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Sensors 2018, 18(2), 583; https://doi.org/10.3390/s18020583

Multisensor Analysis of Spectral Dimensionality and Soil Diversity in the Great Central Valley of California

Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY 10964, USA
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Received: 1 December 2017 / Revised: 17 January 2018 / Accepted: 3 February 2018 / Published: 14 February 2018
(This article belongs to the Special Issue Analysis of Multispectral and Hyperspectral Data)
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Abstract

Planned hyperspectral satellite missions and the decreased revisit time of multispectral imaging offer the potential for data fusion to leverage both the spectral resolution of hyperspectral sensors and the temporal resolution of multispectral constellations. Hyperspectral imagery can also be used to better understand fundamental properties of multispectral data. In this analysis, we use five flight lines from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) archive with coincident Landsat 8 acquisitions over a spectrally diverse region of California to address the following questions: (1) How much of the spectral dimensionality of hyperspectral data is captured in multispectral data?; (2) 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?; (3) How much variability in rock and soil substrate endmembers (EMs) present in hyperspectral data is captured by multispectral sensors? We find nearly identical partitions of variance, low-order feature space topologies, and EM spectra for hyperspectral and multispectral image composites. The resulting feature spaces and EMs are also very similar to those from previous global multispectral analyses, implying that the fundamental structure of the global feature space is present in our relatively small spatial subset of California. Finally, we find that the multispectral dataset well represents the substrate EM variability present in the study area – despite its inability to resolve narrow band absorptions. We observe a tentative but consistent physical relationship between the gradation of substrate reflectance in the feature space and the gradation of sand versus clay content in the soil classification system. View Full-Text
Keywords: soil; spectral dimensionality; spectral resolution; spatial scaling; Great Central Valley; California; AVIRIS; Landsat soil; spectral dimensionality; spectral resolution; spatial scaling; Great Central Valley; California; AVIRIS; Landsat
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Sousa, D.; Small, C. Multisensor Analysis of Spectral Dimensionality and Soil Diversity in the Great Central Valley of California. Sensors 2018, 18, 583.

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