Soil Spectral Inference with R (häftad)
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Format
Häftad (Paperback / softback)
Språk
Engelska
Antal sidor
247
Utgivningsdatum
2022-02-26
Upplaga
1st ed. 2021
Förlag
Springer Nature Switzerland AG
Medarbetare
Malone, Brendan / Minasny, Budiman
Illustrationer
72 Illustrations, color; 389 Illustrations, black and white; XV, 247 p. 461 illus., 72 illus. in col
Dimensioner
234 x 156 x 14 mm
Vikt
372 g
Antal komponenter
1
Komponenter
1 Paperback / softback
ISBN
9783030648985

Soil Spectral Inference with R

Analysing Digital Soil Spectra using the R Programming Environment

av Alexandre M J-C Wadoux, Brendan Malone, Budiman Minasny, Mario Fajardo, Alex B McBratney
Häftad,  Engelska, 2022-02-26
2211
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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.
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Fler böcker av författarna

Alexandre M J-C Wadoux, Brendan Malone, Budiman Minasny, Mario Fajardo, Alex B McBratney

Övrig information

Alexandre Wadoux is Research Associate in soil science at the University of Sydney and member of the Sydney Institute of Agriculture, Australia. Brendan Malone is Senior Research Scientist in soil science at CSIRO Canberra, Australia. Budiman Minasny is Professor in soil-landscape modelling at the University of Sydney, Australia. Mario Fajardo is Postdoctoral Research Fellow at the Precision Agriculture Laboratory of the University of Sydney, Australia. Alex McBratney is Professor of Digital Agriculture & Soil Science at the University of Sydney and Director of the Sydney Institute of Agriculture, Australia.

Innehållsförteckning

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.

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