Estimating leaf photosynthesis of C₃ plants grown under different environments from pigment index, photochemical reflectance index, and chlorophyll fluorescence

Photosynthetic rates vary depending on growth conditions, even within species. Remote sensing techniques have a great potential to predict the photosynthetic rates of leaves with different characteristics. Here, we demonstrate that the photosynthetic rates of leaves acclimated to different light and nutrient conditions can be estimated based on the chlorophyll fluorescence (ChlF), the photochemical reflectance index (PRI), and a chlorophyll index. Chenopodium album plants were grown under different light and nutrient conditions. PRI, ChlF parameters, and CO₂/H₂O gas exchange rates of leaves were simultaneously determined under the various light and CO₂ conditions. PRI was used to assess non-photochemical quenching (NPQ), but the relationship between NPQ and PRI was weakened when the data on leaves grown under different conditions were pooled, because PRI in darkness (PRI) changed with the leaf pigment composition. Among 15 pigment indices, we found that NDVI _green, a reflectance index related to the leaf chlorophyll content, had the best correlation with PRI (r²= 0.89) across the studied leaves, and the correction of PRI by NDVI _green improved the predictability of NPQ (r²= 0.82). Using the steady-state ChlF, the NPQ estimated from PRI and NDVI _green, and the stomatal conductance coefficient, we calculated the CO₂ assimilation rates, which were strongly correlated with the actual rates (RMSE = 4.85 μmol m^{- 2} s^{- 1}), irrespective of growth conditions. Our approach has the potential to contribute to a more accurate estimation of photosynthetic rates in remote sensing. However, further studies on species variations and connecting with radiative transfer models are needed to demonstrate this at the canopy scale.