Tree species maps derived from satellite imagery increasingly support forest administrations and nature conservation authorities with large-scale and up-to-date information. However, many species are often excluded or aggregated in classification tasks due to a limited knowledge of the most suitable predictors. Our study aims to gain a better understanding of optical and polarimetric traits for tree species mapping by examining Sentinel-1 and Sentinel-2 time series from 61 tree species in temperate Europe. For a selection of 32 optical, polarimetric and structural variables, the principal component analysis revealed that Sentinel-2 variables mainly explain the variance in the data by contributing to the “seasonality” and “foliage color” components. Sentinel-1 contribute most to the “texture” component. The Normalized Difference Vegetation Index (NDVI), Tasseled Cap Greenness (TCG) and Radar Vegetation Index (RVI) were chosen as key variables for further analysis. Seasonality was found to be the most dominant aspect in all vegetation indices. Furthermore, the TCG was found to be useful to distinguish between early and late budding species. The RVI showed a large potential to discriminate conifers, which is attributed to the crown volume effect of C-band SAR. Using exploratory data analysis, we further examined the influence of management, biogeographical and meteorological factors on the time series from , , and . The NDVI and TCG are relatively robust to different conditions. For the two conifer species however, we found strong spatial variations of the RVI which are presumably caused by different crown conditions across the study area. Using Sentinel-1 data could therefore lead to uncertainties in tree species mapping across large biogeographical gradients. This study contributes to the improvement of tree species mapping based on optical and dual-polarimetric data and thus benefits forest authorities and other stakeholders in their monitoring tasks and decision-making.

中文翻译：

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Sentinel-1 C 波段 SAR 和 Sentinel-2 多光谱遥感时间序列中欧洲 61 种树种的谱时特征

来自卫星图像的树种地图越来越多地为森林管理部门和自然保护机构提供大规模的最新信息。然而，由于对最合适的预测因子的了解有限，许多物种经常在分类任务中被排除或聚集。我们的研究旨在通过检查欧洲温带 61 种树种的 Sentinel-1 和 Sentinel-2 时间序列，更好地了解树种绘图的光学和偏振特征。对于精选的 32 个光学、偏振和结构变量，主成分分析显示，Sentinel-2 变量主要通过贡献“季节性”和“叶子颜色”成分来解释数据的方差。 Sentinel-1 对“纹理”部分的贡献最大。选择归一化植被指数（NDVI）、缨帽绿度（TCG）和雷达植被指数（RVI）作为进一步分析的关键变量。发现季节性是所有植被指数中最主要的方面。此外，TCG 被发现有助于区分早期和晚期出芽的物种。 RVI 显示出区分针叶树的巨大潜力，这归因于 C 波段 SAR 的树冠体积效应。通过探索性数据分析，我们进一步研究了管理、生物地理和气象因素对 、 、 和 时间序列的影响。 NDVI 和 TCG 对于不同的条件都相对稳健。然而，对于这两种针叶树物种，我们发现 RVI 存在强烈的空间变化，这可能是由研究区域不同的树冠条件引起的。因此，使用 Sentinel-1 数据可能会导致跨大生物地理梯度的树种绘图的不确定性。这项研究有助于改进基于光学和双偏振数据的树种测绘，从而有利于森林当局和其他利益相关者的监测任务和决策。