Soil spectral inference with R : analysing digital soil spectra using the R programming environment / Alexandre M.J.-C. Wadoux, Brendan Malone, Budiman Minasny, Mario Fajardo, Alex B. McBratney.
2021
S593.2 .W33 2021
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Details
Title
Soil spectral inference with R : analysing digital soil spectra using the R programming environment / Alexandre M.J.-C. Wadoux, Brendan Malone, Budiman Minasny, Mario Fajardo, Alex B. McBratney.
ISBN
9783030648961 (electronic bk.)
3030648966 (electronic bk.)
9783030648954
3030648966 (electronic bk.)
9783030648954
Published
Cham : Springer, [2021]
Language
English
Description
1 online resource (xv, 247 pages) : illustrations (some color).
Item Number
10.1007/978-3-030-64896-1 doi
Call Number
S593.2 .W33 2021
Dewey Decimal Classification
631.4/1
Summary
This book provides a didactic overview of techniques for inferring information from soil spectroscopic data, and the codes in the R programming language for performing such analyses. It is intended for students, researchers and practitioners looking to infer soil information from spectroscopic data, focusing mainly on, but not restricted to, the infrared range of the electromagnetic spectrum. Little prior knowledge of the R programming language or digital soil spectra is required. We work through the steps to process spectroscopic data systematically.
Bibliography, etc. Note
Includes bibliographical references.
Access Note
Access limited to authorized users.
Digital File Characteristics
text file
PDF
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed March 30, 2021).
Added Author
Series
Progress in soil science.
Available in Other Form
Print version: 9783030648954
Print version: 9783030648978
Print version: 9783030648985
Print version: 9783030648978
Print version: 9783030648985
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Record Appears in
Table of Contents
1. Introduction
2. Getting Started with R
3. Material
4. Data Handling of Spectra
5. Pre-Processing of Spectra
6. Similarity between Spectra and the Detection of Outlier
7. Selection of the Calibration Sample
8. Estimating Soil Properties and Classes from Spectra
9. Spectral Transformation.
2. Getting Started with R
3. Material
4. Data Handling of Spectra
5. Pre-Processing of Spectra
6. Similarity between Spectra and the Detection of Outlier
7. Selection of the Calibration Sample
8. Estimating Soil Properties and Classes from Spectra
9. Spectral Transformation.