## 3 Results  
The low-order spectral feature space of the AVIRIS-ng reflectance mosaic is shown as orthogonal projections of the low order principal component (PC) distributions in Figure 2. The first three dimensions are found to capture >97% of the overall variance. The first two dimensions of the spectral feature space are bounded by spectral endmembers (EMs) corresponding to soil and rock Substrates, illuminated photosynthetic Vegetation, and Dark  
Figure 1: Index map. (**Left**): 15 flight lines from the 2020 AVIRIS-ng campaign (red) span broad crop and soil diversity in one of the most productive agricultural regions on earth. (**Right**): false color composite mosaic image compiled from 125 subsets, each of 600 \(\times\) 800 pixels (~2.4 \(\times\) 3.2 km). Ground sampling distance for all lines is approximately 3 m. For further information about soil diversity, see [37; 31]; for agricultural diversity see [30]. targets such as shadow, water, and low-albedo (S, V, and D). A clear non-photosynthetic vegetation (N) EM also emerges in the third dimension. Both the 3-EM (SVD) and 4-EM (SVD) models were inverted and compared. V fraction (F\({}_{\rm v}\)) estimates from the the 3-EM and 4-EM models were found to be strongly interrelated (r > 0.99; MI = 1.9). F\({}_{\rm v}\) from the 3-EM model was used for subsequent analysis. Bivariate distributions of VIs from SuperDove (y-axis) and F\({}_{\rm v}\) from mixture model inversion (x-axis) are shown in Figure 3. The DVI and NIRv were very strongly interrelated (DVI:NIRv \(\rho\) > 0.99; MI > 2.4; Figure S1). The DVI and NIRv were also colinear to F\({}_{\rm v}\) (\(\rho\) = 0.95 and MI = 1.4 for both DVI:F\({}_{\rm v}\) and NIRv:F\({}_{\rm v}\)), but substantially biased towards an underestimation at higher values (slope = 1.43, intercept = 0.017; regression in Figure S1). Similarly, the EVI and EVI2 were also strongly colinear (EVI:EVI2 \(\rho\) > 0.99; MI > 2.3; Figure S1). The EVI and EVI2 also correlated strongly with F\({}_{\rm v}\) (\(\rho\) > 0.94; MI > 1.2), showing some overestimation but considerably less bias than the DVI or NIRv. Figure 2: Spectral feature space and endmembers. The first 3 dimensions of the spectral feature space, visualized here, account for over 97% of the variance in the AVIRIS-ng spectra. Substrate, vegetation and dark (S, V, D) endmembers bound the first two dimensions of this feature space. Non-photosynthetic vegetation (N) extends the plane of substrates in the third dimension. Compare to previous results from mosaics of AVIRIS-classic and Landsat [31]. Colors represent point density, from sparser (violet) to denser (green). In contrast, the NDVI and SR showed considerably more complex relationships with F\({}_{\rm v}\). Both \(\rho\) (0.84 and 0.81) and MI (<0.7) were much lower than for other indices (Table S3). Consistent with previous comparisons to multispectral mixture models [27; 38; 39], the NDVI considerably overestimated F\({}_{\rm v}\) throughout most of its range, saturating near 0.6. Sensitivity to background reflectance was also especially apparent for the NDVI--for instance, spectra with F\({}_{\rm v}\) = 0.2 were found to exhibit NDVI values ranging from 0.05 to 0.6. Univariate VI distributions for unvegetated (F\({}_{\rm v}\) < 0.05) spectra shown in Figure 4 quantify the sensitivity of each index to substrate and NPV background reflectance. Histograms show only spectra with F\({}_{\rm v}\) < 0.05 as estimated using the spectral mixture model. Of the indices, the NIRv (yellow) exhibited the least sensitivity to background reflectance, with a mode near 0.05 and standard deviation (s) = 0.018. The DVI (red) demonstrated slightly more sensitivity, with a greater mode (0.08) and dispersion (s = 0.029). The EVI (magenta) and EVI2 (white) performed similarly to each other, with greater modes (0.12) and dispersions (s = 0.038). Interestingly, the SR (green) performed similarly to the EVI and EVI2, with a similar mode (0.14) and lower dispersion (s = 0.018).