ucl department of geography geogg141 principles & practice of remote sensing (pprs) radar iii:...
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UCL DEPARTMENT OF GEOGRAPHYUCL DEPARTMENT OF GEOGRAPHY
GEOGG141Principles & Practice of Remote Sensing (PPRS) RADAR III: ApplicationsRevision
Dr. Mathias (Mat) Disney
UCL Geography
Office: 113, Pearson Building
Tel: 7670 0592
Email: [email protected]
www.geog.ucl.ac.uk/~mdisney
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UCL DEPARTMENT OF GEOGRAPHY
RECAP
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UCL DEPARTMENT OF GEOGRAPHY
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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
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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)
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UCL DEPARTMENT OF GEOGRAPHY
EasternSahara desert
SIR-APenetration 1 – 4 m
Landsat
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UCL DEPARTMENT OF GEOGRAPHY
Safsaf oasis, Egypt
SIR-C L-band 16 April 1994Landsat
Penetration up to 2 m
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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 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
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UCL DEPARTMENT OF GEOGRAPHY
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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
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UCL DEPARTMENT OF GEOGRAPHY
Wetlands in Vietnam - ERS
Oct 97 Jan 99 18 Mar 99 27 May 99
Sept 99 Dec 99 Jan 00 Feb 00
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UCL DEPARTMENT OF GEOGRAPHY
Wetlands...
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UCL DEPARTMENT OF GEOGRAPHY
SIR-C (mission 1 left, mission 2 centre, difference in blue on right)
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UCL DEPARTMENT OF GEOGRAPHY
Floods...
Maastricht
A two date composite of ERS SAR images
30/1/95 (red/green)
21/9/95 (blue)
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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
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UCL DEPARTMENT OF GEOGRAPHY
Agriculture
Gt. Driffield
Composite of 3 ERS SAR images from different dates
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UCL DEPARTMENT OF GEOGRAPHY
OSR - Oil seed rapeWW - Winter wheat
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UCL DEPARTMENT OF GEOGRAPHY
ERS SAR
East Anglia
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UCL DEPARTMENT OF GEOGRAPHY
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UCL DEPARTMENT OF GEOGRAPHY
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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
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UCL DEPARTMENT OF GEOGRAPHY
Water cloud model
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
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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
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UCL DEPARTMENT OF GEOGRAPHY
Simulated backscatter
r2 = 0.81
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UCL DEPARTMENT OF GEOGRAPHY
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UCL DEPARTMENT OF GEOGRAPHY
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UCL DEPARTMENT OF GEOGRAPHY
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UCL DEPARTMENT OF GEOGRAPHY
Canopy moisture
r2 = 0.96
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UCL DEPARTMENT OF GEOGRAPHY
Applications
• Irrigation fraud detection• Irrigation scheduling• Crop status mapping, e.g.
disease, water stress
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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,...
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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)
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UCL DEPARTMENT OF GEOGRAPHY
Thetford, UK
AIRSAR (1991)
C-HH
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UCL DEPARTMENT OF GEOGRAPHY
Thetford, UK
AIRSAR (1991)
multi-freq composite
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UCL DEPARTMENT OF GEOGRAPHY
Thetford, UK
SHAC (SAR and Hyperspectral Airborne Campaign)
http://badc.nerc.ac.uk/view/neodc.nerc.ac.uk__ATOM__dataent_11742960559518010
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)
Coherent RADAR modelling
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UCL DEPARTMENT OF GEOGRAPHY
Coherent RADAR modelling
Thetford, UK
SHAC (SAR and Hyperspectral Airborne Campaign)
http://badc.nerc.ac.uk/view/neodc.nerc.ac.uk__ATOM__dataent_11742960559518010
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)
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UCL DEPARTMENT OF GEOGRAPHY
Optical signal with age for different tree density (HyMAP optical data)
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UCL DEPARTMENT OF GEOGRAPHY
Coherent (polarised) modelled RADAR signal (CASM)
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UCL DEPARTMENT OF GEOGRAPHY
OPTICAL
RADAR
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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
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UCL DEPARTMENT OF GEOGRAPHY
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 (RIP)
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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, pre-
processing 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…
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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?
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UCL DEPARTMENT OF GEOGRAPHY
Revision problems: Planck’s Law
43
• Fractional energy from 0 to F0? Integrate Planck function
• Note Eb(,T), emissive power of bbody at , is function of product T only, so....
Tb
T
TETd
T
TETF
054
00
,,
,,
Radiant energy from 0 to
Total radiant energy
for =0 to =
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UCL DEPARTMENT OF GEOGRAPHY
Revision: Planck’s Law example
44
• 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 = 0.38m * 5770K = 2193mK
• Or 2.193x103 mK i.e. between 2 and 3
• Interpolate between F0 (2x103) and F0 (3x103)
lT (mmK x103) F0® l ( lT)(dimensionless)
2 .0673 .2734 .4815 .6346 .7388 .85610 .91412 .94514 .96316 .97418 .98120 .986
193.0
23
2193.2
102103
102,3
38.003
38.00
338.0038.00
xFxF
xFTF
193.0
067.0273.0
067.0,38.00
TF
• Finally, F00.38 = 0.193*(0.273-0.067)+0.067 = 0.11
• i.e. ~11% of total solar energy lies in UV between 0 and 0.38m
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UCL DEPARTMENT OF GEOGRAPHY
• Orbital period for a given instrument and height? – Gravitational force Fg = GMEms/RsE
2
• 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 = msvs
2/RsE = ms s2RsE
2 /RsE
– so s2 = GME /RsE
3
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Orbits: examples
From:http://csep10.phys.utk.edu/astr161/lect/history/kepler.html
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UCL DEPARTMENT OF GEOGRAPHY
• 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 /42)T2 ]1/3 - RE
– So h = [(6.67x10-11*5.983x1024 /42)(24*60*60)2 ]1/3 - 6.38x106
– h = 42.2x106 - 6.38x106 = 35.8km
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Orbits: examples
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UCL DEPARTMENT OF GEOGRAPHY
• 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
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Orbits: examples