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UCL DEPARTMENT OF GEOGRAPHYUCL DEPARTMENT OF GEOGRAPHY
GEOGG141/ GEOG3051Principles & Practice of Remote Sensing (PPRS)Angular, temporal, radiometric resolution, sampling
Dr. Mathias (Mat) Disney
UCL Geography
Office: 113, Pearson Building
Tel: 7679 0592
Email: [email protected]
http://www2.geog.ucl.ac.uk/~mdisney/teaching/GEOGG141/GEOGG141.html
http://www2.geog.ucl.ac.uk/~mdisney/teaching/3051/GEOG3051.html
UCL DEPARTMENT OF GEOGRAPHY
• Previously introduced– spatial and spectral resolution– narrow v broad band tradeoffs....– signal to noise ratio
• This session– temporal and angular sampling and/or resolution– REMEMBER: sampling NOT same as resolution, but
sometimes used interchangeably– orbits and sensor swath– radiometric resolution
2
Recap
UCL DEPARTMENT OF GEOGRAPHY
• Single or multiple observations• How far apart are observations in time?
– One-off, several or many?
• Depends (as usual) on application– Is it dynamic?– If so, over what timescale?
3
Temporal sampling/resolution
Useful link: http://nasascience.nasa.gov/earth-science
UCL DEPARTMENT OF GEOGRAPHY
• Examples– Vegetation stress monitoring, weather, rainfall
• hours to days– Terrestrial carbon, ocean surface temperature
• days to months to years– Glacier dynamics, ice sheet mass balance, erosion/tectonic
processes• Months to decades
4
Temporal
Useful link: http://nasascience.nasa.gov/earth-science
UCL DEPARTMENT OF GEOGRAPHY
• Sensor orbit– geostationary orbit - over same spot
• BUT distance means entire hemisphere is viewed e.g. METEOSAT
– polar orbit can use Earth rotation to view entire surface
• Sensor swath– Wide swath allows more rapid revisit
• typical of moderate res. instruments for regional/global applications– Narrow swath == longer revisit times
• typical of higher resolution for regional to local applications
5
What determines temporal sampling?
UCL DEPARTMENT OF GEOGRAPHY
• Orbital characteristics – orbital mechanics developed by Johannes Kepler (1571-1630),
German mathematician– Explained observations of Danish nobleman Tyco Brahe (1546-
1601)– Kepler favoured elliptical orbits (from Copernicus’ solar-centric
system)
• Properties of ellipse?
6
Orbits and swaths
UCL DEPARTMENT OF GEOGRAPHY
• Flattened circle – 2 foci and 2 axes: major and minor– Distance r1+r2 = constant = 2a (major axis)
– “Flatness” of ellipse defined by eccentricity, e = 1-b2/a2 = c/a– i.e. e is position of the focus as a fraction of the semimajor axis, a
7
Ellipse
From http://mathworld.wolfram.com/Ellipse.html
Increasing eccentricity
• ecircle = 0
• As e 1, c a and ellipse becomes flatter
r1 r2
f1 f2C
2a
2c
2b
major axis
minor axis
UCL DEPARTMENT OF GEOGRAPHY
• Kepler’s Laws – deduced from Brahe’s data after his death– see nice Java applet
http://www-groups.dcs.st-and.ac.uk/~history/Java/Ellipse.html
• Kepler’s 1st law: – Orbits of planets are elliptical, with sun at one focus
8
Kepler’s laws
From:http://csep10.phys.utk.edu/astr161/lect/history/kepler.html
UCL DEPARTMENT OF GEOGRAPHY
• Kepler’s 2nd law – line joining planet to sun sweeps out equal areas in equal times
9
Kepler’s laws
From:http://csep10.phys.utk.edu/astr161/lect/history/kepler.html
UCL DEPARTMENT OF GEOGRAPHY
• Kepler’s 3rd law – “ratio of the squares of the revolutionary periods for two planets (P1,
P2) is equal to the ratio of the cubes of their semimajor axes (R1, R2)”
– P12/P2
2 = R13/R2
3
• i.e. orbital period increases dramatically with R
• Convenient unit of distance is average separation of Earth from Sun = 1 astronomical unit (A.U.)– 1A.U. = 149,597,870.691 km– in Keplerian form, P(years)2 R(A.U.)3
– or P(years) R(A.U.)3/2
– or R(A.U.) P(years)2/3
10
Kepler’s laws
UCL DEPARTMENT OF GEOGRAPHY
• Orbital period for a given instrument and height? – Gravitational force Fg = GMEms/RsE
2
• G is universal gravitational constant (6.67x10-11 Nm2kg2); ME is Earth mass (5.983x1024kg); ms is satellite mass (?) and RsE is distance from Earth centre to satellite i.e. 6.38x106 + h where h is satellite altitude
– Centripetal (not centrifugal!) force Fc = msvs2/RsE
• where vs is linear speed of satellite (=sRsE where is the satellite angular velocity, rad s-1)
– for stable (constant radius) orbit Fc = Fg
– GMEms/RsE2 = msvs
2/RsE = ms s2RsE
2 /RsE
– so s2 = GME /RsE
3
11
Orbits: examples
UCL DEPARTMENT OF GEOGRAPHY
• Orbital period T of satellite (in s) = 2/– (remember 2 = one full rotation, 360°, in radians)– and RsE = RE + h where RE = 6.38x106 m
– So now T = 2[(RE+h)3/GME]1/2
• Example: polar orbiter period, if h = 705x103m– T = 2[(6.38x106 +705x103)3 / (6.67x10-11*5.983x1024)]1/2
– T = 5930.6s = 98.8mins
• Example: altitude for geostationary orbit? T = ??– Rearranging: h = [(GME /42)T2 ]1/3 - RE
– So h = [(6.67x10-11*5.983x1024 /42)(24*60*60)2 ]1/3 - 6.38x106
– h = 42.2x106 - 6.38x106 = 35.8x106m
12
Orbits: examples
UCL DEPARTMENT OF GEOGRAPHY
• Convenience of using radians– By definition, angle subtended by an arc (in radians) = length of
arc/radius of circle i.e. = l/r– i.e. length of an arc l = r– So if we have unit circle (r=1), l = circumference = 2r = 2– So, 360° = 2 radians
13
Orbits: aside
r
l
UCL DEPARTMENT OF GEOGRAPHY
• Geostationary? – Circular orbit in the equatorial plane, altitude ~36,000km– Orbital period?
• Advantages– See whole Earth disk at once due to large distance– See same spot on the surface all the time i.e. high temporal coverage– Big advantage for weather monitoring satellites - knowing atmos.
dynamics critical to short-term forecasting and numerical weather prediction (NWP)
• GOES (Geostationary Orbiting Environmental Satellites), operated by NOAA (US National Oceanic and Atmospheric Administration)
• http://www.noaa.gov/ and http://www.goes.noaa.gov/
14
Orbital pros and cons
UCL DEPARTMENT OF GEOGRAPHY
• Meteorological satellites - combination of GOES-E, GOES-W, METEOSAT (Eumetsat), GMS (NASDA), IODC (old Meteosat 5)– GOES 1st gen. (GOES-1 - ‘75 GOES-7 ‘95); 2nd gen. (GOES-8++ ‘94)
15
Geostationary
From http://www.sat.dundee.ac.uk/pdusfaq.html
METEOSAT 0° WGOES-W 135° WGOES-E 75° W GMS 140° EIODC 63° E
UCL DEPARTMENT OF GEOGRAPHY
• METEOSAT - whole earth disk every 15 mins
16
Geostationary
From http://www.goes.noaa.gov/f_meteo.html
UCL DEPARTMENT OF GEOGRAPHY
• Disadvantages– typically low spatial resolution due to high altitude– e.g. METEOSAT 2nd Generation (MSG) 1x1km visible, 3x3km IR
(used to be 3x3 and 6x6 respectively)• MSG has SEVIRI and GERB instruments• http://www.eumetsat.int/Home/Main/What_We_Do/Satellites/
Meteosat_Second_Generation/Space_Segment/SP_1119959405658?l=en– Cannot see poles very well (orbit over equator)
• spatial resolution at 60-70° N several times lower• not much good beyond 60-70°
– NB Geosynchronous orbit same period as Earth, but not equatorial
17
Geostationary orbits
From http://www.esa.int/SPECIALS/MSG/index.html
UCL DEPARTMENT OF GEOGRAPHY
• Advantages– full polar orbit inclined 90 to equator
• typically few degrees off so poles not covered• orbital period typically 90 - 105mins
– near circular orbit between 300km (low Earth orbit) and 1000km– typically higher spatial resolution than geostationary– rotation of Earth under satellite gives (potential) total coverage
• ground track repeat typically 14-16 days
18
Polar & near polar orbits
From http://collections.ic.gc.ca/satellites/english/anatomy/orbit/
UCL DEPARTMENT OF GEOGRAPHY
19
(near) Polar orbits: NASA Terra
From http://visibleearth.nasa.gov/cgi-bin/viewrecord?134
UCL DEPARTMENT OF GEOGRAPHY
– inclination 98.2, T = 98.8mins– http://www.cscrs.itu.edu.tr/page.en.php?id=51– http://landsat.gsfc.nasa.gov/project/Comparison.html
20
Near-polar orbits: Landsat
From http://www.iitap.iastate.edu/gccourse/satellite/satellite_lecture_new.html & http://eosims.cr.usgs.gov:5725/DATASET_DOCS/landsat7_dataset.html
• ASIDE: repeat time
• Orbital period is 5928s
• So in this time Earth surface moves l = r = r*(2*5928/(24*60*60))
• So if r = 6.38x106 then l = 2750km
UCL DEPARTMENT OF GEOGRAPHY
• Disadvantages– need to launch to precise altitude and orbital inclination– orbital decay
• at LEOs (Low Earth Orbits) < 1000km, drag from atmosphere• causes orbit to become more eccentric• Drag increases with increasing solar activity (sun spots) - during solar
maximum (~11yr cycle) drag height increased by 100km!– Build your own orbit:
http://lectureonline.cl.msu.edu/~mmp/kap7/orbiter/orbit.htm
21
(near) Polar orbits
From http://collections.ic.gc.ca/satellites/english/anatomy/orbit/
UCL DEPARTMENT OF GEOGRAPHY
• Sun-synchronous– Passes over same point on surface at approx. same local solar time
each day (e.g. Landsat)– Characterised by equatorial crossing time (Landsat ~ 10am)– Gives standard time for observation– AND gives approx. same sun angle at each observation
• good for consistent illumination of observations over time series (i.e. Observed change less likely to be due to illumination variations)
• BAD if you need variation of illumination (angular reflectance behaviour)
• Special case is dawn-to-dusk– e.g. Radarsat 98.6° inclination– trails the Earth’s shadow (day/night border)– allows solar panels to be kept in sunlight all the time)
22
Types of near-polar orbit
UCL DEPARTMENT OF GEOGRAPHY
• Inclination much lower– orbits close to equatorial– useful for making observations solely over tropical regions
• Example– TRMM - Tropical Rainfall Measuring Mission– Orbital inclination 35.5°, periapsis (near point: 366km), apoapsis (far point:
3881km)– crosses equator several times daily– Flyby of Hurrican Frances (24/8/04)– iso-surface
23
Near-ish: Equatorial orbit
From http://trmm.gsfc.nasa.gov/
UCL DEPARTMENT OF GEOGRAPHY
• TLEs (two line elements)– http://www.satobs.org/element.html e.g.
PROBA 1
1 26958U 01049B 04225.33423432 .00000718 00000-0 77853-4 0 2275
2 26958 97.8103 302.9333 0084512 102.5081 258.5604 14.88754129152399
• DORIS, GPS, Galileo etc.– DORIS: Doppler Orbitography and Radiopositioning Integrated by Satellite– Tracking system providing range-rate measurements of signals from a dense
network of ground-based beacons (~cm accuracy)– GPS: Global Positioning System– http://www.vectorsite.net/ttgps.html– http://www.edu-observatory.org/gps/tracking.html
24
Orbital location?
UCL DEPARTMENT OF GEOGRAPHY
• Swath describes ground area imaged by instrument during overpass
25
Instrument swath
one sample
two samples
three samples
satellite ground swath
direction of travel
UCL DEPARTMENT OF GEOGRAPHY
26
MODIS on-board Terra
From http://visibleearth.nasa.gov/cgi-bin/viewrecord?130
UCL DEPARTMENT OF GEOGRAPHY
27
Terra instrument swaths compared
From http://visibleearth.nasa.gov/Sensors/Terra/
UCL DEPARTMENT OF GEOGRAPHY
• MODIS, POLDER, AVHRR etc.– swaths typically several 1000s of km– lower spatial resolution– Wide area coverage– Large overlap obtains many more view and illumination angles
(much better termporal & angular (BRDF) sampling)– Rapid repeat time
28
Broad swath
UCL DEPARTMENT OF GEOGRAPHY
• Note across-track “whiskbroom” type scanning mechanism• swath width of 2330km (250-1000m resolution)• Hence, 1-2 day repeat cycle
29
MODIS: building global view
From http://visibleearth.nasa.gov/Sensors/Terra/
UCL DEPARTMENT OF GEOGRAPHY
• 2400km swath, 1.1km pixels at nadir, but > 5km at edge of swath• Repeats 1-2 times per day
30
AVHRR: global view
From http://edc.usgs.gov/guides/avhrr.html
UCL DEPARTMENT OF GEOGRAPHY
31
POLDER (RIP!): global view
From http://www-loa.univ-lille1.fr/~riedi/BROWSES/200304/16/index.html
• Polarisation and Directionality of Earth’s Reflectance– FOV ±43° along track, ±51° across track, 9 cameras, 2400km swath, 7x6km
resn. at nadir– POLDER I 8 months, POLDER II 7 months....
Each set of points corresponds to given viewing zenith and azimuthal angles for near-simultaneous measurements over a region defined by lat 0°±0.5° and long of 0°±0.5° (Nov 1996)
Each day, region is sampled from different viewing directions so hemisphere is sampled heavily by compositing measurements over time
From Loeb et al. (2000) Top-of-Atmosphere Albedo Estimation from Angular Distribution Models Using Scene Identification from Satellite Cloud Property Retrievals, Journal of Climate, 1269-1285.
UCL DEPARTMENT OF GEOGRAPHY
• Landsat TM/MSS/ETM+, IKONOS, QuickBird etc.– swaths typically few 10s to 100skm– higher spatial resolution– local to regional coverage NOT global– far less overlap (particularly at lower latitudes)– May have to wait weeks/months for revisit
32
Narrow swath
UCL DEPARTMENT OF GEOGRAPHY
33
Landsat: regional to local view
From http://visibleearth.nasa.gov/Sensors/Terra/
• 185km swath width, hence 16-day repeat cycle (and spatial res. 25m)
• Contiguous swaths overlap (sidelap) by 7.3% at the equator
• Much greater overlap at higher latitudes (80% at 84°)
UCL DEPARTMENT OF GEOGRAPHY
34
IKONOS, QuickBird, WorldView: very local view!
• QuickBird: 16.5km swath at nadir, 61cm! panchromatic, 2.44m multispectral
• http://www.digitalglobe.com
• IKONOS: 11km swath at nadir, 1m panchromatic, 4m multispectral
• http://www.spaceimaging.com/
UCL DEPARTMENT OF GEOGRAPHY
• ERS 1 & 2– ATSR instruments, RADAR altimeter, Imaging SAR (synthetic aperture
RADAR) etc. – ERS 1: various mission phases: repeat times of 3 (ice), 35 and 168
(geodyssey) days– ERS 2: 35 days
35
Variable repeat patterns
From http://earth.esa.int/rootcollection/eeo/ERS1.1.7.html
UCL DEPARTMENT OF GEOGRAPHY
• Wide swath instruments have large overlap– e.g. MODIS 2330km (55), so up to 4 views per day at different
angles! – AVHRR, SPOT-VGT, POLDER I and II, etc.– Why do we want good angular sampling?
• Remember BRDF?• See Barnsley et al (1997) paper
– Information in angular signal we can exploit!– Or remove BRDF effects when combining observations from different
times/angles– More samples of viewing/illum. hemisphere gives more info.
36
So.....angular resolution
UCL DEPARTMENT OF GEOGRAPHY
• Can look like noise over time BUT plotted as a function of angle we see BRDF effect
• So must account for BRDF if we want to look at changes over time
37
Angular (BRDF) effects
UCL DEPARTMENT OF GEOGRAPHY
• MODIS and SPOT-VGT: polar plots– http://www.soton.ac.uk/~epfs/methods/polarplot.shtml
• Reasonable sampling BUT mostly across principal plane (less angular info.)• Is this “good” sampling of BRDF
38
Angular sampling: broad swath
Solar principal plane
Cross solar principal plane
view zenith
relative azimuth (view - solar)
UCL DEPARTMENT OF GEOGRAPHY
• POLDER I !• Broad swath (2200km) AND
large 2D CCD array gave huge number of samples– 43 IFOV along-track and
51 IFOV across-track
39
Angular sampling: broad swath
UCL DEPARTMENT OF GEOGRAPHY
• Is wide swath angular sampling REALLY multi-angular?– Different samples on different days e.g. MODIS BRDF product is
composite over 16 days– minimise impact of clouds, maximise number of samples
• “True” multi-angular viewing requires samples at same time– need to use several looks e.g. ATSR, MISR (& POLDER)
40
BUT.......
UCL DEPARTMENT OF GEOGRAPHY
41
Angular sampling: narrow swath
• ATSR-2 and MISR polar plots• Better sampling in principal plane (more angular info.)• MISR has 9 cameras
UCL DEPARTMENT OF GEOGRAPHY
42
Angular sampling: combinations?
• MODIS AND MISR: better sampling than either individually• Combine observations to sample BRDF more effectively
UCL DEPARTMENT OF GEOGRAPHY
• Function of swath and IFOV – e.g. MODIS at nadir ~1km pixel – remember l = r so angle (in rads) = r/l where r this time is 705km
and l ~ 1km so angular res ~ 1.42x10-6 rads at nadir– at edge of swath ~5km pixel so angular res ~ 7x10-6 rads
• SAMPLING more important/meaningful than resolution in angular sense (as for temporal)
43
So, angular resolution
UCL DEPARTMENT OF GEOGRAPHY
• Had spatial, spectral, temporal, angular.....• Precision with which an instrument records EMR
– i.e. Sensitivity of detector to amount of incoming radiation– More sensitivity == higher radiometric resolution
• determines smallest slice of EM spectrum we can assign DN to– BUT higher radiometric resolution means more data
• As is the case for spatial, temporal, angular etc.
• Typically, radiometric resolution refers to digital detectors– i.e. Number of bits per pixel used to encode signal
44
Radiometric resolution
UCL DEPARTMENT OF GEOGRAPHY
• Analogue– continuous measurement levels– film cameras– radiometric sensitivity of film emulsion
• Digital– discrete measurement levels– solid state detectors (e.g. semiconductor CCDs)
45
Radiometric resolution
UCL DEPARTMENT OF GEOGRAPHY
• Bits per pixel– 1 bit (0,1); 2bits (0, 1, 2, 3); 3 bits (0, 1, 2, 3, 4, 5, 6, 7) etc.– 8 bits in a byte so 1 byte can record 28 (256) different DNs (0-255)
46
Radiometric resolution
• 1 to 6 bits (left to right)– black/white (21) up to 64 graylevels (26) (DN values)– human eye cannot distinguish more than 20-30 DN levels in grayscale
i.e. ‘radiometric resolution’ of human eye 4-5 bits
From http://ceos.cnes.fr:8100/cdrom/ceos1/irsd/pages/dre4.htm
UCL DEPARTMENT OF GEOGRAPHY
• Landsat: MSS 7bits, TM 8bits• AVHRR: 10-bit (210 = 1024 DN levels)
– TIR channel scaled (calibrated) so that DN 0 = -273°C and DN 1023 ~50°C
• MODIS: 12-bit (212 = 4096 DN levels)• BUT precision is NOT accuracy
– can be very precise AND very inaccurate– so more bits doesn’t mean more accuracy
• Radiometric accuracy designed with application and data size in mind – more bits == more data to store/transmit/process
47
Radiometric resolution: examples
UCL DEPARTMENT OF GEOGRAPHY
• Coverage (hence angular &/or temporal sampling) due to combination of orbit and swath– Mostly swath - many orbits nearly same
• MODIS and Landsat have identical orbital characteristics: inclination 98.2°, h=705km, T = 99mins BUT swaths of 2400km and 185km hence repeat of 1-2 days and 16 days respectively
– Most EO satellites typically near-polar orbits with repeat tracks every 16 or so days
– BUT wide swath instrument can view same spot much more frequently than narrow
• Tradeoffs again, as a function of objectives
48
Summary: angular, temporal resolution
UCL DEPARTMENT OF GEOGRAPHY
• Number of bits per pixel– more bits, more precision (not accuracy)– but more data to store, transmit, process– most EO data typically 8-12 bits (in raw form)
• Tradeoffs again, as a function of objectives
49
Summary: radiometric resolution