Satellite data are used in several environmental applications, particularly in air quality supervising, climate change monitoring, and natural disaster predictions. However, remote sensing (RS) data occur in huge volume, in near-real time, and are stored inside complex structures. We aim to prove that satellite data are big data (BD). Accordingly, we propose a software as an extract-transform-load tool for satellite data preprocessing. We focused on the ingestion layer that will enable an efficient RSBD integration. As a result, the developed software layer receives data continuously and removes ∼86  %   of the unused files. This layer also eliminates nearly 20% of erroneous datasets. Thanks to the proposed approach, we successfully reduced storage space consumption, enhanced the RS data accuracy, and integrated preprocessed datasets into a Hadoop distributed file system.