SITS-Former: A pre-trained spatio-spectral-temporal representation model for Sentinel-2 time series classification

•We present SITS-Former, which is the first pre-trained representation model for patch-based Sentinel-2 time series classification.•SITS-Former is pre-trained on massive unlabeled Sentinel-2 time series to learn spatio-spectral-temporal features via a missing-data imputation proxy task based on self-supervised learning.•SITS-Former can adapt the learned features to an interested classification task through fine-tuning.•SITS-Former outperforms state-of-the-art approaches and yields a significant improvement over the purely supervised method.