Uncovering the hidden: Leveraging sub-pixel spectral diversity to estimate plant diversity from space

Remotely sensed spectral diversity has emerged as a promising proxy for plant diversity. However, spectral diversity approaches relate image spectra to plant community diversity by only incorporating variation among adjacent pixels, considering each pixel as a homogeneous entity composed of one class, and disregarding the within-pixel variability. Although such approaches might work for remotely-sensed data with fine spatial resolution, they might not be viable solutions to estimate plant diversity using coarse-resolution data from forthcoming spaceborne imagers. To address the limitations associated with spectral diversity approaches, we proposed a novel approach, known as endmember diversity, for remote estimation of plant diversity through quantifying spectral diversity at the sub-pixel level and taking into account the within-pixel variability. The approach consisted of deriving the number and abundance of distinct spectral entities within each pixel via spectral unmixing. In doing so, we considered the spectral signature of each pixel as a mixture of distinct spectral entities, commonly known as endmembers. We then used the per-pixel endmembers and their abundance to calculate different spectral diversity metrics for every pixel. We assessed the performance of the endmember diversity approach at estimating plant taxonomic and phylogenetic diversity based on two experiments using a simulated spectral dataset and a real-world spaceborne DESIS (DLR Earth Sensing Imaging Spectrometer) dataset. In both experiments, we found significant associations between endmember diversity and in situ plant diversity. Additionally, our method applied to DESIS data outperformed a conventional spectral diversity metric based on the coefficient of variation when applied to 1-m airborne imaging spectroscopy data. Collectively, our results demonstrate the capability of forthcoming spaceborne imagers to monitor local plant diversity.