干旱区生态环境知识资源中心(Arid): Spatial prediction of soil surface properties in an arid region using synthetic soil image and machine learning

Arid

DOI	10.1080/10106049.2021.1996639
	Spatial prediction of soil surface properties in an arid region using synthetic soil image and machine learning
	Naimi, Salman; Ayoubi, Shamsollah; Dematte, Jose A. M.; Zeraatpisheh, Mojtaba; Amorim, Merilyn Taynara Accorsi; Mello, Fellipe Alcantara de Oliveira
通讯作者	Ayoubi, S (corresponding author), Isfahan Univ Technol, Coll Agr, Dept Soil Sci, Esfahan 84115683111, Iran.
来源期刊	GEOCARTO INTERNATIONAL
ISSN	1010-6049
EISSN	1752-0762
出版年	2021-10
英文摘要	Evaluation of spatial variability and mapping of soil properties is critical for sustainable agricultural production in arid lands. The main objectives of the present study were to spatialize soil organic carbon (SOC), soil particle size distribution(clay, sand, and silt contents), and calcium carbonate equivalent (CCE) by integrating multisource environmental covariates, including digital elevation model (DEM) and remote sensing data by machine learning (Cubist, Cu and random forest, RF) in an arid region of Iran. Additionally, Synthetic Soil Images (SySI) were achieved from multi-temporal images of bare soil pixels based on Landsats 4, 5, 7, 8, and a DEM. Three hundred topsoil samples (0-30 cm depth) were collected based on the conditioned Latin hypercube sampling (cLHS) approach in Afzar district, Fars province, southern Iran. The models were calibrated and validated by the 10-fold cross-validation approach, and the performance was evaluated using root mean square error (RMSE), the ratio of the performance to interquartile distance (RPIQ), and coefficient of determination (R-2). Also, the prediction accuracy was assessed by the relative RMSE (rRMSE). The performance of the best models based on the RPIQ index showed that the model for predicting clay (1.89) had a good prediction, for sand (1.64), SOC (1.55), and CCE (1.59) had a fair prediction, while the model for silt (1.13) performed poorly. We found that the Cu and RF models had the highest and lowest prediction accuracies for CCE (rRMSE = 14.31%) and SOC (rRMSE = 43.93%), respectively. We discovered that a combination of high-quality RS data (SySI) and variables derived from DEM were reasonably able to predict soil properties. We revealed a strong promise to enhance the accuracy of digital soil mapping, especially in regions with limited soil data. Moreover, the application of RS data can reduce the soil sampling cost and, accordingly, soil mapping.
英文关键词	Spatial prediction environmental covariates synthetic soil image (SySI) Cubist random forest machine learning
类型	Article ; Early Access
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000713902900001
WOS关键词	NEAR-INFRARED-SPECTROSCOPY ; ORGANIC-CARBON ; GEOSTATISTICAL METHODS ; SPECTRAL REFLECTANCE ; LOGISTIC-REGRESSION ; INTERPOLATION ; VARIABILITY ; ASSESSMENTS ; PERFORMANCE ; VARIABLES
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/2XILL650/367621
作者单位	[Naimi, Salman; Ayoubi, Shamsollah] Isfahan Univ Technol, Coll Agr, Dept Soil Sci, Esfahan 84115683111, Iran; [Dematte, Jose A. M.; Amorim, Merilyn Taynara Accorsi; Mello, Fellipe Alcantara de Oliveira] Luiz de Queiroz Coll Agr, Dept Soil Sci, Piracicaba, SP, Brazil; [Zeraatpisheh, Mojtaba] Henan Univ, Henan Key Lab Earth Syst Observat & Modeling, Kaifeng, Peoples R China; [Zeraatpisheh, Mojtaba] Henan Univ, Coll Geog & Environm Sci, Kaifeng, Peoples R China
推荐引用方式 GB/T 7714	Naimi, Salman,Ayoubi, Shamsollah,Dematte, Jose A. M.,et al. Spatial prediction of soil surface properties in an arid region using synthetic soil image and machine learning[J],2021.
APA	Naimi, Salman,Ayoubi, Shamsollah,Dematte, Jose A. M.,Zeraatpisheh, Mojtaba,Amorim, Merilyn Taynara Accorsi,&Mello, Fellipe Alcantara de Oliveira.(2021).Spatial prediction of soil surface properties in an arid region using synthetic soil image and machine learning.GEOCARTO INTERNATIONAL.
MLA	Naimi, Salman,et al."Spatial prediction of soil surface properties in an arid region using synthetic soil image and machine learning".GEOCARTO INTERNATIONAL (2021).