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

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.

Spectral Mixture Analysis as a Unified Framework for the Remote Sensing of Evapotranspiration

“…In practice, however, 6-band Landsat spectra have been shown to essentially represent only three distinct land cover types on ice-free land surfaces ( [47,62]) corresponding to substrate, vegetation, and dark surfaces (S, V, and D). Similar EMs emerge from diverse mixing spaces of higher dimensional 12-band Sentinel-2 imagery [63], and 224-band hyperspectral AVIRIS flight line composites [64]. These studies suggest that an approach based on estimation of three materials from multispectral imagery is likely to be generally applicable across most terrestrial surfaces relevant to ET analysis.…”

Mapping and Monitoring Rice Agriculture with Multisensor Temporal Mixture Models

“…The vast majority of precipitation is delivered between the late fall and the early spring, with summers being characterized by dry, hot days with sparse cloud cover. A wide range of soil types exist in California, sometimes distinguishable on the basis of VSWIR reflectance [21]. Clay-rich soils are common in the northern portion of the valley, providing a nearly ideal natural substrate to support the standing water commonly used in intensive rice agriculture.…”

“…SMA is sensitive to the EMs used, but global analyses of multispectral Landsat [30,31] and Sentinel-2 [32] data, as well as regional analysis of a diverse set of hyperspectral AVIRIS flight lines [21], show that the Earth's ice-free land surface is generally well represented by 3 generic EMs: Substrate (S; rock and soil), Vegetation (V; illuminated photosynthetic foliage), and Dark (D; water and shadow). In addition to providing estimates of fractional vegetation cover which are linearly scalable [31,33] and more accurate than vegetation indices [34,35], SMA based on these globally standardized EMs simultaneously provides estimates of the subpixel abundance of S and D materials with root-mean-square misfits < 0.05 for > 97% of 100,000,000 spectral mixtures from every ice-free biome on Earth [31].…”

Spatial Patterns of Chemical Weathering at the Basal Tertiary Nonconformity in California from Multispectral and Hyperspectral Optical Remote Sensing

“…It is possible that the information in misfit and spectral residual might be used in future studies to provide a more standardized approach for the characterization and mapping of rocks and soils. California might be an ideal landscape for such a study due to the diversity of rocks and soils, and associated spectral complexity, which has been shown to reasonably approximate the global feature space [64].…”