Digital Soil Mapping Products Research Articles

Evaluating the quality of soil legacy data used as input of digital soil mapping models

AbstractMost of the Digital Soil Mapping (DSM) products now available across the globe have been developed from the deposits of punctual soil observations inherited from several decades of soil survey activity. By using these legacy data as inputs for calibrating our DSM models, we implicitly make the assumption that these legacy soil data are accurate and therefore do not affect significantly our DSM products. However, this assumption has never been tested. The objectives of this study were to evaluate the accuracies of soil property measurements retrieved from legacy soil profiles, to analyse the different sources of error that may affect these measurements and to examine their impacts on the soil property predictions delivered by DSM models. The study was focused on a control sampling within the coastal plain of Languedoc (Southern France) at 129 locations where legacy measured soil profiles were collected between 1955 and 1992. At each location, four topsoil (0–20 cm) samples were collected at increasing distances (0, 5, 25 and 100 m) to characterize the local variabilities of the soil properties. Six soil properties—Clay, Silt, Sand, Soil Organic Carbon, Calcium Carbonate contents and Cation Exchange Capacity—were determined for each sample using certified soil laboratory methods. The results revealed that legacy soil property values had large overall errors and large biases. Biases likely induced by differences in soil analysis protocols could be corrected by linear functions calibrated onto the reference data obtained from the control sampling. The contributions of the errors propagated from the manual geo‐referencing errors (mean = 31 m) represented on average 52% of the errors after analytical bias corrections. These errors exhibited large variations from one property to another due to differences in the short‐range spatial variations (0–100 m) of these soil properties. A DSM exercise conducted on our control sampling revealed that the errors of legacy soil data were propagated to the soil property predictions provided by the DSM models. However, this propagation could be largely mitigated by applying the above‐evoked corrections. This study highlights the need to better control the quality of the legacy soil data used in DSM and to account for this source of uncertainty in the DSM models.

Colombian soil texture: building a spatial ensemble model

Abstract. Texture is a fundamental soil property for multiple applications in environmental and earth sciences. Knowing its spatial distribution allows a better understanding of the response of soil conditions to changes in the environment, such as land use. This paper describes the technical development of Colombia's first texture maps, obtained via a spatial ensemble of national and global digital soil mapping products. This work compiles a new database with 4203 soil profiles, which were harmonized at five standard depths (0–5, 5–15, 15–30, 30–60, and 60–100 cm) and standardized with additive log ratio (ALR) transformation. A compilation of 83 covariates was developed and harmonized at 1 km2 of spatial resolution. Ensemble machine learning (EML) algorithms (MACHISPLIN and landmap) were trained to predict the distribution of soil particle size fractions (PSFs) (clay, sand, and silt), and a comparison with SoilGrids (SG) products was performed. Finally, a spatial ensemble function was created to identify the smallest prediction errors between EML and SG. Our results are the first effort to build a national texture map (clay, sand, and silt fractions) based on digital soil mapping in Colombia. The results of EML algorithms showed that their accuracies were very similar at each standard depth, and were more accurate than SG. The largest improvement with the spatial ensemble was found at the first layer (0–5 cm). EML predictions were frequently selected for each PSF and depth in the total area; however, SG predictions were better when increasing soil depth in some specific regions. The final error distribution in the study area showed that sand presented higher absolute error values than clay and silt fractions, specifically in eastern Colombia. The spatial distribution of soil texture in Colombia is a potential tool to provide information for water-related applications, ecosystem services, and agricultural and crop modeling. However, future efforts need to improve aspects such as treating abrupt changes in the texture between depths and unbalanced data. Our results and the compiled database (https://doi.org/10.6073/pasta/3f91778c2f6ad46c3cc70b61f02532db, Varón-Ramírez and Araujo-Carrillo, 2022, https://doi.org/10.6073/pasta/d6c0bf5847aa40836b42dcc3e0ea874e, Varón-Ramírez et al., 2022) provide new insights to solve some of the aforementioned issues.

How well does digital soil mapping represent soil geography? An investigation from the USA

Abstract. We present methods to evaluate the spatial patterns of the geographic distribution of soil properties in the USA, as shown in gridded maps produced by digital soil mapping (DSM) at global (SoilGrids v2), national (Soil Properties and Class 100 m Grids of the USA), and regional (POLARIS soil properties) scales and compare them to spatial patterns known from detailed field surveys (gNATSGO and gSSURGO). The methods are illustrated with an example, i.e. topsoil pH for an area in central New York state. A companion report examines other areas, soil properties, and depth intervals. A set of R Markdown scripts is referenced so that readers can apply the analysis for areas of their interest. For the test case, we discover and discuss substantial discrepancies between DSM products and large differences between the DSM products and legacy field surveys. These differences are in whole-map statistics, visually identifiable landscape features, level of detail, range and strength of spatial autocorrelation, landscape metrics (Shannon diversity and evenness, shape, aggregation, mean fractal dimension, and co-occurrence vectors), and spatial patterns of property maps classified by histogram equalization. Histograms and variogram analysis revealed the smoothing effect of machine learning models. Property class maps made by histogram equalization were substantially different, but there was no consistent trend in their landscape metrics. The model using only national points and covariates was not substantially different from the global model and, in some cases, introduced artefacts from a lithology covariate. Uncertainty (5 %–95 % confidence intervals) provided by SoilGrids and POLARIS were unrealistically wide compared to gNATSGO/gSSURGO low and high estimated values and show substantially different spatial patterns. We discuss the potential use of the DSM products as a (partial) replacement for field-based soil surveys. There is no substitute for actually examining and interpreting the soil–landscape relation, but despite the issues revealed in this study, DSM can be an important aid to the soil surveyor.

Assessment of global, national and regional‐level digital soil mapping products at different spatial supports

AbstractDigital soil mapping provides estimates of soil properties and, in turn, an understanding of the spatial variation in soil across landscapes. In recent years, several digital soil mapping products have been released across the world. An end user in New South Wales (NSW), Australia, could choose between a global product (SoilGrids), a national product (Soil and Landscape Grid of Australia) and a state product (NSW digital soil maps). All predict at grid resolutions of 250 m or less, which approaches the required detail for within‐field site‐specific management such as variable‐rate application of agricultural inputs. In this study, the quality of clay and soil organic carbon (SOC) products (0–30 cm) from these three publicly available digital soil maps (DSM) were validated with an external dataset of 394 soil observations, collected from 14 farms across eastern Australia. Each farm was sampled using stratified random sampling, allowing for unbiased estimates of prediction quality at three spatial supports: point (soil core), strata (management zone) and farm. For the clay attribute, this study observed Lin's concordance correlation coefficient (LCCC) values of 0.22–0.37 at the point spatial support, far below reported values. This improved at the strata spatial support with LCCC values of 0.20–0.60 and the farm spatial support with values of 0.07–0.54. For SOC, LCCC values ranged from 0.25 to 0.45 for points, 0.31–0.55 for strata and 0.31–0.60 for farms. The grid resolution of DSMs depends largely on the resolution of the covariates used, and current efforts look to incorporate more recent, finer resolution covariates to create higher‐resolution soil maps. However, the results of this study suggest that both the grid resolution and spatial support used in DSMs need further consideration. Rather than letting the resolution of covariates determine the resolution of DSMs, this should also be determined by the number of soil observations available for modelling, their distribution in geographic and attribute space and the prediction quality.Highlights DSM products do not meet published accuracies when assessed with an independent dataset The large‐extent DSM products were unable to represent within‐farm variability Aggregation of the DSM products over large spatial supports improved accuracies DSM products should be created at different spatial supports, each with an associated uncertainty

Multiscale evaluations of global, national and regional digital soil mapping products in France

As digital soil mapping (DSM) applications have been developed at multiple extents over the two last decades, large areas of the world are now covered by several DSM products with similar resolution and targeted soil properties. Thus, from these products, end-users must carefully select the one that will best meet their needs. The aim of this study was to evaluate three DSM products obtained at different scales (global, national and regional) over three local territories of increasing area selected in three contrasting regions of France (Alsace, Brittany and Languedoc-Roussillon). Three topsoil (5–15 cm) properties were evaluated: clay content, pH in water and soil organic carbon content. Evaluations were done at both point and soil mapping unit supports, the latter corresponding to quantitative assessment of visual accordance between DSM products and conventional local soil maps of acknowledged quality. The ability to predict soil properties well increased from global to national to regional DSM products. However, none of the DSM products tested was able to predict satisfactorily at the most local (1:25,000) scale. Evaluations of DSM products using local soil maps were generally in accordance with those using points. Evaluations using local soil maps also provided additional information about the utility of DSM products for small areas with too few soil measurements to perform punctual evaluation and for issues concerning areal-support uses of DSM products. These results suggest that when focusing on local areas, users of DSM products should evaluate their performance and, if unsatisfactory, invest in development of local DSM.

Mapping soil organic carbon stocks in Tunisian topsoils

Better knowledge of the amount and spatial distribution of soil organic carbon (SOC) stock at national level is a key element for monitoring, planning and decision-making regarding soil quality management, agriculture or carbon storage options. The present study proposes for the first time a Digital Soil Mapping (DSM) initiative to map SOC stocks in Tunisian topsoils (0–30 cm) at 100 m resolution, using a Quantile Regression Forest (QRF) algorithm, a range of environmental covariates, and a national database of 1540 SOC stock profiles. Our results provided a revised assessment of the SOC stock on the Tunisian territory at 391Tg C in the first 30 cm of soil profile, i.e. an average of 2.53 kg m-2. The map of SOC stocks outperformed global DSM products such as SoilGrids 2.0 in both R2 (0.44 vs. 0.15) and RMSE (1.94 vs. 2.52 kg m−2) and can be used as a benchmark against changes of land use and climate. The importance of the environmental covariates tested indicates the major role of bioclimatic data and, to a lesser extent, remote sensing images and topography-related variables. Our study did not show a significant added value of using additional covariates in relation to nationally available variables or the SOC map predicted by SoilGrids2.0. Finally, our results showed that increasing the quality and quantity of soil profile observations is most likely the best way to improve the future SOC map, starting with the northern region of Tunisia, which has the highest SOC stock predictions and uncertainties in the country. An alternative way would be the exploration of new covariates through sub-national approaches.

Hand-feel soil texture observations to evaluate the accuracy of digital soil maps for local prediction of soil particle size distribution: A case study in Central France

Digital maps of soil properties are now widely available. End-users now can access several digital soil mapping (DSM) products of soil properties, produced using different models, calibration/training data, and covariates at various spatial scales from global to local. Therefore, there is an urgent need to provide easy-to-understand tools to communicate map uncertainty and help end-users assess the reliability of DSM products for use at local scales. In this study, we used a large amount of hand-feel soil texture (HFST) data to assess the performance of various published DSM products on the prediction of soil particle size distribution in Central France. We tested four DSM products for soil texture prediction developed at various scales (global, continental, national, and regional) by comparing their predictions with approximately 3 200 HFST observations realized on a 1:50 000 soil survey conducted after release of these DSM products. We used both visual comparisons and quantitative indicators to match the DSM predictions and HFST observations. The comparison between the low-cost HFST observations and DSM predictions clearly showed the applicability of various DSM products, with the prediction accuracy increasing from global to regional predictions. This simple evaluation can determine which products can be used at the local scale and if more accurate DSM products are required.

National-scale maps for soil aggregate size distribution parameters using pedotransfer functions and digital soil mapping data products

Soil aggregate size distribution (ASD) is a key soil property for quantitative characterization of soil structure. Typically, dry and wet sieving methods are followed to measure mass fractions of soil aggregates under each aggregate size class and their cumulative fractions are used to estimate ASD parameters. In general, available soil ASD databases are small and a comprehensive assessment of existing models to describe ASD data is not available for soil aggregates. With objectives to comprehensively assess available ASD models, parameterize each model through pedotransfer functions (PTF), and generate ASD parameter maps for large region, we evaluated 26 models using 1373 soil ASD datasets generated from four large Indian states. The lognormal distribution-based Buchan model was observed to best describe most of our datasets. In fact, 1305 ASD datasets met the threshold criteria consisting of ‘coefficient of determination (R2) > 0.95′ and ‘corrected Akaike information criterion (AICc) < − 45′ for the Buchan model. Once ASD models were chosen, we used both soil properties and terrain attributes as predictors in the random forest (RF) and extreme gradient boosting models to develop PTFs for their parameters. To check whether soil properties estimated through digital soil mapping (DSM) can replace measured soil properties in the PTFs, we extracted soil properties from available DSM products. With measured soil properties and terrain attributes as predictors, RF-based PTFs yielded R2 values in the range of 0.55–0.76 for ASD parameters. Comparable performance was observed when measured soil properties were replaced by the DSM-estimated soil properties. Such a result enabled us to create national digital soil maps for ASD parameters based on DSM-derived soil properties for the first time.

Digital soil mapping outputs on soil classification and sugarcane production in Brazil

Soil maps at regional and farm levels are vital for the best management agricultural practices (BMAP). The soil is the substrate for plant growth and essential to ensure food security. In this context, soil maps require a detailed cartographic scale for BMAP. This study (i) investigated the use of digital soil mapping (DSM) products, such as soil chemical and physical attributes, indices, mineralogy, and properties to extrapolate late soil survey maps at 1:20,000 scale; (ii) created the digital yield environment for sugarcane based on the DSM products; and (iii) evaluated qualitatively the predict soil maps and relationship with previous studies and the predicted yield environment. The region of interest covers eight municipalities and almost 2598 km2 in São Paulo State, Brazil. The soil survey at farm level conducted covered almost 86.52 km2, ∼3.33% of the total area (96.67% of the unmapped area). We created a point grid (centroid) with the same spatial resolution (30 m) of the rasters used as covariates for soil mapping unit (SMU) predictions. This grid intended to retrieve the representative soil mapping unit of each geometric polygon. It was retrieved 117,413 points representing 27 SMU of seven soil orders at a first categorical level, according to the Brazilian Classification System, and seven yield environment for sugarcane production. SMU predictions and their respective soil orders were performed using the random forest machine learning regression method. The level of association between SMU and yield environments was 0.34 (α = 0.01) by the Cramer's V coefficient with a very strong relationship. Our approach could provide the first digital yield environment for sugarcane based on the DSM products. Furthermore, a qualitative evaluation of our framework was substantiated with previous research in the same study site. This framework could be replicated and fulfil the need for DSM at regional and farm levels for policy-makers and farmers.

Multiscale Evaluations of Global, National and Regional Digital Soil Mapping Products in France Across Regions and Soil Properties

Multiscale Evaluations of Global, National and Regional Digital Soil Mapping Products in France Across Regions and Soil Properties

Applying the Digital Soil Mapping Products to Predict the Soil Types in Brazil

Applying the Digital Soil Mapping Products to Predict the Soil Types in Brazil

Digital mapping of GlobalSoilMap soil properties at a broad scale: A review

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 (<30 cm), and studies on deep soil (100–200 cm) were less represented (21.7%). Relief, organisms, and climate were the three most frequently used environmental covariates in DSM. Nonlinear models (i.e. machine learning) have been increasingly used in DSM for their capacity to manage complex interactions between soil information and environmental covariates. Soil pH was the best predicted soil property (average R2 of 0.60, 0.63, and 0.56 at 0–30, 30–100, and 100–200 cm). Other relatively well-predicted soil properties were clay, silt, sand, soil organic carbon (SOC), soil organic matter (SOM), SOC stocks, and bulk density, and coarse fragments and soil depth were poorly predicted (R2 < 0.28). In addition, decreasing model performance with deeper depth intervals was found for most soil properties. Further research should pursue rescuing legacy data, sampling new data guided by well-designed sampling schemas, collecting representative environmental covariates, improving the performance and interpretability of advanced spatial predictive models, relating performance indicators such as accuracy and precision to cost-benefit and risk assessment analysis for improving decision support; moving from static DSM to dynamic DSM; and providing high-quality, fine-resolution digital soil maps to address global challenges related to soil resources.

Evaluating the impact of using digital soil mapping products as input for spatializing a crop model: The case of drainage and maize yield simulated by STICS in the Berambadi catchment (India)

Digital Soil Mapping (DSM) can be an alternative data source for spatializing crop models over large areas. The objective of the paper was to evaluate the impact of DSM products and their uncertainties on a crop model’s outputs in an 80 km2 catchment in south India. We used a crop model called STICS and evaluated two essential soil functions: the biomass production (through simulated yield) and water regulation (via calculated drainage). The simulation was conducted at 217 sites using soil parameters obtained from a DSM approach using either Random Forest or Random Forest Kriging. We first analysed the individual STICS simulations, i.e., at two cropping seasons for 14 individual years, and then pooled the simulations across years, per site and crop season. The results show that i) DSM products outperformed a classical soil map in providing spatial estimates of STICS soil parameters, ii) although each soil parameters were estimated separately, the correlations between soil parameters were globally preserved, ii) Errors on STICS’ yearly outputs induced by DSM estimations of soil parameters were globally low but were important for the few years with high impacts of soil variations, iii) The statistics of the STICS simulations across years were also affected by DSM errors with the same order of magnitude as the errors on soil inputs and iv) The impact of DSM errors was variable across the studied soil parameters. These results demonstrated that coupling DSM with a crop model could be a better alternative to the classical Digital Soil Assessment techniques. As such, it will deserve more work in the future.

Using bias correction and ensemble modelling for predictive mapping and related uncertainty: A case study in digital soil mapping

In this study, we explored the potential benefits of using bias correction and ensemble modelling for the prediction of soil properties and assessment of related uncertainty. The proposed approach combines resampling techniques applied to soil observations, covariates and hyperparameters to generate a set of simulated values at the same location. The ensemble predictions resulting from the resampling are then used to generate deterministic predictions for the final mapping product along with the related uncertainty. We also introduced bias correction into the modelling framework in order to overcome conditional bias that is commonly encountered in digital soil mapping products. We compared the accuracy of our predictions resulting from bias correction and ensemble modelling with previously published global soil mapping products. Our results demonstrated that bias correction improves the linearity and the ratio of the variance between simulated and observed values and reduces conditional bias by a factor of 25 to 50% for different soil properties. The performance of the deterministic predictions obtained from ensemble modelling is better than most of its individual component models, and is always located in the first quantile of the performance of all members. The analysis of uncertainty suffers from underdispersion, which means that local uncertainty tends to be underestimated by our approach 40 to 60% of the time. A comparison with the performance achieved by global soil mapping products in our area, indicates that global mapping products achieved low performance (R2: −0.48–0.13) and suffered from an important conditional bias (alpha: 0.23–0.59, where alpha is the ratio of variance between predicted and observed values), leading to unrealistic predictions at the local scale. Ultimately, the combination of bias correction and ensemble modelling appears to be both useful and relevant for digital soil mapping and helps to address three common problems: equifinality, assessment of uncertainty and, correction of conditional bias in simulated values. The procedure described in this study is relatively easy to implement and is not computationally intensive. In operational use, the combination of bias correction and ensemble modelling should increase the quality of the information produced for environmental management and modelling, while additionally providing uncertainty maps.

Digital soil maps can perform as well as large-scale conventional soil maps for the prediction of catchment baseflows

The rapid advance of digital soil mapping (DSM) has resulted in the generation of fine resolution soils spatial datasets with associated uncertainty that can be as accurate but more cost-effective and faster to produce than conventional soil maps. There is documented and increasing interest by policy makers and end-users in moving from conventional soil mapping to DSM; however, the wider operational use of DSM depends on demonstrating the effectiveness of DSM in fulfilling user needs and requirements along with providing DSM products that can be easily used by non-specialists. In this study we used the Hydrology of Soil Types (HOST) classification scheme, which was devised to predict flows in ungauged catchments and is used by both the research and policy maker communities in the UK, as the exemplar for comparing the efficiency of HOST class maps produced using both DSM and conventional soil map approaches for predicting catchment hydrological response. The performance for hydrological predictions of a detailed (1:25,000 scale) polygon HOST class map and a HOST-DSM class map produced via spatial disaggregation was assessed by comparing Base Flow Index (BFI) calculated using HOST class proportions from both maps with BFI calculated using flow data from gauges in 39 selected catchments. Results showed that the disaggregated HOST-DSM class map gave similar or even better BFI predictions than the conventional polygon-based HOST class map, while also providing a better insight on spatial soil variability within map units and its effect on hydrological predictions. This study demonstrates the potential of DSM to produce soil hydrological maps that provide comparable or better baseflow predictions than maps produced using detailed and intensive soil surveying. The study results also suggest that translating DSM of classes to more easily interpretable mapping datasets and incorporating prediction uncertainty in the final DSM product, as in the case of BFI maps based on HOST-DSM classes, could help with facilitating the transition towards a wider operational use of DSM.

IRAKA: The first Colombian soil information system with digital soil mapping products

Advances in science and technology combined with the need to handle and manage natural resources have resulted in a gradual increase in soil data availability. This has allowed characterizing, describing, or proposing projects to address issues such as soil degradation, agricultural productivity, threats, and ecosystem services. Despite the availability of soil data, users are increasingly looking for additional, timely, and reliable information in order to respond to current and future situations, such as food security and climate change. IRAKA is a soil information system for the Colombian Cundiboyacense high plateau and the first to provide additional information from available official sources. It has a modular design based on the organization of a soil database whose structure incorporated 299 profiles, 1432 samples, and 55 properties and the integration, harmonization, and standardization of 8 soil surveys conducted in the area. The structured information enabled modeling and validating physical and chemical properties through digital soil mapping and comparing different machine learning techniques, including random forest, ranger, support vector machine, and ensemble models of these techniques. The interpolations of 11 quantitative properties and 1 qualitative property resulted in acceptable coefficients of goodness of fit due to different factors: property variability, representativeness, distribution in the study area, and property description based on environmental covariates. All the information generated has been made available for widespread use across different geographic web services. Thus IRAKA contributes to the management of information and knowledge through techniques and tools that allow users to visualize new information, use the information, and draw attention to new studies and improve databases in which the confidence level is insufficient and does not allow decision making.

Impressions of digital soil maps: The good, the not so good, and making them ever better

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.

Some practical aspects of predicting texture data in digital soil mapping

Soil texture is the most well-known composition in soil science. When separate components of the texture (sand, silt, and clay) are predicted independently in digital soil mapping (DSM), there is no guarantee that the separate estimates will sum to 100%. Log-ratio transformations before DSM modelling are alternatives to guarantee a constant sum of the estimates. Little is known about the effect of non-summing to 100% and transformations of particle-size fractions (PSFs) when DSM products were used to predict soil functional properties using pedotransfer functions (PTFs). Therefore, this study was conducted to investigate the effect of different soil texture modelling methods on the estimation of available soil water capacity (AWC) and the total amount of irrigation water (TIW) required for wheat production on a 4600 ha area in Khuzestan province, southwestern Iran. Specifically, this study aimed i) to assess the performance of random forest models (RF) to predict untransformed (UT) and transformed PSFs using environmental covariates; ii) to study the effects of three widely used log-ratio transformations including additive, centroid and isometric log-ratio transformations (alr, clr, and ilr respectively) on the estimations of AWC and TIW. A total of 150 soil samples were collected from the surface layers (0–30 cm) based on the conditioned Latin hypercube sampling (cLHS) procedure. Results indicated that, in terms of root mean square error (RMSE), RF provided similar accuracies in predicting PSFs for both untransformed and transformed data. However, transformation resulted in biased estimates. In addition, RF prediction based on untransformed data resulted in more correctly soil texture classes allocation when compared to transformed data. The spatial distribution of the sum of the predicted untransformed fractions indicated only small parts of the area conformed to the 100% sum. Almost the same accuracies for estimates of AWC were obtained when both untransformed and transformed predicted texture components were used as the inputs to PTFs. Data transformation can result in biased estimates of AWC. The findings indicated no significant difference between transformation methods in predicting AWC and TIW. The general patterns of the spatial distribution of the predicted AWCs across the whole area were the same for transformed and untransformed data (except for clr transformation).

Digital soil mapping for fire prediction and management in rangelands

BackgroundSoil properties have important effects on fire occurrence and spread, but soils are often overlooked in fire prediction models. Quantifying soil−fire linkages is limited by information in conventional soil maps, but digital soil mapping products (e.g., detailed soil property maps) could improve both wildfire prediction models and post-fire management decisions.ResultsOf our estimated 3.7 Mkm2 of rangeland in the continental US and Alaska, an average of 38 000 km2 burned per year between 2008 and 2017. To highlight the role of soils in fire ecology, we present 1) a conceptual framework explaining why soil information can be useful for fire models, 2) a comprehensive suite of literature examples that used soil property information in traditional soil survey for predicting wildfire, and 3) specific examples of how more detailed soil information can be applied for pre- and post-fire decisions.ConclusionsDigital soil mapping can improve fire prediction models and inform post-fire management decisions.

Merging country, continental and global predictions of soil texture: Lessons from ensemble modelling in France

The increasing demand for soil information has led to the rapid development of Digital Soil Mapping (DSM) products. As a consequence, multiple soil maps are sometimes available for a particular area. Rather than selecting the best map, model ensemble offers a way to capitalize on existing soil information, and to improve the map accuracy. In this study we ensemble four topsoil texture maps of France with different resolution made by different organizations at the national, European, and global scale. We investigated two methods of model ensemble: the Granger-Ramanathan (GR) and Variance-Weighted (VW) methods. Ensemble methods based on area stratification were also tested to take into account local soil information. We also assessed the impact of the number of calibration points on the evaluation indicators. Both ensemble methods improved the accuracy of the map compared to the best of the primary maps, while the GR method outperformed the VW method. We found that the different stratification strategies did not improve the accuracy significantly when compared to the global methods. Finally, we showed that a relatively low number of calibration points is required in the merging process if the sampling is well designed. This study demonstrates that digital soil mapping products at various scales from various data sources can be combined with the ensemble method taking advantage of all existing efforts and taking care of harmonization issues.