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(Article of periodic en Anglais - 2013)

Document title

Spatially distributed modeling of soil organic matter across China : An application of artificial neural network approach

Authors(s) and Affiliation(s)

LI Q.-Q. (1 2) ; YUE T.-X. (2) ; WANG C.-Q. (1) ; ZHANG W.-J. (3) ; YU Y. (4) ; LI B. (1) ; YANG J. (1) ; BAI G.-C. (1) ;
(1) College of Resources and Environment, Sichuan Agricultural Univ., Chengdu, CHINE
(2) Inst. of Geographic Sciences and Natural Resources Research, Beijing, CHINE
(3) State Key Lab. of Hydraulics and Mountain River Engineering, Sichuan Univ., Chengdu, CHINE
(4) College of Forestry, Agricultural Univ., Yaan, CHINE

Abstract

This study proposed a radial basis function neural networks model (RBFNN), combined with principal component analysis (PCA), to predict the spatial distribution of soil organic matter (SOM) content across China. To assess its feasibility, 6 241 soil samples collected during the second national soil survey period were used. This approach obtained lower prediction errors than multiple linear regression and regression kriging. This approach also produced a more realistic spatial pattern of soil organic matter. The result suggests that the proposed method can play a vital role in improving prediction accuracy of SOM within a large area

Source

Article of periodic

published at : Catena / ISSN 0341-8162

Editor : Catena, Cremlingen-Destedt - ALLEMAGNE (1973)

Millesime : 2013, vol. 104 [pp. 210-218]

Bibliographic references : 37 ref.

Collation : 4 fig., 6 tabl.

Language

Anglais

INIST-CNRS, Cote INIST : 16767

Digital Object Identifier

Go to electronic document thanks to its DOI : doi:10.1016/j.catena.2012.11.012

Tous droits réservés © Prodig - Bibliographie Géographique Internationale (BGI), 2013
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