干旱区生态环境知识资源中心(Arid): Soil Organic Carbon Content Prediction Using Soil-Reflected Spectra: A Comparison of Two Regression Methods

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DOI	10.3390/rs13234752
	Soil Organic Carbon Content Prediction Using Soil-Reflected Spectra: A Comparison of Two Regression Methods
	Ribeiro, Sharon Gomes; Teixeira, Adunias dos Santos; de Oliveira, Marcio Regys Rabelo; Costa, Mirian Cristina Gomes; Araujo, Isabel Cristina da Silva; Moreira, Luis Clenio Jario; Lopes, Fernando Bezerra
通讯作者	Teixeira, AD (corresponding author), Univ Fed Ceara, Dept Agr Engn, BR-60455760 Fortaleza, Ceara, Brazil.
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2021
卷号	13 期号:23
英文摘要	Quantifying the organic carbon content of soil over large areas is essential for characterising the soil and the effects of its management. However, analytical methods can be laborious and costly. Reflectance spectroscopy is a well-established and widespread method for estimating the chemical-element content of soils. The aim of this study was to estimate the soil organic carbon (SOC) content using hyperspectral remote sensing. The data were from soils from two localities in the semi-arid region of Brazil. The spectral reflectance factors of the collected soil samples were recorded at wavelengths ranging from 350-2500 nm. Pre-processing techniques were employed, including normalisation, Savitzky-Golay smoothing and first-order derivative analysis. The data (n = 65) were examined both jointly and by soil class, and subdivided into calibration and validation to independently assess the performance of the linear methods. Two multivariate models were calibrated using the SOC content estimated in the laboratory by principal component regression (PCR) and partial least squares regression (PLSR). The study showed significant success in predicting the SOC with transformed and untransformed data, yielding acceptable-to-excellent predictions (with the performance-to-deviation ratio ranging from 1.40-3.38). In general, the spectral reflectance factors of the soils decreased with the increasing levels of SOC. PLSR was considered more robust than PCR, whose wavelengths from 354 to 380 nm, 1685, 1718, 1757, 1840, 1876, 1880, 2018, 2037, 2042, and 2057 nm showed outstanding absorption characteristics between the predicted models. The results found here are of significant practical value for estimating SOC in Neosols and Cambisols in the semi-arid region of Brazil using VIS-NIR-SWIR spectroscopy.
英文关键词	SOC chemometrics soil spectral library spectroradiometer multivariate modelling
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000734679200001
WOS关键词	PRINCIPAL COMPONENTS REGRESSION ; NEAR-INFRARED SPECTROSCOPY ; MATTER CONTENT ; NIR ; QUANTIFICATION ; NITROGEN ; FIELD ; TEXTURE ; GENESIS ; WATER
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/373940
作者单位	[Ribeiro, Sharon Gomes] Univ Fed Ceara, Soil Sci Grad Program, BR-60455760 Fortaleza, Ceara, Brazil; [Teixeira, Adunias dos Santos; Araujo, Isabel Cristina da Silva; Lopes, Fernando Bezerra] Univ Fed Ceara, Dept Agr Engn, BR-60455760 Fortaleza, Ceara, Brazil; [de Oliveira, Marcio Regys Rabelo] Univ Fed Ceara, Agr Engn Grad Program, BR-60455760 Fortaleza, Ceara, Brazil; [Costa, Mirian Cristina Gomes] Univ Fed Ceara, Dept Soil Sci, BR-60455760 Fortaleza, Ceara, Brazil; [Moreira, Luis Clenio Jario] Fed Inst Educ Sci & Technol Ceara, Dept Agron, BR-62930000 Limoeiro Do Norte, Brazil
推荐引用方式 GB/T 7714	Ribeiro, Sharon Gomes,Teixeira, Adunias dos Santos,de Oliveira, Marcio Regys Rabelo,et al. Soil Organic Carbon Content Prediction Using Soil-Reflected Spectra: A Comparison of Two Regression Methods[J],2021,13(23).
APA	Ribeiro, Sharon Gomes.,Teixeira, Adunias dos Santos.,de Oliveira, Marcio Regys Rabelo.,Costa, Mirian Cristina Gomes.,Araujo, Isabel Cristina da Silva.,...&Lopes, Fernando Bezerra.(2021).Soil Organic Carbon Content Prediction Using Soil-Reflected Spectra: A Comparison of Two Regression Methods.REMOTE SENSING,13(23).
MLA	Ribeiro, Sharon Gomes,et al."Soil Organic Carbon Content Prediction Using Soil-Reflected Spectra: A Comparison of Two Regression Methods".REMOTE SENSING 13.23(2021).