Geoinformatics and Remote Sensing, Institute for Geography, Leipzig University, 04103, Leipzig, Germany; Remote Sensing Centre for Earth System Research, Leipzig University, 04103, Leipzig, Germany; German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, 04103, Leipzig, Germany.
Institute of Cartography and Geoinformatics, Eötvös Loránd University, 1117, Budapest, Hungary.
Talanta. 2024 Mar 1;269:125406. doi: 10.1016/j.talanta.2023.125406. Epub 2023 Nov 14.
Understanding the role of non-structural carbohydrates (NSC) in tree-level carbon cycling crucially depends on the availability of NSC data in a sufficient temporal resolution covering extreme conditions and seasonal peaks or declines. Chemical analytical methods should therefore get complemented by less extensive retrieval methods. To this end, we explored the potential of diffuse reflectance spectroscopy for estimating NSC contents at a set of 180 samples taken from leaves, roots, stems and branches of different tree species in different biogeographic regions. Multiple randomized partitioning in calibration and validation data were performed with near-infrared (NIR) and mid-infrared (MIR) as well as combined data. With derivative spectra, NIR markedly outperformed MIR data for NSC estimation; mean RMSE for outer validation samples equalled 2.58 (in % of dry matter) compared to 2.90, r was 0.64 compared to 0.52. We found complementary information related to NSC in both spectral domains, so that a combination with high-level data fusion (model averaging) increased accuracy (RMSE decreased to 2.19, r equalled 0.72). Spectral variable selection with the CARS algorithm further improved results slightly (RMSE = 1.97, r = 0.78). On the level of tissue types, we found a marked differentiation concerning the appropriateness of datasets and approaches. High-level data fusion was successful for leaves, NIR data (together with CARS) provided the best results for wooden tissues. This suggests further studies with a greater number of samples per tissue type but only for selected (main) tree species to finally judge the sensitivities of diffuse reflectance spectroscopy (NIR, MIR) for NSC retrieval.
了解非结构性碳水化合物（NSC）在树木水平碳循环中的作用，关键取决于在足够的时间分辨率下获得 NSC 数据，这些数据涵盖了极端条件、季节性峰值或下降。因此，化学分析方法应该辅以更广泛的检索方法。为此，我们探讨了漫反射光谱在估计不同生物地理区域不同树种叶片、根系、茎和枝的 NSC 含量方面的潜力。使用近红外（NIR）和中红外（MIR）以及组合数据，在校准和验证数据中进行了多次随机分区。与 MIR 数据相比，NIR 数据在 NSC 估算方面表现出色，外部验证样本的平均 RMSE 为 2.58（占干物质的百分比），r 值为 0.64；与 MIR 数据相比，r 值为 0.52。我们在两个光谱域都发现了与 NSC 相关的补充信息，因此，与高水平数据融合（模型平均）相结合可以提高精度（RMSE 降低到 2.19，r 等于 0.72）。使用 CARS 算法进行光谱变量选择进一步略微提高了结果（RMSE = 1.97，r = 0.78）。在组织类型层面上，我们发现数据集和方法的适用性存在明显差异。高水平数据融合适用于叶片，NIR 数据（与 CARS 结合）为木质组织提供了最佳结果。这表明需要进一步针对每种组织类型进行更多样本的研究，但仅针对选定（主要）树种，以最终判断漫反射光谱（NIR、MIR）对 NSC 检索的敏感性。
