Leaf reflectance can surrogate foliar economics better than physiological traits across macrophyte species

Abstract

Macrophytes are key players in aquatic ecosystems diversity, but knowledge on variability of their functional traits, among and within species, is still limited. Remote sensing is a high-throughput, feasible option for characterizing plant traits at different scales, provided that reliable spectroscopy models are calibrated with congruous empirical data.We sampled leaves from six macrophyte species common in temperate areas, covering different phenological stages, seasons, and environmental conditions, and measured leaf reflectance (400-2500 nm) and traits (dealing with photophysiology, pigments and leaf economics). We explored optimal spectral bands combinations and established non-parametric reflectance-based models for selected traits, eventually showing how airborne hyperspectral data can capture spatial-temporal macrophyte variability.Our key finding is that structural, leaf dry matter content, leaf mass per area (LMA), and biochemical, chlorophyll-a content and chlorophyll to carotenoids ratio, traits can be surrogated by leaf reflectance with relative error under 20% across macrophyte species, while performance of reflectance-based models for photophysiological traits depends on species.This finding shows the link between leaf reflectance and structural-biochemical traits for aquatic plants, thus supporting the use of remote sensing for enhancing the level of detail of macrophyte functional diversity analysis, to intra-site and intra-species scales.

Unveiling the transferability of PLSR models for leaf trait estimation: lessons from a comprehensive analysis with a novel global dataset

“…We synthesized 37 datasets from published literatures and the EcoSIS Spectral Library (https://ecosis.org/) (Hosgood et al ., 1994; Jacquemound et al ., 2003; Singh, 2013; Serbin et al ., 2014, 2017, 2018, 2019a,b,c,d, 2021; Serbin & Townsend, 2014; Couture, 2015, 2016; Couture et al ., 2015; Meerdink, 2016; Serbin, 2016; Serbin & Rogers, 2016; Yang et al ., 2016; Kattenborn et al ., 2017; Wu et al ., 2017, 2019a,b,c; Ely et al ., 2018, 2019; Kamoske et al ., 2018; Chlus, 2019; Ge et al ., 2019; Nakaji et al ., 2019; Wang, 2019a,b, 2022; Grzybowski et al ., 2020; Helsen et al ., 2020a,b; Burnett et al ., 2021a,b,c,d; Villa et al ., 2021a,b; Chen et al ., 2022; Chlus & Townsend, 2022a,b; Kothari et al ., 2022a,b,c), compiling a new dataset that covers all major biomes and climate zones over the world (Fig. 1a, Supporting Information Fig.…”