An Integration of Phenological Pattern and Satellite Image Time Series for Zanthoxylum bungeanum Maxim. Mapping Using Deep One-Class Classification

As a commercially and medicinally significant species extensively cultivated across Asia, Zanthoxylum bungeanum Maxim. (ZBM) demands precise spatial mapping to underpin regional economic development and ecological management. Although satellite image time series (SITS) coupled with machine learning have advanced crop-specific identification, accurately delineating ZBM in mountainous smallholder landscapes remains problematic. Similar agronomic practices and microclimatic variability intensify spectral confusion between ZBM and its co-cultivated counterparts, while shifting agricultural policies and diverse crop rotations complicate the establishment of fixed classification schemes and render manual annotation prohibitively laborious. To address these challenges, this study proposes the Phenology-Stage Aware Temporo-Spatial Fusion Transformer with Deep One-Class Loss (PTSFOC), a novel framework for high-precision ZBM mapping using SITS data. PTSFOC addresses spectral ambiguity through the Multi Phenology-Stage Feature Extraction (MPSE) branches and Cross-Phenology-Stage Fusion (CPSF) module, which jointly model spatiotemporal signatures across ZBM's key growth phases (e.g., fruit expansion and ripening) to enhance discriminative capacity. To alleviate labeling burdens, a deep one-class risk estimator trains the network using only positive and unlabeled samples, obviating the need for negative class definitions. Applied to Jiangjin District in Chongqing Municipality, a typical mountainous smallholder region, PTSFOC achieves an F1-score of 0.952, outperforming existing one-class classification methods. This work not only delivers a robust tool for mapping ZBM in complex agricultural landscapes but also offers a transferable strategy for other specialty crops plagued by spectral ambiguity, thereby informing targeted agricultural and ecological policy.