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Remote Sens. 2016, 8(9), 755; doi:10.3390/rs8090755

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

1
Key Laboratory of Aquatic Botany and Watershed Ecology, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China
2
Key Laboratory of Geographic Information System of the Ministry of Education, School of Resource and Environmental Science, Wuhan University, Wuhan 430079, China
3
State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
4
College of Engineering, Peking University, Beijing 100871, China
*
Author to whom correspondence should be addressed.
Academic Editors: Lenio Soares Galvao and Prasad Thenkabail
Received: 19 July 2016 / Revised: 5 September 2016 / Accepted: 8 September 2016 / Published: 14 September 2016
View Full-Text   |   Download PDF [3610 KB, uploaded 23 September 2016]   |  

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. View Full-Text
Keywords: generalized least squares weighting; moisture correction; orthogonal signal correction; soil organic carbon; visible/near-infrared reflectance spectroscopy generalized least squares weighting; moisture correction; orthogonal signal correction; soil organic carbon; visible/near-infrared reflectance spectroscopy
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Jiang, Q.; Chen, Y.; Guo, L.; Fei, T.; Qi, K. Estimating Soil Organic Carbon of Cropland Soil at Different Levels of Soil Moisture Using VIS-NIR Spectroscopy. Remote Sens. 2016, 8, 755.

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