Compute an estimated path radiance for all sensor bands, which can then be used to roughly correct the radiance values for atmospheric scattering. Path radiance estimation is based on a dark object method.
1 2 3 4 5 6 7 8  ## S4 method for signature 'Satellite'
calcPathRadDOS(x, model = c("DOS2", "DOS4"),
esun_method = "RadRef", use_cpp = TRUE)
## S4 method for signature 'numeric'
calcPathRadDOS(x, bnbr, band_wls, radm, rada, szen, esun,
model = c("DOS2", "DOS4"), scat_coef = c(4, 2, 1, 0.7, 0.5),
dos_adjust = 0.01, use_cpp = TRUE)

x 
A Satellite object or the value (scaled count) of a dark object in

model 
Model to be used to correct for 1% scattering (DOS2, DOS4; must
be the same as used by 
esun_method 
If x is a Satellite object, name of the method to be used
to compute esun using one of 
use_cpp 
Logical. If 
bnbr 
Band number for which DNmin is valid. 
band_wls 
Band wavelengths to be corrected; 
radm 
Multiplicative coefficient for radiance transformation (i.e. slope). 
rada 
Additive coefficient for radiance transformation (i.e. offset). 
szen 
Sun zenith angle. 
esun 
Actual (i.e. nonnormalized) TOA solar irradiance, e.g. returned
by 
scat_coef 
Scattering coefficient; defaults to 4.0. 
dos_adjust 
Assumed reflection for dark object adjustment; defaults to 0.01. 
If x is a Satellite object, the minimum raw count value (x) is computed using
calcDODN
. If the TOA solar irradiance is not part of the
Satellite object's metadata, it is computed using
calcTOAIrradRadRef
, calcTOAIrradTable
or
calcTOAIrradModel
.
The dark object subtraction approach is based on an approximation of the atmospheric path radiance (i.e. upwelling radiation which is scattered into the sensors field of view, aka haze) using the reflectance of a dark object (i.e. reflectance ~1%).
Chavez (1988) proposed a method which uses the dark object reflectance
in one band to predict the corresponding path radiances in all other
band_wls
. This is done using a relative radiance model which depends on
the wavelength and overall atmospheric optical thickness (which is estimated
based on the dark object's DN value). This has the advantage that the path
radiance is actually correlated across different sensor band_wls
and
not computed individually for each band using independent dark objects. He
proposed a relative radiance model which follows a wavelength dependent
scattering that ranges from a power of 4 over 2, 1, 0.7 to 0.5 for very
clear over clear, moderate, hazy to very hazy conditions. The relative
factors are computed individually for each 1/1000 wavelength within each band
range and subsequently averaged over the band as proposed by Goslee (2011).
The atmospheric transmittance towards the sensor (Tv) is approximated by 1.0 (DOS2, Chavez 1996) or Rayleigh scattering (DOS4, Moran et al. 1992)
The atmospheric transmittance from the sun (Tz) is approximated by the cosine of the sun zenith angle (DOS2, Chavez 1996) or again using Rayleigh scattering (DOS4, Moran et al. 1992).
The downwelling diffuse irradiance is approximated by 0.0 (DOS2, Chavez 1996) or the hemispherical integral of the path radiance (DOS4, Moran et al. 1992).
Equations for the path radiance are taken from Song et al. (2001).
For some sensors, the band wavelengths are already included. See
lutInfo()[grepl("_BANDS", names(lutInfo()$META))]
if your sensor is
included. To retrieve a sensor, use lutInfo()$<Sensor ID>_BANDS
.
Satellite object with path radiance for each band in the metadata (W m2 micrometer1)
Vector object with path radiance values for each band (W m2 micrometer1)
Chavez Jr PS (1988) An improved darkobject subtraction technique for atmospheric scattering correction of multispectral data. Remote Sensing of Environment 24/3, doi:10.1016/00344257(88)900193, available online at http://www.sciencedirect.com/science/article/pii/0034425788900193.
Chavez Jr PS (1996) Imagebased atmospheric corrections revisited and improved. Photogrammetric Engineering and Remote Sensing 62/9, available online at http://www.asprs.org/PERSJournals1996/PERSSeptember1996.html.
Goslee SC (2011) Analyzing Remote Sensing Data in R: The landsat Package. Journal of Statistical Software,43/4, 125, available online at http://www.jstatsoft.org/v43/i04/.
Moran MS, Jackson RD, Slater PN, Teillet PM (1992) Evlauation of simplified procedures for rretrieval of land surface reflectane factors from satellite sensor output.Remote Sensing of Environment 41/23, 169184, doi:10.1016/00344257(92)90076V, available online at http://www.sciencedirect.com/science/article/pii/003442579290076V.
Song C, Woodcock CE, Seto KC, Lenney MP, Macomber SA (2001) Classification and Change Detection Using Landsat TM Data: When and How to Correct Atmospheric Effects? Remote Sensing of Environment 75/2, doi:10.1016/S00344257(00)001693, available online at http://www.sciencedirect.com/science/article/pii/S0034425700001693
If you refer to Sawyer and Stephen 2014, please note that eq. 5 is wrong.
This function is used by calcAtmosCorr
to
compute the path radiance.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36  path < system.file("extdata", package = "satellite")
files < list.files(path, pattern = glob2rx("LC8*.tif"), full.names = TRUE)
sat < satellite(files)
sat < calcTOAIrradModel(sat)
bcde < "B002n"
sat < calcTOAIrradRadRef(sat, normalize = FALSE)
## performance of baseR
system.time(
val1 < calcPathRadDOS(x = min(getValues(getSatDataLayer(sat, bcde))),
bnbr = getSatLNBR(sat, bcde),
band_wls = data.frame(LMIN = getSatLMIN(sat, getSatBCDESolar(sat)),
LMAX = getSatLMAX(sat, getSatBCDESolar(sat))),
radm = getSatRADM(sat, getSatBCDESolar(sat)),
rada = getSatRADA(sat, getSatBCDESolar(sat)),
szen = getSatSZEN(sat, getSatBCDESolar(sat)),
esun = getSatESUN(sat, getSatBCDESolar(sat)),
model = "DOS2",
scat_coef = 4, use_cpp = FALSE)
)
## and Rcpp version
system.time(
val2 < calcPathRadDOS(x = min(getValues(getSatDataLayer(sat, bcde))),
bnbr = getSatLNBR(sat, bcde),
band_wls = data.frame(LMIN = getSatLMIN(sat, getSatBCDESolar(sat)),
LMAX = getSatLMAX(sat, getSatBCDESolar(sat))),
radm = getSatRADM(sat, getSatBCDESolar(sat)),
rada = getSatRADA(sat, getSatBCDESolar(sat)),
szen = getSatSZEN(sat, getSatBCDESolar(sat)),
esun = getSatESUN(sat, getSatBCDESolar(sat)),
model = "DOS2",
scat_coef = 4, use_cpp = TRUE)
)

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