HSRA-Net: Hierarchical Season-Year Representation Alignment for Tree Species Classification from Long-Term Sentinel-2 Observations

Accurately identifying dominant forest tree species from remote sensing supports biodiversity assessment, carbon stock estimation, and sustainable forest management. Sentinel-2 satellite time series capture phenological dynamics and canopy spectral variation, providing valuable information for regional tree species classification. However, interannual shifts in phenological timing can reduce the temporal correspondence among repeated annual cycles. This temporal misalignment complicates the consistent representation of multiyear observations. To address this problem, this study proposes the Hierarchical Season-Year Representation Alignment Network (HSRA-Net). Using multiyear Sentinel-2 NDVI time series, HSRA-Net builds robust temporal representations, aligns comparable seasonal phases across years, and adaptively integrates informative temporal features. The model was evaluated on dominant tree species in representative temperate and subtropical forests in China using ground reference observations and four temporal scales: intra-growing, annual, biennial, and triennial. It was compared with standard models and representative recent spatiotemporal methods. Among the four temporal scales, HSRA-Net achieved its highest classification accuracy in the triennial scale in both study areas. HSRA-Net achieved OA of 88.90% and 83.17% in the temperate and subtropical regions, respectively. Compared with the intra-growing season, the triennial scale increased OA by 3.72% and 14.31%, respectively. These findings demonstrate that organizing comparable phenological information across repeated annual cycles can improve dominant tree species classification and highlight the effectiveness of HSRA-Net for multiyear temporal representation.