气候变化领域集成服务门户(CCPortal): Machine learning for digital soil mapping: Applications, challenges and suggested solutions

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DOI	10.1016/j.earscirev.2020.103359
	Machine learning for digital soil mapping: Applications, challenges and suggested solutions
	Wadoux A.M.J.-C.; Minasny B.; McBratney A.B.
发表日期	2020
ISSN	0012-8577
卷号	210
英文摘要	The uptake of machine learning (ML) algorithms in digital soil mapping (DSM) is transforming the way soil scientists produce their maps. Within the past two decades, soil scientists have applied ML to a wide range of scenarios, by mapping soil properties or classes with various ML algorithms, on spatial scale from the local to the global, and with depth. The wide adoption of ML for soil mapping was made possible by the increase in data availability, the ease of accessing environmental spatial data, and the development of software solutions aided by computational tools to analyse them. In this article, we review the current use of ML in DSM, identify the key challenges and suggest solutions from the existing literature. There is a growing interest of the use of ML in DSM. Most studies emphasize prediction and accuracy of the predicted maps for applications, such as baseline production of quantitative soil information. Few studies account for existing soil knowledge in the modelling process or quantify the uncertainty of the predicted maps. Further, we discuss the challenges related to the application of ML for soil mapping and suggest solutions from existing studies in the natural sciences. The challenges are: sampling, resampling, accounting for the spatial information, multivariate mapping, uncertainty analysis, validation, integration of pedological knowledge and interpretation of the models. Overall, the current literature shows few attempts in understanding the underlying soil structure or process using the predicted maps and the ML model, for example by generating hypotheses on mechanistic relationships among variables. In this regard, several additional challenging aspects need to be considered, such as the inclusion of pedological knowledge in the ML algorithm or the interpretability of the calibrated ML model. Tackling these challenges is critical for ML to gain credibility and scientific consistency in soil science. We conclude that for future developments, ML could incorporate three core elements: plausibility, interpretability, and explainability, which will trigger soil scientists to couple model prediction with pedological explanation and understanding of the underlying soil processes. © 2020 Elsevier B.V.
英文关键词	Data mining; Geostatistics; Pedometrics; Random forest; Soil science; Spatial data
语种	英语
scopus关键词	algorithm; data mining; geostatistics; machine learning; mapping; pedology; soil property
来源期刊	EARTH-SCIENCE REVIEWS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/209506
作者单位	Sydney Institute of Agriculture & School of Life and Environmental Sciences, The University of Sydney, New South Wales, Australia
推荐引用方式 GB/T 7714	Wadoux A.M.J.-C.,Minasny B.,McBratney A.B.. Machine learning for digital soil mapping: Applications, challenges and suggested solutions[J],2020,210.
APA	Wadoux A.M.J.-C.,Minasny B.,&McBratney A.B..(2020).Machine learning for digital soil mapping: Applications, challenges and suggested solutions.EARTH-SCIENCE REVIEWS,210.
MLA	Wadoux A.M.J.-C.,et al."Machine learning for digital soil mapping: Applications, challenges and suggested solutions".EARTH-SCIENCE REVIEWS 210(2020).