Leaf aging of Amazonian canopy trees as revealed by spectral and physiochemical measurements

Abstract

Summary Leaf aging is a fundamental driver of changes in leaf traits, thereby regulating ecosystem processes and remotely sensed canopy dynamics. We explore leaf reflectance as a tool to monitor leaf age and develop a spectra‐based partial least squares regression ( PLSR ) model to predict age using data from a phenological study of 1099 leaves from 12 lowland Amazonian canopy trees in southern Peru. Results demonstrated monotonic decreases in leaf water (LWC) and phosphorus (P mass ) contents and an increase in leaf mass per unit area (LMA) with age across trees; leaf nitrogen (N mass ) and carbon (C mass ) contents showed monotonic but tree‐specific age responses. We observed large age‐related variation in leaf spectra across trees. A spectra‐based model was more accurate in predicting leaf age ( R 2 = 0.86; percent root mean square error (% RMSE ) = 33) compared with trait‐based models using single ( R 2 = 0.07–0.73; % RMSE = 7–38) and multiple ( R 2 = 0.76; % RMSE = 28) predictors. Spectra‐ and trait‐based models established a physiochemical basis for the spectral age model. Vegetation indices ( VI s) including the normalized difference vegetation index (NDVI), enhanced vegetation index 2 (EVI2), normalized difference water index (NDWI) and photosynthetic reflectance index (PRI) were all age‐dependent. This study highlights the importance of leaf age as a mediator of leaf traits, provides evidence of age‐related leaf reflectance changes that have important impacts on VI s used to monitor canopy dynamics and productivity and proposes a new approach to predicting and monitoring leaf age with important implications for remote sensing.