干旱区生态环境知识资源中心(Arid): Predicting soil organic carbon by integrating Landsat 8 OLI, GIS and data mining techniques in semi-arid region

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DOI	10.1007/s12145-021-00673-8
	Predicting soil organic carbon by integrating Landsat 8 OLI, GIS and data mining techniques in semi-arid region
	Akbari, Mohammad; Goudarzi, Iman; Tahmoures, Mohammad; Elveny, Marischa; Bakhshayeshi, Iman
通讯作者	Akbari, M (corresponding author), Univ Birjand, Dept Civil Engn, Birjand, Iran. ; Elveny, M (corresponding author), Univ Sumatera Utara, Data Sci & Computat Intelligence Res Grp, Medan, Indonesia.
来源期刊	EARTH SCIENCE INFORMATICS
ISSN	1865-0473
EISSN	1865-0481
出版年	2021
英文摘要	Soil organic carbon (SOC) is an important indicator for soil quality and environmental health. It also plays a key role in the semi-arid region. The aims of this study were to derive models for SOC prediction using Landsat 8 OLI data in dry and wet months of a semi-arid region. To this end, the SOC contents were measured in 165 points from agricultural soils (0-15 cm depth) based on a stratified random sampling method. The measured data were divided randomly into a calibration data-set (75%) and validation data-set (25%). The multiple linear regression (MLR), support vector machine (SVM), and artificial neural network (ANN) models were then employed to predict SOC contents by using two Landsat 8 OLI images acquired in dry (June 2019) and wet (February 2019) months. The accuracy of developed models was evaluated by applying the ME (mean error), R-2 (coefficient of determination), and RMSE (root mean square error) indices. The results indicated that the derived ANN model performed better than the developed MLR and SVM models for predicting SOC contents in both dry and wet months. Overall, the best result for SOC contents prediction was obtained by the ANN model in dry month (ME = -0.055, RMSE = 0.163 and R-2 = 0.743). It was concluded that using Landsat 8 OLI image in the dry month brings higher accuracy for SOC prediction.
英文关键词	Agricultural soils Data mining methods Landsat 8 OLI Semi-arid region
类型	Article ; Early Access
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000684894100001
WOS关键词	ARTIFICIAL NEURAL-NETWORK ; REMOTE-SENSING TECHNIQUES ; SPATIAL VARIABILITY ; CALCAREOUS SOILS ; NIR SPECTROSCOPY ; BANEH REGION ; MATTER ; MODEL ; SEQUESTRATION ; REFLECTANCE
WOS类目	Computer Science, Interdisciplinary Applications ; Geosciences, Multidisciplinary
WOS研究方向	Computer Science ; Geology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/362959
作者单位	[Akbari, Mohammad] Univ Birjand, Dept Civil Engn, Birjand, Iran; [Goudarzi, Iman] Politecn Milan, Dept Civil & Environm Engn, Piazza Leonardo da Vinci 32, I-20133 Milan, Italy; [Tahmoures, Mohammad] AREEO, Dept Soil Conservat & Watershed Management, Zanjan Agr & Nat Resources Res Ctr, Zanjan, Iran; [Elveny, Marischa] Univ Sumatera Utara, Data Sci & Computat Intelligence Res Grp, Medan, Indonesia; [Bakhshayeshi, Iman] Univ New South Wales, Sch Built Environm, Sydney, NSW, Australia; [Bakhshayeshi, Iman] Macquarie Univ, Fac Sci & Engn, Sch Engn, Sydney, NSW 2109, Australia
推荐引用方式 GB/T 7714	Akbari, Mohammad,Goudarzi, Iman,Tahmoures, Mohammad,et al. Predicting soil organic carbon by integrating Landsat 8 OLI, GIS and data mining techniques in semi-arid region[J],2021.
APA	Akbari, Mohammad,Goudarzi, Iman,Tahmoures, Mohammad,Elveny, Marischa,&Bakhshayeshi, Iman.(2021).Predicting soil organic carbon by integrating Landsat 8 OLI, GIS and data mining techniques in semi-arid region.EARTH SCIENCE INFORMATICS.
MLA	Akbari, Mohammad,et al."Predicting soil organic carbon by integrating Landsat 8 OLI, GIS and data mining techniques in semi-arid region".EARTH SCIENCE INFORMATICS (2021).