Self-Supervised Transformers for Long-Term Prediction of Landsat NDVI Time Series

Long-term satellite image time-series (SITS) analysis presents significant challenges in remote sensing, especially for heterogeneous Mediterranean landscapes, due to complex temporal dependencies, pronounced seasonality, and overarching global trends. We propose Self-Supervised Transformers for Long-Term Prediction (SST-LTP), a novel framework that combines self-supervised learning, temporal embeddings, and a Transformer-based architecture to analyze multi-decade Landsat data. Our approach leverages a selfsupervised pretext task to train Transformers on unlabeled data, incorporating temporal embeddings to capture both long-term trends and seasonal variations. This architecture effectively models intricate temporal patterns, enabling accurate predictions of the Normalized Difference Vegetation Index (NDVI) across diverse temporal horizons. Using Landsat data spanning 1984–2024, SST-LTP achieves a Mean Absolute Error (MAE) of 0.0338 and an R 2 value of 0.8337, outperforming traditional methods and other neural network architectures. These results highlight SST-LTP as a robust tool for long-term environmental monitoring and analysis.