Spectra-phenology integration for high-resolution, accurate, and scalable mapping of foliar functional traits using time-series Sentinel-2 data

Foliar functional traits are essential for understanding plant adaptation strategies and ecosystem function. Due to limited in-situ observational data, there is a growing interest in upscaling these traits from field sites to regional and global levels. However, limitations persist: (1) global/national scale upscaling that relies on plant functional type (PFT) maps, environmental variables or coarse resolution multispectral images, which fail to capture local-scale trait variability; (2) airborne imaging spectroscopy that enables high-resolution and accurate mapping but is restricted to site scale and is costly; and (3) multispectral satellites like Sentinel-2 that offer global coverage but have limited spectral bands and resolution. While previous research has demonstrated the connection between traits and vegetation phenology, our study seeks to build upon this foundation by further exploring the integration of phenological information for large-scale trait prediction. We examined the integration of Sentinel-2 data with its time series (for phenology information) to map 12 foliar functional traits across 14 National Ecological Observatory Network (NEON) sites in the eastern United States. Our results show that time-series Sentinel-2 models effectively capture the variance in these 12 traits (R2 = 0.60–0.80) when compared with benchmark trait data generated by state-of-the-art airborne imaging spectroscopy. The models adequately capture considerable trait variations observed within sites and PFTs. Our approach outperforms existing methods that rely on environmental variables, or a single Sentinel-2 image as predictors across examined NEON sites in eastern United States. Interestingly, including environmental variables in our models does not significantly improve predictive power. Further analysis reveals that a ‘fast-slow’ principal axis predominantly explains the covariation in Enhanced Vegetation Index amplitude (a proxy for leaf longevity), leaf mass per area, and leaf nitrogen content across PFTs. This finding highlights the importance of incorporating phenological information for trait mapping and suggests a potential mechanism underlying these spectra-based models. Our proposed method, which simultaneously achieves high accuracy, large-scale scalability, and high spatial resolution, represents a promising avenue for future global trait mapping. Validation on a larger scale to fully realize its potential in addressing fundamental ecological questions will be a key future focus.