Exploring photosynthetic dynamics in diverse crop canopies by using hyperspectral and solar-induced fluorescence (SIF) data

To overcome threats to agro-ecosystems, such as a dramatic species decline, an ecological intensification in crop production is needed. One possible strategy is the simultaneous cultivation of legume and cereal plants in a mixed arrangement, namely mixed cropping. Cereal-legume crop mixtures may benefit from diversity effects, i.e. improved use of environmental resources such as light, water and nitrogen. Thus, mixtures have shown to result in higher land productivity with respect to grain yield compared to sole cropping. However, mixture systems are complex and difficult to study due to dynamic species interactions and their heterogeneous canopy structures. 
To better understand structural and functional diversity effects in a mixed cropping system, we non-invasively studied two crops in a field trial in 2021 and 2022. Here, different genotypes of faba bean (Vicia faba L.) and spring wheat (Triticum aestivum L.) were combined in six legume-cereal mixtures. The 1:1 mixtures were compared to each other and against the respective sole crops. To study structural and functional diversity effects in mixtures, we applied proximal and remote sensing tools. We characterized photosynthesis-related plant traits derived from hyperspectral and solar-induced fluorescence (SIF) data recorded with ground-based and airborne sensors. The high-performance airborne spectrometer HyPlant was used to acquire SIF image data with 1 m spatial resolution. Additionally, we collected hyperspectral and SIF point measurements with the mobile field sensor system FloX on different dates during the two growing seasons. We found that HyPlant and FloX datasets of different mixtures and crop types collected in mid-June showed significantly different levels of far-red SIF emission efficiency (ε_F) (p<0.05), while the same was not observed for the absolute far-red SIF measurements. Wheat provided higher ε_F values in comparison to beans. Furthermore, differences between mixture combinations could be observed. This was more prominent in data collected in 2021 compared to 2022. In order to identify seasonal dynamics of mixture performance we extracted photosynthesis-related variables by combining radiative transfer modelling (RTM) with machine-learning regression algorithms (MLRAs) in a hybrid manner. First, we simulated reflectance and SIF data using the ‘Soil Canopy Observation, Photochemistry and Energy fluxes’ (SCOPE) RTM. Next, we calibrated different regression methods (e.g. Gaussian Process Regression, Kernel ridge Regression) with simulated data in order to retrieve relevant variables to characterize the photosynthetic performance, such as absorbed photosynthetically active radiation (APAR) and ε_F. Results for mixed cropping plots were corrected for the species composition calculated using spectral mixture analysis based on multispectral UAV data with high spatial resolution.
In our study we explore how crop performance driven by diversity effects can be explained by hyperspectral and SIF information. We believe that such data will facilitate new insights into the complex relationship underlying the mixture of two species in a diversified legume-cereal system.