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NASA's Earth Surface Mineral Dust Source Investigation (EMIT) mission seeksto use spaceborne imaging spectroscopy (hyperspectral imaging) to map themineralogy of arid dust source regions. Here we apply recent developments inJoint Characterization (JC) and the spectral Mixture Residual (MR) to explorethe information content of data from this novel mission. Specifically, for amosaic of 20 spectrally diverse scenes we find: 1) a generalizedthree-endmember (Substrate, Vegetation, Dark; SVD) spectral mixture model iscapable of capturing the preponderance (99% in 3 dimensions) of spectralvariance with low misfit (99% of pixels with RMSE < 3.7%); 2) manifold learning(UMAP) is capable of identifying spatially coherent, physically interpretableclustering relationships in the spectral feature space; 3) UMAP yields resultsthat are at least as informative when applied to the MR as when applied to rawreflectance; 4) SVD fraction information usefully contextualizes UMAPclustering relationships, and vice-versa (JC); and 5) when EMIT data areconvolved to spectral response functions of multispectral instruments(Sentinel-2, Landsat 8/9, Planet SuperDove), SVD fractions correlate stronglyacross sensors but UMAP clustering relationships for the EMIT hyperspectralfeature space are far more informative than for simulated multispectralsensors. Implications are discussed for both the utility of EMIT data in thenear-term, and for the potential of high SNR spaceborne imaging spectroscopymore generally, to transform the future of optical remote sensing in the yearsand decades to come.

Topological Generality and Spectral Dimensionality in the Earth Mineral  Dust Source Investigation (EMIT) using Joint Characterization and the  Spectral Mixture Residual