RADAR REMOTE SENSING Jan G.P.W. Clevers & Steven M. de Jong Chapter 8 of L&K 1 Wave theory for the EMS: Section 1.2 of L&K E = electrical field M = magnetic field c = speed of light : propagation direction 2
Wavelength frequency relation: 3 Emission: The amount of emitted radiation by the earth s surface depends on: wavelength temperature emissivity spectral radiant exitance (Wm -2 μm -1 ) 10 8 10 6 6000 K (sun) 3000 K Wien function 10 4 800 K 10 2 300 K (earth) 10 0 195 K 79 K 10-2 0.1 VIS 1 10 100 1000 wavelength (μm) 4
Atmospheric windows in the EMS: reflection emission 0.3 0.6 1.0 5.0 10 50 100 200 µm 1mm 1cm 1m 10m atmospheric transmittance UV VIS NIR blue green red MIR MIR TIR TIR blocking effect of atmosphere microwaves human eye (incl. SRF) lidar photography (AP,MSP, SRF) thermal scanners (TIRS) radar (SLAR, SAR) multispectral scanners (MSS) microwave radiometry 0.3 0.6 1.0 5.0 10 50 100 200 µm 1mm 1cm 1m 10m optical window wavelength microwave window 5 A Cloudy Morning in Waterford, Ireland on 9 August 1991 ERS-1 SAR-C at 11.25 a.m. Landsat TM at 10.43 a.m. Area coverage: 50 by 50 k Adv 1. Microwaves penetrate the atmosphere under almost all conditions: haze, clouds, rain 6
Attenuation to radiation of different wavelengths by atmospheric constituents and other particles 7 Radar remote sensing essential for areas with frequent or continuous cloud cover 8
Penetrating Capabilities of Radar Waves Landsat MSS SIR-A (23 cm HH) Selima Sand Desert Sudan. November 1982 9 Adv 2. Given favourable (dry) conditions radar waves can penetrate into objects revealing subsurface information 10
Envisat SAR image C-band, multi-polarization The Netherlands Landsat Optical TM image Colour infrared RGB The Netherlands Adv 3. Microwave reflections/emissions differ from optical/ thermal reflections, so it provide additional information 11 Active & Passive remote sensing systems: RS using reflected solar radiation: (PASSIVE) RS using radiation emitted by objects (thermal, radar): (PASSIVE) ACTIVE RS (radar): 12
Passive microwave RS Emission by the Earth Day and night application Small amounts of energy Large pixel sizes: low spatial resolution Sensitive to moisture of the surface Active Passive 13 Active Microwave Remote Sensing: RADAR = RAdio Detection And Ranging Use of antenna to transmit and receive Distance and intensity measurement Coherent radiation (one frequency, one phase) Day & night 14
How does weather / precipitation radar work? Rain Radar Meteoconsult Wageningen 0.1... 30 mm/hr 15 How does weather / precipitation radar work? Concept of rain detection by radar Return signal is function of: -Transmitted power -Radar wavelength -Distance to target -Size of target -Density of drops 16
How does weather / precipitation radar work? - Pulses emitted: 200 to 300 per second, wavelength ~ 1.5 cm - Between pulses backscatter is registered - Detection limit of rain drop >> 0.5 mm - One station measures a 3D space of 150 km (radius) * 15 km (height) 17 Aircraft with SLAR-antenna: active radar system 18
Microwave Spectral region 19 Radar concept Pulse arrives at object Pulse is scattered Pulse is mirrored Pulse travels back Pulse registered -travel time - intensity - polarization A : microwave pulses (coherent) B : focused beam due to antenna C : backscattered (reflected) energy 20
Side-Looking (Airborne) Radar SL(A)R Time of arrival of the pulse used to re-build the image 21 Measurement of Radar Signal Strength is Decibel (db), ratio of powers In radar remote sensing: measurement of reflectivity/backscatter dynamic range of the reflectivity spans several factors of 10: Log scale. Power in decibel: Pdb = 10 Log 10 (P/P ref ) P : Power : Reference Power P ref Example: Power Ratio: 0.5 Equals '- 3dB' Derived from: Log 10 (0.5) = -0.3010 22
Shuttle Radar Images of the Flevopolder: C-band ~ 5 cm L-band ~ 25 cm 23 Radar Backscatter is function of: 1. Sensor parameters: - frequency / wavelength - polarization (receive/transmit) - incidence angle - viewing geometry 2. Object parameters: - roughness (function of λ) - dielectric constant, moisture 3. Volumes (vegetation): - thickness canopy - shape/density of stem, branches, leaves - orientation 24
Backscatter natural targets X 3 cm C 5.5 cm L 23.5 cm Vegetation: Dry alluvium Glacier: 25 Polarisation: Polarisation = orientation of the electrical field HH : horizontal transmit - horizontal receive VV : vertical transmit - vertical receive HV : horizontal transmit - vertical receive VH : vertical transmit - horizontal receive 26
27 Examples of radar images with polarization: RGB: 28
Speckle in radar images (salt & pepper pattern): E β GR Caused by the coherent summing of radar backscatter within one resolution cell, whereby positive and negative interference occurs System phenomena, not the result of spatial variation of the terrain 29 Speckle in profile: intensity distance constructive destructive 30
Speckle filtering Original Image Filtered image 31 Radar geometry: A : flight direction B : nadir C : swath D : range E : azimuth 32
Range resolution (across-track): E P : pulse length P/2 : slant range resolution 1-2 : one object 3-4 : separable 33 Azimuth resolution (along track): E β GR Azimuth resolution : A : beam width 1-2 : separable GR β β = beam width 3-4 : one object Low spatial resolution of radar is a problem... SAR 34
Synthetic Aperture Radar (SAR): E β GR Target A is recorded as long as it is within the beam Special processing yields a (simulated) long antenna 35 Range/Doppler Coordinates Flight direction R a n g e Flight direction (Azimuth) Doppler Frequency 0 R a n g e Flight direction Range Migration Curve 36
Topographic distortions of radar imagery 37 Summary Topographic Effects Radar Imagery Lillesand Ch.8, p.653 38
Example ERS image (Phillipines) with topographic effects Forest Mountain ridge, Radar Layover City Mountain slope, Radar shadow 39 Object parameters: roughness 40
Rayleigh Roughness model Rough when: h λ/ (8 * sin γ) h: elevation over normal plain λ: wavelength, 23.5 cm γ: depression angle, 70º h 23.5 / (8 * sin(70º)) h 3.1 cm 41 Death valley CA 42
Death valley CA 43 Death valley CA, aerial photograph 44
Death valley CA, airborne L-band, 25 cm 45 Death valley CA, airborne X-band, 3 cm 46
Wind influence (surface roughness) Smooth water surface (rivers) causing low backscatter Rough water surface (rivers) causing relatively high backscatter 47 Special case multiple smooth surfaces: corner reflector Urban areas, bridges etc. 48
Dielectric constant (ε): - measure of object s electrical properties - indication for reflectivity and conductivity of materials Values of dielectric materials: - natural materials ε ~ 3 80 - very dry soil ε ~ 3 - water ε ~ 80 Presence of moisture - increases values & - increase backscatter 49 Airborne Radar Image C - VV potato fields at pre-emergence stage Test site Outlook, Saskatchewan Canada A = irrigated field, B = non-irrigated field 50
Monthly Soil Water Index based on ERS/MetOp C-band radar, 25 km resolution 51 Spaceborne Radar Systems ERS-2 (European, ESA) Repeat pass: 35 days Sensor C band, VV polarization Scene size 100 x 100 km Resolution 30m after resampling β Incidence angle 23 GR RADARSAT (Canadian, private) Repeat pass: 24 days Sensor X band, HH polarization Scene size 50 x 50 km up to 500 x 500 km Resolution 8m 100m Incidence angle 10-59 Envisat ASAR: continuation of ERS 1 & 2 52
InSAR: Interferometric Synthetic Aperture Radar for topographic Mapping Mount Etna, Italy Chapter 8 L&K ppp.688-691 54 InSAR: Interferometric Synthetic Aperture Radar 55
An Imaging Radar Sensor System collects: Backscatter intensity image Phase image or coherence image Phase map of one SAR image contains no useful information, but... 56 If we have two SAR images from viewing points at slightly different incidence angles: λ Phase differences can be computed and processed into a coherence map of phase differences 57
Phase map Phase difference map: φ = φ 1 - φ 2 58 Phase difference is derived from: ρ ρ = range difference (blue) Number of waves can be retrieved from the interferogram after phase unwrapping h ρ ρ ρ ρ Phase difference Δφ = ( ρ ρ ) 2π/λ mod(2π) 59
Next, a DEM is generated on the basis of known reference points α B ρ i ρ i = B cos(α i ) (1) ρ 1 ρ 1 ρ 1 ρ 1 h 1 h 2 h i =ρ i sin α i (2) φ i = ( ρ i ρ i ) 2π/λ (3) No reference to any ellipsoid so only relative heights At least one reference point required to geo-reference the DTM 60 Elevation differences are derived from counting phase differences So by counting all the colour cycles we know how many wavelengths fit inside ρ ρ 61
Bamler, R., InSAR Sommerschule 2002 62 Bamler, R., InSAR Sommerschule 2002 63
64 R. Bamler, MFFU Sommerschule 2000 Application of InSAR: water extraction at Las Vegas and resulting subsidence 65
Application of InSAR: land surface deformation by earth quakes InSAR, each colour is 2.8 cm deformation Izmit, Turkey 17 August 1999 66 230 cm horizontal deformation near railway station Izmit 67
Error sources for InSAR: Baseline accuracy (horizontal and vertical) - orbits geometry Low Coherence (Phase Correlation) between the two SAR images is the most important limitation and error source Decorrelation (loss of coherence) can be due to: Registration errors (spatial and spectral) Temporal de-correlation Baseline de-correlation Orbit errors Inhomogeneous Atmosphere De-correlation has a direct effect on the interferometric phase and thus on the height 68 New German commercial TerraSAR-X SpotLight: 10 km swath width, 1 m spatial resolution 69
Applications of InSAR: Digital elevation mapping Slope maps (e.g. landslide hazards) DEMs Deformation mapping Crustal motion Monitoring Use of temporal coherence information Classification) Change detection 70 Your microwave: S-band, 12 cm Radar Thank you for your attention www.geog.uu.nl/remotesensing