This paper introduces a modular processing chain to derive global high-resolution maps of leaf traits. In particular, we present global maps at 500 m resolution of specific leaf area, leaf dry... Show moreThis paper introduces a modular processing chain to derive global high-resolution maps of leaf traits. In particular, we present global maps at 500 m resolution of specific leaf area, leaf dry matter content, leaf nitrogen and phosphorus content per dry mass, and leaf nitrogen/phosphorus ratio. The processing chain exploits machine learning techniques along with optical remote sensing data (MODIS/Landsat) and climate data for gap filling and up-scaling of in-situ measured leaf traits. The chain first uses random forests regression with surrogates to fill gaps in the database (> 45% of missing entries) and maximizes the global representativeness of the trait dataset. Plant species are then aggregated to Plant Functional Types (PFTs). Next, the spatial abundance of PFTs at MODIS resolution (500 m) is calculated using Landsat data (30 m). Based on these PFT abundances, representative trait values are calculated for MODIS pixels with nearby trait data. Finally, different regression algorithms are applied to globally predict trait estimates from these MODIS pixels using remote sensing and climate data. The methods were compared in terms of precision, robustness and efficiency. The best model (random forests regression) shows good precision (normalized RMSE≤ 20%) and goodness of fit (averaged Pearson's correlation R = 0.78) in any considered trait. Along with the estimated global maps of leaf traits, we provide associated uncertainty estimates derived from the regression models. The process chain is modular, and can easily accommodate new traits, data streams (traits databases and remote sensing data), and methods. The machine learning techniques applied allow attribution of information gain to data input and thus provide the opportunity to understand trait-environment relationships at the plant and ecosystem scales. The new data products – the gap-filled trait matrix, a global map of PFT abundance per MODIS gridcells and the high-resolution global leaf trait maps – are complementary to existing large-scale observations of the land surface and we therefore anticipate substantial contributions to advances in quantifying, understanding and prediction of the Earth system. Show less
Musavi, T.; Mahecha, M.D.; Migliavacca, M.; Reichstein, M.; Weg, M.J. van de; Bodegom, P.M. van; ... ; Kattge, J. 2015
Terrestrial ecosystems strongly determine the exchange of carbon, water and energy between thebiosphere and atmosphere. These exchanges are influenced by environmental conditions (e.g.,... Show moreTerrestrial ecosystems strongly determine the exchange of carbon, water and energy between thebiosphere and atmosphere. These exchanges are influenced by environmental conditions (e.g., localmeteorology, soils), but generally mediated by organisms. Often, mathematical descriptions of theseprocesses are implemented in terrestrial biosphere models. Model implementations of this kind shouldbe evaluated by empirical analyses of relationships between observed patterns of ecosystem function-ing, vegetation structure, plant traits, and environmental conditions. However, the question of how todescribe the imprint of plants on ecosystem functioning based on observations has not yet been systemat-ically investigated. One approach might be to identify and quantify functional attributes or responsivenessof ecosystems (often very short-term in nature) that contribute to the long-term (i.e., annual but alsoseasonal or daily) metrics commonly in use. Here we define these patterns as “ecosystem functional prop-erties”, or EFPs. Such as the ecosystem capacity of carbon assimilation or the maximum light use efficiencyof an ecosystem. While EFPs should be directly derivable from flux measurements at the ecosystem level,we posit that these inherently include the influence of specific plant traits and their local heterogeneity.We present different options of upscaling in situ measured plant traits to the ecosystem level (ecosystemvegetation properties – EVPs) and provide examples of empirical analyses on plants’ imprint on ecosys-tem functioning by combining in situ measured plant traits and ecosystem flux measurements. Finally,we discuss how recent advances in remote sensing contribute to this framework. Show less
