  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.
