Abstract
Soil organic carbon (SOC) is an essential property for soil function, fertility and sustainability of agricultural systems. It can be measured with visible and near-infrared reflectance (VIS-NIR) spectroscopy efficiently based on empirical equations and spectra data for air/oven-dried samples. However, the spectral signal is interfered with by soil moisture content (MC) under in situ conditions, which will affect the accuracy of measurements and calibration transfer among different areas. This study aimed to (1) quantify the influences of MC on SOC prediction by VIS-NIR spectroscopy; and (2) explore the potentials of orthogonal signal correction (OSC) and generalized least squares weighting (GLSW) methods in the removal of moisture interference. Ninety-eight samples were collected from the Jianghan plain, China, and eight MCs were obtained for each sample by a rewetting process. The VIS-NIR spectra of the rewetted soil samples were measured in the laboratory. Partial least squares regression (PLSR) was used to develop SOC prediction models. Specifically, three validation strategies, namely moisture level validation, transferability validation and mixed-moisture validation, were designed to test the potentials of OSC and GLSW in removing the MC effect. Results showed that all of the PLSR models generated at different moisture levels (e.g., 50–100, 250–300 g·kg−1) were moderately successful in SOC predictions (r2pre = 0.58–0.85, RPD = 1.55–2.55). These models, however, could not be transferred to soil samples with different moisture levels. OSC and GLSW methods are useful filter transformations improving model transferability. The GLSW-PLSR model (mean of r2pre = 0.77, root mean square error for prediction (RMSEP) = 3.08 g·kg−1, and residual prediction deviations (RPD) = 2.09) outperforms the OSC-PLSR model (mean of r2pre = 0.67, RMSEP = 3.67 g·kg−1, and RPD = 1.76) when the moisture-mixed protocol is used. Results demonstrated the use of OSC and GLSW combined with PLSR models for efficient estimation of SOC using VIS-NIR under different soil MC conditions.
Highlights
Soil organic carbon (SOC) is a major determinant of soil function, soil fertility, and sustainability of agricultural systems [1]
To reveal the influence of relative moisture content (MC) on visible and near-infrared reflectance (VIS-NIR) reflectance spectra and prediction of SOC content, in this study, two moisture correction methods (i.e., orthogonal signal correction (OSC) and generalized least squares weighting (GLSW) filter methods) and three independent validation strategies were applied to quantify the effect of MC on the accuracy of SOC predictions
This study demonstrated the potentials of two moisture correction methods (i.e., OSC and GLSW filter methods) in removing the influence of relative MCs on VIS-NIR spectroscopy in SOC
Summary
Soil organic carbon (SOC) is a major determinant of soil function, soil fertility, and sustainability of agricultural systems [1]. SOC dynamics strongly interact with the atmospheric CO2 dynamics. 2016, 8, 755 system [2], which further affects climate change. Investigating SOC content and its spatial variability for environmental monitoring and precision agriculture is a strong requirement. Traditional methods for chemical analysis of SOC are relatively complex, time consuming, and expensive. The implementation of soil surveys requires the analysis of numerous soil samples to characterize SOC spatial variability in the field [3]. The development of fast, inexpensive, and environmentally friendly methods to assess SOC spatial variability is indispensable
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