ECE 5010 - Lecture 29johnson/5010/sensing1.pdf · ECE 5010 - Lecture 29 1 Introduction to Remote...

ECE 5010 - Lecture 29

1 Introduction to Remote Sensing

2 Remote Sensing Applications

3 Choices of Frequency Bands

4 Microwave Sensing

5 Orbits and Geometries

Levis, Johnson, Teixeira (ESL/OSU) Radiowave Propagation November 19, 2018 1 / 58

Introduction to Remote Sensing

The remainder of the course focuses on remote sensing

We define remote sensing as remote measurements of the naturalproperties of the Earth, e.g. atmospheric, oceanographic, land surfaceproperties, etc.

There are a variety of physical effects that one could use to remotelysense information (e.g. gravity, sounds, etc.) but we will focusexclusively on electromagnetic measurements

Remote sensing measurements can be performed from ground-based,air-borne, or space-borne platforms

Remote sensing also used in observing the moon and other planets,but our focus will be on the Earth


History of Remote Sensing

Utility of aerial photographs realized beginning with photos fromballoons beginning 1858

Aircraft photography began 1909

Development of radar in World War II began interest in microwaveremote sensing

Basic science of microwave measurements of natural media began inthe 1950’s and 1960’s (many fundamental measurements wereperformed at OSU)

Space age of 1960’s began spaceborne remote sensing

First dedicated remote sensing satellite mission: SeaSat 1978

Continuing international growth in area to present


Remote Sensing Applications

Many applications for this information: weather forecasting,oceanography, atmospheric science, polar monitoring, climate change,operations planning, hydrology, gas emissions monitoring, GISdatabases, etc.

Weather: Improving weather forecasts by incorporating more completeremote sensing information is one of NASA’s and NOAA’s key goals

Climate: Understanding how the global Earth system is changing isanother of NASA’s key Earth Science goals

Note weather and climate needs occur on differing time and spatialscales:

Weather requires higher resolution in space and time; current shortterm forecasts are of primary interest, high variability expectedClimate can tolerate lower resolution to study long term variations intime. Need high accuracy for small variations


Remote Sensing Applications

Remote sensing is desirable in difficult to access regions: poles, globaloceans, dense forests, etc.

A variety of geophysical information is of interest to support theseapplications:

Atmospheric temperature profiles, surface temperatureAtmospheric composition profiles (e.g. water vapor, ozone, trace gases,etc.)Atmospheric winds and surface windsCloud presence and properties, precip presence and propertiesLand surface properties: vegetation biomass, soil moisture, carbon flux,etc.Ice coverage, ice propertiesMany others!


Choices of Frequency Bands

Interaction of electromagnetic waves with the Earth environmentdepends strongly on frequencyGenerally, lower frequencies penetrate more into lossy media, andwaves interact significantly only with objects that are larger than afraction of the wavelengthThe Earth atmosphere/ionosphere has only a small affect on signalsfrom around 1 GHz to 5-6 GHz; these frequencies emphasize surfacepropertiesLower than 1 GHz are good for penetrating into the Earth surface;best to use from ground or airborne platforms since the ionospherecan cause significant effects from spaceFrom around 6 GHz up to around 100 GHz precip and atmosphere arepresent along with surface effectsAbove around 100 GHz, many resonances in atmospheric absorptioncan be used for sensing particular gases, etc, but clouds play a majorrole


1 10 100 10000.001

0.01

0.1

1

10

100

1000

Frequency (GHz)

Zeni

th a

tten

(dB)

0 km5 km10 km


Optical Remote Sensing

There are many remote sensing systems using optical or IRmeasurements to monitor both surface and atmospheric properties

These include both “passive” systems (i.e. only observe natural light)and “active” systems (transmit own light) such as LiDAR

“Hyperspectral” systems (i.e. having many optical frequencychannels) are also possible and are a developing technology

Advantages: fantastic real-aperture resolutions from space (cmscale) can be achieved; small sensor packages; many resonances ofdiffering components of the atmosphere in optical/IR region

Disadvantages: cloud cover can obscure desired measurements;passive sensors sensitive to solar illumination geometry; limitedpenetration into surface

Optical remote sensing is a discipline unto itself; not focus of ourstudies


Microwave Sensing

We will focus on microwave sensing (i.e. using frequencies fromaround 1-100 GHz)

Advantages: less affected (sometimes unaffected) by cloud cover;less sensitive (sometimes not sensitive) to solar illumination; canpenetrate into lossy media

Disadvantages: Longer wavelengths mean larger antennas neededfor good spatial resolution (but synthetic aperture radar overcomesthis); interactions with atmosphere/terrain can be complicated

Active microwave sensing transmits a signal and performs sensing bymeasuring what comes back

RADAR= radio detection and ranging

Passive microwave sensing measures the external thermal noisecoming from the environment

Microwave radiometry


Orbits and Geometries

Although much of what we will learn is applicable to ground, air, orspace-borne systems, much of the focus of microwave sensing is onsatellite sensors

This is because of the interest in obtaining global coverage in manyremote sensing applications

We studied geostationary orbits back in Chapter 5: altitudeapproximately 35,900 km results in an orbital period equal to theEarth’s rotation so that satellite stays “fixed” above a given groundpoint

Orbital mechanics can be very complicated; we will consider only asimplified circular orbit geometry



The “centrifugal force” on an object orbiting in a circle is

Fc =mv2

r= mω2r

where m is the satellite’s mass, v is its velocity amplitude (tangent tocircle), r is the radius of the orbit = a + h where h is the orbitaltitude, and ω is the orbital angular frequencyTo stay in fixed orbit, this must balance the gravitational force:

Fg = mg(a

r

)2

with g = 9.81 kg-m/(sec)2 the Earth gravitational constantIf these are equated we can find v and orbital period T

v =

√ga2

r

T =2πr

v= 2πr

√r

ga2


200 400 600 800 1000 12007

7.2

7.4

7.6

7.8

8

Altitude (km)

Velo

city

(km

/s)

200 400 600 800 1000 120080

90

100

110

120

Altitude (km)

Perio

d (m

inut

es)



Most microwave remote sensing satellites operate in “low earth orbit”(altitudes around 200-2000 km)

In one day, the satellite will orbit the Earth around 14 times

The inclination of an orbit is its angle with respect to the equatorialplane of the Earth

A zero degree inclination will circle only the equator: not good forglobal coverageA 90 degree inclination will go over the poles

Most microwave sensing missions use near-polar orbits

Many use “sun synchronous” orbits: local time remains nearlyconstant at any point of Earth as satellite overpasses

Desirable when solar illumination or diurnal temperature cycles matter

“Local time of the ascending node” is the local solar time at theequator when the satellite passes in a northward direction


Coverage

It is not possible for a single satellite to view the entire globesimultaneously; the “swath” of a satellite is used to describe howmuch of the Earth’s surface is covered in one “measurement”

Typically described in terms of a “swath width” (in km) that is sweptover the Earth as the satellite orbits

If we approximate the surface area of the Earth as 4πa2 = 510 trillionsquare km, and we have approximate 14 orbits per day over theEarth’s circumference, it appears a swath width of 910 km would besufficient to get global coverage in a day

This isn’t right for near polar orbits however because the Earth isrotating: too much coverage at poles and not enough at equator

Many Earth remote sensing missions obtain global coverage in 2-3days



1 Radar Remote Sensing

2 Basic Radar Properties

3 Radar Equation

4 Illuminated Areas and Sensor Types

5 Speckle


Radar Remote Sensing

A radar emits an electromagnetic wave and performs remote sensingby measuring the signal that is returned (“scattered”) from sceneobserved

Ionospheric sounder of Chapter 11 is a radar system

Information about scene can be obtained from:

Time delays of returns (to measure range)Amplitude of returns (to measure “strength” of scattering)Phase of returns (to measure velocity or other properties)Variations in amplitude/phase with polarization, position of radar, etc.

Radar system can be either monostatic (transmitter and receiverlocated at same place) or bistatic (located at different places)

We focus only on monostatic radar (much more common); thesesystems measure “backscattering” from scene


Radar Remote Sensing

In chapter 11, we learned that time delay can be scaled into rangethrough

R =c∆t

2

if it is assumed the wave propagates at the speed of light in free space

If we stay away from ionospheric effects, this is reasonable so timedelay determines range to target

Another important parameter is the range resolution of the radar:how fast can received signal vary in time?

This depends on the signal bandwidth used by the radar; larger signalbandwidths means faster rise times of signal which means finerresolution in time


Radar Resolution

Basic formula for radar range resolution is:

∆R =c

2B=

cτ

2

where B is the bandwidth used by the radar and τ is thecorresponding “pulse width”

Finer resolution in time means finer resolution in range

There are many other properties of the transmit signal (pulserepetition frequency, possibility of using “chirped waveforms”, etc.)that we will not consider here

Treated in more detail in ECE radar course


Radar Equation

Our studies of radar remote sensing will focus on the amplitude ofreturned power; to understand this we need to derive “the radarequation”

Begin by considering a radar transmitter transmitting PT wattsthrough an antenna of gain GT ; resulting power density at range R isthen

ST (R, θ, φ) =PT GT (θ, φ)

4πR2

This power density impinges on a target at range R; presence oftarget causes scattering of field back toward radar

Although the scattering process can be complicated, the scatteredpower density measured at range R from a finite-sized target can bedescribed by σRCS , the “radar cross section” (RCS) :

SR =ST (R, θ, φ)σRCS

4πR2


Radar Equation

The RCS is a function of target size, shape, composition, orientation,and polarization of incident and scattered fields; units of area

At the radar receiver (same location as transmitter), the receivedpower is

PR = Aeff SR =PT GTλ

2GTσRCS

(4π)3 R4

The radar equation enables us to compute received power given theradar (PT , GT , λ) and target (R,σRCS ) parameters

Can also rewrite in terms of effective area of antenna Aeff as

PR =PT A2

eff σRCS

4πλ2R4


Radar Equation

Note power falls off as R4 for a finite-sized target

Note we are assuming free space propagation here; Section 12.5 ofbook discusses the inclusion of non-free space propagation effects

Atmospheric gas, rain, direct-plus-Earth, terrain, etc. all can influenceradar performance

We will focus on systems for which a free space propagationassumption is applicable: Earth-to-space or Earth-to-air at largerelevation angles

We can also compare the received power to the receiver noise powerFkBT0B to determine received signal-to-noise ratios

SNR can be improved by integrating returns “coherently” overmultiple measurements: an average over N measurements multipliesSNR of a single measurement by N


Observing Natural Scenes

In remote sensing, we are typically observing “extended area” targets,not “finite sized” target

For extended area targets, scatterers fill up the entire antenna patten;causes scattering to have a different behavior

Returns from natural targets are therefore best described in terms ofan RCS per unit area σ0,RCS , also called the normalized RCS (NRCS)

The NRCS is defined as the RCS of an area extensive target dividedby the area illuminated by the radar (Aill ) when the RCS wasmeasured; note unitless

The area illuminated by a radar depends on the observing geometry;we need some relationships about antenna gain and “beamwidth” inorder to clarify this


Antenna Beamwidths

For a “high gain” antenna (i.e. gain greater than around 10 dB), thepattern is concentrated in a small angular range (the “main beam” ofthe antenna)Describe the main beam as having “3 dB beamwidths” in azimuth(θAz ) and elevation (θEl ): angles at which the radiated power densityis one half that at the maximumA useful approximation:

G ≈ 28000

θAzθEl≈ 4πAeff

λ2

with the beamwidths in degreesFor aperture-type antenna, Aeff can usually be approximated as theantenna geometrical areaIn radar, we frequently need to consider the “two-way” beamwidthssince we have an antenna pattern on transmit and receive; two-waybeamwidths can be approximated as 1/

√2 times the “one-way”

beamwidths θAz and θEl


Antenna Main Beam


Differing cases of illuminated areas

For a radar looking straight down (“altimeter”), the illuminated areaat range R is determined by the projection of the antenna patternonto the ground:

Aill =π

8(RθAz,rad ) (RθEl ,rad )

The corresponding RCS in the radar equation is

σRCS = σ0,RCSAill =πR2

8(θAz,radθEl ,rad )σ0,RCS

which grows as R2!


Differing cases of illuminated areas

For a “side-looking” radar, the illuminated area is determined in therange direction by the range resolution ∆R, and in the cross rangedirection by the antenna pattern:

Aill =

(RθAz,rad√

2

)(cτ

2secψ

)where ψ is the elevation angle

In this case the area grows only as R and not R2

For a “synthetic aperture” radar, the illuminated area is determinedby the range resolution ∆R and a factor proportional to the radarantenna length in the “along track” direction

Does not vary with range in this case


Types of remote sensing radars

Airborne and spaceborne radars used for remote sensing fall into threeprimary categories

Altimeters: Radars that point straight down

Footprint size (spatial resolution) determined by antenna pattern,typically around 10 km or poorer from spaceRange resolution used to measure heights of surfacesUsed to measure heights of sea, land, ice, ...Many examples: Topex-Poseidon, Jason-1 , Jason-2, ...

Scatterometers: Side-looking, cross-range resolution determined byantenna pattern

Footprint sizes typically 10 km or more from space (lots of multilookingin range)Focus is on NRCS measurement of surfaceUsed for sea surface winds, soil moisture, NRCS, ...Many examples: QuikScat, Aquarius, SMAP, ...


Types of remote sensing radars

Airborne and spaceborne radars used for remote sensing fall into threeprimary categories

Synthetic Aperture Radar: (SAR) Side-looking, uses “focusing” toachieve high spatial resolution

Spatial resolution on the order of 10-100 mFocus is on NRCS “mapping” of surface; also interferometry,polarimetry to determine surface topography and detailed NRCSpropertiesUsed for many land surface studies; global land coverage typically onlymonthlyMany examples: PALSAR, SIR-C, TerraSAR, ...


Speckle

So far we’ve been treating the NRCS σ0,RCS as a deterministicquantity; however it in fact is “random” since it arises from scatteringcontributions from many individual objects

For example, leaves of trees, blades of grass, etc.

This randomness is called “speckle”

Since the scattering arises from a sum of many similar contributions,the received fields can be modeled as Gaussian random variables (asin the Rician and Rayleigh fading models)

Since there is typically no deterministic component of the return, it isgenerally expected that speckle returns for a single measurementfollow Rayleigh statistics (i.e. returned power has an exponential pdf)

Models for the NRCS in differing situations describe the expectedvalue of the NRCS (e.g. like the Pf parameter in the Rayleigh fadingpdf from Chapter 8); a given measurement may differ significantlyfrom the expected value


Combating Speckle

We learned in Chapter 8 that Rayleigh fading has a high degree ofvariability, e.g. mean and standard deviation of received power areequal

This makes single pulse measurements very noisy when trying todetermine the NRCS

Combat this by averaging over many independent measurements or“looks”; almost all remote sensing radars perform “multi-looking” toimprove NRCS determination

If we average over a large number of independent measurements, wecan assume that the resulting average has a Gaussian pdf instead ofexponential (central limit theorem)


Statistics after multi-looking

After multilooking, we can describe received powers from naturalscenes as having a Gaussian distribution with mean power µP equalto that predicted by the radar equation using the expected value ofthe NRCS

The variance of the received power σ2P is equal to the mean power

squared divided by the number of independent looks averaged

Equivalently the standard deviation of the power after multi-looking isequal to the mean received power divided by the square root of thenumber of looks

If our radar takes the received power and calibrates it into a measuredNRCS, we can also assume that the measured NRCS is Gaussian withmean equal to the mean NRCS and standard deviation equal to themean NRCS divided by the square root of the number of looks


How to get independent looks?

We have two kinds of “noise”: thermal noise from receiver (alwaysindependent on every pulse), and speckle noise of scene

Because speckle is caused by scatterers comprising natural terrain, itwill not vary from pulse to pulse

Averaging over multiple pulses doesn’t help speckle unless differingterrain samples are used

“Multi-looking” in radar typically means averaging overmeasurements in different illuminated areas

Altimeter: average in time (=space) as radar movesSide-looking: average in time or degrade range resolutionSAR: degrade spatial resolution

Two types of integration: short term “coherent” to improve SNR (i.e.affects only thermal noise), and “multi-looking” to reduce specklevariations



1 Information in NRCS

2 Permittivity and Roughness

3 Typical scattering behaviors

4 Two NRCS models for soil surfaces


Information in NRCS

Although radar measurements can provide information on range,velocity, and other features, our studies will focus on the informationin the backscattered NRCS

We will focus primarily on “bare” surfaces in what follows, i.e.neglecting vegetation

Bare surface backscatter is influenced by surface permittivity androughness (surface parameters), as well as incidence angle, frequency,and polarization (radar parameters)

Permittivity: Backscatter at all angles generally increases withsurface relative permittivity

We need models for the permittivity of natural media as function ofgeophysical parameters

We consider two examples: sea water and soil


Sea water relative permittivity

Sea water permittivity in the microwave region is a function offrequency, salinity, and temperature

Salinity is specified as the number of grams of salt in one kilogram ofsea water; units are called “practical salinity units” (psu)

Function sea eps.m computes sea relative permittivity given theseparameters

Sea water permittivity in the microwave region is always large, sochanges in permittivity have relatively weak effect on backscatter

Dependencies on salinity and temperature used in microwaveradiometry (we’ll study later) for salinity sensing


Sea water relative permittivity examples

0 10 20 3040

50

60

70

80

Temperature (C)

Rela

tive

Perm

ittivi

ty

1.4 GHz, Salinity 30 psu

Real−Imag

0 5 100

50

100

150

Frequency (GHz)

Rela

tive

Perm

ittivi

ty

10 C, 30 psu

Real−Imag


Soil relative permittivity

Soil relative permittivity in the microwave region is a strong functionof moisture content; moisture content is defined by mv , the fractionof water volume within a given soil volume

Several other parameters: frequency, temperature (C), salinity ofwater (psu), fraction of sand and clay making up soil (the soil“texture”), and the “bulk density” of the soil after drying(grams/cubic cm)

Function soil eps.m computes soil relative permittivity given theseparameters

Strong variation in permittivity with soil moisture offers potential toremotely sense soil moisture


Soil relative permittivity examples

0 0.1 0.2 0.3 0.40

10

20

30

40

Soil moisture (%/%)

Rela

tive

Perm

ittivi

ty

1.7 g/cm3,s=0.51,c=0.13,1.26 GHz, 10 C

Real−Imag

0 5 100

2

4

6

8

10

Frequency (GHz)

Rela

tive

Perm

ittivi

ty

mv=0.1,1.7 g/cm3,s=0.51,c=0.13, 10 C

Real−Imag


Describing roughness

Surface roughness is generally characterized in terms of a powerspectral density W (kx , ky )

If we think of a surface profile as a function z = f (x , y), we can writethe Fourier transform of f (x , y) as F (kx , ky )

This is a Fourier transform between space (x , y) and spatial frequency(kx , ky )

W (kx , ky ) is then defined as⟨|F (kx , ky )|2

⟩W describes how the surface roughness is distributed over variouslength scales

The surface rms height hr can be determined from W (kx , ky )

Surface roughness effects vary with hr/λ; if this is < 0.01 surface isnearly “flat”, if not surface is “rough”


A model for W (kx , ky): Soil surfaces

Natural surface can be complicated, so creating models for W (kx , ky )is difficult

A commonly used model for land surfaces is an “exponentialcorrelation function”:

W (kx , ky ) =h2

r L2

2π

(1 + (kρL)2

)−1.5

Here kρ =√

k2x + k2

y ; this means that the surface is azimuthally

symmetric; statistics are same no matter how surface is rotated inazimuth

Beyond the rms height, the spectrum has one more parameter: L, thesurface correlation length. Describes average length scale over whichsurface varies.

Difficult to measure L so a common approximation is L ≈ 10hr


NRCS variations with incidence angle androughness

A surface that is nearly flat (i.e. hr/λ small) will cause scatteringmainly in the specular direction (Chapter 6)

NRCS should be small unless observing at nadir

As surface roughness becomes larger, surface scatters less energy intospecular direction and more energy into other directions

NRCS near normal incidence get smaller as roughness increasesNRCS at oblique incidence gets larger as roughness increases


Smooth vs. rough surfaces


Typical NRCS behaviors vs. incidence angle


Typical Sea surface NRCS behaviors versusincidence angle


NRCS Model 1: Small Perturbation Method (SPM)

The SPM is an approximate model for scattering from a roughsurface; captures important polarization dependencies of surfacescattering

In radar, we need to describe both the incident polarization and thereceived polarization; typical to use h and v notations forperpendicular and parallel cases from Chapter 6

Notation is to add hh, vv , or hv as subscripts to the NRCS; hh andvv are called “co-pol” and hv or vh is call “cross-pol”

SPM is applicable when the rms height compared to the wavelengthis small (hr/λ < 0.05) and for backscatter at incidence angles fromaround 20 to 80 degrees


SPM Equations

SPM predicts that:

σ0,RCS ,αβ = 16πk40 cos4 (θi ) |gα,β|2 W (2k0 sin θi , 0)

where k0 = 2π/λ is the radar wavenumber and θi is the incidenceangle

The g quantities vary with polarization:

ghh =cos θi −

√ε− sin2 θi

cos θi +√ε− sin2 θi

= Γhh (θi )

gvv =(ε− 1)

[ε(1 + sin2 θi

)− sin2 θi

][ε cos θi +

√ε− sin2 θi

]2ghv = gvh = 0

Note scattering at a particular angle is sensitive to only one value inthe roughness power spectral density: this is called “Bragg scattering”


NRCS Model 2: Dubois Empirical Model

The SPM is usually applicable for natural soil surfaces only atfrequencies around 1 GHz or lowerNumerous empirical models of the soil surface NRCS have beencreated; here we consider the model of P.C. Dubois, et al (IEEETrans. Geosc. Rem. Sensing, 1995)Created through analysis of ground-based data and tested withsatellite observationsDescribed as applicable for frequencies from 1.5 to 11 GHz, soilmoisture up to 0.35, incidence angles 30 to 65 degrees, rms heightless than 0.4 wavelengthsGiven εR = Re {ε}, and λcm the wavelength in cm, the model states:

σ0,RCS ,hh =[10−2.75+0.028εR tan θi

] cos1.5 θi

sin5 θi(k0hr sin θi )

1.4 λ0.7cm

σ0,RCS ,vv =[10−2.35+0.046εR tan θi

] cos3 θi

sin θi

(k0hr sin3 θi

)1.1λ0.7

cm


SPM/Dubois comparison (NRCS in dB)


Radar sensing of soil moisture

Note previous results show that NRCS increases with both soilmoisture and surface roughness

Usually we don’t know the roughness, but we do know the frequencyand incidence angle

From measurement of HH and/or VV NRCS we want to “retrieve”information on soil moisture; need to cancel out roughness effectsomehow or else retrieve roughness too

Measurements of HH and VV offer possibility of retrieving both!

Assuming that multi-looked HH and VV NRCS measurements areGaussian random variables, an “optimum” method for retrieving soilmoisture can be formulated


Soil moisture retrieval

Result is that we simply find the values of mv and hr that minimizethe differences between model and measurements:

(σ0,HH,Measured − σ0,HH,Modeled )2 + (σ0,VV ,Measured − σ0,VV ,Modeled )2

This can be done just by making a very fine table of the model vs. hr

and mv and subtracting the observed data; note assumes that modelis “correct”

Performance should be expected to improve with more multi-looking(at cost of spatial resolution)

Also should be expected to improve in cases where the NRCS variesmore as the soil moisture varies

Homework problem goes through the steps of a retrieval

Similar methods are planned for NASA’s Soil Moisture Active/Passive(SMAP) mission, but will also include a time series of measurements


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