Global Soil Map

High-quality soil maps are urgently needed by diverse stakeholders, but errors in existing soil maps are often unknown, particularly in countries with limited soil surveys. To address this issue, we used field soil data to assess the accuracy of seven spatial soil databases (Digital Soil Map of the World, Namibian Soil and Terrain Digital Database, Soil and Terrain Database for Southern Africa, Harmonized World Soil Database, SoilGrids1km, SoilGrids250m, and World Inventory of Soil Property Estimates) using topsoil texture as an example soil property and Namibia as a case study area. In addition, we visually compared topsoil texture maps derived from these databases. We found that the maps showed the correct topsoil texture in only 13% to 42% of all test sites, with substantial confusion occurring among all texture categories, not just those in close proximity in the soil texture triangle. Visual comparisons of the maps moreover showed that the maps differ greatly with respect to th...

High-quality soil maps are urgently needed by diverse stakeholders, but errors in existing soil maps are often unknown, particularly in countries with limited soil surveys. To address this issue, we used field soil data to assess the accuracy of seven spatial soil databases (Digital Soil Map of the World, Namibian Soil and Terrain Digital Database, Soil and Terrain Database for Southern Africa, Harmonized World Soil Database, SoilGrids1km, Soil-Grids250m, and World Inventory of Soil Property Estimates) using topsoil texture as an example soil property and Namibia as a case study area. In addition, we visually compared topsoil texture maps derived from these databases. We found that the maps showed the correct topsoil texture in only 13% to 42% of all test sites, with substantial confusion occurring among all texture categories, not just those in close proximity in the soil texture triangle. Visual comparisons of the maps moreover showed that the maps differ greatly with respect to the number, types, and spatial distribution of texture classes. The topsoil texture information provided by the maps is thus sufficiently inaccurate that it would result in significant errors in a number of applications, including irrigation system design and predictions of potential forage and crop productivity, water runoff, and soil erosion. Clearly, the use of these existing maps for policy-and decision-making is highly questionable and there is a critical need for better on-site estimates and soil map predictions. We propose that mobile apps, citizen science, and crowdsourcing can help meet this need.

The demand for information on functional soil properties is high and has increased over time. This is especially true for soil organic carbon (SOC) in the framework of food security and climate change. The GlobalSoilMap consortium was established in response to such a soaring demand for up-to-date and relevant soil information. The majority of the data needed to produce GlobalSoilMap soil property maps will, at least for the first generation, come mainly from archived soil legacy data, which could include polygon soil maps and point pedon data, and from available co-variates such as climatic data, remote sensing information, geological data, and other forms of environmental information. Several countries have already released products according to the GlobalSoilMap specifications and the project is rejuvenating soil survey and mapping in many parts of the world. Functional soil property maps have been produced using digital soil mapping techniques and existing legacy information and...

Countrywide estimates of soil organic carbon stock (SOCS) are useful to set up national strategies for sustainable land use management as well as to enhance the accuracy of global SOCS inventories. We appraised the spatial distribution of SOCS at five depth layers (0-15 cm, 15-30 cm, 30-100 cm, 0-30 cm and 0-100 cm) in Cameroon at 100 m spatial resolution, using a national harmonized legacy soil database (Camsodat 0.1) with 1432 georeferenced soil profiles. We assessed the prediction performances of random forest (RF) and generalized boosted regression (GBR), combined with two hybridization approaches of spatial interpolation of the residuals using ordinary kriging (OK) and inverse distance weighting (IDW). The estimates were compared to two global estimates derived from the Harmonized World Soil Database (HWSD) and SoilGrids250m. The SOCS distribution across the country showed a moderate spatial heterogeneity at all depth layers with coefficients of variation between 35% and 47%, and values ranging from 6 to 108 Mg C ha −1 at 0-15 cm, from 4 to 107 Mg C ha −1 at 15-30 cm, from 10 to 276 Mg C ha −1 at 30-100 cm, from 11 to 210 Mg C ha −1 at 0-30 cm and from 21 to 468 Mg C ha −1 at the 0-100 cm layer. Of the selected environmental covariates, terrain and climate attributes were the most relevant to predict the SOCS spatial distribution at country level. The RF model outperformed the GBR model, with about 10% improvement on prediction performance (R 2) for most soil depths. The hybridization further slightly improved performance. However, OK was only slightly better than IDW in the overall assessment. Compared to national estimates, SoilGrids overestimated the SOCS by 15% at 0-30 cm depth, while HWSD underestimated SOCS by 26% at the same depth. Overall, about 5.7 Pg C are stored in the top 1 m of soils in Cameroon, with about 50% of that in the top 30 cm. The national distribution of SOCS is consistent with the pattern of agro-ecological zones. Our assessment provides baseline information for sustainable land management and climate change mitigation, as well as for improving the understanding of the spatial distribution of SOCS in Cameroon.

The food security–climate change nexus rapidly gains momentum. Soil degradation plays an important role in this context while dealing with, for example, the productive capacity of our soil resources or carbon sequestration for climate change mitigation. However, little has been done to assess the pristine soil conditions despite the fact that these provide the basis to put changes into context. Various methodologies have been developed to assess the global distribution of current soil conditions. We used the S‐World methodology that was developed to generate global soil property maps for environmental modelling studies. Up till now, the S‐World methodology assessed current soil conditions by disaggregating the Harmonized World Soil Database using detailed information on climate, topography, land cover, and land use. This study used the S‐World methodology to derive global soil conditions under natural vegetation. A large number of natural areas around the globe were identified for w...

Soil properties have an enormous impact on economic and environmental aspects of agricultural production. Quantitative relationships between soil properties and the factors that influence their variability are the basis of digital soil mapping. The predictive models of soil properties evaluated in this work are statistical (multiple linear regression-MLR) and geostatistical (ordinary kriging and co-kriging). The study was conducted in the municipality of Bom Jardim, RJ, using a soil database with 208 sampling points. Predictive models were evaluated for sand, silt and clay fractions, pH in water and organic carbon at six depths according to the specifications of the consortium of digital soil mapping at the global level (GlobalSoilMap). Continuous covariates and categorical predictors were used and their contributions to the model assessed. Only the environmental covariates elevation, aspect, stream power index (SPI), soil wetness index (SWI), normalized difference vegetation index ...

The demand for information on functional soil properties is high and has increased over time. This is especially true for soil organic carbon (SOC) in the framework of food security and climate change. The GlobalSoilMap consortium was established in response to such a soaring demand for up-to-date and relevant soil information. The majority of the data needed to produce GlobalSoilMap soil property maps will, at least for the first generation, come mainly from archived soil legacy data, which could include polygon soil maps and point pedon data, and from available co-variates such as climatic data, remote sensing information, geological data, and other forms of environmental information. Several countries have already released products according to the GlobalSoilMap specifications and the project is rejuvenating soil survey and mapping in many parts of the world. Functional soil property maps have been produced using digital soil mapping techniques and existing legacy information and...

Countrywide estimates of soil organic carbon stock (SOCS) are useful to set up national strategies for sustainable land use management as well as to enhance the accuracy of global SOCS inventories. We appraised the spatial distribution of SOCS at five depth layers (0-15 cm, 15-30 cm, 30-100 cm, 0-30 cm and 0-100 cm) in Cameroon at 100 m spatial resolution, using a national harmonized legacy soil database (Camsodat 0.1) with 1432 georeferenced soil profiles. We assessed the prediction performances of random forest (RF) and generalized boosted regression (GBR), combined with two hybridization approaches of spatial interpolation of the residuals using ordinary kriging (OK) and inverse distance weighting (IDW). The estimates were compared to two global estimates derived from the Harmonized World Soil Database (HWSD) and SoilGrids250m. The SOCS distribution across the country showed a moderate spatial heterogeneity at all depth layers with coefficients of variation between 35% and 47%, and values ranging from 6 to 108 Mg C ha −1 at 0-15 cm, from 4 to 107 Mg C ha −1 at 15-30 cm, from 10 to 276 Mg C ha −1 at 30-100 cm, from 11 to 210 Mg C ha −1 at 0-30 cm and from 21 to 468 Mg C ha −1 at the 0-100 cm layer. Of the selected environmental covariates, terrain and climate attributes were the most relevant to predict the SOCS spatial distribution at country level. The RF model outperformed the GBR model, with about 10% improvement on prediction performance (R 2) for most soil depths. The hybridization further slightly improved performance. However, OK was only slightly better than IDW in the overall assessment. Compared to national estimates, SoilGrids overestimated the SOCS by 15% at 0-30 cm depth, while HWSD underestimated SOCS by 26% at the same depth. Overall, about 5.7 Pg C are stored in the top 1 m of soils in Cameroon, with about 50% of that in the top 30 cm. The national distribution of SOCS is consistent with the pattern of agro-ecological zones. Our assessment provides baseline information for sustainable land management and climate change mitigation, as well as for improving the understanding of the spatial distribution of SOCS in Cameroon.

The demand for information on functional soil properties is high and has increased over time. This is especially true for soil organic carbon (SOC) in the framework of food security and climate change. The GlobalSoilMap consortium was established in response to such a soaring demand for up-to-date and relevant soil information. The majority of the data needed to produce GlobalSoilMap soil property maps will, at least for the first generation, come mainly from archived soil legacy data, which could include polygon soil maps and point pedon data, and from available co-variates such as climatic data, remote sensing information, geological data, and other forms of environmental information. Several countries have already released products according to the GlobalSoilMap specifications and the project is rejuvenating soil survey and mapping in many parts of the world. Functional soil property maps have been produced using digital soil mapping techniques and existing legacy information and...

The food security-climate change nexus rapidly gains momentum. Soil degradation plays an important role in this context while dealing with, for example, the productive capacity of our soil resources or carbon sequestration for climate change mitigation. However, little has been done to assess the pristine soil conditions despite the fact that these provide the basis to put changes into context. Various methodologies have been developed to assess the global distribution of current soil conditions. We used the S-World methodology that was developed to generate global soil property maps for environmental modelling studies. Up till now, the S-World methodology assessed current soil conditions by disaggregating the Harmonized World Soil Database using detailed information on climate, topography, land cover, and land use. This study used the S-World methodology to derive global soil conditions under natural vegetation. A large number of natural areas around the globe were identified for which land cover, expressed by the Normalized Difference Vegetation Index, could be successfully correlated to environmental conditions such as temperature, rainfall, and topography. Using this relation in regression kriging, the vegetation index under natural conditions was derived for the entire globe. Subsequently, the S-World methodology was used to calculate the soil properties under natural land cover and absence of human land use. Soil property maps for natural and current conditions were compared and showed large local differences. The results indicate that there are major changes due to land cover and land use change and that these changes are concentrated on the globe. The results are the basis for future assessments on, for example, land degradation, food security, or the sustainable development goals.

ISRIC - World Soil Information has a mandate to serve the international community as custodian of global soil information and to increase awareness and understanding of the role of soils in major global issues. To adapt to the current demand for soil information, ISRIC is updating its enterprise data management system, including procedures for registering acquired data, such as lineage, versioning, quality assessment, and control. Data can be submitted, queried, and analysed using a growing range of web-based services —ultimately aiming at full and open exchange of data, metadata, and products— through the ICSU-accredited World Data Centre for Soils.

The research community increasingly analyses global environmental problems like climate change and desertification with models. These global environmental modelling studies require global, high resolution, spatially exhaustive, and quantitative data describing the soil profile. This study aimed to develop a pedological approach that takes stock of available legacy and auxiliary data to create a global, 30 arc second soil property database for modelling. The methodology uses the Harmonized World Soil Database and the ISRIC-WISE 3.1 soil profile database. Auxiliary information at 30 arc second resolution for various landscape properties is used to describe the variation in landscape properties (temperature and precipitation, topography, elevation, land use, and land cover). Complex mapping units of the HWSD were first disaggregated using a digital elevation model and toposequences to generate delineated areas described by a single soil type. Secondly, ranges of soil properties per soil type were determined using the soil profile data. Then a meta-analysis on a broad literature survey was used to develop a simple model that, based on landscape properties at a particular location, determines the position within these ranges and thus provides an estimation of the local soil properties. Finally, the model was implemented at the global level to determine the distribution of soil properties. The methodology, denominated S-World (Soils of the world) resulted in readily available, high resolution, global soil property maps that are now available for environmental modelling.

The purpose of this study was to analyze the relationships between soil attributes and environmental covariates in a tropical hillslope environment on a regional scale to estimate spatial distribution of soil attributes and identify statistical and geostatistical techniques that could represent the variation of the soil attributes. The study was performed in Bom Jardim County, Brazil, and covered an area of 390 km 2 with a soil database of 208 sample points distributed in six depth layers (0.53 pts/km 2 ). The study used 18 environmental covariates derived from DEM and satellite imagery. The models evaluated were linear regression, regression trees and ordinary and regression kriging. An exploratory analysis showed that DEM, NDVI, MRVBF, MSP, b3/b2, b5/b7, SPI, SWI, SLOPE and ASPECT were correlated with soil properties. The models performance had a mean crossvalidation r 2 of 0.13. The best results were achieved with kriging models, with a crossvalidation r 2 of 0.19. A comparison between multiple linear regression and regression trees showed that the tree model yielded the best results. The sample density alone could not explain the results, but an interaction between DEM accuracy, sample density, covariates and geological conditions was suitable as an explanatory factor. Studies of tropical hillslope digital soil mapping on regional scales need to be more exhaustively focused to develop this research area.

ISRIC -World Soil Information has a mandate to serve the international community as custodian of global soil information and to increase awareness and understanding of the role soils in major global issues. To adapt to the current demand for soil information, ISRIC is updating its enterprise data management system, including procedures for registering acquired data such as lineage, versioning, quality assessment and control. Data can be submitted, queried and analysed using a growing range of web-based services -ultimately aiming at full and open exchange of data, metadata and products-through the ICSU-accredited World Data Centre for Soils.

Quantitative soil information is important in Korea useful for management of nutrients for crop production, and balancing the ecosystem. This paper produces digital soil maps of soil organic carbon and clay content at different depths for Korean soils based on the GlobalSoilMap.net specification. A Korean soil database of 380 soil series was compiled, which includes chemical and physical properties such as organic carbon and clay content. We applied the equal-area spline depth functions for soil organic carbon and clay content. Derived mean values of organic carbon and clay content from the fitted spline at standard depths were mapped by corresponding soil series to produce raterized spatial and depth-wise soil property maps. We also compared the results to recently collected soil samples to estimate the accuracy of the maps. Clay content showed better agreement compared to soil organic carbon content.