Crop type mapping in the pre-Sentinel era using variable-length Landsat time-series and self-supervised learning

Crop type mapping is crucial for agricultural land cover monitoring and decision-making. State-of-the-art methods developed using recent Sentinel satellite data have already demonstrated their ability to accurately map crop types. However, crop type mapping for the pre-Sentinel era remains challenging due to the limited availability of higher spatial- and temporal-resolution data. This study addresses this knowledge gap by leveraging variable-length Landsat satellite time-series (L-SITS) data in combination with a self-supervised learning model, SITS-BERT, for crop type mapping. This case study, conducted in two German districts, demonstrates the potential of mapping two different crop type levels (CTL1 - 5 and CTL2 - 9 classes) in the pre-Sentinel era. The SITS-BERT model, pre-trained on unlabelled L-SITS data, was fine-tuned on single-year and 3-year datasets and evaluated using past and future years’ data, compared with the model’s training data. The SITS-BERT model achieved overall accuracies of 0.78–0.83 and 0.64–0.76 for CTL1 and CTL2, respectively, with fine-tuning on single-year data. The model fine-tuned with 3 years achieved higher accuracies (0.81–0.85 and 0.72–0.78). The results showed that the SITS-BERT model finetuned with single-year data outperforms the baseline random forest model trained on single-year fixed-length L-SITS data. The study highlighted that, with this approach, limited number of available SITS observations can still be useful. The findings of this study demonstrated the potential of the SITS-BERT model with L-SITS data for crop-type mapping in the pre-Sentinel era, contributing to a more comprehensive understanding of agricultural land cover dynamics and to the evaluation of agricultural policy impacts.