Radiative transfer Vcmax estimation from hyperspectral imagery and SIF retrievals to assess photosynthetic performance in rainfed and irrigated plant phenotyping trials

Abstract Plant photosynthetic traits may be indicative of stress tolerance and performance in the field, making their accurate assessment critical in phenotyping trials. The maximum rate of carboxylation (Vcmax) is a key parameter for estimating CO 2 assimilation (A), as it controls the CO 2 fixation rate. This study demonstrates the utility of combining airborne-based solar-induced chlorophyll fluorescence (SIF) and hyperspectral imagery through the inversion of the Soil-Canopy Observation of Photosynthesis and Energy (SCOPE) model to estimate Vcmax, using sensor resolutions available in precision agriculture technologies. Vcmax was quantified in three wheat phenotyping experimental fields during the 2015–2018 growing seasons, comprising both rainfed and irrigated conditions. Airborne campaigns were carried out with two hyperspectral sensors, covering the 400–850 nm (20 cm resolution) and 950–1750 nm (70 cm resolution) spectral regions, and with a thermal camera (25 cm resolution) in the 8–14 μm region. Validation between model-estimated and field-measured Vcmax was statistically significant (r 2  = 0.77; p-value ≤2.2e−16), and Vcmax was reliably associated with net assimilation both in irrigated and rainfed conditions (r 2  = 0.65 and 0.5, respectively). By contrast, simulated chlorophyll content (Cab) and airborne-derived structural and chlorophyll indicators (NDVI and PSSRb) lacked significant correlations with assimilation rate in irrigated plots, while the relationship between assimilation rate and the crop water stress index (CWSI) was not significant in rainfed plots. The superior sensitivity of remotely-sensed Vcmax under irrigated conditions was likely related to its robustness to distortions from high canopy densities observed in other indices. The remote sensing retrieval of Vcmax, and the methodology demonstrated in this study is directly relevant for high-throughput plant phenotyping and for precision agriculture applications.