Downscaling the full-spectrum solar-induced fluorescence emission signal of a mixed crop canopy to the photosystem level using the hybrid approach

Abstract

Remote sensing of hyperspectral vegetation reflectance and solar-induced chlorophyll fluorescence (SIF) is essential for evaluating crop functionality and photosynthetic performance. While primarily applied in monocultures, these tools show promise in diverse cropping systems, enhancing ecological intensification. Plant-plant interactions in such systems can influence key physiological processes, such as photosynthesis, making SIF a valuable tool for evaluating how crop diversity affects photosynthetic function and productivity. However, detecting SIF in diverse stands remains challenging due to uncertainties in light re-absorption and scattering. To address these challenges, we propose a hybrid model inversion framework that combines canopy observations with physical modeling to derive leaf biochemical, canopy structural variables, and SIF spectra at leaf and photosystem levels. This approach employs a machine learning retrieval algorithm (MLRA), trained on synthetic spectra from radiative transfer model (RTM) simulations, to quantify re-absorption and scattering effects. Using the SpecFit retrieval algorithm, the temporal evolution of full-spectrum SIF at the canopy level can be derived. To downscale SIF to the photosystem level and retrieve its quantum yield, we corrected the canopy SIF spectrum for re-absorption and scattering effects calculated from TOC reflectance. Spectral measurements were gathered from field experiments conducted over three years, covering various growth stages of cereal and legume monocrops and their mixture. Our method accurately predicts important leaf biochemical and canopy structural variables, such as leaf area (LAI, R² = 0.75) and leaf chlorophyll content (LCC, R² = 0.91), and shows a general high retrieval performance for light absorption (fAPARCₕₗ, R² = 0.99 for the internal model validation). We confirmed the reliability of our method in modeling re-absorption and scattering processes by comparing canopy SIF downscaled to the leaf level with independent leaf-level SIF measurements. While the results show a good prediction accuracy in terms of fluorescence magnitude at the leaf level, we did not find a strong agreement of corresponding leaf and canopy measurements at the single plot level.

Plant phenotyping relevance

作物キャノピーのSIFを葉・光合成系レベルへ推定するハイブリッド逆解析法を開発し、独立測定との比較で検証しているため、植物生理表現型の取得手法が中心である。

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