calcPathRadDOS: Compute path radiance based on the dark object method
In satellite: Handling and Manipulating Remote Sensing Data

Description Usage Arguments Details Value References See Also Examples

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.

## S4 method for signature 'Satellite'
calcPathRadDOS(x, model = c("DOS2", "DOS4"), esun_method = "RadRef")

## 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
)

`x`	A Satellite object or the value (scaled count) of a dark object in `bnbr` (e.g. minimum raw count of selected raster `bnbr`). If x is a Satellite object, the value is computed using `calcDODN`.
`model`	Model to be used to correct for 1% scattering (DOS2, DOS4; must be the same as used by `calcAtmosCorr`).
`esun_method`	If x is a Satellite object, name of the method to be used to compute esun using one of `calcTOAIrradRadRef` ("RadRef"), `calcTOAIrradTable` ("Table") or `calcTOAIrradModel` ("Model")
`bnbr`	Band number for which DNmin is valid.
`band_wls`	Band wavelengths to be corrected; `data.frame` with min (max) in first (second) column, see details.
`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. non-normalized) TOA solar irradiance, e.g. returned by `calcTOAIrradRadRef`, `calcTOAIrradTable` or `calcTOAIrradModel`.
`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 m-2 micrometer-1)

Vector object with path radiance values for each band (W m-2 micrometer-1)

Chavez Jr PS (1988) An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sensing of Environment 24/3, doi: 10.1016/0034-4257(88)90019-3.

Chavez Jr PS (1996) Image-based atmospheric corrections revisited and improved. Photogrammetric Engineering and Remote Sensing 62/9, available online at https://www.researchgate.net/publication/236769129_Image-Based_Atmospheric_Corrections_-_Revisited_and_Improved.

Goslee SC (2011) Analyzing Remote Sensing Data in R: The landsat Package. Journal of Statistical Software, 43/4, 1-25, doi: 10.18637/jss.v043.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 41/2-3, 169-184, doi: 10.1016/0034-4257(92)90076-V.

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/S0034-4257(00)00169-3.

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.

path <- system.file("extdata", package = "satellite")
files <- list.files(path, pattern = glob2rx("LC08*.TIF"), full.names = TRUE)
sat <- satellite(files)
sat <- calcTOAIrradModel(sat)

bds <- "B002n"
val <- calcPathRadDOS(x = min(getValues(getSatDataLayer(sat, bds))),
                      bnbr = getSatLNBR(sat, bds),
                      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)
val