SatMAE-Agri: Masked Spatiotemporal Autoencoding for Self-Supervised Learning on Satellite Image Time Series

Satellite image time series (SITS) provide valuable information for agricultural monitoring, yet supervised learning approaches remain limited by the scarcity of labeled data, particularly in developing regions. To address this challenge, we propose SatMAE-Agri, a masked spatiotemporal autoencoder for self-supervised representation learning from multi-temporal Sentinel-2 satellite imagery. The proposed method extends masked autoencoding to spatiotemporal remote sensing data by jointly modeling spatial structure and temporal evolution. Satellite images are divided into non-overlapping patches and embedded into a latent space, where both spatial and temporal positional encodings are added. A high proportion of spatiotemporal tokens is randomly masked, and the encoder processes only the visible tokens. A lightweight decoder then reconstructs the masked patches, enabling the model to learn meaningful representations without manual annotations. We evaluate the method on Sentinel-2 image time series over agricultural regions in Burundi. Experimental results show that the model successfully reconstructs heavily masked patches and captures consistent spatial and temporal patterns across crop fields. The learned representations are suitable for downstream agricultural tasks such as crop classification and change detection. This work demonstrates that masked spatiotemporal modeling is a promising direction for label-efficient learning in satellite-based agricultural monitoring.