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

doi:10.3390/rs8090755

CORC > 北京大学 > 工学院

	Estimating Soil Organic Carbon of Cropland Soil at Different Levels of Soil Moisture Using VIS-NIR Spectroscopy
	Jiang, Qinghu ; Chen, Yiyun ; Guo, Long ; Fei, Teng ; Qi, Kun
刊名	REMOTE SENSING
	2016
关键词	generalized least squares weighting moisture correction orthogonal signal correction soil organic carbon visible near-infrared reflectance spectroscopy NEAR-INFRARED-SPECTROSCOPY DIFFUSE-REFLECTANCE SPECTROSCOPY AGRICULTURAL SOILS FIELD CONDITIONS CENTRAL YANGTZE LEAST-SQUARES WATER CONTENT SPECTRA PREDICTION QUANTIFICATION
DOI	10.3390/rs8090755
英文摘要	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 gkg(-1)) were moderately successful in SOC predictions (r(pre)(2) = 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(pre)(2) = 0.77, root mean square error for prediction (RMSEP) = 3.08 gkg(-1), and residual prediction deviations (RPD) = 2.09) outperforms the OSC-PLSR model (mean of r(pre)(2) = 0.67, RMSEP = 3.67 gkg(-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.; National Natural Science Foundation of China [31600377, 41501444]; Fundamental Research Funds for the Central Universities [2042015KF1044]; Suzhou Applied and Basic Research Program for Agriculture [SYN201422]; SCI(E); ARTICLE; jianghu_568@163.com; chenyy@whu.edu.cn; guolong027@tom.com; feiteng@whu.edu.cn; qikun@whu.edu.cn; 9; 8
语种	英语
内容类型	期刊论文
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/458910]
专题	工学院
推荐引用方式 GB/T 7714	Jiang, Qinghu,Chen, Yiyun,Guo, Long,et al. Estimating Soil Organic Carbon of Cropland Soil at Different Levels of Soil Moisture Using VIS-NIR Spectroscopy[J]. REMOTE SENSING,2016.
APA	Jiang, Qinghu,Chen, Yiyun,Guo, Long,Fei, Teng,&Qi, Kun.(2016).Estimating Soil Organic Carbon of Cropland Soil at Different Levels of Soil Moisture Using VIS-NIR Spectroscopy.REMOTE SENSING.
MLA	Jiang, Qinghu,et al."Estimating Soil Organic Carbon of Cropland Soil at Different Levels of Soil Moisture Using VIS-NIR Spectroscopy".REMOTE SENSING (2016).