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

doi:10.3390/rs8090755

CORC > 武汉植物园 > 中国科学院武汉植物园

	Estimating Soil Organic Carbon of Cropland Soil at Different Levels of Soil Moisture Using VIS-NIR Spectroscopy
	Jiang, Qinghu 1,2; Chen, Yiyun 2,3; Guo, Long 2; Fei, Teng 2; Qi, Kun 4
刊名	REMOTE SENSING
	2016-09-01
卷号	8 期号:9 页码:16
关键词	generalized least squares weighting moisture correction orthogonal signal correction soil organic carbon visible near-infrared reflectance spectroscopy
ISSN号	2072-4292
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.
资助项目	National Natural Science Foundation of China[31600377] ; National Natural Science Foundation of China[41501444] ; Fundamental Research Funds for the Central Universities[2042015KF1044] ; Suzhou Applied and Basic Research Program for Agriculture[SYN201422]
WOS研究方向	Remote Sensing
语种	英语
出版者	MDPI AG
WOS记录号	WOS:000385488000065
内容类型	期刊论文
源URL	[http://202.127.146.157/handle/2RYDP1HH/30]
专题	中国科学院武汉植物园
通讯作者	Chen, Yiyun
作者单位	1.Chinese Acad Sci, Wuhan Bot Garden, Key Lab Aquat Bot & Watershed Ecol, Wuhan 430074, Peoples R China 2.Wuhan Univ, Sch Resource & Environm Sci, Key Lab Geog Informat Syst, Minist Educ, Wuhan 430079, Peoples R China 3.Chinese Acad Sci, Inst Soil Sci, State Key Lab Soil & Sustainable Agr, Nanjing 210008, Peoples R China 4.Peking Univ, Coll Engn, Beijing 100871, Peoples R China
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,2016,8(9):16.
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,8(9),16.
MLA	Jiang, Qinghu,et al."Estimating Soil Organic Carbon of Cropland Soil at Different Levels of Soil Moisture Using VIS-NIR Spectroscopy".REMOTE SENSING 8.9(2016):16.