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      SoilGrids250m: Global gridded soil information based on machine learning

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          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 methods—random forest and gradient boosting and/or multinomial logistic regression—as 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.

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          Applied Predictive Modeling

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            The shuttle radar topography mission—a new class of digital elevation models acquired by spaceborne radar

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              System for Automated Geoscientific Analyses (SAGA) v. 2.1.4

              The System for Automated Geoscientific Analyses (SAGA) is an open source geographic information system (GIS), mainly licensed under the GNU General Public License. Since its first release in 2004, SAGA has rapidly developed from a specialized tool for digital terrain analysis to a comprehensive and globally established GIS platform for scientific analysis and modeling. SAGA is coded in C++ in an object oriented design and runs under several operating systems including Windows and Linux. Key functional features of the modular software architecture comprise an application programming interface for the development and implementation of new geoscientific methods, a user friendly graphical user interface with many visualization options, a command line interpreter, and interfaces to interpreted languages like R and Python. The current version 2.1.4 offers more than 600 tools, which are implemented in dynamically loadable libraries or shared objects and represent the broad scopes of SAGA in numerous fields of geoscientific endeavor and beyond. In this paper, we inform about the system's architecture, functionality, and its current state of development and implementation. Furthermore, we highlight the wide spectrum of scientific applications of SAGA in a review of published studies, with special emphasis on the core application areas digital terrain analysis, geomorphology, soil science, climatology and meteorology, as well as remote sensing.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                2017
                16 February 2017
                : 12
                : 2
                : e0169748
                Affiliations
                [1 ]ISRIC — World Soil Information, Wageningen, the Netherlands
                [2 ]Faculty of Civil Engineering, University of Belgrade, Belgrade, Serbia
                [3 ]GILab Ltd, Belgrade, Serbia
                [4 ]School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou, China
                [5 ]Institut für Medizinische Biometrie und Statistik, Lübeck, Germany
                [6 ]Agriculture and Agri-Food Canada, Ottawa (Ontario), Canada
                [7 ]Department of Geodesy and Geoinformation, Vienna University of Technology, Vienna, Austria
                [8 ]University of Delaware, Newark (DE), United States of America
                [9 ]LandMapper Environmental Solutions Inc., Edmonton (Alberta), Canada
                [10 ]Envirometrix Inc., Wageningen, the Netherlands
                Pacific Northwest National Laboratory, UNITED STATES
                Author notes

                Competing Interests: Aleksandar Blagotić is employee and web-developer of GILAB DOO. There are no patents, products in development or marketed products to declare. This does not alter our adherence to all the PLOS ONE policies on sharing data and materials.

                • Conceptualization: TH JMJ GBMH MK WS MAG RAM JGBL IW BK.

                • Data curation: MRG WS XG RAM NHB JGBL SM.

                • Formal analysis: TH GBMH MK WS MNW BK.

                • Funding acquisition: TH GBMH.

                • Investigation: TH JMJ GBMH MRG WS BK.

                • Methodology: TH JMJ GBMH MRG MK WS MNW BBM RAM SM BK.

                • Project administration: TH JMJ MK.

                • Resources: JMJ MK AB BBM ER.

                • Software: TH JMJ MRG MK AB WS MNW ER BK.

                • Supervision: TH GBMH.

                • Validation: MRG XG MAG RV RAM NHB JGBL ER IW SM.

                • Visualization: TH JMJ AB MK ER.

                • Writing – original draft: TH JMJ GBMH WS MNW BBM RAM NHB IW BK.

                • Writing – review & editing: TH JMJ GBMH MK WS MNW BBM RAM NHB IW BK.

                Author information
                http://orcid.org/0000-0002-9921-5129
                Article
                PONE-D-16-30775
                10.1371/journal.pone.0169748
                5313206
                28207752
                69c7c75e-c76c-4a3b-b5ad-5121b4429f13
                © 2017 Hengl et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 1 August 2016
                : 21 December 2016
                Page count
                Figures: 14, Tables: 4, Pages: 40
                Funding
                ISRIC is a non-profit organization primarily funded by the Dutch government. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. GILAB DOO provided support in the form of salaries for author AB, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of this author are articulated in the ‘author contributions’ section.
                Categories
                Research Article
                Biology and Life Sciences
                Ecology
                Ecological Metrics
                Species Diversity
                Shannon Index
                Ecology and Environmental Sciences
                Ecology
                Ecological Metrics
                Species Diversity
                Shannon Index
                Research and Analysis Methods
                Mathematical and Statistical Techniques
                Statistical Methods
                Forecasting
                Physical Sciences
                Mathematics
                Statistics (Mathematics)
                Statistical Methods
                Forecasting
                Biology and Life Sciences
                Neuroscience
                Cognitive Science
                Artificial Intelligence
                Machine Learning
                Computer and Information Sciences
                Artificial Intelligence
                Machine Learning
                Biology and Life Sciences
                Agriculture
                Agricultural Soil Science
                Ecology and Environmental Sciences
                Soil Science
                Agricultural Soil Science
                Biology and Life Sciences
                Organisms
                Plants
                Trees
                Engineering and Technology
                Remote Sensing
                Earth Sciences
                Glaciology
                Glaciers
                Ecology and Environmental Sciences
                Soil Science
                Soil Ecology
                Custom metadata
                SoilGrids are available under the Open Database License (ODbl) v1.0 and can be downloaded from www.soilgrids.org and/or ftp.soilgrids.org without restrictions. SoilGrids250m data has already been released in July 2016 (see: http://www.isric.org/content/isric-releases-upgraded-soilgrids-system-two-times-improved-accuracy-predictions) Access to SoilGrids maps is provided via a soil web mapping portal at SoilGrids.org; through a Web Coverage Service (WCS); and via the SoilInfo App, hence access to data is without restrictions. All the code used to generate SoilGrids250m predictions is fully documented via: https://github.com/ISRICWorldSoil/SoilGrids250m/.

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