NASA's Earth Surface Mineral Dust Source Investigation (EMIT) mission seeks to use spaceborne imaging spectroscopy (hyperspectral imaging) to map the mineralogy of arid dust source regions. Here we apply recent developments in Joint Characterization (JC) and the spectral Mixture Residual (MR) to explore the information content of data from this novel mission. Specifically, for a mosaic of 20 spectrally diverse scenes we find: 1) a generalized three-endmember (Substrate, Vegetation, Dark; SVD) spectral mixture model is capable of capturing the preponderance (99% in 3 dimensions) of spectral variance with low misfit (99% of pixels with RMSE < 3.7%); 2) manifold learning (UMAP) is capable of identifying spatially coherent, physically interpretable clustering relationships in the spectral feature space; 3) UMAP yields results that are at least as informative when applied to the MR as when applied to raw reflectance; 4) SVD fraction information usefully contextualizes UMAP clustering relationships, and vice-versa (JC); and 5) when EMIT data are convolved to spectral response functions of multispectral instruments (Sentinel-2, Landsat 8/9, Planet SuperDove), SVD fractions correlate strongly across sensors but UMAP clustering relationships for the EMIT hyperspectral feature space are far more informative than for simulated multispectral sensors. Implications are discussed for both the utility of EMIT data in the near-term, and for the potential of high SNR spaceborne imaging spectroscopy more generally, to transform the future of optical remote sensing in the years and decades to come.