  With the limited availability of labeled data with various atmospheric
conditions in remote sensing images, it seems useful to work with
self-supervised algorithms. Few pretext-based algorithms, including from
rotation, spatial context and jigsaw puzzles are not appropriate for satellite
images. Often, satellite images have a higher temporal frequency. So, the
temporal dimension of remote sensing data provides natural augmentation without
requiring us to create artificial augmentation of images. Here, we propose
S3-TSS, a novel method of self-supervised learning technique that leverages
natural augmentation occurring in temporal dimension. We compare our results
with current state-of-the-art methods and also perform various experiments. We
observed that our method was able to perform better than baseline SeCo in four
downstream datasets. Code for our work can be found here:
https://github.com/hewanshrestha/Why-Self-Supervision-in-Time
