Estimating Soil Organic Carbon of Cropland Soil at Different Levels of Soil Moisture Using VIS-NIR Spectroscopy

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 (r 2 pre = 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 r 2 pre = 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 r 2 pre = 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.

Transferability of Economy Estimation Based on DMSP/OLS Night-Time Light

“…To differ from them, the former is named R 2 cv and the latter is named R 2 p. The highest R 2 cv indicates the best model for regression and for predicting, a higher R 2 p indicates a better accuracy. As for RPD, it is used to investigate the prediction error with variation in the data [32], which is calculated by Equation (3).…”

Prediction of Soil Organic Matter by VIS–NIR Spectroscopy Using Normalized Soil Moisture Index as a Proxy of Soil Moisture

“…Similarly, this method also cannot solve the complex and nonlinear effect of SM because of only using simple linear corrections for target variables [22]. Jiang et al [23] utilized OSC and GLSW algorithms to remove relative SM effects and verified the transferability of OSC-partial least square (OSC-PLS) and GLSW-PLS models between different SM levels. Successful practices were reported in their study.…”

“…Most recently, many researchers have investigated the effect of SM on reflectance spectra, and some methods for removing or minimizing the SM and improving the prediction accuracy of SOM have been also put forward and explored, such as external parameter orthogonalization (EPO) [9,[13][14][15], direct standardization (DS) and piecewise direct standardization (PDS) [11,[16][17][18], "spiking" method [19,20], first derivative [21], slope bias correction (SB) [22], orthogonal signal correction (OSC) and generalized least squares weighting (GLSW) [23,24], spectral classification [25,26] and so on. The above-mentioned EPO, DS and PDS strategies usually require dry soil spectral libraries (SSLs) at a specific scales (global, continental, national or regional) and then use a projection matrix (or transfer matrix) to correct the moist spectra.…”

Ensemble Identification of Spectral Bands Related to Soil Organic Carbon Levels over an Agricultural Field in Southern Ontario, Canada

“…The RF and SVM model fits in our study are similar to those obtained in a very recent study [15] (0.62 vs 0.63 and 0.74 vs 0.73, respectively); our PLS fit was substantially lower (0.37 vs 0.67). This lower fit can be due to more data (i.e., 8426 samples) used in Tsakiridis et al's study [15] or by the sensitivity of PLSR, being a parametric regression technique, towards a skewed distribution of the SOC contents [42]. In light of these results, the SVM model seems to have a superior influence on the ensemble selection than RF and PLS models, based on the initial weighting calculation of the importance value within the ensemble.…”