A Methodology to Derive Global Maps of Leaf Traits Using Remote Sensing and Climate Data

TL;DR

This paper presents a modular methodology combining remote sensing, climate data, and machine learning to generate high-resolution global maps of leaf traits with uncertainty estimates, enhancing understanding of trait-environment relationships.

Contribution

A novel modular processing chain integrating remote sensing, climate data, and machine learning for global leaf trait mapping with uncertainty quantification.

Findings

Generated global maps of key leaf traits at 500 m resolution.

Successfully filled data gaps using random forests regression.

Provided uncertainty estimates for all mapped traits.

Abstract

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 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 maximize the global representativeness of the trait dataset. 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.