1 / 50

Dr. Mathias (Mat) Disney UCL Geography Office: 113, Pearson Building Tel: 7670 0592 1

2009-10 CEGEG046 / GEOG3051 Principles & Practice of Remote Sensing (PPRS) 5: resolution II: angular/temporal. Dr. Mathias (Mat) Disney UCL Geography Office: 113, Pearson Building Tel: 7670 0592 1 Email: mdisney@ucl.geog.ac.uk www.geog.ucl.ac.uk/~mdisney. Recap. Previously introduced

adonica
Download Presentation

Dr. Mathias (Mat) Disney UCL Geography Office: 113, Pearson Building Tel: 7670 0592 1

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 2009-10 CEGEG046 / GEOG3051Principles & Practice of Remote Sensing (PPRS) 5: resolution II: angular/temporal Dr. Mathias (Mat) Disney UCL Geography Office: 113, Pearson Building Tel: 7670 05921 Email: mdisney@ucl.geog.ac.uk www.geog.ucl.ac.uk/~mdisney

  2. Recap • Previously introduced • spatial and spectral resolution • narrow v broad band tradeoffs.... • signal to noise ratio • This week • temporal and angular sampling and/or resolution • REMEMBER: sampling NOT same as resolution, but sometimes used interchagngeably • orbits and sensor swath • radiometric resolution

  3. Temporal sampling/resolution • 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? Useful link: http://nasascience.nasa.gov/earth-science

  4. Temporal • 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 Useful link: http://nasascience.nasa.gov/earth-science

  5. What determines temporal sampling? • 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

  6. Orbits and swaths • 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?

  7. r1 r2 2b minor axis f2 f1 C • ecircle = 0 • As e 1, c a and ellipse becomes flatter Increasing eccentricity 2c 2a major axis Ellipse • 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 From http://mathworld.wolfram.com/Ellipse.html

  8. Kepler’s laws • 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 From:http://csep10.phys.utk.edu/astr161/lect/history/kepler.html

  9. Kepler’s laws • Kepler’s 2nd law • line joining planet to sun sweeps out equal areas in equal times From:http://csep10.phys.utk.edu/astr161/lect/history/kepler.html

  10. Kepler’s laws • 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/P22 = R13/R23 • 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

  11. Orbits: examples • Orbital period for a given instrument and height? • Gravitational force Fg = GMEms/RsE2 • 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 = msvs2/RsE = ms s2RsE2 /RsE • so s2 = GME /RsE3

  12. Orbits: examples • 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 /42)T2 ]1/3 - RE • So h = [(6.67x10-11*5.983x1024/42)(24*60*60)2 ]1/3 - 6.38x106 • h = 42.2x106 - 6.38x106 = 35.8km

  13. l  r Orbits: aside • 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 = 2r = 2 • So, 360° = 2 radians

  14. Orbital pros and cons • 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/

  15. GOES-E 75° W GOES-W 135° W METEOSAT 0° W IODC 63° E GMS 140° E Geostationary • 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) From http://www.sat.dundee.ac.uk/pdusfaq.html

  16. Geostationary • METEOSAT - whole earth disk every 15 mins From http://www.goes.noaa.gov/f_meteo.html

  17. Geostationary orbits • 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 From http://www.esa.int/SPECIALS/MSG/index.html

  18. Polar & near polar orbits • 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 From http://collections.ic.gc.ca/satellites/english/anatomy/orbit/

  19. (near) Polar orbits: NASA Terra From http://visibleearth.nasa.gov/cgi-bin/viewrecord?134

  20. Near-polar orbits: Landsat • 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 • 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 From http://www.iitap.iastate.edu/gccourse/satellite/satellite_lecture_new.html & http://eosims.cr.usgs.gov:5725/DATASET_DOCS/landsat7_dataset.html

  21. (near) Polar orbits • 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 From http://collections.ic.gc.ca/satellites/english/anatomy/orbit/

  22. Types of near-polar orbit • 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)

  23. Near-ish: Equatorial orbit • 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 From http://trmm.gsfc.nasa.gov/

  24. Orbital location? • 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

  25. direction of travel satellite ground swath one sample two samples three samples Instrument swath • Swath describes ground area imaged by instrument during overpass

  26. MODIS on-board Terra From http://visibleearth.nasa.gov/cgi-bin/viewrecord?130

  27. Terra instrument swaths compared From http://visibleearth.nasa.gov/Sensors/Terra/

  28. Broad swath • 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

  29. MODIS: building global view • Note across-track “whiskbroom” type scanning mechanism • swath width of 2330km (250-1000m resolution) • Hence, 1-2 day repeat cycle From http://visibleearth.nasa.gov/Sensors/Terra/

  30. AVHRR: global view • 2400km swath, 1.1km pixels at nadir, but > 5km at edge of swath • Repeats 1-2 times per day From http://edc.usgs.gov/guides/avhrr.html

  31. POLDER (RIP!): global view • 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. From http://www-loa.univ-lille1.fr/~riedi/BROWSES/200304/16/index.html

  32. Narrow swath • 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

  33. Landsat: regional to local view • 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°) From http://visibleearth.nasa.gov/Sensors/Terra/

  34. 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/ IKONOS & QuickBird: very local view!

  35. Variable repeat patterns • 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 From http://earth.esa.int/rootcollection/eeo/ERS1.1.7.html

  36. So.....angular resolution • 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.

  37. Angular (BRDF) effects • 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

  38. relative azimuth (view - solar) view zenith Cross solar principal plane Solar principal plane Angular sampling: broad swath • 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

  39. Angular sampling: broad swath • POLDER I ! • Broad swath (2200km) AND large 2D CCD array gave huge number of samples • 43 IFOV along-track and 51 IFOV across-track

  40. BUT....... • 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)

  41. Angular sampling: narrow swath • ATSR-2 and MISR polar plots • Better sampling in principal plane (more angular info.) • MISR has 9 cameras

  42. Angular sampling: combinations? • MODIS AND MISR: better sampling than either individually • Combine observations to sample BRDF more effectively

  43. So, angular resolution • 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)

  44. Radiometric resolution • 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

  45. Radiometric resolution • Analogue • continuous measurement levels • film cameras • radiometric sensitivity of film emulsion • Digital • discrete measurement levels • solid state detectors (e.g. semiconductor CCDs)

  46. 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 Radiometric resolution • 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) From http://ceos.cnes.fr:8100/cdrom/ceos1/irsd/pages/dre4.htm

  47. Radiometric resolution: examples • 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

  48. Summary: angular, temporal resolution • 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

  49. Summary: radiometric resolution • 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

  50. ASIDE: 2nd ESA Explorer launched 2/11/09 • SMOS (Soil Moisture and Ocean Salinity) probe • Interferometric radiometer • Global maps of soil moisture every three days within an accuracy of 4% at a spatial resolution of 50 km • Global maps of sea-surface salinity down to 0.1 practical salinity units for a 30-day average over an area of 200×200 km • http://www.esa.int/SPECIALS/smos/SEMNEYAOE1G_0.html • AND Proba-2 (PRoject for On-Board Autonomy • SEMINAR: tomorrow (5th) at 5pm in room 305

More Related