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Remote Sensing I Active Remote Sensing Summer 2007 Björn-Martin Sinnhuber Room NW1 - U3215 Tel. 8958 bms@iup.physik.uni-bremen.de www.iup.uni-bremen.de/~bms. Contents. Chapter 1 Introduction Chapter 2 Electromagnetic Radiation Chapter 3 Radiative Transfer through the Atmosphere

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  1. Remote Sensing IActive Remote SensingSummer 2007Björn-Martin SinnhuberRoom NW1 - U3215Tel. 8958bms@iup.physik.uni-bremen.dewww.iup.uni-bremen.de/~bms B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007

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

  3. Chapter 7Active 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

  4. 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

  5. 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

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

  7. 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

  8. Lidar equation • The detected intensity Id(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

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

  10. 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 • H2O profiles http://www.etl.noaa.gov/et2/ B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007

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

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

  13. DIAL LIDAR: Examples Tropospheric O3 Stratospheric O3 B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007

  14. Aerosol LIDAR • Idea: • Backscattering at different wavelengths is used to derive information on aerosol properties • for each wavelength, the backscattering coefficient βMie(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: LMie(z, λ)= αMie(z, λ)/ βMie(z, λ) • Measurement quantity is the backscattering ratio R. B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007

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

  16. Aerosol Lidar: Example Cirrus Clouds • airborne lidar measurements • OLEX instrument (http://www.dlr.de/~flentje/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

  17. LIDAR: Overview B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007

  18. 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 http://www-lite.larc.nasa.gov/index.html B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007

  19. LITE: Example of Aerosol Measurements Clouds (ITCZ) Atlas mountains complex aerosol layer maritime aerosol layer http://www-lite.larc.nasa.gov/index.html B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007

  20. More LIDARS in space • CALIPSO • Launched April 2006 • 532 nm and 1064 nm polarization-sensitive lidar • Nd:YAG, diode pumped laser • clouds and aerosols • http://www-calipso.larc.nasa.gov B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007

  21. Chapter 7Active 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

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

  23. Frequency Bands Used for Spaceborne Radars B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007

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

  25. Horizontal resolution in range direction The Radar is an active remote sensing system. Short pulsesof 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 bythe length of the pulse Δτ : B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007

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

  27. 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

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

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

  30. 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 ΔXa,SAR of the syntheticapperture radar of: B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007

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

  32. Chapter 7Active 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

  33. 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

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

  35. 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

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

  37. 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

  38. Sea ice concentration fromAMSR-E 89 GHz 15 April 2007 www.seaice.de courtesy of Lars Kaleschke B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007

  39. Sea ice concentration fromAMSR-E 89 GHz 08 July 2007 www.seaice.de courtesy of Lars Kaleschke B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007

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

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

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

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

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

  45. 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

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

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

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