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There is a growing demand for high quality soil data to model soil processes and map soil properties. However, wet chemistry measurements on soil properties are subjected to many error sources, such as the observer, the instrument and lack of standardised methodologies. Consequently, soil data are imperfect and uncertain because of these error sources. Uncertainties in measurements of fundamental soil properties can propagate through, e.g., pedotransfer functions, spectroscopic models and digital soil mapping algorithms. Therefore, it is important to provide detailed uncertainty information about soil measurements to potential data users. In practice, uncertainty estimates are rarely specified by providers of analytical soil data.In this research, we aimed to quantify uncertainties in synthetic and real-world pH (1:1 soil-water suspension) and Total Organic Carbon (TOC) measurements. We assumed that uncertainty can be represented by a normal distribution. A linear mixed-effects model was applied to estimate the parameters of the normal distribution, i.e., mean and standard deviation, of both synthetic and real-world datasets. The model included ‘sample ID’ as a fixed effect, and ‘batch’ and ‘laboratory’ as random effects. The use of synthetic datasets allowed us to investigate how well the model parameters could be estimated given a specific experimental measurement design, whereas the real-world case served to explore if the parameter estimates were still accurate for such unbalanced datasets.For a balanced dataset (n=20, n=100, n=200 and n=500), using synthetic pH data for three hypothetical laboratories (two batches per laboratory), the mean estimated standard deviations (σ) of the random effects were σbatch=0.10, σlaboratory=0.24 and σresidual=0.2. These estimates were in agreement with the σ for the respective random effects used to generate the synthetic dataset, meaning that the model could accurately estimate the model parameters. Subsequently, changes were made to the experimental measurement design by randomly removing 20%, 50% and 80% of the data, resulting in unbalanced datasets. In general, the interquartile range (IQR) of σ for each random effect increased with a larger percentage of removed data. However, the increase in IQR was larger for n=20 compared to, e.g., n=200. When comparing 0% and 80% randomly removed data, the IQR for the batch effect increased with 60.3%. Conversely, for n=200 an increase of only 23.5% was observed.Subsequently, the same model was fitted on real-world pH and TOC data, provided by the Wageningen Evaluating Programs for Analytical Laboratories (WEPAL). The unbalanced dataset structure was first reconstructed and filled with synthetically generated data, based on sample means and standard deviations derived from the measured data. The model was fitted on both datasets. For measured pH, the model yielded σbatch=0.27, σlaboratory=0.17 and σresidual=0.10. The IQRs of the estimated σ from synthetic WEPAL data were 0.04 (batch), 0.06 (laboratory) and 0.02 (residual). The model fitted on the measured TOC data estimated σbatch=5.3%, σlaboratory=2.8% and σresidual=2.1%. For the synthetic WEPAL data, IQRs of 1.3% (batch), 1.4% (laboratory) and 0.4% (residual) were determined for the estimated σ. These findings suggest that despite having a highly unbalanced dataset, realistic model parameter estimates can still be obtained.

In digital soil mapping, machine learning (ML) techniques are being used to infer a relationship between a soil property and the covariates. The information derived from this process is often translated into pedological knowledge. This mechanism is referred to as knowledge discovery. This study shows that knowledge discovery based on ML must be treated with caution. We show how pseudo-covariates can be used to accurately predict soil organic carbon in a hypothetical case study. We demonstrate that ML methods can find relevant patterns even when the covariates are meaningless and not related to soil-forming factors and processes. We argue that pattern recognition for prediction should not be equated with knowledge discovery. Knowledge discovery requires more than the recognition of patterns and successful prediction. It requires the pre-selection and preprocessing of pedologically relevant environmental covariates and the posterior interpretation and evaluation of the recognized patterns. We argue that important ML covariates could serve the purpose of providing elements to postulate hypotheses about soil processes that, once validated through experiments, could result in new pedological knowledge. Highlights: We discuss the rationale of knowledge discovery based on the most important machine learning covariates We use pseudo-covariates to predict topsoil organic carbon with random forest Soil organic carbon was accurately predicted in a hypothetical case study Pattern recognition by random forest should not be equated to knowledge discovery.

International audience; Pedometrics, the application of mathematical and statistical methods to the study of the distribution and genesis of soils, has broadened its scope over the past two decades. The primary focus of pedometricians has traditionally been on spatial and spatio-temporal soil inventories with numerical soil classification, geostatistical modelling of spatial variation and mapping. The rapid development of remote and proximal soil sensing as well as data-driven statistical modelling techniques have had a major impact on pedometrics over the past decades. During this time, a general demand for quantitative digital soil information for environmental modelling and management has compelled pedometricians to address other soil-related questions from a quantitative point of view: soil genesis and utility and quality of soil. While scientific progress is largely an autonomous process that is difficult to steer, research efforts could benefit from an agenda with pressing pedometric research topics. This paper defines and discusses ten recent or longstanding pedometrics challenges, with the attempt to identify knowledge gaps and suggest new concepts and methods to overcome them. The ten challenges were selected through a collaborative effort and may serve as a guidance for future pedometrics research and to foster collaboration among soil scientists. The challenges discussed in this paper are also indicators of the current understanding and state of knowledge from which progress can be measured in the future.

International audience; Since the turn of the millennium, digital soil mapping (DSM) has revolutionized the production of fine resolution gridded soil data with associated uncertainty. However, the link to conventional soil maps has not been sufficiently explained nor are the approaches complementary and synergistic. Further training on the digital soil mapping approaches, and associated strengths and weaknesses is required. The user community requires training in, and experience with, the new digital soil map products, especially about the use of uncertainties for risk modelling and policy development. Standards are required for public and private sector digital soil map products to prevent the production of poor-quality information which will become misleading and counter-productive. Machine-learning methods are to be used with caution with respect to their interpretability and parsimony. The use of DSM products for improved pedological understanding and soil survey interpretations requires urgent investigation.

The soil organic carbon (SOC) pool is the largest terrestrial carbon (C) pool and is two to three times larger than the C stored in vegetation and the atmosphere. SOC is a crucial component within the C cycle, and an accurate baseline of SOC is required, especially for biogeochemical and earth system modelling. This baseline will allow better monitoring of SOC dynamics due to land use change and climate change. However, current estimates of SOC stock and its spatial distribution have large uncertainties. In this study, we test whether we can improve the accuracy of the three existing SOC maps of France obtained at national (IGCS), continental (LUCAS), and global (SoilGrids) scales using statistical model averaging approaches. Soil data from the French Soil Monitoring Network (RMQS) were used to calibrate and evaluate five model averaging approaches, i.e., Granger-Ramanathan, Bias-corrected Variance Weighted (BC-VW), Bayesian Modelling Averaging, Cubist and Residual-based Cubist. Cross-validation showed that with a calibration size larger than 100 observations, the five model averaging approaches performed better than individual SOC maps. The BC-VW approach performed best and is recommended for model averaging. Our results show that 200 calibration observations were an acceptable calibration strategy for model averaging in France, showing that a fairly small number of spatially stratified observations (sampling density of 1 sample per 2500 km2) provides sufficient calibration data. We also tested the use of model averaging in data-poor situations by reproducing national SOC maps using various sized subsets of the IGCS dataset for model calibration. The results show that model averaging always performs better than the national SOC map. However, the Modelling Efficiency dropped substantially when the national SOC map was excluded in model averaging. This indicates the necessity of including a national SOC map for model averaging, even if produced with a small dataset (i.e., 200 samples). This study provides a reference for data-poor countries to improve national SOC maps using existing continental and global SOC maps.

International audience; During the last 10 years, several attempts to map soil attributes at the scale of mainland France have been realised. We exemplify them by seven major outputs: maps of organic C stocks, trace elements (TE), microbial density and diversity, soil thickness, available water capacity (AWC), extractable P, and changes in soil pH. We first briefly describe the data and the methods used to produce these maps and summarise their main results. We then focus on their impacts on various categories of the public, i.e. the general public and citizens; farmers; private companies; non-governmental organisations; agricultural development organisations, stakeholders, and national agencies; French governmental bodies; and international organisations. We also analyse the demands that came to the French National Soil Information Centre from 2008 to 2018 and the impact that our activities had in various media. Soil organic C had the largest impact in nearly all categories of end-users, which may be linked to the recent ‘4 per 1000’ initiative launched by the French governmentduring the COP21 and to the fact that farmers are interested in increasing the organic matter content of their soil for increasing the fertility. TE obtained high scores, which may be related to citizens' care about health and to the fact that governmental bodies and national agencies have a major interest in site contamination assessments. The soil P content, pH, and AWC exhibited major impacts on the agricultural sector. Maps of the soil P content and pH were used as geomarketing tools by private companies selling fertilisers and soil amendments, whereas the AWC was already incorporated into decision-making aid tools for irrigation management developed by development organisations for farmers. Microbial diversity generated collaborations with a large network of farmers and had a large media impact. Nevertheless, the visibility of soil information to the general public should be increased. This can be done by using new multimedia and interactive tools. Overall, these selected examples of digital soil mapping of soil attributes at the national scale in France clearly indicate that the soil attributes have substantial impact on various categories of end-users, such as farmers, professional organisations, stakeholders, and policymakers at different levels of decision-making, among others. However, the impacts on the general public and citizens are more difficult to quantify, and increasing the soil awareness of the general public should be of high priority.

We thank the authors for their thought-provoking comments on our paper. Most of the commentators agree that soil organic carbon (SOC) sequestration is important for improving the quality of soil, however they argue that we have overstated the potential of soil carbon sequestration. We welcome the comments and appreciate that the issue of SOC sequestration has always been somewhat factious (Schlesinger, 2000). We shall address the significance of the quantity “4 per mille”, reported sequestration rates, the limitation of carbon sequestration with time, and nutrient requirements. We clarify that our paper (Minasny et al., 2017) mainly deals with potentials for the 20 countries and regions, where SOC sequestration can also be seen as a way to improving the resilience of the soil to future climate change, that is, improving adaptation rather than mitigation. We believe that in some parts of the world where food security is threatened, the benefit of soil carbon management for adaptation should be stressed more than for mitigation. This is the reason why the 4 per mille initiative explicitly includes food security (Chabbi et al., 2017; Soussana et al., 2015). We need to add that the “4 per mille Soils for Food Security and Climate” initiative is just one of many national and global initiatives on SOC sequestration for mitigating climate change. The Intergovernmental Technical Panel on Soils (ITPS) of the Global Soil Partnership (GSP) discussed incorporating the topic of SOC in the IPCC Assessment Report (ARs), from AR6 onwards. The IPCC has also put a focus on soil in their upcoming special report “Climate Change and Land” (http://www.ipcc.ch/report/sr2/). The recent FAO Global Symposium (GSOC17) assembled experts engaged in FAO, GSP and its ITPS, IPCC, UNCCD-SPI and WMO activities to work together for the common goal of appropriate SOC management as part of overall sustainable soil management within the climate change mitigation and adaptation, sustainable development, Land Degradation Neutrality (LDN) and food security agendas (http://www.fao.org/about/meetings/soil-organic-carbon-symposium/en/). The Global Research Alliance on Agricultural Greenhouse Gases (GRA) focused on opportunities to reduce agricultural greenhouse gas emissions and increase soil carbon sequestration while still helping to meet food security objectives (http://globalresearchalliance.org/about/). The Common Agriculture Policy in the EU is currently being revised to include the potential use of SOC as an indicator. The 4 per mille initiative was launched at COP21, where the Paris Agreement was adopted, and one of the main aims of the Paris Agreement is to stop the planet from warming an additional two Celsius degrees. The two-degree target, although suggested by scientists through modelling work, was chosen more for political and pragmatic reasons whereby countries could agree on a target that they could work towards (Tollefson, 2015). And of course, there are many scientific critiques of this target (Knutti et al., 2016). Similarly, the 4 per mille initiative comes from a politically-driven aspiration, and our paper (Minasny et al., 2017) is a response to such an aspiration, to seek and outline possibilities based on current knowledge. The important concept is that soil and agriculture are part of the solution, and it is an interim and evidence-based solution that we can implement. Now we shall respond to each of the commentaries.

Despite the increased usage of global soil property maps, a proper review of the maps rarely takes place. This study aims to explore the options for such a review with an application for the S-World global soil property database. Global soil organic carbon (SOC) and clay content maps from S-World were studied at two spatial resolutions in three steps. First, a comparative analysis with an ensemble of seven datasets derived from five other global soil databases was done. Second, a validation of S-World was done with independent soil observations from the WoSIS soil profile database. Third, a methodological evaluation of S-world took place by looking at the variation of soil properties per soil type and short distance variability. In the comparative analysis, S-World and the ensemble of other maps show similar spatial patterns. However, the ensemble locally shows large discrepancies (e.g., in boreal regions where typically SOC contents are high and the sampling density is low). Overall, the results show that S-World is not deviating strongly from the model ensemble (91% of the area falls within a 1.5% SOC range in the topsoil). The validation with the WoSIS database showed that S-World was able to capture a large part of the variation (with, e.g., a root mean square difference of 1.7% for SOC in the topsoil and a mean difference of 1.2%). Finally, the methodological evaluation revealed that estimates of the ranges of soil properties for the different soil types can be improved by using the larger WoSIS database. It is concluded that the review through the comparison, validation, and evaluation provides a good overview of the strengths and the weaknesses of S-World. The three approaches to review the database each provide specific insights regarding the quality of the database. Specific evaluation criteria for an application will determine whether S-World is a suitable soil database for use in global environmental studies.

ISI Document Delivery No.: HT3RS Times Cited: 0 Cited Reference Count: 79 Chen, Songchao Mulder, Vera Leatitia Martin, Manuel P. Walter, Christian Lacost, Marine Richer-de-Forges, Anne C. Saby, Nicolas P. A. Loiseau, Thomas Hu, Bifeng Arrouays, Dominique French Ministry of Ecology; French Environment and Energy Management Agency (ADEME); French Institute for Research and Development (IRD); French Institute for National Geographic and Forest Information (IGN); National Institute for Agronomic Research (INRA); China Scholarship Council [201606320211]; French Scientific Group of Interest on soils (GIS Sol) The collection and handling of soil data was supported by the French Scientific Group of Interest on soils (GIS Sol), including the French Ministry of Ecology, the French Ministry of Agriculture, the French Environment and Energy Management Agency (ADEME), the French Institute for Research and Development (IRD), the French Institute for National Geographic and Forest Information (IGN) and the National Institute for Agronomic Research (INRA). We thank all the people involved in sampling the sites and populating the database. We would also like to thank Quentin Styc and Philippe Lagacherie for sharing their ideas on dealing with right censored data. Songchao Chen received the support of the China Scholarship Council for three years' of Ph.D. study in INRA and Agrocampus Ouest (under grant agreement no. 201606320211). 0 Elsevier science bv Amsterdam 1872-6259; International audience; Soil thickness (ST) is a crucial factor in earth surface modelling and soil storage capacity calculations (e.g., available water capacity and carbon stocks). However, the observed depths recorded in soil information systems for some profiles are often less than the actual ST (i.e., right censored data). The use of such data will negatively affect model and map accuracy, yet few studies have been done to resolve this issue or propose methods to correct for right censored data. Therefore, this work demonstrates how right censored data can be accounted for in the ST modelling of mainland France. We propose the use of Random Survival Forest (RSF) for ST probability mapping within a Digital Soil Mapping framework. Among 2109 sites of the French Soil Monitoring Network, 1089 observed STs were defined as being right censored. Using RSF, the probability of exceeding a given depth was modelled using freely available spatial data representing the main soil-forming factors. Subsequently, the models were extrapolated to the full spatial extent of mainland France. As examples, we produced maps showing the probability of exceeding the thickness of each GlobalSoilMap standard depth: 5, 15, 30, 60, 100, and 200 cm. In addition, a bootstrapping approach was used to assess the 90% confidence intervals. Our results showed that RSF was able to correct for right censored data entries occurring within a given dataset. RSF was more reliable for thin (0.3 m) and thick soils (1 to 2 m), as they performed better (overall accuracy from 0.793 to 0.989) than soils with a thickness between 0.3 and 1 m. This study provides a new approach for modelling right censored soil information. Moreover, RSF can produce probability maps at any depth less than the maximum depth of the calibration data, which is of great value for designing additional sampling campaigns and decision making in geotechnical engineering.

Within the context of achieving Land Degradation Neutrality by 2030, this work studied to which extent soil variability acts as controlling factor for changes in observed land productivity under extreme climatic events. This was done by analysing 30 years of Normalized Difference Vegetation Index (NDVI) data and coinciding extreme warm, dry and their compound events in continental Europe and the Mediterranean Basin. In order to better understand the response of vegetation activity to extreme climatic events in relation to soil functioning, the data was segmented into different climate zones and further studied as a function of land cover and soil type. This study demonstrated that extreme climatic events cause substantial reductions in the NDVI with the maximum median impact up to 31%, one month after the occurrence of an extreme climatic event. However, the magnitude of NDVI drop largely depended on land cover and soil type. Our analysis showed that for soil types with root depth limitations, lower water retention capacity and the absence of specific symbiotic species in the soil, vegetation activity was more impacted by climate extremes compared to soil types having favourable growing conditions. Natural land cover types, especially taiga and boreal forest, were most sensitive. Consequently, with the expected increase in extreme events, the now stable and productive ecosystems may become unstable and less capable to absorb the CO2 in the future, thereby enhancing climate change and land degradation. Therefore, it is important to have mitigation policies tailored towards maintaining soil functioning in vulnerable ecosystems.

Ce PDF ne doit pas être diffusé.; International audience; AbstractSoil clay content is a key parameter that influences many other soil properties and processes. The potential of adding new and contemporary satellite data for soil property mapping in France is assessed in this study. The soil property maps used for this analysis were produced within the framework of GlobalSoilMap, which was created to deliver global fine grids of soil properties and associated uncertainties using existing soil information and ancillary data to predict these properties based on digital soil mapping techniques. In this study, we evaluate the added value of Moderate Resolution Imaging Spectroradiometer (MODIS), Project for On-Board Autonomy-Vegetation (PROBA-V), and Sentinel-2 (S2) data for predicting the soil clay content at 90 m resolution for mainland France. The rationale behind adding these data is that satellite images and derived products may enable the biogeochemical characteristics of the earth’s surface to be captured more effectively, which in turn enables more precise predictions of the soil clay content. For this methodology, we i) create composite bare soil mosaics and derive the spectral indices from S2 data acquired during sowing periods from 2016 to 2017, ii) extract the first three principal components of harmonized MODIS and PROBA-V normalized difference vegetation index (NDVI) time series acquired in 2003 and 2016 to represent vegetation changes, and iii) test whether the complementary datasets are able to improve the soil clay information compared to a benchmark value. The soil clay content is obtained by using quantile regression forest (QRF) for each GlobalSoilMap depth interval of 0–5 cm, 5–15 cm, 15–30 cm, 30–60 cm, 60–100 cm, and 100–200 cm along with a 10-fold cross-validation having 10 replicates. The results show that the complementary satellite data improve the clay content estimation on bare soil for the topsoil layers (e.g., 0–30 cm) by increasing the R² and decreasing the bias at averages of 0.05 and 1 g kg−1, respectively. Moreover, the first principal component of the harmonized NDVI data is shown to be the second most important variable for estimating the clay content, as indicated by the QRF models. However, the use of only the satellite data and products as input for the QRF does not yield a satisfactory estimate of the clay content. Finally, this work provides a reference for embedding new remote sensing data in existing national soil inventories and national soil information systems. Further research should incorporate new techniques for considering the spatial–temporal variability of the earth’s surface parameters such as soil moisture and roughness.

International audience; In this work we aimed at developing a conceptual framework in which we improve our understanding of the controlling factors for soil organic carbon (SOC) over vast areas at different depths. We postulated that variability in SOC levels may be better explained by modeling SOC within soil-landscape systems (SLSs). The study was performed in mainland France, and explanatory SOC models were developed for the sampled topsoil (0–30 cm) and subsoil (>30 cm), using both directed and undirected data-mining techniques. With this study we demonstrated that there is a shift in controlling factors both in space and depth which were mainly related to (1) typical SLS characteristics and (2) human-induced changes to SLSs. The controlling factors in relation to depth alter from predominantly biotic to more abiotic with increasing depth. Especially, water availability, soil texture, and physical protection control deeper stored SOC. In SLSs with similar SOC levels, different combinations of physical protection, the input of organic matter, and climatic conditions largely determined the SOC level. The SLS approach provided the means to partition the data into data sets that were having homogenous conditions with respect to this combination of controlling factors. This information may provide important information on the carbon storage and sequestration potential of a soil.

International audience; The ‘4 per mille Soils for Food Security and Climate’ was launched at the COP21 with an aspiration to increase global soil organic matter stocks by 4 per 1000 (or 0.4 %) per year as a compensation for the global emissions of greenhouse gases by anthropogenic sources. This paper surveyed the soil organic carbon (SOC) stock estimates and sequestration potentials from 20 regions in the world (New Zealand, Chile, South Africa, Australia, Tanzania, Indonesia, Kenya, Nigeria, India, China Taiwan, South Korea, China Mainland, United States of America, France, Canada, Belgium, England & Wales, Ireland, Scotland, and Russia). We asked whether the 4 per mille initiative is feasible for the region. The outcomes highlight region specific efforts and scopes for soil carbon sequestration. Reported soil C sequestration rates globally show that under best management practices, 4 per mille or even higher sequestration rates can be accomplished. High C sequestration rates (up to 10 per mille) can be achieved for soils with low initial SOC stock (topsoil less than 30 t C ha−1), and at the first twenty years after implementation of best management practices. In addition, areas which have reached equilibrium will not be able to further increase their sequestration. We found that most studies on SOC sequestration only consider topsoil (up to 0.3 m depth), as it is considered to be most affected by management techniques. The 4 per mille number was based on a blanket calculation of the whole global soil profile C stock, however the potential to increase SOC is mostly on managed agricultural lands. If we consider 4 per mille in the top 1m of global agricultural soils, SOC sequestration is between 2-3 Gt C year−1, which effectively offset 20–35% of global anthropogenic greenhouse gas emissions. As a strategy for climate change mitigation, soil carbon sequestration buys time over the next ten to twenty years while other effective sequestration and low carbon technologies become viable. The challenge for cropping farmers is to find disruptive technologies that will further improve soil condition and deliver increased soil carbon. Progress in 4 per mille requires collaboration and communication between scientists, farmers, policy makers, and marketeers.

This paper presents a sparse, remote sensing-based sampling approach making use of conditioned Latin Hypercube Sampling (cLHS) to assess variability in soil properties at regional scale. The method optimizes the sampling scheme for a defined spatial population based on selected covariates, which are assumed to represent the variability of the target variables. The optimization also accounts for specific constraints and costs expressing the field sampling effort. The approach is demonstrated using a case study in Morocco, where a small but representative sample record had to be collected over a 15,000 km2 area within 2 weeks. The covariate space of the Latin Hypercube consisted of the first three principal components of ASTER imagery as well as elevation. Comparison of soil properties taken from the topsoil with the existing soil map, a geological map and lithological data showed that the sampling approach was successful in representing major soil variability. The cLHS sample failed to express spatial correlation; constraining the LHS by a distance criterion favoured large spatial variability within a short distances resulting in an overestimation of the variograms nugget and short distance variability. However, the exhaustive covariate data appeared to be spatially correlated which supports our premise that once the relation between spatially explicit remote sensing data and soil properties has been modelled, the latter can be spatially predicted based on the densely sampled remotely sensed data. Therefore, the LHS approach is considered as time and cost efficient for regional scale surveys that rely on remote sensing-based prediction of soil properties.

International audience

International audience

Legacy soil data have been produced over 70 years in nearly all countries of the world. Unfortunately, data, information and knowledge are still currently fragmented and at risk of getting lost if they remain in a paper format. To process this legacy data into consistent, spatially explicit and continuous global soil information, data are being rescued and compiled into databases. Thousands of soil survey reports and maps have been scanned and made available online. The soil profile data reported by these data sources have been captured and compiled into databases. The total number of soil profiles rescued in the selected countries is about 800,000. Currently, data for 117, 000 profiles are compiled and harmonized according to GlobalSoilMap specifications in a world level database (WoSIS). The results presented at the country level are likely to be an underestimate. The majority of soil data is still not rescued and this effort should be pursued. The data have been used to produce soil property maps. We discuss the pro and cons of top-down and bottom-up approaches to produce such maps and we stress their complementarity. We give examples of success stories. The first global soil property maps using rescued data were produced by a top-down approach and were released at a limited resolution of 1 km in 2014, followed by an update at a resolution of 250 m in 2017. By the end of 2020, we aim to deliver the first worldwide product that fully meets the GlobalSoilMap specifications.

International audience

International audience; Soils are essential for supporting food production and providing ecosystem services but are under pressure due to population growth, higher food demand, and land use competition. Because of the effort to ensure the sustainable use of soil resources, demand for current, updatable soil information capable of supporting decisions across scales is increasing. Digital soil mapping (DSM) addresses the drawbacks of conventional soil mapping and has been increasingly used for delivering soil information in a time- and cost-efficient manner with higher spatial resolution, better map accuracy, and quantified uncertainty estimates. We reviewed 244 articles published between January 2003 and July 2021 and then summarised the progress in broad-scale (spatial extent 10,000 km2) DSM, focusing on the 12 mandatory soil properties for GlobalSoilMap. We observed that DSM publications continued to increase exponentially; however, the majority (74.6%) focused on applications rather than methodology development. China, France, Australia, and the United States were the most active countries, and Africa and South America lacked country-based DSM products. Approximately 78% of articles focused on mapping soil organic matter/carbon content and soil organic carbon stocks because of their significant role in food security and climate regulation. Half the articles focused on soil information in topsoil only (