## 4 Results  
Figure 4 shows the low order partition of variance for each of the 5000 \(\times\) 700 pixel AVIRIS subsets (light gray), as well as the full 5000 \(\times\) 4900 pixel image composite (black). While some variability is observed in the partition of variance for individual spatial subsets of both the AVIRIS and Landsat datasets, the first three or four dimensions clearly contain nearly all of the variance in all cases. Cumulative variance for the first three dimensions of the AVIRIS and Landsat composite images is 97% and 99%, respectively. As expected, inset correlation matrices illustrate three or four relatively distinct spectral regimes (Visible, NIR, SWIR1, and SWIR2). A more complex correlation structure is evident within the AVIRIS NIR spectral regime, illustrating the added value of the hyperspectral sensor. Figure 4: Partition of variance for AVIRIS **(left)** and Landsat **(right)**. Result are shown for each \(5000\times 700\) pixel spatial subset (gray), as well as for the combined dataset (black). While the dimensionality of each spatial subset is variable, the combined dataset shows the first three dimensions clearly separated from the continuum that follows. Cumulative variance is labeled for the first three dimensions, showing that 97% or more of the total variance in contained in the first three dimensions of each dataset. Correlation matrices (inset) clearly show three or four distinct spectral regimes which capture the majority of the variance in the dataset. Figure 5 shows projections of the first three dimensions of the spectral feature spaces for the AVIRIS and Landsat datasets of this study (top) in comparison to the much more extensive spatial sampling of previous studies by [12; 13; 14]. The first three dimensions of the AVIRIS and Landsat feature spaces show striking similarity to each other, despite the roughly \(30\times\) greater spectral sampling of the AVIRIS. Both covariance- and correlation-based transforms were performed. Because the resulting feature spaces were nearly identical, only the feature space from the covariance-based transform is shown here. In addition, both of the feature spaces of this study are remarkably similar to the global Landsat feature spaces. This observation is especially noteworthy given the differences between the studies  
Figure 5: Low dimensional feature space comparison. Northern CA AVIRIS and Landsat used in this study both show similar low-order feature space topology to the much wider range of land covers sampled in the global Landsat 7 analysis of Small & Milesi (2013), as well as the Landsat 7/8 underflight comparison analysis of Sousa & Small (2017). Substrate (S), Vegetation (V), and Dark (D) EMs are also broadly consistent across all four studies, in spite of the substantial disparities in both spectral resolution and spatial extent. Differences in topology are predominantly due to variations in sampling of relatively rare land covers such as evaporites (E) and synthetic materials (Sy). Substrate EMs show the greatest variability, as expected given the diversity in the shape of the reflectance continuum of rock and soil. in data preprocessing. Substrate, Vegetation, and Dark EM spectra show comparable spectral shapes despite the relatively minimal sampling of crystalline basement or large scale sedimentary deposits. The most prominent differences between the feature spaces of this study and previous studies involve the sampling of evaporites and synthetic materials. This study does not sample any large deposits of evaporitic minerals such as Halite or Gypsum. These minerals are spectrally distinct from other soil and rock substrates and they plot separately from the main point cloud that represents the vast majority of land surface reflectance spectra. This study does, however, contain a relatively large fractional area of settlements with synthetic roofs and tarped fields. These relatively exotic spectra do not have appreciable global abundance and so are not present in the previous studies focusing on terrestrial targets.