Quantifying mineral abundances of complex mixtures by coupling spectral deconvolution of SWIR spectra (2.1-2.4 μm) and regression tree analysis

Geoderma

Volumn 207-208, Issue 1, 2013, Pages 279-290

  Mulder, V L ^a   Plotze M ^b   de Bruin, S ^a   Schaepman, M E ^c   Mavris, C ^c   Kokaly, R F ^d   Egli, M ^c  

^a WAGENINGEN UNIVERSITY   (Netherlands)

^b ETH ZURICH   (Switzerland)

^c UNIVERSITY OF ZURICH   (Switzerland)

^d DENVER FEDERAL CENTER   (United States)

Author keywords

Exponential Gaussian optimisation; Regression trees; Soil minerals; Spectral unmixing; SWIR reflectance spectroscopy

Indexed keywords

OPTIMISATIONS; REFLECTANCE SPECTROSCOPY; REGRESSION TREES; SOIL MINERALS; SPECTRAL UNMIXING;

CALCITE; FORESTRY; KAOLINITE; MEAN SQUARE ERROR; MICA; MIXTURES; OPTIMIZATION; QUARTZ; REGRESSION ANALYSIS; SPECTRUM ANALYSIS;

MINERALS;

ABSORBANCE; CALIBRATION; DECONVOLUTION; ERROR ANALYSIS; GAUSSIAN METHOD; MINERAL; OPTIMIZATION; REGRESSION ANALYSIS; SPECTRAL ANALYSIS; SPECTRAL REFLECTANCE;

FORESTRY; KAOLIN; MICA; MINERALS; MIXTURES; OPTIMIZATION; QUARTZ; REGRESSION ANALYSIS; SOIL; TREES;

MOROCCO;

EID: 84879558525     PISSN: 00167061     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.geoderma.2013.05.011     Document Type: Article

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