用深度嵌入提升荷兰森林普查树种分类精度，少样本下效果显著。

National Forest Inventory (NFI) serves as the primary source of forest information, however, maintaining these inventories requires labor-intensive on-site campaigns by forestry experts to identify and document tree species. Embeddings from deep pre-trained remote sensing models offer new opportunities to update NFIs more frequently and at larger scales. This work systematically investigates how deep embeddings improve tree species classification accuracy in the Netherlands with few annotated data. We evaluate this question on three embedding models: Presto, Alpha Earth, and TESSERA, using three tree species datasets of varying difficulty. Data-wise, we compare the available embeddings from Alpha Earth and TESSERA with dynamically calculated embeddings from a pre-trained Presto model, for which we extracted time series from Sentinel-1 , Sentinel-2, and weather data, along with elevation data downloaded from Google Earth Engine. Our results demonstrate that publicly available remote sensing time series deep embeddings outperform the current state-of-the-art hand crafted features in NFI species classification in the Netherlands, yielding performance gains of roughly 7 to 9 percentage points in overall accuracy and up to 17 points in macro-F1 on the NFI datasets at the reference plot level. This indicates that classic hand defined features are too simple for this task and highlights the potential of using deep embeddings for data-limited applications such as NFI classification. Country-scale maps built from the pre-computed embeddings reach accuracy consistent with the plot-level results and with an independent comparison against the NFI. By leveraging openly available satellite data and deep embeddings from pre-trained models, this approach consistently improves classification accuracy compared to traditional methods and can effectively complement existing forest inventory processes.