02406naa a2200301 a 450000100080000000500110000800800410001902200140006002400550007410000190012924501690014826000090031752014700032665000260179665000190182265000090184165000210185065000140187165000250188565000090191065000160191965300210193570000160195670000190197270000170199170000210200877300750202920938282018-08-02       2018    bl uuuu     u00u1 u    #d  a1753-89557 ahttps://doi.org/10.1080/17538947.2017.13498412DOI1 aFRIEDEL, M. J.  aMapping fractional landscape soils and vegetation components from Hyperion satellite imagery using an unsupervised machine-learning workflow.h[electronic resource]  c2018  aAn unsupervised machine-learning workflow is proposed for estimating fractional landscape soils and vegetation components from remotely sensed hyperspectral imagery. The workflow is applied to EO-1 Hyperion satellite imagery collected near Ibirací, Minas Gerais, Brazil. The proposed workflow includes subset feature selection, learning, and estimation algorithms. Network training with landscape feature class realizations provide a hypersurface from which to estimate mixtures of soil (e.g. 0.5 exceedance for pixels: 75% clay-rich Nitisols, 15% iron-rich Latosols, and 1% quartz-rich Arenosols) and vegetation (e.g. 0.5 exceedance for pixels: 4% Aspen-like trees, 7% Blackberry-like trees, 0% live grass, and 2% dead grass). The process correctly maps forests and iron-rich Latosols as being coincident with existing drainages, and correctly classifies the clay-rich Nitisols and grasses on the intervening hills. These classifications are independently corroborated visually (Google Earth) and quantitatively (random soil samples and crossplots of field spectra). Some mapping challenges are the underestimation of forest fractions and overestimation of soil fractions where steep valley shadows exist, and the under representation of classified grass in some dry areas of the Hyperion image. These preliminary results provide impetus for future hyperspectral studies involving airborne and satellite sensors with higher signal-to-noise and smaller footprints.  aHyperspectral imagery  aRemote sensing  aSoil  aVegetation cover  aSatélite  aSensoriamento Remoto  aSolo  aVegetação  aMachine learning1 aBUSCEMA, M.1 aVICENTE, L. E.1 aIWASHITA, F.1 aKOGA-VICENTE, A.  tInternational Journal of Digital Earthgv. 11, n. 7, p. 670-690, 2018.