Abstract

BackgroundLeaf chlorophyll content (LCC) provides valuable information about plant physiology. Most of the published chlorophyll vegetation indices at the leaf level have been based on the spectral characteristics of the adaxial leaf surface, thus, they are not appropriate for estimating LCC when both the adaxial and abaxial leaf surfaces influence the spectral reflectance. We attempted to address this challenge by measuring the spectral reflectance of the adaxial and abaxial leaf surfaces of several plant species at different growth stages using a portable field spectroradiometer. The relationships between more than 30 published reflectance indices with LCC were analyzed to determine which index estimated LCC most effectively. Additionally, since the relationships determined on one set of samples might have poor predictive performances when applied to other samples, a robust wavelength region is required to render the spectral index generally applicable, regardless of the leaf surface or plant species.ResultsThe Modified Datt (MDATT) index, which is the ratio of reflectance difference defined as (Rλ3 − Rλ1)/(Rλ3 − Rλ2), exhibited the strongest correlation (R2 = 0.856, RMSE = 6.872 μg/cm2), with LCC of all the indices tested when all the leaf samples from the adaxial and abaxial surfaces were combined. The optimal wavelength regions, which were derived from the contour maps of R2 between the MDATT index and LCC for the datasets of one side or both leaf surfaces of each plant species and their intersection, indicated that the red-edge to near-infrared wavelength (723–885 nm) was optimal for λ1, while the red-edge region (697–771 nm) was optimal for λ2 and λ3. In these optimal wavelength regions, when the MDATT index was used to estimate LCC, an R2 higher than 0.8 could be obtained. The correlation of the MDATT index with LCC was the same when the positions of λ2 and λ3 were exchanged in the index.ConclusionsMDATT is proposed as an optimal index for the remote estimation of vegetation chlorophyll content across several plant species in different growth stages when reflectance from both leaf surfaces is considered. The red-edge to near-infrared wavelength (723–885 nm) for λ1, as well as the red-edge region (697–771 nm) for λ2 or λ3, are considered to be the most robust for constructing the MDATT index for estimating LCC, regardless of the leaf surface or plant species.

Highlights

  • Leaf chlorophyll content (LCC) provides valuable information about plant physiology

  • One of the aims of the present study is to examine which index associated with a specific formula is the most effective for estimating LCC across various plant species with different structural features when adaxial, abaxial, or bifacial reflectance is considered

  • There was a clear difference between the upper and lower surfaces in white poplar and narrow-leaved oleaster as their abaxial surfaces are covered in dense hair, but less variation was found in the leaves of the other species

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Summary

Introduction

Leaf chlorophyll content (LCC) provides valuable information about plant physiology. Most of the published chlorophyll vegetation indices at the leaf level have been based on the spectral characteristics of the adaxial leaf surface, they are not appropriate for estimating LCC when both the adaxial and abaxial leaf surfaces influence the spectral reflectance. The normalized difference vegetation index (NDVI), based on the reflectance contrast between the red and the NIR (near infrared) [8], has been most commonly used for characterizing canopy LCC. Broadband NDVIs are only effective in distinguishing broad differences in vegetation conditions [9, 13] but not effective in assessing detailed canopy LCC due to their saturation at a high leaf area index (LAI). The vegetation indices are initially developed at the leaf level using hyperspectral data because an assessment of the practicality of such a method at leaf level is regarded as a first step for further research at the canopy scale and for the remote estimation of LCC from satellite observation, which is essential in ecosystem modeling [24, 25]

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