2009-10 CEGEG046 / GEOG3051 Principles & Practice of Remote Sensing (PPRS) 6: ground segment, pre-processing & scanning

Dr. Mathias (Mat) Disney UCL Geography Office: 113, Pearson Building Tel: 7670 0592 Email: [email protected]

www.geog.ucl.ac.uk/~mdisney

Recap

• Last week – orbits and swaths – Temporal & angular sampling/resolution + radiometric resolution • This week – data size, storage & transmission – pre-processing stages (transform raw data to “ products ” ) – sensor scanning mechanisms 2

Data volume?

• Size of digital image data easy (ish) to calculate – size = (nRows * nColumns * nBands * nBitsPerPixel) bits – in bytes = size / nBitsPerByte – typical file has header information (giving rows, cols, bands, date etc.) nColumns (0,0) nColumns (0,0) nBands nBands (r,c)

Time

(r,c) 3

Aside

• Several ways to arrange data in binary image file – Band sequential (BSQ) – Band interleaved by line (BIL) – Band interleaved by pixel (BIP) From http://www.profc.udec.cl/~gabriel/tutoriales/rsnote/cp6/cp6-4.htm

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Data volume: examples

• Landsat ETM+ image? Bands 1-5, 7 (vis/NIR) – size of raw binary data (no header info) in bytes?

– 6000 rows (or lines) * 6600 cols (or samples) * 6 bands * 1 byte per pixel = 237600000 bytes ~ 237MB • actually 226.59 MB as 1 MB  1x10 6 1048576 bytes bytes, 1MB actually 2 20 bytes = • see http://www.matisse.net/mcgi-bin/bits.cgi

– Landsat 7 has 375GB on-board storage (~1500 images) Details from http://ltpwww.gsfc.nasa.gov/IAS/handbook/handbook_htmls/chapter6/chapter6.htm

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Data volume: examples

• MODIS reflectance 500m tile (not raw swath....)?

– 2400 rows (or lines) * 2400 cols (or samples) * 7 bands * 2 bytes per pixel (i.e. 16-bit data) = 80640000 bytes = 77MB – Actual file also contains 1 32-bit QC (quality control) band & 2 8-bit bands containing other info.

• BUT 44 MODIS products, raw radiance in 36 bands at 250m • Roughly 4800 * 4800 * 36 * 2 ~ 1.6GB per tile, so 100s GB data volume per day!

Details from http://edcdaac.usgs.gov/modis/mod09a1.asp and http://edcdaac.usgs.gov/modis/mod09ghk.asp

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Transmission, storage and processing

• Ground segment – receiving stations capture digital data transmitted by satellite • A: direct if Ground Receiving Station (GRS) visible • B: storage on board for later transmission • C: broadcast to another satellite (typically geostationary telecomms) known as Tracking and Data Relay Satellite System (TDRSS) From http://www.ccrs.nrcan.gc.ca/ccrs/learn/tutorials/fundam/chapter2/chapter2_15_e.html

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Transmission, storage and processing

• Ground receiving station – dish to receive raw data (typically broadcast in  wave) – data storage and archiving facilities – possibly processing occurs at station (maybe later) – dissemination to end users From http://www.ccrs.nrcan.gc.ca/ccrs/learn/tutorials/fundam/chapter2/chapter2_15_e.html

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Transmission, storage and processing

• Ground receiving station, Kiruna, Sweden From http://www.esa.int/SPECIALS/ESOC/SEMZEEW4QWD_1.html#subhead1 9

Transmission, storage and processing

• Scale?

– can be very small-scale these days – dish or aerial for METEOSAT-type data – desktop PC and some disk space 10

E.g. MODIS direct broadcast (DB)

• • • MODIS DB – ideal for smaller organisations, developing nations etc.

– Only need 3m dish and some hardware Pre-processing stage can be VERY complex!

Before you let users loose....

From http://daac.gsfc.nasa.gov/DAAC_DOCS/direct_broadcast/ 11

(Pre)Processing chain

• Task of turning raw top-of-atmosphere (TOA) radiance values (raw DN) into useful information • geophysical variables, products etc. DERIVED from radiance – Can be very complex, time- (and space) consuming – BUT pre-processing determines quality of final products • e.g. reflectance, albedo, surface temperature, NDVI, leaf area index (LAI), suspended organic matter (SOM) content etc. etc.

– typically require ancillary information, models etc. – combined into algorithm for turning raw data into information 12

(Pre?) Processing chain

• Typically: – radiometric calibration – radiometric correction – atmospheric correction – geometric correction/registration 13

Radiometric calibration

• Account for sensor response – cannot assume sensor response is linear – account for non-linearities via pre-launch and/or in-orbit calibration • On-board black body (A/ATSR), stable targets (AVHRR), inter-sensor comparisons etc.

DN out DN in 14

Processing chain

• Typically: – radiometric calibration – radiometric correction – atmospheric correction – geometric correction/registration 15

Radiometric correction

• Remove radiometric artifacts – dropped lines • detectors in CCD may have failed – fix by interpolating DNs either side?

– Automate?

• Topographic effects?

See http://www.chris-proba.org.uk

CHRIS-PROBA image over Harwood Forest, Northumberland, UK, 9/5/2004 16

Radiometric correction

• Remove radiometric artifacts – striping • deterioration of detectors with time (& non-linearities) • Filter in Fourier domain to remove periodic striping From http://visibleearth.nasa.gov/cgi-bin/viewrecord?7386

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Fourier domain filtering

• Filter periodic noise/aretfacts Fourier transform (to freq. domain) Convolve with Fourier domain filter Apply inverse FT From http://homepages.inf.ed.ac.uk/rbf/HIPR2/freqfilt.htm

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Processing chain

• Typically: – radiometric calibration – radiometric correction – atmospheric correction – geometric correction/registration 19

Remember? Interactions with the atmosphere

R 4 R 1 R 2 R 3 target target target •Notice that target reflectance is a function of •Atmospheric irradiance (path radiance: R 1 ) •Reflectance outside target scattered into path (R 2 ) •Diffuse atmospheric irradiance (scattered onto target: R 3 ) •Multiple-scattered surface-atmosphere interactions (R 4 ) From: http://www.geog.ucl.ac.uk/~mdisney/phd.bak/final_version/final_pdf/chapter2a.pdf

target 20

Atmospheric correction: simple

• So....need to remove impact of atmosphere on signal i.e. turn raw TOA DN into at-ground reflectance • Simple methods?

– Convert DN to apparent radiance L app – sensor dynamic range – Convert L app sensor) to apparent reflectance (knowing response of – Convert to intrinsic surface property - at-ground reflectance in this case, by accounting for atmosphere 21

Atmospheric correction: simple

• Simple methods – e.g. empirical line correction (ELC) method – Use target of “ known ” , low and high reflectance targets in one channel e.g. non turbid water & desert, or dense dark vegetation & snow – Assuming linear detector response, radiance, L = gain * DN + offset – e.g. L = DN(L max - L min )/255 + L min Radiance, L Offset assumed to be atmospheric path radiance (plus dark current signal) L max L min Regression line L = G*DN + O (+ ) Target DN values DN 22

Atmospheric correction: simple

• Drawbacks – require assumptions of: • Lambertian surface (ignore angular effects) • Large, homogeneous area (ignore adjacency effects) • Stability (ignore temporal effects) – Also, per-band not per pixel so assumes • atmospheric effects invariant across image • illumination invariant across image • ok for narrow swath (e.g. airborne) but no good for wide swath 23

Example: airborne data

Haze due to scan angle of instruments Airborne Thematic Mapper (ATM) data over Harwood Forest, Northumberland, UK, 13/7/2003 See: http://www.nerc.ac.uk/arsf Compact Airborne Spectrographic Imager (CASI) data over Harwood Forest, Northumberland, UK, 13/7/2003 24

Atmospheric correction: complex

• Atmospheric radiative transfer modelling – use detailed scattering models of atmosphere including gas and aerosols • Second Simulation of Satellite Signal in Solar Spectrum (6s) Vermote et al. (1997) • MODTRAN/LOWTRAN (Berk et al. 1998) • SMAC Rahman and Dedieu (1994) • FLAASH, ACORN, ATREM etc.

http://www-loa.univ-lille1.fr/Msixs/msixs_gb.html

http://geosci.uchicago.edu/~archer/cgimodels/radiation.html

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Atmospheric correction: complex

• 6S radiative transfer model: calculate upward and Direct + diffuse reflectance downward direct and diffuse fluxes from target (we want) +  r * ( q

s

, q

v

, D f i.e. what we ) =

t g

( q

s

, q

v

Transmitted, ) é é é é r

a

measure ( q

s

, q

v

, D f Path radiance,  ) + m

s

+

td

Direct & diffuse from sun  é éé é éé r

c e

t m

v

1 + r r

e e

surroundings 

td S

Diffuse (mscatt) ( ) é éé é é é é between ground and atmos  ρ* (θ s , θ v , Δϕ) = Top-of-atmosphere spectral reflectance, as a function of view and sun zenith θ s,v and relative azimuth, Δϕ; t g ρ a = total gaseous transmission i.e. solar radiation to surface, then escaping on the way up; = atmospheric reflectance, function of molecular aerosols optical properties; τ = atmos. optical depth (e -t/μs and e -t/μv = direct transmittance in sun & view directions, where μ s , μ v are cos(θ s ) and cos(θ v ) respectively; td(θ s ), td(θ v ) = diffuse transmittance in sun & view directions; ρ c = reflectance of target (what we want); ρ e

=

reflectance of surrounding area; S = spherical (direct + diffuse) albedo of the atmosphere i.e. 1-ρ e S accounts for multiple scattering between ground (outside target) and atmosphere…..

Atmospheric correction: complex

• Radiative transfer models such as 6S require: – Geometrical conditions (view/illum. angles) – Atmospheric model for gaseous components (Rayleigh scattering) • H 2 O, O 3 , aerosol optical depth,  (opacity) – Aerosol model (type and concentration) (Mie scattering) • Dust, soot, salt etc.

– Spectral condition • bands and bandwidths – Ground reflectance (type and spectral variation) • surface BRDF (default is to assume Lambertian….) • If no info. use default values (Standard Atmosphere) From: http://www.geog.ucl.ac.uk/~mdisney/phd.bak/final_version/final_pdf/chapter2a.pdf

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Atmospheric correction

• Can measure  from ground and/or use multi angle viewing to obtain different path lengths through atmos e.g. MISR, CHRIS – infer optical depth and path radiance AND aerosols – so use data themselves to infer atmos. scattering From:http://visibleearth.nasa.gov/cgi-bin/viewrecord?129

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Atmospheric correction: summary

• Convert TOA radiance to at-ground reflectance • VERY important to get right (can totally dominate signal) • Simple methods – e.g. ELC but rough and ready and require many assumptions • Complex methods – e.g. 6S but require much ancillary assumptions – BUT can use multi-angle measurements to correct – i.e. treat atmosphere as PART of surface parameter retrieval problem • different view angles give different PATH LENGTH 29

Processing chain

• Typically: – radiometric calibration – radiometric correction – atmospheric correction – geometric correction/registration 30

Geometric correction

• Account for distortion in image due to motion of platform and scanner mechanism – Particular problem for airborne data: distortion due to roll, pitch, yaw From:http://liftoff.msfc.nasa.gov/academy/rocket_sci/shuttle/attitude/pyr.html

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Geometric correction

• Airborne data over Barton Bendish, Norfolk, 1997 • Resample using ground control points – various warping and resampling methods – nearest neighbour, bilinear or bicubic interpolation....

– Resample to new grid (map) 32

BRDF effects?

• Multi-temporal observations have varying sun/view angles • To compare images from different dates, need same view/illum. conditions i.e. account for BRDF effects – fit BRDF model & use to normalise reflectance e.g. to nadir view/illum.

• e.g. MODIS NBAR nadir BRDF-adjusted reflectance ( http://geography.bu.edu/brdf/userguide/nbar.html

) AVHRR bands 1 & 2 uncorrected Corrected to sza = 45 ° vza = 0 ° From:http://www.ccrs.nrcan.gc.ca/ccrs/rd/apps/landcov/corr/brdf_e.html

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BRDF effects?

• Field measurements of BRDF: goniometer e.g. European Goniometric Facility (EGO) at JRC, & FIGO in CH – http://www.geo.unizh.ch/rsl/research/SpectroLab/goniometry/index.shtml

Movable sensor head: alter view zen. angle Azimuthal rail: alter view azimuth angle ASIDE: Chapter (12) in Liang (2004) book on validation, sampling; Also Jensen chapter (11) 34

Pre-processing: summary

• Convert raw DN to useful information – calibrate instrument response and remove radiometric blunders – remove atmospheric effects – remove BRDF effects?

– resample onto grid • Results in more fundamental property e.g. surface reflectance, emissivity etc.

– NOW apply scientific algorithm to convert reflectance to LAI, fAPAR, albedo, ocean colour etc. etc. etc.

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Sensor scanning characteristics

• Range of scanning mechanisms to build up images • Different applications, different image characteristics and pros/cons for each type – scanning mechanisms: electromechanical • discrete detectors • whiskbroom scanners • pushbroom scanners – digital frame cameras 36

Discrete detectors

• Mirror can rotate or scan – individual detectors record signal in different bands – How do we split signal into separate bands?

• Dichroic mirror or prism Separate bands Lens Scan mirror Sensor path Dichroic mirrors Adapted from Jensen, 2000, p. 184 37

Scanning mechanisms: across track

• 3 main types of electromechanical (detectors, optics plus mechanical scanning) mechanisms – across track or “ whiskbroom ” scanner (mechanical) – linear detectors array (electronic) – beam splitter / dichroic / prism / filters splits incoming signal into separate wavelength regions Dichroic lens/prism Sensor motion From Jensen, J. (2000) Remote sensing: and Earth resource perspective, p. 184 38

Scanning mechanisms: across track

• • Whiskbroom scanner – Mirror either rotates fully, or oscillates – Oscillation can have delays at either end of scan (vibration?) – Restricted “ dwell time ” requires tradeoff with no. of bands to give acceptable SNR – motion of platform and mirror causes image distortion Diameter of IFOV on surface  H  – H = flying height;  = nominal angular IFOV in radians – e.g. For 2.5 mrad IFOV, H = 3000m, D = 2.5x10

3 x3000 = 7.5m

– Typically .5 to 5 mrad - tradeoff of spatial resolution v SNR IFOV sweeps surface Adapted from Lillesand, Kiefer and Chipman, 2004 p. 332 Examples: Landsat MSS, TM and ETM, AVHRR, (MODIS) See Jensen Chapter 7 39

Scanning mechanisms: along track

• Pushbroom scanner – pixels recorded line by line, using forward motion of sensor – less distortion across track but overlap to avoid gaps – No moving parts so less to go wrong and longer “ dwell time ” – BUT needs v. good calibration to avoid striping – Ground-sampled distance (GSD) in x-track direction fixed by CCD element size – GSD along-track fixed by detector sampling interval (  T) used for AD conversion Sensor motion Examples: SPOT HRVIR and Vegetation, MISR, IKONOS, QuickBird See Jensen Chapter 7 From: http://ceos.cnes.fr:8100/cdrom/ceos1/irsd/pages/datacq4.htm & J. Jensen (2000) Sensor motion 40

Scanning mechanisms

• Central perspective / digital frame camera area arrays – Multitple CCD arrays – Silicon (vis/NIR), HgCdTe (SWIR/LWIR)?

– Similar image distortion to film camera • distortion increases radially away from focal point Sensor motion From: http://ceos.cnes.fr:8100/cdrom/ceos1/irsd/pages/datacq4.htm & Jensen (2000) 41

Aside: CCD

•

Charge Couple Device

From http://www.na.astro.it/datoz-bin/corsi?l1a

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Aside: CCD

•Photons arrive (through optics and filters) and generate free electrons in CCD elements (few x10 6 on a CCD) •More photons == more electrons collected •Charge coupling: CCD design allows all packets of charged electrons to be moved one row at a time by varying voltage of adjacent rows across CCD - cascade effect •i.e. Count is done at one point (lower corner) – so delay due to read time •http://electronics.howstuffworks.com/digital-camera2.htm

•http://www.oceanoptics.com/Products/howccddetectorworks.asp

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Aside: CCD

•

Si (Silicon) CCD

– vis/NIR up to ~ 1.1 m

• InGaAs (Indium Gallium Arsenide)

– IR (~0.9 - 1.6 m)

• InSb (Indium Antimonide)

– mid-IR ~3.5 - 4 m

• HgCdTe (Mercury Cadmium Telluride)

– IR (~10 - 12 m) 44

Summary

• Ground receiving – transfer data from sensor to ground station (storage v. transmission?) – can be small-scale these days e.g. MSG, MODIS DB etc.

• Pre-processing chain – atmospheric, geometric correction, radiometric correction and calibration • can obtain raw data (level 0 product), some pre-processing (level 1) or fully processed to reflectance, radiance etc. (level 1b/2/3 etc.) – then REAL work begins!

• Scanning mechanisms – various depending on application – have pros/cons - usual tradeoff of reliability, spatial res. V SNR and geometric distortions ( see Lillesand, Kiefer, Chipman section 5.9

) – Reading – Rahman and Dedieu (1994); Vermote et al. (1997) 45