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Remote Sensing I Summer 2007 Björn-Martin Sinnhuber Room NW1 - U3215 Tel. 8958 [email protected] www.iup.uni-bremen.de/~bms. Contents. Lecture 1 Introduction to R emote S ensing Lecture 2 Electromagnetic R adiation - PowerPoint PPT PresentationTRANSCRIPT
B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007
Remote Sensing I
Summer 2007
Björn-Martin SinnhuberRoom NW1 - U3215Tel. [email protected]/~bms
B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007
Lecture 1 Introduction to Remote Sensing
Lecture 2 Electromagnetic Radiation
Lecture 3 Interaction of Radiation with Gases and Matter: Spectroscopy
Lecture 4 Atmospheric Radiative Transfer
Lecture 5 Retrieval Techniques / Inverse Methods
Remote Sensing of the Atmosphere:
Lecture 6 Passive Microwave Remote Sensing
Lecture 7 Infra-Red Techniques
Lecture 8 Optical (UV / Visible) Remote Sensing
Lecture 9 Active Remote Sensing: Radar and Lidar
Remote Sensing of the Earth Surface:
Lecture 10 Sea Ice Remote Sensing
Lecture 11 Remote Sensing of the Ocean with Satellite Altimeters
Lecture 12 Summary
Contents
B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007
Lecture 1 Introduction
• General Introduction
• Examples of Remote Sensing Applications
• Introduction to Satellite Orbits
B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007
Photo taken
by crew of
Apollo 17
7 Dec 1972
B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007
from maps.google.com
B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007
A Note on Spatial Resolution
The maximum achievable resolution with an optical systemis 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
D
sin
h
xsin
D
hx
α
x
h
(Rayleigh criterion)
B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007
Resolution: An example
D
hx
Assume some typical values: h: 800 km, D: 4m (huge!),λ: 500 nm:
cm10m1.0m4
m10500m10800 93
B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007
ENVISAT: Launched 1 March 2002
B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007
MERIS/ENVISAT
B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007
SeaWIFS, 26. Feb. 2000
B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007MERIS/ENVISAT, Cloud Top Pressure
B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007
Ocean colour: MERIS/ENVISAT, 443 nm
B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007
Ocean colour: MERIS/ENVISAT, 560 nm
B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007
Ocean colour: MERIS/ENVISAT, Chlorophyll
B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007
Absorption windows of atmospheric constituents
B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007
Observing the Ozone Layer
http://ww
w.iu
p.physik.uni-b
reme
n.de/g
ome
nrt/
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
B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007
The Arctic Ozone Layer
Ten years of GOME observtions
B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007
The Electromagnetic Spectrum
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-1 Thermal 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
B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007
Solar Spectrum and Terrestrial Spectrum
Sun Earth
Short Wave Long Wave
B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007
MODIS / Terra, Gulfstream Temperature
B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007
B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007
AMSU-B Data (183 ±1 GHz)
Dry areas in the UT
(NOAA 16, Channel 18,
15.6.2004.
Figure: Oliver Lemke)
Microwave Remote Sensing
B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007
Satellite Limb Sounding
(Figure: Oliver Lemke)
B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007
Microwave Limb Sonder (MLS) onboard UARS
B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007
Airborne Microwave Remote Sensing
B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007
ASUR frequency range and primary species
B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007
A picture from the SOLVE campaignin Kiruna, Sweden, January 2000
B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007
Validation of satellite data is important ...
B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007
Ground-based Radiometer for Atmospheric Measurements (RAM)
B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007
Measured Microwave Spectrum by the RAM
B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007
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
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: Axy
The matrix A is also called as the weighting function matrix.Finding x from measured y would require inversion of A:
yAx 1
yAx g
However, this is generally not possible (inverse of A does not exist).Therefore one has to find some „generallized“ inverse of A:
B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007
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
Radar Image
ENVISAT ASAR
15 April 2005
B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007
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
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
Example: SCIAMACHY Tropospheric NO2
biomass burningpollution
Courtesy of Andreas Richter
B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007
• NO2 reductions in Europe and parts of the US
• strong increase over China
• consistent with significant NOx emission changes
• 7 years of GOME data
• DOAS retrieval + CTM-stratospheric correction
• seasonal and local AMF based on 1997 MOART-2 run
• cloud screening
1996 - 2002
GOME annual changes in tropospheric NO2
GOME NO2: Temporal Evolution
A. Richter et al., Increase in tropospheric nitrogen dioxide over China observed from space, Nature, 437 2005
B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007
Lightning Flashes, Optical Transient Detector (OTD)
B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007
Lecture 1 Introduction
• General Introduction
• Examples of Remote Seinsing Applications
• Introduction to Satellite Orbits
B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007
Satellite Orbits
21 ea
a
satellite
r
Earth
apogee
perigee
a: major axis
e: excentricity
cos1
)1( 2
e
ear
B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007
For a circular satellite orbit around a spherically homogenous planet the gravitational force Fg and the centrifugal force Fc are in balance:
2
r
RmgFg
rmr
mvFc
22
For the Earth g=9.81 m/s2 and R=6380 km.
B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007
r
v
r
RgFF cg
22
r
gRv
2
22
2
gR
rr
v
rT
Orbital period given by:
B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007 From Elachi
Rrh
B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007 From Elachi
B.-M. Sinnhuber, Remote Sensing I, University of Bremen, Summer 2007
2/73
2
cos
2
3
r
IgRJ
dt
d
The orbital node changes due to precession, primarily due to theoblateness of the Earth. The rate of change for the orbital nodeis 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 From Elachi