干旱区生态环境知识资源中心(Arid): Improving the spatial prediction of soil organic carbon using environmental covariates selection: A comparison of a group of environmental covariates

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DOI	10.1016/j.catena.2021.105723
	Improving the spatial prediction of soil organic carbon using environmental covariates selection: A comparison of a group of environmental covariates
	Zeraatpisheh, Mojtaba; Garosi, Younes; Owliaie, Hamid Reza; Ayoubi, Shamsollah; Taghizadeh-Mehrjardi, Ruhollah; Scholten, Thomas; Xu, Ming
通讯作者	Xu, M (corresponding author), Henan Univ, Henan Key Lab Earth Syst Observat & Modeling, Kaifeng 475004, Peoples R China. ; Xu, M (corresponding author), Henan Univ, Coll Geog & Environm Sci, Kaifeng 475004, Peoples R China.
来源期刊	CATENA
ISSN	0341-8162
EISSN	1872-6887
出版年	2022
卷号	208
英文摘要	In the digital soil mapping (DSM) framework, machine learning models quantify the relationship between soil observations and environmental covariates. Generally, the most commonly used covariates (MCC; e.g., topographic attributes and single-time remote sensing data, and legacy maps) were employed in DSM studies. Additionally, remote sensing time-series (RST) data can provide useful information for soil mapping. Therefore, the main aims of the study are to compare the MCC, the monthly Sentinel-2 time-series of vegetation indices dataset, and the combination of datasets (MCC + RST) for soil organic carbon (SOC) prediction in an arid agroecosystem in Iran. We used different machine learning algorithms, including random forest (RF), Cubist, support vector machine (SVM), and partial least square regression (PLSR). A total of 237 soil samples at 0-20 cm depths were collected. The 5-fold cross-validation technique was used to evaluate the modeling performance, and 50 bootstrap models were applied to quantify the prediction uncertainty. The results showed that the Cubist model performed the best with the MCC dataset (R-2 = 0.35, RMSE = 0.26%) and the combined dataset of MCC and RST (R-2 = 0.33, RMSE = 0.27%), while the RF model showed better results for the RST dataset (R-2 = 0.10, RMSE = 0.31%). Soil properties could explain the SOC variation in MCC and combined datasets (66.35% and 50.82%, respectively), while NDVI was the most controlling factor in the RST (50.22%). Accordingly, results showed that time-series vegetation indices did not have enough potential to increase SOC prediction accuracy. However, the combination of MCC and RST datasets produced SOC spatial maps with lower uncertainty. Therefore, future studies are required to explicitly explain the efficiency of time-series remotely-sensed data and their interrelationship with environmental covariates to predict SOC in arid regions with low SOC content.
英文关键词	Soil organic carbon Spatial prediction Environmental covariates Time-series vegetation indices Machine learning Uncertainty
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000700576300013
WOS关键词	DATA-MINING TECHNIQUES ; LAND-USE CHANGE ; TOPOGRAPHY ; NITROGEN ; MATTER ; REGION ; FOREST ; MANAGEMENT ; FERTILITY ; NORTHEAST
WOS类目	Geosciences, Multidisciplinary ; Soil Science ; Water Resources
WOS研究方向	Geology ; Agriculture ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/362830
作者单位	[Zeraatpisheh, Mojtaba; Xu, Ming] Henan Univ, Henan Key Lab Earth Syst Observat & Modeling, Kaifeng 475004, Peoples R China; [Zeraatpisheh, Mojtaba; Xu, Ming] Henan Univ, Coll Geog & Environm Sci, Kaifeng 475004, Peoples R China; [Garosi, Younes; Ayoubi, Shamsollah] Isfahan Univ Technol, Coll Agr, Dept Soil Sci, Esfahan 8415683111, Iran; [Owliaie, Hamid Reza] Univ Yasuj, Coll Agr, Yasuj, Iran; [Taghizadeh-Mehrjardi, Ruhollah; Scholten, Thomas] Univ Tubingen, Dept Geosci Soil Sci & Geomorphol, D-72070 Tubingen, Germany; [Taghizadeh-Mehrjardi, Ruhollah; Scholten, Thomas] Univ Tubingen, SFB 1070 ResourceCultures, Gartenstr 29, D-72074 Tubingen, Germany; [Taghizadeh-Mehrjardi, Ruhollah; Scholten, Thomas] Univ Tubingen, DFG Cluster Excellence Machine Learning, Tubingen, Germany
推荐引用方式 GB/T 7714	Zeraatpisheh, Mojtaba,Garosi, Younes,Owliaie, Hamid Reza,et al. Improving the spatial prediction of soil organic carbon using environmental covariates selection: A comparison of a group of environmental covariates[J],2022,208.
APA	Zeraatpisheh, Mojtaba.,Garosi, Younes.,Owliaie, Hamid Reza.,Ayoubi, Shamsollah.,Taghizadeh-Mehrjardi, Ruhollah.,...&Xu, Ming.(2022).Improving the spatial prediction of soil organic carbon using environmental covariates selection: A comparison of a group of environmental covariates.CATENA,208.
MLA	Zeraatpisheh, Mojtaba,et al."Improving the spatial prediction of soil organic carbon using environmental covariates selection: A comparison of a group of environmental covariates".CATENA 208(2022).