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The European Green Deal emphasizes the importance of healthy soils for our planet and society. In order to monitor soil health, modelling soil organic matter (SOM) in space and over time is necessary to assess changes in the fertility of agricultural soils, combat climate change and maintain ecosystem services. In digital soil mapping, most recent statistical modelling approaches have used time series of remote sensing data, which became available from the early 1980s onwards, together with soil observations to make predictions in space and over time. While this has clear advantages, it does not provide spatially explicit, explanatory information for time periods before the 1980s, even though observations in soil databases are often from beforehand. In this study, we modelled changes in SOM in 3D space over 65 years on a national scale in the Netherlands. We used SOM observations from 345 000 locations from 0 to 2 m depth between 1953 and 2018. The covariates were comprised of proxies of soil forming factors either considered to be static (e.g. relief, parent material) or dynamic over these 65 years. As dynamic covariates, we used indices of digitized historic (1960 – 1980) and more recent (1986 – 2018) land use maps. These dynamic covariates were chosen for two reasons. Firstly, land use and land cover change are the main drivers of SOM change over the time period of several decades. This is especially true in the Netherlands, where the anthropogenic influence on soils has been tremendous. Approximately 82 % of the land surface are agricultural, urban or infrastructure areas, while 15 % consists of (managed) peatlands and up to 20 % has been reclaimed from the sea. Secondly, by including carefully mapped historic land use, we were able to take advantage of a longer time series of soil data to make space-time predictions of SOM over a longer time period. Predictions were made using the quantile regression forest (QRF) algorithm, whereby sampling depth and year were included during calibration. SOM predictions were validated in two ways: a) over the 65-year period using a 10-fold cross-validation and b) specifically for 1998 and 2018, where designed-based statistical inference was possible using a probability sample. We computed the mean error (ME), root mean squared error (RMSE), model efficiency coefficient (MEC) as accuracy metrics and the prediction interval coverage probability (PICP) as an evaluation of the prediction uncertainty. Results showed that spatial patterns were realistic and properly reproduced but that prediction of temporal dynamics was more challenging. This research is also of interest for spatio-temporal soil modelling in other regions of the world that have soil data from the early and mid-19th century and historical land use and land cover data.

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

Since the establishment of Digital Soil Mapping (DSM) as a research field, the main focus has been on implementing new methods to improve the predictive performance of soil maps. However, considerably less effort has been invested in investigating the best way to communicate the quality of soil mapping products with users. This is essential for soil maps to be adopted by a broader community, future research guidance and most importantly, to ensure that they are used correctly. We introduce a high-resolution 3D soil modelling and mapping platform for the Netherlands (BIS-3D) using a quantile regression forest (QRF) for spatial interpolation approach that includes an assessment of the map quality using GlobalSoilMap (GSM) accuracy thresholds. Our objectives are twofold: a) providing accurate and high-resolution (25m) soil pH, soil organic carbon, and soil texture (clay, silt, and sand) maps over 3D space including prediction uncertainty; and b) providing an intuitive way to communicate accuracy of soil maps for users by means of accuracy thresholds. In this work, the first outputs of the modelling and mapping platform BIS-3D are being presented.QRF models were trained and validated, yielding average predictions for each target location and depth as well as the 90% prediction interval. Predicted soil maps were evaluated using an independent validation data set based on a stratified random sampling design covering the entire Netherlands (1151 locations). Furthermore, at every validation location, predictions were assessed as A, AA or AAA quality using the GSM specifications.First results for soil pH (KCl) using 15887 soil observations between depths 0-2 m and 180 covariates reveal a mean square error skill score (SSmse) = 0.88, RMSE = 0.49 and bias = 0.01 for out of bag predictions. Model evaluation using the independent validation set resulted in SSmse = 0.66, RMSE = 0.81 and bias = 0.12 across all depths. Prediction accuracy was highest for depths between 0-15 cm (SSmse = 0.66, RMSE = 0.76) and 60-100 cm (SSmse = 0.69, RMSE = 0.78) and lowest for 100-200 cm (SSmse = 0.61, RMSE = 0.86). The soil measurement (observation) was within the 90% prediction interval of model predictions in 83% of the cases, indicating that QRF is slightly over-optimistic in quantifying the prediction uncertainty. 61% of predictions that were independently validated over all depths were within the highest GSM accuracy threshold (AAA = +/- 0.5 pH), 23% were AA (+/- 1.0 pH), 9% were A (+/- 1.5 pH) and the remaining 7% were below A. A categorical physical geography map was the most important covariate, although other covariates associated with relief, geomorphology, land use and temperature were also effective. However, such variable importance measurements are merely indications and should be handled with care. The BIS-3D can easily be extended for predicting additional soil properties and it may provide a basis for decision makers to easily assess to what extent and in which areas soil maps can be used for their applications.

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.

Soils have critical relevance to global issues, such as food and water security, climate regulation, sustainable energy, desertification and biodiversity protection. All these examples require accurate national soil property information and there is a need to scientific support to develop reliable baseline soil information and pathways for measuring and monitoring soils. Soil sustainable management is a global issue, but effective actions require high-resolution data about soil properties. Two projects, GlobalSoilMap and SoilGrids, aim at delivering the first generation of high-resolution soil property grids for the globe, the first one by a bottom-up approach (from country to globe), the latter by top-down (global). The GLobAl Digital SOIL MAP (GLADSOILMAP) consortium brings together world scientific leaders involved in both projects. The consortium aims at developing and transferring methods to improve the prediction accuracy of soil properties and their associated uncertainty, by using legacy soil data and ancillary spatial information. This approach brings together new technologies and methods, existing soil databases and expert knowledge. The consortium aims at transferring methods to achieve convergence between top-down and bottom-up approaches, and to generate methods for delivering maps of soil properties. These maps are essential for communities from climate and environmental modeling to decision making and sustainable resources management at a scale that is relevant to soil management. The consortium will ensure links with the numerous actors in geosciences of the world, and will contribute to improving their skills in digital mapping and their national and international legibility. The actions include 4 main Work Packages (WP) subdivided into several tasks that are summarized below: WP0 Management of the projectWP1 Legacy and ancillary data for Digital Soil Mapping (DSM)Test the potential of new ancillary data for DSMExplore methodologies to merge and/or harmonize different productsPropose methods for harmonizing products to a common date WP2 Methods for sampling, modelling and mapping soils in space and timeTesting and developing new methods/models for predictionTesting methods for estimating complete probability distribution WP3 Methods for estimating model and map uncertaintyDevelop methods of uncertainty spatial assessmentDevelop methods do deal with censored data/soft dataSolve the question of influence on the age of the rescued soil data on predictions WP4 Scientific outreach and capacity buildingProduce an exhaustive review of GlobalSoilMap initiatives and results all over the worldRevise and update the GlobalSoilMap specifications by keeping them at the state-of-the-art levelShow relevance of gridded, Global, DSM by use cases and communication to end users The added value of the consortium is to allow a direct scientific exchange between members that should result in synthesis papers, in the identification of the major knowledge gaps, and in extending, deepening and disseminating knowledge of DSM, with the final aim to contribute to the achievement of global soil maps. Another added value of the consortium will certainly be to foster the creation of new ideas. Acknowledgements: the Consortium GLADSOILMAP is supported by LE STUDIUM Loire Valley Institute for Advanced studies.

In the 21st century, soils are at the crossroads of global issues (i.e., food security, water security, biodiversity protection, climate change, and ecosystem services) and essential to achieve some of the Sustainable Development Goals. Although soils are central to these global issues, their management requires local actions and knowledge, which requires fine-resolution soil information. With an emphasis on broad-scale studies (>10,000 km2), this review outlines recent progress in the development of GlobalSoilMap, an initiative to provide a global fine-resolution grid of soil properties with quantified uncertainties using the bottom-up approach. This review provides an overview related to the soil data source, environmental covariates, spatial prediction, modelling and mapping techniques, uncertainty qualification, and target soil properties. The main findings of this review are: (1) A great increase of publication was observed after 2012, reaching a peak in recent years; (2) Australia and China were the most active countries; (3) Geoderma was the most frequent journal that was preferred by authors to publish related studies; (4) More than a half of the studies did not report soil sampling design; (5) Data splitting was the most frequent strategy for model evaluation, and independent validation was rarely used; (6) Nonlinear predictive model (i.e., machine learning) was becoming popular than ever before; (7) Relief, organisms and climate were the top three SCORPAN factors used in modelling; (8) Soil organic carbon (or soil organic matter) was the top soil property of interest.This review also highlights the perspectives of GlobalSoilMap for further improving the quality of soil information globally and making it practical in decision making.

National audience

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.

This work evaluated two different digital soil mapping methods for mapping the presence of iron pans in South-West France. The presence of iron pans limit rooting depth, thereby affecting available water content for plants and increasing vulnerability of trees to storms. In some cases, it may also limit the water infiltration rate and cause anaerobic conditions limiting rooting depth, biological activity and plant growth. This work evaluates the potential of a road-side survey sampling and subsequent digital soil mapping techniques to map the probability of iron pan presence in a French region. We tested 1) indicator kriging without taking into account ancillary covariates and compared the mapped probability with 2) logistic regression kriging which did account for ancillary covariates. The probability of iron pan occurrence was mapped at a regional level with a reasonable precision but adding ancillary co-variates to the model gave more realistic predictions. Likely, the goodness of the prediction is still hampered by the resolution of the employed DEM and by the spatial distribution of the observation points. Furthermore, we made some suggestions of the possible usage of the iron pan map; probability maps could help regional planning for forestry, agriculture and geotechnical projects.

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.

There is an increasing demand for soil data at different scales. Due to changing data requirements, environmental studies need to make key decisions in terms of i) which datasets to use, and ii) how to derive the necessary data from these datasets. This is particularly true for properties that are not included in standard soil profile descriptions like many soil hydrological properties. Often, data are disaggregated to account for spatial variability and processed using e.g., pedotransfer functions to derive the soil hydrological properties. However, the effect of disaggregation and the use of pedotransfer functions is often unclear. This paper aims to illustrate the potential effects by looking at three different case studies at the global, regional and local scale. The three cases clearly show that the disaggregation of data has considerable effect on the estimate of the water holding capacity and its spatial distribution. At the regional and local scale, actual measurements of the water holding capacity also differed considerably from the results of the pedotransfer functions. It is concluded that the choice of datasets and the procedures to derive the water holding capacity really matter. Too often, studies take a published dataset and a method without additional evaluation. It is recommended to pay more attention to these elements and carry out ensemble runs using different datasets and carry out comparative analysis using different methods to provide better insight in the accuracy of the final outcomes.

International audience; This work presents the first high-resolution map of soil organic carbon (SOC) in mainland France, including soils below 30 cm. The research was performed within the framework of GlobalSoilMap (GSM). SOC predictions for different depth layers (0–5 cm, 5–15 cm, 15–30 cm, 30–60 cm, 60–100 cm and > 100 cm) were made at 90 and 500 m resolution for mainland France, along with their upper and lower confidence intervals. The maps were developed using data mining and an elaborate cross-validation scheme. The 90 m maps were compared to 500 m resolution GlobalSoilMap maps and the SoilGrids1km (SG1km) product. The latter is a global model for predicting soil properties for the same depth layers, at 1 km resolution. At 90 and 500 m resolution, the predicted SOC content was unbiased and showed good agreement with the measured SOC, despite the poor model diagnostics and decrease of performance with depth. It was found that the subsoil (> 30 cm) carbon pool for France contributes 49% to the total soil carbon stock. The use of coarser resolution prediction grids resulted in smoother spatial patterns and wider confidence intervals; however, it did not bias the estimated carbon stocks. Applying GlobalSoilMap specifications to France, using a large soil dataset and all the exhaustive spatially available data outperformed SG1km predictions. The overall spatial patterns of the SG1km SOC content were found to be very similar to the GlobalSoilMap maps. However, the SG1km overestimated the SOC content and carbon stocks (> 75% for the total carbon stock, and 100% for the stocks below 30 cm) and showed a similar spatial distribution over the different soil depth layers. The main reason for the overestimation was that the local data used in SG1km was rather small (56 samples) and not representative in terms of SOC content or represented soil types; the profiles had far higher SOC content and this may have propagated in the modelled vertical profile and the kriging part of the residuals. Improvements for SG1km may entail the use of a representative national subsample from large national soil databases. Furthermore, a bottom-up approach such as GlobalSoilMap may be more favourable when considering prediction accuracies, data privacy policies and local acceptance of generated products.

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 methodology for assessing mineral abundances of mixtures having more than two constituents using absorption features in the 2.1–2.4 μm wavelength region. In the first step, the absorption behaviour of mineral mixtures is parameterised by exponential Gaussian optimisation. Next, mineral abundances are predicted by regression tree analysis using these parameters as inputs. The approach is demonstrated on a range of prepared samples with known abundances of kaolinite, dioctahedral mica, smectite, calcite and quartz and on a set of field samples from Morocco. The latter contained varying quantities of other minerals, some of which did not have diagnostic absorption features in the 2.1–2.4 μm region. Cross validation showed that the prepared samples of kaolinite, dioctahedral mica, smectite and calcite were predicted with a root mean square error (RMSE) less than 9 wt.%. For the field samples, the RMSE was less than 8 wt.% for calcite, dioctahedral mica and kaolinite abundances. Smectite could not be well predicted, which was attributed to spectral variation of the cations within the dioctahedral layered smectites. Substitution of part of the quartz by chlorite at the prediction phase hardly affected the accuracy of the predicted mineral content; this suggests that the method is robust in handling the omission of minerals during the training phase. The degree of expression of absorption components was different between the field sample and the laboratory mixtures. This demonstrates that the method should be calibrated and trained on local samples. Our method allows the simultaneous quantification of more than two minerals within a complex mixture and thereby enhances the perspectives of spectral analysis for mineral abundances.

The objective of this work was to compare three spatially explicit modeling approaches for soil depth (SD) for France (540 K km 2 ), produced using: i) a straight forward digital soil mapping (DSM) approach, based on regression treemodeling ( RTM ), ii) gradient boosting modeling ( GBM ), and iii) multi-resolution kriging ( MrK ) for large datasets. SD was defined from the USDA Soil Survey Manual as the depth (in cm) to a lithic or paralithic contact. SD was determined for 2116 sites from the French Soil Monitoring network (RMQS) and 14,113 sites from French Soil Inventory (IGCS) program. The RMQS dataset had a better spatial coverage and was collected following a standardized procedure. The RMQS dataset was used for calibration; the IGCS dataset was used for validation. SD ranged from 0 to 300 cm for RMQS (mean = 102 cm), and from 3 to 295 cm for IGCS (mean = 93 cm). Exhaustive environmental data were used to characterize the climate, the organisms, the topography and the other known soil forming factors, following the Soil-Landscape paradigm. The final maps were compared using the same strategy; the maps' accuracy and difference were assessed by comparing the SD predictions to observed data (RMQS and IGCS datasets) and to an available soil map (1:1M). The three DSM approaches predicted the SD trend mainly from the covariates derived from the digital elevation model (DEM). Then, the most important covariates were soil properties, climate covariates, and finally land use. The three predictive maps showed similar accuracy, and were consistent with the 1:1M SD map. The four maps presented similar spatial pattern at the country scale, but the RTM and GBM map showed the higher spatial heterogeneity, while the MrK map was smoother. The three maps had poor performance to estimate the shallower and deeper SD values. This issue was discussed and three options were presented to solve it (implementing calibration dataset, addition of kriged residuals, quantile transformation). Finally, this work showed that the choice of the modeling approach should be done considering the users' goals.

This article reviews the use of optical and microwave remote sensing data for soil and terrain mapping with emphasis on applications at regional and coarser scales. Remote sensing is expected to offer possibilities for improving incomplete spatial and thematic coverage of current regional and global soil databases. Traditionally, remotely sensed imagery have been used to support segmentation of the landscape into rather homogeneous soil–landscape units for which soil composition can be established by sampling. Soil properties have also been inferred from optical and microwave data using physically-based and empirical methods. Used as a secondary data source, remotely sensed imagery may support spatial interpolation of sparsely sampled soil property data. Soil properties that have been measured using remote or proximal sensing approaches include mineralogy, texture, soil iron, soil moisture, soil organic carbon, soil salinity and carbonate content. In sparsely vegetated areas, successful use of space borne, airborne, and in situ measurements using optical, passive and active microwave instruments has been reported. On the other hand, in densely vegetated areas, soil data acquisition typically relied on indirect retrievals using soil indicators, such as plant functional groups, productivity changes, and Ellenberg indicator values. Several forms of kriging, classification and regression tree analyses have been used jointly with remotely sensed data to predict soil properties at unvisited locations aiming at obtaining continuous area coverage. We expect that remotely sensed data from existing platforms and planned missions can provide an important data source supporting digital soil mapping. Yet, most studies so far have been performed on a local scale and only few on regional or smaller map scale. Although progress has been made, current methods and techniques still bear potential to further explore the full range of spectral, spatial and temporal properties of existing data sources. For example, space borne spectroscopy has been of limited use in retrieving soil data when compared to laboratory or field spectroscopy. To date, there is no coherent methodology established, where approaches of spatial segmentation, measurements of soil properties and interpolation using remotely sensed data are integrated in a holistic fashion to achieve complete area coverage. Such approaches will enhance the perspectives of using remotely sensed data for digital soil mapping.

This work aims at improving the mapping of major mineral variability at regional scale using scale-dependent spatial variability observed in remote sensing data. Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data and statistical methods were combined with laboratory-based mineral characterization of field samples to create maps of the distributions of clay, mica and carbonate minerals and their abundances. The Material Identification and Characterization Algorithm (MICA) was used to identify the spectrally-dominant minerals in field samples; these results were combined with ASTER data using multinomial logistic regression to map mineral distributions. X-ray diffraction (XRD) was used to quantify mineral composition in field samples. XRD results were combined with ASTER data using multiple linear regression to map mineral abundances. We tested whether smoothing of the ASTER data to match the scale of variability of the target sample would improve model correlations. Smoothing was done with Fixed Rank Kriging (FRK) to represent the medium and long-range spatial variability in the ASTER data. Stronger correlations resulted using the smoothed data compared to results obtained with the original data. Highest model accuracies came from using both medium and long-range scaled ASTER data as input to the statistical models. High correlation coefficients were obtained for the abundances of calcite and mica (R2 = 0.71 and 0.70, respectively). Moderately-high correlation coefficients were found for smectite and kaolinite (R2 = 0.57 and 0.45, respectively). Maps of mineral distributions, obtained by relating ASTER data to MICA analysis of field samples, were found to characterize major soil mineral variability (overall accuracies for mica, smectite and kaolinite were 76%, 89% and 86% respectively). The results of this study suggest that the distributions of minerals and their abundances derived using FRK-smoothed ASTER data more closely match the spatial variability of soil and environmental properties at regional scale.

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 (