This function aggregates VNIR (Visible and Near-Infrared) spectral data by calculating the mean of every 10 columns 
while removing specific detector artifact regions (~1000nm and ~1800nm) and unwanted spectral bands.

Creates a spectroscopy guideline with a highly accurate prediction model for soil properties using machine learning or deep learning algorithms such as LASSO, Random Forest, Cubist, etc., and decide which algorithm generates the best model for different soil types.

Pengyuan Chen

MLSP

Machine Learning Models for Soil Properties

Christopher Clingensmith

Chenglong Ye

Sabine Grunwald

Katsutoshi Mizuta

raw function

<dl><dt>vnir.matrix</dt>
<dd>A numeric matrix or data frame containing VNIR spectral data. Each row corresponds to a sample, 
and each column corresponds to a spectral band.</dd></dl>

Arguments

Aggregate VNIR Spectra by Columns — raw

<dl>

<dt>vnir.matrix</dt>
<dd>A numeric matrix or data frame containing VNIR spectral data. Each row corresponds to a sample, 
and each column corresponds to a spectral band.</dd>

</dl>

raw: Aggregate VNIR Spectra by Columns

Description

Usage

Value

Arguments

Details

Examples