Tree ring width (TRW) is crucial for assessing biomass increments, carbon uptake, forest productivity, and forest health. Due to the limitations involved in measuring TRW, utilizing canopy attributes based on vegetation indices (VIs) offers a promising alternative. This study investigated the species-specific relationship between the VIs derived from the Sentinel optical (Sentinel-2) and SAR (Sentinel-1) time series and TRW. For each of the seven dominant Central European tree species, we aimed to identify the most suitable VI that shows the strongest relationship with the interannual variation in TRW. We also developed species-specific models using the random forest (RF) approach and a variety of VIs to predict TRW. Additionally, the impact of detrending TRW on its correlation with VIs and on the accuracy of TRW modeling was assessed. The results showed that the VIs that had the strongest correlation with TRW differed among the analyzed tree species. The results confirmed our hypothesis that the use of novel VIs, such as the green normalized difference vegetation index (GNDVI), or red-edge-based VIs can increase our ability to detect growth-related canopy attributes. Among all the models constructed based on raw and detrended TRWs, 12–39 % of the annual variance in TRW was explained by the integrated optical and SAR-based features. Comparing the raw and detrended TRWs indicated that detrending is necessary for certain species, even in short-term studies (i.e., less than 6 years). We concluded that Sentinel-based VIs can be used to improve the understanding of species-specific variation in forest growth over large areas. These results are useful for modeling and upscaling forest growth, as well as for assessing the effect of extreme climate events, such as droughts, on forest productivity.

中文翻译：

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将遥感生长相关的树冠属性与年际树轮宽度变化联系起来：一项使用 Sentinel 光学和 SAR 时间序列的物种特异性研究


树木年轮宽度 （TRW） 对于评估生物量增量、碳吸收、森林生产力和森林健康状况至关重要。由于测量 TRW 所涉及的限制，利用基于植被指数 （VI） 的冠层属性提供了一种很有前途的替代方案。本研究调查了源自 Sentinel 光学 （Sentinel-2） 和 SAR （Sentinel-1） 时间序列的 VI 与 TRW 之间的物种特异性关系。对于七种主要中欧树种中的每一种，我们旨在确定最合适的 VI，它与 TRW 的年际变化关系最强。我们还使用随机森林 （RF） 方法和各种 VI 开发了物种特异性模型来预测 TRW。此外，还评估了去除 TRW 趋势对其与 VI 的相关性以及 TRW 建模准确性的影响。结果表明，与 TRW 相关性最强的 VIs 在分析的树种之间存在差异。结果证实了我们的假设，即使用新的VI，例如绿色归一化差异植被指数 （GNDVI） 或基于红色边缘的 VI 可以提高我们检测与生长相关的冠层属性的能力。在基于原始和去趋势的 TRW 构建的所有模型中，TRW 年方差的 12-39% 由集成的光学和基于 SAR 的特征解释。比较原始和去除趋势的 TRW 表明，即使在短期研究中（即不到 6 年），某些物种也有必要去除趋势。我们得出的结论是，基于 Sentinel 的 VI 可用于提高对大面积森林生长中物种特异性变化的理解。 这些结果可用于建模和扩大森林生长规模，以及评估极端气候事件（如干旱）对森林生产力的影响。