Sinnhuber & Bracher, Remote Sensing, University of Bremen, Summer 2008 Remote Sensing Summer 2008 Björn-Martin Sinnhuber and Astrid Bracher Room NW1 - U3215 Tel

Sinnhuber & Bracher, Remote Sensing, University of Bremen, Summer 2008 Lecture 1 Introduction to Remote Sensing „Rules of the Game“ Examples of Remote Sensing Applications

Sinnhuber & Bracher, Remote Sensing, University of Bremen, Summer 2008 General Principles of Remote Sensing Lecture 1 Introduction to Remote Sensing Lecture 2 Electromagnetic Radiation Lecture 3Radiative Transfer Lecture 4 Satellite Remote Sensing Lecture 5 Retrieval Techniques / Inverse Methods Remote Sensing of the Atmosphere: Lecture 6 Microwave Tehniques Lecture 7 Infra-Red Techniques Lecture 8 Spectroscopy Lecture 9Optical (UV / Visible) Remote Sensing Lecture 10Active Techniques and Meteorological Applications Remote Sensing of the Ocean Surface: Lecture 11 Sea Ice Remote Sensing Lecture 12 Remote Sensing of Ocean Currents and SST Lecture 13 Ocean Colour & Summary Outline

Sinnhuber & Bracher, Remote Sensing, University of Bremen, Summer 2008 General Information: „The rules of the game“ Lecture 13 lectures, every Monday 13:15-14:45 ECTS: 4 One „rapporteur“ gives brief summary (5 min.) of previous lecture. Mandatory for each student. Fix your date in the list (check on website)! Exercises 10 exercises: turned out Mondays, given back next Monday, 10 points total for each exercise Exercises are discussed every Thursdays 13:15-14:00 (not 1st and last week, not holidays 1st and 15th May) with Gregor Kiesewetter phone: Exam Written exam 14 July 2008; 9:30-11:30 Prerequisite: >70 points in all exercises and one report

Sinnhuber & Bracher, Remote Sensing, University of Bremen, Summer 2008 Literature Charles Elachi Introduction to the Physics and Techniques of Remote Sensing Graeme L. Stephens Remote Sensing of the Lower Atmosphere Martin Seelye An Introduction to Ocean Remote Sensing

Sinnhuber & Bracher, Remote Sensing, University of Bremen, Summer 2008 Lecture 1 Introduction to Remote Sensing „Rules of the Game“ Examples of Remote Sensing Applications

Sinnhuber & Bracher, Remote Sensing, University of Bremen, Summer 2008 Photo taken by crew of Apollo 17 7 Dec 1972

Sinnhuber & Bracher, Remote Sensing, University of Bremen, Summer 2008 from maps.google.com

Sinnhuber & Bracher, Remote Sensing, University of Bremen, Summer 2008 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 α x h (Rayleigh criterion)

Sinnhuber & Bracher, Remote Sensing, University of Bremen, Summer 2008 Resolution: An example Assume some typical values: h: 800 km, D: 4m (huge!), λ: 500 nm:

Sinnhuber & Bracher, Remote Sensing, University of Bremen, Summer 2008 ENVISAT: Launched 1 March 2002

Sinnhuber & Bracher, Remote Sensing, University of Bremen, Summer 2008 MERIS/ENVISAT

Sinnhuber & Bracher, Remote Sensing, University of Bremen, Summer 2008 SeaWIFS, 26. Feb. 2000

Sinnhuber & Bracher, Remote Sensing, University of Bremen, Summer 2008 MERIS/ENVISAT, Cloud Top Pressure

Sinnhuber & Bracher, Remote Sensing, University of Bremen, Summer 2008 Ocean colour: MERIS/ENVISAT, 443 nm

Sinnhuber & Bracher, Remote Sensing, University of Bremen, Summer 2008 Ocean colour: MERIS/ENVISAT, 560 nm

Sinnhuber & Bracher, Remote Sensing, University of Bremen, Summer 2008 Ocean colour: MERIS/ENVISAT, Chlorophyll

Sinnhuber & Bracher, Remote Sensing, University of Bremen, Summer 2008 Absorption windows of atmospheric constituents

Sinnhuber & Bracher, Remote Sensing, University of Bremen, Summer 2008 Observing the Ozone Layer 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) Antarctic Ozone Hole

Sinnhuber & Bracher, Remote Sensing, University of Bremen, Summer 2008 The Arctic Ozone Layer Ten years of GOME observtions

Sinnhuber & Bracher, Remote Sensing, University of Bremen, Summer 2008 The Electromagnetic Spectrum 100 m cm MHz 10 m cm -1 Radio 100 MHz 1 m cm -1 1 GHz 10 cm 0.1 cm GHz Microwave 1 cm 1 cm GHz 1 mm 10 cm -1 1 THz sub-mm – Far IR 0.1 mm 100 cm THz 10 μm 1000 cm -1 Thermal IR al IR 100 THz Near IR 1 μm 10 4 cm THz Ultraviolet 100 nm 10 5 cm -1 Wavelength Frequency Wave number Visible nm

Sinnhuber & Bracher, Remote Sensing, University of Bremen, Summer 2008 Solar Spectrum and Terrestrial Spectrum

Sinnhuber & Bracher, Remote Sensing, University of Bremen, Summer 2008 MODIS / Terra, Gulfstream Temperature

Sinnhuber & Bracher, Remote Sensing, University of Bremen, Summer 2008

AMSU-B Data (183 ±1 GHz) Dry areas in the UT (NOAA 16, Channel 18, Figure: Oliver Lemke) Microwave Remote Sensing

Sinnhuber & Bracher, Remote Sensing, University of Bremen, Summer 2008 Satellite Limb Sounding (Figure: Oliver Lemke)

Sinnhuber & Bracher, Remote Sensing, University of Bremen, Summer 2008 Microwave Limb Sonder (MLS) onboard UARS

Sinnhuber & Bracher, Remote Sensing, University of Bremen, Summer 2008 Airborne Microwave Remote Sensing

Sinnhuber & Bracher, Remote Sensing, University of Bremen, Summer 2008 ASUR frequency range and primary species

Sinnhuber & Bracher, Remote Sensing, University of Bremen, Summer 2008 A picture from the SOLVE campaign in Kiruna, Sweden, January 2000

Sinnhuber & Bracher, Remote Sensing, University of Bremen, Summer 2008 Validation of satellite data is important...

Sinnhuber & Bracher, Remote Sensing, University of Bremen, Summer 2008 Ground-based Radiometer for Atmospheric Measurements (RAM )

Sinnhuber & Bracher, Remote Sensing, University of Bremen, Summer 2008 Measured Microwave Spectrum by the RAM

Sinnhuber & Bracher, Remote Sensing, University of Bremen, Summer 2008 Pressure Broadening of Spectral Lines 50km / 0.5 hPa 20km / 50 hPa 10km / 200 hPa

Sinnhuber & Bracher, Remote Sensing, University of Bremen, Summer 2008 A Note on Profile Retrieval Often we can describe the relation between the (unknown) atmospheric profile x and the measured spectrum y by a linear equation: 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:

Sinnhuber & Bracher, Remote Sensing, University of Bremen, Summer 2008 Lidar In-space Technology Experiment (LITE) on Discovery in September 1994 as part of the STS-64 mission

Sinnhuber & Bracher, Remote Sensing, University of Bremen, Summer 2008 Radar Image ENVISAT ASAR 15 April 2005

Sinnhuber & Bracher, Remote Sensing, University of Bremen, Summer 2008 Sea ice concentration from AMSR-E 89 GHz 15 April courtesy of Lars Kaleschke

Sinnhuber & Bracher, Remote Sensing, University of Bremen, Summer 2008 Sea ice concentration from AMSR-E 89 GHz 15 April False colour image courtesy of Lars Kaleschke

Sinnhuber & Bracher, Remote Sensing, University of Bremen, Summer 2008 Sea ice concentration from AMSR-E 89 GHz 06 April False colour image courtesy of Lars Kaleschke

Sinnhuber & Bracher, Remote Sensing, University of Bremen, Summer 2008 Example: SCIAMACHY Tropospheric NO 2 biomass burning pollution Courtesy of Andreas Richter

Sinnhuber & Bracher, Remote Sensing, University of Bremen, Summer 2008 NO2 reductions in Europe and parts of the US strong increase over China consistent with significant NO x emission changes 7 years of GOME data DOAS retrieval + CTM-stratospheric correction seasonal and local AMF based on 1997 MOART-2 run cloud screening GOME annual changes in tropospheric NO 2 GOME NO 2 : Temporal Evolution A. Richter et al., Increase in tropospheric nitrogen dioxide over China observed from space, Nature,