Cross Calibration and Validation of Landsat 8 OLI and Sentinel 2A MSI

Abstract

This work describes a proposed radiometric cross calibration between the Landsat 8 Operational Land Imager (OLI) and Sentinel 2A Multispectral Instrument (MSI) sensors. The cross calibration procedure involves i) correction of the MSI data to account for spectral band differences with the OLI; and ii) correction of BRDF effects in the data from both sensors using a new model accounting for the view zenith/azimuth angles in addition to the solar zenith/view angles. Following application of the spectral and BRDF corrections, standard least-squares linear regression is used to determine the cross calibration gain and offset in each band. Uncertainties related to each step in the proposed process are determined, as is the overall uncertainty associated with the complete processing sequence. Validation of the proposed cross calibration gains and offsets is performed on image data acquired over the Algodones Dunes site. In general, the estimated cross calibration offsets in all bands were small, on the order of 0.0075 or less in magnitude. The cross calibration gains generally varied less than 1.0% from unity; for the Blue and Red bands, the gains varied by approximately -2.5% and - 1.4% from unity, respectively. For a forced zero offset, the estimated gain in all but the Blue band changed little; the Blue band gain varied by approximately 1.86% from unity. Consequently, cross calibration of the Blue band requires both the gain and nonzero offset. To maintain processing consistency, it is recommended to use the gain and (nonzero) offset in all bands. Overall, the net uncertainty in the proposed process was estimated to be on the order of 6.76%, with the largest uncertainty component due to each sensor’s calibration uncertainty, on the order of 5% and 3% for the MSI and OLI, respectively. Other significant contributions to the uncertainty include: seasonal changes in solar zenith and azimuth angles, on the order of 2.27%; target site non-uniformity, on the order of 1.8%; variability in atmospheric water vapor and/or aerosol concentration, on the order of 1.29%; and potential shifts in each sensor’s spectral filter central wavelength and/or bandwidth, on the order of 0.82% and 0.28%, respectively