Monitoring tree crop expansion is vital for zero-deforestation policies like the European Union's Regulation on Deforestation-free Products (EUDR). However, these efforts are hindered by a lack of highresolution data distinguishing diverse agricultural systems from forests. Here, we present the first 10m-resolution tree crop map for South America, generated using a multi-modal, spatio-temporal deep learning model trained on Sentinel-1 and Sentinel-2 satellite imagery time series. The map identifies approximately 11 million hectares of tree crops, 23% of which is linked to 2000-2020 forest cover loss. Critically, our analysis reveals that existing regulatory maps supporting the EUDR often classify established agriculture, particularly smallholder agroforestry, as "forest". This discrepancy risks false deforestation alerts and unfair penalties for small-scale farmers. Our work mitigates this risk by providing a high-resolution baseline, supporting conservation policies that are effective, inclusive, and equitable.

翻译：监测木本作物扩张对于欧盟《零毁林产品条例》等零毁林政策至关重要。然而，当前缺乏能够区分多样化农业系统与森林的高分辨率数据，制约了相关监测工作。本研究通过训练基于Sentinel-1和Sentinel-2卫星影像时间序列的多模态时空深度学习模型，首次绘制了南美洲10米分辨率的木本作物分布图。该地图识别出约1100万公顷木本作物种植区，其中23%与2000-2020年间的森林覆盖减少区域存在空间关联。关键发现表明，当前支持欧盟零毁林条例的监管地图常将既有农业用地（特别是小农复合农林系统）误判为"森林"。这种偏差可能导致虚假的毁林警报，并对小规模农户造成不公正处罚。本研究通过提供高精度基准数据，为制定高效、包容且公平的自然保护政策提供支撑，有效降低了上述风险。