Claudia NotarnicolaEURAC-Institute for Applied Remote Sensing

Arpa Emilia Romagna, Bologna, 25 Ottobre 20101

• Importance of soil moisture• Basic principles for detection of soil moisture

with radar and optical data• Model simulations• Inversion methodologies

– Semi-empirical algorithms– Bayes procedure– Neural networks– Support Vector RegressionsExperimental results for different test sites

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Fig.

4.1

8, C

ain

et a

l. (p

. 97)

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Effect Varies with TopographySlopeAspectTopographic Convergence

Vegetation GrowthCrops have ideal growth temperature

Heat stress (out of LE to evaporate, increases H)Plant diseases due to condensation

(LE=Latent Energy Heat Flux, H=Surface Sensible Heat Flux)

Local Surface Temperatures Moderated by Soil Moisture

Wet soils = cold, Dry soils = warm (heat capacity)Diurnal and seasonal flux of sensible heatLatent heat use (evaporation cools, condensation warms)

(Hatchett, 2008) 4

Soil Moisture linked to Mesoscale Convection (e.g. Betts and Ball 1998, Sullivan et al. 2000)

Remains open research question due to many feedbacks/complicating factorsSometimes wet soils suppress convection, dry soils aid propagation (Taylor and Ellis, 2006)

Role of EvaporationPatchiness of wet/dry, creating gradients (Sahel, Central Plains US) that force surface PBLFindell and Eltahir 2003 found that antecedent wet soils aided convection in SE US

(Hatchett, 2008) 5

Soil moisture estimation

Microwave sensors (SAR,

Scatterometer, Radiometer)

f= 1-10 GHz

Thermal (8-14 μm) and visible

bands

•Directly linked todielectric properties ofsoils•Necessity to compensateeffects of vegetation

•Provide information onvegetation (NDVI, VWC)•Linked to thermal inertia•Related to albedo

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Ground resolution

Repeat cycle

SMOS

25 km

SMAP

1-3 km

1 day

3 days

ALOS26 days

10days

100 m<100 m

SAOCOM16 days

MODIS

Meteosat<1 day

NPOESS

400 m

LANDSAT LDCM

METOP

SACD/Aquarius

ASTER

Future SAR missionsFuture thermal missionsMissions including microwave acquisitions (SAR-L-C -Xband or radiometer)Missions including thermal acquisitions

SENTINEL

Radarsat2

COSMO-SkyMed

TERRASAR-X

ASAR

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Imaging radar is an active illumination system. An antenna, mounted on a platform, transmits a radar signal in a side-looking direction towards the Earth's surface. The reflected signal, known as the echo, is backscattered from the surface and received a fraction of a second later at the same antenna (monostatic radar).

For coherent radar systems such as Synthetic Aperture Radar (SAR), the amplitude and the phase of the received echo - which are used during the focusing process to construct the image - are recorded.

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1. Emission, through the antenna, of apulsed e.m. signal in a precise direction

2. Capability to detect with high accuracythe signal backscattered by the target

3. Ranging technique: determination of thetarget distance through themeasurements of the time delay betweenthe trasmitted and received signal

4. The directional beam scans wide areasdetecting different objects

5. Capability to analyse the coherent signalin the frequency domain

1+2+3+4

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Characteristics of the radar techniques

Characteristic of the SAR technique

SAR takes advantage of the Doppler history of the radar echoes generated by the forward motion of the spacecraft to synthesise a large antenna (see Figure). This allows high azimuth resolution in the resulting image despite a physically small antenna. As the radar moves, a pulse is transmitted at each position. The return echoes pass through the receiver and are recorded in an echo store.

SAR requires a complex integrated array of onboard navigational and control systems, with location accuracy provided by both Doppler and inertial navigation equipment. For sensors such as ERS-1/2 SAR and ENVISAT ASAR, orbiting about 900km from the Earth, the area on the ground covered by a singletransmitted pulse (footprint) is about 5 km long in the along-track (azimuth) direction.

GeneralSAR images represent an estimate of the radar backscatter for that area on theground. Darker areas in the image represent low backscatter, while brighter areasrepresent high backscatter. Bright features mean that a large fraction of the radarenergy was reflected back to the radar, while dark features imply that very littleenergy was reflected.

Backscatter for a target area at a particular wavelength will vary for a variety ofconditions, such as the physical size of the scatterers in the target area, the target'selectrical properties and the moisture content, with wetter objects appearing bright,and drier targets appearing dark. (The exception to this is a smooth body of water,which will act as a flat surface and reflect incoming pulses away from the sensor.These bodies will appear dark). The wavelength and polarisation of the SAR pulses,and the observation angles will also affect backscatter.

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A useful rule-of-thumb in analysing radar images is that the higher or brighter the backscatter on the image, the rougher the surface being imaged. Flat surfaces that reflect little or no radio or microwave energy back towards the radar will always appear dark in radar images.

Vegetation is usually moderately rough on the scale of most radar wavelengths and appears as grey or light grey in a radar image.

Surface scattering

Volume scattering

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Surfaces inclined towards the radar will have a stronger backscatter than surfaceswhich slope away from the radar and will tend to appear brighter in a radar image.Some areas not illuminated by the radar, like the back slope of mountains, are inshadow, and will appear dark.

When city streets or buildings are lined up in such a way that the incoming radarpulses are able to bounce off the streets and then bounce again off the buildings(called a double-bounce) and directly back towards the radar they appear verybright (white) in radar images. Roads and freeways are flat surfaces and so appeardark. Buildings which do not line up so that the radar pulses are reflected straightback will appear light grey, like very rough surfaces.

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The backscattered signal results from:- surface scattering- volume scattering-multiple volume-surface scattering

The relative importance of these contributions depend on- surface roughness*- dielectric properties of the medium

All of these factors depend on- the radar frequency- the polarisation- the incidence angle

*A rough surface is classically described in terms of surface root mean square height s and correlation length L normalised respect to the inciden wave number k (Fung, 1994).

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The surface roughness (wrt to the wavelength) governs the scattering pattern

The dielectric constant (moisture content) of the medium governs the strenght of thebackscatter

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Intermediate roughnessMedium return

High roughnessStrong return

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•F. T. Ulaby, R. K. Moore, and A. K. Fung, Microwave Remote Sensing: Active and Passive. Norwood, MA: Artech House, 1986, vol. 2–3.18

Irrigated fields

Higher backscatter

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The relationship betweenradar backscatter at Cband 23°VV and soilmoisture is modulated bysurface roughness

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Smooth soil(pseudo-random)

Surface parameters• s = 1.3 cm• lc = 3.7 cm• P = 59.5 cm

Rough soil(pseudo-periodic)

Surface parameters• s = 2.8 cm• lc = 6.8 cm• P = 40.3 cm

TDR probe

• Measuring range:0..100%

• Accuracy:Range 0..40%:+-1%

• Repeating accuracy:+/- 0.5%

30-4

0 cm

SMC 21

no depolarisation

no HV or VH backscatter

Fresnel Reflectivity RH > RV

some depolarisation

HV or VH backscatter > 0

Fresnel Reflectivity RH = RV

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Depending on the frequency and polarization, waves can penetrate into thevegetation and, on dry conditions, to some extent, into the soil (for instance drysnow or sand). Generally, the longer the wavelength, the stronger the penetrationinto the target is. With respect to the polarization, cross-polarized (VH/HV) acquisi-tions have a significant less penetration effect than copolarized (HH/VV) one.

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The main scatterers in a canopy are the elements having dimension of the order of the wavelength.

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High depolarisation (HV, VH)• Increase with increasing number of scatterers• When the number (or volume) of scatterers is very high, the signal saturates

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At C band, scatterers in forest are leaves and stemsSaturation occurs more quickly than at lower frequenciesForest response is relatively stable at C band

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At C band,HH and VV: The dominant scattering mechanism is the ground scattering,attenuated by the vegetation layer--> the backscatter increases with increasing soil moisture--> the backscatter decreases with increasing vegetation biomass--> it is difficult to retrieve vegetation biomass and soil moisture usingone polarisationHV or VH: volume scattering dominant. However, very low backscatterdue to small size of the scatterers

HH/VV: differential attenuation,related to vegetation biomass 29

Airborne CV-580 C-Band SARSouth of Ottawa, 1998 July 9Linear Polarization CompositeR = HH G = HV B = VV

The availability of multiple polarizations will greatly improve the potential for crop type mapping 30

They estabish a relationship (direct) between the surface parameters and the backscatteredradiations. They are useful to understand the sensitivity of the sensor responses to thesurface paameter changes.

Empirical models: they find a statistical relationship among the above mentioned parameters

Electromagnetic models: if the surface is characterized by statistical parameters, theexpected values for the electromagnetic fields after the interaction with the surface can beretrieved as a function of the statistical surface parameters

Different approximations:Small Perturbation Method (SPM): is useful for sufaces whose horizontal and verticaldimensions are smaller with respect to the signal wavelength (low frequency approximation)

Kirchhoff models: is based on the tangent plane approximation for surfaces whosehorizontal dimension, curvature ray, and correlation length are bigger than the signalwavelength

Methods of Moment (MOM), Integral Equation Model (IEM): exact calculation of the e.m.fields without restrictive hyphotesis

•F. T. Ulaby, R. K. Moore, and A. K. Fung, Microwave Remote Sensing: Active and Passive. Norwood, MA: Artech House, 1986, vol. 2–3.31

The relationship between the sensor wavelength and the dimensions of thescattering elements and their relative distances change the type of collectiveeffect which determines the scattering

PO/GO: are both “facet scattering” models, where the surface is approximatedwith plane surfaces with different orientations

GO: the main hyphotesis is that the only contribution to the signalderives from the specular directions. Different facets do not interacteach other. So that the main surface parameter is the local slope.PO: as the surface becomes flat (σ<<) there is a relationship amongthe different scattering elements.

SPM: as the horizontal dimension decreases the concept of facet disappers,and the scattering elements is point-like with respect to the signal wavelengthand becomes source of spherical e.m. waves according to the Huyghensprinciple. Then the backscattering coefficients depend on one single spectralcomponent of the roughness spectrum that coincides with the spatial frequancyable to produce the Bragg resonance.

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The radar backscatter from a vegetated surface is mainly composed of three contributions:

Where

is the bascattering coefficient of bare soil descrease by the two-way attenuation of the vegetation

is the direct backscatter contribution of the vegetation

represents multiple scattering involving the vegetation elements and the ground surface

•F. T. Ulaby, R. K. Moore, and A. K. Fung, Microwave Remote Sensing: Active and Passive. Norwood, MA: Artech House, 1986, vol. 2–3.34

Techniques and methods to evaluate soil and vegetation water content includes asmain instruments passive and active microwave methods but also some indirectmeasurements based on radiometric techniques in the optical-thermal range.

authors Sensors Applications Validation approach

Cai et al. (2009) MODIS Retrieval of Soil Moisture over bare agricultural areas or lightly vegetated

Comparison with ground measurements

Verstraeten et al. (2006)

METEOSAT Retrieval of Soil Moisture index over forest stands

Comparison with SWI from ERS Scatterometer and EUROFLUX data

Tramutoli et. al. (2000)

AVHRR Retrieval of Soil Moisture index over agricultural areas

Comparison with API index

Sobrino et al. (1999) AVHRR Estimation of apparent thermal inertia

Comparison with ground measurements

Xue & Cracknell(1995)

AVHRR Estimation of thermal inertia

Comparison with ground measurements

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• Physical Thermal Inertia (TI)• Response to temperature change• Physical TI = √(density*thermal conductivity*heat capacity)

The generalized theory to calculate the thermal inertia from global remote sensing datawas given by Price (1977), but it is of difficult application because it requires theknowledge of many physical parameters such as wind speed, air humidity generally toodifficult to be estimated from remotely sensed data.A theoretical expression for the thermal inertia as a function of soil moisture isprovided by Ma and Xue (1990) with the following expression:

where ds is the soil bulk density, d is the water density, w is the percentage of soil moisture. Considering this expression there is a unique relationship between soil moisture and the thermal inertia values.

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12:00

Tem

pera

ture

00:00 24:00Time

bare soil

water

vegetation

• Apparent Thermal Inertia (ATI)• ATI = (1-albedo) / (Temperature max - Temperature min)• Thermal image pair solar noon and pre-dawn

Problems: the presence of vegetation on ground may change soil responsesAir masses close to the soil-atmophere interface can complicate the retrieval

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