Can Vegetation Indices Serve as Proxies for Potential Sun-Induced Fluorescence (SIF)? A Fuzzy Simulation Approach on Airborne Imaging Spectroscopy Data

Subhajit Bandopadhyay,Anshu Rastogi,Sergio Cogliati,Uwe Rascher,Maciej Gąbka,Radosław Juszczak

doi:10.3390/rs13132545

Abstract

In this study, we are testing a proxy for red and far-red Sun-induced fluorescence (SIF) using an integrated fuzzy logic modelling approach, termed as SIFfuzzy and SIFfuzzy-APAR. The SIF emitted from the core of the photosynthesis and observed at the top-of-canopy is regulated by three major controlling factors: (1) light interception and absorption by canopy plant cover; (2) escape fraction of SIF photons (fesc); (3) light use efficiency and non-photochemical quenching (NPQ) processes. In our study, we proposed and validated a fuzzy logic modelling approach that uses different combinations of spectral vegetation indices (SVIs) reflecting such controlling factors to approximate the potential SIF signals at 760 nm and 687 nm. The HyPlant derived and field validated SVIs (i.e., SR, NDVI, EVI, NDVIre, PRI) have been processed through the membership transformation in the first stage, and in the next stage the membership transformed maps have been processed through the Fuzzy Gamma simulation to calculate the SIFfuzzy. To test whether the inclusion of absorbed photosynthetic active radiation (APAR) increases the accuracy of the model, the SIFfuzzy was multiplied by APAR (SIFfuzzy-APAR). The agreement between the modelled SIFfuzzy and actual SIF airborne retrievals expressed by R2 ranged from 0.38 to 0.69 for SIF760 and from 0.85 to 0.92 for SIF687. The inclusion of APAR improved the R2 value between SIFfuzzy-APAR and actual SIF. This study showed, for the first time, that a diverse set of SVIs considered as proxies of different vegetation traits, such as biochemical, structural, and functional, can be successfully combined to work as a first-order proxy of SIF. The previous studies mainly included the far-red SIF whereas, in this study, we have also focused on red SIF along with far-red SIF. The analysis carried out at 1 m spatial resolution permits to better infer SIF behaviour at an ecosystem-relevant scale.

Highlights

IntroductionSun-induced fluorescence (SIF) has emerged as a promising remote sensing (RS) signal in the contemporary era to understand and monitor the terrestrial vegetation activity creativecommons.org/licenses/by/ 4.0/)
Normalized Difference Vegetation Index (NDVI), Simple Ratio (SR), Enhanced Vegetation Index (EVI), and NDVIre are greenness indices that are related to light absorption and interception, whereas EVI and NDVIre better present the canopy structure, they can be more related to fesc, while Photochemical Reflectance Index (PRI) is the component related to the light use efficiency and non-photochemical quenching (NPQ) processes [13,17]
Both SIFfuzzy and SIFfuzzy-absorbed photosynthetic active radiation (APAR) worked quite accurately to approximate the Sun-induced fluorescence (SIF) signals, where SIFfuzzy were closer to SIF at 687 nm (SIF687) values, whereas SIFfuzzy-APAR were better correlated with SIF at 760 nm (SIF760) as expressed by higher R2 and lower root mean square error (RMSE)

Summary

Introduction

Sun-induced fluorescence (SIF) has emerged as a promising remote sensing (RS) signal in the contemporary era to understand and monitor the terrestrial vegetation activity creativecommons.org/licenses/by/ 4.0/). The SIF signal is influenced by three main factors which may impact the accuracy of SIF estimation at the canopy level: (1) light interception or light absorption by canopy plant cover, expressed by absorbed photosynthetic active radiation (APAR). [13], indicating the dependence of SIF on vegetation traits; (2) SIF reabsorption and scattering—most of the photons emitted by plants are reabsorbed or scattered within the canopy [12,14,15], the fluorescence signal is dependent on the escape fraction of SIF (fesc) which may vary for different vegetation; (3) the light use efficiency (LUE)—absorbed light used for photosynthesis has a strong impact on the functional/physiological regulation of the leaf which is highly dynamic [16]. The relation between the efficiency of leaf photosynthesis and the intensity of SIF is non-linear as it is controlled by three different paths of energy use/dissipation, i.e., photosynthetic electron flow, non-photochemical quenching (NPQ) and fluorescence emission [17]

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