IGSNRR OpenIR
Incorporation of high accuracy surface modeling into machine learning to improve soil organic matter mapping
Wang, Zong1,2; Du, Zhengping1,2; Li, Xiaoyan3; Bao, Zhengyi1,2; Zhao, Na1,2; Yue, Tianxiang1,2
2021-10-01
Source PublicationECOLOGICAL INDICATORS
ISSN1470-160X
Volume129Pages:13
Corresponding AuthorZhao, Na(zhaon@lreis.ac.cn) ; Yue, Tianxiang(yue@lreis.ac.cn)
AbstractDigital soil mapping approaches related to soil organic matter (SOM) are crucial to quantify the process of the carbon cycle in terrestrial ecosystems and thus, can better manage soil fertility. Recently, many studies have compared machine learning (ML) models with traditional statistical models in digital soil mapping. However, few studies focused on the application of hybrid models that combine ML with statistical models to map SOM content, especially in loess areas, which have a complicated geomorphologic landscape. In this study, the trend prediction used two ML models, i.e., gradient boosting modeling and random forest (RF), and a traditional stepwise multiple linear regression plus interpolated residuals generated from two classic geostatistical models, i. e., ordinary kriging and inverse distance weighting, and a high accuracy surface modeling (HASM) were implemented to map SOM content in the Dongzhi Loess Tableland area of China. A total of 145 topsoil samples and heterogeneous environmental variables were collected to develop the hybrid models. Results showed that 18 variables related to soil properties, climate variables, terrain attributes, vegetation indices, and location attributes played an important role in SOM mapping. The models that incorporate ML algorithms and interpolated residuals to predict SOM variation were found to have a better ability to handle complex environment relationships. The HASM model outperformed traditional geostatistical models in interpolating the residuals. In contrast, RF combined with HASM residuals (RF_HASM) gave the best performance, with the lowest mean absolute error (1.69 g/kg), root mean square error (2.30 g/kg), and the highest coefficient of determination (0.57) and concordance correlation coefficient (0.69) values. Moreover, the spatial distribution pattern obtained with RF_HASM yielded a spatial distribution of SOM that better fit the actual distribution pattern of the study area. In conclusion, these results suggest that RF_HASM is particularly capable of improving the mapping accuracy of SOM content at the regional scale.
KeywordSoil organic matter Dongzhi Loess Tableland Gradient boosting modeling Random forest High accuracy surface modeling
DOI10.1016/j.ecolind.2021.107975
WOS KeywordSPATIAL PREDICTION ; RANDOM FOREST ; REGRESSION TREE ; CARBON STOCKS ; VARIABILITY ; VARIABLES ; GEOSTATISTICS ; PROPERTY ; PROVINCE ; TEXTURE
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[41930647] ; National Natural Science Foundation of China[42071374] ; Strategic Priority Research Program (A) of the Chinese Academy of Sciences[XDA20030203] ; Innovation Project of State Key Laboratory of Resources and Environmental Information System[O88RA600YA]
Funding OrganizationNational Natural Science Foundation of China ; Strategic Priority Research Program (A) of the Chinese Academy of Sciences ; Innovation Project of State Key Laboratory of Resources and Environmental Information System
WOS Research AreaBiodiversity & Conservation ; Environmental Sciences & Ecology
WOS SubjectBiodiversity Conservation ; Environmental Sciences
WOS IDWOS:000681696400007
PublisherELSEVIER
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/164726
Collection中国科学院地理科学与资源研究所
Corresponding AuthorZhao, Na; Yue, Tianxiang
Affiliation1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
3.Beijing Normal Univ, Fac Geog Sci, Sch Nat Resources, Beijing 100875, Peoples R China
Recommended Citation
GB/T 7714
Wang, Zong,Du, Zhengping,Li, Xiaoyan,et al. Incorporation of high accuracy surface modeling into machine learning to improve soil organic matter mapping[J]. ECOLOGICAL INDICATORS,2021,129:13.
APA Wang, Zong,Du, Zhengping,Li, Xiaoyan,Bao, Zhengyi,Zhao, Na,&Yue, Tianxiang.(2021).Incorporation of high accuracy surface modeling into machine learning to improve soil organic matter mapping.ECOLOGICAL INDICATORS,129,13.
MLA Wang, Zong,et al."Incorporation of high accuracy surface modeling into machine learning to improve soil organic matter mapping".ECOLOGICAL INDICATORS 129(2021):13.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Wang, Zong]'s Articles
[Du, Zhengping]'s Articles
[Li, Xiaoyan]'s Articles
Baidu academic
Similar articles in Baidu academic
[Wang, Zong]'s Articles
[Du, Zhengping]'s Articles
[Li, Xiaoyan]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Wang, Zong]'s Articles
[Du, Zhengping]'s Articles
[Li, Xiaoyan]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.