Optimizing Sentinel-2 image selection in a Big Data context

Abstract

Processing large amounts of image data such as the Sentinel-2 archive is a computationally demanding task. However, for most applications, many of the images in the archive are redundant and do not contribute to the quality of the final result. An optimization scheme is presented here that selects a subset of the Sentinel-2 archive in order to reduce the amount of processing, while retaining the quality of the resulting output. As a case study, we focused on the creation of a cloud-free composite, covering the global land mass and based on all the images acquired from January 2016 until September 2017. The total amount of available images was 2,128,556. The selection of the optimal subset was based on quicklooks, which correspond to a spatial and spectral subset of the original Sentinel-2 products and are lossy compressed. The selected subset contained 94,093 image tiles in total, reducing the amount of images to be processed to 4.42% of the full set.