气候变化领域集成服务门户(CCPortal): Digital soil mapping using machine learning-based methods to predict soil organic carbon in two different districts in the Czech Republic

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DOI	10.17221/119/2023-SWR
	Digital soil mapping using machine learning-based methods to predict soil organic carbon in two different districts in the Czech Republic
	Nozari, Shahin; Pahlavan-Rad, Mohammad Reza; Brungard, Colby; Heung, Brandon; Boruvka, Lubos
发表日期	2024
ISSN	1801-5395
EISSN	1805-9384
起始页码	19
结束页码	1
卷号	19 期号:1
英文摘要	Soil organic carbon (SOC) is an important soil characteristic as well as a way how to mitigate climate change. Information on its content and spatial distribution is thus crucial. Digital soil mapping (DSM) is a suitable way to evaluate spatial distribution of soil properties thanks to its ability to obtain accurate information about soil. This research aims to apply machine learning algorithms using various environmental covariates to generate digital SOC maps for mineral topsoils in the Liberec and Domazlice districts, located in the Czech Republic. The soil class, land cover, and geology maps as well as terrain covariates extracted from the digital elevation model and remote sensing data were used as covariates in modelling. The spatial distribution of SOC was predicted based on its relationships with covariates using random forest (RF), cubist, and quantile random forest (QRF) models. Results of the RF model showed that land cover (vegetation) and elevation were the most important environmental variables in the SOC prediction in both districts. The RF had better efficiency and accuracy than the cubist and QRF to predict SOC in both districts. The greatest R2 value (0.63) was observed in the Domazlice district using the RF model. However, cubist and QRF showed appropriate performance in both districts, too.
英文关键词	cubist; DSM; quantile random forest; random forest; SOC
语种	英语
WOS研究方向	Agriculture ; Water Resources
WOS类目	Soil Science ; Water Resources
WOS记录号	WOS:001148213900001
来源期刊	SOIL AND WATER RESEARCH
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/295139
作者单位	Czech University of Life Sciences Prague; New Mexico State University; Dalhousie University
推荐引用方式 GB/T 7714	Nozari, Shahin,Pahlavan-Rad, Mohammad Reza,Brungard, Colby,et al. Digital soil mapping using machine learning-based methods to predict soil organic carbon in two different districts in the Czech Republic[J],2024,19(1).
APA	Nozari, Shahin,Pahlavan-Rad, Mohammad Reza,Brungard, Colby,Heung, Brandon,&Boruvka, Lubos.(2024).Digital soil mapping using machine learning-based methods to predict soil organic carbon in two different districts in the Czech Republic.SOIL AND WATER RESEARCH,19(1).
MLA	Nozari, Shahin,et al."Digital soil mapping using machine learning-based methods to predict soil organic carbon in two different districts in the Czech Republic".SOIL AND WATER RESEARCH 19.1(2024).