The Influence of Spectral Pretreatment on the Selection of Representative Calibration Samples for Soil Organic Matter Estimation Using Vis-NIR Reflectance Spectroscopy

Yi Liu,Tiezhu Shi,Yongsheng Hong,Yang Zhang,Yiyun Chen,Yaolin Liu,Junjie Wang,Teng Fei

doi:10.3390/rs11040450

Abstract

In constructing models for predicting soil organic matter (SOM) by using visible and near-infrared (vis–NIR) spectroscopy, the selection of representative calibration samples is decisive. Few researchers have studied the inclusion of spectral pretreatments in the sample selection strategy. We collected 108 soil samples and applied six commonly used spectral pretreatments to preprocess soil spectra, namely, Savitzky–Golay (SG) smoothing, first derivative (FD), logarithmic function log(1/R), mean centering (MC), standard normal variate (SNV), and multiplicative scatter correction (MSC). Then, the Kennard–Stone (KS) strategy was used to select calibration samples based on the pretreated spectra, and the size of the calibration set varied from 10 samples to 86 samples (80% of the total samples). These calibration sets were employed to construct partial least squares regression models (PLSR) to predict SOM, and the built models were validated by a set of 21 samples (20% of the total samples). The results showed that 64−78% of the calibration sets selected by the inclusion of pretreatment demonstrated significantly better performance of SOM estimation. The average improved residual predictive deviations (ΔRPD) were 0.06, 0.13, 0.19, and 0.13 for FD, log(1/R), MSC, and SNV, respectively. Thus, we concluded that spectral pretreatment improves the sample selection strategy, and the degree of its influence varies with the size of the calibration set and the type of pretreatment.

Highlights

Soil organic matter (SOM) has become a popular topic in the past decade because of its vital role in ecosystem quality, food security, and global climate change [1,2,3]
Five parts are usually involved in SOM estimation: (i) field sampling; (ii) measurements, in which the SOM content is determined and soil reflectance spectra are obtained; (iii) preprocessing, in which spectroscopic reflectance spectra are preprocessed; (iv) calibration, in which a subset of samples is selected for building multivariate regression models that relate SOM content to reflection data; and (v) validation, in which a subset of independent samples is used to assess the accuracy of the built multivariate regression models [11,12]
This study aimed to explore the effects of spectral pretreatment on sample selection

Summary

Introduction

Soil organic matter (SOM) has become a popular topic in the past decade because of its vital role in ecosystem quality, food security, and global climate change [1,2,3]. Visible and near-infrared (vis–NIR) spectroscopy is an inexpensive and quick technique for the measurement of soil properties (e.g., SOM), and has been continuously developed over the past 30 years [6,7]. Five parts are usually involved in SOM estimation: (i) field sampling; (ii) measurements, in which the SOM content is determined and soil reflectance spectra are obtained; (iii) preprocessing, in which spectroscopic reflectance spectra are preprocessed; (iv) calibration, in which a subset of samples is selected for building multivariate regression models that relate SOM content to reflection data; and (v) validation, in which a subset of independent samples is used to assess the accuracy of the built multivariate regression models [11,12]. Special care must be given to the sample selection strategy [14,15]

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