Digital Soil Mapping Research Articles

Contribution of Remote Sensing Data and GIS to Digital Soil Mapping in a Semi-Arid Zone of North Cameroon (Central Africa)

Contribution of Remote Sensing Data and GIS to Digital Soil Mapping in a Semi-Arid Zone of North Cameroon (Central Africa)

Assessing Soil Prediction Distributions for Forest Management Using Digital Soil Mapping

Texture, soil organic matter (SOM), and soil depth (SoD) are crucial properties in forest management because they can supply spatial information on forest site productivity and guide fertilizer applications. However, soil properties possess an inherent uncertainty that must be mapped to enhance decision making in management applications. Most digital soil mapping predictions primarily concentrate on the mean of the distribution, often neglecting the estimation of local uncertainty in soil properties. Additionally, there is a noticeable scarcity of practical soil examples to demonstrate the prediction uncertainty for the benefit of forest managers. In this study, following a digital soil mapping (DSM) approach, a Quantile Regression Forest (QRF) model was developed to generate high-resolution maps and their uncertainty regarding the texture, SoD, and SOM, which were expressed as standard deviation (Sd) values. The results showed that the SOM (R2 = 0.61, RMSE = 2.03% and with an average Sd = 50%), SoD (R2 = 0.74 and RMSE = 19.4 cm), clay (R2 = 0.63, RMSE = 10.5% and average Sd = 29%), silt (R2 = 0.59, RMSE = 6.26% and average Sd = 33%), and sand content (R2 = 0.55, RMSE = 9.49% and average Sd = 35%) were accurately estimated for forest plantations in central south Chile. A practical demonstration of precision fertilizer application, utilizing the predictive distribution of SOM, effectively showcased how uncertainty in soil attributes can be leveraged to benefit forest managers. This approach holds potential for optimizing resource allocation and maximizing economic benefits.

Gaussian process regression for three-dimensional soil mapping over multiple spatial supports

This study investigates the complexity of spatial soil modelling, particularly focusing on the challenge of variable vertical support in traditional soil data collection. Traditional soil sampling, described in terms of horizons, often fails to accurately pinpoint the specific depths for specific soil properties. This gap is significant, as depth-specific data is crucial for a thorough understanding of soil formation processes and for assessing potential environmental impacts. In digital soil mapping (DSM), the prevalent reliance on standardised depth intervals and mass-preserving spline functions for data resampling results in a modelling approach that tends to disregard depth-related details, thereby introducing potential uncertainties.To address these limitations, this work explores how effectively Gaussian process regression (GPR) can model soil in 3D. This technique comprises two key components: a mean function and a semivariogram-like kernel. Unlike conventional methods that make a single prediction, GPR provides detailed probability distributions. This capability allows for the quantification of prediction uncertainty at various points, offering insights for decision-making and risk assessment purposes. Moreover, GPR has the capability to make volume or block estimates and assess associated uncertainties. Enabling volume-based predictions enriches the range of strategies available for land management.In this research, we employ GPR as a novel 3D soil modelling technique and compare its performance with traditional spline-based methods. The comparison is conducted through a case study on a farm in northern New South Wales, Australia, focusing on the 3D mapping of soil pH and electrical conductivity (EC). Our results demonstrate the ability of GPR to estimate soil properties across various volumes, utilising data from multiple vertical supports, thereby offering a more versatile approach for soil modelling in diverse spatial contexts.

Digital soil mapping for soil types using machine learning approaches at the landscape scale in the arid regions of Iran

The aims of this research are (i) to compare random forest (RF), boosted regression tree (BRT), and multinomial logistic regression (MnLR) models to prepare the prediction maps of soil great group and subgroup levels, (ii) determination of the most important environmental covariates influencing the production of digital soil mapping (DSM) in an arid climate, (iii) to evaluate the efficiency of spectra indices extracted from Sentinel-2A digital images and data capability of ALOS-PALSAR radar data, and (iv) investigating the effect of sub-surface genetic horizons in the modeling of different types of soil map classes distribution. The principal component analysis method was employed to select the best set from the pool of environmental covariates (n = 46) such as geomorphometric parameters (GPs), RS indices, and diagnostic soil properties (DSP). The relative importance results indicate that Gypsic (GYP) subsurface horizon, standardized height (StH), slope length (SL), and normalized different vegetation index (NDVI) had an important role in the prediction of soil classes compared to the other selected covariates. DSM methodology was used in this research by incorporating of RF model and representative soil-forming factors that can be used for preparing the maps of soil classes in low-relief areas with a similar soil-landscape relationship. Totally, in this study places a spotlight on the profound impact of sub-surface genetic horizons, shedding light on their pivotal role in accurately modeling soil class distributions. These findings not only advance our comprehension of soil variability in arid regions but also hold immense implications for the burgeoning field of pedometrics.

Evaluation of the soil carbon sequestration potential and toward digital soil mapping under semi-arid Mediterranean ecological condition

Evaluation of the soil carbon sequestration potential and toward digital soil mapping under semi-arid Mediterranean ecological condition

The Western Greece Soil Information System (WΕSIS)—A Soil Health Design Supported by the Internet of Things, Soil Databases, and Artificial Intelligence Technologies in Western Greece

Soil quality is vital for ecosystem stability, impacting human, plant, and animal health. Traditional soil quality assessments are labor-intensive and costly, making them unsuitable for smart agriculture. To overcome this, Internet of Things (IoT) and artificial intelligence (AI) technologies are employed for sustainable agriculture, enabling real-time data collection and analysis, trend identification, and soil health optimization. The Western Greece Soil Information System (WΕSIS) offers open-access data and services for soil health and sustainability. It includes modules for soil quality indicators, sustainable fertilization management zones, soil property distribution, prediction, mapping, statistical analysis, water management, land use maps, digital soil mapping, and crop health calculation. Integrating the IoT and AI allows for real-time and remote monitoring of soil conditions, managing soil interventions adaptively and in a data-driven way, enhancing soil resources’ efficiency and sustainability, and increasing crop yield and quality. AI algorithms assist farmers and regional stakeholders in optimizing production lines, methodologies, and field practices, reducing costs and increasing profitability. This promotes a circular economy, a soil- and climate-resilient future, biodiversity protection targets, and enhanced soil fertility and productivity. The proposed IoT/AI technical architecture can underpin the development of soil health monitoring platforms, integrating data from various sources, automating data collection, and providing decision support tools.

National-scale digital soil mapping performances are related to covariates and sampling density: Lessons from France

Accurate soil property and class predictions through spatial modelling necessitate a thoughtful selection of explanatory variables and sample size, as their choice greatly impacts model performance. Within the framework of Global Soil Nutrient and Nutrient Budget maps (GSNmap), the FAO Global Soil Partnership (GSP) launched a country-driven digital soil mapping (DSM) approach. The GSP asked the countries if they could implement the DSM prediction of ten soil properties, using their national point data and a set of widely available covariates (GSP_Cov). In this study, we examined the effect of including additional national-based covariates and soil observations on the performance of the prediction models using mainland France as a pilot. The learning soil dataset was based on a systematic 16-to-16 km grid. For a subset of soil properties, we also assessed using repeated k-fold cross-validation the effect of adding to this dataset many other irregularly spread measurements. The GSP_Cov included common widely available covariates that represented information about terrain, climate, and organisms. The second set of covariates consisted of the GSP_Cov, extended to extra covariates available at a national level, such as previously existing soil maps, geological maps, remote sensing products and others. Random Forest approach in combination with the Boruta selection method was employed for mapping ten soil properties: soil organic carbon (SOC), pH (water), total nitrogen (N), available phosphorus (P), available potassium (K), cation exchange capacity (CEC), bulk density (BD), and texture (clay, silt, and sand). The results revealed noteworthy enhancements in prediction performance for more than half of the properties, although, for some of them, the improvements were negligible. The most significant improvements were obtained for pH, CEC and texture, where geological variables and a previous pH map significantly contributed to the increase in accuracy. Adding numerous points (around 25,000) to the learning dataset improved the performance of soil particle-size fractions predictions. By broadening the spectrum of covariates and better covering the feature and geographical spaces considered in soil prediction models, this research underscores the importance of implementing a more diverse range of covariates at a national scale and of densifying soil information to enlarge the feature and geographical spaces of multidimensional soil/covariates combinations. This information should be taken into account in national and continental digital soil mapping endeavours.

A framework for optimizing environmental covariates to support model interpretability in digital soil mapping

A common practice in digital soil mapping (DSM) is to incorporate many environmental covariates into a machine-learning algorithm to predict the spatial patterns of soil attributes. Variance inflation factor (VIF), principal component analysis (PCA), and recursive feature elimination (RFE) are three statistical methods that can be used to reduce the number of covariates. This study aims 1) to compare VIF and PCA approaches; 2) to identify an approach to determine the minimum number of covariates in DSM to ensure model parsimony using RFE after using VIF; and 3) to examine methods to interpret the impact of covariates on the variability of the predicted soil properties. The study area was the province of British Columbia (BC), Canada. This study used legacy data for four soil properties to make digital soil maps: soil organic carbon (SOC%), pH, clay%, and coarse fragment (CF%). Seven models were made for each soil property to determine the influence on validation results by using a different number of covariates produced by various methods on validation results. The results showed that the number of covariates could be reduced from 70 to 4 to 12 with only a little or no difference in concordance correlation coefficient (CCC) validation results. The CCC results of pH models using 70 and 7 covariates were both 0.74, and for other soil properties, this difference was negligible. The validation results obtained from PCA models showed that the performance of PCA in reducing the number of covariates was not as effective as when using VIF. Moreover, this study showed that covariates related to precipitation were the most important for modeling SOC%, soil pH, and clay%. Topographic covariates were the most influential covariates for modeling soil CF%. This study emphasizes the potential benefits of combining various data reduction methods to achieve optimal outcomes and generate the most parsimonious and interpretable models.

Digital Mapping of Key Static Soil Attributes of Tamil Nadu, India using Legacy Soil Information

Abstract Acquiring spatial soil information is pivotal for land resource management, environmental and soil modelling. Digital soil mapping approach helps in prediction of spatial soil properties by establishing the relationship between soil and environmental covariates. In the current study, we attempted to predict and map spatial distribution of soil depth, coarse fragments (CF), and soil textural classes over 0.13 million sq km area of Tamil Nadu, India. About 2105 samples were used for the prediction of soil attributes viz., soil depth and coarse fragments using random forest (RF) regression model, multiple linear regression (MLR), and support vector machine (SVM), while the same set of soil data was used to predict the soil textural classes as categorical variables using Random Forest classifier. Different environmental covariates such as derivatives of digital elevation models, IRS LISS-III data and bioclimatic variables were related for predicting the soil properties. The predicted soil depth and CF ranged from 46-200 cm and 1-42 per cent, respectively. The RF model outperformed by explaining the variability (R2) of 43% for soil depth and 21% for coarse fragments with root mean square error (RMSE) of 38 cm and 13%, respectively, whereas, MLR and SVM could achieve the maximum variability of R2 of 0.20 and 0.25 for soil depth and R2 of 0.07 and 0.09 for CF. With respect to soil textural classes, RF classifier performed well with overall accuracy of 63.8% and kappa index of 0.43. Variable importance ranking of Random Forest model showed that elevation, multiresolution valley bottom flatness (MrVBF), multiresolution ridge top flatness (MrRTF) and remote sensing variables (NDVI & EVI) displayed decisive role in prediction of the soil depth, coarse fragments and soil textural classes. In this study, 250 m resolution detailed soil depth, CF and textural class maps were prepared which will be useful for different environmental modelling and proper agricultural management purposes.

Comparison of SWAT and WEPP for Modeling Annual Runoff and Sediment Yield in Agewu - Mariyam Watershed, Northern Ethiopia

The Soil and Water Assessment Tool (SWAT) and the Geographic Water Erosion Prediction Project (Geo-WEPP) were applied to compare modeling of annual runoff and sediment yield in the Agewmariam watershed, eastern Amhara Region, Ethiopia. Spatial and temporal data distributions were required as inputs to run both models. Soil texture and other soil properties were measured in the field and in the laboratory, and soil maps were generated from global digital soil maps. Land use maps were created by manually digitizing Google Earth images. Watersheds were defined using watershed DEMs and gradient maps were created for each runoff event. Runoff samples were collected and analyzed for sediment concentrations in the laboratory; average annual runoff and sediment volumes were estimated using the WEPP and SWAT models. The results were satisfactory compared to the observed values, with R2 values of 0.86 and 0.91 for the SWAT and WEPP models, respectively, and NSE values of 0.54 and 0.71 for the monthly runoff. The estimated annual mean runoff and sediment yield at the watershed outlets were 65.54 mm, 146.14 mm, 43t/ha/yr and 41.7t/ha/yr for the WEPP and SWAT models, respectively. Several sub watersheds were determined to be susceptible to soil erosion and were prioritized, so more attention was given to this area to reduce runoff and soil erosion. Therefore, the SWAT and WEPP models were suitable for estimating annual runoff and sediment volumes. Sediment yields simulated from both models were high and alarming and far exceeded the allowable rate of soil loss.

Spatializing soil elemental concentration as measured by X-ray fluorescence analysis using remote sensing data

Spatial soil information has significantly contributed to public policy and planning. The use of X-ray fluorescence spectrometry (XRF) in soil science, primarily in laboratory settings or for isolated point analyses, has been well documented. This study broadens the application of XRF data by integrating it with remote sensing data for a comprehensive understanding of soil patterns and processes. Focusing on a 2574 km2 area in Brazil, we utilized a lab-based XRF instrument to measure the total concentrations of key chemical elements in the soil, such as Al, Si, Ti, and Fe. These geochemical data were then spatialized using the digital soil mapping (DSM) framework. We employed a synthetic bare soil image and elevation data as covariates in the analysis. The spatial accuracy assessment yielded R2 values ranging from 0.65 to 0.81 for Fe, 0.62 to 0.78 for Ti, 0.52 to 0.63 for Si, and 0.48 to 0.63 for Al. The digital soil maps of geochemical elements aligned well with existing pedological, and geological maps as evaluated using multiple correspondence analysis. The integration of spatialized XRF data with DSM and remote sensing techniques shows significant promise in assessing soil variations across landscapes based on their chemical composition.

Digital soil mapping and crop modeling to define the spatially-explicit influence of soils on water-limited sugarcane yield

Digital soil mapping and crop modeling to define the spatially-explicit influence of soils on water-limited sugarcane yield

Identifying soil groups and selecting a high-accuracy classification method based on multi-textural features with optimal window sizes using remote sensing images

Determining the spatial distribution of soil groups accurately is crucial for managing soil resources. However, limitations persist in the mapping of soil groups using multi-textural features derived from remote sensing images. Identification of the optimal window size for multi-textural feature extraction and the most effective classification method for soil group recognition using remote sensing multi-textural features remains unresolved. In this study, we investigated soil groups in a representative area of the Jiaodong Peninsula. We extracted the mean and entropy texture parameters for various window sizes (3 × 3 to 25 × 25 in odd increments) from Landsat 8 images to determine the optimal sizes for multi-textural feature extraction. The efficacy of identifying soil groups via textural features was analyzed using maximum likelihood classification (MLC), support vector machine (SVM), artificial neural network (ANN), and random forest (RF) methods to ascertain the most suitable classification approach. The results indicate that the optimal window sizes were 19 × 19 for the mean parameter and 23 × 23 for the entropy parameter. The SVM method outperformed the MLC, ANN, and RF methods in terms of the classification accuracy. Notably, the SVM classification method reached a peak accuracy of 71.61% when combining multi-textural features with the optimal windows. This demonstrates the feasibility of different soil groups using multi-textural information from remote sensing images. These findings have notable implications in guiding digital soil mapping using multi-textural features.

A Systematic Review on Digital Soil Mapping Approaches in Lowland Areas

Digital soil mapping (DSM) around the world is mostly conducted in areas with a certain relief characterized by significant heterogeneities in soil-forming factors. However, lowland areas (e.g., plains, low-relief areas), prevalently used for agricultural purposes, might also show a certain variability in soil characteristics. To assess the spatial distribution of soil properties and classes, accurate soil datasets are a prerequisite to facilitate the effective management of agricultural areas. This systematic review explores the DSM approaches in lowland areas by compiling and analysing published articles from 2008 to mid-2023. A total of 67 relevant articles were identified from Web of Science and Scopus. The study reveals a rising trend in publications, particularly in recent years, indicative of the growing recognition of DSM’s pivotal role in comprehending soil properties in lowland ecosystems. Noteworthy knowledge gaps are identified, emphasizing the need for nuanced exploration of specific environmental variables influencing soil heterogeneity. This review underscores the dominance of agricultural cropland as a focus, reflecting the intricate relationship between soil attributes and agricultural productivity in lowlands. Vegetation-related covariates, relief-related factors, and statistical machine learning models, with random forest at the forefront, emerge prominently. The study concludes by outlining future research directions, highlighting the urgency of understanding the intricacies of lowland soil mapping for improved land management, heightened agricultural productivity, and effective environmental conservation strategies.

Sample Size Optimization for Digital Soil Mapping: An Empirical Example

In the evolving field of digital soil mapping (DSM), the determination of sample size remains a pivotal challenge, particularly for large-scale regional projects. We introduced the Jensen-Shannon Divergence (DJS), a novel tool recently applied to DSM, to determine optimal sample sizes for a 2790 km2 area in Ontario, Canada. Utilizing 1791 observations, we generated maps for cation exchange capacity (CEC), clay content, pH, and soil organic carbon (SOC). We then assessed sample sets ranging from 50 to 4000 through conditioned Latin hypercube sampling (cLHS), feature space coverage sampling (FSCS), and simple random sampling (SRS) to calibrate random forest models, analyzing performance via concordance correlation coefficient and root mean square error. Findings reveal DJS as a robust estimator for optimal sample sizes—865 for cLHS, 874 for FSCS, and 869 for SRS, with property-specific optimal sizes indicating the potential for enhanced DSM accuracy. This methodology facilitates a strategic approach to sample size determination, significantly improving the precision of large-scale soil mapping. Conclusively, our research validates the utility of DJS in DSM, offering a scalable solution. This advancement holds considerable promise for improving soil management and sustainability practices, underpinning the critical role of precise soil data in agricultural productivity and environmental conservation.

High-resolution digital mapping of soil erodibility in China

Soil erodibility (K) is the intrinsic susceptibility of a soil to water erosion. Currently, its detailed and accurate spatial distribution information especially over large areas is urgently required for national and regional soil erosion assessment and soil conservation decision making. This study combined pedotransfer function with digital soil mapping techniques to develop a high-resolution map of soil erodibility across China. The First, based on a recent national soil survey, we adopted the erosion-productivity impact calculator (EPIC) to calculate soil erodibility values at 4710 soil sampling points. Then, with the caclulated values of points, we used five techniques including polygon linking (PL), ordinary kriging (OK), Cubist, extreme gradient boosting (XGBoost), and random forests (RF) to generate spatial distribution of soil erodibility. The three latter machine learning techniques modeled the quantitative relationships between soil erodibility and a set of environmental covariates. The results showed that machine learning methods exhibited much more spatial details than the PL and OK did. Among the five techniques the RF achieved the highest accuracy with R2 of 0.49 and RMSE of 0.0077 t ha h ha−1 MJ−1 mm−1 based on 10-fold cross-validation. Spatial uncertainty analysis of the RF predictions showed that high uncerntainty occurred in northwestern China and low uncertainty in the center and southeast. We found that topographical and climatic variables are major environmental factors indirectly controlling spatial variation of soil erodibility while the soil particle composition and SOC contents directly influence the variation.

Explorative analysis of varying spatial resolutions on a soil type classification model and it's transferability in an agricultural lowland area of Lombardy, Italy

In Digital Soil Mapping (DSM), assessing the transferability of soil type classification models across different spatial resolutions is a pivotal step in ensuring their robustness and applicability to diverse terrains. This study investigates the impact of spatial resolutions on soil type mapping within an intensively used agricultural lowland region in Lombardy, Italy, based on a Random Forest algorithm. Employing Digital Elevation Models (DEMs) at resolutions of 5 m, 10 m, and 25 m, this study aims to identify the optimal spatial resolution for accurate soil type maps and explores the transferability of models across different resolutions. The nested Leave-One-Out Cross-Validation (nested-LOOCV) results indicate a substantial impact of resolution on model performance, with higher resolutions demonstrating superior accuracy. The model developed at 10 m resolution emerges as the most robust performer, achieving an overall accuracy of 40.3%. Model transferability analysis reveals challenges when transitioning from finer to coarser resolutions, while models at coarser resolutions adapt favourably to higher resolution data. The implications extend to DSM, emphasizing the need for careful consideration of spatial resolution in model development and transfer. The findings provide valuable insights for researchers and practitioners, urging tailored approaches based on the scale and objectives of the study area. The study encourages future research to focus on advanced techniques enhancing model transferability within DSM. Overall, this research contributes to the optimization of soil classification models, advancing our understanding of soil taxonomy in agriculturally vital lowland areas.

Disposal of solid waste from civil construction: a screening proposal for a suitability system and case study in Nepomuceno, Minas Gerais

ABSTRACT Most Brazilian municipalities do not have regulated areas for solid waste disposal in civil construction. Usually, residues are disposed of vacant lots and dumps, posing risks to the population health and the environment. Soils are the primary means for the disposal or recycling of waste, highlighting the importance of well-characterized soils and their respective landscape. This study aimed to establish a land suitability system for solid residues in civil construction and apply such information in a case study in Southeastern Brazil. An unprecedented digital soil map with a resolution of 30 m was created using the random forest classifier algorithm and soil field prospection information. A guide listing favorable soil-landscape attributes that most prevent soil erosion, water bodies or water table contamination was elaborated and discussed. Thus, such information was linked through a suitability system to classify areas with potential for receiving waste on a daily volume basis as follows: large size: >500 m³ day -1 , medium size: >100 m³ day -1 and <300 m³ day -1 , and small size <100 m³ day -1 . Topography and soil depth were the most limiting factors of the areas in the case study. The proposed attributes as criteria for the suitability system complement the current state legislation. A total of 236 ha closer to the urban perimeter connected by roads in good condition were classified as suitable for managing medium- and small-scale daily volume, whose destination might reduce transportation and installation costs in the study area.

ISoLIM: a similarity-based spatial prediction software for the big data era

ABSTRACT Spatial prediction software is a valuable tool for predicting the spatial distribution of geographic variables and have a wide applicability with numerous non-expert users. However, as the input data to spatial prediction tasks gets more various in sources and larger in volume in the big data era, existing software are faced with challenges when processing such data on personal computers, which is one of the main platforms for running the software. This study uses the design and implementation of a similarity-based spatial prediction desktop software, the intelligent Soil Land Inference Model (iSoLIM), as an example to enable existing software to easily execute spatial prediction tasks with multi-source and large-volume data. The implemented similarity-based method was originally designed for digital soil mapping and has been applied to many other geographic variables. In the presented software, an expandable knowledge representation schema is adopted to intelligently manage multi-source knowledge data and enable knowledge integration; block division-based parallelization is adopted for intelligent computation arrangement on large-volume data. Experiments attest that, compared to existing software, iSoLIM can achieve higher accuracy from multi-source knowledge data by knowledge integration; and shows higher efficiency when processing data in large volume.

Predicting soil organic carbon with different approaches and spatial resolutions for the southern Iberian Peninsula, Spain

Quantification and monitoring of soil organic carbon (SOC) stocks across local-to-global scales are needed to assess soil resource management practices and adapt environmental policies. Multiple SOC estimates are available worldwide; however, verification and validation are required to quantify the discrepancies and provide improved estimates. Here, we evaluated four different digital soil mapping (DSM) approaches (i.e., linear models, support vector machine, random forest, and quantile regression forest) to estimate SOC concentration (SOCc) and SOC stocks (SOCs) in the Region of Murcia (11,313 km2), a complex topographic and climatic area in the southern Iberian Peninsula, at three spatial resolutions (100, 250, and 1000 m). We estimated SOC spatially using a local harmonized database of 255 soil profiles for modeling and 1100 topsoils for external validation. We found that the quantile regression forest (QRF) approach had the best data-model agreement at 100 m spatial resolution, with the highest accuracy percentage (79%), external validation (correlation coefficient of 52%), and spatial interpretability of patterns, especially for SOCc. The QRF model showed a mean SOCc of 12.18 g/kg with an overall uncertainty of 10.54 g/kg and an accuracy percentage of 79%, whereas the total SOCs was 27,572 GgC with an uncertainty of 0.016 GgC. Our results showed that using local environmental covariates and local soil information to predict SOC within this region resulted in a relative improvement in the prediction accuracy of ∼40% for SOCc and ∼ 65% for SOCs compared to the SOC products derived from national and global databases. Our results showed a large discrepancy between the national and global estimates for reporting SOC locally. Consequently, local-to-regional efforts are needed to describe SOC spatial variability better to reduce uncertainty and improve the assessment of soil resources. We provide the resulting SOC maps with associated spatial uncertainty on the public Environmental Data Initiative Repository at https://portal.edirepository.org/nis/mapbrowse?packageid=edi.1238.2.