This study explores the application of transfer learning with transformer models for early crop classification using multi-year satellite image time series (SITS) data. The goal is to produce accurate agricultural land class predictions with minimal reference data. The research uses Sentinel-2 satellite imagery and reference crop labels from Slovenia for 2019, 2020, and 2021. The study evaluates the effectiveness of pre-trained models in adapting to new years with limited reference data, showing that transfer learning significantly improves performance compared to models trained from scratch. A pre-trained model fine-tuned with only 1500 samples (less than 0.3% of the dataset) achieved an average F1 score of 82.5% for the target year, outperforming models trained on the same year without reference data. The study also demonstrates that early classification can be achieved with a smaller sample size, with models outperforming the baseline after 210 days of the year. The results highlight the potential of transfer learning in reducing the need for extensive reference data and improving the efficiency of crop classification in real-world scenarios. The study contributes to the field of remote sensing and agricultural monitoring by showing the effectiveness of transformer-based models in handling multi-year SITS data for early crop classification.This study explores the application of transfer learning with transformer models for early crop classification using multi-year satellite image time series (SITS) data. The goal is to produce accurate agricultural land class predictions with minimal reference data. The research uses Sentinel-2 satellite imagery and reference crop labels from Slovenia for 2019, 2020, and 2021. The study evaluates the effectiveness of pre-trained models in adapting to new years with limited reference data, showing that transfer learning significantly improves performance compared to models trained from scratch. A pre-trained model fine-tuned with only 1500 samples (less than 0.3% of the dataset) achieved an average F1 score of 82.5% for the target year, outperforming models trained on the same year without reference data. The study also demonstrates that early classification can be achieved with a smaller sample size, with models outperforming the baseline after 210 days of the year. The results highlight the potential of transfer learning in reducing the need for extensive reference data and improving the efficiency of crop classification in real-world scenarios. The study contributes to the field of remote sensing and agricultural monitoring by showing the effectiveness of transformer-based models in handling multi-year SITS data for early crop classification.