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Response of microscopical hyperspectral data to past climatic variable

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Abstract

Tree growth is ordinarily affected by changes in environmental conditions, and the annual sequence of favorable and unfavorable climates is regularly recorded in wide and narrow tree rings. Therefore, data extracted from tree rings can be adopted to reconstruct the past climate. In dendroclimatology, the commonly used proxy indices to obtain paleoclimatic parameters include tree-ring width, blue intensity, density, and isotopic series. Notwithstanding, reconstructing the past local climatic parameters with high accuracy is a challenge. Here, we demonstrate how data from microscopic-hyperspectral scanning of the dendrochronological samples, together with the tree-ring width data, facilitates the precise estimation of one paleoclimatic parameter, the mean deviation of monthly air temperature. In a proof-of-concept analysis experiment, samples were collected from two sites located in the middle part of China, measured with a hyperspectral scanning system, model achieved an averaged root mean square error (RMSE) of 0.524℃ on the mean deviation of monthly air temperature from March to July, which demonstrates significant improvements in accuracy compared with the classical single-indexed tree-ring width method, especially when the number of tree-ring samples is limited. Further, our results suggest that climatic data stored in the tree-ring spectrum could be divulged by analyzing their surface hyperspectral reflectance. We hope our work will be a genesis for more intricate models in dendroclimatology that use microscopic-hyperspectral scanning as a standard tool, which promises enhanced accuracy for past climate reconstruction. Besides tree rings, the capacity of the microscopic-hyperspectral scanning technique can be exploited on other samples collected in paleoclimatology, including core samples from rocks, sediment, ice, or surface samples on shells, boreholes, and corals, to derive valuable information from their surface spectral patterns.

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Data availability

The datasets generated and analyzed during the current study are not publicly available due to relevant laboratory confidentiality rules.

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Funding

This project is supported by the National Natural Science Foundation of China (NSFC) Project No. 42271476, the National Natural Science Foundation of China (NSFC) Project No. 41771227, and the Guangdong Science and Technology Strategic Innovation Fund (the Guangdong-Hong Kong-Macau Joint Laboratory) Project No. 2020B1212030009.

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Authors and Affiliations

  1. School of Resource and Environmental Sciences, Wuhan University, Wuhan, 430079, China

    Lingjun Wang, Teng Fei, Yadan Xu, Haochen Zhang & Yonghong Zheng

  2. Guangdong–Hong Kong-Macau Joint Laboratory for Smart Cities, Shenzhen, 518060, China

    Teng Fei

  3. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, China

    Meng Bian

  4. Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, 100085, China

    Haifeng Zhu

Authors
  1. Lingjun Wang
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  2. Teng Fei
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  3. Meng Bian
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  4. Yadan Xu
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  5. Haochen Zhang
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  6. Yonghong Zheng
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  7. Haifeng Zhu
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Contributions

Teng Fei generated the initial idea which was developed together with Meng Bian, Yadan Xu and Yonghong Zheng.

Yonghong Zheng and Haifeng Zhu provided the tree-ring samples.

Lingjun Wang and Haochen Zhang led the practical spectroscopy work, performed image analyses, and made the figures.

Teng Fei and Lingjun Wang led the writing of the paper, with all authors contributing to manuscript development.

During revising the manuscript, Bian Meng and Teng Fei provided essential ideas based on the reviewers’ comments and assisted with additional experiments. Lingjun Wang performed the main revisions. Yadan Xu and Haochen Zhang also helped with the additional experiments.

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Correspondence to Teng Fei or Yonghong Zheng.

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The authors declare no competing interests.

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Wang, L., Fei, T., Bian, M. et al. Response of microscopical hyperspectral data to past climatic variable. Theor Appl Climatol 150, 1145–1155 (2022). https://doi.org/10.1007/s00704-022-04219-w

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  • DOI: https://doi.org/10.1007/s00704-022-04219-w

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