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Mapping the extent and locations of peatland at landscape scale has implications for carbon inventories, conservation and ecosystem services assessments. The main aim of this paper was to model and map the extent of northern peat soils while taking into account its uncertainty, and in particular exploring: 1. the use of radar Sentinel 1 as alternative to optical sensors to reduce problems due to clouds while taking advantage of the seasonality changes and 2. the use of deep learning for peat classification and as application of Digital Soil Mapping. The data sets defining presence or absence of peat in the soil were obtained from different sources and different sampling schemes, densities and distributions. Scotland was used as test case, because of its cloudy weather and fragmented distribution of different types of peat. An extension of the scorpan-kriging approach was used. The trend was estimated with different approaches: Generalized Additive Models, RandomForest and deep learning (convolutional neural networks). Each approach produced the probability of belonging to a class and the predicted class for each pixel. The results were assessed using out-of-sample measures. In this study 108 combinations of data sets and models (including trend approaches, sets of covariates and modelling of the spatial structure) were assessed. Overall, spatially explicit models performed better. The choice of the statistical method can have a significant impact on the predictive performances, while the sets of environmental covariates had a lower impact. Sentinel-1 with morphological features proved to be a good alternative to optical data for peat mapping. It is important to have balanced data sets representing the distribution of the data, because merging heterogeneous sources of data from different populations does not necessarily improve predictions. The use of deep learning and convolutional neural network provided initial promising results. There were large differences in the modelling approaches. These differences and uncertainties need to be taken into account for further modelling such as earth surface modelling or carbon accounting.

Land degradation leads to ecosystem degradation, reducing ecosystem functioning and depleting ecosystems' resilience. The majority of factors linked to land degradation are closely related with the depletion of below- and above-ground stocks of organic carbon. Organic carbon stock is important for climate change mitigation and for restoring soil functions such as those crucial to support food security. In this study, we mapped carbon stocks to infer land degradation in a small area in the Ethiopian Great Rift Valley. The study aimed to assess carbon stock status and to identify limitations and advantages of using global data in mapping at local scale relative to using local data. Two different datasets were developed; i) a “global dataset” characterised by data from datasets with global coverage data, and ii) a “hybrid dataset” that coupled data from global datasets, soil data derived from a local survey, and land cover data derived from a supervised classification of satellite images. The results showed that i) global datasets introduced inaccuracy that must be taken into account for advocating interventions at a local scale, and ii) global datasets could be used at a small catchment level for decision-making, if a simple rank of values is sufficient, but they might provide an optimistic picture of land degradation because they overestimate stocks.

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.

Reliable spatially explicit information about soil is important for global environmental challenges. Soil texture is one of the soil most important characteristics as it drives several physical, chemical, biological, and hydrological properties and processes. Despite the importance, there is scarcity of information on soil texture, especially at the resolution required for environmental modelling. Many recent efforts modelled soil texture with different approaches focussing on the spatial relationships with environmental covariates. This study aimed at i) modelling and map soil particle classes for Scotland at medium resolution (250 m), for topsoil and the whole profile, using an operational DSM approach following specifications from the GlobalSoilMap project; ii) assessing the spatial uncertainty of the modelling approach, and iii) evaluating the impact of spatial and modelling uncertainty on soil texture classification of the topsoil. An extension of the scorpan -kriging approach, i.e. hybrid geostatistical Generalized Additive Models (GAMs), combining GAM with Gaussian simulations was used on Additive-Log-Transformed soil particle classes. The R2 calculated with the validation dataset was between 0.55 and 0.60 and the RMSE values were below 13%. The set of covariates used in this study explained about 40% of the variance of the data. The significant covariates included morphological features, vegetation index and information about the phenological season. The results also showed a large percentage of the variability to be spatially structured. The assessment of the uncertainty on the soil texture classification showed variability and class shift. The resulting datasets can be used as input for further modelling in a number of areas, and they are also important for soil functions modelling in the context of provision of Ecosystem services. The accompanying uncertainty can be used to provide supporting information for land management choices.

Peatlands are an important potential sink or source of carbon and play a significant role in climate change regulation. Understanding peatland as 3D-landforms is as important as mapping their spatial extent. The main aim of this work was to estimate a 3D representation of peat properties and assess the associated spatial uncertainty, to provide baseline information for climate and land use change analyses. In this study a combination of 3D Generalized Additive Models and 3D geostatistics was applied to a raised basin bog using legacy data to map carbon content. The study presents a novel approach based on methods providing quantification of the spatial uncertainty and the possibility to model complex relationships. The approach fully exploits the 3D spatial relationships between the survey points while supported by environmental variables. The methods proved to be general and highly flexible. The results of this study showed that it is possible to model peat properties to obtain a detailed volumetric assessment of the peat, including carbon stocks from a limited set of legacy data. The estimates of spatial uncertainty are important when including the results in further environmental and climate-change models or for decision making to provide alternatives and prioritisation.

The variation of soil properties down a profile is usually considered continuous. The aim of this study was to develop and test a methodology to model the continuous vertical and lateral distributions of SOC stocks in Scottish soils making explicit the modelling and spatial uncertainty of the results. A comparison with regression kriging and other depth function methods is provided to show that better performances can be achieved taking into account non-linear relationships between covariates and soil properties. The analysis was run for the whole of Scotland. The carbon stocks were calculated for each point, i.e. each horizon in each available profile. The stock value at each cell for each of the considered depth layers was defined using a hybrid GAM-geostatistical 3D model, combining: 1) the fitting of a GAM to estimate the trend of the variable, using a 3D smoother with related covariates; and 2) kriging or Gaussian simulations of GAM residuals as spatial component to account for local details. The use of GAM makes the approach flexible, because it is able to deal with both linear and non-linear relationships between soil properties and the considered covariates. The results confirmed that MODIS data are a useful source of information for DSM especially at national scale. When comparing the proposed approach with similar methods such as regression kriging, the results showed better agreement with the data in the validation set with a global R2 of 0.60. The median values obtained are comparable with the values reported from previous studies on stocks in Scotland using different methods. The uncertainty is large indicating a wide range of credible values for each pixel.

Knowledge of soil properties with complete area coverage is needed for policy-making, land resource management, and monitoring environmental impacts. Remote sensing offers possibilities to support Digital Soil Mapping, especially in data-poor regions. The aim of this work was to test the potential of time-series of MODIS (Moderate Resolution Imaging Spectroradiometer) vegetation and drought indices to provide relevant information to model topsoil properties in a Boreal-Atlantic region (Scotland) focussing on differentiation between soils with high and soils with low organic matter contents. For each of the three considered years, 345 MODIS data sets were included in the exploratory analysis; 15 data products for 23 dates (bi-weekly) per year. Terrain parameters derived from Shuttle Radar Topography Mission were also included. A methodology was implemented to exploit fully the high number of covariates, to identify the band, index or product that best correlates with the soil property of interest. In particular the proposed approach i. relies on freely globally available data-sets; ii. uses statistical criteria to select the combination of covariates providing the highest predictive capability, among the data considered and available; iii. deals with both continuous (using Generalized Additive Models, GAMs) and multinomial categorical (using Random Trees) types of variables; iv. takes into account fully the spatial autocorrelation of the data; v. provides estimates of the spatial uncertainty for each pixel; and vi. is computationally efficient when compared with methods such as forward stepwise. The models fitted show a fairly good agreement with existing data sets, presenting a consistent spatial pattern. The use of MODIS data as covariates increased the predictive capabilities of GAMs using only terrain parameters. The misclassification error for organic matter classes was between 25 and 35%. The assessment provided of the spatial uncertainty of the modelled values can be used in further modelling and in the assessment of consequences of climate-change and trade-off in land use changes. This approach can contribute to improving our understanding and modelling of soil processes and function over large, and relatively sparsely sampled, areas of the world.

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.

Assessment of the spatial distribution of environmental variables and of the associated uncertainty is a key issue in environmental modelling. The water content of soil plays an important role in many ecological and hydrological processes for land suitability evaluation. In this study we present a flexible procedure to interpolate soil-related variables that uses covariates to estimate the spatial trend of the variables and quantifies the uncertainty dealing with non-linear relationships. The procedure further extends approaches based on generalized additive models. The use of Gaussian simulations of the error allows the assessment of spatial uncertainty. The method was applied to available soil water capacity for three different nested extents: national, regional, and catchment. The models fitted have different significant covariates and different estimated values according to the region considered. The results suggest that the estimates from the model fitted at the appropriate extent are the most accurate. Taking into account the uncertainty of the trend, the results provided a realistic estimation of the variability and they are spatially consistent with the geomorphological patterns. Estimating the variability with the proposed procedure is useful for further environmental and land use modelling and it can be integrated with uncertainty from other variables, such as those derived from climate models.

The Scottish Government wish to preserve the carbon stocks already stored or sequestered in both organic and mineral soils and see land-use change as one of the key drivers affecting storage of soil organic carbon (SOC). A key component to develop any strategy to maintain the existing carbon stocks is the quantification of these stocks both in terms of the carbon content and its spatial distribution. To date, two different methods that use the same existing legacy data have been used to quantify carbon stocks in Scotland: a traditional approach and a hybrid generalised additive model (GAM)—geostatistical 3D model. Each of the methods revealed differences in the spatial patterns of SOC stocks. Understanding these differences will enable the development of more robust and accurate models that can be used to assess changes in stocks due to changing land use. Here, we compare these methods for the Scottish mainland, Western Isles, and Orkney. The traditional approach was based on calculating average organic carbon values from a subset (6000) of around 40,000 observations stored within the Scottish Soil Database. The total SOC stock was then determined by multiplying the areal extent of each soil series/land-use combination by the calculated profile stock. The uncertainty was also quantified based on standard error of the measured carbon contents and the uncertainty in the bulk density pedotransfer functions. A hybrid GAM-geostatistical 3D model combined the fitting of a GAM using a 3D smoother with related covariates and the kriging or Gaussian simulations of the residuals to spatially account for local details. The uncertainty was also calculated and was found to be large, indicating a wide range of credible values for each pixel. The deviation from the median ranges was between 5 and 75 % for the interpolated values depending on location.

We describe the classification of landscapes characterised bymineral soil using a model that calculates soil moisture availability on amonthly basis. Scotland is used as a case study area. The model uses potentialsoil moisture deficit, estimated using broad scale (40 × 40 km)climate patterns, in conjunction with meteorological station measurements toobtain finer scale values of climatic soil moisture deficit. Point estimates ofsoil available water are obtained for soil characteristics using appropriatepedotransfer functions, and geostatistical techniques are used to upscale theresults and interpolate to a 1-km grid. Known heterogeneityin soil physical characteristics is used to provide local corrections to thepotential soil moisture deficit, estimated using the climatic variables above.Temporal profiles of monthly water content are modelled for each1-km location and classified into six classes usingunsupervised cluster analysis. The spatial distribution of these classesreflects regional variations in the availability of moisture and energy, onwhich finer-grained topographic patterns are superimposed. In the case study,the broad scale spatial heterogeneity of heathlands and grasslands on mineralsoils in Scotland is shown to be strongly related to the soil moistureclassification. The results can be used in studies investigating the patternsofdistribution of communities at the landscape and regional scale.