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.

草原具有极高的分类学与功能多样性，尤其是在精细空间尺度下，其斑块化分布与物种更替特性为遥感监测带来了挑战。多数遥感平台的空间分辨率往往高于均质草原斑块的尺寸，由此产生混合像元，进而阻碍植被制图工作。为解决这一问题，本研究将多端元光谱混合分析（Multiple Endmember Spectral Mixture Analysis, MESMA）应用于取自国家生态观测网络（National Ecological Observatory Network, NEON）机载观测平台（Airborne Observatory Platform, AOP）的1平方米高分辨率高光谱影像，以评估美国大平原4处草原中4个主要禾本科植物演化支（须芒草族Andropogoneae、黍亚科Panicoideae、虎尾草亚科Chloridoideae以及早熟禾亚科Pooideae）的盖度与优势度预测精度。本研究通过多种端元选择策略对MESMA的性能进行评估，涵盖叶片水平与样地水平光谱端元，以及局地专属端元与多站点共享端元两类方案。采用最优端元组合时，总体分类精度可达约90%（马修斯相关系数约为0.84）。尽管没有单一策略能在所有场景下均占据优势，但总体而言，取自目标样地的叶片水平端元，以及整合全站点数据的样地水平端元，均可获得更高的总体分类精度。上述结果表明，相较于叶片水平端元，样地水平端元在不同站点间的可迁移性更强。本研究结果进一步证实，纳入演化亲缘关系相关信息可有效优化光谱解混结果。本研究推动了草原群落组成的亚像元制图技术发展，可为生态建模、土地变化预测，以及评估草原对环境变化的响应与群落组成特征提供重要参考。