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N. Pierdicca 1, L. Guerriero 2, E. Santi 3, A. Egido 4 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 Modelling the GNSS Reflectometry Signal over Land: sensitivity to soil moisture and biomass

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AIT-CeTeM – Telerilevamento a Microonde, Bari 4,5 dicembre 2012 Introduction: GNSS-R GNSS Reflectometry performs bistatic measurements, with most of the signal coming from around the specular direction Specular reflection and diffuse scattering from the Earth surface are combined This is similar to the radar altimeter, which however works in monostatic configuration 2

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AIT-CeTeM – Telerilevamento a Microonde, Bari 4,5 dicembre 2012 Introduction 3 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 vegetation monitoring This presentation resumes some issues addressed and some experience gained on land GNSS-R signal modeling

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AIT-CeTeM – Telerilevamento a Microonde, Bari 4,5 dicembre 2012 4 Content The projects The problem Simulator description Validation results and sensitivity Conclusions

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AIT-CeTeM – Telerilevamento a Microonde, Bari 4,5 dicembre 2012 LeiMon project 5 The GNSS receiver antenna The monitored fields (West and East side) The crane

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AIT-CeTeM – Telerilevamento a Microonde, Bari 4,5 dicembre 2012 GRASS project 6 The airplane The flight track and monitored fields The antenna

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AIT-CeTeM – Telerilevamento a Microonde, Bari 4,5 dicembre 2012 v 7 LeimonLeimonOverviewOverviewLeimonLeimonOverviewOverview Moisture & rain Biomass West field Roughness Left-right & rain Right-Right & rain

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AIT-CeTeM – Telerilevamento a Microonde, Bari 4,5 dicembre 2012 Coherent vs. incoherent power Coherent Coherent reflection along specular direction E coh = Incoherent Incoherent scattering diffused in any direction E incoh =E- pq Different dependence on ranges (r t and r r ) and resolution (dA) makes the relative magnitude of incoherent and coherent components varies with receiver height, besides dependence on surface roughness, vegetation. TX

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AIT-CeTeM – Telerilevamento a Microonde, Bari 4,5 dicembre 2012 Signal correlation time Incoherent component fluctuates (speckle) with correlation time T c dependent on system configuration In LeiMon ground based steady receiver T C 15 s In GRASS airborne receiver T C 7-8 ms Long coherent integration (T I >>T C ) can reduce incoherent, zero mean, component when possible Alternatively, long incoherent integration is required to mitigate fading, still preserving the incoherent signal power T C reflected direct Confirmed by the slow fluctuation of LeiMon signal (red line)

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AIT-CeTeM – Telerilevamento a Microonde, Bari 4,5 dicembre 2012 Incoherent component model Indefinite mean surface plane with roughness at wavelength scale. Bistatic scattering of locally incident plane waves by AIEM (Fung, Chen) Absent or homogeneous vegetation cover. RTE solution considerind attenuation and multiple scattering by a discrete medium (Tor Vergata model) 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

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AIT-CeTeM – Telerilevamento a Microonde, Bari 4,5 dicembre 2012 Coherent component model Scattering of spherical wave by Kirchoff approxi. (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.

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AIT-CeTeM – Telerilevamento a Microonde, Bari 4,5 dicembre 2012 12 |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 provided by the electromagnetic model 2 The GPS correlation (triangle) function S 2 The attenuation sinc function due to Doppler misalignment. The longer the time T i the narrower the filter in Doppler space 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

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AIT-CeTeM – Telerilevamento a Microonde, Bari 4,5 dicembre 2012 13 August 26th SMC=10% East field Z =0.6 cm West field Z =1cm April 8th SMC=30% East field Z =3cm West field Z =1.75cm Wetter and smoother fields exhibits higher down/up Simulator reproduces quite well LR signal versus at incidence angles 45°. Higher angle may suffer from poor antenna characterization (pattern, polarization mismatch) Model validation: LeiMon angular trend Reflected (down antenna) over direct (up antenna)

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AIT-CeTeM – Telerilevamento a Microonde, Bari 4,5 dicembre 2012 14 August 26th SMC=10% East Z =0.6 cm April 8th SMC=30% East Z =3cm Comparison of LR theoretical simulations and data shows that incoherent component strongly contributes to total signal when soil is rough. LeiMon coherent & incoherent: soil coherent total

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AIT-CeTeM – Telerilevamento a Microonde, Bari 4,5 dicembre 2012 15 Overall LR LeiMon model vs data comparison over bare soil 10%

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AIT-CeTeM – Telerilevamento a Microonde, Bari 4,5 dicembre 2012 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

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AIT-CeTeM – Telerilevamento a Microonde, Bari 4,5 dicembre 2012 Model vs data: RR component 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) LeiMon experimentGRAA experiment

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AIT-CeTeM – Telerilevamento a Microonde, Bari 4,5 dicembre 2012 Sensitivity to SMC 18 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

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AIT-CeTeM – Telerilevamento a Microonde, Bari 4,5 dicembre 2012 Best fitting of LR versus z and m v, for the East field data, by equation: Sensitivity to m v : 1 to 3 dB/10% Sensitivity to z : -4 to -2 dB/cm Data (blue diamonds) and fitting surface (gray) Slopes of the surface measures sensitivity LeiMon LR data bivariate fitting

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AIT-CeTeM – Telerilevamento a Microonde, Bari 4,5 dicembre 2012 20 LeiMon: LR sensitivity to crop biomass At 35°, the coherent component is attenuated by about 1 dB each 1kg/m 2, which would predict a large sensitivity to PWC (Ulaby et al., 1983; Jackson et al., 1982; ONeill,1983) The Simulator predicts a quite large incoherent component according to the short coherent 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

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AIT-CeTeM – Telerilevamento a Microonde, Bari 4,5 dicembre 2012 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 m 3 /ha) GRASS: LR sensitivity to forest biomass 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

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AIT-CeTeM – Telerilevamento a Microonde, Bari 4,5 dicembre 2012 22Conclusions A simulator has been developed which provides DDMs, 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.

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AIT-CeTeM – Telerilevamento a Microonde, Bari 4,5 dicembre 2012 23

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