T
Tomislav Hengl
Researcher at University of Amsterdam
Publications - 105
Citations - 10848
Tomislav Hengl is an academic researcher from University of Amsterdam. The author has contributed to research in topics: Soil carbon & Soil water. The author has an hindex of 34, co-authored 98 publications receiving 7918 citations. Previous affiliations of Tomislav Hengl include International Institute of Minnesota & Wageningen University and Research Centre.
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Journal ArticleDOI
SoilGrids250m: Global gridded soil information based on machine learning
Tomislav Hengl,Jorge Mendes de Jesus,Gerard B. M. Heuvelink,Maria Ruiperez Gonzalez,Milan Kilibarda,Aleksandar Blagotić,Wei Shangguan,Marvin N. Wright,Xiaoyuan Geng,Bernhard Bauer-Marschallinger,Mario Guevara,Rodrigo Vargas,R. A. MacMillan,Niels H. Batjes,Johan G. B. Leenaars,Eloi Ribeiro,Ichsani Wheeler,Stephan Mantel,Bas Kempen +18 more
TL;DR: Improvements in the relative accuracy considering the amount of variation explained, in comparison to the previous version of SoilGrids at 1 km spatial resolution, range from 60 to 230%.
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A generic framework for spatial prediction of soil variables based on regression-kriging
TL;DR: In this paper, a methodological framework for spatial prediction based on regression-kriging is described and compared with ordinary kriging and plain regression, which can adopt both continuous and categorical soil variables in a semi-automated or automated manner.
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About regression-kriging: From equations to case studies
TL;DR: This paper discusses the characteristics of regression-kriging (RK), its strengths and limitations, and illustrates these with a simple example and three case studies, and addresses pragmatic issues: implementation of RK in existing software packages, comparison ofRK with alternative interpolation techniques, and practical limitations to using RK.
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Finding the right pixel size
TL;DR: This paper discusses empirical and analytical rules to select a suitable grid resolution for output maps and based on the inherent properties of the input data to derive the true optimal grid resolution that maximizes the predictive capabilities or information content of a map.
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Soil carbon debt of 12,000 years of human land use
TL;DR: A machine learning-based model was fitted using a global compilation of SOC data and the History Database of the Global Environment land use data in combination with climatic, landform and lithology covariates, demonstrating that there are identifiable regions which can be targeted for SOC restoration efforts.