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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] Recap. Last week

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2009-10 CEGEG046 / GEOG3051Principles & 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]



  • 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












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.)


  • Several ways to arrange data in binary image file
    • Band sequential (BSQ)
    • Band interleaved by line (BIL)
    • Band interleaved by pixel (BIP)



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  1x106 bytes, 1MB actually 220 bytes = 1048576 bytes
      • see
    • Landsat 7 has 375GB on-board storage (~1500 images)

Details from


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 and


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)



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



Transmission, storage and processing

  • Ground receiving station, Kiruna, Sweden



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

E.g. MODIS direct broadcast (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....



(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

(Pre?) Processing chain

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



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.

Processing chain

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

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

Radiometric correction

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



Radiometric correction

  • Remove radiometric artifacts
    • striping
      • deterioration of detectors with time (& non-linearities)
      • Filter in Fourier domain to remove periodic striping



Fourier domain filtering

  • Filter periodic noise/aretfacts

Fourier transform (to freq. domain)

Convolve with Fourier domain filter

Apply inverse FT



Processing chain

  • Typically:
    • radiometric calibration
    • radiometric correction
    • atmospheric correction
    • geometric correction/registration
remember interactions with the atmosphere













Remember? Interactions with the atmosphere
  • Notice that target reflectance is a function of
    • Atmospheric irradiance (path radiance: R1)
    • Reflectance outside target scattered into path (R2)
    • Diffuse atmospheric irradiance (scattered onto target: R3)
    • Multiple-scattered surface-atmosphere interactions (R4)



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 Lapp – sensor dynamic range
    • Convert Lapp to apparent reflectance (knowing response of sensor)
    • Convert to intrinsic surface property - at-ground reflectance in this case, by accounting for atmosphere

Radiance, L

Offset assumed to be atmospheric path radiance (plus dark current signal)

Regression line L = G*DN + O (+)


Target DN values

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(Lmax - Lmin)/255 + Lmin




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

Haze due to scan angle of instruments

Airborne Thematic Mapper (ATM) data over Harwood Forest, Northumberland, UK, 13/7/2003

Compact Airborne Spectrographic Imager (CASI) data over Harwood Forest, Northumberland, UK, 13/7/2003

Example: airborne data



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.


Atmospheric correction: complex

  • 6S radiative transfer model: calculate upward and downward direct and diffuse fluxes

Direct + diffuse reflectance from target (we want) + surroundings 

Transmitted, 

Direct & diffuse from sun 

Path radiance, 

TOA reflectance,  i.e. what we measure

Diffuse (mscatt) between ground and atmos 

ρ* (θs, θv, Δϕ) = Top-of-atmosphere spectral reflectance, as a function of view and sun zenith θs,v and relative azimuth, Δϕ;

tg = total gaseous transmission i.e. solar radiation to surface, then escaping on the way up;

ρa= 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 μs, μv are cos(θs) and cos(θv) respectively;

td(θs), td(θv) = diffuse transmittance in sun & view directions;

ρc= reflectance of target (what we want); ρe = reflectance of surrounding area;

S = spherical (direct + diffuse) albedo of the atmosphere i.e. 1-ρeS 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)
      • H2O, O3, 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)



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



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

Processing chain

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

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



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)

Corrected to sza = 45° vza = 0 °

AVHRR bands 1 & 2 uncorrected

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 (



Movable sensor head: alter view zen. angle

Azimuthal rail: alter view azimuth angle

BRDF effects?

  • Field measurements of BRDF: goniometer e.g. European Goniometric Facility (EGO) at JRC, & FIGO in CH

ASIDE: Chapter (12) in Liang (2004) book on validation, sampling; Also Jensen chapter (11)


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.

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

Separate bands


Scan mirror

Sensor path

Dichroic mirrors

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

Adapted from Jensen, 2000, p. 184


Dichroic lens/prism

Sensor motion

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

From Jensen, J. (2000) Remote sensing: and Earth resource perspective, p. 184


IFOV sweeps surface

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 = 2.5x10-3x3000 = 7.5m
    • Typically .5 to 5 mrad - tradeoff of spatial resolution v SNR

Adapted from Lillesand, Kiefer and Chipman, 2004 p. 332

Examples: Landsat MSS, TM and ETM, AVHRR, (MODIS)

See Jensen Chapter 7


Sensor motion

Sensor motion

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

Examples: SPOT HRVIR and Vegetation, MISR, IKONOS, QuickBird

See Jensen Chapter 7

From: & J. Jensen (2000)


Sensor motion

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

From: & Jensen (2000)


Aside: CCD

  • Charge Couple Device



Aside: CCD

  • Photons arrive (through optics and filters) and generate free electrons in CCD elements (few x106 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

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)


  • 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)