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.

/pdf/soilgrids250m-global-gridded-soil-information-based-on-2xwvokp8f3.pdf

SoilGrids250m: Global gridded soil information based on machine learning

Soil invertebrates and ecosystem services

Resumen en: The aim of this study was a survey of the estimated costs of soil erosion, an issue of fundamental importance in view of the current worldwide discussion...

http://www.redalyc.org/pdf/1802/Resumenes/Abstract_180218547001_2.pdf

Revista Brasileira de Ciência do Solo

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.

/pdf/soilgrids1km-global-soil-information-based-on-automated-xaly8v3j4o.pdf

SoilGrids1km--global soil information based on automated mapping.

Using Imaging Spectroscopy to study soil properties

The scarcity of comprehensive characterizations of soils associated to gentle summit depressions of the Northeastern Brazilian Coastal Plains justifies this work, which had as objective to provide basic information for the more diverse agricultural and non-agricultural uses. For that, representative soils (Spodosols or similar soils) from these environments were selected in Alagoas, Sergipe and Bahia states. This approach included characterization of morphological, mineralogical and micromorphological properties of the soil profiles, employing standard procedures. The morphological characterization corroborated the effect of the podzolization process during the formation of these soils. The mineralogy of the clay fraction of these soils was basically composed of kaolinite and quartz, which, associated to the very sandy texture, helped in the understanding of the obtained data. The soil micromorphological study, besides confirming the field morphology, mainly in regard to the strong cementation, aggregated value to the work in terms of the secure identification of the clay illuviation process (non-identified in the field), in association with the dominant podzolization process.

MORPHOLOGY, MINERALOGY AND MICROMORPHOLOGY OF SOILS ASSOCIATED TO SUMMIT DEPRESSIONS OF THE NORTHEASTERN BRAZILIAN COASTAL PLAINS Morfologia, mineralogia e micromorfologia de solos de depressões de topo de Tabuleiros Costeiros do Nordeste Brasileiro

Miguel Cooper

Papers

MORPHOLOGY, MINERALOGY AND MICROMORPHOLOGY OF SOILS ASSOCIATED TO SUMMIT DEPRESSIONS OF THE NORTHEASTERN BRAZILIAN COASTAL PLAINS Morfologia, mineralogia e micromorfologia de solos de depressões de topo de Tabuleiros Costeiros do Nordeste Brasileiro