Radiance (LT) from paths 1, 3, and 5 contains intrinsic valuable spectral information about the target of interest. Conversely, the path radiance (Lp) from paths 2 and 4 includes diffuse sky irradiance or radiance from neighboring areas on the ground. This path radiance generally introduces unwanted radiometric noise in the remotely sensed data and complicates the image interpretation process.

Radiometric Variables

Path 1 contains spectral solar irradiance ( ) that was attenuated very little before illuminating the terrain within the IFOV. Notice in this case that we are interested in the solar irradiance from a specific solar zenith angle ( ) and that the amount of irradiance reaching the terrain is a function of the atmospheric transmittance at this angle ( ). If all of the irradiance makes it to the ground, then the atmospheric transmittance ( ) equals one. If none of the irradiance makes it to the ground, then the atmospheric transmittance is zero

λoE

oθ

oTθ

oTθ

Path 2 contains spectral diffuse sky irradiance ( ) that never even reaches the Earth’s surface (the target study area) because of scattering in the atmosphere. Unfortunately, such energy is often scattered directly into the IFOV of the sensor system. As previously discussed, Rayleigh scattering of blue light contributes much to this diffuse sky irradiance. That is why the blue band image produced by a remote sensor system is often much brighter than any of the other bands. It contains much unwanted diffuse sky irradiance that was inadvertently scattered into the IFOV of the sensor system. Therefore, if possible, we want to minimize its effects. Green (2003) refers to the quantity as the upward reflectance of the atmosphere (

λdE

λduE

Path 3 contains energy from the Sun that has undergone some Rayleigh, Mie, and/or nonselective scattering and perhaps some absorption and reemission before illuminating the study area. Thus, its spectral composition and polarization may be somewhat different from the energy that reaches the ground from path 1. Green (2003) refers to this quantity as the downward reflectance of the atmosphere ( ).

λddE

Path 4 contains radiation that was reflected or scattered by nearby terrain ( ) covered by snow, concrete, soil, water, and/or vegetation into the IFOV of the sensor system. The energy does not actually illuminate the study area of interest. Therefore, if possible, we would like to minimize its effects. Path 2 and Path 4 combine to produce what is commonly referred to as Path Radiance, Lp.

nλρ

Path 5 is energy that was also reflected from nearby terrain into the atmosphere, but then scattered or reflected onto the study area.

Jensen 2007

The total radiance reaching the sensor from the target is:

pTS LLL +=

The total radiance recorded by the sensor becomes:

( ) λθρπ θλθ

λ

λλ dETETL dooovT += ∫ cos1 2

1

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(Pre?) Processing chain • Typically:

– radiometric calibration – radiometric correction – atmospheric correction – geometric correction/registration

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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.

DNout

DNin

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Processing chain

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

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Radiometric correction • Remove radiometric

artifacts – dropped lines

• detectors in CCD may have failed

– fix by interpolating DNs either side?

– Automate?

• Topographic effects?

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

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Radiometric correction • Remove radiometric artifacts

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

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Fourier domain filtering • Filter periodic noise/aretfacts

Fourier transform (to freq. domain)

Convolve with Fourier domain filter

Apply inverse FT

15

Processing chain • Typically:

– radiometric calibration – radiometric correction – atmospheric correction – geometric correction/registration

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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)

R 1

target

R 2

target

R 3

target

R 4

target

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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

Empirical Line Calibration

a) Landsat Thematic Mapper image acquired on February 3, 1994 was radiometrically corrected using empirical line calibration and paired NASA JPL and Johns Hopkins University spectral library beach and water in situ spectroradiometer measurements and Landsat TM image brightness values (BVi,j,k). b) A pixel of loblolly pine with its original brightness values in six bands (TM band 6 thermal data were not used). c) The same pixel after empirical line calibration to scaled surface reflectance. Note the correct chlorophyll absorption in the blue (band 1) and red (band 3) portions of the spectrum and the increase in near-infrared reflectance.

Jensen 2005

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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

DN

Radiance, L

Target DN values

Regression line L = G*DN + O (+)

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

Lmax

Lmin

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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

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Example: airborne data

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

Haze due to scan angle of instruments

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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) • MODTRAN/LOWTRAN • SMAC etc.

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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, τ (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)

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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

Atmospheric Correction Using ATCOR

a) Image containing substantial haze prior to atmospheric correction. b) Image after atmospheric correction using ATCOR (Courtesy Leica Geosystems and DLR, the German Aerospace Centre).

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Processing chain

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

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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

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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)

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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

AVHRR bands 1 & 2 uncorrected Corrected to sza = 45° vza = 0 °