Deriving photosystem-level red chlorophyll fluorescence emission by combining leaf chlorophyll content and canopy far-red solar-induced fluorescence: Possibilities and challenges

Abstract

Solar-induced chlorophyll fluorescence (SIF) emitted from photosystem I (PSI) and photosystem II (PSII) is characterized by two peaks centered in the red and far-red spectral regions. SIF provides a unique remotely sensible signal to track plant photosynthetic dynamics. Compared with far-red SIF, red SIF (RSIF) is more strongly linked to PSII and thus with plant photosynthetic activity, but is subject to stronger reabsorption within leaves and canopies. This hinders the understanding and use of canopy RSIF observations (RSIFₒbₛ), which is only a small fraction of the total RSIF emitted by the photosystems (RSIFₜₒₜₐₗ). Deriving RSIFₜₒₜₐₗ from RSIFₒbₛ is still challenging due to retrieval uncertainty, limited availability of RSIFₒbₛ and spectral overlap with chlorophyll absorption. To address the challenges associated with deriving RSIFₜₒₜₐₗ, we propose an exploratory method framework that combines canopy far-red SIF observations (FRSIFₒbₛ) and leaf chlorophyll content (LCC) to derive RSIFₜₒₜₐₗ. We first downscale FRSIFₒbₛ from canopy to leaf, and then leverage LCC information to estimate RSIF at the leaf level. Finally, we incorporate LCC information in the subsequent downscaling of RSIF from leaf to photosystem. To evaluate our approach, we use ground-based observation data in three crop types (rice, wheat, and maize) and SCOPE model simulations. Our results demonstrate that the seasonal patterns of RSIFₜₒₜₐₗ show a close agreement with the seasonal patterns of gross primary production (GPP) and absorbed photosynthetic active radiation (APAR). More importantly, RSIFₜₒₜₐₗ slightly outperforms FRSIFₒbₛ in estimating GPP for the three crop types. Our study has also revealed a strong linear relationship between the escape probability of RSIFₜₒₜₐₗ (fₑₛc_R) and the RSIFₒbₛ/FRSIFₒbₛ ratio affected by LCC. The simplicity and robustness of our approach, along with its potential application in satellite remote sensing, will contribute to the improvement of large-scale GPP estimation and photosynthetic phenology detection. Moreover, our investigation of fₑₛc_R will contribute to a better understanding the physiological and non-physiological dynamics of RSIFₒbₛ.

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