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Remote Sensing I Summer 2007 Björn-Martin Sinnhuber Room NW1 - U3215 Tel. 8958 bms@iup.physik.uni-bremen.de www.iup.uni-bremen.de/~bms. Contents. Lecture 1 Introduction to R emote S ensing Lecture 2 Electromagnetic R adiation

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  1. Remote Sensing ISummer 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 Lecture 1Introduction to Remote Sensing Lecture 2 Electromagnetic Radiation Lecture 3Interaction of Radiation with Gases and Matter: Spectroscopy Lecture 4Atmospheric Radiative Transfer Lecture 5 Retrieval Techniques / Inverse Methods Remote Sensing of the Atmosphere: Lecture 6 Passive Microwave Remote Sensing Lecture 7Infra-Red Techniques Lecture 8Optical (UV / Visible) Remote Sensing Lecture 9Active Remote Sensing: Radar and Lidar Remote Sensing of the Earth Surface: Lecture 10 Sea Ice Remote Sensing Lecture 11Remote Sensing of the Ocean with Satellite Altimeters Lecture 12 Summary B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007

  3. Lecture 1Introduction • General Introduction • Examples of Remote Sensing Applications • Introduction to Satellite Orbits B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007

  4. Photo takenby crew ofApollo 17 7 Dec 1972 B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007

  5. from maps.google.com B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007

  6. A Note on Spatial Resolution The maximum achievable resolution with an optical system is given by with α: opening angle, D: diameter of the optical aperture,λ: wavelength. Because with x: object size and h: sensor height we get (Rayleigh criterion) α h x B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007

  7. Resolution: An example Assume some typical values: h: 800 km, D: 4m (huge!),λ: 500 nm: B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007

  8. ENVISAT: Launched 1 March 2002 B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007

  9. MERIS/ENVISAT B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007

  10. SeaWIFS, 26. Feb. 2000 B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007

  11. MERIS/ENVISAT, Cloud Top Pressure B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007

  12. Ocean colour: MERIS/ENVISAT, 443 nm B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007

  13. Ocean colour: MERIS/ENVISAT, 560 nm B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007

  14. Ocean colour: MERIS/ENVISAT, Chlorophyll B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007

  15. Absorption windows of atmospheric constituents B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007

  16. Antarctic Ozone Hole Observing the Ozone Layer http://www.iup.physik.uni-bremen.de/gomenrt/ Global measurements of total ozone columns Measurement type: Satellite-based passive remote sensing Instrument:Global Ozone Monitoring Experiment (GOME) /ERS-2 Measured quantity: Total ozone columns (from backscattered solar radiation) B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007

  17. The Arctic Ozone Layer Ten years of GOME observtions B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007

  18. 100 m 10-4 cm-1 10 MHz 10 m 10-3 cm-1 Radio 100 MHz 1 m 10-2 cm-1 1 GHz 10 cm 0.1 cm-1 10 GHz Microwave 1 cm 1 cm-1 100 GHz 1 mm 10 cm-1 1 THz sub-mm – Far IR 0.1 mm 100 cm-1 10 THz 10 μm 1000 cm-1Thermal IR al IR 100 THz Near IR 1 μm 104 cm-1 1000 THz Ultraviolet 100 nm 105 cm-1 Wavelength Frequency Wave number Visible 400-700 nm The Electromagnetic Spectrum B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007

  19. Solar Spectrum and Terrestrial Spectrum B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007

  20. MODIS / Terra, Gulfstream Temperature B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007

  21. B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007

  22. AMSU-B Data (183 ±1 GHz) Microwave Remote Sensing Dry areas in the UT (NOAA 16, Channel 18, 15.6.2004. Figure: Oliver Lemke) B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007

  23. Satellite Limb Sounding (Figure: Oliver Lemke) B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007

  24. Microwave Limb Sonder (MLS) onboard UARS B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007

  25. Airborne Microwave Remote Sensing B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007

  26. ASUR frequency range and primary species B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007

  27. A picture from the SOLVE campaignin Kiruna, Sweden, January 2000 B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007

  28. Validation of satellite data is important ... B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007

  29. Ground-based Radiometer for Atmospheric Measurements (RAM) B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007

  30. Measured Microwave Spectrum by the RAM B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007

  31. Pressure Broadening of Spectral Lines 50km / 0.5 hPa 20km / 50 hPa 10km / 200 hPa B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007

  32. The matrix A is also called as the weighting function matrix. Finding x from measured y would require inversion of A: However, this is generally not possible (inverse of A does not exist). Therefore one has to find some „generallized“ inverse of A: A Note on Profile Retrieval Often we can describe the relation between the (unknown)atmospheric profile x and the measured spectrum y by alinear equation: B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007

  33. Lidar In-space Technology Experiment (LITE) on Discovery in September 1994 as part of the STS-64 mission http://www-lite.larc.nasa.gov/index.html B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007

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

  35. 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

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

  37. pollution biomass burning Example: SCIAMACHY Tropospheric NO2 Courtesy of Andreas Richter B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007

  38. GOME annual changes in tropospheric NO2 1996 - 2002 GOME NO2: Temporal Evolution • 7 years of GOME data • DOAS retrieval + CTM-stratospheric correction • seasonal and local AMF based on 1997 MOART-2 run • cloud screening • NO2 reductions in Europe and parts of the US • strong increase over China • consistent with significant NOx emission changes A. Richter et al., Increase in tropospheric nitrogen dioxide over China observed from space, Nature, 4372005 B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007

  39. Lightning Flashes, Optical Transient Detector (OTD) B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007

  40. Lecture 1Introduction • General Introduction • Examples of Remote Seinsing Applications • Introduction to Satellite Orbits B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007

  41. Satellite Orbits satellite apogee Earth perigee a: major axis e: excentricity B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007

  42. For a circular satellite orbit around a spherically homogenous planet the gravitational force Fg and the centrifugal force Fc are in balance: For the Earth g=9.81 m/s2 and R=6380 km. B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007

  43. Orbital period given by: B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007

  44. B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007 From Elachi

  45. B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007 From Elachi

  46. The orbital node changes due to precession, primarily due to the oblateness of the Earth. The rate of change for the orbital node is approximately given by: Here J2=0.00108 is the second harmonic of the Earth geopotential.I is the inclination angle. B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007

  47. B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007 From Elachi

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