1. 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

2. UCL DEPARTMENT OF GEOGRAPHY RECAP

3. UCL DEPARTMENT OF GEOGRAPHY

4. 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

5. 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)

6. UCL DEPARTMENT OF GEOGRAPHY Eastern Sahara desert SIR-A Penetration 1 – 4 m Landsat

7. UCL DEPARTMENT OF GEOGRAPHY Safsaf oasis, Egypt SIR-C L-band 16 April 1994Landsat Penetration up to 2 m

8. 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

9. UCL DEPARTMENT OF GEOGRAPHY

10. 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

11. 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

12. UCL DEPARTMENT OF GEOGRAPHY Wetlands...

13. UCL DEPARTMENT OF GEOGRAPHY SIR-C (mission 1 left, mission 2 centre, difference in blue on right)

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

15. 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

16. UCL DEPARTMENT OF GEOGRAPHY Agriculture Gt. Driffield Composite of 3 ERS SAR images from different dates

17. UCL DEPARTMENT OF GEOGRAPHY OSR - Oil seed rape WW - Winter wheat

18. UCL DEPARTMENT OF GEOGRAPHY ERS SAR East Anglia

19. UCL DEPARTMENT OF GEOGRAPHY

21. 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

22. 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 σ 0 = scattering coefficient m s = soil moisture θ = incidence angle L = leaf area index Vegetation

23. UCL DEPARTMENT OF GEOGRAPHY Values of A, B, C, D ParameterValueUnits / description A-10.351dB B1.945Fractional canopy moisture C-23.640dB D0.262Fractional soil moisture

24. UCL DEPARTMENT OF GEOGRAPHY Simulated backscatter r 2 = 0.81

25. UCL DEPARTMENT OF GEOGRAPHY

28. Canopy moisture r 2 = 0.96

29. UCL DEPARTMENT OF GEOGRAPHY Applications Irrigation fraud detection Irrigation scheduling Crop status mapping, e.g. disease, water stress

30. 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,...

31. 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)

32. UCL DEPARTMENT OF GEOGRAPHY Thetford, UK AIRSAR (1991) C-HH

33. UCL DEPARTMENT OF GEOGRAPHY Thetford, UK AIRSAR (1991) multi-freq composite

34. UCL DEPARTMENT OF GEOGRAPHY 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) Coherent RADAR modelling

35. 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)

36. UCL DEPARTMENT OF GEOGRAPHY Optical signal with age for different tree density (HyMAP optical data)

37. UCL DEPARTMENT OF GEOGRAPHY Coherent (polarised) modelled RADAR signal (CASM)

38. UCL DEPARTMENT OF GEOGRAPHY OPTICAL RADAR

39. 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

40. 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)

41. 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…

42. 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?

43. UCL DEPARTMENT OF GEOGRAPHY Revision problems: Planck’s Law 43 Fractional energy from 0 to F 0  ? Integrate Planck function Note E b (,T), emissive power of bbody at, is function of product T only, so.... Radiant energy from 0 to Total radiant energy for =0 to = 

44. 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 F 0  So, T = 0.38  m * 5770K = 2193  mK Or 2.193x10 3  mK i.e. between 2 and 3 Interpolate between F 0  (2x10 3 ) and F 0  (3x10 3 ) T (  mK x10 3 )F 0  ( T) (dimensionless) 2.067 3.273 4.481 5.634 6.738 8.856 10.914 12.945 14.963 16.974 18.981 20.986 Finally, F 0  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

45. UCL DEPARTMENT OF GEOGRAPHY Orbital period for a given instrument and height? –Gravitational force F g = GM E m s /R sE 2 where G is universal gravitational constant (6.67x10 -11 Nm 2 kg 2 ); M E is Earth mass (5.983x10 24 kg); m s is satellite mass (?) and R sE is distance from Earth centre to satellite i.e. 6.38x10 6 + h where h is satellite altitude –Centripetal (not centrifugal!) force F c = m s v s 2 /R sE where v s is linear speed of satellite (=  s R sE where  is the satellite angular velocity, rad s -1 ) –for stable (constant radius) orbit F c = F g –  GM E m s /R sE 2 = m s v s 2 /R sE = m s  s 2 R sE 2 /R sE –so  s 2 = GM E /R sE 3 45 Orbits: examples From:http://csep10.phys.utk.edu/astr161/lect/history/kepler.html

46. UCL DEPARTMENT OF GEOGRAPHY Orbital period T of satellite (in s) = 2  /  –(remember 2  = one full rotation, 360°, in radians) –and R sE = R E + h where R E = 6.38x10 6 m –So now T = 2  [(R E +h) 3 /GM E ] 1/2 Example: geostationary altitude? T = ?? –Rearranging: h = [(GM E /4  2 )T 2 ] 1/3 - R E –So h = [(6.67x10 -11 *5.983x10 24 /4  2 )(24*60*60) 2 ] 1/3 - 6.38x10 6 –h = 42.2x10 6 - 6.38x10 6 = 35.8km 46 Orbits: examples

47. UCL DEPARTMENT OF GEOGRAPHY Example: polar orbiter period, if h = 705x10 3 m –T = 2  [(6.38x10 6 +705x10 3 ) 3 / (6.67x10 -11 *5.983x10 24 )] 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)*  –= (6.38x10 6 +705x10 3 )* 0.431 = 3054km 47 Orbits: examples