Evaluating Multi Angle Photochemical Reflectance Index and Solar Induced Fluorescence for the Estimation of Gross Primary Production in Maize
The photochemical reflectance index (PRI) has been suggested as an indicator of light use efficiency (LUE) and for use in the improvement of estimating gross primary production (GPP) in LUE models. Over the last two decades, solar induced fluorescence (SIF) observations from remote sensing have been used to evaluate the distribution of GPP over a range of spatial and temporal scales. However, both PRI and SIF observations have been decoupled from photosynthesis under a variety of non physiological factors, i.e. sun view geometry and environmental variables. These observations are important for estimating GPP but rarely reported in the literature. In our study, multi angle PRI and SIF observations were obtained during the 2018 growing season in a maize field. We evaluated a PRI based LUE model for estimating GPP, and compared it with the direct estimation of GPP using concurrent SIF measurements. Our results showed that the observed PRI varied with view angles and that the averaged PRI from the multi angle observations exhibited better performance than the single angle observed PRI for estimating LUE. The PRI based LUE model when compared to SIF, demonstrated a higher ability to capture the diurnal dynamics of GPP (the coefficient of determination (R2) 0.71) than the seasonal changes (R2 0.44) while the seasonal GPP variations were better estimated by SIF (R2 0.50) Based on random forest analyses, relative humidity (RH) was the most important driver affecting diurnal GPP estimation using the PRI based LUE model. The SIF based linear model was most influenced by photosynthetically active radiation (PAR) The SIF based linear model did not perform as well as the PRI based LUE model under most environmental conditions, the exception being clear days (the ratio of direct and diffuse sky radiance 2) Our study confirms the utility of multi angle PRI observations in the estimation of GPP in LUE models and suggests that the effects of changing environmental conditions should be taken into account for accurately estimating GPP with PRI and SIF observations.
Remote. Sens.