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Estimation of leaf photosynthetic capacity parameters using spectral indices developed from fractional-order derivatives.

Authors :
Song, Guangman
Wang, Quan
Jin, Jia
Source :
Computers & Electronics in Agriculture. Sep2023, Vol. 212, pN.PAG-N.PAG. 1p.
Publication Year :
2023

Abstract

• FOD indices are more robust for estimating leaf photosynthetic parameters. • 1.50- and 1.60-order D-type indices performed best for Vcmax and Jmax estimations. • High order-derived indices are more sensitive to random noise. The maximum carboxylation rate (Vcmax) and the maximum electron transport rate (Jmax) are representative parameters describing the gas exchange properties of leaves. Non-destructive measurements of these parameters are valuable for tracing the physiological status and productivity of a plant. The spectral vegetation index, as a simple and efficient approach, has been applied to estimate these two parameters. However, most indices used are derived from original or integer-order rather than fractional-order derivatives (FOD), despite increasing evidence of their advantages in terms of tracing diverse plant properties. In this study, we attempt to derive photosynthetic capacity parameters from FOD indices based on a composite hyperspectral spectroscopy dataset covering various conditions. Our results show that the FOD can be utilized to develop more robust indices for obtaining photosynthetic capacity parameters. In particular, the D-type indices calculated from the 1.50-order and 1.60-order derivatives produced the highest accuracies for the estimation of Vcmax and Jmax. The performance of these proposed indices, however, differed for different plant functional types and had a high sensitivity to random noise. The results of the present research show that FOD indices have the potential to estimate leaf photosynthetic parameters, and are therefore to support hyperspectral information on estimating plant properties. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
01681699
Volume :
212
Database :
Academic Search Index
Journal :
Computers & Electronics in Agriculture
Publication Type :
Academic Journal
Accession number :
171365790
Full Text :
https://doi.org/10.1016/j.compag.2023.108068