Estimating Soil Organic Carbon of Cropland Soil at Different Levels of Soil Moisture Using VIS-NIR Spectroscopy
Open Access
- 14 September 2016
- journal article
- research article
- Published by MDPI AG in Remote Sensing
- Vol. 8 (9), 755
- https://doi.org/10.3390/rs8090755
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.Keywords
This publication has 41 references indexed in Scilit:
- Removing the effect of soil moisture from NIR diffuse reflectance spectra for the prediction of soil organic carbonGeoderma, 2011
- Estimation of the age of a weathered mixture of volatile organic compoundsAnalytica Chimica Acta, 2011
- Near- versus mid-infrared diffuse reflectance spectroscopy for soil analysis emphasizing carbon and laboratory versus on-site analysis: Where are we and what needs to be done?Geoderma, 2010
- Using data mining to model and interpret soil diffuse reflectance spectraGeoderma, 2010
- Measuring soil organic carbon in croplands at regional scale using airborne imaging spectroscopyGeoderma, 2010
- Effects of soil sample pretreatments and standardised rewetting as interacted with sand classes on Vis-NIR predictions of clay and soil organic carbonGeoderma, 2010
- Comparison among principal component, partial least squares and back propagation neural network analyses for accuracy of measurement of selected soil properties with visible and near infrared spectroscopyGeoderma, 2010
- Continuum removal versus PLSR method for clay and calcium carbonate content estimation from laboratory and airborne hyperspectral measurementsGeoderma, 2008
- Performance of some variable selection methods when multicollinearity is presentChemometrics and Intelligent Laboratory Systems, 2005
- Global soil characterization with VNIR diffuse reflectance spectroscopyGeoderma, 2005