干旱区生态环境知识资源中心(Arid): Using environmental variables and Fourier Transform Infrared Spectroscopy to predict soil organic carbon

Arid

DOI	10.1016/j.catena.2021.105280
	Using environmental variables and Fourier Transform Infrared Spectroscopy to predict soil organic carbon
	Goydaragh, Maryam Ghebleh; Taghizadeh-Mehrjardi, Ruhollah; Jafarzadeh, Ali Asghar; Triantafilis, John; Lado, Marcos
通讯作者	Goydaragh, MG (corresponding author), Univ Tabriz, Fac Agr, Dept Soil Sci, Tabriz, Iran. ; Taghizadeh-Mehrjardi, R (corresponding author), Univ Tubingen, Dept Geosci Soil Sci & Geomorphol, Tubingen, Germany.
来源期刊	CATENA
ISSN	0341-8162
EISSN	1872-6887
出版年	2021
卷号	202
英文摘要	Soil Organic Carbon (SOC) content is a key element for soil fertility and productivity, nutrient availability and potentially represents a measurement of the sink for greenhouse gas abatement. Improving our knowledge on the spatial distribution of SOC is hence essential for sustainable nutrient management and carbon storage capacity. The objective of this study was to evaluate the performance of six tree-based machine-learning models when using environmental variables (i.e., remote sensing and terrain attributes - scenario 1), Fourier Transform Infrared Spectroscopy (FTIR) data (scenario 2) and combination of environmental variables and FTIR data (scenario 3) as predictors in prediction of SOC content. The models included Random Forest, Cubist, Conditional Inference Forest, Conditional Inference Trees, Extreme Gradient Boosting and Classification, Regression Trees. Furthermore, we explored if the Bat optimization algorithm can improve the prediction accuracy of the models. The study was conducted across a 7000 ha field in the Miandoab County, Northern Iran, with a total of 80 soil samples collected systematically in a regular grid (700 x 1000 m). According to Leave-One-Out Cross-Validation, the best prediction performance was achieved by the Cubist+Bat model when environmental variables and FTIR spectra (scenario 3) were used (Coefficient of determination = 0.73, Concordance Correlation Coefficient = 0.77, Root Mean Square Error = 0.36, Mean Absolute Error = 0.31, Median Absolute Error = 0.28). FTIR data had the highest influence on the prediction accuracy of SOC. Therefore, it can be concluded that the combination of environmental variables and FTIR data with Cubist+Bat model as a precise approach to monitor SOC in semi-arid soils of Iran. The final Digital Soil Map (DSM) of SOC revealed that improvements in prediction might be possible with the collection of more soil samples in areas where the land use and topography changed over short spatial scales.
英文关键词	Aridisols Digital soil mapping Hybrid models Machine-learning Mid-IR spectral data
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000643594100045
WOS关键词	DIFFUSE-REFLECTANCE SPECTROSCOPY ; SUPPORT VECTOR MACHINE ; RANDOM FOREST ; SPATIAL PREDICTION ; REGIONAL-SCALE ; MIDINFRARED SPECTROSCOPY ; FTIR SPECTROSCOPY ; GENETIC ALGORITHM ; ELEVATION DATA ; LAND-USE
WOS类目	Geosciences, Multidisciplinary ; Soil Science ; Water Resources
WOS研究方向	Geology ; Agriculture ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/368676
作者单位	[Goydaragh, Maryam Ghebleh; Jafarzadeh, Ali Asghar] Univ Tabriz, Fac Agr, Dept Soil Sci, Tabriz, Iran; [Taghizadeh-Mehrjardi, Ruhollah] Univ Tubingen, Dept Geosci Soil Sci & Geomorphol, Tubingen, Germany; [Taghizadeh-Mehrjardi, Ruhollah] Ardakan Univ, Fac Agr & Nat Resources, Ardakan, Iran; [Triantafilis, John] Univ New South Wales, Fac Sci, Sch Biol Earth & Environm Sci, Sydney, NSW 2052, Australia; [Goydaragh, Maryam Ghebleh; Lado, Marcos] Univ A Coruna, Fac Sci, Ctr Invest Cient Avanzadas, A Zapateira S-N, La Coruna 15071, Spain
推荐引用方式 GB/T 7714	Goydaragh, Maryam Ghebleh,Taghizadeh-Mehrjardi, Ruhollah,Jafarzadeh, Ali Asghar,et al. Using environmental variables and Fourier Transform Infrared Spectroscopy to predict soil organic carbon[J],2021,202.
APA	Goydaragh, Maryam Ghebleh,Taghizadeh-Mehrjardi, Ruhollah,Jafarzadeh, Ali Asghar,Triantafilis, John,&Lado, Marcos.(2021).Using environmental variables and Fourier Transform Infrared Spectroscopy to predict soil organic carbon.CATENA,202.
MLA	Goydaragh, Maryam Ghebleh,et al."Using environmental variables and Fourier Transform Infrared Spectroscopy to predict soil organic carbon".CATENA 202(2021).