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Comparison of additive and isometric log-ratio transformations combined with machine learning and regression kriging models for mapping soil particle size fractions

文献类型：期刊论文

作者Wang, Zong2,4; Shi, Wenjiao3,4; Zhou, Wei1,2,4,6; Li, Xiaoyan5; Yue, Tianxiang2,4
刊名GEODERMA
出版日期2020-04-15
卷号365页码:16
关键词Soil particle size fractions Log-ratio transformation Boosted regression tree Random forest Regression kriging
ISSN号0016-7061
DOI10.1016/j.geoderma.2020.114214
通讯作者Yue, Tianxiang(yue@lreis.ac.cn)
英文摘要Digital soil mapping approaches relating to the soil particle size fractions (psf) face the challenge around how to establish the statistical or geostatistical models from large sets of environmental variables, especially in a situation with sparse soil profile data. Recently, many machine learning (ML) models have sprung up with advantages over statistical models. However, few studies focused on the comprehensive comparative analyses between ML and geostatistical models in the soil psf mapping. And the exploration of optimal combination of data transformation and model simulation was even less. Therefore, two transformed methods such as additive log-ratio (ALR) and isometric log-ratio (ILR) transformations combine with two ML models such as boosted regression tree (BRT), random forest (RF) and a classic geostatistical model of regression kriging (RK) were implemented to map soil psf in the Heihe River basin, China. A total of 640 samples and thirteen scorpan factors were collected and used for the comprehensive comparative analysis. Results showed that the scorpan factors such as temperature, precipitation, elevation, soil type, soil organic carbon, vegetation types and normalized difference vegetation index had important impacts on the soil psf mapping. ILR transformation was better than ALR transformation with advantage of improving stability of data distributions and ML models could also improve the mapping performance in comparison with RK models for better handling candidate factors. For these ML models, the RF models had better accuracy performance than the BRT models. In contrast, ILR transformation combined with RF model (ILR_RF) had the best performance, with the lowest root mean square error values (sand, 15.35%; silt, 14.20%; and clay, 6.66%), Aitchison distance value (0.86), standardized residual sum of squares value (0.60), and the highest concordance correlation coefficient value (0.73) and coefficient of determination value (56.69%) for clay content. In addition, ILR_RF had a relatively higher right ratio of soil texture type (68.44%) and better predict performance for most soil texture types. The predicted maps generated from ILR_RF presented more reasonable and smoother transitions. In the future, more ML models should be explored and more variables related to soil psf should be introduced into the models to improve the predictive performance.
WOS关键词HEIHE RIVER-BASIN ; SPATIAL PREDICTION ; ORGANIC-CARBON ; RANDOM FOREST ; COMPOSITIONAL DATA ; CLASSIFICATION ; TEXTURE ; TREE ; REGION ; PERFORMANCE
资助项目National Natural Science Foundation of China[41421001] ; National Natural Science Foundation of China[41590844] ; National Natural Science Foundation of China[41930647] ; Innovation Project of State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Science and Natural Resources Research, Chinese Academy of Sciences[O88RA600YA] ; Key Laboratory of Land Surface Pattern and Simulation
WOS研究方向Agriculture
语种英语
WOS记录号WOS:000518707700009
出版者ELSEVIER
资助机构National Natural Science Foundation of China ; Innovation Project of State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Science and Natural Resources Research, Chinese Academy of Sciences ; Key Laboratory of Land Surface Pattern and Simulation
源URL[http://ir.igsnrr.ac.cn/handle/311030/133124]  
专题中国科学院地理科学与资源研究所
通讯作者Yue, Tianxiang
作者单位1.Southwest Univ, Sch Geog Sci, Chongqing 400715, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China
4.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
5.Beijing Normal Univ, Coll Resources Sci & Technol, Beijing 100875, Peoples R China
6.Chongqing Jiaotong Univ, Coll Architecture & Urban Planning, Dept Geog & Land & Resources, Xuefu Rd 66, Chongqing 400074, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Zong,Shi, Wenjiao,Zhou, Wei,et al. Comparison of additive and isometric log-ratio transformations combined with machine learning and regression kriging models for mapping soil particle size fractions[J]. GEODERMA,2020,365:16.
APA Wang, Zong,Shi, Wenjiao,Zhou, Wei,Li, Xiaoyan,&Yue, Tianxiang.(2020).Comparison of additive and isometric log-ratio transformations combined with machine learning and regression kriging models for mapping soil particle size fractions.GEODERMA,365,16.
MLA Wang, Zong,et al."Comparison of additive and isometric log-ratio transformations combined with machine learning and regression kriging models for mapping soil particle size fractions".GEODERMA 365(2020):16.

入库方式： OAI收割

来源：地理科学与资源研究所

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