Vegetation communities play a key role in governing the atmospheric-terrestrial fluxes of water, carbon, nutrients, and energy. The expanse and heterogeneity of vegetation in sub-arctic peatland systems makes monitoring change at meaningful spatial resolutions and extents challenging. We use a field-collected spectral endmember reference library to unmix hyperspectral imagery and map vegetation coverage at the level of plant functional type (PFT), across three wetland sites in sub-arctic Alaska. This study explores the optimization and parametrization of multiple endmember spectral mixture analysis (MESMA) models to estimate coverage of PFTs across wetland classes. We use partial least squares regression (PLSR) to identify a parsimonious set of critical bands for unmixing and compare the reference and modeled coverage. Unmixing, using a full set of 110-bands and a smaller set of 4-bands, results in maps that effectively discriminate between PFTs, indicating a small investment in fieldwork results in maps mirroring the true ground cover. Both sets of spectral bands differentiate between PFTs, but the 4-band unmixing library results in more accurate predictive mapping with lower computational cost. Reducing the unmixing reference dataset by constraining the PFT endmembers to those identified in the field-site produces only a small advantage for mapping, suggesting extensive fieldwork may not be necessary for MESMA to have a high explanatory value in these remote environments.

The vegetation of the vast circumboreal subarctic wetlands plays an important role in moderating or exacerbating ongoing climate impacts, making the monitoring of change in vegetation foundational to understanding and predicting the carbon balance at high latitudes. We use nested scales of intersecting spectral data to estimate and map fractional vegetation composition of three sub-arctic peat accumulating wetlands using multiple endmember spectral mixture analysis (MESMA). We develop a bottom-up reference library for unmixing based on nested scales of data beginning with the highest resolution of a ground collected handheld spectral measurements, progressing to 1-meter\textsuperscript{2} resolution using fused hyperspectral-LiDAR data, and then subsequently map predictively at the spatial resolution of the 10-meter\textsuperscript{2} multi-spectral imagery of the European Space Agency’s Sentinel-2A. We assess the accuracy of the MESMA unmixing with a confusion matrix between field sampling plots and satellite (Sentinel-2A) MESMA pixel-plots, and visual assessment. We perform MESMA on imagery four years apart, to estimate the vegetation compositional turnover, at three separate sites representing three different types of wetlands. The spectral libraries we develop return kappa statistics between 0.79 and 0.95, and unmix between 92.4 and 99.1 percent of the wetland imagery. The confusion matrix used to evaluate the model's classification of vegetation, results in misclassification rates ranging from 0.07 to 0.45. Our results demonstrate that MESMA can provide important information about vegetation dynamics at a high resolution in these highly heterogeneous wetland systems. These findings and examples highlight the future potential for extracting meaningful ecological information about expansive, heterogeneous subarctic wetlands.