCP real antenna polarization) It singles out coherent and incoherent signal components. Validated using data over controlled experimental sites (bare soil, sunflower, forest) (LeiMon and GRASS). Simulator results and experimental data show a fair agreement at LR polarization and angles <45° (the antenna beamwidth) The incoherent component may be high in the ground based LeiMon configuration, whereas was reduced by coherent integration in airborne GRASS Sensitivity to SMC is significant and well reproduced by the simulator Sensitivity to vegetation is reproduced and it is quite low when the incoherent contribution cannot be reduced.

  23. Thank you for your attention

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