干旱区生态环境知识资源中心(Arid): Predicting soil depth in a large and complex area using machine learning and environmental correlations

Arid

DOI	10.1016/S2095-3119(21)63692-4
	Predicting soil depth in a large and complex area using machine learning and environmental correlations
	Liu, Feng; Yang, Fei; Zhao, Yu-guo; Zhang, Gan-lin; Li, De-cheng
通讯作者	Zhang, GL
来源期刊	JOURNAL OF INTEGRATIVE AGRICULTURE
ISSN	2095-3119
出版年	2022
卷号	21 期号:8 页码:2422-2434
英文摘要	Soil depth is critical for eco-hydrological modeling, carbon storage calculation and land evaluation. However, its spatial variation is poorly understood and rarely mapped. With a limited number of sparse samples, how to predict soil depth in a large area of complex landscapes is still an issue. This study constructed an ensemble machine learning model, i.e., quantile regression forest, to quantify the relationship between soil depth and environmental conditions. The model was then combined with a rich set of environmental covariates to predict spatial variation of soil depth and straightforwardly estimate the associated predictive uncertainty in the 140 000 km2 Heihe River basin of northwestern China. A total of 275 soil depth observation points and 26 covariates were used. The results showed a model predictive accuracy with coefficient of determination (R2) of 0.587 and root mean square error (RMSE) of 2.98 cm (square root scale), i.e., almost 60% of soil depth variation explained. The resulting soil depth map clearly exhibited regional patterns as well as local details. Relatively deep soils occurred in low lying landscape positions such as valley bottoms and plains while shallow soils occurred in high and steep landscape positions such as hillslopes, ridges and terraces. The oases had much deeper soils than outside semi-desert areas, the middle of an alluvial plain had deeper soils than its margins, and the middle of a lacustrine plain had shallower soils than its margins. Large predictive uncertainty mainly occurred in areas with a lack of soil survey points. Both pedogenic and geomorphic processes contributed to the shaping of soil depth pattern of this basin but the latter was dominant. This findings may be applicable to other similar basins in cold and arid regions around the world.
英文关键词	digital soil mapping spatial variation uncertainty machine learning soil -landscape model soil depth
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000841761100001
WOS关键词	THICKNESS ; LANDSCAPE ; UNCERTAINTY ; FOREST ; WATER ; EROSION ; CHINA ; MODEL
WOS类目	Agriculture, Multidisciplinary
WOS研究方向	Agriculture
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/393540
推荐引用方式 GB/T 7714	Liu, Feng,Yang, Fei,Zhao, Yu-guo,et al. Predicting soil depth in a large and complex area using machine learning and environmental correlations[J],2022,21(8):2422-2434.
APA	Liu, Feng,Yang, Fei,Zhao, Yu-guo,Zhang, Gan-lin,&Li, De-cheng.(2022).Predicting soil depth in a large and complex area using machine learning and environmental correlations.JOURNAL OF INTEGRATIVE AGRICULTURE,21(8),2422-2434.
MLA	Liu, Feng,et al."Predicting soil depth in a large and complex area using machine learning and environmental correlations".JOURNAL OF INTEGRATIVE AGRICULTURE 21.8(2022):2422-2434.