Cross-Year Crop and Land-Cover Classification with Limited Labels

Abstract

Accurate multi-year crop mapping is often constrained by the cost of constructing dense labels for every target year. This study evaluated a cross-year crop and land-cover classification framework using Sentinel-2 imagery in Jiyuan City, China. Dense semantic-segmentation labels were constructed for three representative sites in 2020 and 2021 and used to train DeepLab V3+, U-Net, and U-Net++. Two input schemes, spectral bands alone and spectral bands combined with six vegetation indices, were compared, and the trained models were applied to 2019 and 2022–2024 without additional year-specific dense semantic-segmentation labels. U-Net++ with vegetation-index-enhanced inputs achieved the best overall performance among the tested networks, with annual overall accuracy ranging from 87.35% to 93.18%. Winter wheat was consistently identified with high producer’s and user’s accuracies, whereas other crops and urban areas remained more uncertain. The resulting annual maps supported analysis of winter-wheat spatial dynamics across 2019–2024. These results demonstrate the practical applicability of a fixed source-year semantic-segmentation model for multi-year crop mapping within the study area, while broader temporal and spatial transferability require further validation.