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Modern Applied Statistics With S

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

A census of the biomass on Earth is key for understanding the structure and dynamics of the biosphere. However, a global, quantitative view of how the biomass of different taxa compare with one another is still lacking. Here, we assemble the overall biomass composition of the biosphere, establishing a census of the ≈550 gigatons of carbon (Gt C) of biomass distributed among all of the kingdoms of life. We find that the kingdoms of life concentrate at different locations on the planet; plants (≈450 Gt C, the dominant kingdom) are primarily terrestrial, whereas animals (≈2 Gt C) are mainly marine, and bacteria (≈70 Gt C) and archaea (≈7 Gt C) are predominantly located in deep subsurface environments. We show 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. Our analysis reveals that the global marine biomass pyramid contains more consumers than producers, thus increasing the scope of previous observations on inverse food pyramids. Finally, we highlight that the mass of humans is an order of magnitude higher than that of all wild mammals combined and report the historical impact of humanity on the global biomass of prominent taxa, including mammals, fish, and plants.

https://www.pnas.org/content/pnas/115/25/6506.full.pdf

The biomass distribution on Earth

Human activity and related land use change are the primary cause of accelerated soil erosion, which has substantial implications for nutrient and carbon cycling, land productivity and in turn, worldwide socio-economic conditions. Here we present an unprecedentedly high resolution (250 × 250 m) global potential soil erosion model, using a combination of remote sensing, GIS modelling and census data. We challenge the previous annual soil erosion reference values as our estimate, of 35.9 Pg yr−1 of soil eroded in 2012, is at least two times lower. Moreover, we estimate the spatial and temporal effects of land use change between 2001 and 2012 and the potential offset of the global application of conservation practices. Our findings indicate a potential overall increase in global soil erosion driven by cropland expansion. The greatest increases are predicted to occur in Sub-Saharan Africa, South America and Southeast Asia. The least developed economies have been found to experience the highest estimates of soil erosion rates.

/pdf/an-assessment-of-the-global-impact-of-21st-century-land-use-rdg7ojdzqi.pdf

An assessment of the global impact of 21st century land use change on soil erosion.

The majority of the Earth's terrestrial carbon is stored in the soil. If anthropogenic warming stimulates the loss of this carbon to the atmosphere, it could drive further planetary warming. Despite evidence that warming enhances carbon fluxes to and from the soil, the net global balance between these responses remains uncertain. Here we 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. We find that the effects of warming are contingent on the size of the initial soil carbon stock, with considerable losses occurring in high-latitude areas. By extrapolating this empirical relationship to the global scale, we provide estimates of soil carbon sensitivity to warming that may help to constrain Earth system model projections. Our empirical relationship suggests that global soil carbon stocks in the upper soil horizons will fall by 30 ± 30 petagrams of carbon to 203 ± 161 petagrams of carbon under one degree of warming, depending on the rate at which the effects of warming are realized. Under the conservative assumption that the response of soil carbon to warming occurs within a year, a business-as-usual climate scenario would drive the loss of 55 ± 50 petagrams of carbon from the upper soil horizons by 2050. This value is around 12-17 per cent of the expected anthropogenic emissions over this period. Despite the considerable uncertainty in our estimates, the direction of the global soil carbon response is consistent across all scenarios. This provides strong empirical support for the idea that rising temperatures will stimulate the net loss of soil carbon to the atmosphere, driving a positive land carbon-climate feedback that could accelerate climate change.

/pdf/quantifying-global-soil-carbon-losses-in-response-to-warming-4fxdkawwiw.pdf

Quantifying global soil carbon losses in response to warming

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.

9 Geostatistical mapping can be defined as analytical production of maps by using field observations, auxiliary 10 information and a computer program that calculates values at locations of interest. The purpose of this guide 11 is to assist you in producing quality maps by using fully-operational open source software packages. It will first 12 introduce you to the basic principles of geostatistical mapping and regression-kriging, as the key prediction 13 technique, then it will guide you through software tools — R+gstat/geoR, SAGA GIS and Google Earth — 14 which will be used to prepare the data, run analysis and make final layouts. Geostatistical mapping is further 15 illustrated using seven diverse case studies: interpolation of soil parameters, heavy metal concentrations, 16 global soil organic carbon, species density distribution, distribution of landforms, density of DEM-derived 17 streams, and spatio-temporal interpolation of land surface temperatures. Unlike similar books from the “use 18 R” series, or purely GIS user manuals, this book specifically aims to bridge the gap between statistical and 19 geographical computing. Materials presented in this book have been used for the five–day advanced training 20 course “GEOSTAT: spatio-temporal data analysis with R+SAGA+Google Earth”, that is periodically organized 21 by the author and collaborators. Visit the book’s homepage to obtain a copy of the data sets and scripts used 22 in the exercises. 23 This is an Open Access Publication! 24

A Practical Guide to Geostatistical Mapping

Geomorphometry is the science of quantitative land-surface analysis. It draws upon mathematical, statistical, and image-processing techniques to quantify the shape of earth's topography at various spatial scales. The focus of geomorphometry is the calculation of surface-form measures (land-surface parameters) and features (objects), which may be used to improve the mapping and modelling of landforms to assist in the evaluation of soils, vegetation, land use, natural hazards, and other information. This book provides a practical guide to preparing Digital Elevation Models (DEM) for analysis and extracting land-surface parameters and objects from DEMs through a variety of software. It further offers detailed instructions on applying parameters and objects in soil, agricultural, environmental and earth sciences. This is a manual of state-of-the-art methods to serve the various researchers who use geomorphometry. Soil scientists will use this book to further learn the methods for classifying and measuring the chemical, biological, and fertility properties of soils and gain a further understaing of the role of soil as a natural resource. Geologists will find value in the instruction this book provides for measuring the physical features of the soil such as elevation, porosity, and structure which geologists use to predict natural disasters such as earthquakes, volcanoes, and flooding.

Tomislav Hengl

Papers

SoilGrids1km--global soil information based on automated mapping.

A Practical Guide to Geostatistical Mapping

Geomorphometry : concepts, software, applications

Global Soil Information Facilities