Arid
DOI10.1080/10106049.2022.2138565
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
ISSN1010-6049
EISSN1752-0762
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.
条目包含的文件
条目无相关文件。
个性服务
推荐该条目
保存到收藏夹
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Shahabi, Aram]的文章
[Nabiollahi, Kamal]的文章
[Davari, Masoud]的文章
百度学术
百度学术中相似的文章
[Shahabi, Aram]的文章
[Nabiollahi, Kamal]的文章
[Davari, Masoud]的文章
必应学术
必应学术中相似的文章
[Shahabi, Aram]的文章
[Nabiollahi, Kamal]的文章
[Davari, Masoud]的文章
相关权益政策
暂无数据
收藏/分享

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。