Millions of wave mode synthetic aperture radar (SAR) images are routinely acquired by the ESA's two sentinel-1 satellites every month over the open ocean. These SAR images capture clear imprints of atmospheric boundary layer (ABL) roll vortices. This provides a new and unique opportunity to investigate the characteristics of ABL rolls globally and statistically. In this study, we take advantage of the deep learning classification tool that has been successfully developed to automatically identify ABL rolls. For each SAR image classified with ABL rolls, roll wavelength and orientation are extracted through spectral analysis. Surface meteorological variables are also collocated with each SAR image to address the atmospheric conditions of roll occurrence. Results show that roll vortices are prevalent over the whole ocean and mainly occur in unstable to near-neutral stratification. Roll characteristics follow the theoretical expectation and are in good agreement with previous studies.