干旱区生态环境知识资源中心(Arid): Estimation of Soil Organic Matter in Arid Zones with Coupled Environmental Variables and Spectral Features

Arid

DOI	10.3390/s22031194
	Estimation of Soil Organic Matter in Arid Zones with Coupled Environmental Variables and Spectral Features
	Wang, Zheng; Ding, Jianli; Zhang, Zipeng
通讯作者	Ding, JL
来源期刊	SENSORS
EISSN	1424-8220
出版年	2022
卷号	22 期号:3
英文摘要	The soil organic matter (SOM) content is a key factor affecting the function and health of soil ecosystems. For measurements of land reclamation and soil fertility, SOM monitoring using visible and near-infrared spectroscopy (Vis-NIR) is one approach to quantifying soil quality, and Vis-NIR is important for monitoring the SOM content in a broad and nondestructive manner. To investigate the influence of environmental factors and Vis-NIR spectroscopy in estimating SOM, 249 soil samples were collected from the Werigan-Kuqa oasis in Xinjiang, China, and their spectral reflectance, SOM content and soil salinity were measured. To classify and improve the prediction accuracy, we also take into account the soil salinity content as a variable indicator. Relevant environmental variables were extracted using remote sensing datasets (land-use/land-cover (LULC), digital elevation model (DEM), World Reference Base for Soil Resources (WRB), and soil texture). On the basis of Savitzky-Golay (S-G) smoothing and first derivative (FD) preprocessing of the original spectrum, three clusters were obtained by K-means clustering through the use of Vis-NIR and used as spectral classification variables. Using Vis-NIR as Model 1, Vis-NIR combined with spectral classification as Model 2, environmental variables as Model 3, and the combination of all the above variables (Vis-NIR, spectral classification, environmental variables, and soil salinity) as Model 4, a SOM content estimation model was constructed using partial least squares regression (PLSR). Using the 249 soil samples, the modeling set contained 166 samples and the validation set contained 83 samples. The results showed that Model 2 (validation r(2) = 0.78) was better than Model 1 (validation r(2) = 0.76). The prediction accuracy for Model 4 (validation r(2) = 0.85) was better than Model 2 (validation r(2) = 0.78). Among these, Model 3 was the worst (validation r(2) = 0.39). Therefore, the combination of environmental variables with Vis-NIR spectroscopy to estimate SOM content is an important method and has important implications for improving the accuracy of SOM predictions in arid regions.
英文关键词	remote sensing visible and near-infrared spectroscopy soil organic matter environmental variables partial least squares regression
类型	Article
语种	英语
开放获取类型	Green Published, gold
收录类别	SCI-E
WOS记录号	WOS:000755642500001
WOS关键词	CARBON ; PREDICTION ; MODEL ; SALINIZATION ; MOISTURE ; NITROGEN ; CHINA ; NIR
WOS类目	Chemistry, Analytical ; Engineering, Electrical & Electronic ; Instruments & Instrumentation
WOS研究方向	Chemistry ; Engineering ; Instruments & Instrumentation
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/376447
推荐引用方式 GB/T 7714	Wang, Zheng,Ding, Jianli,Zhang, Zipeng. Estimation of Soil Organic Matter in Arid Zones with Coupled Environmental Variables and Spectral Features[J],2022,22(3).
APA	Wang, Zheng,Ding, Jianli,&Zhang, Zipeng.(2022).Estimation of Soil Organic Matter in Arid Zones with Coupled Environmental Variables and Spectral Features.SENSORS,22(3).
MLA	Wang, Zheng,et al."Estimation of Soil Organic Matter in Arid Zones with Coupled Environmental Variables and Spectral Features".SENSORS 22.3(2022).