干旱区生态环境知识资源中心(Arid): Spatial prediction of soil properties through hybridized random forest model and combination of reflectance spectroscopy and environmental covariates

Arid

DOI	10.1080/10106049.2022.2138565
	Spatial prediction of soil properties through hybridized random forest model and combination of reflectance spectroscopy and environmental covariates
	Shahabi, Aram; Nabiollahi, Kamal; Davari, Masoud; Zeraatpisheh, Mojtaba; Heung, Brandon; Scholten, Thomas; Taghizadeh-Mehrjardi, Ruhollah
通讯作者	Nabiollahi, K
来源期刊	GEOCARTO INTERNATIONAL
ISSN	1010-6049
EISSN	1752-0762
出版年	2022
卷号	37 期号:27 页码:18172-18195
英文摘要	Spatial information on land and soil resources are critical towards addressing land degradation for ensuring sustainable soil and crop management. To address these needs, digital soil mapping techniques have emerged as an efficient and low-cost solution. Although digital soil mapping has typically leveraged geospatial environmental variables (e.g. remote sensing), the application and integration of spectroscopic data with those environmental variables remain limited. Hence, this study combines visible and near-infrared (Vis-NIR) spectroscopy, remote sensing, and topographic data and applies random forests, hybridized with particle swarm optimization algorithm (RF + PSO), to predict the spatial variability of soil clay content, electrical conductivity (EC), and calcium carbonate equivalent (CCE) for 370 km(2) of agricultural land in western Iran. Using a conditioned Latin hypercube approach, 220 soil samples at the 0-20 cm depth increment were acquired throughout the study area. Three sets of environmental covariates were tested: Scenario A (Vis-NIR spectroscopy data), Scenario B (environmental data), and Scenario C (Vis-NIR spectroscopy + environmental data). According to the 10-fold cross-validation procedure with 100 replications, the RF + PSO model showed an acceptable level accuracy for all scenarios, although the accuracy of the RF + PSO model using the Scenario C data was higher than all other scenarios: the Lin's Concordance Correlation Coefficient values were 0.77, 0.83, and 0.74 for the clay contents, EC, and CCE, respectively. The results demonstrated that the combination of Vis-NIR spectroscopic data and commonly available environmental covariates provided the best input data for the hybridized model and enhanced its performance.
英文关键词	Sentinel hyperspectral machine learning environmental covariates clay content electric conductivity calcium carbonate semi-arid region Iran Kurdistan
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000890344500001
WOS关键词	NEAR-INFRARED SPECTROSCOPY ; SEMIARID REGION ; ORGANIC-CARBON ; REGRESSION ; SALINITY ; QUALITY ; CLAY ; PLSR
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/392903
推荐引用方式 GB/T 7714	Shahabi, Aram,Nabiollahi, Kamal,Davari, Masoud,et al. Spatial prediction of soil properties through hybridized random forest model and combination of reflectance spectroscopy and environmental covariates[J],2022,37(27):18172-18195.
APA	Shahabi, Aram.,Nabiollahi, Kamal.,Davari, Masoud.,Zeraatpisheh, Mojtaba.,Heung, Brandon.,...&Taghizadeh-Mehrjardi, Ruhollah.(2022).Spatial prediction of soil properties through hybridized random forest model and combination of reflectance spectroscopy and environmental covariates.GEOCARTO INTERNATIONAL,37(27),18172-18195.
MLA	Shahabi, Aram,et al."Spatial prediction of soil properties through hybridized random forest model and combination of reflectance spectroscopy and environmental covariates".GEOCARTO INTERNATIONAL 37.27(2022):18172-18195.