干旱区生态环境知识资源中心(Arid): Estimation of soil inorganic carbon with visible near-infrared spectroscopy coupling of variable selection and deep learning in arid region of China

Arid

DOI	10.1016/j.geoderma.2023.116589
	Estimation of soil inorganic carbon with visible near-infrared spectroscopy coupling of variable selection and deep learning in arid region of China
	Bai, Zijin; Chen, Songchao; Hong, Yongsheng; Hu, Bifeng; Luo, Defang; Peng, Jie; Shi, Zhou
通讯作者	Peng, J
来源期刊	GEODERMA
ISSN	0016-7061
EISSN	1872-6259
出版年	2023
卷号	437
英文摘要	Soil inorganic carbon (SIC) is the primary component of the soil carbon pool in arid and semiarid regions and strongly impacts the global carbon cycle, ecosystem services, and soil functions. The global climate change and intensify of human activities, could substantially change SIC, which highlights the importance of monitoring SIC. Rapid and accurate estimation of SIC concentration is critical for soil inorganic carbon pool monitoring. Currently, visible near-infrared (Vis-NIR) spectroscopy is a promising technique for estimating SIC via a rapid and cost-effective manner. Thus, in this study, we collected 315 topsoil samples from the Alar Reclamation Area in South Xinjiang, China, and measured their Vis-NIR spectra and SIC content. Then, we used deep learning algorithms, including a one-dimensional convolutional neural network (1D-CNN), two-dimensional convolu-tional neural network (2D-CNN), long short-term memory network (LSTM), and deep belief network (DBN), combined with variable selection algorithms (particle swarm algorithm (PSO), interval random frog (IRF), competitive adaptive reweighting algorithm (CARS), ant colony algorithm (ACO), and iteratively retaining informative variables (IRIV) to estimate SIC. Results showed that all five variable selection algorithms could effectively extract the featured spectral information of SIC, and reduce the number of spectral variables by >97%, simplifying the model structure. The variable selection algorithm could markedly improve the SIC spectral estimation accuracy, and the corresponding estimation accuracy follows the order: IRF > IRIV > PSO > CARS > ACO. All four deep learning models have high prediction accuracy, and the modeling accuracy of each method follow the order: LSTM > 1D-CNN > 2D-CNN > DBN. The combined IRF and LSTM model achieved the highest estimation accuracy (R2 = 0.93, RMSE = 1.26 g kg-1 in the calibration dataset; R2 = 0.92, RMSE = 1.37 g kg -1 in the validation dataset). This study demonstrated that deep learning combined with variable selection algorithms can detect SIC content quickly and accurately.
英文关键词	Visible near-infrared spectroscopy Deep learning Variable selection Soil inorganic carbon Northwest China
类型	Article
语种	英语
开放获取类型	hybrid
收录类别	SCI-E
WOS记录号	WOS:001041571600001
WOS关键词	RANDOM FROG ; PREDICTION
WOS类目	Soil Science
WOS研究方向	Agriculture
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/396695
推荐引用方式 GB/T 7714	Bai, Zijin,Chen, Songchao,Hong, Yongsheng,et al. Estimation of soil inorganic carbon with visible near-infrared spectroscopy coupling of variable selection and deep learning in arid region of China[J],2023,437.
APA	Bai, Zijin.,Chen, Songchao.,Hong, Yongsheng.,Hu, Bifeng.,Luo, Defang.,...&Shi, Zhou.(2023).Estimation of soil inorganic carbon with visible near-infrared spectroscopy coupling of variable selection and deep learning in arid region of China.GEODERMA,437.
MLA	Bai, Zijin,et al."Estimation of soil inorganic carbon with visible near-infrared spectroscopy coupling of variable selection and deep learning in arid region of China".GEODERMA 437(2023).