Suppr超能文献

叶片光谱学作为预测亚马孙中部森林树木异戊二烯排放和萜烯储存情况的一种工具。

Leaf spectroscopy as a tool for predicting the presence of isoprene emissions and terpene storage in central Amazon forest trees.

作者信息

Robin Michelle, Durgante Flavia Machado, Mallmann Caroline Lorenci, Hadlich Hilana Louise, Römermann Christine, Falcão Lucas de Souza, Lacerda Caroline Dutra, Duvoisin Sérgio, Wittmann Florian, Piedade Maria Teresa Fernandez, Schöngart Jochen, Gomes Alves Eliane

机构信息

Biogeochemical Processes Department, Max Planck Institute for Biogeochemistry, Jena, Germany.

Department of Wetlands Ecology, Karlsruhe Institute of Technology, Karlsruhe, Germany.

出版信息

Plant Methods. 2025 Jun 4;21(1):78. doi: 10.1186/s13007-025-01400-w.

Abstract

BACKGROUND

Volatile isoprenoids (VIs), such as isoprene, monoterpenes, and sesquiterpenes, participate in various forest-atmosphere processes ranging from plant cell regulation to atmospheric particle formation. The Amazon Forest is the greatest and most diverse source of VI emissions, but the lack of leaf-level studies and the logistical challenges of measuring in such remote and highly biodiverse sites bring high levels of uncertainty to modeled emission estimates. Studies indicate that leaf spectroscopy is an effective tool for estimating leaf morphological, physiological, and chemical traits, being a promising tool for more easily assessing VI emissions from vegetation. In this study, we tested the ability of leaf reflectance spectroscopy to predict the presence of VI emissions and storage in central Amazon Forest trees. We measured leaf-level isoprene emission capacity (E; emission measured at standard conditions: light of 1000 µmol m s photosynthetically active radiation and leaf temperature of 30 ˚C), stored monoterpene and sesquiterpene contents, and hyperspectral visible to short-wave infrared (VSWIR) reflectance from dry and fresh leaves of 175 trees from 124 species of angiosperms.

RESULTS

We found that dry leaf hyperspectral reflectance data, and fresh leaf reflectance measured at selected wavelengths (616, 694, and 1155 nm), predicted the presence of isoprene emissions with accuracies of 0.67 and 0.72, respectively. Meanwhile, fresh leaf hyperspectral reflectance data predicted monoterpene and sesquiterpene storage with accuracies of 0.65 and 0.67, respectively.

CONCLUSIONS

Our results indicate the possibility of using spectral readings from botanical collections or field inventories to orient sampling efforts toward potential isoprene-emitting or terpene-storing trees, or to identify key spectral features (most informative selected wavelengths) for potential future incorporation into remote sensing models. The use of spectral tools for detecting potential isoprene-emitting and terpene-storing species can help to improve current VI emission datasets, reduce modeling emission uncertainties, and contribute to a better understanding of the roles of VIs within forest-atmosphere interactions, atmospheric chemistry, and the carbon cycle.

摘要

背景

挥发性异戊二烯(VIs),如异戊二烯、单萜和倍半萜,参与从植物细胞调节到大气颗粒物形成等各种森林 - 大气过程。亚马逊森林是挥发性异戊二烯排放的最大且最多样化的来源,但缺乏叶级研究以及在如此偏远和生物多样性极高的地点进行测量的后勤挑战给模拟排放估算带来了高度不确定性。研究表明,叶片光谱学是估算叶片形态、生理和化学特征的有效工具,是更轻松评估植被挥发性异戊二烯排放的有前途的工具。在本研究中,我们测试了叶片反射光谱预测亚马逊森林中部树木挥发性异戊二烯排放和储存情况的能力。我们测量了叶级异戊二烯排放能力(E;在标准条件下测量的排放:光合有效辐射为1000 μmol m⁻² s⁻¹且叶片温度为30˚C)、储存的单萜和倍半萜含量,以及来自124种被子植物的175棵树的干叶和鲜叶的高光谱可见到短波红外(VSWIR)反射率。

结果

我们发现,干叶高光谱反射率数据以及在选定波长(616、694和1155 nm)测量的鲜叶反射率分别以0.67和0.72的准确率预测了异戊二烯排放的存在。同时,鲜叶高光谱反射率数据分别以0.65和0.67的准确率预测了单萜和倍半萜的储存情况。

结论

我们的结果表明,利用植物标本馆收集的数据或实地清查的光谱读数,有可能将采样工作导向潜在的异戊二烯排放或萜烯储存树木,或识别关键光谱特征(信息量最大的选定波长),以便未来可能纳入遥感模型。使用光谱工具检测潜在的异戊二烯排放和萜烯储存物种有助于改进当前的挥发性异戊二烯排放数据集,减少模拟排放的不确定性,并有助于更好地理解挥发性异戊二烯在森林 - 大气相互作用、大气化学和碳循环中的作用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f280/12135534/6ccceaebaa30/13007_2025_1400_Fig1_HTML.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验