Abstract

Visible and near-infrared reflectance (VIS-NIR) spectroscopy is widely applied to estimate soil organic carbon (SOC). Intense and diverse human activities increase the heterogeneity in the relationships between SOC and VIS-NIR spectra in anthropogenic soil. This fact results in poor performance of SOC estimation models. To improve model accuracy and parsimony, we investigated the performance of two variable selection algorithms, namely competitive adaptive reweighted sampling (CARS) and random frog (RF), coupled with five spectral pretreatments. A total of 108 samples were collected from Jianghan Plain, China, with the SOC content and VIS-NIR spectra measured in the laboratory. Results showed that both CARS and RF coupled with partial least squares regression (PLSR) outperformed PLSR alone in terms of higher model accuracy and less spectral variables. It revealed that spectral variable selection could identify important spectral variables that account for the relationships between SOC and VIS-NIR spectra, thereby improving the accuracy and parsimony of PLSR models in anthropogenic soil. Our findings are of significant practical value to the SOC estimation in anthropogenic soil by VIS-NIR spectroscopy.

Highlights

  • Soil holds the most massive storage of organic carbon in the terrestrial ecosystems [1,2]

  • With soil samples collected from the study area, we investigated the effect of two spectral variable selection techniques to improve the performance of the partial least squares regression (PLSR) models in soil organic carbon (SOC) estimation in anthropogenic soils

  • Competitive adaptive reweighted sampling (CARS) and random frog (RF) algorithms in combination with five different spectral pretreatments were used to select spectral variables, which were used as input in partial least squares regression (PLSR) to estimate soil organic carbon (SOC) in the Jianghan Plain of China

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Summary

Introduction

Soil holds the most massive storage of organic carbon in the terrestrial ecosystems [1,2]. Soil organic carbon (SOC) storage has been altered due to this transformation [4]. Visible and near-infrared (VIS-NIR) spectroscopy serves as a time-saving and cost-effective proximal soil-sensing technique to estimate SOC content [12,13,14,15]. VIS-NIR spectra can be measured in situ or the lab [16]. VIS-NIR spectra measured in situ suffer from the influences of soil surface roughness, soil moisture, water vapor, light intensity, and other external environmental interference [17]. The measurement of VIS-NIR spectra performed in the lab could effectively avoid these influences

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