GEN52

Cover image: Multitemporal ERS-1 SAR image of Rugen Island and North MecklenburgBaltic Coast, Germany (5 September 1991, 23 September 1991 and 29 September 1991as Blue, Green Red). The size of the area is 55.6 km x 55.6 km. Colours are mainly rela­ted to changes in surface roughness due to differences in soil preparation and crops gro­wing in the fields during this period of the year: white fields are potatoes, yellow aremeadows and winter wheat, light green are unharvested corn. Pasture is very dark green.The ERS-1 image was acquired at Fucino and processed by ESA/Earthnet.

RSC Series No 67

RADAR ThfAGERY: THEORY AND INTERPRETATION

LECTURE NOTES

REMOTE SENSING CENTRE

RESEARCH AND TECHNOLOGY DEVELOPMENT DIVISION

AGRICULTURE DEPARTMENT

FOOD AND AGRICULTURE ORGANIZATION OF THE UNITED NATIONSRome, 1993

The designations employed and the presentation of material in thispublication do not imply the expression of any opinion whatsoever onthe part of the Food and Agriculture Organization of the UnitedNations concerning the legal status of any country, territory, city orarea or of its authorities, or concerning the delimitation of its frontiersor boundaries.

© FAO 1993

All rights reserved. No part of this publication may be reproduced, stored in aretrieval system, or transmitted in any form or by any means, electronic, mechani­cal, photocopying or otherwise, without the prior permission of the copyright owner.Applications for such permission, with a statement of the purpose and extent of thereproduction, should be addressed to the Director, Publications Division, Food andAgriculture Organization of the United Nations, Viale delle Terme di Caracalla,00100 Rome, Italy.

AUTHORS

These lecture notes were prepared by J.F.Dallemand, ESA/FAO consultant,J.Lichtenegger (ESA-ESRIN, Frascati), R.K.Raney (Canada Centre for Remote Sensing,Ottawa) and R.Schumann (ESA-ESRIN, Frascati). This document is partly based on theFAO/ESA publication "Principles of radar imagery" (FAO Remote Sensing Centre Series No46, Rome, 1989) and on other remote sensing publications on imaging radars theory orapplications listed in the bibliography.

ACKNOWLEDGEMENTS

The authors of this document wish to thank Mr J.Arets, Mrs K.Barbance, MrsS.Cheli of the International Affairs Department of the European Space Agency (ESA), andMr Z.D.Kalensky, Chief of the Remote Sensing Centre of United Nations Food andAgriculture Organization (FAQ) for their support and encouragement in the preparation ofthis work. Thanks are due to H.Palmer, C.Lieber, J.S.Latham (FAQ Remote Sensing Centre)for the text revision and illustrations preparation.

TABLE OF CONTENTS Page

I. INTRODUCTION 1

1. Advantagesof microwavetechniques 12. Historicalbackground 2

Il. RADAR WAVES ANDBACKSCATTERING COEFFICIENT 3

1. Electromagneticradiationused by imagingradars 32. All-weathercapabilityof imagingradars 53. Reflectivitymeasuredby imagingradars 74. Parameters that affect radar backscatter 7

4.1 Influenceof frequency 74.2 Influenceof polarization 74.3 Influenceof roughness 94.4 Influenceof incidenceangle 94.5 Influenceof moisture 10

ID. BASIC PRINCIPLES OF IMAGING RADARS 11

1. Introduction 11

2. Real ApertureRadar 13

2.1 Range resolution 142.2 Azimuthresolution 16

3. SyntheticApertureRadar 17

3.1 SARPrinciple 173.2 SARProcessing 19

IV. ELEMENTS OF RADAR IMAGERY INTERPRETATION 21

1. Radiometry 21

1.1 Tone 211.2 Speckle 221.3 Specklefilters 221.4 Commentson radar image interpretation 231.5 Texture and image analysis 24

vm. BIBLIOGRAPHY 86

2. Image geometry 26

2.1 Specific aspects of image geometry2.2 Backscatter and local incidence angle2.3 Shadowing, layover and foreshortening2.4 Radar image geocoding

26283234

V. APPLICATIONS 38

1. Forestry2. Land use mapping and crop identification3. Geology and geomorphology4. Interferometry5. Hydrology6. Oceanography, sea ice and coastal zones studies7. Environmental monitoring

38424545464748

VI. CONCLUSIONS 52

VII. GWSSARY 69

ANNEXES 92

1. Characteristics of Shuttle-borne Imaging Radars2. Characteristics of long-term Imaging Radars3. ERS-1 presentation4. Spacebome SAR data availability

929394102

LIST OF FIGURES Page

Fig.1Fig.2Fig.3Fig.4Fig.5Fig.6Fig.7Fig.8Fig.9Fig.10Fig.11Fig.12Fig.13Fig.14Fig.15Fig.16Fig.17Fig.18Fig.19Fig.20Fig.21Fig.22Fig.23Fig.24Fig.25Fig.26Fig.27Fig.28Fig.29Fig.30Fig.31Fig.32Fig.33Fig.34Fig.35Fig.36Fig.37Fig.38Fig.39Fig.40Fig.41Fig.42

Electromagnetic spectrum and microwave spectrum 4Transmittivity in the microwave spectrum 6Backscatter of natural targets in band X, C and L 8Examples of surface-scattering patterns 10Elements of an imaging Radar system 11Principle of an imaging Radar 12Pulse ranging 13Effect of pulse length on range resolution 14Along-track location 16Geometry of a synthetic aperture array 17Example of Doppler frequency 18Different size of resolution cell and inter-sample spacing 20Relation between image tone and radar backscatter 21Imaging radar geometry 26Image geometry of radar and aerial photography 26Two different types of display 27Incidence angles for aircrafts and spacecrafts SARs 28Local incidence angle and incidence angle 29Relation between image tone, terrain slope and image scale 30Comparison of imaging geometries of spaceborne and airborne SARs 31Relation between radar shadow and range 32Radar shadow and relief displacement for large incidence angle 32Layover 33Principle of SAR image geocoding 36System for terrain correction of SAR data 37Radar backscatter contributions of a forest scene 38Summary of frequency requirements for five science disciplines 50Summary of polarization requirements for five science disciplines 51Effect of the wavelength used 53Effect of polarization used 54Example of speckle (Agricultural zone of Flevopolder, Netherlands) 55Example of SAR image geocoding and terrain correction 56Airborne SAR image (Pointe Noire, Congo) 57Airborne SAR image (Keddah, Malaysia) 58Use of texture analysis for forest classification 59ERS-1 SAR image of Brazilian Amazonia 60Optical/microwave colour composite (Grombalia, Tunisia) 61Optical/microwave colour composite (Songkhla, Thailand) 62Multitemporal ERS-1 SAR image (Nitra, Slovakia) 63Example of filtered SAR image (Nitra, Slovakia) 64Comparison between Landsat TM and SIR-A images (Algeria) 65ERS-1 image of English Channel (Isle of Wight) 66

Tab.1: Three Texture components of a radar imageTab.2: Summary of SAR applications results in tropical forest areas

2540

Fig.43Fig.44Fig.45Fig.46Fig.47Fig.48

Venice multitemporal ERS-1 imageUse of ERS-1 SAR image for oil spill detectionERS-1 reference orbitERS-1 SAR Image Mode geometryERS-1 orbit scenarioActivities of ERS-1 PAFs

676894979899

LIST OF TABLES

Active Microwave InstrumentAlong Track Scanning Radiometer and Microwave SounderAdvanced Very High Resolution RadiometerComputer Compatible Tape"Centre National d'Etudes Spatiales"Digital Terrain ModelEarth Observing SystemEnvironmental Research Institute of MichiganEuropean Remote Sensing SatelliteEuropean Space AgencyEuropean Space Research InstituteFood and Agriculture OrganizationGround Control PointGeographic Information SystemGlobal Positioning SystemHigh-Density Digital TapesHorizontally Polarized Transmit, Horizontally Polarized ReceiveHigh Resolution VisibleHorizontally Polarized Transmit, Vertically Polarized ReceiveInstitute of Electrical and Electronics EngineersInternational Geoscience and Remote Sensing Symposium"Institut Geographique National"InfraredInternational Training CenterJapanese Earth Resources Satellite-IJoint Research Center (EEC)Land SatelliteLow Bit Rate data (ERS-1)Multispectral ScannerNational Aeronautics and Space AdministrationNational Oceanic and Atmospheric AdministrationPrecise Range and Range-rate EquipmentPulse Repetition FrequencyRAdio Detection And RangingSynthetic Aperture RadarShuttle Imaging Radar AShuttle Imaging Radar BShuttle Imaging Radar CSide-Looking Airborne RadarSide-Looking Radar"Systeme pour l'Observation de la Terre"Tracking and Data Relay Satellite SystemThematic MapperUniversal Tranverse Mercator ProjectionVertically Polarized Transmit, Horizontally Polarized ReceiveVisibleVertically Polarized Transmit, Vertically Polarized Receive

LIST OF ABREVIATIONS AND ACRONYMS

AMIATSR-MAVHRRCCTCNESDTMEosERIMERS-1ESAESRINFAQGCPGISGPSHDDTHHHRVHVIEEEIGARSSIGNIRITCJERS-1JRCLandsatLBRMSSNASANOAAPRAREPRFRadarSARSIR-ASIR-BSIR-CSLARSLRSPOTTDRSSTMUTMVHVISvv

••••••••••••••••••••.......................... ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~--

1

I. INTRODUCTION

Since July 1972, date of the launch of the first ERTS (Earth ResourcesTechnology Satellite), satellite remote sensing has become more and more operational, evenat semi-detailed scales. Nevertheless, for applications in agriculture and renewable resources,one of the factors limiting an operational use of spaceborne images has been the problem ofavailability of data. This point is very important in agriculture when several images arenecessary to allow an optimum differentiation between land use classes. Due to atmosphericconditions and cloud cover, multitemporal land use mapping is often difficult and sometimesimpossible. This problem has been partially overcome by ;- SPOT off nadir viewing capability that increases the probability of acquisition of an imagefor a given area with partial cloud cover,

- combined analysis of SPOT and Landsat data.However, especially in the case of tropical zones, there is a pronounced lack of information.Radar imagery which is independant of solar illumination and atmospheric conditions can bean extremely important source of geographic information for developing countries in tropicalzones that often have a limited thematic cartography.

The usefulness of airborne radar images in the tropics was first demonstratedduring the early seventies by such important projects as NIRAD (Nigeria), Radam (Brazil)and Proradam (Colombia). Use of spaceborne radar was not demonstrated until Seasat in1978. Further experiments were made with Shuttle Imaging Radars (SIR-) A and Bin 1981and 1984 respectively. Long term radar missions designed for civilian applications startedwith the launches of the ALMAZ-1 in 1987, ALMAZ-2 in 1991 (former USSR) and theEuropean ERS-1 in 1991. ERS-1 continues to provide data from its microwave instruments,including an imaging radar. Seven months after the European launch, Japan launched JERS-1, with both microwave and optical imaging systems, in another long-term mission. Variousother microwave missions are under preparation, including RADARSAT in Canada, POEM(Polar orbiting Earth Mission) by ESA, and a continuation of the SIR series of short durationexperimental missions by the USA with support from Germany and Italy. Thanks to thecontinuation of present Earth-observation optical programmes (Landsat, SPOT), newdevelopments will occur in the field of combined analysis of optical and microwave data.

1. Advantages of microwave techniques

In relation to optical sensors, interest in imaging radars is related to:

- small dependance on atmospheric conditions: use of imaging radars is independant of solarradiation so that imagery can be obtained at any time during day/night and through 100%cloud cover, although tropical rain events may be troublesome. '- control of the emitted electromagnetic radiation: power, frequency, polarization.- ability to choose a depression angle and an azimuth angle to meet the objectives of thestudy.- access to parameters describing properties of targets different from those available usingoptical sensors.- possibility to obtain information on subsurface features, when low soil density and moisturepermit.

2

2. Historical background

As with visible and infra-red remote sensing, imaging radars used for Earthobservation result from the efforts of both civilian and military laboratories. Initiallyrestricted to military applications, imaging radars have now become a useful tool for suchcivilian applications as natural resources applications. The transmission of electromagneticwaves and their reflection from various metallic and non-metallic objects was firstdemonstrated in 1886 by Hertz. In 1903, Hulsmeyer demonstrated the possibility of usingradar for ship detection. Ground-based pulse radars were developed in the 1920s and 1930sfor detecting ships and aircraft by Taylor and his colleagues at the U.S. Naval ResearchLaboratory. After an initial use of continuous-wave systems (1922), the development of pulseradars started. During 1930's, Sir Watson-Watt developed a practical aircraft detection radarsystem.

The first imaging radars were used during World War II, with the developmentof the Plan Position Indicator (PPI) as an aid to nightime bombing. In the case of the PPI,the antenna beam was rotated through 360 degrees to produce a circular image of the groundwhen mounted on an aircraft, or of the airspace when based on the ground. Reflected signalswere displayed on a cathode ray tube (CRT). Radar development was classified during theWar and for that reason codes were attributed to the wavelengths used, such as L-band, C­hand or X-band. After World War II, Side-looking airborne radars (SLAR) were developedfor terrain surveillance. In this case, the radar illuminates a strip of terrain parallel to theflight path. Backscattered signals are recorded on a film using a CRT. The disadvantage ofthis kind of real aperture radar is that unrealistic antenna length would be needed to achievea high azimuth resolution. This problem was resolved by the U.S. military in the 1950sthrough the development of Synthetic Aperture Radar (SAR). A SAR is also a side-lookinginstrument, but it uses clever signal processing to provide a higher azimuth resolution thatis independant of range and does not require an increase in the real antenna size.

SLAR and SAR techniques were declassified in the late 1960safter which civilianremote sensing missions started in North America and in tropical regions. Research iscurrently being conducted on multi-frequency and multi-polarization imaging. With ERS-1in 1991, long term earth-observation radar missions started, with a growing interest fromspecialists in oceanography, geology as well as land resources applications. Assessment andmonitoring of the extent of natural disaters, such as floods and wind storms, are examplesof growing importance for application of satellite imaging radar systems.

3

II. RADAR WAVES AND BACKSCATTERING COEFFICIENT

1. Electromagnetic radiation used by imaging radars: microwaves

The basic principle of imaging radars is to emit electromagnetic radiation towardsthe earth surface and to record the quantity and time delay of energy backscattered. Thisinformation is carried by the electromagnetic waves defined by:

- direction of propagation,- amplitude,- wavelength,- polarisation,- phase.

The relation between frequency (f) and wavelength (A)can be shown as:

f = CIA f = frequency in Hertz [cycles per second (Hertz)] (1 GHz = H>9 Hz)

c = 3 x 108 [metres per second]

A = wavelength [metres]

For earth observation, the wavelengths used are situated usually between 1 centimeter and1 meter.Figure 1 presents the electromagnetic spectrum and the letter designations of the microwaveportions commonly used.

4

EE::&.::&."' r-,ci ci''

• <( • <( E'(")

• <( o<( ::&.'I E•<( 0 'I ::&.0 (") 0 0 f"1t I

ci ci (") (") (") QI I (")

'

WAVELENGTHE EE ::&. E E E E

::&. u E E E ""' ""'0 (") u u E 0""'

00 0 ci g 0 0 0 0(") (") (") (") (") (") (") M (")

WAVELENGTH ().)

GAMMA_J _J-RAY'----- X RAY L_ UV

10.

L L L'.'.'.'_' , vhf hf , ml IfRADAR ~DIOJ.BANDS~

INFRARED MICROWAVE-'MEDIUM -~ 1 GHz

AUDIO AC

lQ I J 101010111 10'10 10 10

~FREQUENCY, Hz

ABLACK REPRESENTSATMOSPHERICATTENUATION

~~~-'-~C~OSMtCNOISf'RAIN FOG RAOIO

ATTENUATION ASTRONOMY

INTERACTION MECHANISMSOR PHENOMENA DETECTED

DISSOCIATION

HEATING

BAND p LI s=jc~rI

K-t-O+-V+- W

B I o.39 I1.55 13.9 5.75110.9 136 46 56

FREQUENCY (GHz) 0.3 1.0 3.0 10.0 30.0 100.0

WAVELENGTH (cm) 100 30 10 3 1 0.3

TYPICAL VALUES 70 23 10 5.6 3p L s c x Ka

Fig.1: Electromagnetic spectrum A and microwave spectrum B (from ASPRS)

5

2. All-weather capability of imaging radars

An imaging radar is an active system: it provides its own illumination source (themicrowave radiation it emits) and does not need an external illumination source (sunlight forexample) as is the case for aerial photography, SPOT and most channels of Landsat ThematicMapper. A radar can obtain images at any time of the day or night. In addition, microwavescan easily penetrate clouds since atmospheric attenuation is null or relatively low in the caseof microwaves (A > 3 cm). This all-weather capability is one of the main advantages ofimaging radars in relation to optical sensors. Figure 2 presents the variation of transmittivityin function of microwave frequencies.

3. Reflectivity measured by imaging radars

An imaging radar produces an image in which the digital number at each pixelposition is determined by the strength of the radar signal reflected from the correspondinglocation in the scene. Digital image files usually are created using the square root of power,since less dynamic range is required for data in this form. For technical purposes, radarperformance may be analyzed in terms of the power of the reflected signals. The powerreceived from each radar pulse transmitted may be expressed in terms of the physicalparameters of the radar and illumination geometry through the Radar Equation. The receivedpower is a function of radar wavelength and wave polarization, among other variables.Images are built by integrating over many pulses, and the mean power in the image from aterrain patch may be derived from the single pulse radar equation. For users, the importantrelationship is the SAR Image Radar Equation, which has the form:

<PR> = CT G2(0;) dX dR <flR3 sin(O;)

where

<PR> = Mean received power, per pixel

(Ji = Incidence angle

G(O;) = One-way antenna power gain

R = Range (distance) between antenna and reflecting terrain patch

CT = Constant of the system, including transmitted power, wavelength,various gains and losses, etc.

dx = Pixel spacing in the azimuth direction

dR = Pixel spacing in the slant range direction

<fl = Average reflectivity, per unit area, of the scene

6

This expression includes the most important parameter dependencies of concern to a user,which are incidence angle and range. The signal level is very sensitive to the antenna gainpattern, which often is the limiting factor in system brightness calibration.

u° is the radar cross section per surface unit of the target. u° is commonly called the radarbackscattering coefficient and is a parameter characterising the target. u0 is dimensionlessbut, as associated to a very high dynamic range(of the order of 1<>5), it is generally expressedin decibels:

u0 dB = 10 log u0

1.0

0.9

0.8

0.788 0.6

~0.5

>.!::::>c;; 0.4Clle

0.3Cllcf!I- 0.2

0.1

!..········-···········..···········-··················I

i

'Tropical

/

1(T0=303K., Mv=5.1Kgcm-2)

. Standardf (T0= 288K, Mv= 2.53g cm-2)-lf PolarI (T0 = 249 K, Mv= 0.24 s cm-2)II'i!

r::I ' '\

\\I TropicalI r, II I \'iI I \I ',

\\\

120 160 200 ·240 280

Frequency (GHz}

Infra-redI IH

Visible

ULF LF HF ~HF

Band I Microwaves

1 Hz 103Hz 106Hz 109 HzFrequency 1 KHz 1 MHz 1 GHz

Wavelength 300km 300m 30cm

1012 Hz1 THz

0.3mm 0.3 nm

Fig.2: Transmittivity in the microwave spectrum (From Raney, CCRS)

7

4. Parameters affecting radar backscatter

The radar backscattering coefficient a0 provides information about the imaged surface. It isa function of:- radar observation parameters (frequency f, polarisation p and incidence angle of theelectromagnetic waves emitted)- surface parameters (roughness, geometric shape and dielectric properties of the target).

4.1 Influence of frequency

The frequency of the incident radiation determines:- the penetration depth of the waves for the target imaged,- the relative roughness of the surface considered (see 11.4.3).

•Penetration depth tends to be longer with longer wavelengths. If we consider theexample of a forest, the radiation will only penetrate the first leaves on top of the trees ifusing the X-band (A.= 3 cm). The information content of the image is related to the toplayer and the crown of the trees. On the other hand, in the case of L-band (A.= 23 cm), theradiation penetrates leaves and small branches; the information content of the image is thenrelated to branches and eventually tree trunks.

The same phenomena apply to various types of surfaces or targets (Fig.5). Butit should be noted that:- penetration depth is also related to the moisture of the target (see II.4.5).- microwaves do not penetrate water more than a few millimeters.

4.2 Influence of polarization

Polarization describes the orientation of the electric field component of anelectromagnetic wave. Imaging radars can ·have different polarization configurations.However, linear polarization configurations HH, VV, HV, VH are more commonly used.The first term corresponds to the polarization of the emitted radiation, the second term to thereceived radiation, so that XHvrefers to X band, H transmit, and V receive for example.

In certain specific cases, polarization can provide information on different layersof the target, for example flooded vegetation. The penetration depth of the radar wave varieswith the polarization choosen. Polarization may provide information on the form and theorientation of small scattering elements that compose the surface or target. More than onebounce of backscattering tends to depolarize the pulse, so that the cross polarized return inthis case would be larger than with single bounce reflection.

8

VEGETATION

DRYALLUVIUM

''''",,,,,~::~~):)i~q:~:::~:~:}::~:::E:~

==II='' II »» //­= , , ,., /,- ~ // ~ \\ . II ~ ,,"///////T \: ~ h //,/I///,'////////////,. .v..../////////////////////////////////////

,//////////////////////////.//////////////////////////"/////////////////////////,'/////////////////////////.///////////////////////////////////////,/////////////

,,,,,::·:·:·:·::::::~::::~:i:~.~~::::::.:/; 1- ,''""~" s- ,, '·t'""'.~·..·:

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,,,,,'''-)...~.;::::::::;,::::_)-:~~''::::;:::::;:::·:'.,, \ ,, !~"'-.~"""'--·- - .'l // "==' ~~/,~ ~~1;11;;

"" ~ 1- "-,"-.._,;,""'~.::;, // ,,//- ''!\: ..,.////;"//T>. . .... \\ ///////

/////,////////////////////.

/////////////////////////~

//////////////////////////'/////////////////////////'/////////////////////////./////////////////////////,//////////////////////////

GLACIERICE

L-BAND23 cm

C-BAND6 cm

X-BAND3 cm

Fig.3: Backscatter of natural targets in band X, C and L (from NASA)

9

4.3 Influence of roughness

Roughness is a relative concept depending upon wavelength and incidence angle.A surface is considered "rough" if its surface structure has dimensions that are comparableto the incident wavelength.

According to the Rayleigh criterion, a surface is considered:

- smooth if h <s cos e

- rough if h >s cos e

where h mean height of surface variationsA. wavelengthe incidence angle

An example of the effect of surface roughness can be observed in the zones ofcontact between land and water. Inland water bodies tend to be relatively smooth, with mostof energy being reflected away from the radar and only a slight backscatter towards theradar. On the contrary, land surfaces tend to have a higher roughness. Water bodiesgenerally have a dark tonality on radar images, except in the case of wind-stress or currentthat increase the water surface roughness, which provokes a high backscatter. In themicrowave region, this difference between respective properties of land and water can beextremely useful for such applications as flood extent measurement or coastal zones erosion.Figure 4 illustrates the range of backscatter from natural rough surfaces.

4.4 Influence of incidence angle

The incidence angle is defined by the angle between the perpendicular to theimaged surface and the direction of the incident radiation. For most natural targets,backscatter coefficient a0 varies with the incidence angle.

Experimental work was conducted by Ulaby et al. (1978) using five soils withdifferent surface roughness conditions but with similar moisture content. It appeared thatwhen using the L-band (1.1 GHz) the backscatter of smooth fields was very sensitive to nearnadir incidence angles; on the other hand, in the case of rough fields, the backscatter wasalmost independent of the incidence angle choosen.

INCIDENTWAVE.

REFLECTEDWAVE

10

4.5 Influence of moisture

The complex dielectric constant is a measure of the electric properties of surfacematerials. It consists of two parts (permittivity and conductivity) that are both highlydependent on the moisture content of the material considered. In the microwave region, mostnatural materials have a dielectric constant around 3 to 8, in dry conditions. Water has a highdielectric constant ( = 80), at least 10 times higher than for dry soil. As a result, a changein moisture content generally provokes a significant change in the dielectric properties ofnatural materials; increasing moisture is associated with an increased radar reflectivity.The electromagnetic wave penetration in an object is an inverse function of water content.In the specific case of vegetation, penetration depth depends on moisture, density andgeometric structure of the plants (leaves, branches).

(a)SMOOTH SURFACE

(bl

BACK SCATTEREDCOMPONENT

SLIGHTLY ROUGH SURFACE

(C)ROUGH SURFACE

Fig.4: Examples of surface-scattering patterns

11

III. BASIC PRINCIPLES OF IMAGING RADARS

1. Introduction

Among microwave systems, we should distinguish between:

- radar (RAdio Detection And Ranging) (active system). systems for ships and airplanes monitoring. imaging systems (real aperture or synthetic aperture radars)

- non-imaging active systems (scatterometers, altimeters)- microwave radiometer (passive system). linear. imaging

A scatterometer is a sensor that measures the strength of signals reflected from an object overa range of incidence angles.The basic principle of a radar is transmission and reception of pulses. Short (microsecond)high energy pulses are emitted and the returning echoes recorded, providing information on:- relative strength- time interval between pulse emission and return from the object- phase.- direction of receptionThe same antenna is often used for transmission and reception.Figure 5 presents the basic elements of an imaging radar system.

MODULATOR

TRANSMITTER

RECEIVER

EMITTE0DPULSE BAC~SCATTERED

PULSE

TIMING&

CONTROL RADARELECTRONICS

Fig.5: Elements of an imaging radar system

PROCESSOR

RADARPROCESSOR

12

The two types of imaging radars most commonly used are:

- RAR Real Aperture RAdars,- SAR Synthetic Aperture Radars.

Real Aperture radars are often called SLAR (Side Looking Airborne Radars).Both Real Aperture and Synthetic Aperture Radars are side-looking systems with illuminationdirection usually perpendicular to the flight line. The difference lies in the resolution in thealong-track, or azimuth direction. Real Aperture Radars have azimuth resolution determinedby the antenna beamwidth, so that it is proportional to the distance between the radar and thetarget (slant-range).

Synthetic Aperture Radars use signal processing to synthesise an aperture that ishundreds of times longer than the actual antenna by operating on a sequence of signalsrecorded in system memory. These systems have azimuth resolution (along-track resolution)that is independant of the distance between the antenna and the target. The nominal azimuthresolution for a SAR is half of the real antenna size, although larger resolution may beselected so that other aspects of image quality may be improved. Generally, depending onthe processing, resolutions achieved are of the order of 1-2 metres for airborne radars and5-50 metres for spaceborne radars. Figure 6 illustrates data acquisition by an imaging radar.

Fig.6: Principle of an imaging Radar (from Raney, CCRS)

13

2. Real Aperture Radar

A description of a SLAR image construction can be found in GIB-9/67.

A narrow beam of energy is directed perpendicularly to the flight path of theaeroplane. A pulse of energy is transmitted from the radar antenna, and the relative intensityof the reflections is used to produce an image of a narrow strip of terrain. Reflections fromlarger ranges arrive back at the radar after proportionately larger time, which becomes therange direction in the image. When the next pulse is transmitted, the radar will have movedforward a small distance and a slightly different strip of terrain will be imaged. Thesesequential strips of terrain will then be recorded side by side to build up the azimuthdirection. The image consists of the two dimensional data array.

Nadir Point

/A 8

Ground Range Swath

Fig.7: Pulse rangingPoint A =Near rangePoint B = Far range() = incidence angle at mid-swath

14

On Figure 7, the strip of terrain to be imaged is from point A to point B. Point A beingnearest to the nadir point is said to lie at near range and point B, being furthest, is said tolie at far range. The distance between A and B defines swath width. The distance betweenany point within the swath and the radar is called its slant range. Ground range for any pointwithin the swath is its distance from the nadir point (point on the ground directly underneaththe radar).

2.1 Range resolution

For the radar to be able to distinguish two closely spaced elements, their echoes mustnecessarily be received at different times. On Figure 8, the pulse length L is approachingbuildings A and B. The slant range distance between the two buildings is d. Since the radarpulse must travel two ways, the two buildings lead to two distinguished echoes if:

A

d > ~2

BFig.8: Effect of pulse length on range resolution (from Goodyear GIB)

15

The part of the pulse backscattered by building A is PA and the part of the pulsebackscattered by building B is PB. It appears in Figure 12 that to reach the target and comeback, PB has covered an extra distance L/2 and thus is at L distance behind PA.

Because of this the end of PA and the beginning of PB overlap when they reach the antenna.As a consequence they are imaged as one single large target which extends from A to B.

If the slant range distance between A and B was slightly higher than L/2, the two pulseswould not overlap and the two signals would be recorded as separate images.

Range resolution (across track resolution) is approximately equal to L/2, i.e. half the pulselength.

Ground range resolution is:

R =r L 1 CT-2 sin (}2 sin (}

with c = speed of lightT = duration of focussed pulse(} = incidence angle

Incidence angle is the angle between the vertical at the terrain and the line going from theantenna to the object.

To improve range resolution, radar pulses should be as short as possible.However, it is also necessary for the pulses to transmit enough energy to enable the detectionof the reflected signals. If the pulse is shortened, its amplitude must be increased to keep thesame total energy in the pulse. One limitation is the fact that the equipment required totransmit a very short, high-energy pulse is difficult to build. For this reason, most long rangeradar systems use the "chirp" approach which is an alternative method of pulse compressionby frequency modulation. In the case of the chirp technique, instead of a short pulse with aconstant frequency a long pulse is emitted with a modulated frequency. The frequencymodulation must be processed after reception to focus the pulse to a much shorter value. Forthe user, the result is the same as if a very short pulse had been used throughout the system.

X3

16

2.2 Azimuth resolution

Azimuth resolution describes the ability of an imaging radar to separate twoclosely spaced scatterers in the direction parallel to the motion vector of the sensor. Figure9 shows when objects 1 and 2 are in the radar beam simultaneously, for almost all pulses,they both cause reflections, and their echoes will be received at the same time. However, thereflected echo from object 3 will not be received until the radar moves forward. When theobject 3 is illuminated, objects 1 and 2 are no longer illuminated, thus the echo from 3 willbe recorded separately. For a real aperture radar, two targets in the azimuth or along track­resolution can be separated only if the distance between them is larger than the radar beamwidth. Hence the beamwidth is taken as the azimuth resolution for these systems.

ALONG TRACK DIRECTION

Fig.9: Along-track location (from Goodyear GIB)B = antenna beam width

For all types of radars, the beamwidth is a constant angular value with range. For adiffraction limited system, for a given wavelength (A.), the azimuth beam width (13) dependson the physical length (dH)of the antenna in the horizontal direction according to:

=

For example, to obtain a beam width of 10 milliradians using 50 millimeters wavelength, itwould be necessary to use an antenna 5 metres long. The real aperture azimuth resolutionrazis given by:

raz= azimuth resolution

R = antenna/target slant range

17

For example with a real aperture radar of beamwidth 10 milliradians, at a slant range Requal to 700 kilometers, the azimuth resolution razwill be:

raz= 700 x 0.01raz= 7 km

Real aperture radars do not provide fine resolution from orbital altitudes, although they havebeen built and operated successfully (for example COSMOS 1500 of the Soviet Union). Forsuch radars, azimuth resolution can be improved only by longer antenna or shorterwavelength. The use of shorter wavelength generally leads to a higher cloud and atmosphericattenuation, reducing the all-weather capability of imaging radars.

3. Synthetic Aperture Radar (SAR)

Synthetic Aperture Radars were developed as a means of overcoming theselimitations. These systems achieve good azimuth resolution that is independent of the slantrange to the target, yet use small antennae and relatively long wavelengths to do it.

3.1 SAR Principle

A synthetic aperture is produced by using the forward motion of the radar. Asit passes a given scatterer, many pulses are reflected in sequence. By recording and thencombining these individuals signals, a "synthetic aperture" is created in the computerproviding a much improved azimuth resolution (Fig.10).

z*II

..Jl-:i:II

~l,...*

::c

Fig.10: Geometry of a synthetic aperture arrayPoint Pis illuminated from 1, 2, to N (from Elachi)

fr = 12 CYCLESPER MINUTE

18

It is important to note that the detailed structure of the echoes produced by a given targetchange during the time the radar passes by. This change is explained by the Doppler effectwhich is used to focus the signals in the azimuth processor. We will illustrate this point withan analogy.

Let us consider, as in the case of Figure 11, a plunger going up and down in thewater, producing circles of radiating waves, each with a frequency of 10 cycles per minute.These waves travel at a known speed. The plunger is a source of waves analogous to thosefrom a radar. We are interested in the appearance of this wave field at a distance. Considera boat is moving along line V. At position B, a passenger on the boat would count 10 wavesper minute since he is moving neither toward or away from the waves (source). However,at position A, the boat is moving towards the waves and the passenger will count a highernumber of waves, perhaps 12 per minute: the travelling speed of the waves is slightlyincreased by the speed of the ship.On the contrary, at position C, the boat is moving away from the buoy and the apparentfrequency is lower, perhaps 8 cycles per minute: the waves are moving in the same directionas the boat. Doppler frequency is the difference between received and emitted frequencieswhere the difference is caused by relative motion between the source and the observer.Equivalently, the relative spacing between crests of the wave field could be recorded alongthe line AC, measured as if the wave field were motionless. This leads to a phase model ofthe signals that is equivalent to the Doppler model.

fr= 8 CYCLESPER MINUTE

fr = 10 CYCLESPER MINUTE

PHASE HISTORY

ft = TRANSMITTED FREQUENCYfr = RECEIVED FREQUENCY

Fig.11: Example of Doppler frequency(from Goodyear GIB)

19

During the movement of the boat from position A to position C, the recording by theobserver of the number of waves would look like the curve at the right of Figure 13.

Instead of a plunger, let us now consider an aeroplane emitting a radar signal. The boatcorresponds to a target appearing to move through the antenna beam as the radar moves past.The record of the signals backscattered by the target and received would be similar to therecord of the passenger in the boat. Such a record is called the Doppler history (or phasehistory) of the returned signals. When the target is entering the beam, the Doppler shift ispositive because the source to target distance is decreasing. The phase history is then storedto be used during the SAR processing.

When the illumination is abeam the target, the Doppler frequency is nul. At the end of thetarget illumination, the returned signal has a negative Doppler shift, because the range isincreasing.

3.2 SAR Processing

The objective of SAR processing is to reconstruct the imaged scene from themany pulses reflected by each single target, received by the antenna and registered inmemory.

SAR processing is a simple process although it requires much computation. It canbe considered as a two-dimensional focussing operation. The first of these is the relativelystraightforward one of range focussing, requiring the "de-chirping" of the received echoes.Azimuth focussing depends upon the doppler histories produced by each point in the targetfield and is similar to the de-chirping operation used to focus in the range direction. This iscomplicated however by the fact that these doppler histories are range dependent, so azimuthcompression must have the same range dependancy. It is necessary also to make variouscorrections to the data for sensor motion and earth rotation for example, as well as for thechanges in target range as the sensor flies past it.

It is important to note (Fig.12) that the pixel of the final SAR image does nothave the same dimensions as the resolution cell during the data acquisition, due to thevariation of range resolution with incidence angle. Thus it is necessary to perform a pixelresampling with a uniform grid. Even more fundamental, at least two pixels are required torepresent each resolution cell, which is a consequence of digital sampling rules. Byconvention, pixel spacing in SAR imagery is chosen to conform to standard map scales,hence must be a discrete multiple (or divisor) of 100 meters. For example, ERS-1 data,having nominal resolution of 28 meters in range and azimuth is delivered with 12.5 meterpixel spacings.

20

Amplitude___J !...___ Resolution- - .....-1 · r '(separation)

Resolution(impulse width)

D A B

Digital Number

Distancec

_,r- Resolution

Fig.12: Resolution describes the minimal discernable spacing between two similar pointresponses (A and B), but often is applied to the width of one response (C). Aweaker response (D) requires a larger separation for detection. Pixels refer to thediscrete sample positions used for digital imagery. There must be at least twopixels within a resolution distance. (These comments extend to the two­dimensional image).

D A B

Pixel spacing

c Pixels

21

IV. ELEMENTS OF RADAR IMAGERY INTERPRETATION

Radar images have certain characteristics that are fundamentally different fromimages obtained using optical sensors such.as Landsat, SPOT or aerial photographs. Thesespecific characteristics are the consequence of the imaging radar technique, and are relatedto radiometry (speckle, texture) or geometry. During radar image analysis, the interpretermust keep in mind the fact that, even if the image is presented as an analog product onphotographic paper, the radar "sees" the scene in a very different way from the human eyeor from an optical sensor; the grey levels of the scene are related to the relative strength ofthe microwave energy backscattered by the landcape elements. Shadows in radar image arerelated to the oblique incidence angle of microwave radiation emitted by the radar system andnot to geometry of solar illumination. The false visual similarity between the two types ofimages usually leads to confusion for beginners in interpretation of radar images.

1. Radiometry

1.1 Tone

The average backscattering coefficient u0 (see section II.3) differs according tothe types of surface so it may be reasonably expected that surfaces with different values ofu0 would produce different grey levels in the image. Fig. presents a simplified illustrationof the relation between image tone and radar backscatter. The dark or bright tonalityassociated for example with an image of agricultural fields is the result of an averagingprocess.

D

WATER

Fig.13: Relation between image tone and radar backscatter(D = Dark, M = Medium, L = Light)

22

1.2 Speckle

A detailed analysis of the radar image shows that even for a single surface type,important grey level variations may occur between adjacent resolution cells. These variationscreate a grainy texture, characteristic of radar images. This effect, caused by the coherentradiation used by radar systems, is called speckle. It happens because each resolution cellassociated with an extended target contains several scattering centers whose elementaryreturns, by positive or negative interference, originate light or dark image brightness. Thiscreates a "salt and pepper" appearance. An example of speckle is shown on the radar imageof Flevopolder agricultural zone, in Netherlands (Fig.31).Speckle is a system phenomenon and is not the result of spatial variation of averagereflectivity of the radar illuminated surface. For a high resolution radar, there may be usefulscene texture which differs from the speckle. This is the case for example of forested zonesin which the combined effects of radar illumination and tree shadowing create a roughertexture granularity than the speckle. In this case, there exists a spatial variability of thephysical reflectivity of the illuminated zone. In a radar image we may find:- zones where the only image texture is related to speckle that we may call regions "withouttexture" (extended homogeneous target),

- zones "with texture" that have spatial variations in scene reflectivity in addition to speckle.Thus, in the case of "no texture" zones, it becomes possible to study the statisticaldistribution of the backscattered radar signal, which helps to estimate certain radarcharacteristics.

Speckle can be reduced by two methods:

- SAR image multi-look processing. This technique consists of dividing the available data intoseveral sectors which are processed independantly. These so-called "looks" are averaged toreduce the grey level random variations provoked by speckle. For N statistically independant(non-overlapping) data sectors, the speckle variance is reduced by a factor of N. Likewise,the resolution is degraded by a factor of N. In such a way, we can for example have 8-lookimages. A compromise has to be found between desired spatial resolution and an acceptablelevel of speckle.- Filtering techniques.

1.3 Speckle filters

We can consider that an extended area is characterised by:- the radar backscattering coefficient a0 i.e. radiometric information,- spatial variability, i.e. textural information.

The presence of speckle reduces the separability of the various land use classes,based on radiometry and texture. It is thus important to treat speckle so as to improve thepossibility of separation, but with minimal loss of information. If we take the example ofhomogeneous areas such as agricultural zones with a defined field pattern, the filters to beused must preserve the aveage backscattering value, and maintain sharp edges betweenadjacent fields. In the case of textured areas, such as forest for example, the filters shouldalso preserve the spatial variability (textural information) relating to the scene.

23

Adjacent cell averaging is generally applied on a moving window of 3 X 3, 5 X5 pixels etc ... In homogeneous areas, this type of filter provides satisfactory results but theblurring effect on edges and textural components is a limiting factor. For this reason,adaptive SAR filters were developed by for example Lee (1981), Frost et al. (1982), Lopeset al. (Gamma Maximum A Posteriori filter, CESR, 1990). Taking into account a localmeasure of the scene heterogeneity, these adaptive filters reduce speckle with preservationof radiometric and textural information. A description of selected SAR filters and anassessment of their performance is contained in the guide to the ESA/ESRIN SAR filteringsoftware (Aschbacher et al., AIT, 1992). Figure 39 presents an example of a filtered SARimage.

1.4 Comments on radar image interpretation

Elements of interpretation of radar imagery can be found in several publicationsfor example in "The use of Side-Looking Airborne Radar imagery for the production of aland use and vegetation study of Nigeria" (Allen, 1979).Grey levels in a radar image are related to the microwave backscattering properties of thesurface (Fig.13). The intensity of the backscattered signal varies according to roughness,dielectric properties and local slope. Thus the radar signal refers mainly to geometricalproperties of the target. In contrast, measurements in the visible/infra-red region use opticalsensors where target response is related to colours, chemical composition and temperature.

The following parameters are used during radar imagery interpretation:

- tone- texture- shape- structure- size.

Several principles of photointerpretation can be used for radar imagery interpretation and wecan distinguish three steps:- photo reading. This corresponds to boundaries recognition on the basis of thepreviously listed parameters.- photo analysis. This corresponds to the recognition of what is within the boundariespreviously identified.- deductive interpretation of image. At this stage, the interpreter uses all his thematicknowledge and experience to interpret the data.

Before describing texture, we can propose the following definitions:

Tone: radar imagery tone can be defined as the average intensity of the backscattered signal.High intensity returns appear as light tones on a positive image, while low signal returnsappear as dark tones on the imagery.

24

Shape: It can be defined as spatial form with respect to a relative constant contour orperiphery, or more simply the object's ouline. Some features (streets, bridges, airports ... )can be distinghished by their shape. It should be noted that shape is as seen by the obliqueillumination: slant range distance of the radar.

Structure: The spatial arrangement of features throughout a region with recurringconfiguration.

Size: The size of an object may be used as a qualitative recognition element on radarimagery. The size of known features on the imagery provides a relative evaluation of scaleand dimensions of other terrain features.

1.5 Texture and image analysis

The concept of texture is discussed by Laur (1989). Before describing thespecificity of radar image texture, it is necessary to define the concept of textural element;i.e. the texture elementary unit, smallest homogeneous element of same radiometryconstituting the texture.

In principle, the texture of a radar image can be described by consideringmacrotexture, mesotexture and microtexture. Let us consider the following example toillustrate these three texture categories. An observer flies with a sensor above a wooded area,high enough not to recognize single leaves but low enough to distinguish clearly tree crowns.First, he identifies the different stands; these might be oaks, pines, beeches, etc. Thedifferent stands define the macrotexture or structure of the scene. The crowns are the keyelement to identify the content of a stand and define the mesotexture, a crown being a textureunit in this example. The grey level of each resolution cell depends on the quantity andorientation of the leaves that scatter the incoming light. Horizontal leaves reflect much morethan vertical ones. This difference between resolution cells of the same class appears in theimage as microtexture, called speckle or image noise on a radar image.

The texture of a radar image can be divided into three components (Tab. I):

- micro-texture, i.e. speckle, that appears as grains of the same size as or larger than theresolution cell, and having a random brightness. This texture is inherent to the radar system;it does not correspond to a real variation from one resolution cell to another. Thus, speckleis essentially an image texture arising from the system, and not from the scene. Speckledegrades image readibility. However, despite this major disadvantage, speckle may bestatistically characterised. This point may be exploited by speckle filtering methods.

- meso-texture or "scene texture" is the natural variation of average radar backscatter on ascale of several resolution cells or more. Taking forests as an example, the high backscatterfrom the part of the tree facing the radar appears near the shadow of the opposed part of thetree away from the radar. The result is a grainy texture whose elementary unit covers severalresolution cells (depending on the spatial resolution of the system). It is this component ofimage texture that is most useful in the interpretation of the radar image.

- macro-texture corresponds to variations in radar brightness that extend over manyresolution cells. It can be, for example, field boundaries, forest shadows, roads or geologiclineaments. The structure parameter is extremely important for radar imagery interpretation,especially in geology and oceanography. It is generally assessed using edge or other patterndetection techniques.

Based on this discussion, some observations may be made:- speckle is superimposed on scene texture and structure, creating problems during texturalanalysis or edge detection of imagery. In general, speckle is a product function: it ismultiplicative, not additive although speckle modelling for scenes of rapidly changingcontrast is more complex.- tone, i.e. average value of the mean cross section a0• is a local concept. It is the spatialvariation of tone that provides "texture".- texture is related to the spatial distribution of resolution cells and depends on the scene, notof the system. Nevertheless, radiometric quality must be relatively constant in the sampleswhere textural measurements are performed.- texture and structure are limited by the spatial resolution.- any post-processing of a SAR image (filtering, texture analysis) should be performed onradiometrically original images (slant range). Ground range images are resampled and thusmay be radiometrically distorted.

25

Macro-texture(Structure)

•textural cell

>>resolution cell

•w

I - scene dependantI

Table 1 illustrates the three components of radar image texture.

Radar image texture

Tab.1: Three texture components of a radar image (adapted from Laur)

Micro-texture(Speckle)

w

textural cellrv

resolution cell

~

- randomly distributed

Mesa-texture(Scene Texture)

'

textural cell>

resolution cell

'---

[ - scene dependant

.•...··

26

2. Image geometry

2.1 Specific aspects of image geometry

A radar image has a rectilinear geometry that is a consequence of the mode ofacquisition of the data (Fig.14). The radar "sees" the ground according to a perspective linethat corresponds to antenna/target slant range. The echoes of the emitted pulses are registeredin a time sequence: they provide the across-track component of the image. The along-trackcomponent is the result of the repeated transmissions during the sensor motion along theflight line.

RADAR

RADARBEAM

GROUNDRANGE

Fig.14: Imaging radar geometry

Figure 15 presents a comparison between respective geometries of radar image and obliqueaerial photos.

Look direction

..•

..•.3~

..•.3

'-'-'-~-'-~~.....L.~~~---J~..•.L_~~~~~~--.~~~~~--1

..•. ....•...••. ...•. ...••.

3

Geometry ofSlant rangeRadar Imagery

3

Geometry ofGround rangeRadar imagery

Geometry ofOblique aerialPhotography

Fig.15: Image geometry of radar and aerial photograph (from MacCoy)

27

Figure 16 shows two types of display of radar data:

- slant range image, in which distances are measured between the antenna and thetarget.

- ground range image in which distances are measured between the platform groundtrack and the target, and placed in the correct position on the chosen reference plane.

Slant range data are the natural result of radar range measurements. Transformation toground range requires correction at each data point for local terrain slope and elevation.

/./////Radar Slant range

display

Ground Rangedisplay

Fig.16: Two different types of display

28

The geometric distortions present on a radar image can be divided into:

- Elevation distortions. This occurs in those cases where points have an elevation differenfrom the mean terrain elevation.

- Range distortions. Radar measures slant ranges but, for an image to represent correctlthe surface, it must be ground range corrected.

2.2 Backscatter and local incidence angle

For a given type of terrain feature such as coniferous forest, the strength of the echrbackscattered towards the antenna depends on the incidence angle (which varies with range:and local topography. Figures 17 and 18·illustrate incidence angle and local incidence angleA slope facing the radar beam will have a stronger backscatter than if it is facing away frorrthe radar beam, and thus will have a lighter tonality on the image (Fig.19). It is importanto remember that the imaging geometries may be different in the case of airborne ancspacebome radars (Fig.20).

-. ·-·-·-·-Fig.17: Incidence angles for aircrafts and spacecraft SARs (from Raney, CCRS)

r

29

Fig.18: Local incidence angle and incidence angle (from Raney, CCRS)

Fig.19: Relation between image tone, terrain slope, and image scale(from Raney, CCRS)

Elevationprofile inthe scene

Brightness

and scaleof image

30

Ground rangederived usingterrain model(geocoded with DTM)

Slan'7'range projection at average groun~ plane scale

31

SPACEBORNE SAR

AIRBORNE SAR

,.SOkm

IMAGE SWATH

Airborne SAR typical altitudes 5-10 km- Incidence angles for wide swath coverage vary considerably across the swath,in this example between 45° and 80°

Spaceborne SAR typical altitudes 250-800 km- Incidence angles across the same swath vary only slightly, typically = + 2°

Fig.20: Comparison of imaging geometries of spaceborne and airborne SARs(from Werle)

32

2.3 Shadowing, Foreshortening and layover

A slope away from the radar illumination with an angle that is steeper than the sensordepression angle provokes radar shadows. It should be also noted that the radar shadows oftwo objects of the same height are longer in the far range than in the near range (Fig.21).Shadow regions appear as dark (zero signal) with any changes due solely to system noise,sidelobes, and other effects normally of small importance.

-:~-11

/ '<,./-..., - - -

/ I\ X -----/ / "<, ----1 '\ <,I \ "..........._: \ ~.I \ ~

RADARSHADOW· INCIDENCEANGLE---- ll AT FAR RANGE

----- lh--- <;!--:•NEARRANGE" ccfARRANGE"

Fig.21: Relation between radar shadow and range (from Smit)

Distortion :.-1

Fig.22: Radar shadow and relief displacement for large incidence angle(from Raney, CCRS)

33

Foreshortening is a dominant effect in SAR images of mountainous areas (See SectionIV.2.3). Especially in the case of steep-looking spaceborne sensors, the across-track slant­range di fferences between two points located on foreslopes of mountains are smaller thanthey would be in flat areas. This effect results in an across-track compression of theradiometric information backscattered from foreslope areas which may be compensatedduring the geocoding process if a terrain model is available. It is worth noting that shorteningeffects are still present on ellipsoid corrected data.

If, in the case of a very steep slope, valley points of mountains have a larger slant range thanthe mountain top, then the foreslope is "reversed" in the slant range image (Fig.23). Thisphenomenon is called layover: the ordering of surface elements on the radar image is thereverse of the ordering on the ground. Generally, these layover zones, facing radarillumination, appear as bright features on the image due to the low incidence angle.In addition, in such a layover zone, radiometric information is the result of thesuperimposition of the response of many objects. Geocoding can not resolve the ambiguitiesdue to the representation of several points on the ground by one single point on the image;these zones also appear bright on the geocoded image.

Scene

Fig.23: Layover (from Raney, CCRS)

34

Based upon the previous considerations on SAR image geometry, the following remarkscan be formulated in order to assist the interpreter:- for regions of low relief, larger incidence angles give a slight enhancement to topographicfeatures. So does very small incidence angles.- for regions of high relief, layover is minimized and shadowing exaggerated by largerincidence angles. Smaller incidence angles are preferable to avoid shadowing.- intermediate incidence angles correspond to low relief distortion and good detection of land(but not water) features.- small incidence angles are necessary to give acceptable levels of backscattering from oceansurfaces.- planimetric applications necessitate the use of ground range data, which usually requiresuse of digital elevation data and image transformation.

2.4 SAR image geocoding

Descriptions of SAR data geocoding can be found in the Proceedings of the Workshopon Synthetic Aperture Radar Image Rectification Techniques (DIBAG Report 29, 1987).Some principles are summarized in FAO RSC/ESA Publication No 55 (1991).

The principle of side-looking SAR is measurement of the electromagnetic signal roundtrip time for the determination of slant ranges to objects and the strength of the returnedsignal. This principle causes several types of geometrical distortions. Severe distortions occurif pronounced terrain relief is present in the imaged zone. The amount of distortion dependson the particular side-looking geometry and on the magnitude of the undulation of the terrainsurface (See Section IV.2.3).In many applications such as agriculture and vegetationmapping, the terrain-induced distortions degrade the usefulness of SAR images and in somecases may even prevent information extraction.

SAR data geocoding is a very important step for many users because SAR data shouldbe geometrically correct in order to be compared or integrated with other types of data(satellite images, maps, etc). Geocoding an image consists of introducing spatial shifts on theoriginal image in order to have a correspondance betweenthe position of points on the finalimage and their location in a given cartographic projection.

Radiometric distortions also exist in connection with terrain relief and often cannot becompletely corrected. In addition, resampling of the image can introduce radiometric errors.For these reasons, the thematic user of the image needs information on what he should expectin terms of interpretability of geocoded images for a given thematic application. Alayover/shadowing mask and a local incidence angles map are both helpful for manyapplications. Figure illustrates the principle of SAR geocoding.

-----

35

Figure 25 illustrates a SAR geocoding system constituted from 3 data bases:

- orbital parameters,- raw radar data,- geographic data base (Digital Terrain Model, Control Points and parameters ofcartographic projection)

ERS-1 SAR looks at the Earth surface with a 23° incidence angle. Due to this, imagescontain almost no shadow but may contain a large amount of layover and foreshortening.With the geocoded data, ERS-1 PAF (Processing and Archiving Facilities) will provide onrequest a data file indicating the layover and shadowed zones as well as the local incidenceangle for each picture element. This file will be useful for the interpreter prior to thematicmapping. If a Digital Elevation Model is available, it may be possible to correct the terraininfluence in SAR backscatter by using empirical backscatter models (Bayer et al., 1991).

Figure 32 illustrates an example of SAR data geocoding using a Seasat scene of Geneva(Switzerland). The slant range image (A) presents strong geometric distortions, evident inthe shape of the lake. The same SAR image (B) is presented after geometric correction andin a cartographic projection.

Geocoded Image

36

/ Grey value Interpolation(Resampling)

Radar Image

Grey Value Assignment Map to ImageTransformation

Fig.24: Principle of SAR image geocoding (From DIBAG)

Elevation· Dat1--~~~~~~~

PreparationProducteneration

pea-Database IOEMs

Map ProjectionGCPs Parameters

Orbit Data

37

SAR Processing

- Detected SlantRange ImageProcessor GeneratedParameters (eg Doppler)

Construction ofGeometric Model

(inc. Earth Ellipsoid)

- Geocoded ImageProcessed

Fig.25: System for terrain correction of SAR data (from Sowter et al.)

\ \ \c

38

V. APPLICATIONS

1. ForestryIn the case of vegetation, different information is provided by visible, near infrared and

radar sensors. Reflectance is mainly related to colour and pigmentation in the visible, tocellular structure in the near infrared. Radar, depending on the selected band, has a highsensitivity to other parameters such as the upper layer roughness, to the plant structure andorientation, and plant moisture. The energy backscattered can be differentiated into severalcomponents (Fig.26):- direct backscatter at forest canopy (A),- multiple scattering and volume scattering within the canopy (B),- direct backscattering at ground surface (C),- trunk/ground (or water) interactions (D),- shadow cast by trees (E).

D

A

Fig.26: Radar backscatter contributions of a forest scene

For example, if we use higher radar frequency bands (starting from C-band = 5.6 cm), theforest is opaque and the backscatter mainly depends on leaf geometry and leaf orientation.X-band has a high sensitivity to twigs and foliage. On the other hand, if we consider L-band= 24 cm and more importantly P band = 70 cm, the forest foliage is more or lesstransparent with additional backscatter components from branches, trunks, soil/undergrowthlayer and from soil-trunk interactions. Radar backscatter varies to a lesser extent accordingto polarization (Fig.30). Multi-frequency or multi-polarization data sets have not yet beenused at an operational level. As well as frequency and polarisation, the incidence angle usedis an important parameter in determining the amount of backscatter produced (See"MAESTRO-AGRISCATT Radar Techniques for Forestry and Agriculture Applications,ESA WPP31, 1992). The fact that information content varies for images produced with thevarious wavelengths (optical to microwave) would suggest that combinations of data over thesame site from different sensors would show a certain complementarity.

39

For forestry purposes, a system for radar imagery interpretation based on physiographicelements was proposed by Stellingwerf et al. (ITC, 1983); airborne radar imagery wasconsidered a useful tool for forest mapping and is still used at an operational level. A largepart of the research on radar imagery interpretation was carried out in temperate zones(United States, Canada, Europe) but radar images have been extremely useful for tropicalforestry mapping at reconnaissance level. In these zones where acquisition of aerialphotographs is very difficult due to persistent cloud cover, airborne radar imagery (Fig.33and 34) is often the only option, despite a rather high cost per square kilometer comparedto other sensors. Important projects during the seventies, such as Radam (Brazil, 8.5 millionkm2 mapped), Proradam (Colombia, 1.8 million km2 mapped) and NIRAD (Nigeria), arewell documented. More recently, at the end of 1990, a complete SAR coverage of EquatorialGuinea (26 000 km2) was achieved within the FAQ "Forest Assessment and ManagementProject" using the INTERA high resolution STAR-1 System. Based on the interpretation of1: 100 000 scale radar stereo pairs, a 1: 200 000 scale vegetation map was produced.

In tropical zones, until 1992 most of the existing practical experience has been basedon interpretation of X and L bands, with only limited data set acquired in C-band. The strongpotential of P band for tropical forest monitoring was stressed and originated the TREIS(Tropical Radar Environmental Information System) concept (Raney and Specter, 1991)which is based on SAR acquisition at low data rate, medium resolution (about 100 meters)and complete coverage of tropical forests twice a year.

~able 2 (Raney et al., 1990) presents the results obtained for tropical forestryapplications using airborne or spaceborne SAR data. This Table was prepared before thelaunch of ERS-1 and thus only includes the results obtained from Seasat, SIR-A and SIR-Bspaceborne missions as well as certain airborne data sets. Even using high resolution radarimagery, it should be remembered that a field check is always necessary, as is the case forother remote sensing methods. In temperate zones, radar imagery cannot substitute detailedaerial photographs for forest species mapping.

Spaceborne SAR images such as those from ERS-1 or ERS-2 will probably be used formapping purposes at 1:100000 scale. Thus, due to acquisition constraints (receiving stationsin operation, selected orbits) and volume of data to be processed, high resolution spacebornedata are not suitable for a complete ("wall to wall") tropical forest cartography. Conversely,due to the high revisit capability, they might be extremely useful for deforestation monitoringactivities in zones under environmental pressure, or for monitoring of sampling zones. Figure36 shows an ERS-1 SAR image of the Amazonian region (Brazil).

40

Application Country Band Resolution ReferenceArea

Road detection Sarawak x 6m Thomson and Dams 1990Peninsular x 6m Wan Ahmad et al. 1988Malaysia(Cameroon1 L 40 m Werle 1989)Belize L,P,C2 lOm Zebker et al. 1990

Forest inventory Peninsular x 6m Ahmad et al. 1988information Malaysia

(Nigeria x > 30 m Sicco Smit 1978)(Colombia K.,X,L 10-22,16,40 m de Molina and Molina 1986)

Clearcut Cameroon L 40 m Werle 1986detection Venezuela L 40 m Werle 1986

Indonesia L 40 m Werle 1986Brazil L 40 m Stone & Woodwell 1987Colombia K.,X,L 10-22,16,40 m de Molina & Molina 1986Costa Rica x 6m Dams et al 1987Belize L,P,C2 lOm Zebker et al.

Clearcut Cameroon L 40 m Werle 1989revegetation Venezuela L 40 m Werle 1989

Indonesia L 40 m Werle 1989Costa Rica x 6m Dams et al. 1987

Settlements Nigeria x 30 m Hunting, 1984Paraguay L 40 m Werle 1989Indonesia L 40 m Werle 1989Sarawak x 6m Thomson and Dams 1990Colombia x 6, 12 m de Molina and Molina 1989

Agricultural Paraguay L 40 m Werle 1986conversion Sarawak x 6m Thomson and Dams 1990

Peninsular x 6m Thomson and Dams 1990MalaysiaColombia x 6,12 m de Molina and Molina 1989

Tree plantations Guatemala K. 10-22 m Dellwig et al. 1978Indonesia L 40 m Werle 1989Australia x 12 m Lowry et al. 1986Congo x 6m Thomson and Dams, 1990Peninsular x 6m Wan Ahmad et al. 1988MalaysiaBelize L,P(C) lOm Zebker et al.1990

41

Applications Country Band Resolution Referencearea

Mangrove forests Indonesia X,L 40 m King 1985Panama x, Lewis & MacDonald 1973Sarawak x 6m Thomson and Dams 1990Australia x 12 m Lowry et al. 1986(Bangladesh L = 16-33 m Imhoff et al. 1986)(Florida, USA L 25 m MacDonald et al. 1981)

Flooding Bangladesh L 28 m Imhoff et al. 1986Australia L 40m Werle 1989Indonesia L 28 m Ford & Sabins, 1Q85Guatemala L 25 m Pope 1987Colombia x 6,12 m de Molina and Molina 1989Indonesia L = 20-30 m Ford & Casey 1988Borneo

Drainage Nigeria x > 30 m Sicco Smit 1980Colombia K.,X,L 10-22,16,40 m de Molina & Molina 1986Brazil X,L 16,40 m Ford & da Cunha 1985Cameroon L 40 m Werle 1989Sarawak x 6m Thomson and Dams 1990

Broad vegetation Brazil x 16 m Furley, 1986classes Nicaragua ~ 10-20 m Trevett 1986

Togo K. > 20m Gelnett et al. 1978Nigeria x 30 m Hunting 1984Colombia x = 10-22 m de Molina et al.1973Colombia Trevett 1986Colombia K.,X,L 10-22,16,40 m de Molina & Molina 1986Colombia x 6,12 m de Molina & Molina 1989Australia x 6m Lowry et al. 1986Indonesia L = 20-30 m Ford & Casey 1988Borneo(Brazil x = 16 m Disperati and Keech 1978)

Tab.2: Summary of SAR applications in tropical forests(from Raney, Ahern, Dams and Werle, 1990)

Notes:I.Studies in parentheses ( ) report negative findings for that application.2.Partial success, depending on local conditions.

# WavelengthsK, = 1 cm; X = 3 cm; C = 6 cm; S = 10 cm; L = 23 cm; P = 68 cm

42

2. Land use mapping and crop identification

Radar images generally allow the differentiation of land use types (urban areas,agricultural zones, forest, water bodies) due to SAR sensitivity to surface roughness andmoisture content as noted above. Nevertheless it should be remembered that radarinterpretations are site-specific and that an accurate land use mapping requires field data and,if possible, access to other types of remote sensing data.

In 1988/89, the FAO Remote Sensing Centre and ESA executed two pilot studiesin Tunisia, in collaboration with the Ministry of Agriculture, within the framework of thepreparation to the future use of ERS-1 SAR data. The objective of these studies was to assessthe usefulness of SAR images for land use mapping and to compare SAR data with opticaldata. One of the test areas was the semi-arid zone of M'Saken, in Central Tunisia. This zoneis characterised by a slightly undulating topography with olive tree plantations, cereals andvegetables along the rivers. Optical data (1978 and 1981 Landsat MSS, 1986 Landsat TMand 1987 SPOT) were compared to microwave data (1978 Seasat and 1981 SIR-A). Animage-to-image registration was carried out, taking as reference the Seasat data. Then1: 100 000 scale photographic enlargements were visually interpreted. This enabled land useclass separation with observations from SAR data on soil moisture and local topography. Soiland water conservation embankments of 30 centimeters height, thus inferior to the pixel sizewere identified on Seasat data, due to their orientation perpendicular to radar illumination.Maximum likelihood classifications were then performed, with or without integration of SARdata. Classification accuracy was calculated using a digitized aerial photograph. Withouttexture characterisation, the integration of SAR data did not substantially improve theclassification result (Ref. Rebillard 1985 and FAQ RSC/ESA No 51, 1989); this was partlydue to the geometrical mismatch between the SAR and the optical data, as SAR data werenot terrain corrected (a Digital Terrain Model was not available).

A second test area was chosen in Grombalia, in the Cap Bon Peninsula, NorthEast of Tunisia. This region is associated with the intensive agriculture of irrigated citrus,vegetables, vineyards and cereals. Optical data (1978 Landsat MSS, 1988 Landsat TM andSPOT) were compared to microwave data (1978 Seasat). A Digital Terrain Model wasproduced from existing 1:50 000 scale topographic maps and then used for satellite datageocoding in Lambert projection. As in the case of M'Saken previously presented, visualinterpretation provided satisfactory results with regard to the identification of field pattern,citrus plantations, hilly zones, urban areas. In the case of mountainous zones, vegetationmapping from SAR data, even geocoded, suffered serious limitations (Ref. FAO RSC/ESANo 52, 1990).

43

Fig.37 shows an optical microwave colour composite of the Grombalia zone, with thefollowing colour codes:

- Red- Green

= Landsat TM 4 (Near Infra-red)= Landsat TM 2 +TM 3 (= Panchromatic component)

2

- Blue = Seasat SAR (Component mainly related to surface roughness).

This type of colour composite is useful because it enables integration of two different typesof information:- information on surface roughness (from the radar band),- information on vegetation (from visible and near infra-red band). It is thus possible toimprove the separation between crops, especially between fruit trees/low crops, and to betterassess the soil preparation. For example, on a Landsat TM color composite, it maysometimes be difficult to separate fruit trees and vineyards from extensive pasture or cerealsat the beginning of the cycle. In all these cases, bare soil will occupy a large portion of thepixels. SAR provides useful additional information because vineyards/fruit trees zonesgenerally have a high surface roughness compared to pasture/cereal zones.In these two Tunisia studies, the combined visual interpretation of optical and microwavedata improved in a significant way the interpretation result. It appeared also that radar shouldnot be considered only as a substitute to optical sensors but as a complement, mainly due toradar sensitivity to soil roughness and terrain moisture. On the other hand, it was observedthat the digital classification accuracy depends on:- high precision data registration,- SAR data post processing (filtering).

Another land use mapping study was executed by ESA in the South of Thailand(Ref. ESA EOQ No 31, 1990). The main agricultural activity of the test area, near Songkhla,is rice farming and palm tree cultivation.SPOT data used were geocoded in the UTM cartographic" projection. SIR-A data weredigitized from a film, then resampled in order to have a 20 metres pixel size. Unfiltered SARdata were compared to EPS (Edge preserving smoothing) filtered data.A colour composite (Fig.38) was produced from SPOT XS band 3 (in red), from filteredSAR image (in green) and from SPOT XS band 1 (in blue). It is worth noting that on thiscolour composite, the clouds from the SPOT image are associated with different colours ifthey are located above palm trees (white) or rice fields and swamps (magenta). This is theconsequence of the type of information provided by the all-weather radar sensor:- white = rough surface, i.e. high vegetation,- magenta = srriooth surface, i.e. rice fields.A supervised digital classification was then executed taking into account cloud covered zones,zones with cloud shadow and cloud free zones. On the final classified image, cloud influencehas disappeared: this type of method enables the use of a partially cloud covered SPOTimage for land use mapping purposes.

44

For agricultural crop identification, one advantage of radar in relation to othersensors is that it allows data acquisition at optimum periods for crop separation during thegrowth cycle, independant of the weather conditions. The backscattered energy from anagricultural crop may be divided into several components: volume scattering by thevegetation, multiple reflections from soil and vegetation, and soil backscatter. Generally, thebest correlation between crop characteristics and radar backscatter is observed in the shorterwavelengths at moderate to large incidence angles. The shorter wavelengths are associatedto a greater backscatter from the vegetation canopy, and the moderate to large incidenceangles to a low component from the soil. For example, X band has a low penetrationcapability and is thus associated with direct backscatter from the canopy, with a limitedcontribution from the internal part of the canopy and from the soil layer. L band canpenetrate the canopy layer and it is possible to have a backscatter component from the soil.The ideal approach would be to analyse multitemporal, multifrequency (L,C,X band), andmultipolarisation data. Such an approach is not yet possible at an operational level but itimproves crop separability, which is related to such parameters as plant height, canopy andleaf geometry, and row direction. Some of these topics were discussed during the analysisof the European SAR 580 campaign ( See JRC-ESA, 1985 and ESA TM-01, 1988).In addition to imaging radar experiments, scatterometer experiments are also conducted asthey allow a control of radar parameters but also of target parameters (crop type, plantgeometry, growth stage, moisture, soil characteristics, cultivation practices).

Even if the potential of radar data for crop identification has been verifiedthrough several airborne campaigns, radar data have never been used operationally for cropidentification/agricultural statistics due to:- high cost of airborne SAR data,- lack of spacebome SAR data with a satisfactory temporal resolution.The availability of ERS-1 SAR data may allow a development of new applications in someagricultural sampling zones. For example, during the ERS-1 commissioning phase, 3 imagesof the Wieringermeer Polder (Netherlands) were acquired on the following dates: 27 July,30 July and 2 August. Such a repetitivity would have been impossible with optical data. Ona multitemporal SAR colour composite (First date = red, second date = green, third date= blue), it is possible to identify which fields were harvested during this period, and alsothe date of harvest.

The multitemporal ERS-1 SAR images of Riigen, Germany (Front cover) andNitra, Slovakia (Fig.39) provide further examples of the usefulness of SAR data for cropidentification. In the case of Nitra, two ERS-1 SAR images were used to generate a colourcomposite assigning red (10 July 1992) and green colours (16 September 1992). As a result,colours span only from red to yellow and green, including black, but without blue or bluishtones. On the 10 July, harvest of cereals (bright fields in the image) had not yet started.Sugarbeet and especially sunflower (rather bright) had a much higher backscatter than com(darker). On the 16 September, all cereals other than com were harvested (rather dark) andcom was fully developped (bright). The colour attribution to land use types in the image isas follows:- green yellow: com- various shades of orange, with a fine texture: sugarbeet- red: cereals other than com (wheat...)- very dark greens: meadow/pastureland- light brown (coarse texture): woods- yellow: urban areas, groups of houses.

45

Spaceborne SAR data will be useful in the future for crop identification and acreageestimates. Their usefulness for crop condition assessment and yield forecasting still has tobe demonstrated.

3. Geology and geomorphology

Radar images have been used for more than 20 years by geologists for lineamentsdetection and mapping, oil and mineral exploration. The advantages of radar images forgeologic studies are:- synoptic view at low cost,- radar's sensitivity to topography, surface roughness and moisture content.Topography enhancement is related to the incidence angle selected and good results may beobtained from the analysis of several images acquired with different viewing angles.Generally, the drainage network appears clearly on SAR images, with also an enhancementof the topography; this allows a satisfactory assessment of the geomorphology of a studyarea. Fig.41 illustrates radar penetration capability of a dry soil. Two images of the Southof Hoggar, Algeria, are presented:- Landsat Thematic Mapper image,- SAR image acquired during SIR-A mission.On the Landsat Thematic Mapper image, to the right we can see a highly eroded rockformation and on the left a sand covered plain. It is interesting to note that on the SIR-Aimage we can see many details of the subsurface structure, such as a drainage network, aresult of radar penetration of dry sand.In addition, since frequent data acquisitions of spaceborne SAR data become possible, moreimmediate assessment of earthquake damages can be envisaged.

Radar interferometry is based on two image acquisitions of the same zone fromslightly different orbits of the satellite. The phase information of the two image data files arethen superimposed. The two phase values at each pixel are subtracted, leading to aninterferogramme that records only the differences in phase between the two original images.The phase differences give the altitude variation of each pixel and enables the production ofa Digital Elevation Model. This technique might be used in the future to produce DEMs oftropical/equatorial zones at a low cost. Topographic mapping from interferometric SARobservations was described by Zebker and Goldstein (1986). Large parts of the worldpresently are not covered by accurate topographic maps. Other methods of topographicmapping are based on ground measurements which are very expensive, or on aerialphotographs/SPOT stereo pairs, which have cloud cover limitations. First results of radarinterferometry show height calculations with an accuracy of about 4 meters.

4. Interferometry

46

In 1992, an experiment was carried out using several ERS-1 SAR images of theBonn area (Germany). This technique called "differential radar interferometry" is arefinement of the interferometry technique previously presented. In this case, the first twoimages are substracted to form an interferogramme, then the second and third to formanother interferogramme, etc.... Ten images were used in the Bonn experiment. Bysubtracting the interferogrammes, it is possible to produce a final interferogramme that showswith greater accuracy the changes that occured between the image acquisitions. In the caseof the Bonn experiment, corner reflectors had been placed on the ground and moved slightly.As a result of the image processing, it was possible to identify which corner reflectors hadbeen moved and to measure displacements of less than one centimeter. Differentialinterferometry (see Gabriel et al., 1989) opens up new fields of application such as detectingmovements of the Earth's surface, or to monitor areas with landslide risk.

5. Hydrology

Microwave remote sensing for hydrologic modelling and soil moisture estimationwas described by Engman (1990). In the case of soils, microwave measurements are verysensitive to the moisture content of the upper soil layer. The need for soil moisturemeasurements through active or passive microwave results from the high spatial and temporalvariability of soil moisture which is a function of such parameters as soil properties,vegetation cover and pluviometry. Since most ground measurements are valid only at a singlelocation and are difficult to extrapolate, microwave remote sensing has a strong potential. Inorder to delineate hydrological units, it is thus important to define methods to quantify soilmoisture spatial variability. It is also useful to produce soil moisture maps at global orcontinental level, with a high repetitivity. This type of information, for which satellite remotesensing is a useful acquisition tool due to its synoptic view, can be used as input data formeteorologic models. For many hydrologic applications, changes in soil moisture may bemore relevant than the actual absolute value of the soil moisture.On radar images, it is generally difficult to separate the backscattered components related tosurface roughness, vegetation cover and soil moisture. However, for monitoring purposesduring short periods, the change provoked by roughness variation is minor and oftennegligeable compared with the influence of moisture variation. It should be noted that indirectinformation on soil moisture may also be obtained in other parts of the electromagneticspectrum such as middle and thermal infrared. A limiting factor with present radar systemsis that they are sensitive to soil moisture only in the top 5-10 cm of the soil which isinsufficient to assess the moisture condition of the root zone of many plants. Beaudoin etal.(1990) studied the effect of the multiscale surface geometry on the sensitivity of C-bandSAR data to soil moisture.

47

6. Oceanography, sea ice and coastal zone studies

Microwave energy incident on the ocean surface is scattered in many directions.Part of this energy is backscattered towards the radar and carries information on the seasurface. The intensity of the return signal is a function of several parameters such as theradar viewing angle, frequency, polarisation and also of the sea surface roughness relativeto the incident radiation wavelength. The sea surface roughness is influenced by the presenceof short wavelength capillary waves riding on wind waves and ocean swell. The frequencyand amplitudes of the capillary waves depend on wind speed over the ocean surface, seacurrents and, in the case of shallow water with significant current flow, sea bottomtopography. Fig.42 shows an ERS-1 SAR image of the Isle of Wight (United Kingdom). Wecan see the English Channel in light wind conditions with southwesterly swells of some 2 kmwave length. The many ships are represented by bright points, a few of them have visibleshort wakes. This suggests that the dark trail, over 60 km long and 2 to 5 km large must bea very recent oil spill. Most irregular features in the sea are related to the sea bottomtopography and the prevailing current. Since microwaves do not penetrate water more thana few millimetres, such features can only be seen because of the local modification of the seasurface. This also accounts for the bright stripes close to the southern coast of the Isle ofWight, revealing the currents around Saint Catherine's Point.

In addition to the SAR, information acquired by the other modes of the ActiveMicrowave Instrument (AMI) of ERS-1 about the ocean surface include:- wave length and wave direction by the wave mode,- wind speed and wind direction by the scatterometer,- significant wave height and wind speed by the altimeter.In contrast to SAR imagery, this information is part of the LBR (Low Bit Rate) data streamwhich is acquired world wide, recorded on board and then down linked to ESA stations. Thedata are distributed in real time to meteorological services and forecasting centres. Theyserve as observations for shiprouting, fishing, oil platforms and coastal studies. These dataare an essential input to global weather and sea state forecasting models.

In the arctic zones, early spaceborne radar images were used to detect thepresence of icebergs and their movements, and also to assess ice condition. This type ofapplication allows an optimisation of ice-breaker movements and was one of the originalconcepts of the Canadian Radarsat project. Shortly after the satellite launch, ERS-1 SARimages were used to guide m/v Astrolab on her expedition through the Northeast passage.An ice validation experiment for ERS-1 data was carried out in the Barent Sea in March1992 using an ice strengthened vessel and a helicopter for in situ data. An additional researchvessel operated at the ice edge. During the cruise ERS-1 images were received in near realtime via telefax in order to steer the vessel in zones of major interest. Results show that withSAR data, different ice types could be separated such as young ice, refrozen leads, multiyearice in large flows, rubble fields, ridges, smooth first year ice. While in winter conditions theice edge is well defined in SAR images, summer conditions with winds and wet ice presentsome difficulties (Stein et al., 1992).

48

Fig.43 shows an ERS-1 SAR multitemporal image of Venice (Italy) and illustratesthe usefulness of SAR imagery for coastal zones management. The image is a colourcomposite of three dates: 10 May 1992, 23 August 1992, 27 September 1992 asBlue/Green/Red. Areas and objects keeping unchanged their response to the radar throuhoutthe period are shown in white or any grey level, while changes are indicated in colours. Nochange is observed in built-up areas (light grey and white spots), in woodlands (homogeneousgrey) or in pasture (very dark). Changes are observed mainly in field crops. Magenta standsfor wheat fields because they are fully vegetated in May and ploughed (rough surface) inSeptember. Fields appearing in red or yellow are late crops (corn, sugar beet). The coloursin the Adriatic Sea are driven by the roughening effect of the wind at the time of the dataacquisitions. Due to the high sensitivity of the radar even light winds are registered (colours).The elongated features are believed to be films of oil produced by micro-organisms, floatingon the water and damping the wind effect. The tiny colour spots in the sea and in theharbours of Venice and Mestre are ships in their position at the different dates, i.e. the redspots reveal the presence of ships only on 27 September 1992.

7. Environmental monitoring

In 1979, SAR 580 data (bands Land X) were used for flooding assessments inthe Red River Valley, in Manitoba, Canada (Lowry et al., 1979). Radar images weremosaiqued, transfered to 1:50 000 maps, then to 1:250 000 and 1:500 000 maps. Satisfactoryresults were obtained and it was possible to measure rather accurately the maximum extentof the flood. Due to persistent cloud cover in these circumstances, airborne or spaceborneoptical data cannot be acquired for such an objective.

Oil pollution detection using SAR data is another promising field ofenvironmental applications. In general, the grey level of a target on a SAR image is a resultof its surface geometry: the rougher the surface, the brighter it appears in the image. On awater body, surface roughness is induced by wind and current but can be reduced by analteration of the surface tension due to a top layer, even very thin, of natural or spilled oil.Typically, a water surface appears black if the wind speed does not exceed 3 meters/second.In the case of higher wind speeds over dark appearing water surfaces, the presence of asurface layer film is probable. It has also to be noted that at wind speeds exceeding 10meters/second, it has not been possible to detect an effect of natural or artificial oil films onthe short frequency windwaves. Hence, oil spill monitoring using SAR data is limited by thislower and upper wind speed.

Figure 44 shows a very dark area in and around the bay of La Corufia (Spain).This image was taken after the accident of the "Aegean Sea" oil tanker on the 4th December1992. On the 13 December 1992, La Corufia Meteorological Station observed variable wind1 to 3 meters/second from east to south, assumed to be stonger over the sea. After theaccident on the 4 December, winds remained light from south to west but were very strongwesterly and northwesterly up to 23meters/second around the 7 December, turning tonortheasterly 10 to 12 December, with a short maximum of 13 meters/second on the 11December. The very dark area can be caused by a calm or very low wind while in the outerbay the windspeed is most probably higher than 3 meters/second. However, observationsfrom helicopters suggest that the whole bay is polluted by some 70.000 tons of crude oil spilt

49

by the tanker grounded near Torre de Hercules at the entrance of the Ria de Corufia, Thenortheasterly wind of the previous days caused the slick to drift along the coast. Due to theparticularly favourable meteorological conditions, and assuming constant wind speed, thethickness or degradation of the oil can be mapped due to the different grey tones on theimage. Black would be the thickest film (strong damping effect), while the dark smears atthe southwest may contain more dispersed oil. The oil in the dark grey part extending tosome 20 km from the coast in the southwest is probably further disintegrated. Beyond thiszone, the ocean looks homogeneous and brighter with no visible trace of contamination. Thecoast at the north also seems to be polluted, probably due to a period of southerly winds orcurrents preceding the 12 December.

Assuming precise wind measurements and the knowledge of the oil type as wellas the spilling time, it will be possible to assess the damping of the small wind waves ondifferent parts of the slick. Such information is valuable for the cleaning operation. It canalso be used as input data and for updating drifting and dispersion models for forecasting andalerting.

Figures 27 and 28 (from NASA Earth Observing System Reports, Vol.Ilf SAR,1989) present a summary of frequency and polarization requirements for five disciplines:glaciology, hydrology, vegetation studies, oceanography and geology. Examples ofapplications of ERS-1 SAR data are presented in the Proceedings of the First ERS-1Symposium "Space at the Service of our Environment" (Cannes, 1992, Ref. ESA SP-359).

50

C-BAND X-BANDL-BAND

GLACIOLOGY

• SEA ICE TYPE DISCRIMINATION

e SEA ICE DYNAMICS

• LAKE AND RIVER ICE OBSERVATIONS

• ICE SHEETS AND SHELVES

• SNOWPACK EXTENT, CONDITION

HYDROLOGY

• SOIL MOISTURE

• SURFACE ROUGHNESS, EROSION

• LANDFORM PATTERNS

• LAND-WATER BOUNDARIES

• SNOWPACK EXTENT, CONDITION

VEGETATION

• STANDING BIOMASS

e CANOPY MOISTURE

• SURFACE BOUNDARY LAYER STATE

OCEANOGRAPHY

• CURRl::NTS, FRONTS, AND EDDIES

• INTERNAL, SURFACE WAVES

• SURFACE WIND STRESS

• BATHYMETRIC FEATURES

(OR)

r'·mfttllf:\tlttf@@M:::JKj

GEOLOGY

• CRUSTAL STRUCTURE; TECTONICS

e ARID LANDS STUDIES

• DESERTIFICATION

I> : t:INEXT BEST

~THIRD CHOICEBEST UNKNOWN

Fig.27: Summary of frequency requirements for five science disciplines(from NASA SAR EoS, Vol.Ilf, 1989)

51

ttGLACIOLOGY HH

(OIR)vv HV

• SEA ICE TYPE DISCRIMINATION ···:. ..(UR)

• SEA ICE DYNAMICS :-:-... ••.:::,:::!=~w~:::-. -: : ··::-:·;:..-.:.-:~=--~~~

!UHi• LAKE AND RIVER ICE OBSERVATIONS

(OR)• ICE SHEETS AND SHELVES

I I• SNOWPACK EXTENT, CONDITION

HYDROLOGY

e SOIL MOISTURE

• SURFACE ROUGHNESS, EROSION(OR)

• LANDFORM PATTERNS

e LAND-WATER BOUNDARIES --=: • . •. ~:::.«=:::?~:>:;:;:.;:::$.<:-~-..:&&:~~:::11I I

e SNOWPACK EXTENT, CONDITION

VEGETATION

• STANDING BIOMASS

e CANOPY MOISTURE =·:::::~::::~::.::~:::::::::::::::=:~;:::::::;:::.-:::::;-:::;::~====~===:~:%::::I

• SURFACE BOUNDARY LAYER STATE

e CANOPY GEOMETRY

OCEANOGRAPHY

e CURRENTS, FRONTS, AND EDDIESI

• INTERNAL, SURFACE WAVES

• SURFACE WIND STRESS

e BATHYMETRIC FEATURES

GEOLOGY

• CRUST AL STRUCTURE, TECTONICS ··;. . 5:;:?=:::~J?:::».-:!:;.::::*-~:;:r:~:;.:

• ARID LANDS STUDIES t:::-.-:· x -:.::::::::: .-:-:I

• DESERTIFICATIONI

f/??@@,lTHIRD CHOICEBEST NEXT BEST UNKNOWN

Fig.28: Summary of polarization requirements for five science disciplines(from NASA SAR EoS, Vol.Hf, 1989)

52

V. CONCLUSIONS

Airborne radar imagery has already demonstrated its usefulness for renewable resourcestudies in tropical or equatorial countries, for example Panama, Brazil, Nigeria andColombia. Seasat (1978), SIR-A (1981) and SIR-B (1984) short term missions provided alarge data set of L band spaceborne data that were analysed by researchers and users in thefield of renewable and non-renewable resources.

In Europe, SAR 580 AGRISAR, AGRISCATI and MAESTRO airborne campaignsprovided encouraging results. Important progress has resulted from the exploitation of ERS-1satellite by the European Space Agency since 1991. ERS-1, to be followed by ERS-2, is thefirst long term orbital mission to include an imaging radar (See Annex III); for the first time,it becomes possible for areas within station visibility to take advantage of the high revisitcapability of imaging radars.

In 1993 Canada initiated an airborne global SAR programme, the Globesar 93. In theframework of this programme, SAR image data are recorded in the C and X-band modesover test areas in Africa, Asia/Pacific, Middle East and Europe. The objective of thisprogramme is to strengthen capacities of participating countries in the efficient applicationof radar imagery for sustainable management of natural and environmental protection.

JERS-1 satellite (Japan) was launched in February 1992, and Radarsat is scheduled forlaunch in 1995. These two satellites are equipped with an imaging radar. It thus appears thatduring the next few years a larger quantity of spaceborne radar data will be available.However, from the user point of view, what is more important is not the quantity of availabledata but the information that can be extracted from satellite images for land resources studies.

In order to be able to extract meaningful thematic information it is important to developoperational tools of microwave/optical data interpretation and analysis. These tools shouldinclude SAR filtering and SAR texture analysis options. In addition, the remote sensingcommunity will have to get more familiar with SAR geocoding which is a necessary step tobe able to integrate SAR data with existing maps or other remote sensing data, in aGeographic Information System for example. In hilly zones, SAR geocoding requires aDigital Terrain Model and is thus associated presently at high production costs. Satisfactorythematic results can be obtained in moderate to planar terrain through an optical/microwaveimage to image registration. The contribution of microwave SAR data for the study of thesurface physical parameters is useful and SAR data are considered as complementary tooptical satellite data (NOAA-AVHRR, SPOT, Landsat, MOS).

53

A

B

Fig.29: Effect of the wavelength used, SAR 580 images, HH, 12 July 1984Region of Freiburg (Germany), Scale 1:60 000A = L-band B = X-band (Sieber, Noack, 1986)

54

A

B

Fig.30: Effect of the polarization usedA. Landes (France) JPL SAR, L-band, 16 August 1989Scale 1:100000, RH-Red, VVGreen, HV-BlueB. Thetford (United Kingdom), JPL SAR, C-band, 16 August 1989Scale 1:100 000, RH-Red, VV-Green, HV-Blue

55

Fig.31: Example of speckle, Agricultural zone of Flevopolder (Netherlands)SAR 580 airborne image, 6.4 km x 6.4 km, 28 August 1978, 3 looks

56

46'

A

46

7° 974 800---------·- l 2223 000

824 7002223 000

B

2067000824 700 6'

20670007" 974800

Fig.32: Example of SAR image geocoding and terrain correction,A = Slant range image B = Geocoded imageSeasat scene of Geneva, 19 August 1978Acquisition: ESA/Earthnet Oakhanger/RAEProcessing: DLR Oberpfaffenhofen, Geocoding: RSL, Zurich University

57

Fig.33: Airborne SAR image (Pointe Noire, Congo)Scale 1:100 000, X-band, HH, 6 m x 6 m resolution, 1989INTERA STAR System, INTERA Technologies, Calgary, CanadaA.Pine trees B.Eucalyptus

58

1 New residential development2 New industrial development3 Clearing in forest cover4 Major river5 Mangrove/Nypa palm6 Transmission towers7 Secondary road8 Rubber plantation

Fig.34: Airborne SAR image (Kedah, Malaysia)Scale 1:100 000, X-band, 6 m x 6 m resolution, 1989INTERA STAR System, INTERA Technologies, Calgary, Canada

59

A

500 M

B

Fig.35: Use of texture analysis for forest classification (Orleans, France)SAR 580 airborne data, X-band, 6 meters resolutionA.Original data, (yellow white = high backscatter, dark red = low backscatter)B.Image after textural segmentation, (green = forest, orange yellow = fields)(Laur et al., 1987)

60

Fig.36: ERS-1 SAR image of "Serra dos Calabis" region, Brazilian Amazonia(Matto-Grosso State), 1: 500 000 scale. Acquired by Cuiaba Receiving Station,Date 3 May 1992, Processed at D PAF, 3 looks, 12.5 m x 12.5 m pixel spacing.Deforested zones (low backscatter, black and dark tonalities) can be identifiedin the surrounding forest (high backscatter, light tonality).

61

Blue = Seasat SAR(ESA EOQ No 33, 1991)

Fig.37: Optical/microwave colour composite, Grombalia region (Tunisia)Red = Landsat TM 4 (Near Infrared)Green = Landsat TM 2 + TM 3

2

62

Fig.38: Optical/microwave colour composite (Songkhla region, Thailand)SPOT XS 3/Speckle filtered SIR-A/ SPOT XS 1 as Red/Green/Blue.Clouds appear in different colours, either they are located over sugar palms(white) or paddy fields and swamps (magenta). Sugar palms plantations (green)become distinguishable from paddy fields (brown to red)(ESA EOQ No 31, 1990).

63

Fig.39: Multitemporal ERS-1 SAR image (Nitra region, Slovakia)10 July 1992 = Red16 September 1992 = Green

64

Fig.40: Example of filtered ERS-1 SAR image (Nitra, Slovakia)A. ERS-1 image (10 July 1992)B. ERS-1 image (18 September 1992)C. Multitemporal SAR image (10 July = Red, 18 September = Green)D. Filtered multitemporal SAR image.

A B

65

/

\'I

I'•;

I '

Fig.41: Radar penetration capability of dry sand (South of Hoggar, Algeria).Comparison at 1:750 000 scale between:A.Laµdsat Thematic Mapper image, 19 January 1985(Acquisition ESA/Earthnet, distribution Eurimage)B. SIR-A image, L band, 14 November 1981(Source Elachi JPL, NSSDC and World Data Centre A for Rockets and Satellites,GAF processing)

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Fig.42: ERS-1 SAR image of Isle of Wight, United Kingdom, 29 December 1991100 km x 100 km, 3 looks, 12.Sm x 12.Sm pixel, processed at Frascati(Earthnet/PCS/VMP)

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Fig.43: ERS-1 SAR multitemporal image of Venice (Italy)(10May 1992 = Blue, 23 August 1992 = Green, 27 Septembre 1992 = Red)Acquired at Fucino, Processed by 1-PAF, Size 95 km x 95 km

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Fig.44: Use of SAR imagery for oil spill detection and monitoring.ERS-1 image of La Corufia, Spain acquired on 13 December 1992,100 km x 100 km, 3 looks, 12.Sm x 12.Sm pixel, processed at Frascati(Earthnet/PCS/VMP). The image was acquired further to the accident of 4December 1992. The oil polluted zone appears in black due to the modificationof the ocean surface roughness.

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RADAR GLOSSARY (from Raney, CCRS)

This Glossary is designed to include most of the specialized terminology arising in basicsynthetic aperture radar (SAR) applications and image interpretation. Italicized wordsin the text suggest cross references. Boldface words suggest definitions implied by thecontext, and words which are important, but have no individual listing.

Almaz: Synthetic aperture radar (Ssband) satellite launched by the USSR in May, 1991,which operated until October 1992. (See Proceedings IEEE, Special Section, June 1991).

Azimuth ambiguities: (See Doppler frequency).

Absorption: Reduction in strength of an electromagnetic wave propagating through a medium,determined by dielectric properties of the material.

Along-track: Dimension parallel to the path of the vehicle carrying the radar, sometimescalled the cross range or azimuth direction for side-looking radars.

Amplitude: Measure of the strength of a signal, and in particular the strength or "height" ofan electromagnetic wave (units of voltage). Amplitude may imply a complex signal, whereasthe term magnitude is not ambiguous.

Antenna: Device to radiate electromagnetic energy on transmission by a radar, and to collectsuch energy during reception. An antenna pattern is designed with spatial directivity, whichco_ncentrates the energy into a beam in both the vertical (elevation) and the horizontal(azimuth) directions. The electrical losses of an antenna together with its directivity determinethe antenna gain. In general, the beamwidtb in any plane is inversely proportional to theaperture width in that plane, and directly proportional to the wavelength of the radiation.Polarization on transmit and on receive is determined by the antenna.

Attenuation: Decrease in the strength of a signal, usually described by a multiplicative factorin the mathematical description of signal level. A signal is attenuated by application of a gainless than unity. Common causes of attenuation of an electromagnetic wave include lossesthrough absorption and by volume scattering in a medium as a wave passes through.

Aspect angle: Description of the geometric orientation in the horizontal plane of an objectcinthe scene with respect to the illuminating wavefront. (See incidence angle).

Automatic gain control: Adaptive change in radar gain in the along-track direction, tocompensate changes in average scene reflectivity.

Azimuth: The relative position of an object within the field of view of an antenna in theplane intersecting the moving radar's line of flight. The term commonly is used to indicatelinear distance or image scale in the along-track direction.

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Azimuth-resolution: Resolution characteristic of the azimuth dimension, usually applied tothe image domain. Azimuth resolution is fundamentally limited by the Doppler bandwidthof the system. Excess Doppler bandwidth is usually to allow extra looks, at the expense ofazimuth resolution.

Backscatter: The (microwave) signal reflected by elements of an illuminated scene back inthe direction of the radar. It is so named to make clear the difference between energyscattered in arbitrary directions, and that which returns to the radar and thus may be receivedand recorded by the sensor.

Bandwidth: A measure, according to a standard definition (see width), of the span offrequencies available in a signal or other distribution, or of the frequency limiting stages inthe system. Typical bandwidths in the range channel of a SAR are on the order of 20Megahertz, and in the azimuth channel are on the order of 1 Kilohertz. The azimuthfrequency domain is also known as the Doppler domain. Bandwidth is a fundamentalparameter of any imaging system, and determines the ultimate resolution available.

Beamwidth: A measure, according to a standard definition, of the width of the radiationpattern of an antenna. For SAR applications, both the vertical beamwidth (affecting the widthof the illuminated swath) and the horizontal or azimuth pattern (which determines, indirectly,the azimuth resolution) are frequently used concepts. Beamwidth may be measured in theone-way or two-way form, and in either voltage or power.

Bragg scattering: Enhanced backscatter due to coherent combination of signals reflected froma rough surface having linear features orthogonal to the illuminating wavefront and whosespacing is equal to half of the wavelength as projected onto the surface.

Brightness: Property of a radar image (digital or optical) in which the observed strength ofthe radar reflectivity is expressed as being proportional to a digital number (digital image file)or to a gray scale mapping, which, for a photographic positive, shows "bright" as "white".

Calibration: Process whereby one may related the digital numbers describing an image tophysical quantities such as reflectivity, geometry (position or size), or phase.

C-band: Microwave band in which the wavelengths are at or near 5.6 cm.

CCRS: Canada Centre for Remote Sensing, Ottawa, Ontario. CCRS is the leading centre inCanada for the development of imaging radar and other remote sensing applications andtechnology.

CCT: Computer compatible tape.

Chirp: Typical phase coding or modulation applied to the range pulse of an imaging radardesigned to achieve a large time-bandwidth product. The resulting phase is quadratic in time,which has a linear derivative: such coding is often called linear frequency modulation, orlinear fm.

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Coherent: Property of a signal or data set in which the phase of the constituents ismeasurable, and plays a significant role in the way in which several signals or data combine.The power Peoh of coherent signals S; combine according to the sum of the signals, magnitudesquared, Pc•••= I s, + Si + ... I 2• (See noncoherent.)

Coherent reflector: Simple or complex surface (such as a comer reflector) from whichreflected wave components are coherent with respect to each other, and thus combine to yieldlarger effective power than would be observed from a diffuse scattering surface of the samearea.

Complex (number): In the sense of radar systems, this implies that the representation of asignal or data file needs both magnitude and phase measures. In the digital SAR context, acomplex number is often represented by an equivalent pair of numbers, the in-phase (I)component and the quadrature (Q) component. For any complex number a, the relationshipsare a = re'" = I + jQ, where I = r cose, Q = r sine, and j = (-l)'h. In the exponentialnotation, r is the magnitude and cp is the phase of the number a, which is the complexamplitude (sometimes, simply called "amplitude" which could be confused with"magnitude"). For coherent systems such as SAR, the role of complex numbers is anessential part of the signal, since signal phase is used in the processor to obtain highresolution.

CSA: Canadian space Agency, Ottawa, Ontario, Canada.

Conductivity: Property of a material to allow electrical current to flow with very little loss.For natural surfaces, conductivity in general is increased with increased moisture content.

Conservation of Confusion: Principle, for imagery derived from a given SAR, that theamount of "information" in the data is a constant. One expression of this rule is that theproduct of the range and the azimuth resolution divided by the number of statisticallyindependent looks is a constant, which serves as a figure of merit of the system. (In thiscontext, "information" is related to the statistical degrees of freedom in the data ensemble,and not necessarily to knowledge about objects in the scene.)

Conservation of Coordinates: Principle, for synthetic aperture radar imagery, that imageposition is not changed by pitch, roll, or yaw rotations of the radar, since range isdetermined by the speed of light, and azimuth is determined by the along track radarvelocity.

Conservation of Energy: Principle, assuming that all available data is used for each case, thatthe average value of the estimated reflectivity from a scene is a constant for a given SAR andprocessor, independent of the number of looks used, and independent of any time varyingnoncoherence in the scene (such as from a moving surface of water) or in the radar/Processorcombination.

Comer reflector: Combination of two or more intersecting specular surfaces that combineto enhance the signal reflected back in the direction of the radar. Strongest reflection isobtained when the materials are good conductors.

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Decibel (dB): Measurement of signal strength, properly applied to a ratio of powers. Fora signal power P compared by ratio to a reference power p••r, the formal definition isPdB = 10 log10(P/P rer). DeciBels often are used in radar, such as in measures of reflectivity,for which the dynamic range may span several factors of ten. The unit is named in honourof Alexander Graham Bell, inventor of the telephone.

Depression angle: Usually refers to the line of sight from the radar to an illuminated objectas measured from the horizontal plane at the radar. For image interpretation, use of the termis not recommended because it does not account for the effects of Earth curvature, and itdoes not conveniently include effects of local slope in the scene. It is more appropriate forengineering description of the vertical antenna pattern at the radar itself. (See incidenceangle.)

Detection: Processing stage at which the strength of the signal is determined for each pixelvalue. Detection removes phase information from the data file. The preferred detectionscheme uses a magnitude squared method, I s I 2, which is energy conserving, and has unitsof voltage squared per pixel. (See image.)

Dielectric: Material which has neither "perfect" conductivity nor is perfectly "transparent"to electromagnetic radiation. The electrical properties of all intermediate materials, such asice, natural foliage, or rocks, may be described by two quantities: relative dielectric constant;and loss tangent. Reflectivity of a smooth surface and the penetration of microwaves into thematerial are determined by these two quantities.

Dielectric constant: Fundamental (complex) parameter, also known as the complexpermittivity, that describes the electrical properties of a lossy medium. (See permeability.)By convention, the relative dielectric constant of a given material is used, defined as the(absolute) dielectric constant divided by the dielectric constant of "free space". The (relative)dielectric constant is usually defined as e = t' - jt". (It is common practice to refer to thereal component t' as "the dielectric constant", whose partner, the loss tangent, accounts fort" .)

Diffuse: Reflection typically made up of many individual reflections having random phasewith respect to each other, such as from a natural forest canopy or agricultural field. Theterm is also used to describe a surface that reflects (microwave) illumination in this fashion.(The opposite term is specular or coherent.)

Digital number (DN): Numerical number, between zero and 255 for example, assigned toeach spatial grid position in the file representing the brightness levels of an image. Thedigital numbers may be related to sigma nought of scene elements through the process ofcalibration.

Dihedral: Corner reflector formed by two surfaces orthogonally intersecting. For enhancedbackscatter, the dihedral must be open to the radar, and have the axis of intersection at rightangles to the direction of illumination.

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Distributed scatterers: Elements of a scene consisting of many small scatterers of randomlocation, phase, and reflectivity in each resolution cell. (See diffuse.)

Distribution: General purpose mathematical description of a signal characterized by valueswith magnitude significantly larger than zero over only a relatively small span in time ordistance. A distribution may have extensive low level tails or sidelobes. Examples ofdistributions include the pulse transmitted by a radar, and the description in space of thepattern of an antenna.

Doppler (frequency): Shift in frequency caused by relative motion along the line of sightbetween the sensor and the observed scene. In SAR, it is more formally the first derivativeof the signal phase in the azimuth direction. The span of useful Doppler frequenciesilluminated by the antenna must be smaller than the azimuth pulse repetition frequency (prf),otherwise false image features (azimuth ambiguities) will occur.

Dynamic range: A description of the variety of signal amplitudes (or power levels) availablein a system, or present in a data file. Dynamic range is specified either i) to be withinminimum and maximum values, or ii) with respect to the ratio of maximum to minimumvalues. The most important specification is linear dynamic range over which signals combineaccording to the property of linearity.

EOS SAR: Satellite proposed by JPL to carry a three frequency quadrature polarimetric SARfor the Earth Observation Satellite series. If approved by NASA, it would be operational onlyafter the year 2000.

Electromagnetic (em) wave: A wave described by variations in electric and magnetic fields,elegantly formulated by J.C. Maxwell in 1873. Light waves, radio waves, and microwavesare well known examples. All such waves propagate at the speed of light in "free space",which includes most realistic atmospheric conditions. Three material parameters arenecessary and sufficient to describe em waves in a given medium: dielectric constant (orpermittivity); permeability; and conductivity.

Elevation displacement: Image distortion in the range direction of a side and downwardlooking radar caused by terrain features in the scene being above (or below) the referenceelevation contour, and thus in fact being closer to (or further from) the radar than theirplanimetric position. The effect may be used to create radar stereo images (see parallax).It may be removed from an image through independent knowledge of the terrain profile. Inmany applications, an approximate correction may be derived through shape from shadingtechniques.

Energy: For a waveform of time-limited duration such as a radar pulse reflected by anobject, the pulse energy is given by the power of the signal integrated over the duration ofthe signal (Units of watt-seconds = joules).

ERS-1: Satellite launched by ESA in July 1991. The main payload (AMI) instrument includesa SAR at C-band, VVpolarization and 23° incidence angle. [See Proceedings IEEE, SpecialSection, June 1991].

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ESA: European Space Agency, with headquarters in Paris, France.

Foreshortening: Spatial distortion whereby terrain slopes facing a side-looking radar'sillumination are mapped as having range extent reduced from their true size, which is aspecial case of elevation displacement. The effect is more pronounced for steeper slopes, andfor radars that use steeper incidence angles.

Fourier transform: Mathematical operation used to derive thefrequency domain descriptionof a distribution. An efficient digital implementation is the "fast Fourier transform", or FFT.The inverse Fourier transform returns a frequency domain description to the originaldistribution. The digital inverse form is known as the IFFT.

Frequency: Rate of oscillation of a wave. In the microwave region, frequencies are on theorder of 1 GHz (Gigahenz) to 100 GHz. ("Giga" implies multiplication by a factor of lCJ.)For electromagnetic waves, the product of wavelength and frequency is equal to the speedof propagation, which, in free space, is the speed of light.

Frequency domain: For every distribution f in time there is an equivalent representation Fwhose independent variable isfrequency. The frequency domain representation is the Fouriertransform of the original distribution. F and fare equivalent in the sense that they carry thesame information, but expressed in an alternative way. The concept is often generalized todistributions in the space domain, for which the Fourier transform is in the spatial frequencydomain, having units of cycles per unit length.

Gain: Change in signal level due to processing functions that increase the magnitude of thesignal. Examples include: signal amplification in a radar receiver; processing gain in theprocessor; and antenna gain, a result of the directivity of the pattern.

Gaussian: The classical distribution characterized by a "bell-shaped" curve, it plays severalroles in SAR. For example, it is the "normal" probability distribution that describes the in­phase and the quadrature components of the signal corresponding to a purely diffusescattering surface, which are sometimes described as Gaussian.scatterers.

Ground range: Range direction of a side-looking radar image as projected onto the nominallyhorizontal reference plane, similar to the spatial display of conventional maps. Forspacecraft data, an Earth geoid model is used, whereas for airborne radar data, a planarapproximation is sufficient. Ground range projection requires a geometric transformationfrom slant range to ground range, leading to relief or elevation displacement, f oreshortening,and layover unless terrain elevation information is used.

HDDT: High density digital tape.

Hertz: Named after H.R. Hertz, a 19th century German physicist, it is the standard unit forfrequency, equivalent to one cycle per second.

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Histogram: Graph which plots number of samples versus digital number (the statisticaldistribution of brightness) of data selected from a region of an actual image file.

Image: Mapping of the observed radar reflectivity of a scene. for radars with digital imageprocessing, the image consists of a file of digital numbers assigned to spatial positions on agrid of pixels, and presented either as hard copy (such as a photographic print) or soft copy(such as a digital data record). All radar images are subject to statistical variations, mainlyspeckle and noise, which must be accommodated in either visual or numerical imageinterpretation. The most commonly used image formats occur after detection. Aftercalibration (and compensation for speckle and noise effects), image files from magnitudesquared detection are proportional, on average, to sigma nought a", Magnitude scaling(formed by taking the square root of the detected, look-summed file to yield and imageproportional to (u0)1h) is the "standard" for most SAR image files. A magnitude image oftenyields a photographic copy that is more readily interpreted visually, and requires lessdynamic range and data storage space. A digital SAR image file may be retained in complexformat (before detection) for specialized applications.

Impulse response: Also known as the point spread function, it is the two-dimensionalbrightness pattern in an image (after processing) corresponding to the signal reflected by anobject whose sigma falls within the dynamic range of the system, and for which the widthof the imaged pattern is determined by the radar and processor rather than by the size of theobject. (A trihedral comer reflector is the most commonly used object for generating animpulse response in a test image). A "good" impulse response has a relatively large valuefor the pixel that maps the point scatterer location, and very small values for all surroundingpixels. The impulse response is a basic building block in describing a given radar's imagingperformance, since an image is built up from the linear combination of impulse responsesfrom all individual scatterers illuminated by the radar. The width (resolution) of the impulseresponse central peak is the most important characteristic of the impulse response, togetherwith the shape of the impulse response distribution both close to and remote from its centre.

In-phase (I): Component of the signal that has the same phase as the complex referencefrequency.

Incidence angle: Angle between the line of sight from the radar to an element of an imagedscene, and a vertical direction characteristic of the scene. The definition of "vertical" for thispurpose is important. One must distinguish between the (nominal) "incidence angle"determined by the large scale geometry of the radar and the Earth's geoidal surface, and thelocal incidence angle which takes into account the mean slope with each pixel of the image.Smaller incidence angle refers to viewing line of sight being closer to the (local) vertical,hence "steeper" (See aspect angle). In general, reflectivity from distributed scatterersdecreases with increasing incidence angle.

Intensity: Strength of a field or of a distribution, such as an image file, proportional tomagnitude, squared.

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Interferometer: Device such as an imaging radar that uses two different paths for imaging,and deduces information from the coherent interference between the two signals. In SARapplications, spatial interferometry has been demonstrated to measure terrain height, and timedelay interferometry is used to measure movement in the scene such as oceanic currents.

J-ERS-1: Satellite launched by Japan in February 1992. It includes an L-band SAR, HHpolarization and 38.5° incidence angle. [See Proceedings of the IEEE, June 1991].

JPL: Jet Propulsion Laboratory, Pasadena, California. Over the years JPL and their airborneradar systems have established themselves as one of the world's leaders in civilian SARtechnology development.

L-band: Microwave band in which the wavelengths are at or near 23.5 cm.

Layover: Extreme form of elevation displacement or foreshortening in which the top of areflecting object (such as mountain) is closer to the radar (in slant range) than are the-lowerparts of the object. The image of such a feature appears to have fallen over towards theradar. The effect is more pronounced for radars having smaller incidence angle.

Linearity: Property according to which an operation on a sum of signals is equivalent to thesame operation applied to each of the signals individually, and the resulting numbers addedtogether. If C is a multiplicative constant, then a linear operation on any two numbers x andy satisfies Cx + Cy = C(x + y) + C0• {The additive constant C0 is needed to account forrealistic behaviour of many practical systems). Linearity, over the dynamic range of thesystem, is an essential attribute of most measurement devices such as an imaging radar.

Looks: Each of the sub-images used to form the output summed image, implemented in aSAR processor. Speckle, the radiometric uncertainty in each estimate of the scene'sreflectivity, is reduced by the averaging implied by adding together different detected imagesof the same scene. For N statistically independent looks (which may be implemented invarious ways), the standard deviation of each estimate is reduced by~. Multiple looks maybe generated by averaging over N, range and/or N. azimuth resolution cells. For animprovement in radiometric resolution using multiple looks there is an associated degradationin spatial resolution. Note that there is a difference between the number of looks physicallyimplemented in a processor, and the effective number of looks as determined by the statisticsof the image data.

Loss tangent: Ratio of the imaginary part of the dielectric constant to the real part, writtenat tan o = E" If.'. Low loss materials satisfy tan2 o< < 1.

Magnitude: One of three parameters required to describe a wave. Magnitude is the amplitudeof the wave irrespective of the phase. For a complex signal described by in-phase (I) andquadrature (Q) components, the magnitude is given by (12 + Q2)'h. For complex amplitudea, magnitude is, by definition, I a I . (See detection).

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Matched filter: Mathematical model of the detailed structure of a specific two-dimensionaldistribution, applied in a processor to cancel the phase structure of the desired set of signals.The matched filter (first derived by North in 1942) maximizes the signal-to-noise ratio of theprocessor output when the input is a known signal against an additive noise background.

Microwave: An electromagnetic wavelength in (or near) the span 1-100 cm.

Multi-look: (See looks).

Motion compensation: Adjustment of a radar system and/or the recorded data to removeeffects of radar platform motion, including rotation and translation, and variations in alongtrack velocity. Motion compensation is essential for aircraft SARs, but usually is not neededfor spacecraft SARs.

Nadir: Locus of points on the surface of the Earth directly below the radar as it progressesalong its line of flight.

NASA: National Aeronautics and Space Administration, with headquarters in WashingtonD.C., USA.

Noise: Any unwanted or contaminating signal competing with the desired signal. In a SAR,two common kinds of noise are additive (receiver) noise and signal dependent noise, usuallyeither additive or multiplicative. Additive receiver noise is always present in any system, andmay be reduced, relative to the signal level, by increasing the power of the radar. Signaldependent noises, such as azimuth ambiguities or quantization noise, arise from systemimperfections, and are dependent on the strength of the signal itself. "Good" SAR systemsusually keep these noise levels below acceptable levels, by design. (Speckle is sometimesconsidered to be a kind of signal dependent multiplicative noise in a SAR system.)

Noise equivalent sigma nought (o"NiJ: A measure of the sensitivity of a given SAR. it is thestrength of an hypothetical imaged distributed terrain feature that would have the sameaverage image level as does the (additive) system noise inherent to the radar. Smaller noiseequivalent sigma nought values are better. Within physical limitations, smaller a" NP.q may beachieved by increasing the power of the radar transmitter, or by decreasing the noise figureof the electronics.

Noncoherent (or incoherent): Property of a signal or data set in which the phases of theconstituents are not statistically correlated, or systematically related in any fashion. Thepower PNcoh of non-coherent signals si combine according to the power of each of theindividual signals, PNcoh = [s1]2 + [s2]2 + [s2]2 + ... (See coherent).

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One-way: The radar illuminates the scene through the transmit pattern of the antenna. Itreceives the backscattered energy through the receive pattern of the antenna. Thus thereceived pulse must travel in two ways, out to each object at range R and back again thesame distance. Numbers relating to only one direction of propagation are denoted as "one­way", and the corresponding numbers that include the round trip are called "two-way". Thedifference is important in measuring effective antenna pattern widths, in signal phase, andin the relationship between tow-way delay time t and range distance R, such that R = ct/2.(See speed of light).

P-band: As has been adopted by the SAR community, the microwave band in which thewavelengths are at or near 75cm.

Parallax: Apparent change in the position of an object due to an actual change in the pointof view of observation. For a SAR, true parallax occurs only with viewpoint changes thatare away from the nominal flight path of the radar. In contrast to aerial photography,parallax cannot be created by forward and aft looking "exposures". Parallax may be used tocreate stereo viewing of radar images.

Penetration: Act of (microwave) entering a medium such as dry sand or forest leaf canopy.Microwave penetration, in general, is proportional to the wavelength, and inverselyproportional to the loss tangent. The penetration depth op for most natural materials (excepthighly conductive media such as water) encountered in radar remote sensing is given byVE'IE"k), where k is the wavenumber, and E' is the (relative) dielectric constant of themedium.

Period: Time duration of one cycle of a wave, or of one cycle of any regularly recurringpattern. Period is inversely equal to frequency (Units of time, seconds).

Permeability: Parameter that describes the magnetic properties of a material. For remotesensing applications, (magnetic) permeability p. is essentially the same for all materials ofinterest, and plays a insignificant role in image interpretation.

Permittivity: (see dielectric constant).

Phase: The angle f/J of a complex number.

Pixel: Term derived from "picture element" in a digital representation to indicate the spatialposition of a sample of an image file, which consist of a spatial array of digital numbers.A two-dimensional ensemble of pixels forms the geometric grid on which an image is built.The fundamental parameter describing this grid is the inter-pixel spacing in each of the twoimage directions. (To confuse matters, pixel spacing is often referred to as "pixel" or "pixelsize" in the literature. Pixel "size" is to be avoided).

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Polarization: Orientation of the electric (E) vector in an electromagnetic wave, frequently"horizonal" (H) or "vertical" (V) in conventional imaging radar systems. Polarization isestablished by the antenna, which may be adjusted to be different on transmit and on receive.Reflectivity of microwaves from an object depends on the relationship between thepolarization state and the geometric structure of the object. Common shorthand notation forband and polarization properties of an image file is to state the band, with a subscript for thetransmit and the receive state of polarization, in that order. Thus, for example, ~indicatesL-band, horizontal transmit polarization, and vertical receive polarization. Possible states ofpolarization in addition to vertical and horizontal include all angular orientations of the Evector, and time varying orientations leading to elliptical and circular polarizations. (Seequadrature polarization).

Post-processing: Steps that may be applied to digital SAR image files to adjust selectedattributes of the image, such as geometric accuracy or radiometric corrections, includingspeckle reduction and contrast enhancement, or any other form of value-added processing.

Power: For a given signal, proportional to the magnitude, squared. (Units are watts).

Quadrature (Q): Signal component that is 90° out of phase with respect to the referencefrequency.

Processing: Sometimes denoted "preprocessing", it is the means of converting the receivedreflected signal into an image. Processing consists of image focusing through matched.filterintegration, detection, and multi-look summation. The output files of a SAR processor usuallyare presented with unity aspect ratio (so that range and azimuth image scales are the same).Images may be either in slant range or ground range projection. Both of these spatialadjustments require resampling of the image file.

Propagation: The movement of energy in the form of waves through space or other media.Electromagnetic waves move at the speed of light c in free space, but the speed v ofpropagation through other materials is reduced according to the dielectric constant of thematerial in question, according to v=ctYEµ..

Pulse: Group of waves with a distribution confined to a short interval of time. Such adistribution is described in the time domain (or in spatial dimensions) by its width and itsamplitude or magnitude, from which its energy may be found. In radar, use is made ofmodulated or coded pulses which must be processed to decode or compress the original pulseto achieve the impulse response observed in the image. Coded pulses have a time-bandwidthproduct that is much larger than unity. The resolution that may be achieved after processingis determined by the bandwidth of the original pulse.

Pulse repetition frequency (prf): Rate of recurrence of the pulses transmitted by a radar.

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Quadrature polarization ("quad pol") radar: System designed to simultaneously collectimaging data of a scene in two orthogonal polarization states on transmit and the same twopolarization states on receive. From such a data set a complete scattering matrix of thereflectivity of the scene may be synthesized, leading to the concept of polarization signature.The best known example of a "quad pol" radar is the AirSAR of JPL.

Radar: Electromagnetic sensor characterized by RAdio Detection And Ranging, from whichthe acronym RADAR is derived. Predicted in the early part of the 20th century, the firstimportant system was built in England in 1938. Basic building blocks of a radar are thetransmitter, the antenna (normally used for both transmission and for reception), thereceiver, and the data handling equipment. A synthetic aperture radar system, byimplication, includes an image processor, even though it may be remotely located in time orspace from the radar electronics.

Radar cross section (RCS): Measure of radar reflectivity, expressed in terms of the physicalsize of an hypothetical uniformly scattering sphere that would give rise to the same level ofreflection as that observed from the sample target. (See sigma).

Radar equation: Mathematical expression that describes the average received signal level (or,sometimes, the image signal level), compared to the additive noise level, in terms of systemparameters. Principal parameters include transmitted power, antenna gain, noise power, andradar range R. The range effect is sometimes called the spreading factor, since effectivepower decreases significantly with a small increase in range. All else equal, the powerreceived by a SAR per image pixel is proportion to R3.

RADARSAT: Satellite to be launched by Canada in 1995. It will carry a C-band SAR, HHpolarization, and incidence angles spanning (20° - 60°) selectable in a variety of modes.(See Proceedings of the IEEE, June 1991).

Radiation: Act of giving off electromagnetic energy, particularly by the antenna of a radarwhen excited by the transmitter.

Radiometric resolution: The expected spread of variation in each estimate of scene reflectivityas observed in an image. Small radiometric resolution is "better". Radiometric resolution fora given radar may be improved by averaging, but at the cost of spatial resolution. (Seelooks).

Range: Line of sight distance between the radar and each illuminated scatterer (see one-way).In SAR usage, the term also is applied to the dimension of an image away from the line offlight of the radar. (See slant range and ground range).

Range ambiguities: Unwanted echoes that fall into the image from ranges that in fact areoutside of the intended swath, due to the range sampling operation of the radar. Rangeambiguities may be minimized by antenna pattern and imaging mode control, are observedonly rarely in imagery from well designed system.

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Range curvature: Describes the changing distance between the radar and an object during thetime that the object is illuminated by the antenna. Range curvature is more important for longrange systems such as satellite SARs, and must be compensated in the processor as a partof image focusing.

Range resolution: Resolution characteristic of the range dimension, usually applied to theimage domain, either in the slant range plane or in the ground range plane. Range resolutionis fundamentally determined by the system bandwidth in the range channel.

Reflectivity: Property of illuminated objects to reradiate a portion of the incident energy.Reflectivity, in general, is larger in the specular direction for smaller surface roughness. Forside looking radars, backscatter is the observable portion of the energy reflected.Backscatter, in general, is increased by greater surface roughness. In general, reflectivity isincreased for higher conductivity of the scattering surface-.The relative strength of radarreflectivity is tabulated by sigma, for discrete objects, and by sigma nought for natural terrainsurfaces.

Relief displacement: Alternative term for elevation displacement.

Resolution Cell: A three-dimensional cylindrical volume surrounding each point in the scene.The cell range depth is slant range resolution, its width is azimuth resolution, and its height,which is conformal to the illumination wavefront, is limited only by the vertical beam widthof the antenna pattern.

Resolution: Generally (but loosely) defined as the width of the "point spread function", the"Green's function", or the "impulse response function", depending on whether one has anoptics, a physics, or an electronic systems background. More properly, "resolution" refersto the ability of a system to differentiate two image features corresponding to two closelyspaced small objects in the illuminated scene when the brightness of the two objects inquestion are comparable and fall within the dynamic range of the radar in question.(Definition adapted from Lord Rayleigh, 1879). "Higher resolution" refers to a systemhaving a smaller impulse response width.

Roughness: Variation of surface height within an imaged resolution cell. A surface appears"rough" to microwave illumination when the height variations become larger than a fractionof the radar wavelength. The fraction is qualitative, but may be shown to decrease withincidence angle.

SAR: Synthetic Aperture Radar, so-called because azimuth resolution is achieved throughcomputer operations on a set of (coherently recorded) signals such that the processor is ableto function like a large antenna aperture in computer memory, thus realizing azimuthresolution improvement in proportion to aperture size. The SAR concept was introduced byC. Wiley (USA) in 1951.

S-band: Microwave band in which the wavelengths are at or near lOcm.

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Signal: Generalized terminology used to signify a mathematical description of a wave, pulseor other sequence of interest. It often suggests the ensemble of data corresponding toobserved scattering from the scene, either before reception, within the radar or processor,or in the image file. Normally there is a distinction between "signal" and noise.

Scene: Object space illuminated by the radar.

Seasat: NASA satellite that was in operation July-September of 1978. Seasat was the first(civilian) satellite to carry a SAR. It operated at L-band, using horizontal polarization at 22°incidence angle. Data from Seasat is still important for applications and processing techniquedevelopment.

Sensitivity time control (STC): Pre-programmed change in radar gain during the time thatreflections are received after each pulse to offset unwanted changes in average signalamplitude due to weaker backscatter from greater ranges and varying incidence angles acrossthe imaged swath.

Shadow: From an optical point of view as seen from the position of a radar, a region hiddenbehind an elevated feature in the scene would be out of sight. This region corresponds to thatwhich does not get illuminated by the radar energy, and thus is also not visible in theresulting radar image. The region is filled with "no reflectivity", which appears as smalldigital numbers, or a dark region in hard copy.

Sidelobes: Non-zero levels in a distribution that are separated from the desired centralresponse. Sidelobes arise naturally in antenna patterns, for example, although in general theyare a nuisance, and must be suppressed as much as possible. Large sidelobes may lead tounwanted multiple images of a single feature.

Sigma (e): The conventional measure of the strength of a radar signal reflected from ageometric object (natural or manufactured) such as a comer reflector. Sigma specifies thestrength of reflection in terms of the geometric cross section of a conducting sphere thatwould give rise to the same level of reflectivity. (Units of area, such as metres squared).(See radar cross section).

Sigma nought (u"): The conventional measure of the strength of radar signals reflected by adistributed scatterer, usually expressed in dB. It is a normalized dimensionless number,comparing the strength observed to that expected from an area of one square metre. Sigmanought is defined with respect to the nominally horizontal plane, and in general has asignificant variation with incidence angle, wavelength, and polarization, as well as withproperties of the scattering surface itself. (See speckle, and statistics).

Signal-to-noise ratio: Quantitative basis for comparing the relative level of a desired signal,such as a SAR image, to unwanted elements, traditionally taken to be additive noise.

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SIR-A and SIR-B: NASA sponsored radar missions in the Shuttle, each lasting about oneweek. SIR-A (November 1981) was at L-band, HH polarization, nominally 50° incidenceangle, and was optically processed. SIR-B (October 1984) was also at L-band, HHpolarization, offered a variety of incidence angles from about 20° to 50°, and was digitallyprocessed.

SIR-C/X-SAR: A Shuttle radar being built for missions in 1993, 1994 and 1996. It will carrya quadrature polarimetric SAR at C- and L-bands, and an X-band HH polarized SAR(contributed by Germany and Italy). It will offer a variety of incidence angle, wavelengthband selection, resolution and polarization modes.

Slant range: Image direction as measured along the sequence of light-of-sight rays from theradar to each and every reflecting point in the illuminated scene. Since a SAR looks downand to the side, the slant range to ground range transformation has an inherent geometricscale which changes across the image swath. (See ground range).

Speckle: Statistical fluctuation or uncertainty associated with the brightness of each pixel inthe image of a scene. A single look SAR system achieves one estimate of the reflectivity ofeach resolution cell in the image. Speckle may be reduced, at the expense of resolution, inthe SAR processor by using several looks.

Swath: Width of the imaged scene in the range dimension, measured either in ground rangeor in slant range.

Specular: Coherent reflection from a smooth surface in a plane normal to the surface at anangle opposite to the local incidence angle. (From speculum, mirror in Latin).

Speed of light (c): Approximately 300,000,000 metres per second, the speed of light in "freespace", a condition typical of electromagnetic propagation through most atmosphericconditions found on Earth. Denser media, such as the atmosphere of Venus, that have a lowloss dielectric constant, retard the speed of propagation according to their materialproperties.

Statistics: Set of numbers that describes average properties of a random process, such assigma nought, the reflectivity observed from a nominally uniform scattering surfacedistributed in two dimensions, say x and y. Each observation of <I (x,y) is a sample functionhaving a variety of values at each location due to speckle, whose probability distributionfunction is determined primarily by the number NL of independent looks used in theprocessor. The average value of the corresponding image brightness, for calibrated data, is(mean) reflectivity o", and the average spread in brightness values is given by the standarddeviation, approximately given by o" r/NL.

Texture: Second order spatial average of brightness. Scene texture is the spatial variation ofthe average reflectivity. Image texture consists of scene texture multiplied by speckle.

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Time-bandwidth product: Parameter (TBP) found from the width of a distribution in the time(or space) domain multiplied by the width of the same distribution observed in thefrequencydomain. (Typically, the azimuth (Doppler modulated) signal and the range chirp coded pulseeach have TBP larger than 100).

Tone: First order spatial average of image brightness.

Trihedral: Comer reflector formed from three mutually orthogonal surfaces.

Transmission: Energy sent by the radar, normally in the form of a sequence of pulses, toilluminate a scene of interest.

Voltage: Standard unit of magnitude of an electrical signal, named after Count A. Volta,inventor of the battery (about 1800).

Volume scattering: Multiple scattering events occurring inside a medium, generally neitherdense nor having a large loss tangent, such as the canopy of a forest. The relativeimportance of volume scattering is governed by the dielectric properties of the material.

Wave: Propagating periodic displacement of an energy field. A surface wave on the waterserves to visualize the key properties of an electromagnetic wave. At any instant of time,a wave e is described by its "height" (amplitude) and its "length" (wavelength). Equallyimportant is the phase of the wave, which is the number that describes the position of the"crests" or "troughs" with respect to a given reference position. At any specific location inspace, propagation of the wave occurs. From this perspective, its frequency may beobserved. A wave propagates within a given medium at a speed given by the product of itswavelength and its frequency. In radar, waves are very well represented by families ofsinusoidal functions, so-called harmonic oscillation.

Wavefront: Three dimensional surface in space for which the field radiated by an antennahas the same phase at all points. At a large distance R from an .antenna, the wavefront is aspherical surface with radius R over the angular window established by the antenna pattern.For most geometries encountered in remote sensing, the wavefront may be approximated bya plane tangent to the spherical surface, within a tolerance of much less than a wavelengthover a spatial scale of several resolution cells.

Wavelength (A.): Minimum distance between two events of a recurring feature in a periodicsequence, such as the crests in a wave. (Units of length, such as metres).

Wavenumber (k): By convention, the ratio 27r/'A, where 'A is the wavelength.

Width, equivalent rectangle: A standard definition to measure the effective width of adistribution. The width is that of a rectangular distribution with the same amplitude as themaximum of the distribution, and having the same area in the rectangle as is in the measureddistribution.

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Width, 3dB: Width of a distribution equal to the distance between the outer two points onthe distribution having power level half of that at the peak.

X-band: Microwave band with wavelengths at or near 3cm.

86

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ANNEX I

Annex 1: Characteristics of Shuttle borne Imaging radars

Parameters SIR-A SIR-B SIR-C/X SAR(US) (US) (US)

Flight year 1981 1984 1993

Resolution (m) 40 25* 15*

Swath 50 30*. 35-100width (km)

Frequency (GHz) 1.28(L) 1.28(L) 1.28(L)5.3(C)9.6(X)

Polarization HH HH Cross./VV

Incidence angle 47° 15-60° 15-60°

Bit rate NIA 34 46/band(Megabits/sec)

Processing Opt. Dig. Dig.

Antenna length (m) 9.4 10.7 12.1

Orbit 259 225 250altitude (km)

* Varies with incidence angle and acquisition mode

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ANNEX II

Parameters Seasat ALMAZ ERS-1 JERS-1 Radarsat ERS-2 A-SAR(US) (USSR) (ESA) (Japan) (Canada) (ESA) (ESA)

Year of 1978 1991 1991 1992 "1995 1994 1998launch

Resolution (m) 25 15-30 25 20 28 25 25-100

Swath 100 40 100 75 50-500 100 100-400width (km)

Frequency (GHz) 1.28(L) 3(S) 5.3(C) 1.3(L) 5.3(C) 5.3 (C) 5.3 (C)

Polarization HH HH vv HH HH vv HH/VV

Incidence angle 22° 30-60° 23° 35° 20°-45° 23° 13°-39°

Bit rate 110 105 60 < 105 105 > 100(Megabits/sec)

Antenna 10.74 15 10 12 14 10 10length (m)

Orbit 800 300 785 568 800 785 800Altitude (km)

Annex 2: Characteristics of long-term orbiting Imaging Radars

Altitude = 785 km cuI

Local crossingtime = 10.30a.m.

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ANNEX ill

ERS-1 Presentation

Launched on 17 July 1991 by ESA, the ERS-1 satellite is an earth-observationsatellite mainly designed for oceanographic applications; it will also provide data useful forland applications (geology, agriculture, vegetation monitoring, deforestation assessment,hydrology ... ). ERS-1 has an inclinated polar orbit (Fig. 45).

Direction oftravel trajectory

Fig.45: ERS-1 reference orbit

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1 Instruments and operation

ERS-1 has the following capacity:

-AMI Imaa:e Mode ("Active Microwave Instrument").The Synthetic Aperture Radar, when it operates in Image Mode, acquires highresolution images (30 meters) in a 100 km swath located 250 km at the right of thesatellite track. Figure 46 presents the ERS-1 SAR geometry in Image Mode.

ERS-1 SAR images have a high radiometric quality due to the sensor performancewithin the following specifications:. dynamic range > 21 db. radiometric resolution :5; 2.5 db. absolute radiometric accuracy :5; 0.9 db.

The following points should be noted:

. due to power demand, the SAR can only operate in Image Mode for 12 minutesmaximum per orbit.

. the 100 Mbit/s data flow is too high to enable on-board recording and thus SARimages can only be acquired within the reception zone of ground stations.

Among the several payload instruments, ERS-1 SAR has the higher usefulness for earthresources studies due to the complementarity between radar images and optical images(SPOT, Landsat).

- AMI Wind Mode.The purpose of the Wind Mode is to obtain information on wind speed and direction atthe surface level over the oceans. The wind vector data can then be used in globalstatistics, climatological datasets and models for weather forecasting.

- AMI Wave Mode.In Wave Mode, the Synthetic Aperture Radar allows the estimation of directional wavespectra (wave energy as a function of wavelength and direction at the ocean surface)from backscattered radiation.

- Radar Altimeter (RA).\

The radar altimeter (Ku-band, 13.8 GHz) gives high precision measurements of thesatellite altitude, and estimates wave heights and wind speed over the oceans. It ispossible to extract information on ocean circulation and also on ice surface (edges andtopography) for use in geodesy.

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- ATSR-M. (Along Track Scanning Radiometer and Microwave Sounder).This sensor is a scanning radiometer (4 infrared bands) and microwave sounder (2bands) providing measurements of cloud top and sea surface temperatures andinformation on atmospheric water content. It also provides parameters useful for sensorscalibration.

_ PRARE (Precise Range and Range-rate Equipment).It is a ranging system for precise orbit determination.

- LASER RETROREFLECTORThis instrument allows precise data on the satellite orbit to be obtained.

Concerning ERS-1, the following points must be noted:

- the simultaneous operation of all the sensors together is not possible and programmingis required, especially in the case of use of AMI in Image Mode or Wave Mode.

- Landsat and SPOT satellites keep the same orbit during their operational life and thushave a fixed nominal temporal resolution, considering vertical viewing. ERS-1 has adifferent concept and will have different orbits provoked by altitude variation during themission, and thus a variable coverage and temporal resolution. This point is importantfor mapping tropical and equatorial regions because these zones are only partiallycovered during the 3-day cycle. Figure 47 presents the ERS-1 orbit scenario.

- ERS-1 can acquire SAR data in Image Mode during an ascendant orbit as well asduring a descendant orbit. The consequence is a different illumination direction, whichmay, to a certain extent, modify a scene and provide additional information.

- the nominal incidence angle is 23° at mid-swath. In some areas with high topographicvariations, the layover effect is increased and the shadowing effect reduced due to theincidence angle. For short periods, the roll-tilt mode is used with an incidence angle of34°.

- each ERS-1 Processing and Archiving Facility (PAF) has specific attributions and doesnot process all the possible products (Fig.48).

Fig.46: ERS-1 SAR geometry in the Image Mode

SAR

Altitude(785 kmnominal)

//

Sub-/satellitetrack

97

/1Satelliteflight vector

Jf/

//

~ 294km/

/

//

//

/

100 km

98

MULTIDISCIPLINARYPHASE

LAUNCH

ITTlTTTT!-,,-,,-----1 1992E4@J-===: I 1993 ,r~· i

1991 1994,.

ROLL - TILTMODE

FIRST ICE PHASE SECOND ICE PHASE

COMMISSIONING PHASE GEODETIC PHASE

D TRANFER PERIODS BETWEEN PHASES

Fig.47: ERS-1 orbit scenario

99

EARTHNET ERS-1CENTRAL FACILITY

LBRPROCESSING

FACILITYIFREMERBREST(F)

PRODUCTS

-RA (OCEAN)-AMI (WIND)-AMI WAVE

SAR/ALTPROCESSING

FACILITYDLR

OBERPFAFFEN­HOFEN (D)

SAR/LBRPROCESSING

FACILITYRAE

FARNBOROUGH(UK)

PRODUCTS PRODUCTS

-SAR- RA (ICE/LAND)-ATSR-(AMIWAVE)

-SAR- PRECISION ORBITDETERMINATION

- ALTIMETER GEOPHYSICALPRODUCTS

U S E R S

Fig.48: Activities of ERS-1 Processing and Archiving Facilities

U S E R S

RAW DATA

i"'

SAR/LBRPROCESSING

FACILITYASI

MATERA (I)

PRODUCTS

- SAR-LBROVERMEDITERRANEAN

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2 Imaee Mode SAR Products

Two types of ERS-1 SAR Image Mode products are available:

- Fast Delivery (FD) products,- off-line products.

SAR image Mode Fast Delivery Products cover an area of about 100 km X 100 km. Theinterpixel spacing is 20 metres in ground range and 16 meters in azimuth direction. Data areavailable in 16 bit/pixel format. The product size is 5000 pixels in ground range and 6300in azimuth. These FD SAR images are processed in 3 looks. A relative calibration (gainnormalization) is performed but no absolute calibration is executed. Each SAR Fast deliveryProcessor at the ESA stations (Fucino, Kiruna, Maspalomas) is able to process 3 images inless than 90 minutes. FD products are processed on user's request immediately afteracquisition. Within a few hours from acquisition, they are then transmitted from Fucino or _Kiruna to nominated Centres within Europe via satellite link . For studies requiring a higherradiometric or geometric quality, the user will have to apply for SAR off line products.

- Basic 8-Look ImageThe Basic 8-Look Image product is a 99 km by 99 km, 8-Look (overlapping), groundrange intensity image with a spatial resolution of 30 m in range and azimuth. The imagesize is 7920 pixels by 7920 pixels, with pixel dimensions of 12.5 m by 12.5 m. Theimage volume is 59.8 Mbytes.

The following SAR image off-line Products are available:

- Complex Full-Resolution Slant Range (quarter scene only)

- Complex Full-Resolution Ground Range ImageThe Complex Full-Resolution Ground Range Image product is a 99 km by 99 km,single look, complex SAR image in ground range with a spatial resolution of 30 m inrange and 6 m in azimuth. The image size is 7920 pixels (range) by 31680 pixels(azimuth), with pixel dimensions of 12.5 m (range) and 3.125 m (azimuth). The imagevolume is 478.6 Mbytes.

- Complex Multi-Look Ground Range ImageThe Complex Multi-Look Ground Range Image product is a 99 km X 99 km, eightlook, complex SAR image in ground range with a spatial resolution of 30 m in rangeand azimuth. The image size is 7920 pixels by 7920 pixels, with pixel dimensions of12.5 m by 12.5 m. The image volume is 957.1 Mbytes.

- Basic Full-Resolution ImageThe Basic Full-Resolution Image product is a 99 km by 99 km, single look, groundrange intensity image with a spatial resolution of 30 m in range and 6 m in azimuth.The image size is 7920 pixels (range) by 31680 pixels (azimuth), with pixel dimensionsof 12.5 m (range) by 3.125 m (azimuth). The image volume is 239.8 Mbytes.

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- Basic 3-Look Image (PRI)

The Basic 3-Look Image product is 100 km in range by at least 102 km in azimuth, 3-Look (non-overlapping), ground range intensity image with a spatial resolution of 33min range and 30 min azimuth. The image size is 8000 pixels by at least 8200 pixels(azimuth), with pixel dimensions of 12.5 m by 12.5 m. The image volume is 131.2Mbytes. This product is corrected for the antenna pattern and is calibratedradiometrically. Users have access to a calibration constant that can be used to derivebackscatter coefficients from pixel digital numbers (Laur, 1992).

- Multi-Look Geocoded Image (GEC)

The Multi-Look Geocoded Image product is a SAR image rectified onto a specified mapprojection (Universal Transverse Mercator) oriented grid north. The imaged area, ineach dimension, is not greater than 140 km and not less than 99 km depending on thelatitude of the image and the flight direction of the satellite. The image size is 7920-112000 pixels by 7920-11200 pixels, with pixel dimensions of 12.5 m by 12.5 m. Theimage volume is between 59.7 Mbytes and 119.6 Mbytes.

In terms of geometric corrections, the following products are available:

- Precision ImageSAR image generated from raw data with instrumental and orbital corrections applied,precisely located and calibrated.

- Ellipsoid geocoded ImageGeocoded SAR image generated from raw data with instrumental and orbital correctionsapplied, precisely located and rectified onto a map projection.

- Terrain Geocoded Image (GTC}Geocoded SAR image generated from raw data with instrumental corrections applied,precisely located, rectified onto a map projection and corrected for terrain variations.

- Radar Incidence Angle MaskGeocoded digital layer, which indicates the local geometric behaviour of the radar beamversus the terrain slope. The incidence angle per pixel and the layover and shadowconditions are coded. It is only generated as by-product of Terrain Geocoded Image.

Canada/USA- Radarsat International,Data Centre13800 Commerce Parkway,Richmond, British ColombiaV6V 213, CanadaFax: (604) 224-0404

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ANNEX IV SAR DATA AVAILABILITY

ERS-1: - ERS-1 Help Desk,ESRIN, Via Galileo Galilei, CP 64I-0044 Frascati, ItalyTel: 39-6-94180 600Fax: (+39-6) 94180 510

Europe/Middle East/North Africa- Eurimage ERS-1 Customer ServicesESRIN, Via Galileo Galilei, CP 64I-0044 Frascati, ItalyFax: (+ 39-6) 94180510

Seasat, SIR-A, SIR-B: - World Data Center A for Rockets and SatellitesCode 930.2Goddard Space Flight CenterGreenbelt, Maryland 20771, USAFax: (301) 286-4952

Rest of the World:- Spot Image ERS-1 Customer ServiceBP 4859-16 Bis Avenue Edouard Belin31000 Toulouse Cedex FranceFax:(33)61281354

Seasat Europe- Earthnet, ESRIN, CP 64I-0044, Frascati, ItalyFax: (+ 39-6) 94180361

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ALMAZ: - NPO Machinostroyenia33 Gagarin StreetReutov Moscow Region 143952, USSRFax: 302-20-01

- EurimageViale E.D'Onofrio, 21200155 Rome, ItalyFax: (+39-6) 40694232

JERS-1: - NASDAWorld Trade Center Building2-4-1, Hamamatsu-cho, Minato-kuTokyo 105 JapanTelex: J28424(AAB:NASDA 128424)

- EurimageViale E.D'Onofrio, 21200155 Rome, ItalyFax: (+39-6) 40694232