## 1 Introduction  
The availability of hyperspectral data for scientific purposes is rapidly increasing. The recent opening of the AVIRIS [1] and HICO [2] data archives offers scientists a wealth of high quality observations. Planned satellite missions such as HyspIRI [3], EnMAP [4], and HYPXIM [5] promise to offer even greater spatial and temporal coverage of narrowband imaging observations in the coming years. These newly available and planned hyperspectral datasets complement the existing wealth of public multispectral satellite observations. The Landsat archive offers over 35 years of rigorously intercalibrated multispectral data [6]. The recent and planned launches of the Sentinel satellite constellation promise to add considerably to this collection [7], substantially decreasing the revisit time between subsequent multispectral observations [8]. The future availability of systematic global archives of both hyperspectral and short revisit time multispectral observations is expected to offer significant potential for studies that leverage both the spectral resolution of hyperspectral sensors and the temporal resolution of multispectral sensors. A growing number of data fusion techniques attempt to explicitly merge hyperspectral and multispectral data (e.g., [9; 10]). More generally, hyperspectral imagery can be used to better understand fundamental properties of multispectral data. Regardless of the specific application, any combined use of hyperspectral and multispectral data will benefit from a better understanding of a fundamental question: How much of the spectral dimensionality of hyperspectral data is captured in multispectral data? As first recognized by the pioneering work of [PERSON] and [PERSON] [11], multispectral observations generally form a three-dimensional pyramidal feature space. Subsequent global analyses of Landsat [12; 13; 14] confirm this finding on spatially extensive, spectrally diverse sets of data and show that these spaces generally contain >97% of their variance in the first three dimensions. Consistent global endmembers (EMs) can be identified from the apexes of the space representing soil and rock substrates, photosynthetic vegetation, and dark targets such as shadow and water. However, most analyses are regional and sample only a small fraction of the diversity of land covers present on the surface of the Earth. Another fundamental question is thus: Is the characteristic pyramidal structure of the multispectral feature space also present in the low order dimensions of the hyperspectral feature space at comparable spatial scales? Finally, the majority of the EM variability observed in global multispectral analyses is in the substrate EM [11; 14; 15]. This is due to the wide range of soil and rock compositions that cover the land surface of the earth. It is likely that the global availability of hyperspectral imagery will add additional constraints to the properties of multispectral substrate EMs. A third fundamental question is thus: How much of the variability in Substrate EMs present in hyperspectral data is captured by multispectral sensors? In this analysis, we use coincident AVIRIS and Landsat 8 acquisitions to address these three fundamental questions. We find the nearly identical partition of variance, low order feature space topology, and EM spectra for the hyperspectral and multispectral cases. The resulting feature spaces and EMs are also very similar to those found in previous global multispectral analyses, implying that the relatively simple pyramidal structure of the broadband feature space also represents the low order dimensions of the hyperspectral feature space. Finally, we find that the multispectral dataset well represents the basic structure of the plane of substrates found in the hyperspectral data, despite the fact that AVIRIS resolves a wide range of absorptions indistinguishable to Landsat OLI.