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

Remote Sensing

Dennis Helder¹  MM Farhad²  Morakot Kaewmanee³  Larry Leigh³ 

[1] Department of Electrical Engineering and Computer Science, South Dakota State University (SDSU), Brookings, SD 57007, USA;Geospatial Engineer, Advanced Remote Sensing, Sioux Falls, SD 57107, USA;Image Processing Lab, Engineering Office of Research, South Dakota State University (SDSU), Brookings, SD 57007, USA;

关键词: landsat 8;    sentinel 2a;    brdf;    cross calibration;   

DOI  :  10.3390/rs12050806

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown