A source-free unsupervised domain adaptation framework for large-scale, in-season soybean mapping

Reliable in-season soybean mapping remains difficult in heterogeneous and data-poor agricultural regions because phenology, management, and cloud-driven sampling differences induce substantial domain shifts, causing models trained in one region to generalize poorly to new landscapes. Since collecting dense, region-specific labels every season is often impractical, unsupervised domain adaptation (UDA) offers a practical route for adapting models using unlabeled target imagery. However, standard UDA commonly assumes access to source-domain imagery during adaptation, which increases data-sharing, storage, and computational burdens and limits scalable deployment. We propose a source-free unsupervised domain adaptation framework, Contrastive Representation Optimized Prototype Segmentation (CROPS), to deliver large-area, in-season soybean maps using only a pretrained model and unlabeled target…