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

N. Pierdicca1, L. Guerriero2 , E. Santi3, A. Egido4

1 DIET - Sapienza Univ. of Rome, Rome, Italy2 DISP - University of Tor Vergata, Rome, Italy3 CNR/IFAC, Sesto Fiorentino. Italy4 Starlab, Barcelona, Spain

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

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 theEarth surface are combined

This is similar to the radar altimeter, which however works in monostatic configuration

2

TX

RX

TXTX

RX

TX


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

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

Content

The projects The problem Simulator description Validation results and sensitivity Conclusions


LeiMon project

5

The GNSS receiver antenna

The monitored fields (West and East side)

The crane


GRASS project

6

The airplane

The flight track and monitored fields

The antenna


v

7

Leimon

Overview

Moistu

re & ra

in

Biomas

s

West fi

eld

Roughnes

s

Left-

right

& rain

Right-Right

& rain


Coherent vs. incoherent power

Coherent reflectionalong specular direction

Ecoh=<E>

Incoherent scattering diffused in any

direction Eincoh=E-<E>

22

2)(

4 rt

pt

rq

tp

pqcohq

r rrWDD

W

dArr

DDWW o

pqrt

ssrqii

tp

ptincohq

r

223

2)( ),(),(

4

pq

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.

TX

°(i,s)

RX

TX


Signal correlation time Incoherent component fluctuates (speckle) with correlation time

Tc dependent on system configuration In LeiMon ground based steady receiver → TC15 s In GRASS airborne receiver → TC7-8

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

≈TC

reflected

direct

Confirmed by the slow fluctuation of LeiMon signal (red line)


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


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.


|Y |2 Processed signal power at the receiver vs. delay t and 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.° Bistatic scattering coefficient provided by the electromagnetic model2 The GPS correlation (triangle) function S2 The attenuation sinc function due to Doppler misalignment. The longer

the time Ti 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 t and frequency f is modeled by integral Bistatic Radar Equation which includes time delay domain response 2t’-t and Doppler domain response S2 (f’-f ) of the system (Zavorotny and Voronovich, 2000).

dARR

ffSGGPTfYTR

RTTi 022

22

3

222 )'()'(

)4()ˆ,ˆ( tt

t

--

The Bistatic Radar Equation


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 trendReflected (down antenna) over direct (up antenna)


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


-20

-18

-16

-14

-12

-10

-8

-16 -14 -12 -10 -8

Sim

ulat

ed D

N/U

P (o

nly

cohe

rent

) [dB

]

Leimon DN/UP data [dB]

LR

15 West

15 East

25East

25West

35East

35West

Serie1

-20

-18

-16

-14

-12

-10

-8

-20 -18 -16 -14 -12 -10 -8

SIm

ulat

ed D

N/U

P [d

B]

Leimon data DN/UP [dB]

LR

15 West

15 East

25East

25West

35East

35West

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

Model vs LeiMon data: bare soil LR


Model vs GRASS data: overall LR

RMSE2dBBias 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


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 experiment GRAA experiment


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


Best fitting of LR versus z and mv, for the East field data, by equation:

vv mamaaaLR 3210

Sensitivity to mv : 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


LeiMon: LR sensitivity to crop biomass

At 35°, the coherent component is 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 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

sec1.02 - PWCet


• 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)

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


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.


Documents

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