Digital Soil Mapping of Soil Properties in the “Mar de Morros” Environment Using Spectral Data

Patrícia Morais Da Matta Campbell,Luiz Alberto Da Silva Rodrigues Pinto,José Alexandre Melo Demattê,Marcos Gervasio Pereira,Elpídio Inácio Fernandes Filho,Clécia Cristina Barbosa Guimarães And,Márcio Rocha Francelino

doi:10.1590/18069657rbcs20170413

Abstract

ABSTRACT Quantification of soil properties is essential for better understanding of the environment and better soil management. The conventional techniques of laboratory analysis are sometimes costly and detrimental to the environment. Thus, development of new techniques for soil analysis that do not generate residues, such as spectroscopy, is increasingly necessary as a viable way to estimate a wide range of soil properties. The objective of this study was to predict the levels of organic carbon (OC), clay, and extractable phosphorus [...]

Highlights

Pedometry uses modern techniques of mining and data analysis to quantify soil properties, and it is one of the most promising fields of soil property prediction from its relationship with spectral responses in different reflectance ranges (Adeline et al, 2017; Nouri et al, 2017)
The objective of this study was to predict the levels of organic carbon (OC), clay, and extractable phosphorus (P), from the spectral responses of soil samples in the visible and near infrared (Vis-NIR), medium infrared (MIR), and Vis-NIR-MIR using different preprocessing methods combined with five prediction models
The MSC (Multiplicative Scatter Correction), Continuous Removal (CR) (Continuum removal), and SNV (Standard Normal Variate) preprocesses were most efficient for predicting clay, OC, and P, respectively, while the PLSR - Partial Least Squares Regression (OC and P) and SVM - Support Vector Machine gave the best predictions and are recommended for modeling these properties in the study area

Summary

Introduction

Pedometry uses modern techniques of mining and data analysis to quantify soil properties, and it is one of the most promising fields of soil property prediction from its relationship with spectral responses in different reflectance ranges (Adeline et al, 2017; Nouri et al, 2017). Reflectance spectroscopy, both in near-infrared (NIR) and mediuminfrared (MIR) spectra, in addition to visible bands, has drawn attention for its potential use in non-destructive, fast, and efficient methods for quantifying soil properties (Bashagaluke et al, 2015). The precise quantification of different soil properties is performed using large libraries, with many samples (Brown et al, 2006; Fernández-Pierna and Dardenne, 2008; Vasques et al, 2008; Genot et al, 2011; Viscarra Rossel and Webster, 2012), and a way to deal with this large number of covariate samples, such as infrared spectra, is the selection of those with the highest predictive power (Minasny and McBratney, 2008)

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