Remote Sensing (Aug 2023)
Combining Chlorophyll Fluorescence and Vegetation Reflectance Indices to Estimate Non-Photochemical Quenching (NPQ) of Rice at the Leaf Scale
Abstract
Non-photochemical quenching (NPQ) is an indicator of crop stress. Until now, only a limited number of studies have focused on how to estimate NPQ using remote sensing technology. The main challenge is the complicated regulatory mechanism of NPQ. NPQ can be divided into energy-dependent (qE) and non-energy-dependent (non-qE) quenching. The contribution of these two components varies with environmental factors, such as light intensity and stress level due to the different response mechanisms. This study aims to explore the feasibility of estimating NPQ using photosynthesis-related vegetation parameters available from remote sensing by considering the two components of NPQ. We concurrently measured passive vegetation reflectance spectra by spectrometer, as well as active fluorescence parameters by pulse-amplitude modulated (PAM) of rice (Oryza sativa) leaves. Subsequently, we explored the ability of the selected vegetation parameters (including the photochemical reflectance index (PRI), inverted red-edge chlorophyll index (IRECI), near-infrared reflectance of vegetation (NIRv), and fluorescence quantum yield (ΦF)) to estimate NPQ. Based on different combinations of these remote sensing parameters, empirical models were established to estimate NPQ using the linear regression method. Experimental analysis shows that the contribution of qE and non-qE components varied under different illumination conditions. Under high illumination, the NPQ was attributed primarily to the qE component, while under low illumination, it was equally attributed to the qE and non-qE components. Among all tested parameters, ΦF was sensitive to the qE component variation, while IRECI and NIRv were sensitive to the non-qE component variation. Under high illumination, integrating ΦF in the regression model captured NPQ variations well (R2 > 0.74). Under low illumination, ΦF, IRECI, and NIRv explained 24%, 62%, and 65% of the variation in NPQ, respectively, while coupling IRECI or NIRv with ΦF considerably improved the accuracy of NPQ estimation (R2 > 0.9). For all the samples under both low and high illumination, the combination of ΦF with at least one of the other parameters (including IRECI, NIRv and PAR) offers a more versatile and reliable approach to estimating NPQ than using any single parameter alone. The findings of this study contribute to the further development of remote sensing methods for NPQ estimation at the canopy scale in the future.
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