Abstract

Accurate vineyard mapping is important for agricultural resource monitoring, land-use management, and inventory updating in arid regions. However, vineyard identification in Xinjiang, China, is challenged by fragmented parcels, exposed soil backgrounds, irrigation-driven heterogeneity, and strong accumulated-temperature gradients. This study developed a multi-source remote-sensing framework for vineyard mapping and inventory updating by integrating Sentinel-1/2 data, terrain variables, growing-degree-day-derived agrothermal zones (ATZs), and seasonal-difference features. RF, LightGBM, XGBoost, 1D-CNN, and a Stacking ensemble were evaluated using polygon-level in-distribution testing and Leave-One-ATZ-Out cross-zone validation. The in-distribution test was used to assess vineyard separability under similar sample distributions, whereas cross-ATZ validation was used to evaluate model transferability across heterogeneous thermal domains. Tree-based models and Stacking achieved near-ceiling performance under the in-distribution setting, but cross-ATZ validation revealed substantial performance degradation, indicating that conventional local validation can overestimate operational transferability. RF achieved the highest mean cross-zone F1-score, while Stacking achieved the highest cross-zone AP and Recall and provided a flexible probability surface for thresholding, mosaicking, vector post-processing, and patch-level mapping. In Gaochang District, the Stacking-derived result identified 22,998.84 ha of potential vineyard area, achieved an Area Recall of 0.7908 against the historical inventory, and covered 88.03% of existing parcels at ≥30% spatial overlap. The workflow also identified 1356 candidate vineyard patches for inventory updating covering 2502.32 ha for subsequent inventory verification. These results demonstrate that multi-source feature integration and probability-based ensemble mapping can support vineyard mapping and inventory updating in arid regions, while cross-zone validation is essential for assessing operational generalization.

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