Radiometric Cross Calibration and Validation Using 4 Angle BRDF Model between Landsat 8 and Sentinel 2A

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 OLI and (ii) normalization of Bidirectional Reflectance Distribution Function (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 normalization, 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. The results of this work indicate that the blue band has the most significant offset, requiring use of the estimated cross-calibration offset in addition to the estimated gain. The highest difference was observed in the blue and red bands, which are 2.6% and 1.4%, respectively, while other bands shows no significant difference. 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, target site nonuniformity, variability in atmospheric water vapor, and/or aerosol concentration.

Hyperspectral Empirical Absolute Calibration Model Using Libya 4 Pseudo Invariant Calibration Site

“…They found that the solar and view geometries were best defined in the Cartesian coordinates to preserve the nature of the data and to achieve a robust fit for the BRDF model. Moreover, the four-angle multi-linear interaction model provided the best BRDF model categorization, and after normalization, the estimated temporal stability was better than 3% over Libya 4 [20]. Considering the research of Farhad et al, conversion of the angles from Spherical to the Cartesian domain and developing an empirical BRDF model using four angles rather than just two angles can be contributed as improvement features in the absolute calibration model.…”

Extended Pseudo Invariant Calibration Site-Based Trend-to-Trend Cross-Calibration of Optical Satellite Sensors

“…An absolute calibration BRDF model deriving linear and quadratic functions of the solar zenith angle was developed [27] using Libya 4. To fully account for the complexity of the BRDF effects, the BRDF model was developed including all the four angles as derived by Farhad et al [28] This model converts the view and solar angles from a spherical coordinate basis to a linear Cartesian basis and obtains a TOA reflectance of the surface as a continuous function of independent variables. Kaewmanee [29] further extended the model developed by Farhad et al, using an interaction term, which characterized the BRDF model well, with better uncertainty after normalization.…”

“…The mean cross-calibration for the trend-to-trend approach was calculated by taking an average of the blue data in Figure 18 and, for the Libya 4 coincident scene, pairs approaching the mean cross-calibration gain were calculated by taking the average of the red datapoints in Figure 18. The black data in Figure 19 were derived by Farhad et al [28] for the cross-calibration of OLI and MSI sensors using PICS, where the error bars represent the uncertainty derived, which was approximately 6.8%. For two perfectly calibrated sesnors, the value of the cross-calibration gain ratio is expected to be unity.…”

Comparative Study on Polarization Spectral Parameters of Fruit in Southern Xinjiang Based on Roujean Model

“…In the military field, Melvin et al [5] applied it to remote detection of mines and improvised explosive devices. In the aviation field, Yeom et al [6] used it to detect thin clouds on the ground, which improved the efficiency of determining thin cloud pixels; Farhad et al [7] used it to develop a new cross calibration technology for satellite sensors, so as to improve the data coordination between them. In the marine field, aval et al [8] used it to improve the target recognition of the underwater environment.…”