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Wageningen, Netherlands

Soils play a key role in providing a range of ecosystem services. Quality-assessed soil information, with quantified uncertainty levels, is needed to address a range of global issues. Traditional mapping methods, which recognize that soil classes are "important carriers of soil information", were used to prepare an updated harmonized dataset of derived soil properties for the world at a nominal resolution of 30 by 30 arc sec (WISE30sec). The map unit composition was determined using an overlay of the Harmonized World Soil Database, with minor corrections, and the Köppen-Geiger climate zones map as categorical co-variate. Property estimates for the respective component soil units were derived using taxonomy-based transfer rules that draw on a statistical analysis of some 21,000 soil profiles. Best estimates (mean ± standard deviation) for twenty soil properties were calculated for seven depth intervals (up to 2 m depth or less when thinner): organic carbon content, total nitrogen, C/N ratio, pH(H2O), CECsoil, CECclay, effective CEC, total exchangeable bases (TEB), base saturation, aluminium saturation, calcium carbonate content, gypsum content, exchangeable sodium percentage (ESP), electrical conductivity, particle size distribution (content of sand, silt and clay), proportion of coarse fragments (>2 mm), bulk density, and available water capacity (-33 to -1500 kPa); also the dominant soil drainage class. Coefficients of variation tend to be large. WISE30sec may be used for applications at a broad scale (<1:1 M) upon consideration of the underlying data lineage, generalizations, and the associated uncertainties. As an example, the database was used to calculate the global soil organic carbon (SOC) stock to 2 m depth. Some 30% (607 ± 87 Pg C) of this stock (2060 ± 215 Pg C) is held in the Northern Circumpolar Region, which is considered most sensitive to climate change. © 2016 Elsevier B.V.

Hengl T.,ISRIC World Soil Information | Roudier P.,Landcare Research | Beaudette D.,USDA NRCS | Pebesma E.,University of Munster
Journal of Statistical Software | Year: 2015

plotKML is an R package that provides methods for writing the most common R spatial classes into KML files. It builds up on the existing XML parsing functionality (XML package), and provides similar plotting functionality as the lattice package. Its main objective is to provide a simple interface to generate KML files with a small number of arguments, and allows users to visually explore spatio-temporal data available in R: points, polygons, gridded maps, trajectory-type data, vertical profiles, ground photographs, time series vector objects or raster images, along with the results of spatial analysis such as geostatistical mapping, spatial simulations of vector and gridded objects, optimized sampling designs, species distribution models and similar. A generic plotKML() function automatically determines the parsing order and visualizes data directly from R; lower level functions can be combined to allow for new user-created visualization templates. In comparison to other packages writing KML, plotKML seems to be more object oriented, it links more closely to the existing R classes for spatio-temporal data (sp, spacetime and raster packages) than the alternatives, and provides users with the possibility to create their own templates. © 2015, Journal of Statistical Software All rights received.

Brevik E.C.,Dickinson State University | Hartemink A.E.,ISRIC World Soil Information
Catena | Year: 2010

Soils knowledge dates to the earliest known practice of agriculture about 11,000 BP. Civilizations all around the world showed various levels of soil knowledge by the 4th century AD, including irrigation, the use of terraces to control erosion, various ways of improving soil fertility, and ways to create productive artificial soils. Early soils knowledge was largely based on observations of nature; experiments to test theories were not conducted. Many famous scientists, for example, Francis Bacon, Robert Boyle, Charles Darwin, and Leonardo da Vinci worked on soils issues. Soil science did not become a true science, however, until the 19th century with the development of genetic soil science, led by Vasilii V. Dokuchaev. In the 20th century, soil science moved beyond its agricultural roots and soil information is now used in residential development, the planning of highways, building foundations, septic systems, wildlife management, environmental management, and many other applications in addition to agriculture. © 2010 Elsevier B.V.

The Carbon Benefits Project (CBP) is developing a standardized system for sustainable land management projects to measure, model and report changes in carbon stocks and greenhouse gas (GHG) emissions for use at varying scales. A global framework of soil organic carbon (SOC) stocks under native vegetation for application in data poor regions, using the simple assessment option of the CBP system, is presented. It considers default classes for climate and mineral soils as required for IPCC Tier 1 (empirical) level GHG inventories. Suitable soil profiles were extracted from an expanded version of the ISRIC-WISE database. Probable outliers within each climate-soil cluster were removed using a robust outlier-rejection procedure. Mean SOC stocks, to the IPCC reference depth of 30cm (SOC30), vary greatly within each cluster. Overall, present estimates of SOC30 are lower than those listed in the 2006 IPCC Guidelines (though not necessarily in the statistical sense) that drew on a smaller selection of profiles from a more limited geographic area. They represent globally averaged values of SOC stocks under native vegetation that may differ from country/region specific values. Finer criteria for defining climate zones and soil classes, and replacement of default reference stocks and stock change factors with region-specific values, will be necessary to reduce uncertainty. © 2011 Elsevier B.V.

Hengl T.,ISRIC World Soil Information | Heuvelink G.B.M.,Wageningen University | Tadic M.P.,Meteorological and Hydrological Service of Croatia | Pebesma E.J.,University of Munster
Theoretical and Applied Climatology | Year: 2012

A computational framework to generate daily temperature maps using time-series of publicly available MODIS MOD11A2 product Land Surface Temperature (LST) images (1 km resolution; 8-day composites) is illustrated using temperature measurements from the national network of meteorological stations (159) in Croatia. The input data set contains 57,282 ground measurements of daily temperature for the year 2008. Temperature was modeled as a function of latitude, longitude, distance from the sea, elevation, time, insolation, and the MODIS LST images. The original rasters were first converted to principal components to reduce noise and filter missing pixels in the LST images. The residual were next analyzed for spatio-temporal auto-correlation; sum-metric separable variograms were fitted to account for zonal and geometric space-time anisotropy. The final predictions were generated for time-slices of a 3D space-time cube, constructed in the R environment for statistical computing. The results show that the space-time regression model can explain a significant part of the variation in station-data (84%). MODIS LST 8-day (cloud-free) images are unbiased estimator of the daily temperature, but with relatively low precision (±4. 1°C); however their added value is that they systematically improve detection of local changes in land surface temperature due to local meteorological conditions and/or active heat sources (urban areas, land cover classes). The results of 10-fold cross-validation show that use of spatio-temporal regression-kriging and incorporation of time-series of remote sensing images leads to significantly more accurate maps of temperature than if plain spatial techniques were used. The average (global) accuracy of mapping temperature was ±2. 4°C. The regression-kriging explained 91% of variability in daily temperatures, compared to 44% for ordinary kriging. Further software advancement-interactive space-time variogram exploration and automated retrieval, resampling and filtering of MODIS images-are anticipated. © 2011 The Author(s).

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