An operational radiometric calibration procedure for the Landsat sensors based on pseudo-invariant target sites

The Statewide Landcover and Trees Study (SLATS) use both Landsat-7 ETM+ and Landsat-5 TM imagery to monitor short-term woody vegetation changes throughout Queensland, Australia. In order to analyse more subtle long-term vegetation change, time-based trends resulting from artefacts introduced by the sensor system must be removed. In this study, a reflectance-based vicarious calibration approach using high-reflectance, pseudo-invariant targets in western Queensland was developed. This calibration procedure was used to test the existing calibration models for ETM+ and TM, and develop a consistent operational calibration procedure which provides calibration information for the MSS sensors. Ground based data, sensor spectral response functions and atmospheric variables were used as input to MODTRAN radiative transfer code to estimate top-of-atmosphere radiance. The estimated gains for Landsat-7 ETM+ (1999–2003), -5 TM (1987–2004), -5 MSS (1984–1993) and -2 MSS (1979–1982) are presented. Results confirm the stability and accuracy of the ETM+ calibration, and the suitability of this data as a radiometric standard for cross-calibration with TM. Vicarious data support the use of the existing TM calibration model for the red and two shortwave-infrared bands. However, alternative models for blue, green and near-infrared bands are presented. The models proposed differ most noticeably at dates prior to 1995, with differences in estimated gains of up to 9.7%, 10.8% and 6.9% for the blue, green and near-infrared bands respectively. Vicarious gains for Landsat-2 MSS and Landsat-5 MSS are presented and are compared with those applied by the on-board calibration system. Updated calibration coefficients to scale MSS data to the SLATS vicarious measurements are given. The removal of time based calibration trends in the SLATS data archive will enable the measurement of vegetation changes over the 26 year period covered by Landsat -2, -5 and -7.