Effects of Subsetting by Parent Materials on Prediction of Soil Organic Matter Content in a Hilly Area Using Vis-NIR Spectroscopy.

Xuezheng Shi,Jingdong Mao,Shengxiang Xu,Meiyan Wang,Yongcun Zhao

doi:10.1371/journal.pone.0151536

Abstract

Assessment and monitoring of soil organic matter (SOM) quality are important for understanding SOM dynamics and developing management practices that will enhance and maintain the productivity of agricultural soils. Visible and near-infrared (Vis–NIR) diffuse reflectance spectroscopy (350–2500 nm) has received increasing attention over the recent decades as a promising technique for SOM analysis. While heterogeneity of sample sets is one critical factor that complicates the prediction of soil properties from Vis–NIR spectra, a spectral library representing the local soil diversity needs to be constructed. The study area, covering a surface of 927 km2 and located in Yujiang County of Jiangsu Province, is characterized by a hilly area with different soil parent materials (e.g., red sandstone, shale, Quaternary red clay, and river alluvium). In total, 232 topsoil (0–20 cm) samples were collected for SOM analysis and scanned with a Vis–NIR spectrometer in the laboratory. Reflectance data were related to surface SOM content by means of a partial least square regression (PLSR) method and several data pre-processing techniques, such as first and second derivatives with a smoothing filter. The performance of the PLSR model was tested under different combinations of calibration/validation sets (global and local calibrations stratified according to parent materials). The results showed that the models based on the global calibrations can only make approximate predictions for SOM content (RMSE (root mean squared error) = 4.23–4.69 g kg−1; R2 (coefficient of determination) = 0.80–0.84; RPD (ratio of standard deviation to RMSE) = 2.19–2.44; RPIQ (ratio of performance to inter-quartile distance) = 2.88–3.08). Under the local calibrations, the individual PLSR models for each parent material improved SOM predictions (RMSE = 2.55–3.49 g kg−1; R2 = 0.87–0.93; RPD = 2.67–3.12; RPIQ = 3.15–4.02). Among the four different parent materials, the largest R2 and the smallest RMSE were observed for the shale soils, which had the lowest coefficient of variation (CV) values for clay (18.95%), free iron oxides (15.93%), and pH (1.04%). This demonstrates the importance of a practical subsetting strategy for the continued improvement of SOM prediction with Vis–NIR spectroscopy.

Highlights

Soil organic matter (SOM) is a key attribute of soil and environmental quality because it affects physical, chemical and biological functions, which in turn influence soil productivity [1]
The specific objectives are to: (i) investigate whether predictions from a partial least-squares regression (PLSR) model built only from a subset of samples that are similar with respect to parent materials will provide better predictions than a global model built from a set of all possible samples, and (ii) evaluate the effects of the raw vs. derivatives of spectral reflectance and the importance of wavelengths in the Visible and near-infrared (Vis–NIR) for estimating SOM content
Based on comprehensive analysis of the relationship between SOM content and corresponding reflectance spectra in the four different parent materials from a hilly area in Yujiang County, we have provided the basis for future study of sample subsetting for large datasets based on parent material types

Summary

Introduction

Soil organic matter (SOM) is a key attribute of soil and environmental quality because it affects physical, chemical and biological functions, which in turn influence soil productivity [1]. It is essential to carry out a study concerning the Vis–NIR estimation of SOM content with samples derived from different parent materials when the local soil spectral libraries are unavailable. In this context, a heterogeneous set of soil samples were included in our study that covered a relatively wide range of different parent materials and a wide variation in SOM. The specific objectives are to: (i) investigate whether predictions from a PLSR model built only from a subset of samples that are similar with respect to parent materials will provide better predictions than a global model built from a set of all possible samples, and (ii) evaluate the effects of the raw vs derivatives of spectral reflectance and the importance of wavelengths in the Vis–NIR for estimating SOM content

Materials and Methods

Results and Discussion

Conclusions