Seasonal variability of multiple leaf traits captured by leaf spectroscopy at two temperate deciduous forests

Abstract

31Understanding the temporal patterns of leaf traits is critical in determining the seasonality 32 and magnitude of terrestrial carbon and water fluxes. However, robust and efficient ways 33 to monitor the temporal dynamics of leaf traits are lacking. Here we assessed the 34 potential of using leaf spectroscopy to predict leaf traits across their entire life cycle, 35 forest sites, and light environments (sunlit vs. shaded) using a weekly sampled dataset 36 across the entire growing season at two temperate deciduous forests. The dataset includes 37 field measured leaf-level directional-hemispherical reflectance/transmittance together 38 with seven important leaf traits [total chlorophyll (chlorophyll a and b), carotenoids, 39 mass-based nitrogen concentration (N mass ), mass-based carbon concentration (C mass ), and 40 leaf mass per area (LMA)]. All leaf properties, including leaf traits and spectra, varied 41 significantly throughout the growing season, and displayed trait-specific temporal 42 patterns. We used a Partial Least Square Regression (PLSR) analysis to estimate leaf 43 traits from spectra, and found a significant capability of PLSR to capture the variability 44 across time, sites, and light environment of all leaf traits investigated (R 2 =0.6~0.8 for 45 temporal variability; R 2 =0.3~0.7 for cross-site variability; R 2 =0.4~0.8 for variability from 46 light environments). We also tested alternative field sampling designs and found that for 47 most leaf traits, biweekly leaf sampling throughout the growing season enabled accurate 48 characterization of the leaf trait seasonal patterns. Increasing the sampling frequency 49 improved in the estimation of N mass , C mass and LMA comparing with foliar pigments. Our 50 results, based on the comprehensive analysis of spectra-trait relationships across time, 51 sites and light environments, highlight the capacity and potential limitations to use leaf 52 3 spectra to estimate leaf traits with strong seasonal variability, as an alternative to time-53 consuming traditional wet lab approaches. 54 4

Chlorophyll fluorescence tracks seasonal variations of photosynthesis from leaf to canopy in a temperate forest

“…). According to the previous study on the seasonality of the vegetation indices and plant physiological traits based on the NDVI (Normalized Difference Vegetation Index) measured by an NDVI camera and leaf chlorophyll concentration at this site, we identified spring (DOY 132‐167), summer (DOY 168‐260), and autumn (DOY 261‐311) (Yang et al ., ).…”

Biological processes dominate seasonality of remotely sensed canopy greenness in an Amazon evergreen forest

“…This extension is enabled by recent studies that demonstrate strong relationships of leaf traits and spectra with leaf age across diverse growth environments and species in tropical forests Wu et al, 2017) and other biomes (e.g. Yang et al, 2014Yang et al, , 2016Meerdink et al, 2016). Our work builds on these leaf-scale studies to provide support for the importance of remotely…”

“…This extension is enabled by recent studies that demonstrate strong relationships of leaf traits and spectra with leaf age across diverse growth environments and species in tropical forests (Chavana‐Bryant et al ., ; Wu et al ., ) and other biomes (e.g. Yang et al ., , ; Meerdink et al ., ). Our work builds on these leaf‐scale studies to provide support for the importance of remotely detectable temporal convergent relationships in traits and spectra at the canopy scale as well, suggesting the potential for deriving temporal variability in plant traits using canopy reflectance spectroscopy techniques.…”

“…Beyond the tropical forests studied here, this consideration is also probably important for any biome that has seasonally changing traits and spectra (e.g. Meerdink et al, 2016;Yang et al, 2016).…”

Using canopy greenness index to identify leaf ecophysiological traits during the foliar senescence in an oak forest

“…, Yang et al. ), even though mismatches between G cc and chlorophyll concentration may exist (Yang et al. ).…”