We present a new 10-meter map of dominant tree species in Swedish forests accompanied by pixel-level uncertainty estimates. The tree species classification is based on spatiotemporal metrics derived from Sentinel-1 and Sentinel-2 satellite data, combined with field observations from the Swedish National Forest Inventory. We apply an extreme gradient boosting model with Bayesian optimization to relate field observations to satellite-derived features and generate the final species map. Classification uncertainty is quantified using Shannon's entropy of the predicted class probabilities, which provide a spatially explicit measure of model confidence. The final model achieved an overall accuracy of 85% (F1 score = 0.82, Matthews correlation coefficient = 0.81), and mapped species distributions showed strong agreement with official forest statistics (Spearman's rho = 0.94).

翻译：本研究提出了一种新的10米分辨率瑞典森林优势树种分布图，并附有像素级不确定性估计。树种分类基于Sentinel-1和Sentinel-2卫星数据衍生的时空指标，结合瑞典国家森林资源清查的实地观测数据。我们采用贝叶斯优化的极端梯度提升模型，将实地观测数据与卫星特征相关联，生成最终树种分布图。分类不确定性通过预测类别概率的香农熵进行量化，该指标提供了空间显式的模型置信度度量。最终模型的总体精度达到85%（F1分数=0.82，马修斯相关系数=0.81），制图所得树种分布与官方森林统计数据高度吻合（斯皮尔曼秩相关系数ρ=0.94）。