B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007 Remote Sensing I Active Remote Sensing Summer 2007 Björn-Martin Sinnhuber Room NW1 - U3215 Tel

B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007 Contents Chapter 1Introduction Chapter 2Electromagnetic Radiation Chapter 3Radiative Transfer through the Atmosphere Chapter 4Weighting Functions and Retrieval Techniques Chapter 5Atmospheric Microwave Remote Sensing: Chapter 6Atmospheric IR & UV/visible Remote Sensing Chapter 7Active Techniques and Sea Ice Remote Sensing Chapter 8Remote Sensing of Ocean Colour

B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007 Chapter 7 Active Techniques LIDAR for Atmospheric Remote Sensing (LIDAR = Light Detection and Ranging) Synthetic Aperture Radar (SAR) Sea Ice Remote Sensing

B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007 LIDAR-Types and Target Quantities Applications: altimeter Rayleigh Lidar: temperature DIAL (Differential Absorption)-Lidar: trace gases multi wavelength aerosol Lidar: aerosol amount and aerosol properties (size distribution, type) Raman-Lidar: trace gases Doppler-Lidar: particle velocities Fluorescence-Lidar: temperature in the upper atmosphere

B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007 LIDAR: Instrument Laser: short pulses (small dead range above instrument) high pulse power (high backscattered signal) typical lasers: –solid state laser (e.g. Nd-YAG) –gas laser (e.g. XeCl) –dye lasers Detector: excellent quantum efficiency needed (low signal) low noise needed (low signal) typical detectors –Photomultiplier –Photodiodes –CCDs wavelength selective (use of filters)

B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007 LIDAR: Example G. Beyerle, PhD thesis, 1994

B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007 LIDAR: Measurement Example two wavelengths (353 nm and 532 nm minimum altitude: 11 km maximum altitude: 45 km background signals of calibration exponential scale signature of volcanic aerosol signature of PSCs

B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007 Lidar equation The detected intensity I d (z,λ) is proportinal to Emitted intensity Backscatter coefficient Observed solid angle (with A area of telescope) Transmission along the light path Sensitivity of the detector in this channel (including geometric overlap):

B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007 Lidar equation Taking these factors together will give the so called Lidar-Equation: with

B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007 DIAL LIDAR Idea: two wavelengths are emitted, one at an absorption line, the other one off the absorption but close enough to have small changes in scattering properties and absorption by other absorbers Application: ozone profiles H 2 O profiles

B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007 DIAL Lidar equation Start from the Lidar-equation for two wavelength on/off: Forming the ratio between the received signals I on and I off:... And then the logarithm: :

B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007 DIAL Lidar equation Differentiating wrt altitude z gives: If the two wavelength are nearby, scattering properties will be similar, and we finally get:

B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007 DIAL LIDAR: Examples Stratospheric O 3 Tropospheric O 3

B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007 Aerosol LIDAR Idea: Backscattering at different wavelengths is used to derive information on aerosol properties for each wavelength, the backscattering coefficient β Mie (z, λ) is computed from the Lidar equation using the Klett-algorithm: –profiles of temperature and pressure as Input –use of reference height with known backscatter coefficient (Rayleigh only) –Mie scattering ratio determined from model: L Mie (z, λ)= α Mie (z, λ)/ β Mie (z, λ) Measurement quantity is the backscattering ratio R.

B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007 Aerosol Lidar: Example PSC

B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007 Aerosol Lidar: Example Cirrus Clouds airborne lidar measurements OLEX instrument ( /olex.html ) /olex.html very good detection limit high spatial and vertical resolution detection of cirrus clouds, thin and even “subvisible“ particle size from colour ratio particle phase from depolarisation

B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007 LIDAR: Overview Measurement QuantityWavelengthMeasurement Principle Ozone concentration (in Rayleigh atmosphere) 308 nm & 355 nmDIAL-technique Ozone concentration (also in the presence of Mie scatterers) 332 nm & 387 nmRaman-DIAL-technique Water vapour mixing ratios387 nm & 408 nmH 2 O-Ramanlidar-technique stratospheric temperature above 30 km height 355 nmRayleigh integration method tropospheric and stratospheric temperature (also in the presence of Mie scatterers) 530,85 & 529,35 nmRotational Raman method Backscatter ratio volume and particle Extinction coefficient, volume and particle Backscatter coefficient at three wavelengths Colour ratio 308 nm & 332 nm 355 nm & 387 nm 532 nm & 608 nm combination of Raman scattering and elastic scattering (Raman lidar technique) volume and particle Depolarisation355 nm, 387 nm polarisation depending Depolarisation lidar technique

B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007 Lidar In-space Technology Experiment (LITE) Instrument: flashlamp-pumped Nd:YAG laser 1064 nm, 532 nm, and 355 nm 1-meter diameter lightweight telescope PMT for 355 nm and 532 nmavalanche photodiode (APD) for 1064 nm Mission Aims: test and demonstrate lidar measurements from space collect measurements on –clouds –aerosols (stratospheric & tropospheric) –surface reflectance Operation: on Discovery in September 1994 as part of the STS-64 mission 53 hours operation

B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007 LITE: Example of Aerosol Measurements Atlas mountains Clouds (ITCZ) complex aerosol layer maritime aerosol layer

B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007 More LIDARS in space CALIPSO Launched April nm and 1064 nm polarization-sensitive lidar Nd:YAG, diode pumped laser clouds and aerosols

B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007 Chapter 7 Active Techniques LIDAR for Atmospheric Remote Sensing (LIDAR = Light Detection and Ranging) Synthetic Aperture Radar (SAR) Sea Ice Remote Sensing

B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007 Radar Image ENVISAT ASAR 15 April 2005

B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007 Frequency Bands Used for Spaceborne Radars Band designationWavelength (mm)Frequency (GHz) Ka K X C L

B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007 Imaging Radar Because Radars opperate at relatively long wavelengths, they are not (or only very little) affected by scattering in the atmosphere, thus they can „see“ through clouds. The reflected signal depends (among other factors) on the surface roughness, which provides important additional information not directly available from other observations.

B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007 Horizontal resolution in range direction The Radar is an active remote sensing system. Short pulses of EM radiation are sent out and the reflected signal is detected. The travel time τ of the signal is given by: where x is the distance travelled and c is the speed of light. The horizontal resolution (in range direction) is then given by the length of the pulse Δτ :

B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007 Real Aperture Radar h S Opening angle β ΔXaΔXa ΔXrΔXr Viewing angle θ Azimuth direction Range direction

B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007 Antenna beamwidth The opening angle β (beamwidth) of an antenna with aperture D at a wavelength of λ is given by: The azimuth resolution for a real aperture radar is then

B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007 Azimuth resolution for real aperture radar Example: h=800km, λ=23cm, D=1m then ΔX a =260km This is a coarse resolution!

B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007 Synthetic aperture radar L = ΔX a L flight direction Ground object The ground object is seen here by a number of successive observations.

B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007 Synthetic aperture radar Successive images can be combined to create an effective (synthetic) apperture of size L: This synthetic apperture has an effective opening angle of: Which results in an effective resolution ΔX a,SAR of the synthetic apperture radar of:

B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007 Synthetic aperture radar means: 1.The achivable resolution is in the order of meters (good!) 2.It will become even better for smaller antenna sizes 3.Resolution is independent of wavelength, orbit height etc. The effective resolution of a synthetic apperture radar,

B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007 Chapter 7 Active Techniques LIDAR for Atmospheric Remote Sensing (LIDAR = Light Detection and Ranging) Synthetic Aperture Radar (SAR) Sea Ice Remote Sensing

B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007 Sea ice remote sensing from space Optical (e.g. MODIS) –High resolution (about 100 meters) –Can‘t „see“ through clouds; difficult to distinguish clouds and sea ice Radar –Can look through clouds –High spatial resolution (< 1km) –Images difficult to interprete Passive Microwave –Can differentiate between open water, first year and multiyear ice –Can „see“ through clouds –Low spatial resolution (several km)

B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007 MODIS - Antarctica 21 March 2005

B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007 Sea ice remote sensing from space Optical (e.g. MODIS) –High resolution (about 100 meters) –Can‘t „see“ through clouds; difficult to distinguish clouds and sea ice Radar –Can look through clouds –High spatial resolution (< 1km) –Images difficult to interprete Passive Microwave –Can differentiate between open water, first year and multiyear ice –Can „see“ through clouds –Low spatial resolution (several km)

B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007 ENVISAT ASAR - Antarctica 15 April 2005

B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007 Sea ice remote sensing from space Optical (e.g. MODIS) –High resolution (about 100 meters) –Can‘t „see“ through clouds; difficult to distinguish clouds and sea ice Radar –Can look through clouds –High spatial resolution (< 1km) –Images difficult to interprete Passive Microwave –Can differentiate between open water, first year and multiyear ice –Can „see“ through clouds –Low spatial resolution (several km)

B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007 Sea ice concentration from AMSR-E 89 GHz 15 April courtesy of Lars Kaleschke

B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007 Sea ice concentration from AMSR-E 89 GHz 08 July courtesy of Lars Kaleschke

B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007 Passive microwave remote sensing

B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007 Passive microwave remote sensing

B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007 Emissivity of Sea Ice Frequency [GHz] Emissivity Water Multiyear Ice First year Ice Summer

B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007 Clouds Polarization ratio Gradient ratio

B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007 Sea ice concentration

B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007 Validation with Modis Observations MODIS 645, 555, 469 nm AMSR-E 89 GHz courtesy of Lars Kaleschke

B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007 Optical tick clouds are still transparent at 89 GHz courtesy of Lars Kaleschke

B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007 Validation with High Resolution SAR Image courtesy of Lars Kaleschke ERS-2 SAR