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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
Wang, Zong1,3; Shi, Wenjiao2,3; Zhou, Wei1,3,5,6; Li, Xiaoyan4; Yue, Tianxiang1,3
2020-04-15
Source PublicationGEODERMA
ISSN0016-7061
Volume365Pages:16
Corresponding AuthorYue, Tianxiang(yue@lreis.ac.cn)
AbstractDigital 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.
KeywordSoil particle size fractions Log-ratio transformation Boosted regression tree Random forest Regression kriging
DOI10.1016/j.geoderma.2020.114214
WOS KeywordHEIHE RIVER-BASIN ; SPATIAL PREDICTION ; ORGANIC-CARBON ; RANDOM FOREST ; COMPOSITIONAL DATA ; CLASSIFICATION ; TEXTURE ; TREE ; REGION ; PERFORMANCE
Indexed BySCI
Language英语
Funding ProjectNational 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
Funding OrganizationNational 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
WOS Research AreaAgriculture
WOS SubjectSoil Science
WOS IDWOS:000518707700009
PublisherELSEVIER
Citation statistics
Cited Times:3[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/133125
Collection中国科学院地理科学与资源研究所
Corresponding AuthorYue, Tianxiang
Affiliation1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China
3.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
4.Beijing Normal Univ, Coll Resources Sci & Technol, Beijing 100875, Peoples R China
5.Southwest Univ, Sch Geog Sci, Chongqing 400715, Peoples R China
6.Chongqing Jiaotong Univ, Coll Architecture & Urban Planning, Dept Geog & Land & Resources, Xuefu Rd 66, Chongqing 400074, Peoples R China
Recommended Citation
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.
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