SoilGrids250m: Global gridded soil information based on machine learning
| Author(s) | Heng, Tomislav | |
| Author(s) | Mendes de Jesus, Jorge | |
| Author(s) | Heuvelink, Gerard B. M. | |
| Author(s) | Ruiperez Gonzalez, Maria | |
| Author(s) | Kilibarda, Milan | |
| Author(s) | Blagotić, Aleksandar | |
| Author(s) | Shangguan, Wei | |
| Author(s) | Wright, Marvin N. | |
| Author(s) | Geng, Xiaoyuan | |
| Author(s) | Bauer-Marschallinger, Bernhard | |
| Author(s) | Guevara, Mario Antonio | |
| Author(s) | Vargas, Rodrigo | |
| Author(s) | MacMillan, Robert A. | |
| Author(s) | Batjes, Niels H. | |
| Author(s) | Leenaars, Johan G. B. | |
| Author(s) | Ribeiro, Eloi | |
| Author(s) | Wheeler, Ichsani | |
| Author(s) | Mantel, Stephan | |
| Author(s) | Kempen, Bas | |
| Ordered Author | 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 Antonio Guevara, Rodrigo Vargas, Robert A. MacMillan, Niels H. Batjes, Johan G. B. Leenaars, Eloi Ribeiro, Ichsani Wheeler, Stephan Mantel, Bas Kempen | |
| UD Author | Guevara, Mario Antonio | en_US |
| UD Author | Vargas, Rodrigo | en_US |
| Date Accessioned | 2018-08-08T14:53:21Z | |
| Date Available | 2018-08-08T14:53:21Z | |
| Copyright Date | Copyright © 2017 Hengl et al. | en_US |
| Publication Date | 2017-02-16 | |
| Description | Publisher's PDF | en_US |
| Abstract | This paper describes the technical development and accuracy assessment of the most recent and improved version of the SoilGrids system at 250m resolution (June 2016 update). SoilGrids provides global predictions for standard numeric soil properties (organic carbon, bulk density, Cation Exchange Capacity (CEC), pH, soil texture fractions and coarse fragments) at seven standard depths (0, 5, 15, 30, 60, 100 and 200 cm), in addition to predictions of depth to bedrock and distribution of soil classes based on the World Reference Base (WRB) and USDA classification systems (ca. 280 raster layers in total). Predictions were based on ca. 150,000 soil profiles used for training and a stack of 158 remote sensing-based soil covariates (primarily derived from MODIS land products, SRTM DEM derivatives, climatic images and global landform and lithology maps), which were used to fit an ensemble of machine learning methodsÐrandom forest and gradient boosting and/or multinomial logistic regressionÐas implemented in the R packages ranger, xgboost, nnet and caret. The results of 10±fold cross-validation show that the ensemble models explain between 56% (coarse fragments) and 83% (pH) of variation with an overall average of 61%. 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%. Improvements can be attributed to: (1) the use of machine learning instead of linear regression, (2) to considerable investments in preparing finer resolution covariate layers and (3) to insertion of additional soil profiles. Further development of SoilGrids could include refinement of methods to incorporate input uncertainties and derivation of posterior probability distributions (per pixel), and further automation of spatial modeling so that soil maps can be generated for potentially hundreds of soil variables. Another area of future research is the development of methods for multiscale merging of SoilGrids predictions with local and/or national gridded soil products (e.g. up to 50 m spatial resolution) so that increasingly more accurate, complete and consistent global soil information can be produced. SoilGrids are available under the Open Data Base License. | en_US |
| Department | University of Delaware. Department of Plant and Soil Sciences. | en_US |
| Citation | Hengl T, Mendes de Jesus J, Heuvelink GBM, Ruiperez Gonzalez M, Kilibarda M, Blagotić A, et al. (2017) SoilGrids250m: Global gridded soil information based on machine learning. PLoS ONE 12(2): e0169748. doi:10.1371/journal. pone.0169748 | en_US |
| DOI | 10.1371/journal.pone.0169748 | en_US |
| ISSN | 1932-6203 | en_US |
| URL | http://udspace.udel.edu/handle/19716/23665 | |
| Language | en_US | en_US |
| Publisher | PLOS (Public Library of Science) | en_US |
| dc.rights | Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. | en_US |
| dc.rights | This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. | en_US |
| dc.source | PLOS One | en_US |
| dc.source.uri | http://journals.plos.org/plosone/ | en_US |
| Title | SoilGrids250m: Global gridded soil information based on machine learning | en_US |
| Type | Article | en_US |
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