SoilGrids1km--global soil information based on automated mapping.
Tomislav Hengl,Jorge Mendes de Jesus,R. A. MacMillan,Niels H. Batjes,Gerard B. M. Heuvelink,Eloi Ribeiro,Alessandro Samuel-Rosa,Bas Kempen,Johan G. B. Leenaars,Markus G. Walsh,Maria Ruiperez Gonzalez +10 more
TLDR
SoilGrids1km provides an initial set of examples of soil spatial data for input into global models at a resolution and consistency not previously available and results of regression modeling indicate that the most useful covariates for modeling soils at the global scale are climatic and biomass indices, lithology, and taxonomic mapping units derived from conventional soil survey.Abstract:
Background: Soils are widely recognized as a non-renewable natural resource and as biophysical carbon sinks. As such, there is a growing requirement for global soil information. Although several global soil information systems already exist, these tend to suffer from inconsistencies and limited spatial detail. Methodology/Principal Findings: We present SoilGrids1km — a global 3D soil information system at 1 km resolution — containing spatial predictions for a selection of soil properties (at six standard depths): soil organic carbon (g kg21), soil pH, sand, silt and clay fractions (%), bulk density (kg m23), cation-exchange capacity (cmol+/kg), coarse fragments (%), soil organic carbon stock (t ha21), depth to bedrock (cm), World Reference Base soil groups, and USDA Soil Taxonomy suborders. Our predictions are based on global spatial prediction models which we fitted, per soil variable, using a compilation of major international soil profile databases (ca. 110,000 soil profiles), and a selection of ca. 75 global environmental covariates representing soil forming factors. Results of regression modeling indicate that the most useful covariates for modeling soils at the global scale are climatic and biomass indices (based on MODIS images), lithology, and taxonomic mapping units derived from conventional soil survey (Harmonized World Soil Database). Prediction accuracies assessed using 5–fold cross-validation were between 23–51%. Conclusions/Significance: SoilGrids1km provide an initial set of examples of soil spatial data for input into global models at a resolution and consistency not previously available. Some of the main limitations of the current version of SoilGrids1km are: (1) weak relationships between soil properties/classes and explanatory variables due to scale mismatches, (2) difficulty to obtain covariates that capture soil forming factors, (3) low sampling density and spatial clustering of soil profile locations. However, as the SoilGrids system is highly automated and flexible, increasingly accurate predictions can be generated as new input data become available. SoilGrids1km are available for download via http://soilgrids.org under a Creative Commons Non Commercial license.read more
Citations
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Modern Applied Statistics With S
TL;DR: The modern applied statistics with s is universally compatible with any devices to read, and is available in the digital library an online access to it is set as public so you can download it instantly.
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%.
Journal ArticleDOI
The biomass distribution on Earth
TL;DR: The overall biomass composition of the biosphere is assembled, establishing a census of the ≈550 gigatons of carbon (Gt C) of biomass distributed among all of the kingdoms of life and shows that terrestrial biomass is about two orders of magnitude higher than marine biomass and estimate a total of ≈6 Gt C of marine biota, doubling the previous estimated quantity.
Journal ArticleDOI
An assessment of the global impact of 21st century land use change on soil erosion.
Pasquale Borrelli,David A. Robinson,Larissa R. Fleischer,Emanuele Lugato,Cristiano Ballabio,Christine Alewell,Katrin Meusburger,Sirio Modugno,Brigitta Schütt,Vito Ferro,Vincenzo Bagarello,Kristof Van Oost,Luca Montanarella,Panos Panagos +13 more
TL;DR: An unprecedentedly high resolution global potential soil erosion model is presented, using a combination of remote sensing, GIS modelling and census data, that indicates a potential overall increase in global soil erosion driven by cropland expansion.
Journal ArticleDOI
Quantifying global soil carbon losses in response to warming
Thomas W. Crowther,Katherine Todd-Brown,Clara W. Rowe,William R. Wieder,Joanna C. Carey,Megan B. Machmuller,L. Basten Snoek,Shibo Fang,Guangsheng Zhou,Steven D. Allison,John M. Blair,Scott D. Bridgham,Andrew J. Burton,Yolima Carrillo,Peter B. Reich,Peter B. Reich,James S. Clark,Aimée T. Classen,Feike A. Dijkstra,Bo Elberling,Bridget A. Emmett,Marc Estiarte,Serita D. Frey,Ji-Xun Guo,John Harte,Lifen Jiang,Bart R. Johnson,György Kröel-Dulay,Klaus Steenberg Larsen,Hjalmar Laudon,Jocelyn M. Lavallee,Jocelyn M. Lavallee,Yiqi Luo,Yiqi Luo,Massimo Lupascu,Linna Ma,Sven Marhan,Anders Michelsen,Jacqueline E. Mohan,Shuli Niu,Elise Pendall,Josep Peñuelas,Laurel Pfeifer-Meister,Christian Poll,Sabine Reinsch,Lorien L. Reynolds,Inger Kappel Schmidt,Seeta A. Sistla,Noah W. Sokol,Pamela H. Templer,Kathleen K. Treseder,Jeffrey M. Welker,Mark A. Bradford +52 more
TL;DR: In this article, the authors present a comprehensive analysis of warming-induced changes in soil carbon stocks by assembling data from 49 field experiments located across North America, Europe and Asia, and provide estimates of soil carbon sensitivity to warming that may help to constrain Earth system model projections.
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