干旱区生态环境知识资源中心(Arid): Combination of MIR spectroscopy and environmental covariates to predict soil organic carbon in a semi-arid region

Arid

DOI	10.1016/j.catena.2020.104844
	Combination of MIR spectroscopy and environmental covariates to predict soil organic carbon in a semi-arid region
	Sabetizade, Marmar; Gorji, Manouchehr; Roudier, Pierre; Zolfaghari, Ali Asghar; Keshavarzi, Ali
通讯作者	Gorji, M
来源期刊	CATENA
ISSN	0341-8162
EISSN	1872-6887
出版年	2021
卷号	196
英文摘要	Soil organic carbon (SOC) sequestration provides an opportunity to mitigate climate change impacts, since soils are the largest store of terrestrial carbon. Accurate estimates of SOC content across landscapes are therefore important to monitor and manage efficiently these SOC stocks. Mid-infrared (MIR) spectroscopy has been increasingly applied as a rapid, cost-effective, and accurate method for predictive soil analysis. This study assessed the performance of MIR spectroscopy for SOC prediction at a regional scale for remote landscapes in Iran. The potential for combining environmental covariates, including remotely sensed covariates and terrain attributes, with MIR variables to improve prediction was also tested. Soil samples were collected from 151 locations at two depths (0-5 and 5-15 cm) across a large study area (350 km(2)) and analysed for gravimetric SOC content. Partial least squares regression (PLSR) was used to model SOC from MIR spectra recorded on the samples and to obtain latent variables (LV) that were then used, either on their own or alongside environmental covariates, as input to a Cubist rule-based model. The Cubist model using the LV alone outperformed the PLSR model and produced a high prediction accuracy with an R-2 of 0.96, RPIQ of 5.61, and RMSE of 0.16% on the validation set. The inclusion of environmental covariates alongside LV did not improve the performance of the model compared with the model on LV alone (R-2 = 0.94, RPIQ = 4.81, RMSE = 0.19%). The high performance of the developed models indicates the high potential of MIR spectroscopy for SOC prediction in data-scarce areas.
英文关键词	SOC Soil spectroscopy Remote sensing DEM Recursive feature elimination Cubist
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000583955200026
WOS关键词	REFLECTANCE SPECTROSCOPY ; AGRICULTURAL SOILS ; NIR SPECTROSCOPY ; LAND-USE ; MATTER ; STOCKS ; NITROGEN ; REGRESSION ; FRACTIONS ; AIRBORNE
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/2XILL650/327954
作者单位	[Sabetizade, Marmar; Gorji, Manouchehr; Keshavarzi, Ali] Univ Tehran, Fac Agr Engn & Technol, Soil Sci Dept, Tehran, Iran; [Sabetizade, Marmar; Roudier, Pierre] Manaaki Whenua Landcare Res, Palmerston North, New Zealand; [Roudier, Pierre] Te Punaha Matatini, Private Bag 92019, Auckland 1142, New Zealand; [Zolfaghari, Ali Asghar] Semnan Univ, Fac Desert Sci, Semnan, Iran
推荐引用方式 GB/T 7714	Sabetizade, Marmar,Gorji, Manouchehr,Roudier, Pierre,et al. Combination of MIR spectroscopy and environmental covariates to predict soil organic carbon in a semi-arid region[J],2021,196.
APA	Sabetizade, Marmar,Gorji, Manouchehr,Roudier, Pierre,Zolfaghari, Ali Asghar,&Keshavarzi, Ali.(2021).Combination of MIR spectroscopy and environmental covariates to predict soil organic carbon in a semi-arid region.CATENA,196.
MLA	Sabetizade, Marmar,et al."Combination of MIR spectroscopy and environmental covariates to predict soil organic carbon in a semi-arid region".CATENA 196(2021).