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Leaf Biochemistry Parameters Estimation of Vegetation Using the Appropriate Inversion Strategy.

Lin Du1,2,3, Jian Yang1,2, Jia Sun1

  • 1School of Geography and Information Engineering, China University of Geosciences, Wuhan, China.

Frontiers in Plant Science
|July 17, 2020
PubMed
Summary

Artificial neural networks (ANNs) with feature weighting (FW) and principal component analysis (PCA) effectively estimate vegetation biochemistry parameters. These methods improve spectral data analysis for chlorophyll, carotenoid, water thickness, and leaf mass per area estimation.

Keywords:
artificial neural networksband selectionspectral band correlationspectral propertyvegetation biochemistry parameter

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Area of Science:

  • Ecology
  • Remote Sensing
  • Plant Physiology

Background:

  • Vegetation biochemistry parameters are crucial indicators of ecosystem health and photosynthetic activity.
  • Accurate estimation is challenged by spectral band correlations and parameter sensitivity to inversion models.
  • Dimensionality reduction and efficient inversion strategies are vital for reliable parameter estimation.

Purpose of the Study:

  • To enhance the estimation predictability of four key vegetation biochemistry parameters: chlorophyll a and b (Cab), carotenoid (Car), equivalent water thickness (EWT), and leaf mass per area (LMA).
  • To investigate the effectiveness of band-selection-based artificial neural networks (ANNs) combined with feature weighting (FW) and principal component analysis (PCA) for spectral data analysis.
  • To compare different inversion strategies for optimizing parameter estimation accuracy.

Main Methods:

  • Utilized band-selection-based ANNs integrated with FW and PCA to reduce spectral correlations.
  • Analyzed reflectance (R), transmittance (T), and combined R&T spectral properties.
  • Implemented simultaneous and separate inversion strategies for the four biochemistry parameters using FW- and PCA-ANNs.
  • Selected spectral subsets from R and T spectra for EWT and LMA inversion.

Main Results:

  • FW- and PCA-ANN models demonstrated significant improvements in predictability using fewer spectral characteristics.
  • Concurrent inversion of EWT and LMA yielded satisfactory results (R²).
  • Separate inversion was optimal for Cab and Car, achieving the best R² values.
  • Reflectance, transmittance, and combined R&T spectra showed varied performance in parameter inversion.

Conclusions:

  • FW- and PCA-ANN approaches offer efficient methods for reducing spectral dimensionality and improving vegetation biochemistry parameter estimation.
  • The optimal inversion strategy (simultaneous vs. separate) depends on the specific biochemistry parameter being analyzed.
  • Different spectral properties (R, T, R&T) have distinct impacts on the accuracy of parameter inversion, necessitating careful selection for specific applications.