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Principles of Remote Sensing 10: RADAR 3 Applications of imaging RADAR. Dr. Mathias (Mat) Disney UCL Geography Office: 113, Pearson Building Tel: 7670 0592 Email: [email protected] www.geog.ucl.ac.uk/~mdisney. AGENDA. Single channel data Radar penetration Multi-temporal data

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Principles of remote sensing 10 radar 3 applications of imaging radar

Principles of Remote Sensing 10: RADAR 3Applications of imaging RADAR

Dr. Mathias (Mat) Disney

UCL Geography

Office: 113, Pearson Building

Tel: 7670 0592

Email: [email protected]

www.geog.ucl.ac.uk/~mdisney


Agenda
AGENDA

  • Single channel data

    • Radar penetration

  • Multi-temporal data

  • Vegetation, and modelling

    • Agriculture & water cloud model

    • Forest structure and coherent models

  • Multi-parameter


  • Observations of forests
    Observations of forests...

    • C-band (cm-tens of cm)

      • low penetration depth, leaves / needles / twigs

    • L-band

      • leaves / branches

    • P-band

      • can propagate through canopy to branches, trunk and ground

    • C-band quickly saturates (even at relatively low biomass, it only sees canopy); P-band maintains sensitivity to higher biomass as it “sees” trunks, branches, etc

    • Low biomass behaviour dictated by ground properties


    • Surfaces - scattering depends on moisture and roughness

    • Note - we could get penetration into soils at longer wavelengths or with dry soils (sand)

    • Surfaces are typically

      • bright if wet and rough

      • dark if dry and smooth

    • What happens if a dry rough surface becomes wet ?

    • Note similar arguments apply to snow or ice surfaces.

    • Note also, always need to remember that when vegetation is present, it can act as the dominant scatterer OR as an attenuator (of the ground scattering)


    Eastern sahara desert
    EasternSahara desert

    Landsat

    SIR-A

    Penetration 1 – 4 m


    Safsaf oasis egypt
    Safsaf oasis, Egypt

    Penetration up to 2 m

    Landsat

    SIR-C L-band 16 April 1994


    Single channel data
    Single channel data

    • Many applications are based on the operationally-available spaceborne SARs, all of which are single channel (ERS, Radarsat, JERS)

    • As these are spaceborne datasets, we often encounter multi-temporal applications (which is fortunate as these are only single-channel instruments !)

    • When thinking about applications, think carefully about “where” the information is:-

      • scattering physics

      • spatial information (texture, …)

      • temporal changes


    Multi temporal data
    Multi-temporal data

    • Temporal changes in the physical properties of regions in the image offer another degree of freedom for distinguishing them but only if these changes can actually be seen by the radar

    • for example - ERS-1 and ERS-2:-

      • wetlands, floods, snow cover, crops

      • implications for mission design ?


    Wetlands in vietnam ers
    Wetlands in Vietnam - ERS

    Oct 97 Jan 99 18 Mar 99 27 May 99

    Sept 99 Dec 99 Jan 00 Feb 00




    Floods
    Floods... on right)

    Maastricht

    A two date composite of ERS SAR images

    30/1/95 (red/green)

    21/9/95 (blue)


    Snow cover
    Snow cover... on right)

    Glen Tilt - Blair Atholl

    ERS-2 composite

    red = 25/11/96

    cyan=19/5/97

    Scott Polar Research Institute


    Agriculture
    Agriculture on right)

    Gt. Driffield

    Composite of 3 ERS SAR images from different dates


    OSR - Oil seed rape on right)

    WW - Winter wheat


    ERS SAR on right)

    East Anglia


    Radar modelling
    Radar modelling on right)

    • Surface roughness

    • Volume roughness

    • Dielectric constant ~ moisture

    • Models of the vegetation volume, e.g. water cloud model of Attema and Ulaby, RT2 model of Saich

    Multitemporal SHAC radar image

    Barton Bendish


    Water cloud model
    Water cloud model on right)

    A – vegetation canopy backscatter at full cover

    B – canopy attenuation coefficient

    C – dry soil backscatter

    D – sensitivity to soil moisture

    σ0 = scattering coefficient

    ms = soil moisture

    θ = incidence angle

    L = leaf area index

    Vegetation



    Response to moisture
    Response to moisture on right)

    Source: Graham 2001


    Detection
    Detection? on right)

    SAR image

    In situ irrigation

    Source: Graham 2001


    Simulated backscatter
    Simulated backscatter on right)

    r2 = 0.81


    Canopy moisture
    Canopy moisture on right)

    r2 = 0.96


    Applications
    Applications on right)

    • Irrigation fraud detection

    • Irrigation scheduling

    • Crop status mapping, e.g. disease, water stress


    Multi parameter radar
    Multi-parameter radar on right)

    • More sophisticated instruments have multi-frequency, multi-polarisation radars, with steerable beams (different incidence angle)

    • Also, different modes

      • combinations of resolutions and swath widths

    • SIR-C / X-SAR

    • ENVISAT ASAR, ALOS PALSAR,...


    Flevoland April 1994 on right)

    (SIR-C/X-SAR)

    (L/C/X composite)

    L-total power (red)

    C-total power (green)

    X-VV (blue)


    Thetford, UK on right)

    AIRSAR (1991)

    C-HH


    Thetford, UK on right)

    AIRSAR (1991)

    multi-freq composite


    Coherent RADAR modelling on right)

    Thetford, UK

    SHAC (SAR and Hyperspectral Airborne Campaign)

    http://www.neodc.rl.ac.uk/?option=displaypage&Itemid=66&op=page&SubMenu=66

    Disney et al. (2006) – combine detailed structural models with optical AND RADAR models to simulate signal in both domains

    Drat optical model + CASM (Coherent Additive Scattering Model) of Saich et al. (2001)


    Coherent RADAR modelling on right)

    Thetford, UK

    SHAC (SAR and Hyperspectral Airborne Campaign)

    http://www.neodc.rl.ac.uk/?option=displaypage&Itemid=66&op=page&SubMenu=66

    Disney et al. (2006) – combine detailed structural models with optical AND RADAR models to simulate signal in both domains

    Drat optical model + CASM (Coherent Additive Scattering Model) of Saich et al. (2001)




    OPTICAL optical data)

    RADAR


    An ambitious list of applications
    An ambitious list of Applications... optical data)

    • Flood mapping, Snow mapping, Oil Slicks

    • Sea ice type, Crop classification,

    • Forest biomass / timber estimation, tree height

    • Soil moisture mapping, soil roughness mapping / monitoring

    • Pipeline integrity

    • Wave strength for oil platforms

    • Crop yield, crop stress

    • Flood prediction

    • Landslide prediction


    Conclusions
    CONCLUSIONS optical data)

    • Radar is very reliable because of cloud penetration and day/night availability

    • Major advances in interferometric SAR

    • Should radar be used separately or as an adjunct to optical Earth observation data?

    ALOS


    Speckle filtering
    Speckle filtering optical data)

    Mean

    Median

    Lee

    Lee-Sigma

    Local Region

    Frost

    Gamma Maximum a Posteriori (MAP)

    Simulated annealing: modelling what the radar backscatter would have been like without the speckle


    Original SAR data optical data)

    Frost filter

    Gamma MAP filter

    Simulated annealing

    Retford, UK ERS-2 SAR data April – September 1998


    Original SAR data optical data)

    Frost filter


    Gamma MAP filter optical data)

    Simulated annealing

    Recommendation : use these two


    Discussion question
    Discussion question optical data)

    • What sort of radars are preferred for the following applications to be successfully realised and what is the physical basis?

      • Forest mapping

      • Flood extent

      • Soil moisture in vegetated areas

      • Snow mapping


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