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