Automatic sub-pixel co-registration of Landsat-8 Operational Land Imager and Sentinel-2A Multi-Spectral Instrument images using phase correlation and machine learning based mapping

This study investigates misregistration issues between Landsat-8/Operational Land Imager and Sentinel-2A/Multi-Spectral Instrument at 30 m resolution,and between multi-temporal Sentinel-2A images at 10 m resolution using a phase-correlation approach and multiple transformation functions.Co-registration of 45 Landsat-8 to Sentinel-2A pairs and 37 Sentinel-2A to Sentinel-2A pairs were analyzed.Phase correlation proved to be a robust approach that allowed us to identify hundreds and thousands of control points on images acquired more than 100 days apart.Overall,misregistration of up to 1.6 pixels at 30 m resolution between Landsat-8 and Sentinel-2A images,and 1.2 pixels and 2.8 pixels at 10 m resolution between multi-temporal Sentinel-2A images from the same and different orbits,respectively,were observed.The non-linear random forest regression used for constructing the mapping function showed best results in terms of root mean square error(RMSE),yielding an average RMSE error of 0.07±0.02 pixels at 30 m resolution,and 0.09±0.05 and 0.15±0.06 pixels at 10 m resolution for the same and adjacent Sentinel-2A orbits,respectively,for multiple tiles and multiple conditions.A simpler 1st order polynomial function(affine transformation)yielded RMSE of 0.08±0.02 pixels at 30 m resolution and 0.12±0.06(same Sentinel-2A orbits)and 0.20±0.09(adjacent orbits)pixels at 10 m resolution.