TSP-Former: A Phenology-Guided Transformer for Tobacco Mapping Using Satellite Image Time Series

Abstract

Tobacco is a phenology-sensitive and economically significant crop that requires accurate and timely spatial mapping to support agricultural planning and public health regulation. However, single-date spectral similarity among crops and regional differences in planting practices limit the generalizability of existing approaches, particularly deep learning (DL) models. To address these challenges, we propose a novel phenologyguided DL framework that leverages satellite image time series (SITS) to capture crop-specific growth dynamics. Specifically, we introduce the tobacco spectral-phenological variable (TSP), which captures change rates in Red Edge-2 during peak growth. It serves as crop-specific prior knowledge for model guidance. Based on this, we develop TSP-Former, a transformer architecture that incorporates two novel modules: a central prior attention module (CPAM), which adaptively fuses spectral information with phenological priors, and an NDVI-enhanced temporal decoder (NDTD), which reinforces temporal learning by emphasizing phenologically critical stages using NDVI-weighted sequences. Extensive experiments across four major tobacco regions using Sentinel-2 imagery demonstrate the method’s superior cross-regional robustness. TSP-Former achieves an average weighted F1-score of 87.1% and an overall accuracy of 85.9%, significantly outperforming random forest and competing DL approaches. Notably, in challenging regions characterized by substantial phenological shifts, the proposed method surpasses the emerging remote sensing foundation model, AlphaEarth with a fine-tuned lightweight multilayer perceptron, by over 15% in accuracy. These findings highlight the effectiveness of integrating phenological priors into temporal deep models, enabling robust and transferable crop mapping across heterogeneous and data-constrained regions, with clear implications for scalable agricultural monitoring and policy development.