Optical satellite imagery with high temporal and spatial resolution, such as acquired by Sentinel-2, is increasingly becoming available and is used to derive maps of tree species. Such mapping products are required in the scope of operational and sustainable forest management. Existing studies that employ Sentinel-2 imagery have already evaluated different classification algorithms but are often confined to areas smaller than a single Sentinel-2 scene. In this study, the area of interest (a large part of the Province of Tyrol (Austria)) is covered by two Sentinel-2 tiles, of which approximately 5000 km² are forested. In order to deal with seasonal metrics under recurrent cloud cover conditions, we exploit one year of Sentinel-2 imagery by using land surface phenology (LSP) and seasonal cloud-free composites for mapping five different tree species groups (Broadleaved-, Larch- (Larix), Pine- (Pinus), Dwarf Pine- (Pinus mugo) and Spruce/Fir (Abies alba/Picea abies) stands). Although a regular multitemporal classification setup based on three cloud-free images reached an overall accuracy of around 84.4 % and outperformed monotemporal setups by around 10 % points, the availability of single cloud-free images was limited in the mountainous region. Thus, alternative approaches, using combined measures for the entire time series of Sentinel-2 imagery, i.e. three-monthly temporal reflectance composites and phenological metrics, were tested and could even improve overall accuracy by 1–2 % points. As a conclusion, we agree with previous studies that multitemporal imagery can help improving the mapping accuracy. However, leveraging satellite image time series for large-scale mapping of tree species should not only rely on high-quality cloud-free single images and should strongly be supported by i.e. seasonal composites or multi-image metrics. Therefore, development and provisioning of such datasets should be fostered.

具有高时空分辨率的光学卫星图像（例如由Sentinel-2采集的图像）变得越来越可用，并用于获取树种地图。在业务和可持续森林管理的范围内需要此类测绘产品。现有使用Sentinel-2图像的研究已经评估了不同的分类算法，但通常局限于比单个Sentinel-2场景小的区域。在这项研究中，感兴趣的区域（奥地利蒂罗尔州的很大一部分）被两块Sentinel-2瓷砖覆盖，其中约有5000km²的森林被覆盖。为了处理经常性云覆盖条件下的季节性指标，（Larix）， Pine- （Pinus），Dwarf Pine- （Pinus mugo）和云杉/冷杉（Abies alba / Picea abies）站立）。尽管基于三张无云图像的常规多时间分类设置的总体准确度约为84.4％，并且比单时相设置的性能高出约10％，但是在山区，单个无云图像的可用性受到限制。因此，测试了替代方法，对Sentinel-2图像的整个时间序列使用组合测量，即三个月的时间反射率合成和物候指标，甚至可以将整体精度提高1-2％。结论是，我们同意以前的研究，即多时相影像可以帮助提高制图精度。但是，利用卫星图像时间序列进行树种的大规模制图不仅应依靠高质量的无云单一图像，而且应得到以下方面的大力支持：季节性合成或多图像指标。因此，应促进此类数据集的开发和供应。