Photochemical Reflectance Index (PRI) is Sensitive to the Ecohydrology of a Semi-arid Mixed Conifer Forest

A lack of accurate, reliable data on coupled carbon and water fluxes for Earth's expansive ecosystems remains a major barrier to a complete understanding of the terrestrial carbon cycle. The remotely sensed Photochemical Reflectance Index (PRI) is one of the few pigment-based vegetation indices sensitive to rapid plant physiological responses. PRI presents new opportunities to study ecosystems on a diurnal time scale, as well as seasonal processes in evergreen systems where complex vegetation dynamics are not reflected by the small annual changes in chlorophyll content or leaf structure. The Southwest U.S. provides a natural setting for examining the influence of environmental drivers on the productivity of drought-sensitive forests, as well as for evaluating our ability to track these relationships using optical methods. To determine the sensitivity of PRI to seasonal changes in gross primary productivity and ecohydrological variability, this research combined canopy spectral measurements with eddy covariance flux and sap flow methods at a mixed evergreen conifer forest in Arizona, USA. In addition, top-of-canopy leaf-level gas exchange, chlorophyll fluorescence, and hyperspectral measurements were used to determine the sensitivity of PRI to diurnal changes in needle photosynthetic function. Multiple linear regression analysis showed that PRI responded to dynamic water and energy limitations of this system. Further, we report for the first time a significant relationship between seasonal PRI and sap flow in a natural forest. These results demonstrate that PRI is an effective indicator of photosynthetic response to seasonal ecohydrological constraints. Throughout the day, PRI remained relatively constant at both leaf and canopy scales, and we suggest that saturated light conditions drive retention of xanthophylls in a de-epoxidized state. This research contributes evidence that remotely sensed PRI has potential to fill spatial and temporal gaps in our ability to distinguish how water availability influences forest productivity and associated carbon dynamics.