Foliage Biophysical Trait Prediction from Laboratory Spectra in Norway Spruce Is More Affected by Needle Age Than by Site Soil Conditions

Abstract

Scaling leaf-level optical signals to the canopy level is essential for airborne and satellite-based forest monitoring. In evergreen trees, biophysical and optical traits may change as foliage ages. This study aims to evaluate the effect of age in Norway spruce needle on biophysical trait-prediction based on laboratory leaf-level spectra. Mature Norway spruce trees were sampled at forest stands in ten headwater catchments with different soil properties. Foliage biophysical traits (pigments, phenolics, lignin, cellulose, leaf mass per area, water, and nitrogen content) were assessed for three needle-age classes. Complementary samples for needle reflectance and transmittance were measured using an integrating sphere. Partial least square regression (PLSR) models were constructed for predicting needle biophysical traits from reflectance—separating needle age classes and assessing all age classes together. The ten study sites differed in soil properties rather than in needle biophysical traits. Optical properties consistently varied among age classes; however, variation related to the soil conditions was less pronounced. The predictive power of PLSR models was needle-age dependent for all studied traits. The following traits were predicted with moderate accuracy: needle pigments, phenolics, leaf mass per area and water content. PLSR models always performed better if all needle age classes were included (rather than individual age classes separately). This also applied to needle-age independent traits (water and lignin). Thus, we recommend including not only current but also older needle traits as a ground truth for evergreen conifers with long needle lifespan.

Leaf Age Matters in Remote Sensing: Taking Ground Truth for Spectroscopic Studies in Hemiboreal Deciduous Trees with Continuous Leaf Formation

“…From the remote sensing perspective, the regression for all leaf developmental categories pooled together is more realistic, because it covers the full range of pigment contents and, thus, provides a more robust model. Similar findings were reported for PLSR modelling of chlorophyll and carotenoids contents in Norway spruce using several needle age classes [20].…”

“…[65], it is necessary to sample both leaf developmental categories, pre-formed and neoformed leaves, especially if the sampling is conducted only once within the vegetation season. The same approach, to construct the models represented by different leaf age groups, was recommended by [81] for ecosystems with unsynchronized phenology (e.g., evergreen tropical forests) and by [20,36] for evergreen conifers.…”

“…We focused on the effect of leaf developmental category and leaf phenology on VI linear model performance. The effect of leaf age on modelling of biophysical traits from spectra is currently a topical theme for evergreen conifers bearing several needle age classes at once [20,36,80]; and also tropical tree species with leaf developmental asynchrony [81,82]. Moreover, in some tropical trees with asynchronous leaf development and long leaf lifespan (e.g., 360 days), even the leaf age could be modelled from leaf spectra [83].…”

“…Therefore, it is not surprising that PCA applied on values of 64 VIS gives similar score distribution as the PCA applied on the whole spectral range (Figures 8 and 15a,b). With the current wide availability of hyperspectral data at leaf and canopy levels, the multivariate approaches to leaf trait modelling, such as PLSR, exploiting the whole spectral range, are preferentially used [20,46,81]. Although our source data were hyperspectral, we built the present study on vegetation indices, which are still widely used due to rise of various affordable multispectral sensors [80,[94][95][96] or satellite multispectral data [97].…”

“…Within-canopy variation of leaf structural and biophysical traits should be considered when designing spectroscopic studies at the leaf level [20] or taking ground truth for remote sensing data calibration. From this point of view, sun-and shade-acclimated leaves are usually taken into account and sampling designs are based on the vertical gradient within the canopy, reflecting differences in leaf anatomical, biophysical, and optical traits [21].…”