Sub-pixel detection of dominant grass evolutionary lineages at four sites across the Great Plains, U.S. using hyperspectral data

Grasslands exhibit high taxonomic and functional diversity, particularly at fine spatial scales, posing challenges for remote sensing due to patchiness and species turnover. The spatial resolution of most remote sensing platforms often exceeds the size of homogeneous grassland patches, resulting in mixed pixels that hinder vegetation mapping. To address this, we applied Multiple Endmember Spectral Mixture Analysis (MESMA) to high-resolution (1 m2) hyperspectral imagery from the NEON Airborne Observatory Platform (AOP) to assess the predictive accuracies of fractional cover and dominance of four major grass evolutionary lineages, Andropogoneae, Panicoideae, Chloridoideae, and Pooideae, across four U.S. Great Plains grasslands. MESMA performance was evaluated using different endmember selection strategies, including leaf- vs. plot-level spectral endmembers and site-specific vs. multiple-site endmembers. Overall classification accuracy reached ~90% (Matthews Correlation Coefficient ~0.84) using optimal endmember combinations. While no single approach was universally superior, in general, leaf-level endmembers from focal sites and plot-level endmembers aggregated across all sites yielded higher overall accuracies. These results demonstrate that plot-level endmembers are more transferable across sites compared to leaf-level endmembers. Our results furthermore demonstrate that incorporating information about evolutionary relatedness can improve spectral unmixing results. This study advances sub-pixel mapping of grassland composition, offering insights for ecological modelling, land change prediction, and assessing grassland responses to environmental change and community composition.