UCL
DEPARTMENT
OF GEOGRAPHY
UCL DEPARTMENT
OF GEOGRAPHY
GEOGG141
Principles & Practice of Remote Sensing
(PPRS) RADAR III: Applications
Revision
Dr. Mathias (Mat) Disney
UCL Geography
Office: 113, Pearson Building
Tel: 7670 0592
Email: mdisney@ucl.geog.ac.uk
www.geog.ucl.ac.uk/~mdisney
UCL DEPARTMENT OF GEOGRAPHY
RECAP
UCL DEPARTMENT OF GEOGRAPHY
UCL DEPARTMENT OF GEOGRAPHY
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
UCL DEPARTMENT OF GEOGRAPHY
• 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)
UCL DEPARTMENT OF GEOGRAPHY
Eastern
Sahara desert
Landsat
SIR-A
Penetration 1 – 4 m
UCL DEPARTMENT OF GEOGRAPHY
Safsaf oasis, Egypt
Penetration
up to 2 m
Landsat
SIR-C L-band 16 April 1994
UCL DEPARTMENT OF GEOGRAPHY
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 multitemporal 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
UCL DEPARTMENT OF GEOGRAPHY
UCL DEPARTMENT OF GEOGRAPHY
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 ?
• ALOS-PALSAR (2005-2011) revisits
UCL DEPARTMENT OF GEOGRAPHY
Wetlands in Vietnam - ERS
Oct 97
Sept 99
Jan 99
Dec 99
18 Mar 99
Jan 00
27 May 99
Feb 00
UCL DEPARTMENT OF GEOGRAPHY
Wetlands...
UCL DEPARTMENT OF GEOGRAPHY
SIR-C (mission 1 left, mission 2 centre, difference in blue on right)
UCL DEPARTMENT OF GEOGRAPHY
Floods...
Maastricht
A two date composite of ERS
SAR images
30/1/95 (red/green)
21/9/95 (blue)
UCL DEPARTMENT OF GEOGRAPHY
Snow cover...
Glen Tilt - Blair Atholl
ERS-2 composite
red = 25/11/96
cyan=19/5/97
Scott Polar Research Institute
UCL DEPARTMENT OF GEOGRAPHY
Agriculture
Gt. Driffield
Composite of
3 ERS SAR
images from
different dates
UCL DEPARTMENT OF GEOGRAPHY
OSR - Oil seed rape
WW - Winter wheat
UCL DEPARTMENT OF GEOGRAPHY
ERS SAR
East Anglia
UCL DEPARTMENT OF GEOGRAPHY
UCL DEPARTMENT OF GEOGRAPHY
UCL DEPARTMENT OF GEOGRAPHY
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
UCL DEPARTMENT OF GEOGRAPHY
Water cloud model
(
)
æ -2 BL ö
æ -2 BL ö
é
ù
çç
÷÷
ç
÷
s 0 = A cosq ê1 - exp è cosq ø ú + C + Dm . exp è cosq ø
s
êë
úû
A – vegetation canopy backscatter
at full cover
B – canopy attenuation coefficient
C – dry soil backscatter
σ0 = scattering coefficient
ms = soil moisture
θ = incidence angle
L = leaf area index
D – sensitivity to soil moisture
Vegetation
UCL DEPARTMENT OF GEOGRAPHY
Values of A, B, C, D
Parameter
Value
Units / description
A
-10.351
dB
B
1.945
Fractional canopy moisture
C
-23.640
dB
D
0.262
Fractional soil moisture
UCL DEPARTMENT OF GEOGRAPHY
Simulated backscatter
Actual backscatter (dB)
-11
-10
-9
-8
-7
-6
-6
r2 = 0.81
-8
-9
(
)
æ -2 BL ö
æ -2 BL ö
é
çç
÷÷
ç
÷ù
0
è cos q ø
è cos q ø
s = A cos q ê1 - exp
ú + C + Dm . exp
s
êë
úû
-10
-11
CHIPS simulated backscatter (dB)
-7
r2 = 0.81
UCL DEPARTMENT OF GEOGRAPHY
UCL DEPARTMENT OF GEOGRAPHY
UCL DEPARTMENT OF GEOGRAPHY
UCL DEPARTMENT OF GEOGRAPHY
Canopy moisture
1
2
Simulated fractional canopy moisture
r = 0.96
0.8
r2 = 0.96
0.6
0.4
0.2
0
0
0.2
0.4
0.6
Measured fractional canopy moisture
0.8
1
UCL DEPARTMENT OF GEOGRAPHY
Applications
• Irrigation fraud detection
• Irrigation scheduling
• Crop status mapping, e.g.
disease, water stress
UCL DEPARTMENT OF GEOGRAPHY
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,...
UCL DEPARTMENT OF GEOGRAPHY
Flevoland April 1994
(SIR-C/X-SAR)
(L/C/X composite)
L-total power (red)
C-total power (green)
X-VV (blue)
UCL DEPARTMENT OF GEOGRAPHY
Thetford, UK
AIRSAR (1991)
C-HH
UCL DEPARTMENT OF GEOGRAPHY
Thetford, UK
AIRSAR (1991)
multi-freq
composite
UCL DEPARTMENT OF GEOGRAPHY
Coherent RADAR modelling
Thetford, UK
SHAC (SAR and Hyperspectral
Airborne Campaign)
http://badc.nerc.ac.uk/view/neodc.n
erc.ac.uk__ATOM__dataent_11742
960559518010
Disney et al. (2006) – combine detailed structural models with optical AND
RADAR models to simulate signal in both domains
http://www.sciencedirect.com/science/article/pii/S0034425705003445
Drat optical model + CASM (Coherent Additive Scattering Model) of Saich et
al. (2001)
UCL DEPARTMENT OF GEOGRAPHY
Coherent RADAR modelling
Thetford, UK
SHAC (SAR and Hyperspectral
Airborne Campaign)
http://badc.nerc.ac.uk/view/neodc.n
erc.ac.uk__ATOM__dataent_11742
960559518010
Disney et al. (2006) – combine detailed structural models with optical AND
RADAR models to simulate signal in both domains
http://www.sciencedirect.com/science/article/pii/S0034425705003445
Drat optical model + CASM (Coherent Additive Scattering Model) of Saich et
al. (2001)
UCL DEPARTMENT OF GEOGRAPHY
Optical signal with age for different tree density (HyMAP optical data)
UCL DEPARTMENT OF GEOGRAPHY
Coherent (polarised) modelled RADAR signal (CASM)
UCL DEPARTMENT OF GEOGRAPHY
OPTICAL
RADAR
UCL DEPARTMENT OF GEOGRAPHY
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
UCL DEPARTMENT OF GEOGRAPHY
CONCLUSIONS
ALOS (RIP)
• 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?
UCL DEPARTMENT OF GEOGRAPHY
Revision
• Exam: 3 hrs, answer 4 from 7 (2 from Dietmar, 5 from me)
• Types of question based on PREVIOUS material be
similar each year (not surprisingly!)
– Planck function, orbital calculations, definitions of terms, preprocessing stages
– Factors controlling measured signal from vegetation across
vis/SWIR, or angular behaviour
– RADAR principles eg RADAR equation, resolutions
– Principles of SAR interferometry and applications
– General questions - systems to address a given problem
• KEY: address that problem
• Does Q give scope for moving beyond one platform or wavelength? If
so then DO SO…
UCL DEPARTMENT OF GEOGRAPHY
Revision
• Types of question based on NEW material for 2011
– LiDAR
• Principles of lidar remote sensing?
• What is it good for and limitations?
• Example applications
– Radiative Transfer modelling
• Basis of RT model – building blocks?
– Structure, leaf scattering, soil scattering
• Scalar RT equation
– what do terms mean?
– How can we go about solving?
UCL DEPARTMENT OF GEOGRAPHY
Revision problems: Planck’s Law
•Fractional energy from 0 to  F0? Integrate Planck function
•Note Eb(,T), emissive power of bbody at , is function of product
T only, so....
Radiant energy from 0 to 
E0  , T 
Eb  , T 
F0  , T  
  d  , T 
4
5
T

T
0
T
Total radiant energy
for  =0 to  = 
43
UCL DEPARTMENT OF GEOGRAPHY
Revision: Planck’s Law example
•Q: what fraction of the total power radiated by a black body
at 5770 K fall, in the UV (0    0.38µm)?
•Need table of integral values of F0
•So, T = 0.38m * 5770K = 2193mK
T (mK x103)
•Or 2.193x103 mK i.e. between 2 and 3
2
3
4
5
6
8
10
12
14
16
18
20
•Interpolate between F0 (2x103) and F0 (3x103)




F00.38  , T   F00.38 2 x103
2.193  2

 0.193
F00.38 3x103  F00.38 2 x103
3 2


F00.38  , T   0.067
 0.193
0.273  0.067
F0(T)
(dimensionless)
.067
.273
.481
.634
.738
.856
.914
.945
.963
.974
.981
.986
•Finally, F00.38 = 0.193*(0.273-0.067)+0.067 = 0.11
•i.e. ~11% of total solar energy lies in UV between 0 and 0.38m
44
UCL DEPARTMENT OF GEOGRAPHY
Orbits: examples
• Orbital period for a given instrument and height?
– Gravitational force Fg = GMEms/RsE2
• where G is universal gravitational constant (6.67x10-11 Nm2kg2); ME is Earth
mass (5.983x1024kg); ms is satellite mass (?) and RsE is distance from
Earth centre to satellite i.e. 6.38x106 + h where h is satellite altitude
– Centripetal (not centrifugal!) force Fc = msvs2/RsE
• where vs is linear speed of satellite (=sRsE where  is the satellite angular
velocity, rad s-1)
– for stable (constant radius) orbit Fc = Fg
–  GMEms/RsE2 = msvs2/RsE = ms s2RsE2 /RsE
– so s2 = GME /RsE3
From:http://csep10.phys.utk.edu/astr161/lect/history/kepler.html
45
UCL DEPARTMENT OF GEOGRAPHY
Orbits: examples
• Orbital period T of satellite (in s) = 2/
– (remember 2 = one full rotation, 360°, in radians)
– and RsE = RE + h where RE = 6.38x106 m
– So now T = 2[(RE+h)3/GME]1/2
• Example: geostationary altitude? T = ??
– Rearranging: h = [(GME /42)T2 ]1/3 - RE
– So h = [(6.67x10-11*5.983x1024 /42)(24*60*60)2 ]1/3 - 6.38x106
– h = 42.2x106 - 6.38x106 = 35.8km
46
UCL DEPARTMENT OF GEOGRAPHY
Orbits: examples
• Example: polar orbiter period, if h = 705x103m
– T = 2[(6.38x106 +705x103)3 / (6.67x10-11*5.983x1024)]1/2
– T = 5930.6s = 98.8mins
• Example: show separation of successive ground tracks
~3000km
–
–
–
–
–
Earth angular rotation = 2/24*60*60 = 7.27x10-5 rads s-1
So in 98.8 mins, point on surface moves 98.8*60*7.27x10-5 = .431 rads
Remember l =r* for arc of circle radius r &  in radians
So l = (Earth radius + sat. altitude)* 
= (6.38x106 +705x103)* 0.431 = 3054km
47