Data-based investigation of the effects of canopy structure and shadows on chlorophyll fluorescence in a deciduous oak forest

Abstract. Data from satellite, aircraft, drone, and ground-based measurements have already shown that canopy scale sun-induced chlorophyll fluorescence (SIF) is tightly related to photosynthesis, which is linked to vegetation carbon assimilation. However, our ability to effectively use those findings are hindered by confounding factors, including canopy structure, fluctuations in solar radiation and in sun-canopy-geometry that highly affect the SIF signal. Thus, disentangling these factors has become paramount in order to use SIF for monitoring vegetation functioning at canopy scale and beyond. Active chlorophyll fluorescence measurements (F_yieldLIF), which directly measures the apparent fluorescence yield, have been widely used to detect physiological variation of the vegetation at leaf scale. Recently, the measurement of F_yieldLIF has become feasible at the canopy scale, opening up new opportunities to decouple structural, biophysical, and physiological components of SIF at the canopy scale.  In this study, based on top-of-canopy measurements above a mature deciduous forest, reflectance (R), SIF, SIF normalized by incoming photosynthetically active radiation (SIF_y), F_yieldLIF, and the ratio between SIF_y and F_yieldLIF (named Φ_k) were used to investigate the effects of canopy structure and shadows on the diurnal and seasonal dynamics of SIF. Further, random forest (RF) models were also used to not only predict F_yieldLIF and Φ_k, but also provide an interpretation framework by considering additional variables, including the R in the blue, red, green, red-edge, and near-infrared bands, SIF, SIF_y, and sun zenith (SZA) and azimuth (SAA) angles. Results revealed that the SIF signal is highly affected by the canopy structure and sun-canopy geometry effects compared to F_yieldLIF. This was evidenced by the weak correlations obtained between SIF_y and F_yieldLIF at the diurnal timescale. Furthermore, the daily mean SIF_y captured the seasonal dynamics of daily mean F_yieldLIF and explained 58 % of its variability. The findings also revealed that reflectance in the near-infrared (R-NIR) and the NIR_v (the product of NIR by the normalized difference vegetation index) are good proxies of Φ_k at the diurnal timescale, while their correlations with Φ_k decrease at the seasonal timescale. With F_yieldLIF and Φ_k as outputs and the abovementioned variables as predictors, this study also showed that the RF models can explain between 86 % and 90 % of F_yieldLIF, and 60 % and 70 % of Φ_k variations under clear sky conditions. In addition, the predictor importance estimates for F_yieldLIF RF models revealed that R at 410, 665, 740, and 830 nm, SIF, SIF_y, SZA, and SAA emerged as the most useful and influential factors for predicting F_yieldLIF, while R at 410, 665, 705, and 740 nm, SZA, and SAA are crucial for predicting Φ_k. This study highlighted the complexity of interpreting diurnal and seasonal dynamics of SIF in forest canopies. These dynamics are highly dependent on the complex interactions between the structure of the canopy, the vegetation biochemical properties, the illumination angles (SZA and SAA) and the light conditions (ratio of diffuse to direct solar radiation). However, such measurements are necessary to better separate the variability in SIF attributable to radiation and measurement conditions from the subtler variability attributable to plant physiological processes.