Principles of Remote Sensing 10: RADAR 3 Applications of imaging RADAR Dr. Mathias (Mat) Disney UCL Geography Office: 113, Pearson Building Tel:

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... 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 SIR-A Penetration 1 – 4 m Landsat

Safsaf oasis, Egypt SIR-C L-band 16 April 1994Landsat Penetration up to 2 m

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 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 Oct 97 Jan Mar May 99 Sept 99 Dec 99 Jan 00 Feb 00

Wetlands...

SIR-C (mission 1 left, mission 2 centre, difference in blue on right)

Floods... Maastricht A two date composite of ERS SAR images 30/1/95 (red/green) 21/9/95 (blue)

Snow cover... Glen Tilt - Blair Atholl ERS-2 composite red = 25/11/96 cyan=19/5/97 Scott Polar Research Institute

Agriculture Gt. Driffield Composite of 3 ERS SAR images from different dates

OSR - Oil seed rape WW - Winter wheat

ERS SAR East Anglia

Radar modelling 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 A – vegetation canopy backscatter at full cover B – canopy attenuation coefficient C – dry soil backscatter D – sensitivity to soil moisture σ 0 = scattering coefficient m s = soil moisture θ = incidence angle L = leaf area index Vegetation

Values of A, B, C, D ParameterValueUnits / description A dB B1.945Fractional canopy moisture C dB D0.262Fractional soil moisture

Response to moisture Source: Graham 2001

Detection? SAR image In situ irrigation Source: Graham 2001

Simulated backscatter r 2 = 0.81

Canopy moisture r 2 = 0.96

Applications Irrigation fraud detection Irrigation scheduling Crop status mapping, e.g. disease, water stress

Multi-parameter radar 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 (SIR-C/X-SAR) (L/C/X composite) L-total power (red) C-total power (green) X-VV (blue)

Thetford, UK AIRSAR (1991) C-HH

Thetford, UK AIRSAR (1991) multi-freq composite

Thetford, UK SHAC (SAR and Hyperspectral Airborne Campaign) playpage&Itemid= 66&op=page&Sub Menu=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

Thetford, UK SHAC (SAR and Hyperspectral Airborne Campaign) playpage&Itemid= 66&op=page&Sub Menu=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

Optical signal with age for different tree density (HyMAP optical data)

Coherent (polarised) modelled RADAR signal (CASM)

OPTICAL RADAR

An ambitious list of Applications... 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 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 –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 Frost filter Gamma MAP filter Simulated annealing Retford, UK ERS-2 SAR data April – September 1998

Original SAR data Frost filter

Gamma MAP filter Simulated annealing Recommendation : use these two

Discussion question 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