Use of Sun-induced chlorophyll fluorescence in linear and non-linear light use efficiency models for remote estimation of plant photosynthesis

In this study, we address two relevant gaps when monitoring plant photosynthesis using remote sensing techniques; these are i) assess the seasonal trends and relationships observed between photosynthesis, optical vegetation indices, and chlorophyll fluorescence in crop systems and ii) evaluate the contribution of Sun-induced chlorophyll fluorescence (SIF) on linear and non-linear light-use efficiency-based (LUE) models for the remote estimation of plant photosynthesis. Coincident measurements of net plant photosynthesis (A_net), optical vegetation indices (i.e., Red edge index and photochemical reflectance index (PRI) among others), PSII operating efficiency (ΦPSII), and SIF were made at leaf level once a week in a wheat field under different nitrogen treatments. In LUE models, three key variables explain the seasonal variability of photosynthesis; these are the fraction of absorbed photosynthetically active radiation (fAPAR), LUE, and a correction factor related to meteorological conditions that limit LUE. In this study, the Red edge index was highly correlated with fAPAR (R²>0.70, p-value<0.05); however, neither PRI nor SIF were able to explain the seasonal changes of LUE (R²<0.10).  ΦPSII seasonal values (0.10 – 0.40) measured during the experiment indicated strong downregulation of the photosynthetic machinery. This explained why, in this study, SIF did not present a linear relationship with LUE. Our results confirmed that under stress conditions the non-photochemical quenching mechanisms (NPQ) control the energy dissipation pathway, breaking the linear relationship between photochemistry and fluorescence. Additionally, our study proved that changes in A_net could be better explained when optical vegetation indices, chlorophyll fluorescence, and meteorological conditions are combined in non-linear LUE-based models (R² increased from 0.10 for the linear model to 0.60 for the non-linear model). These results confirmed the need to build non-linear models for the remote quantification of photosynthesis.