Discrete anisotropic radiative transfer modelling of solar-induced chlorophyll fluorescence: Structural impacts in geometrically explicit vegetation canopies

Solar-induced fluorescence (SIF) is a subtle but informative optical signal of vegetation photosynthesis. Remotely sensed SIF integrates environmental, physiological and structural changes that alter photosynthesis at leaf, plant canopy scales. Radiative transfer models are ideally suited to investigate the complex sources variability in guide interpretation retrievals from airborne space-borne platforms. Here, we coupled Fluspect-Cx model leaf properties chlorophyll-a with Discrete Anisotropic Transfer (DART) upscale individual leaves three-dimensional (3D) structurally explicit canopies. For one-dimensional homogeneous (turbid-like) canopies, DART-SIF was nearly identical simulated two existing models, SCOPE mSCOPE (RMSE <0.221 W.m−2.μm−1.sr−1). DART simulations geometrically 3D canopies offered four important insights regarding influence structure on multi-angular top-of-canopy signal. First, architecture maize crops, represented by density (leaf area index), clumping (canopy closure) had larger impact than modelled photosynthetic efficiency distinction between sun-adapted shade-adapted foliage. Second, crop stand levels identified as one key driving factors anisotropy red far-red (686 740 nm) for both eucalyptus Third, non-photosynthetic woody material significant forest stands. Wood shadowing decreased photosynthetically active radiation absorbed green leaves, consequently emissions, 10% sparse 17% dense The wood obstruction (blocking) effect, quantified relative difference escape probabilities without nadir viewing direction, 4–6% it smaller sometimes positive (by less 2%) SIF. Fourth, radiative budget profiles revealed majority originated top 25% canopy. Interestingly, introduction bark-covered elements did not balance omnidirectional factor this upper part raise significantly them case These results demonstrate importance investigating interactions better understanding spatiotemporal remote sensing observations.