Spectroscopy and Spectral Analysis, Volume. 44, Issue 10, 2993(2024)

Estimation of Chlorophyll Content in Spartina Alterniflora Leaves Basedon Continous Wavelet Transformation and Random Forest Algorithm

GUAN Cheng1... LIU Ming-yue1,2,3,4,*, MAN Wei-dong1,2,3,4, ZHANG Yong-bin1, ZHANG Qing-wen1, FANG Hua1, LI Xiang1 and GAO Hui-feng1 |Show fewer author(s)
Author Affiliations
  • 1College of Mining Engineering, North China University of Science and Technology, Tangshan 063210, China
  • 2Hebei Industrial Technology Institute of Mine Ecological Remediation, Tangshan 063210, China
  • 3Collaborative Innovation Center of Green Development and Ecological Restoration of Mineral Resources, Tangshan 063210, China
  • 4Tangshan Key Laboratory of Resources and Environmental Remote Sensing, Tangshan 063210, China
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    Chlorophyll content is a key indicator of the physiological status of plants, and accurate estimation of chlorophyll content is important for characterizing its component content traits and quantifying its physiological status. In this paper, the hyperspectral reflectance and chlorophyll content (SPAD) of Spartina alterniflora in the Duliu-river wetland were used as the data source, the original spectrum was mathematically transformed and processed with continuous wavelet transformation (CWT). The spectral features were extracted using Sequential Projection Algorithm (SPA). And the hyperspectral estimation model of leaf chlorophyll content of Spartina alterniflora was developed based on random forest regression (RFR) algorithm. The results showed that: (1) CWT had more accurate time resolution and higher frequency in the low scale spectra, corresponding to a narrow wavelet function, which could better distinguish the differences between the spectra and highlight the characteristic spectral information. (2) Except for reciprocal and logarithmic first derivative spectrals, the spectral mathematical transform and CWT methods could effectively respond to the spectral detail features. CWT was generally better than the spectral mathematical transform, and the correlation between L10 scale and first derivative spectral reached 0.78 and 0.77. (3) First derivative spectral, reciprocal first derivative spectral, logarithmic derivative spectral and CWT could enhance the ability of spectral estimation of Spartina alterniflora chlorophyll content. The RF models based on first derivative spectral (R2=0.776, RMSE=0.510, RPD=1.893) and CWT with the multiscale of L2, L3 and L4 (R2=0.871, RMSE=0.305, RPD=3.846) were the optimal models. This study shows that hyperspectral techniques could be used as a non-destructive means of detecting chlorophyll content in leaves of Spartina alterniflora, and that the hyperspectral estimation model built by combining multiple scales after continuous wavelet decomposition could more estimate chlorophyll content in leaves of Spartina alterniflora.

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    GUAN Cheng, LIU Ming-yue, MAN Wei-dong, ZHANG Yong-bin, ZHANG Qing-wen, FANG Hua, LI Xiang, GAO Hui-feng. Estimation of Chlorophyll Content in Spartina Alterniflora Leaves Basedon Continous Wavelet Transformation and Random Forest Algorithm[J]. Spectroscopy and Spectral Analysis, 2024, 44(10): 2993

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    Paper Information

    Received: Jul. 18, 2023

    Accepted: Jan. 16, 2025

    Published Online: Jan. 16, 2025

    The Author Email: Ming-yue LIU (liumy917@ncst.edu.cn)

    DOI:10.3964/j.issn.1000-0593(2024)10-2993-08

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