Leveraging Remote Sensing and Crowd-Sourced Biodiversity Data for Enhanced Plant Functional Trait Mapping

Abstract High-resolution maps of plant functional traits are crucial for understanding terrestrial ecosystem processes; however, their integration into ecosystem models has been hindered by uncertainties and a lack of spatially detailed data. Here we combine optical remote sensing, global crowd-sourced biodiversity records and plant trait databases to map community trait distributions worldwide at 1-km resolution, estimating community-weighted means (CWMs) and higher-order moments (standard deviation, skewness, and kurtosis) for specific leaf area (SLA), leaf nitrogen (LNC) and leaf phosphorus (LPC) concentrations. Benchmarking against sPlotOpen plot-level CWMs shows low explained variance (R² = 0.10–0.27 across traits), indicating limited plot-scale predictive skill under current limited open global benchmarks and scale mismatches. Agreement increases when using a canopy-weighted comparator (TWM; R² = 0.22–0.38; relative RMSE ≈ 12–18%), consistent with the top-of-canopy sensitivity of optical sensors. By providing spatially explicit trait distributions and their higher-order moments, our findings deliver improved detail for understanding biodiversity patterns and ecosystem functioning and provide landscape-scale insights into trait-mediated coexistence.