radiocorr {landsat}

Radiometric correction of Landsat data

Description

Implements several different methods for absolute radiometric correction of satellite data.

Usage

radiocorr(x, gain, offset, Grescale, Brescale, sunelev, satzenith = 0, edist,
	Esun, Lhaze, method = "apparentreflectance")

Arguments

x

Image to be corrected, in matrix, data frame, or SpatialGridDataFrame format.

gain

Band-specific sensor gain. Require either gain and offset or Grescale and Brescale to convert DN to radiance.

offset

Band-specific sensor offset. Require either gain and offset or Grescale and Brescale to convert DN to radiance.

Grescale

Band-specific sensor Grescale (gain). Require either gain and offset or Grescale and Brescale to convert DN to radiance.

Brescale

Band-specific sensor Brescale (bias). Require either gain and offset or Grescale and Brescale to convert DN to radiance.

sunelev

Sun elevation in degrees

satzenith

Satellite sensor zenith angle (0 for Landsat)

edist

Earth-Sun distance in AU.

Esun

Exo-atmospheric solar irradiance, as given by Chandler et al. 2009 or others.

Lhaze

Haze value, such as SHV from DOS() function. Not needed for apparent reflectance.

method

Radiometric correction method to be used. There are currently four methods available: "apparentreflectance", "DOS" (Chavez 1989), "COSTZ" (Chavez 1996), "DOS4" (SWS+2001).

Details

Uses one of four image-based radiometric correction methods to adjust a satellite image to compensate for atmospheric conditions.

Value

Returns a radiometrically-corrected image in the same format as x.

Author(s)

Sarah Goslee

References

Chavez, Jr., P. S. 1989. Radiometric calibration of Landsat Thematic Mapper multispectral images. Photogrammetric Engineering and Remote Sensing 55:1285-1294.

Chavez, Jr., P. S. 1996. Image-based atmospheric corrections revisited and improved. Photogrammetric Engineering and Remote Sensing 62:1025-1036.

Song, C.; Woodcock, C. E.; Seto, K. C.; Lenney, M. P. & Macomber, S. A. 2001. Classification and change detection using Landsat TM data: when and how to correct atmospheric effects? Remote Sensing of Environment 75:230-244.

See Also

DOS

Examples

	data(july1)
	data(july3)

	# One approach to choosing a Starting Haze Value is to take the lowest DN value
	# with a frequency greater than some predetermined threshold, in this case 1000 pixels. 
	SHV <- table(july1@data[,1])
	SHV <- min(as.numeric(names(SHV)[SHV > 1000]))
	# this is used as Lhaze in the radiocorr function

	# Grescale, Brescale, sun elevation comes from metadata for the SHV band
	july.DOS <- DOS(sat=7, SHV=SHV, SHV.band=1, Grescale=0.77569, Brescale=-6.20000,
		sunelev=61.4, edist=ESdist("2002-07-20"))$DNfinal.mean

	# DOS() returns results for the complete set of scattering coefficients
	# need to choose the appropriate one based on general atmospheric conditions
		###	-4.0: Very Clear	SHV <= 55
		###	-2.0: Clear		SHV 56-75
		### 	-1.0: Moderate		SHV 76-95
		###	-0.7: Hazy		SHV 96-115
		###	-0.5: Very Hazy		SHV >115
	# for july, SHV == 70, so use -2.0: Clear
	july.DOS <- july.DOS[ , 2]

	# Use DOS value as Lhaze in radiocorr() for DOS correction to reflectance
	july3.DOSrefl <- radiocorr(july3, Grescale=0.77569, Brescale=-6.20000, 
		sunelev=61.4, edist=ESdist("2002-07-20"), Esun=1533, 
		Lhaze=july.DOS[3], method="DOS")