Radiance (L T ) from paths 1, 3, and 5 contains intrinsic
valuable spectral information about the target of interest.
Conversely, the path radiance (L p ) 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. Radiance received at a remote
sensor
Slide 4
Path 1 contains spectral solar irradiance ( E o ) that was
attenuated very little before illuminating the terrain within the
IFOV. We are interested in the solar irradiance from a specific
solar zenith angle ( o ) The amount of irradiance reaching the
terrain is a function of the atmospheric transmittance at this
angle (T o ). 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.
Radiance received at a remote sensor
Slide 5
Path 2 contains spectral diffuse sky irradiance ( E d ) that
never reaches the the target study area because of scattering in
the atmosphere. This energy is often scattered into the IFOV of the
sensor system. Rayleigh scattering of blue light contributes much
to this diffuse sky irradiance. Hence blue band image produced by a
remote sensor system is often much brighter than any of the other
bands and contains much unwanted diffuse sky irradiance that was
scattered into the IFOV of the sensor system. Therefore, if
possible, we want to minimize its effects. This quantity is
referred to as the upward reflectance of the atmosphere (E du ).
Radiance received at a remote sensor
Slide 6
Path 3 contains modified 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. Its
spectral composition and polarization may be somewhat different
from the energy that reaches the ground from path 1. This quantity
is also referred to as the downward reflectance of the atmosphere
(E dd ). Radiance received at a remote sensor
Slide 7
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, L
p. Radiance received at a remote sensor
Slide 8
Path 5 is energy that was also reflected from nearby terrain
into the atmosphere, but then scattered or reflected onto the study
area. Generally insignificant. Radiance received at a remote
sensor
Slide 9
Images are arrays of pixels, where each pixel is represented by
a brightness value or grey level, generally between 0 and 255.
These values are called DNs. We can determine the radiance at the
sensor for any pixel from its DN value, between 0 and 255: where L
max and L min are maximum and minimum measurable radiances of the
sensor. k and L min are also called gain and offset of the
detector. This information is provided by the sensor manufacturer.
Radiance received at a remote sensor
Slide 10
BAND123457 L min (W/m 2 /sr/m) -1.5-2.8-1.2-1.5-0.37-0.15 L max
(W/m 2 /sr/m) 152.1296.8204.3206.227.1914.38 Preflight TM-4 and
TM-5 spectral range values (from NASA, 1986, Table C-8) DN value of
a pixel in bands 1 and 7 is 100 Maximum DN value in both bands is
255 Radiance at the pixel in band 1? In band 7?? For band 1, k =
(152.1+1.5)/255 = 0.602353 L pix = (100 x 0.602353 ) 1.5 = 58.73
W/m 2 /sr/m For band 7, k = (14.38+0.15)/255 = 0.05698 L pix = (100
x 0.05698) 0.15 = 5.54 W/m 2 /sr/m
Slide 11
Remote sensing systems sense wavebands, rather than specific
wavelengths. The available irradiance (E o ) in a specific wave
band between 1 and 2 in the area of interest is or where = 2 - 1 is
very small and E sun is the average irradiance in the band . d 2
(in AU)accounts for varying distance of earth from the Sun. If the
reflectance of the pixel of interest is R, then the radiant
exitance of the pixel is: We know that The irradiance (E sun ) of
the sun in a specific length () at a solar zenith angle of is
Radiance received at a remote sensor or
Slide 12
R is the reflectance. If the atmospheric transmission in the
direction v is T v, then the radiance L sensor arriving at the
sensor after traversing the atmosphere is: However the atmosphere
scatters and absorbs a proportion of the solar irradiance. If the
downward scattered or diffused sky irradiance is E dd and T o is
the atmospheric transmission, i.e., the proportion of radiance
transmitted by the atmosphere, in the direction o, then the total
irradiance at the pixel = The radiance from the pixel due to this
irradiance = Radiance received at a remote sensor Where L path is
path radiance
Slide 13
Radiance received at a remote sensor
Slide 14
Objectives of atmospheric corrections The high goal of remote
sensing: To identify the composition of objects on ground from
remote sensing data Spectral reflectance curves are used for this
purpose However, radiance-at-the-sensor is contaminated by path
radiance due to the atmosphere, hence spectral reflectance
estimated from remote sensing data are incorrect We have to correct
the radiance-at-the-sensor to remove atmospheric effects
Slide 15
When is the atmospheric correction really required??
Mono-temporal data : NO Classification: NO Change monitoring and
detection: YES Composition mapping, spectral analysis: YES
Slide 16
Sensor calibration gain and offset Atmospheric correction image
measurement ground measurements atmospheric models sensor view path
atmospheric radiance sensor view path atmospheric transmittance DN
Radiance at sensor Solar and topographic correction Radiance at
ground solar exo-atmospheric spectral irradiance solar path
atmospheric transmittance down-scattered radiance solar angle, DEM
Surface reflectance Radiometric calibration
Slide 17
Atmospheric corrections: Techniques Histogram minimum method
aka dark object subtraction the bootstrap approach Empirical line
method Radiative transfer models Physical-based approach
Slide 18
Estimation of L P : Dark object subtraction Dark-object
subtraction techniques derive the corrected DN (digital number)
values solely from the digital data with no outside information.
This type of correction involves subtracting a constant DN value
from the entire digital image. The assumption is that there is a
high probability that at least a few pixels within an image which
should be black (0% reflectance). If there are no pixels with zero
values, that is the effect of atmospheric scattering For example,
there are about 45 million pixels in a single TM band so there very
high probability that at least one of them should be black.
Slide 19
Estimation of L P : Dark object subtraction
Slide 20
450-515 nm Band 1525-605 nm Band 2 630-690 nm Band 3 775-900 nm
Band 4 1550-1750 nm Band 5 2090-2350 nm Band 7 LANDSAT ETM+ BANDS
Water absorption Water bodies have 0% reflectance in the IR region,
hence zero DN Non-zero values over water bodies in the IR
consequence of path radiance. Subtract the non-zero value over
water bodies from all pixels. That would make water body perfectly
non-reflecting. In Visible bands, shadows should be black in
absence of path radiance. Hence non-zero values over shadowed areas
can be used for dark pixel correction. Estimation of L P : Dark
object subtraction
Slide 21
Histograms of pixel values in all bands pixel values of low
reflectance areas near zero exposures of dark colored rocks deep
shadows clear water Lowest pixel values in visible and
near-infrared are approximation to atmospheric path radiance
Minimum values subtracted from image Estimation of L P : Dark
object subtraction
Slide 22
How will you calculate path radiance for all bands ?? For
example, calculate reflectance for a pixel whose DN value is 53 in
band 1.
Slide 23
Estimation of L P : Dark object subtraction How will you
calculate path radiance for all bands ?? For example, calculate
reflectance for a pixel whose DN value is 53 in band 1. BAND123457
L min (W/m 2 /sr/m) -1.5-2.8-1.2-1.5-0.37-0.15 L max (W/m 2 /sr/m)
152.1296.8204.3206.227.1914.38
Slide 24
Estimation of L P : Dark object subtraction How will you
calculate path radiance for all bands ?? For example, calculate
path radiance for a pixel whose DN value is 53 in band 1. ETM+
Solar Spectral Irradiances Bandwatts/(m 2 * m) 11997 21812 31533
41039 5230.8 784.90 81362. Day of YearDistanceDay of
YearDistanceDay of YearDistanceDay of YearDistanceDay of
YearDistance 1.9833174.994461521.014032271.01281305.99253
15.9836591.999261661.015772421.00969319.98916
32.985361061.003531821.016672581.00566335.98608
46.987741211.007561961.016462741.00119349.98426
60.990841351.010872131.01497288.99718365.98333
Slide 25
DN values of correlated bands are plotted Least square line fit
using standard regression methods Resulting offset is approximation
for the atmospheric path radiance offset subtracted from image
Estimation of L P : Dark object subtraction Regression
technique
Slide 26
One dark (X 1 ) and one bright (X 2 ) object selected on the
image which can be clearly identified on the ground also Ground
reflectance of X 1 and X 2 measured using field radiometer (R X1
and R X2 ). Radiance-at-the-sensor of X 1 and X 2 calculated from
the image (L X1 and L X2 ). The two points plotted on a graph,
joined by a line, and the slope (s) and intercept (a) of the line
measured. Equation of the line derived, used for converting all
radiance values into reflectance values R i = L i s - a R -
Reflectance a - Offset s - Slope = (R x1 -R x2 )/(L x1 -L x2 )
Empirical line method a
Slide 27
Estimation of L P : Dark object subtraction Regression
technique Does it always work? The key criterion of atmospheric
correction algorithm - .. Quantify atmospheric influences on
satellite image radiometry but at the same time insensitive to
surface reflection effects
Slide 28
Estimation of L P : Dark object subtraction Regression
technique So how to correct this image?
Slide 29
Manually select several clear and hazy area pixels in the image
Two spectral bands are selected based on the following criteria:
The spectral responses of different land cover types, under clear
atmospheric conditions, should be highly correlated in the two
bands. This will result in a well-defined surface response vector
in spectral space called clear line (CL) The effect of haze should
be markedly different in the two bands so that increased
atmospheric contamination manifests in increased shift away from
the CL Typically we would select blue and red bands Apply a
transformation whose coefficients define a direction orthogonal to
the CL and whose response magnitude is proportional to the
deviation from this line Estimation of L P : Haze Removal Algorithm
Haze Optimization Transform (HOT) Y. Zhang et al., 2002 (RSE)
Slide 30
Estimation of L P : Haze Removal Algorithm Haze Optimization
Transform (HOT) Schematic diagram of the TM1 TM3 spectral space
illustrating the conceptual components of the HOT. Under clear sky
conditions, radiances of common surface cover types, coded as A K,
exhibit high correlation and define a clear line (CL). The effect
of haze of increasing optical depth, illustrated by the numerical
sequences 1 18, is to pixels to migrate away from the CL. The HOT
quantifies the atmospheric contamination level at a pixel location
by its perpendicular distance, in spectral space, from the CL. Y.
Zhang et al., 2002 (RSE)
Slide 31
Estimation of L P : Haze Removal Algorithm Haze Optimization
Transform (HOT) Y. Zhang et al., 2002 (Rem Sens Env)
Slide 32
Estimation of L P : Haze Removal Algorithm Haze Optimization
Transform (HOT) 1.Select two correlated bands (bands showing
similar reflectance characteristics for all objects) but affected
by scattering due to atmospheric components to different degrees.
Example: Bands 1 (Blue) and 3 (Red) of ETM/TM Haze vector Clear
line slope = and offset on x axis =
Slide 33
Estimation of L P : Haze Removal Algorithm Haze Optimization
Transform (HOT) 2. Mask out areas with obvious haze 3. Select some
very clear areas that are unaffected by clouds/haze)
Slide 34
Estimation of L P : Haze Removal Algorithm Haze Optimization
Transform (HOT) Haze vector Clear line slope = and offset on x axis
= 4. Plot DN (Blue band X axis) vs DN (Red band Y axis) of pixels
from clear area Band 1 (Blue) Band 3
(Red)..........................................................................................................................................
Clear line 5. Fit the pixel DNs to the clear line generated by
linear regression (slope = and offset on x axis. Haze vector 6.
Haze vector is orthogonal to clearline
Slide 35
Estimation of L P : Haze Removal Algorithm Haze Optimization
Transform (HOT) 10. Calculate HOT for all pixels as the offset of a
pixel from the clear line in the haze vector direction Clear
line.................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................
8. Plot all DN (Blue) v/s DN(Red) for all pixels on the image 7.
Plot clear line Band 1 (Blue) Band 3 (Red) Clear line 9. Haze
vector is orthogonal to clear line, hence you can identify haze
pixels.....................................................................
Haze vector
Slide 36
Estimation of L P : Haze Removal Algorithm Haze Optimization
Transform (HOT) 11. Generate HOT Image and determine the HOT values
for clear areas and hazy areas (Not the same image as in the
previous slide)
Slide 37
Estimation of L P : Haze Removal Algorithm Haze Optimization
Transform (HOT) 12. Plot histogram for different HOT levels for
clear and hazy areas Clear areas Haze areas Increasing HOT = >
Increasing Haze
Slide 38
Estimation of L P : Haze Removal Algorithm Haze Optimization
Transform (HOT) 13. Plot histogram lower bound versus HOT for bands
TM1TM3 14. Estimate radiometric adjustment using a method similar
to dark object subtraction to normalize the image to the
radiometric level of the clearest areas. From Step 13 plot, note
that, for Band TM 1 (Blue), the histogram lower bound for clear
pixels (i.e., HOT= 30) is approximately 20 DNs. Consider a hazy
pixel with an observed HOT level of 40. It is a member of a
histogram with a lower bound 27. This implies that this hazy pixel
should have its band 1 DN level reduced by 7 during the radiometric
adjustment phase. This procedure can be used to adjust all bands
for which the histogram analysis has been done. Clear pixel DN
Slide 39
Estimation of L P : Haze Removal Algorithm Haze Optimization
Transform (HOT)
Slide 40
Result Estimation of L P : Haze Removal Algorithm Haze
Optimization Transform (HOT) Results
Slide 41
Advantages and disadvantages of image-based techniques??
Slide 42
Model-based atmospheric corrections Absorption Transmittance
(T): Absorbance (A): or for gases Beers Law: For monochromatic
plane-parallel light entering a medium perpendicular to the surface
of the medium: c - molar concentration; L- light path length, and -
molar absorption coefficient for the medium Molar absorption
coefficient is sometimes called molar extinction coefficient
however, this is only in the idealized case when scattering is
zero. L Hence Beers law can be used to estimate concentrations
Slide 43
Molar absorption coefficient, extinction coefficient and
attenuation coefficnet Model-based atmospheric corrections However,
transmittance is function of absorptance + scattering, hence we
need to define a new term, called optical depth (or optical
thickness, ) , as a measure of transmittance. Optical depth is
defined as the negative natural logarithm of the fraction of
radiation that is not scattered or absorbed on a path. Hence
optical depth is dimensionless, and in particular is not a length,
though it is a monotonically increasing function of path length,
and approaches zero as the path length approaches zero. => in
the absence of scattering. Hence, optical depth is conceptually
analogous to absorbance, but not the same. Includes both absorbance
and scattering extinction (or attenuation) coefficient
Slide 44
Optical thickness or Optical depth Optical thickness () has
three components: Optical thickness due to molecular scattering by
atmospheric gases Mainly affects shorter wavelengths Optical
thickness due to molecular absorption by atmospheric gases Mainly
due to 7 gases: water vapour (H 2 O), carbon dioxide (CO 2 ), ozone
(O 3 ), nitrous oxide (N 2 O), carbon monoxide (CO), methane (CH 4
) and oxygen (O 2 ) Water vapour absorption is significant and
varies with time and space. Optical thickness due to atmospheric
aerosol Aerosol scattering is significant and varies with time and
space.
Slide 45
Optical depth due to molecular absorption by atmospheric
gases
Slide 46
Rayleigh Scattering Optical depth due to molecular scattering
and absorption by atmospheric gases
Slide 47
Strong water vapor bands are located near 1.38 and 1.88 micron.
No signals are detected under clear sky conditions.
Slide 48
Radiance spectrum over a pixel (mineral Kaolinite) Reflectance
spectrum of Kaolinite after atmospheric corrections Strong water
vapor bands are located near 1.38 and 1.88 micron. No signals are
detected under clear sky conditions.
Slide 49
Ground pixel reflectance is given by: What is known? Sun-earth
distance Radiance at the sensor Zenith angle Incoming solar
spectral irradiance What is unknown? Path radiance Transmittance
Radiative transfer codes are used to estimate the unknowns
Slide 50
6) Radiative Transfer The physical phenomenon of energy
transfer in a medium In our case, it refers to electromagnetic
radiation in the atmosphere The propagation of the radiation
through the atmosphere is affected by the processes of absorption
and scattering, as well as atmospheric emissions The equations of
radiative transfer describe the interactions mathematically
Radiative Transfer However, we need not worry about radiative
transfer equations leave that to atmospheric physicists - because
computer codes are available that can model the atmospheric
transmission of solar radiation using radiative transfer equations.
However we need to worry about providing input variables to the
equations.
Slide 51
Radiative Transfer codes MODTRAN (Moderate resolution
atmospheric transmission) 6SV (Second Simulation of a Satellite
Signal in the Solar Spectrum) DISORT (Discrete Ordinates Radiative
Transfer Program for a Multi-Layered Plane-Parallel Medium)
Radiative transfer codes simulate the Path Radiance (L path ) and
atmospheric transmittance (T ), based on user provided values for
different atmospheric parameters.
Slide 52
Atmospheric correction algorithms Main algorithms are (all
commercial) ATmospheric CORrection (ATCOR) : PCI Geomatica, ERDAS
ATmosphere REMoval (ATREM) Fast Line-of-sight Atmospheric Analysis
of Spectral Hypercubes (FLAASH) (ENVI) Atmospheric correction
algorithms are used for estimating the values of atmospheric
parameters, based on a user provided inputs.
Slide 53
Flow chart of radiative-transfer based models AC
algorithmLUTsRT Code Key atmospheric parameters AC algorithm AC -
Atmospheric correction RT - Radiative Transfer
Slide 54
INPUTS TO RADIATIVE TRANSFER MODELS: Solar azimuth Location
Wavelength (bands) Ground elevation Sensor view angle Atmospheric
optical depth WITH ABOVE PARAMETERS, RADIATIVE TRANSFER MODELS
SIMULATE PATH RADIANCE AND TRANSMITTANCE FOR ESTIMATING SURFACE
REFLECTANCE Atmospheric parameters to be input to Radiative
Transfer codes for simulation of path radiance and
transmittance
Slide 55
Atmospheric optical depth - A.Due to molecular scattering by
gases B.Due to molecular absorption by gases C.Due to absorption
and scattering by aerosols Estimation of atmospheric optical depth
PROBLEM: Atmosphere is not homogenous, vertically or horizontally:
Not realistically possible to measure concentrations of all of the
above gases and aerosols over the entire atmospheric column (at
least up to 100 km height) for every pixel. Since optical depth is
a function of concentration: We need to estimate the concentration
of the seven important gases (O 2, O 3, N 2 O, CO 2, CO, CH 4, H 2
O) + aerosols in the direction of solar incidence.
Slide 56
PRACTICAL SOLUTION USED IN ALL ALGORITHMS: Define standard
atmospheres mid-latitude summer atmosphere US standard atmosphere
1976 standard tropical atmosphere desert tropical (arid) atmosphere
fall (autumn) atmosphere mid-latitude winter subarctic winter
Vertically profile different standard atmospheres at a number of
locations for: air pressure, air temperature, Gaseous
concentrations (except H 2 O) due to aerosols? due to water
vapour?
Slide 57
Aerosol type and concentration Water vapour concentration The
unkown parameters ..
Slide 58
Water vapour concentration Must have bands in one of the
following bands: 1050-1210 nm (for the 1135 nm water feature)
870-1020 nm (for the 940 nm water feature) 770-870 nm (for the 820
nm water feature) The band depth can be used to estimate the water
vapor content (pixel wise)
Slide 59
Aerosol : nature and concentration Aerosol Aerosol C Aerosol
For Aerosol Estimate from visibility: Get the user input for
visibility Use Koschmieder equation (VIS = 3.912/) to estimate
extinction coefficient from visibility (Aerosol optical depth not
critical if the visibility is high, that is >40 km) For C
Aerosol Concentration difficult to estimate for every atmospheric
condition, therefore standard types are used: - rural, urban,
desert, maritime The concentration of aerosols measured for
different visibility ranges and different aerosol types, and are
stored in lookup tables
Slide 60
Typical user inputs values are: sensor type (LANDSAT/ASTER/etc
etc) solar azimuth sensor viewing angle Latitude-Longitude standard
atmosphere in the image visibility aerosol type average ground
elevation Water vapour absorption channels (if the sensor type is
provided, this is taken from the header file)
Slide 61
The catalogue (LUT) consists atmospheric correction functions
for: 1.Different standard atmospheres (altitude profile of
pressure, air temperature, gases concentration) mid-latitude summer
atmosphere US standard atmosphere 1976 standard tropical atmosphere
desert tropical (arid) atmosphere fall (autumn) atmosphere
mid-latitude winter subarctic winter. 2.Different aerosol types:
rural, urban, desert, maritime 3.Different aerosol concentrations
(aerosol optical depth) defined by the visibility. The range
provided is 5-40 km, calculated values are: 5, 7, 10, 15, 23, 40
km. Values for 4 and 80 km are obtained by linear extrapolation.
The conditions range from hazy to very clear. ATCOR-2 LUT derived
using MODTRAN 4.Water vapour concentrations (calculated from
absorption bands depths optionally user defined) 5.Different ground
elevations ranging from 0 to 1 km (calculated values are for 0,
0.5, and 1km ASL; other values interpolated.) 6.Solar zenith angles
ranging from 0 o - 70 o in the steps of 10 o 7.Different functions
for each sensor and each band - the atmospheric correction
functions depend on the spectral response of the sensor, thus there
are different functions for each sensor and each band The above
parameters can be specified by the user, or are read from the image
header. For illustration - in ATCOR-2, the number of entries in the
look-up tables for the six reflective bands of Landsat TM is about
9000, i.e. 12 x 7 x 6 x 3 x 6 = 9072, including 12 atmospheres, 7
solar zenith angles, 6 visibilities, 3 ground elevations, and 6
bands. Measured atmospheric data can also be used to calculate new
files of look-up tables for the catalogue.
Slide 62
After estimation of path radiance, global flux and atmospheric
transmission, apply the following equation to derive surface
reflectance
Slide 63
Bidirectional Reflectance Distribution Function
Slide 64
Slide 65
Slide 66
The bidirectional reflectance distribution function (BRDF) is a
theoretical concept that describes the relationship between 1)the
geometric characteristics of the solar irradiance, and 2)the remote
sensing system viewing geometry; hence the bidirectional
terminology (Sandmeier, 1996; Jensen, 2000)
Slide 67
Bidirectional Reflectance Distribution Function Very difficult
to acquire BRDF information about a surface because a)the Sun is
constantly moving across the sky, and/or b)it is difficult to
acquire multiple images of the terrain from various angles of view
in a short period of time. This problem resulted in the invention
of the goniometer; a specialized instrument that measures spectral
reflectance in a specified number of directions distributed
throughout the hemisphere above a particular surface in a very
short time (5-10 minutes), allowing scientists to generate a useful
BRDF for that surface.
Slide 68
Bidirectional Reflectance Distribution Function Bidirectional
reflectance factor (R) dL r is the energy reflected from a surface
in a specific direction divided by the radiance dL ref, reflected
from a loss-less Lambertian reference panel measured under
identical illumination geometry. The R ref is a calibration
coefficient determined for the spectral reflectance panel used. The
bidirectional reflectance factor (R) is then normalized to an
anisotrophy factor (ANIF) to analyze the spectral variability in
BRDF data. The ANIF is calculated by normalizing bidirectional
reflectance data R to nadir reflectance, R o using the equation
(Sandmeier et al., 1998a; Sandmeier and Itten, 1999)
Slide 69
Bidirectional Reflectance Distribution Function (Jensen and
Schill, 2000) Atmospheric absorption -75 o -45 o -15 o 0o0o 15 o 45
o 75 o Zenith angle (Sun zenith angle 75 degrees) Wavelength
Reflectance
Slide 70
Bidirectional Reflectance Distribution Function (Jensen and
Schill, 2000) Atmospheric absorption 0o0o 30 o 60 o 90 o 150 o 270
o Azimuth angle Wavelength Reflectance
Slide 71
Bidirectional Reflectance Distribution Function Bidirectional
reflectance factor (R) An understanding of BRDF is needed in remote
sensing to correct for Sun illumination angle and sensor viewing
angle effects for - Mosaicking images, Deriving albedo, Improving
land cover classification accuracy, Enhancing cloud detection, and
Correcting for atmospheric conditions To identify bands that are
least impacted by BRDF, recognize optimal sun/sensor angle-of-views
(Myneni et al., 1995; Woodcock et al., 1997).
Slide 72
Bidirectional Reflectance Distribution Function The accurate
computation of BRDF required for: Making corrections to reflectance
measurements of features measured from nadir or off-nadir pointing
remote sensing systems. To identify bands that are least impacted
by BRDF, recognize optimal sun/sensor angle-of-views, and provide
insight into radiometrically adjusting remotely sensed data to
minimize BRDF effects.