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Soil erosion is a hazard in every part of the world. The small-scale farmers often grapple with low agricultural production and food insecurity. This study was conducted to estimate soil loss in response to land use/cover change in 1990, 2000, 2010, and 2020 around Ghibe hydroelectric III dam in Southern Ethiopia. The Revised Universal Soil Loss Equation (RUSLE) model was applied using a geographic information system (GIS). Digital elevation model with 30 m to determine topographic factor and supportive practice (P) factor, rainfall from the National Meteorological Institute to calculate the erosivity (R) factor, a digital soil map of the world for erodibility (K) factor, and Landsat 5TM images of 1990, 2000, 2010, and the Landsat 8 OLI of 2020 to determine trends of land use/cover change and the cover management (C) factor were employed. The raster layers of topography, cover management, rainfall erosivity, soil erodibility, and conservation techniques were processed and multiplied using the GIS platform. The overall accuracy of supervised classification was 89.89. The results showed that the percentage of cropland and built-up areas increased by 11.93 and 32.44%, respectively throughout the three study periods. Conversely, the proportion of forest land, grassland, bare land, and bushland declined by 8.2, 9.3, 10.13, 8.6%, respectively. The average annual soil loss rates increased from 30.95 t ha−1 yr−1 in 1990 to 43.85 t ha−1 yr−1in 2020. This is significantly higher than the maximum threshold rate of erosion for Ethiopian highlands (11 t ha−1 yr−1). The local government officers, non-governmental organizations, and farmers who are trained should strengthen watershed management techniques. [ABSTRACT FROM AUTHOR]

We present a data-driven, in situ proximal multi-sensor digital soil mapping approach to develop digital twins for multiple agricultural fields. A novel Digital Soil CoreTM (DSC) Probe was engineered that contains seven sensors, each of a distinct modality, including sleeve friction, tip force, dielectric permittivity, electrical resistivity, soil imagery, acoustics, and visible and near-infrared spectroscopy. The DSC System integrates the DSC Probe, DSC software (v2023.10), and deployment equipment components to sense soil characteristics at a high vertical spatial resolution (mm scale) along in situ soil profiles up to a depth of 120 cm in about 60 s. The DSC Probe in situ proximal data are harmonized into a data cube providing vertical high-density knowledge associated with physical–chemical–biological soil conditions. In contrast, conventional ex situ soil samples derived from soil cores, soil pits, or surface samples analyzed using laboratory and other methods are bound by a substantially coarser spatial resolution and multiple compounding errors. Our objective was to investigate the effects of the mismatched scale between high-resolution in situ proximal sensor data and coarser-resolution ex situ soil laboratory measurements to develop soil prediction models. Our study was conducted in central California soil in almond orchards. We collected DSC sensor data and spatially co-located soil cores that were sliced into narrow layers for laboratory-based soil measurements. Partial Least Squares Regression (PLSR) cross-validation was used to compare the results of testing four data integration methods. Method A reduced the high-resolution sensor data to discrete values paired with layer-based soil laboratory measurements. Method B used stochastic distributions of sensor data paired with layer-based soil laboratory measurements. Method C allocated the same soil analytical data to each one of the high-resolution multi-sensor data within a soil layer. Method D linked the high-density multi-sensor soil data directly to crop responses (crop performance and behavior metrics), bypassing costly laboratory soil analysis. Overall, the soil models derived from Method C outperformed Methods A and B. Soil predictions derived using Method D were the most cost-effective for directly assessing soil–crop relationships, making this method well suited for industrial-scale precision agriculture applications. [ABSTRACT FROM AUTHOR]

Topography is one of the dominant factors in regional soil formation and development. Soil distribution has a certain pattern from high to low in space, and this pattern has a high degree of consistency with slope position. Most of the current research on soil mapping uses landscape types generated by existing methods directly as environmental covariates, and there are few landscape classification methods specifically oriented toward soil surveys. There is rarely any research on landform classification using relative slope position (RSP) and elevation. Therefore, we designed a landform classification method based on RSP and elevation, Terrainforms (TF), and combined the landform type with land use type to construct soil–landscape units for soil type and attribute spatial prediction. In this study, two commonly used landform classification methods, Geomorphons and Landforms, were also used to compare with this design method. It was found that the constructed soil–landscape units had a high consistency with the soil spatial distribution. The landform types based on RSP and elevation obtained the second-highest prediction accuracy in both soil type and soil organic carbon (SOC), and the constructed soil–landscape types obtained the highest prediction accuracy. The results show that the landform classification method based on RSP and elevation is not easily limited by the analysis scale, and is an efficient and accurate landform classification method. The TF landform type and its constructed soil–landscape types can be used as an important environmental variable in soil prediction and sampling, which can provide some guidance and reference for landform classification and digital soil mapping. [ABSTRACT FROM AUTHOR]

Accurate high-resolution soil moisture maps are crucial for a better understanding of hydrological processes and energy cycles. Mapping strategies such as downscaling and interpolation have been developed to obtain high-resolution soil moisture maps from multi-source inputs. However, research on the optimization performance of integrating downscaling and interpolation, especially through the use of mutual covariates, remains unclear. In this study, we compared four methods—two standalone methods based on downscaling and interpolation strategies and two combined methods that utilize soil moisture maps as mutual covariates within each strategy—in a case study of daily soil moisture mapping at a 1 km resolution in the Tibetan Plateau. We assessed mapping performance in terms of prediction accuracy and differences in spatial coverage. The results indicated that introducing interpolated soil moisture maps into the downscaling strategy significantly improved prediction accuracy (RMSE: −5.94%, correlation coefficient: +14.02%) but was limited to localized spatial coverage (6.9% of grid cells) near in situ sites. Conversely, integrating downscaled soil moisture maps into the interpolation strategy resulted in only modest gains in prediction accuracy (RMSE: −1.07%, correlation coefficient: +1.04%), yet facilitated broader spatial coverage (40.4% of grid cells). This study highlights the critical differences between downscaling and interpolation strategies in terms of accuracy improvement and spatial coverage, providing a reference for optimizing soil moisture mapping over large areas. [ABSTRACT FROM AUTHOR]

Detailed soil property maps are increasingly important for land management decisions and environmental modeling. The US Soil Survey is investing in production of the Soil Landscapes of the United States (SOLUS), a new set of national predictive soil property maps. This paper documents initial 100‐m resolution maps of 20 soil properties that include various textural fractions, physical parameters, chemical parameters, carbon, and depth to restrictions. Many of these properties have not been previously mapped at this resolution. A hybrid training strategy helped increase training data by roughly 10‐fold over previous similar studies by combining commonly used laboratory data with underutilized field descriptions tied to soil survey map unit component property estimates (to help represent within polygon variability) as well as randomly selected soil survey map unit weighted average property estimates. Relative prediction intervals were used to help select which training data sources improved model performance. Conventional and spatial cross‐validation strategies yielded generally strong coefficients of determination between 0.5 and 0.7, but with substantial variability and outliers among the various properties, types of training data, and depths. Internal review of the maps highlighted both strengths and weaknesses of the maps, but most of the critical comments were in areas with high model uncertainty that can be used to guide future improvements. Generally, previously glaciated areas and complex large alluvial basins were harder to model. The new SOLUS 100‐m maps will be updated in the future to address identified issues and feedback as users interact with the data. Core Ideas: A total of 20 soil properties were mapped for conterminous United States at 100‐m resolution.New training data sources improved predictive soil property mapping in areas with previously sparse data.Relative prediction intervals were useful for optimizing training sets and corroborated independent map reviews [ABSTRACT FROM AUTHOR]

This study aims to assess the relationships between SOC content and main soil-forming factors and identify key factors explaining the spatial distribution of SOC. The research was conducted in the Southern Ural Mountains throughout 420 km from north to south in the Republic of Bashkortostan. The predominant soil types are mountainous gray forest (Eutric Retisols (Loamic, Cutanic, Humic)), dark gray forest (Luvic Retic Greyzemic Someric Phaeozems (Loamic)) soils, and gray-humus lithozems (Eutric Leptosols (Loamic, Humic)). Forest stands are mainly composed of birch (Betula pendula), pine (Pinus sylvestris), spruce (Picea obovata Ledeb.), and fir (Abies sibirica Ledeb.). A data set of 306 soil samples taken from the top layer (0–20 cm) was studied using the "random forest" machine learning method. Ninety four spatial environmental covariates were used as explanatory variables, including remote sensing data, climate (temperature, precipitation, cloudiness, etc.), digital elevation model and its derivatives, land uses, bioclimatic zones, etc. The results showed that the SOC content varied widely from 0.8 to 32%. The random forest predictive model explained 55% of SOC variation (R2) with a root mean squared error (RMSE) of 1.35%. Key variables included surface temperature, absolute elevation, precipitation, and cloudiness, which together reflect the Dokuchaev vertical and horizontal zonality laws. The findings emphasize the importance of considering multiple environmental factors in subsequent research focused on assessing the spatial distribution of SOC. [ABSTRACT FROM AUTHOR]

Maintaining soil organic carbon (SOC) is critical for global food security as it is essential for soil functions that sustain crop yields. There has been an increase in predictive soil mapping, which when combined with extensive crop yield datasets, enables a better understanding of crop yield and SOC relationships. This study focused on updating maps of SOC content in Saskatchewan using recently digitized historical SOC datasets and predictive soil mapping, and using the maps to examine the relationship between SOC and crop yield. A database of 5014 SOC values was used to map SOC contents using a Random Forest model and a range of environmental covariates. The final SOC model had a R2 of 0.48, root mean square error of 0.98%, concordance correlation coefficient of 0.67, and a bias of 0.12%. The relationship between mapped SOC values and crop yield data, with 100,000–200,000 records depending on crop type, was then assessed using a linear mixed effects model after normalizing the data by rural municipality to remove broad‐scale climate effects. Overall, an increase in SOC by 1% led to an increase on average of 263 kg ha−1 for wheat (Triticum aestivum L.), 293 kg ha−1 for barley (Hordeum vulgare L.), 133 kg ha−1 for canola (Brassica napus L.), and 135 kg ha−1 for field peas (Pisum sativum L.). These results show that increasing SOC was associated with greater yields for four major crops in Saskatchewan, with the largest gains occurring when the initial SOC contents are lower. Core Ideas: Soil organic carbon directly increases crop yields.Yield response to soil organic carbon shows diminishing returns.Yield response to soil organic carbon is likely region and crop specific. [ABSTRACT FROM AUTHOR]

The aim of this study was to narrow the research gap of ambiguity in which machine learning algorithms should be selected for evaluation in digital soil organic carbon (SOC) mapping. This was performed by providing a comprehensive assessment of prediction accuracy for 15 frequently used machine learning algorithms in digital SOC mapping based on studies indexed in the Web of Science Core Collection (WoSCC), providing a basis for algorithm selection in future studies. Two study areas, including mainland France and the Czech Republic, were used in the study based on 2514 and 400 soil samples from the LUCAS 2018 dataset. Random Forest was first ranked for France (mainland) and then ranked for the Czech Republic regarding prediction accuracy; the coefficients of determination were 0.411 and 0.249, respectively, which was in accordance with its dominant appearance in previous studies indexed in the WoSCC. Additionally, the K-Nearest Neighbors and Gradient Boosting Machine regression algorithms indicated, relative to their frequency in studies indexed in the WoSCC, that they are underrated and should be more frequently considered in future digital SOC studies. Future studies should consider study areas not strictly related to human-made administrative borders, as well as more interpretable machine learning and ensemble machine learning approaches. [ABSTRACT FROM AUTHOR]

Machine learning (ML) algorithms can be applied to predict the suitability of soil for crop cultivation based on digital soil mapping. We used three distinct models viz. Multinomial Logistic Regression (MnLR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) to predict the suitability of wheat and pearl millet in the Barmer district of the Thar Desert. After the computation of crop suitability classes, ML techniques were used to develop suitability maps of wheat and pearl millet in the study area. The study found that the RF and XGBoost models worked well to classify crop suitability. The RF model showed that 11.9% of the total area was highly suitable, 1.6% was moderately suitable, 14.9% was marginally suitable, and 71.6% was not suitable for wheat crop. RF model for pearl millet showed that 15.5% of the area is highly suitable. Soil suitability mapping for wheat showed a Kappa index ranging from 0.23 to 0.57 and an overall accuracy ranging from 0.79 to 0.86, whereas the prediction of suitability for pearl millet showed a moderate range of Kappa index from 0.31 to 0.58 and accuracy from 0.63 to 0.77. The area under curve (AUC) for wheat crop was 0.72, 0.88, and 0.91 for MnLR, RF, and XGBoost models, respectively. Overall, the RF model performed better than the MnLR model, showing a 16% increase in accuracy. Therefore, the developed suitability maps using ML provide valuable details on agricultural potential in the Indian desert region while harmonizing its impact on the environment and the economy. [ABSTRACT FROM AUTHOR]

The current issue affecting land and water resources is soil erosion. Its numerous detrimental effects include deforestation, unfavorable farming methods, and decreased fertility, all of which impede socioeconomic growth. This research aimed to evaluate the effects of land use land cover (LULC) dynamics on soil erosion and its spatial distribution patterns over the Hare Watershed for the periods 2001–2021. Revised Universal Soil Loss Equation (RUSLE) model coupled with a geographic information system (GIS) was applied to estimate potential soil losses; this involved utilizing data on rainfall erosivity (R) using interpolating rainfall data, topography (LS) using digital elevation model (DEM), soil erodibility (K) using the William equation and digital soil map, conservation practices (P) using DEM and satellite images, and vegetation cover (C) using satellite images. To account for the change in LULC over the past 20 years, three separate maps of C‐factors were developed for the years 2001, 2011, and 2021. The remaining four factors were kept constant. Finally, after the successful running of the model, the estimated annual average soil loss rate (A) of the watershed is 211.38, 223.79, and 235.64 t·ha−1yr−1 for the years 2001, 2011, and 2021, respectively. This is a result of grazing areas and shrubland being lost for agricultural land. Moreover, in the past period, the watershed was found to show an increase in the yearly mean soil loss rate of 24.26 t·ha−1yr−1. Consequently, the northeastern, northwest, and southern regions of the watershed require the implementation of suitable land management. [ABSTRACT FROM AUTHOR]

Soil particle size fractions play a critical role in determining soil health attributes, including soil aeration, water infiltration and retention capacity, nutrients, and organic matter dynamics. Traditional soil mapping methods rely predominantly on ground-based surveys and laboratory analysis which are reported to be timeconsuming and expensive. To address these challenges, there has been a global shift towards digital soil mapping (DSM) techniques that utilize remote sensing data. This review, conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guideline, aims to provide a comprehensive synthesis of the current state of soil texture prediction using remote sensing data. In particular, the review extract and synthesizes the satellite images used, identify the derived environmental covariates and their relative importance, and assesses the prediction models/algorithms used in the prediction of soil texture. Synthesis and analysis of 70 articles show that clay content is the most predicted of the three soil particle fractions accounting for 37% of the reviewed studies predominantly from topsoil layer (74.29%). Sentinel 2 and Landsat 8 are reported as the most frequently used satellite images. Among the covariates derived from these images, NDVI (80.4%) and SAVI (60.8%) are by far the most derived band ratios (indices). Red (37.3%), NIR (35.3%), Green (33.3%), Blue (33.3%), and SW2 (29.4%) bands were the five most incorporated as covariates for soil texture prediction amongst individual satellite bands. Regarding the DSM algorithms, Random Forest (RF) appeared in most reviewed articles followed by Support Vector Machines (SVM), and Quantile Regression Forest (QRF). The comparative model performance analysis showed that RF and Artificial neural network (ANN) had a good trade-off across validation metrics indicating their best performance in the prediction of both clay, sand, and silt. The RF performance showed a decreasing trend with increasing depth interval for clay and sand prediction and inconsistent for silt prediction. [ABSTRACT FROM AUTHOR]

Snapshots derived from the World Soil Information Service (WoSIS) are served freely to the international community. These static datasets provide quality-assessed and standardised soil profile data that can be used to support digital soil mapping and environmental applications at broad scale levels. Since the release of the preceding snapshot in 2019, refactored ETL (extract, transform and load) procedures for screening, ingesting and standardising disparate source data have been developed. In conjunction with this, the WoSIS data model was overhauled, making it compatible with the ISO 28258 and Observations and Measurements (O&M) domain models. Additional procedures for querying, serving and downloading the publicly available standardised data have been implemented using open software (e.g. GraphQL API). Following up on a short discussion of these methodological developments we discuss the structure and content of the "WoSIS 2023 snapshot". A range of new soil datasets was shared with us, registered in the ISRIC World Data Centre for Soils (WDC-Soils) data repository and subsequently processed in accordance with the licences specified by the data providers. An important effort has been the processing of forest soil data collated in the framework of the EU-HoliSoils project. We paid special attention to the standardisation of soil property definitions, description of the soil analytical procedures and standardisation of the units of measurement. The 2023 snapshot considers soil chemical properties (total carbon, organic carbon, inorganic carbon (total carbonate equivalent), total nitrogen, phosphorus (extractable P, total P and P retention), soil pH, cation exchange capacity and electrical conductivity) and physical properties (soil texture (sand, silt and clay), bulk density, coarse fragments and water retention), grouped according to analytical procedures that are operationally comparable. Method options are defined for each analytical procedure (e.g. pH measured in water, KCl or CaCl2 solution, molarity of the solution, and soil / solution ratio). For each profile we also provide the original soil classification (i.e. FAO, WRB and USDA system with their version) and pedological horizon designations as far as these have been specified in the source databases. Three measures for "fitness for intended use" are provided to facilitate informed data use: (a) positional uncertainty of the profile's site location, (b) possible uncertainty associated with the operationally defined analytical procedures and (c) date of sampling. The most recent (i.e. dynamic) dataset, called wosis_latest, is freely accessible via various web services. To permit consistent referencing and citation, we also provide a static snapshot (in this case, December 2023). This snapshot comprises quality-assessed and standardised data for 228 000 geo-referenced profiles. The data come from 174 countries and represent more than 900 000 soil layers (or horizons) and over 6 million records. The number of measurements for each soil property varies (greatly) between profiles and with depth, this generally depending on the objectives of the initial soil sampling programmes. In the coming years, we aim to gradually fill gaps in the geographic distribution of the profiles, as well as in the soil observations themselves, this subject to the sharing of a wider selection of "public" soil data by prospective data contributors; possible solutions for this are discussed. The WoSIS 2023 snapshot is archived and freely available at 10.17027/isric-wdcsoils-20231130 (Calisto et al., 2023). [ABSTRACT FROM AUTHOR]

Texture features have been consistently overlooked in digital soil mapping, especially in soil salinization mapping. This study aims to clarify how to leverage texture information for monitoring soil salinization through remote sensing techniques. We propose a novel method for estimating soil salinity content (SSC) that combines spectral and texture information from unmanned aerial vehicle (UAV) images. Reflectance, spectral index, and one-dimensional (OD) texture features were extracted from UAV images. Building on the one-dimensional texture features, we constructed two-dimensional (TD) and three-dimensional (THD) texture indices. The technique of Recursive Feature Elimination (RFE) was used for feature selection. Models for soil salinity estimation were built using three distinct methodologies: Random Forest (RF), Partial Least Squares Regression (PLSR), and Convolutional Neural Network (CNN). Spatial distribution maps of soil salinity were then generated for each model. The effectiveness of the proposed method is confirmed through the utilization of 240 surface soil samples gathered from an arid region in northwest China, specifically in Xinjiang, characterized by sparse vegetation. Among all texture indices, TDTeI1 has the highest correlation with SSC (|r| = 0.86). After adding multidimensional texture information, the R2 of the RF model increased from 0.76 to 0.90, with an improvement of 18%. Among the three models, the RF model outperforms PLSR and CNN. The RF model, which combines spectral and texture information (SOTT), achieves an R2 of 0.90, RMSE of 5.13 g kg−1, and RPD of 3.12. Texture information contributes 44.8% to the soil salinity prediction, with the contributions of TD and THD texture indices of 19.3% and 20.2%, respectively. This study confirms the great potential of introducing texture information for monitoring soil salinity in arid and semi-arid regions. [ABSTRACT FROM AUTHOR]

Although regional soil mapping is commended, site-specific studies are required for mapping and quantifying soil organic carbon (SOC) stocks at a landscape scale for management and rehabilitation purposes. Site-specific studies are especially important in remote mountainous areas where soil data are largely absent. The aim of this study was to quantify and map the SOCs of an alpine region in the Maloti-Drakensberg. The samples collected in-field along with digital soil mapping techniques were used to map the SOCs. The models were SoLIM's rule based (RB) and sample based (SB), random forest (RF), least absolute shrinkage and selection operator (LASSO), regression kriging with cubist (RK-CB) and universal kriging. From the results, the mean SOC for the validation dataset of the study area was 12.44 kg organic carbon (OC) m−2. The best model was RK-CB (mean = 12.43 kg OC m−2), with R2 and root mean square error (RMSE) of 0.61 and 4.01 kg OC m−2, respectively. The underperforming model was SoLIM-RB (mean = 13.27 kg OC m−2), with R2 = 0.27 and RMSE = 5.75 kg OC m−2. The RK-CB model from this study significantly outperformed a region-scaled model, proving that site-specific studies in small catchments should be preferred to, especially if there are no soil data available for that area. [ABSTRACT FROM AUTHOR]

Soil is a crucial component in achieving sustainable development goals due to its significant role in addressing environmental challenges. It is essential to differentiate soils that have similar management requirements. This necessity has prompted soil scientists to employ numerical classification models to categorize soils based on their similarities. In this study, we utilized two types of clustering models, traditional and modern, to classify soils from certain areas of the Qazvin Plain. Using one-way and two-way clustering models, we grouped 297 soils from the region based on a comprehensive set of their morphological, physicochemical, and environmental attributes. The classifications derived from these two models were assessed using internal and external evaluation indicators, with the distribution map of soil subgroups serving as a ground truth reference map. The results indicated that the hierarchical clustering model, with a lower Davis-Bouldin index (DB: 1.38) and a higher adjusted Rand index (ARI: 0.49), outperformed the biclustering model. However, the classifications from the bidirectional clustering model corresponded reasonably well with the topographical and soil changes in the region, as evidenced by the higher Shannon’s difference index in the bidirectional clustering model (1.82) compared to the hierarchical clustering model (1.62). Overall, the study’s findings underscore the utility of the co-clustering model as a contemporary data mining technique for soil classification and identification of soil management similarity patterns. [ABSTRACT FROM AUTHOR]

Essential soil nutrients are dynamic in nature and require timely management in farmers' fields. Accurate prediction of the spatial distribution of soil nutrients using a suitable sampling density is a prerequisite for improving the practical utility of spatial soil fertility maps. However, practical research is required to address the challenge of selecting an optimal sampling density that is both cost-effective and accurate for preparing digital soil nutrient maps across regional extents. This study examines the impact of sampling density on spatial prediction accuracy for a range of soil fertility parameters over a regional extent of 8303 km2 located in eastern India. Surface soil samples were collected from 1024 sample points. The performance of six levels of sampling densities for spatial prediction of 14 soil properties was compared using ordinary kriging. From the sample points, randomization was used to select 224 points for validation and the remaining 800 for calibration. Goodness-of-fit for the semi-variograms was evaluated by R2 of model fit. Lin's concordance correlation coefficient (CCC) and root mean square error (RMSE) were evaluated through independent validation as spatial prediction accuracy parameters. Results show that the impact of sampling density on prediction accuracy was unique for each soil property. As a common trend, R2 of model fit and CCC scores improved, and RMSE values declined with the increasing sampling density for all soil properties. On the other hand, the rate of gain in the accuracy metrics with each increment in the sampling density gradually decreased and ultimately plateaued. This indicates that there exists a sampling density threshold beyond which the extra effort on additional sampling adds less to the spatial prediction accuracy. The findings of this study provide a valuable reference for optimizing soil nutrient mapping across regional extents. [ABSTRACT FROM AUTHOR]

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To manage soil and agriculture sustainably, it is crucial to keep updated on changes in the pools of soil organic carbon (SOC). Using tools from digital soil mapping (DSM), this case study simulates the dynamics of total and active organic carbon. The aim of present study was to assess the performance of various machine learning techniques for SOC fractions. The SRTM DEM and its derivatives were used to predict total organic carbon (TOC) and its fractions for a 4000 ha study area in Central State Farm, Sardargragh, Rajasthan. For prediction, Extreme Gradient Boosting (XG Boost), Cubist, and Random Forest (RF) models were used. For modeling purposes, the SOC fractions like very labile C (VLC), labile C (LC), less labile C (LLC) was analyzed. The range of the WBC content was 0.97 to 6.85 g kg-1. Both the calibration and validation sets of VLC showed outstanding results from the RF model (R2c = 0.948, RMSEc = 0.174 g kg-1and R2v = 0.204, RMSEv = 0.213 g kg-1). The XG Boost model, on the other hand, had the next lowest accuracy and explained just around 10.06–36.8% of the variation of C fractions. The cubist model performed the worst in both the calibration and validation sets. Clay is the most significant predictor of the TOC, WBC, LLC, NLC and PC. The predicted SOC varied from 3.06 to 9.41 g kg-1, 0.34 to 1.42 g kg-1, 0.73 to 2.45 g kg-1, 0.28 to 3.30 g kg-1 and 1.05 to 4.12 g kg-1 in TOC, VLC, LC, LLC and NLC, respectively. RF proved to be the most accurate and least uncertain model when it came to predicting regional SOC. Based on the findings of our simulation; it appears that SOC may be approximated rather accurately and with ease. In general, stakeholders, decision-makers, and applicants in agricultural management approaches toward precision agriculture may find their high-resolution maps helpful. [ABSTRACT FROM AUTHOR]

This study investigates soil loss in the Wadi Bin Abdullah watershed using the Revised Universal Soil Loss Equation (RUSLE) combined with advanced tools, such as remote sensing and the Geographic Information System (GIS). By leveraging the ALOS PALSAR Digital Elevation Model (DEM), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) rainfall data, and the Digital Soil Map of the World (DSMW), the research accurately evaluates soil loss loads. The methodology identifies significant variations in soil loss rates across the entire watershed, with values ranging from 1 to 1189 tons per hectare per year. The classification of soil loss into four stages—very low (0–15 t/ha/yr), low (15–45 t/ha/yr), moderate (45–75 t/ha/yr), and high (>75 t/ha/yr)—provides a nuanced perspective on soil loss dynamics. Notably, 20% of the basin exhibited a soil loss rate of 36 tons per hectare per year. These high rates of soil erosion are attributed to certain factors, such as steep slopes, sparse vegetation cover, and intense rainfall events. These results align with regional and global studies and highlight the impact of topography, land use, and soil properties on soil loss. Moreover, the research emphasizes the importance of integrating empirical soil loss models with modern technological approaches to identify soil loss-prone locations and precisely quantify soil loss rates. These findings provide valuable insights for developing environmental management strategies aimed at mitigating the impacts of soil loss, promoting sustainable land use practices, and supporting resource conservation efforts in arid and semi-arid regions. [ABSTRACT FROM AUTHOR]

[Objective] The selection of environmental variables and the selection of spatial reasoning methods for soil types were studied to provide references for improving the accuracy of digital soil mapping in counties. (Method) Taking Yanyuan county in Sichuan province as the research area, we selected climate factors, terrain data, and remote sensing images as auxiliary factors for inference mapping. Field sampling point data and environmental covariate factors were used to obtain the basic data of soil environment knowledge. The decision tree classification method was subsequently used for importance ranking, feature selection, and combination optimization of environmental features. Soil classification mapping accuracy of several soil classification methods, including decision tree classification, support vector machine, random forest, and SoLIM model, was compared. Based on the theory of soil environment relationship, the article explored ways to improve the accuracy of digital soil mapping in mountainous counties with three-dimensional climate characteristics. [Result] (i) Climate and topographic characteristics played an important role in soil classification in the study area. The screening accuracy of soil classification was 83.22% by using a single climate factor as the environmental variable of soil classification. The screening accuracy was increased to 85.78% and 89.43% by adding topographic and biological characteristics in turn. (ii) Compared with other models, the random forest model achieved better mapping results. Using sampling point data as validation data, the overall accuracy of the soil classification was 77. 10%, with Kappa coefficient of 0.72. (iii) The influence of climatic factors and topographic factors on regional climatic hydrothermal conditions mainly determined the spatial heterogeneity of soil types in the study area. Environmental factors, such as average annual temperature, annual accumulated temperature, annual precipitation, relative humidity, elevation and topographic wetness were closely related to the spatial distribution of major soil types. 【Conclusion) In the mountain counties with three-dimensional climate characteristics, the use of climate, topographic and remote sensing data for digital soil classification mapping based on random forest method has good results. [ABSTRACT FROM AUTHOR]

In digital soil mapping, modelling soil thickness poses a challenge due to the prevalent issue of right‐censored data. This means that the true soil thickness exceeds the depth of sampling, and neglecting to account for the censored nature of the data can lead to poor model performance and underestimation of the true soil thickness. Survival analysis is a well‐established domain of statistical modelling that can deal with censored data. The random survival forest is a notable example of a survival‐related machine learning approach used to address right‐censored soil property data in digital soil mapping. Previous studies that employed this model either focused on mapping the probability of soil thickness exceeding certain depths, and thereby not mapping soil thickness itself, or dismissed it due to perceived poor performance. In this study, we propose an alternative survival model to map soil thickness that is based on the inverse probability of censoring weighting. In this approach, calibration data are weighted by the inverse of the probability that soil thickness exceeds a certain depth, that is, a survival probability. These weights can then be used with most machine learning models. We used the weights with a regular random forest, and compared it with a random survival forest, and other strategies for handling right‐censored data, through a comprehensive synthetic simulation study and two real‐world case studies. The results suggest that the weighted random forest model produces competitive predictions, establishing it as a viable option for mapping right‐censored soil property data. [ABSTRACT FROM AUTHOR]

The scientific field of precision agriculture employs increasingly innovative techniques to optimize inputs, maximize profitability, and reduce environmental impact. However, obtaining a high number of soil samples is challenging in order to make precision agriculture viable. There is a trade-off between the amount of data needed and the time and resources spent to obtain these data compared to the accuracy of the maps produced with more or fewer points. In the present study, the research was based on an exhaustive dataset of apparent electrical conductivity (aEC) containing 3906 points distributed along 26 transects with spacing between each of up to 40 m, measured by the proximal soil sensor EM38-MK2, for a grain-producing area of 72 ha in São Paulo, Brazil. A second sparse dataset was simulated, showing only four transects with a 400 m distance and, in the end, only 162 aEC points. The aEC map via ordinary kriging (OK) from the grid with 26 transects was considered the reference, and two other mapping approaches were used to map aEC via sparse grid: kriging with external drift (KED) and geographically weighted regression (GWR). These last two methods allow the increment of auxiliary variables, such as those obtained by remote sensors that present spatial resolution compatible with the pivot scale, such as data from the Landsat-8, Aster, and Sentinel-2 satellites, as well as ten terrain covariates derived from the Alos Palsar digital elevation model. The KED method, when used with the sparse dataset, showed a relatively good fit to the aEC data (R2 = 0.78), with moderate prediction accuracy (MAE = 1.26, RMSE = 1.62) and reasonable predictability (RPD = 1.76), outperforming the GWR method, which had the weakest performance (R2 = 0.57, MAE = 1.78, RMSE = 2.30, RPD = 0.81). The reference aEC map using the exhaustive dataset and OK showed the highest accuracy with an R2 of 0.97, no systematic bias (ME = 0), and excellent precision (RMSE = 0.56, RPD = 5.86). Management zones (MZs) derived from these maps were validated using soil texture data from clay samples measured at 0–10 cm depth in a grid of 72 points. The KED method demonstrated the highest potential for accurately defining MZs for irrigation, producing a map that closely resembled the reference MZ map, thereby providing reliable guidance for irrigation management. [ABSTRACT FROM AUTHOR]

The current demand for wood products is growing globally. Hybrid poplars are fast growing trees and it is beneficial to expand their cultivation area. Digital mapping techniques based on land suitability assessment can effectively support land use decision-making processes in this perspective. The aim of this study was to develop a model to produce land suitability maps to determine the potential production area of hybrid poplar (Populus × canadensis Moench) in Italy. The evaluation was based on a fuzzy logic procedure to generate a raster map with a pixel resolution of 250 m over the country. The modelling approach is planning-oriented: the objective is to predict, on a national scale, the suitability of the land for poplar cultivation by taking into account environmental factors for which geodatabases with adequate and comparable spatial resolution are available on a national scale in Italy. Georeferenced databases of experimental poplar plantations were used as calibration dataset. The assessment identified around 145,000 hectares of highly suitable land and around 1,926,500 hectares of suitable land for hybrid poplar cultivation in Italy. These areas are mainly located in northern Italy (around 36% of the territory) and, to a lesser extent, in central Italy (around 19%) and in southern Italy and the islands (less than 3%). The modelling approach adopted can be easily replicated in other geographical areas or at finer scales, provided that appropriate data are available. [ABSTRACT FROM AUTHOR]

The alpine terrestrials of the Maloti-Drakensberg in southern Africa play crucial roles in ecosystem functions and livelihoods, yet they face escalating degradation from various factors including overgrazing and climate change. This study employs advanced Digital Soil Mapping (DSM) techniques coupled with remote sensing to map and assess wetland coverage and degradation in the northern Maloti-Drakensberg. The model achieved high accuracies of 96% and 92% for training and validation data, respectively, with Kappa statistics of 0.91 and 0.83, marking a pioneering automated attempt at wetland mapping in this region. Terrain attributes such as terrain wetness index (TWI) and valley depth (VD) exhibit significant positive correlations with wetland coverage and erosion gully density, Channel Network Depth and slope were negative correlated. Gully density analysis revealed terrain attributes as dominant factors driving degradation, highlighting the need to consider catchment-specific susceptibility to erosion. This challenge traditional assumptions which mainly attribute wetland degradation to external forces such as livestock overgrazing, ice rate activity and climate change. The sensitivity map produced could serve as a basis for Integrated Catchment Management (ICM) projects, facilitating tailored conservation strategies. Future research should expand on this work to include other highland areas, explore additional covariates, and categorize wetlands based on hydroperiod and sensitivity to degradation. This comprehensive study underscores the potential of DSM and remote sensing in accurately assessing and managing wetland ecosystems, crucial for sustainable resource management in alpine regions. [ABSTRACT FROM AUTHOR]

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Soil organic matter (SOM) is a key soil component. Determining its spatial distribution is necessary for precision agriculture and to understand the ecosystem services that soil provides. However, field SOM studies are severely limited by time and costs. To obtain a spatially continuous distribution map of SOM content, it is necessary to conduct digital soil mapping (DSM). In addition, there is a vital need for both accuracy and interpretability in SOM mapping, which is difficult to achieve with conventional DSM models. To address the above issues, particularly mapping SOM content, a spatial coefficient of variation (SVC) regression model, the Geographic Gaussian Process Generalized Additive Model (GGP-GAM), was used. The root mean squared error (RMSE), mean average error (MAE), and adjusted coefficient of determination (adjusted R 2 ) of this model for SOM mapping in Leizhou area are 7.79, 6.01, and 0.33 g kg−1, respectively. GGP-GAM is more accurate compared to the other three models (i.e., Geographical Random Forest, Geographically Weighted Regression, and Regression Kriging). Moreover, the patterns of covariates affecting SOM are interpreted by mapping coefficients of each predictor individually. The results show that GGP-GAM can be used for the high-precision mapping of SOM content with good interpretability. This DSM technique will in turn contribute to agricultural sustainability and decision making. [ABSTRACT FROM AUTHOR]

The variability of soil organic carbon (SOC) extends across three dimensions. However, quantitative analyses of the factors influencing spatiotemporal variations of SOC in various soil depth is scarce. This study leverages legacy data from two soil surveys conducted in the Dongting Lake Basin during the 1980s and the 2010s, employing Random Forest models to generate three-dimensional SOC maps. Through correlation analysis and permutation importance, we identified the primary factors driving temporal and spatial changes of SOC. The results showed that in the 2010s, SOC storage up to a depth of 1 m in the Dongting Lake Basin was approximately 2.95 Pg, increasing at an average rate of 0.0047 Pg C per year since the 1980s. Regions with higher average SOC contents were predominantly found in the western, southern, and eastern parts of the basin, despite significant losses over the 30-year period. In contrast, the central and northern areas, which initially had lower SOC contents in the 1980s, exhibited increases by the 2010s. Soil depth was the most influential predictor of SOC patterns in both the 1980s and 2010s. Meanwhile, relief and organism factors were primarily responsible for spatial variations in SOC, with the influence of organism factors diminishing by the 2010s. The temporal variations of SOC were chiefly attributed to changes in soil conservation practices, extreme precipitation events, and grain production. Consequently, it is imperative to prioritize ecological restoration and conservation tillage practices to mitigate the impacts of extreme weather conditions and safeguard food security.

Faced with increasing global soil degradation, spatially explicit data on cropland soil organic matter (SOM) provides crucial data for soil carbon pool accounting, cropland quality assessment and the formulation of effective management policies. As a spatial information prediction technique, digital soil mapping (DSM) has been widely used to spatially map soil information at different scales. However, the accuracy of digital SOM maps for cropland is typically lower than for other land cover types due to the inherent difficulty in precisely quantifying human disturbance. To overcome this limitation, this study systematically assessed a framework of “information extraction-feature selection-model averaging” for improving model performance in mapping cropland SOM using 462 cropland soil samples collected in Guangzhou, China in 2021. The results showed that using the framework of dynamic information extraction, feature selection and model averaging could efficiently improve the accuracy of the final predictions (R2: 0.48 to 0.53) without having obviously negative impacts on uncertainty. Quantifying the dynamic information of the environment was an efficient way to generate covariates that are linearly and nonlinearly related to SOM, which improved the R2 of random forest from 0.44 to 0.48 and the R2 of extreme gradient boosting from 0.37 to 0.43. Forward recursive feature selection (FRFS) is recommended when there are relatively few environmental covariates (500). The Granger-Ramanathan model averaging approach could improve the prediction accuracy and average uncertainty. When the structures of initial prediction models are similar, increasing in the number of averaging models did not have significantly positive effects on the final predictions. Given the advantages of these selected strategies over information extraction, feature selection and model averaging have a great potential for high-accuracy soil mapping at any scales, so this approach can provide more reliable references for soil conservation policy-making.

Accurate and high-resolution spatial soil information is crucial for efficient and sustainable land use, management, and conservation. Since the establishment of digital soil mapping (DSM) and the GlobalSoilMap working group, significant advances have been made in spatial soil information globally. However, accurately predicting soil variation over large and complex areas with limited samples remains a challenge, especially for China, which has diverse soil landscapes. To address this challenge, we utilized 11,209 representative multi-source legacy soil profiles (including the Second National Soil Survey of China, World Soil Information Service, First National Soil Survey of China, and regional databases) and high-resolution soil-forming environment characterization. Using advanced Quantile Regression Forest algorithms and a high-performance parallel computing strategy, we developed comprehensive maps of 23 soil physical, chemical and fertility properties at six standard depth layers from 0 to 2 meters in China with a 90 m spatial resolution (China dataset of soil properties for land surface modeling version 2, CSDLv2). Data-splitting and independent samples validation strategies were employed to evaluate the accuracy of the predicted maps quality. The results showed that the predicted maps were significantly more accurate and detailed compared to traditional soil type linkage methods (i.e., CSDLv1, the first version of the dataset), SoilGrids 2.0, and HWSD 2.0 products, effectively representing the spatial variation of soil properties across China. The prediction accuracy of most soil properties at the 0–5 cm depth interval ranged from good to moderate, with Model Efficiency Coefficients for most soil properties ranging from 0.75 to 0.32 during data-splitting validation and from 0.88 to 0.25 during independent sample validation. The wide range between the 5 % lower and 95 % upper prediction limits may indicate substantial room for improvement in current predictions. The relative importance of environmental covariates in predictions varied with soil properties and depth, indicating the complexity of interactions among multiple factors in the soil formation processes. As the soil profiles used in this study mainly originate from the Second National Soil Survey of China during 1970s and 1980s, they could provide new perspectives of soil changes together with existing maps based on 2010s soil profiles. The findings make important contributions to the GlobalSoilMap project and can also be used for regional Earth system modeling and land surface modeling to better represent the role of soil in hydrological and biogeochemical cycles in China. This dataset is freely available and can be accessed at https://doi.org/10.11888/Terre.tpdc.301235 (Shi et al, 2024). [ABSTRACT FROM AUTHOR]

Peatlands, which only cover 3 to 5 percent of the global land area, can store up to twice the amount of carbon as the world's forests. Although recognised for their significant role in the global carbon cycle, discovering the global extent of peatlands and their carbon stock remains challenging. Referring to the UNEP's global peatland map, here we present PEATGRIDS, a data product containing global maps of peat thickness and carbon stock created created using the digital soil mapping approach. We compiled over 25,000 observations of peatland thickness, bulk density (BD) and carbon content (CC), globally. Using the Random Forest (RF) algorithm, we estimated peat thickness and peat BD and CC at ~1 km resolution at multiple depths (0–2 m) globally. The estimates were generated using 19 land surface covariates from digital maps and remote sensing images of land use, soil characteristics, topographical features, and climate parameters. The RF models for peat thickness were trained on 25,200 points grouped into six geographic regions. Validation of the peat thickness estimates showed a good performance, with the coefficient of determination (R2)ranging from 0.15 to 0.72. The prediction for peat BD and CC followed the same model architecture and were trained on 17,000 and 7,000 points, respectively. Overall, BD and CC models performed well and consistently across soil layers with average R2 values of 0.61 for BD and 0.48 for CC. Based on the estimated peat thickness, BD and CC, the carbon stock of global peatland was estimated to be 1,029 Pg C for peat dominated area of 6.57 million km2. PEATGRIDS is made available at https://doi.org/10.5281/zenodo.12559239 (Widyastuti et al., 2024) to support further analyses and modelling of peatlands across the globe. [ABSTRACT FROM AUTHOR]

Soil salinization is a critical environmental and socio‐economic concern with global implications, and its severity is expected to amplify under changing climate. The impact of climate change on salinization in Central Asia is still not fully understood. This study addresses this gap by employing a digital soil mapping (DSM) framework. Cubist, random forest (RF), and quantile regression forests (QRF) are utilized to project variations in soil surface salinity (0‐10 cm) in Central Asia from 2025 to 2100 under two shared socio‐economic pathways (SSPs): SSP2‐4.5 and SSP5‐8.5. These models are developed using data from 20 global climate models (GCMs) obtained from the Coupled Model Intercomparison Project Phase 6 (CMIP6). The results reveal that the RF model exhibits superior predictive capability in estimating soil salinity. RF performed on the calibration set with a coefficient of determination (R2) of 0.86, root mean square error (RMSE) of 9.84 and 9.90 dS m−1, ratio of performance to interquartile distance (RPIQ) of 3.09 and 3.07, and a Nash–Sutcliffe efficiency (NSE) of 0.86. The multi‐GCM ensemble means revealed the potential for varying degrees of salinization in Central Asia, with higher levels predominantly observed in the southeast and southwest of the study area, particularly downstream of the river and in the lakeside areas. Temporal analysis of soil salinity evolution reveals an overall increase in salinity across the region, with more notable changes projected under SSP5‐8.5. Specifically, the projected increase rate in soil salinity for Central Asia was 0.0005 dS m−1/year under SSP2‐4.5 and 0.01 dS m−1/year under SSP5‐8.5. Turkmenistan is notable for possessing the highest regional average of soil salinity, with the exception of a declining trend observed within this area. The remaining regions of Central Asia exhibit an upward trend in average soil salinity, particularly noteworthy under the SSP5‐8.5 scenario, where variations in soil salinity are more obvious. These findings hold significant potential in enhancing our understanding of how Central Asia responds to global change, advances toward sustainable development, and enhances comprehension of the dynamics within the region. [ABSTRACT FROM AUTHOR]

Rapid and accurate mapping of soil properties in farmlands is crucial for guiding agricultural production and maintaining food security. Traditional methods using spectral features from remote sensing prove valuable for estimating soil properties, but are restricted to short periods of bare soil occurrence within agricultural settings. Addressing the challenge of predicting soil properties under crop cover, this study proposed an improved soil modeling framework that integrates dynamic crop growth information with machine learning techniques. The methodology's robustness was tested on six key soil properties in an agricultural region of China, including soil organic carbon (SOC), total nitrogen (TN), total phosphorus (TP), dissolved organic carbon (DOC), dissolved organic nitrogen (DON), and pH. Four experimental scenarios were established to assess the impact of crop growth information, represented by the normalized difference vegetation index (NDVI) and phenological parameters. Specifically, Scenario I utilized only natural factors (terrain and climate data); Scenario II added phenological parameters based on Scenario I; Scenario III incorporated time-series NDVI based on Scenario I; and Scenario IV combined all variables (traditional natural factors and crop growth information). These were evaluated using three advanced machine learning models: random forest (RF), Cubist, and Extreme Gradient Boosting (XGBoost). Results demonstrated that incorporating phenological parameters and time-series NDVI significantly improved model accuracy, enhancing predictions by up to 36% over models using only natural factors. Moreover, although both are crop growth factors, the contribution of the time-series NDVI variable to model accuracy surpassed that of the phenological variable for most soil properties. Relative importance analysis suggested that the crop growth information, derived from time-series NDVI and phenology data, collectively explained 14–45% of the spatial variation in soil properties. This study highlights the significant benefits of integrating remote sensing-based crop growth factors into soil property inversion under crop-covered conditions, providing valuable insights for digital soil mapping. [ABSTRACT FROM AUTHOR]

Soils play a central role in ecosystem functioning, and thus, mapped soil property information is indispensable to supporting sustainable land management. Digital Soil Mapping (DSM) provides a framework to spatially estimate soil properties. However, broad-scale DSM remains challenging because of non-purposively sampled soil data, large data volumes for processing extensive soil covariates, and high model complexities due to spatially varying soil–landscape relationships. This study presents a three-dimensional DSM framework for Switzerland, targeting the soil properties of clay content (Clay), organic carbon content (SOC), pH value (pH), and potential cation exchange capacity (CECpot). The DSM approach is based on machine learning and a comprehensive exploitation of soil and remote sensing data archives. Quantile Regression Forest was applied to link the soil sample data from a national soil data base with covariates derived from a LiDAR-based elevation model, from climate raster data, and from multispectral raster time series based on satellite imagery. The covariate set comprises spatially multiscale terrain attributes, climate patterns and their temporal variation, temporarily multiscale land use features, and spectral bare soil signatures. Soil data and predictions were evaluated with respect to different landcovers and depth intervals. All reference soil data sets were found to be spatially clustered towards croplands, showing an increasing sample density from lower to upper depth intervals. According to the R2 value derived from independent data, the overall model accuracy amounts to 0.69 for Clay, 0.64 for SOC, 0.76 for pH, and 0.72 for CECpot. Reduced model accuracies were found to be accompanied by soil data sets showing limited sample sizes (e.g., CECpot), uneven statistical distributions (e.g., SOC), and low spatial sample densities (e.g., woodland subsoils). Multiscale terrain covariates were highly influential for all models; climate covariates were particularly important for the Clay model; multiscale land use covariates showed enhanced importance for modeling pH; and bare soil reflectance was a major driver in the SOC and CECpot models. [ABSTRACT FROM AUTHOR]

(1) Background: The use of multiscale prediction or the optimal scaling of predictors can enhance soil maps by applying pixel size in digital soil mapping (DSM). (2) Methods: A total of 200, 50, and 129 surface soil samples (0–30 cm) were collected by the CLHS method in three different areas, namely, the Marvdasht, Bandamir, and Lapuee plains in southwest Iran. Then, four soil properties—soil organic matter (SOM), bulk density (BD), soil shear strength (SS), and mean weighted diameter (MWD)—were measured at each sampling point as representative attributes of soil physical and chemical quality. This study examined different-scale scenarios ranging from resampling the original 30 m digital elevation model and remote sensing indices to various pixel sizes, including 60 × 60, 90 × 90, 120 × 120, and up to 2100 × 2100 m. (3) Results: After evaluating 22 environmental covariates, 11 of them were identified as the most suitable candidates for predicting soil properties based on recursive feature elimination (RFE) and expert opinion methods. Furthermore, among different pixel size scenarios for SOM, BD, SS, and MWD, the highest accuracy was achieved at 1200 × 1200 m (R2 = 0.35), 180 × 180 m (R2 = 0.67), 1200 × 1200 m (R2 = 0.42), and 2100 × 2100 m (R2 = 0.34), respectively, in Marvdasht plain. (4) Conclusions: Adjusting the pixel size improves the capture of soil property variability, enhancing mapping precision and supporting effective decision making for crop management, irrigation, and land use planning. [ABSTRACT FROM AUTHOR]

Soil organic matter (SOM) and the ratio of soil organic carbon to total nitrogen (C/N ratio) are fundamental to the ecosystem services provided by soils. Therefore, understanding the spatial distribution and relationships between the SOM components mineral-associated organic matter (MAOM), particulate organic matter (POM), and C/N ratio is crucial. Three ensemble machine learning models were trained to obtain spatial predictions of the C/N ratio, MAOM, and POM in German agricultural topsoil (0–10 cm). Parameter optimization and model evaluation were performed using nested cross-validation. Additionally, a modification to the regressor chain was applied to capture and interpret the interactions among the C/N ratio, MAOM, and POM. The ensemble models yielded mean absolute percent errors (MAPEs) of 8.2% for the C/N ratio, 14.8% for MAOM, and 28.6% for POM. Soil type, pedo-climatic region, hydrological unit, and soilscapes were found to explain 75% of the variance in MAOM and POM, and 50% in the C/N ratio. The modified regressor chain indicated a nonlinear relationship between the C/N ratio and SOM due to the different decomposition rates of SOM as a result of variety in its nutrient quality. These spatial predictions enhance the understanding of soil properties' distribution in Germany. [ABSTRACT FROM AUTHOR]

Many soil functions and processes are controlled by the soil particle size distribution. Accordingly, nationwide geoinformation on this soil property is required to enable climate-smart and resilient land management. This study presents a new deep learning approach to simultaneously model the contents of the three particle sizes of sand, silt, and clay and their variations with depth throughout the landscape. The approach allows for the consideration of the natural soil horizon boundaries and the inclusion of the surrounding landscape context of each soil profile to investigate the soil–landscape relation. Applied to the agricultural soil landscape of Germany, the approach generated a three-dimensional continuous data product with a resolution of 100 m in geographic space and a depth resolution of 1 cm. The approach relies on a patch-wise multi-target convolutional neural network (CNN) model. Genetic algorithm optimization was applied for CNN parameter tuning. Overall, the effectiveness of the CNN algorithm in generating multidimensional, multivariate, national-scale soil data products was demonstrated. The predictive performance resulted in a median root mean square error of 17.8 mass-% for the sand, 14.4 mass-% for the silt, and 9.3 mass-% for the clay content in the top ten centimeters. This increased to 20.9, 16.5, and 11.8 mass-% at a 40 cm depth. The generated data product is the first of its kind. However, even though the potential of this deep learning approach to understand and model the complex soil–landscape relation is virtually limitless, its limitations are data driven concerning the approximation of the soil-forming factors and the available soil profile data. [ABSTRACT FROM AUTHOR]

Soil aggregate stability is crucial for maintaining the arrangement of solid particles and pore space in the soil, even under mechanical stresses. Traditional direct measurements of soil aggregate stability are time-consuming and expensive. This study aimed to spatially predict the soil aggregate stability indices, including the mean weight diameter of aggregates, the geometric mean diameter of aggregates, and the percentage of water stable aggregates, using five machine learning models and environmental covariates in the framework of digital soil mapping. A total of 100 samples were collected from the surface layer (0-15 cm) of soils in the Aji-Chai watershed, northwestern Iran, and their SAS indices were determined by standard laboratory methods. Four scenarios (S) were employed to evaluate the most influencing auxiliary variables, including (S1): topographic attributes, (S2): topographic attributes + remote sensing data, (S3): S2 + thematic maps (geology, land use/cover maps), and (S4): S3 + selected soil properties. Among the various machine learning models, the random forest showed exceptional performance and reduced uncertainty for S4, compared to the other machine learning models and desired scenarios. The coefficient of determination, concordance correlation coefficient, and normalized root mean squared error values of the random forest model were 0.86, 0.87, and 31.42% for mean weight diameter; 0.80, 0.84, and 31.59% for geometric mean diameter; and 0.54, 0.68, and 20.75% for water stable aggregates, respectively. Additionally, properties such as soil organic matter and clay, followed by remote sensing data, demonstrated the highest relative importance when compared to the other covariates in predicting the soil aggregate stability indices. In conclusion, the random forest ML-based model seems to be able to accurately predict soil aggregate stability indices at the watershed scale. The generated maps can serve as a valuable baseline for land use planning and decision-making. These findings contribute to the scientific understanding of soil physical quality indicators and their application in sustainable land management practices. [ABSTRACT FROM AUTHOR]

Digital soil mapping can facilitate acquiring soil information efficiently and precisely. In recent years, owing to the rapid development of computer disciplines and widespread recognition of soil-landscape models, digital soil modeling using machine learning has become a mainstream idea to provide new models for soil spatial distribution interpretation. These models differ from traditional mapping techniques such as geostatistics, expert knowledge, and individual representation. This study reviews the recent findings in the field of digital soil mapping nationally and internationally, and provides a complete and systematic description of digital soil mapping from three perspectives：basic theory, mapping method and outlook of soil mapping using machine learning technology, and digital soil mapping methods including the selection of feature information, selection of mapping models, and accuracy verification of soil maps. Finally, future research directions of digital soil mapping are discussed to provide reference for comprehensive, real-time, and accurate acquisition of spatial distribution of soil information. [ABSTRACT FROM AUTHOR]

Sustainable agriculture is becoming increasingly important in the face of growing environmental challenges. One key aspect of sustainable agriculture is managing soil resources effectively. In this context, digital soil mapping (DSM) has emerged as a powerful tool to understand soil variability better and inform land management decisions. This paper proposes a comprehensive data warehouse for DSM that supports climate change analysis. Our architecture integrates frequent itemset mining (FMI) and association rules mining (ARM) to extract insights from large‐scale soil data. We review related studies in soil data warehousing and ARM, identify gaps, and propose a data warehouse architecture leveraging the galaxy multidimensional model for DSM based on the SCORPAN model, which incorporates all relevant soil forming factors. We employ and compare A‐priori, FP‐growth, and ECLAT algorithms to efficiently mine frequent itemsets and generate association rules. Our intensive experiments evaluation demonstrates that FP‐growth outperforms the other algorithms in accuracy, scalability, and speed and requires less memory. Additionally, we utilized correlation metrics for ARM, such as lift, cosine, kulc, and Imbalance ratio, to obtain the most significant and relevant association rules. These rules provide valuable insights into the complex relationships between soil properties and environmental factors, which can inform land management decisions and improve sustainable agriculture practices. This work contributes to the growing body of research on DSM and data‐driven approaches to sustainable agriculture. [ABSTRACT FROM AUTHOR]

This paper quantifies the annual dynamics of the shortwave radiation reflected from bare arable land as a result of smoothing previously plowed land located in three different agricultural subregions of the European Union and associated countries. This estimate takes into account the annual variation of the bare arable land area, obtained from Sentinel 2 satellite imagery; the spatial variability of soil units within croplands, obtained from digital soil and land-cover maps; and the laboratory spectral reflectance characteristics of these units, obtained from soil samples stored in the LUCAS soil database. The properties of the soil units, which cover an area of at least 4% of each subregion, were characterized. The highest amounts of shortwave radiation reflected under clear-sky conditions from air-dried, bare arable land surfaces—approximately 850 PJ day−1 and 1.10 EJ day−1 for land shaped by a plow (Pd) and smoothing harrow (Hs), respectively—were found in the summer around 8 August in the western subregion. However, the lowest radiation occurred in the spring on 10 April at 340 PJ day−1 for Pd and 430 PJ day−1 for Hs in the central subregion. The largest and the smallest amounts of this radiation throughout the year—only as a result of smoothing, by Hs, land that was previously treated by Pd—was estimated at 42 EJ for the western and southern subregions and 19 EJ for the central subregion, respectively. [ABSTRACT FROM AUTHOR]

Nature‐inclusive scenarios of the future can help address numerous societal challenges related to soil health. As nature‐inclusive scenarios imply sustainable management of natural systems and resources, land use and soil health are assumed to be mutually beneficial in such scenarios. However, the interplay between nature‐inclusive land use scenarios and soil health has never been modelled using digital soil mapping. We predicted soil organic matter (SOM), an important indicator of soil health, in 2050, based on a recently developed nature‐inclusive scenario and machine learning in 3D space and time in the Netherlands. By deriving dynamic covariates related to land use and the occurrence of peat for 2050, we predicted SOM and its uncertainty in 2050 and assessed SOM changes between 2022 and 2050 from 0 to 2 m depth at 25 m resolution. We found little changes in the majority of mineral soils. However, SOM decreases of up to 5% were predicted in grasslands used for animal‐based production systems in 2022, which transitioned into croplands for plant‐based production systems by 2050. Although increases up to 25% SOM were predicted between 0 and 40 cm depth in rewetted peatlands, even larger decreases, on reclaimed land even surpassing 25% SOM, were predicted on non‐rewetted land in peat layers below 40 cm depth. There were several limitations to our approach, mostly due to predicting future trends based on historic data. Furthermore, nuanced nature‐inclusive practices, such as the adoption of agroecological farming methods, were too complex to incorporate in the model and would likely affect SOM spatial variability. Nonetheless, 3D‐mapping of SOM in 2050 created new insights and raised important questions related to soil health behind nature‐inclusive scenarios. Using machine learning explicit in 3D space and time to predict the impact of future scenarios on soil health is a useful tool for facilitating societal discussion, aiding policy making and promoting transformative change. [ABSTRACT FROM AUTHOR]

To ensure soil preservation, it is essential to incorporate the soil's ability to provide ecosystem services into the spatial planning process. For well‐informed planning decisions, stakeholders need spatially explicit information on the state of the soils and the functions they fulfil, with sufficient spatial resolution and quantified uncertainty. It has been shown that Digital Soil Mapping (DSM) products can provide such information. However, in some cases, fine spatial resolution coupled with high levels of uncertainty may lead stakeholders to overlook the inherent uncertainties in the information. Spatial aggregation of DSM products opens up a promising avenue for obtaining maps that are more tailored to the users' scales of decision making while facilitating uncertainty communication. In this perspective, we propose a new spatial aggregation approach relying on spatially constrained agglomerative clustering (AC). The spatial aggregation approach is applied to a 25‐m‐resolution soil potential multifunctionality index (SPMI) map developed for the coastal plain of the Occitanie Region. This DSM product was increasingly aggregated to obtain SPMI maps of different resolutions displaying two distinct areal metrics: proportions of area above a given threshold of SPMI, and mean SPMI. Each map was evaluated through a set of indicators selected for their potential impact on user decision making: mean spatial resolution, overall predicted uncertainty, quantity of information and mean within‐unit variability. The maps were compared with respect to these indicators to other maps obtained with alternative aggregation methods employed in DSM literature (maps aggregated according to some administrative units and QuadMaps). We show that all the tested aggregation methods produced a substantial decrease of the map uncertainty with moderate loss of spatial resolution. However, only AC preserved the fine spatial pattern of the initial DSM product while enabling fine tuning of the uncertainty displayed to end‐users. We show that AC can simplify the identification of extensive regions characterized by low uncertainty without losing information regarding soil multifunctionality, thereby facilitating and enhancing the efficiency of planning decisions. [ABSTRACT FROM AUTHOR]

Fire alters soil hydrologic properties leading to increased risk of catastrophic debris flows and post‐fire flooding. As a result, US federal agencies map soil burn severity (SBS) via direct soil observation and adjustment of rasters of burned area reflectance. We developed a unique application of digital soil mapping (DSM) to map SBS in the Creek Fire which burned 154,000 ha in the Sierra Nevada. We utilized 169 ground‐based observations of SBS in combination with raster proxies of soil forming factors, pre‐fire fuel conditions, and fire effects to vegetation to build a digital soil mapping model of soil burn severity (DSMSBS) using a random forest algorithm and compared the DSMSBS map to the established SBS map. The DSMSBS model had a cross‐validation accuracy of 48%. The established technique had 46% agreement between field observations and pixels. However, since the established technique is manual, it could not be compared to the DSMSBS model via cross‐validation. We produced SBS class uncertainty maps, which showed high prediction probabilities around field observations, and low probabilities away from field observations. SBS prediction probabilities could aid post‐fire assessment teams with sample prioritization. We report 107 km2 more area classified as high and moderate SBS compared to the established technique. We conclude that blending soil forming factors based mapping and vegetation burn severity mapping can improve SBS mapping. This represents a shift in SBS mapping away from validating remotely sensed reflectance imagery and toward a quantitative soil landscape model, which incorporates both fire and soils information to directly predict SBS. Core Ideas: A digital soil mapping method to map wildfire soil burn severity (DSMSBS) was developed.Soil field observations were combined with rasters of environmental covariates and fire effects.Excellent fidelity between field observations of soil burn severity (SBS) and the final DSMSBS map was reported.Direct classification of SBS improves SBS mapping compared to validation of remotely sensed burn severity.Class probabilities generated for SBS may aid post‐fire assessment. [ABSTRACT FROM AUTHOR]

The soil supports many ecosystem services (ES) essential to human well‐being. Rapid developments in digital soil mapping (DSM) allow the mapping of soil types and soil properties with improved resolution and accuracy. However, the potential of DSM to improve the assessment and mapping of ES is not fully exploited. To better understand this potential, we synthesized the peer‐reviewed literature. We examined what empirical studies reveal about the role of soil properties in the assessment of four major ES provided by the forest: (I) timber production, (II) soil carbon storage, (III) regulation of water flow and provision of clean water, and (IV) the soil as a habitat for organisms. Results revealed that soil properties are strongly related to the provision of ES. Therefore, using DSM could greatly improve the assessment of the ES provided by forests. Several variables were related to specific ES regardless of region or ecosystem types, but others were found to be situation‐specific (climate and soil type) and need to be considered at the proper scale or within a proper land classification framework. DSM products have the potential to greatly improve the assessment of ES by turning qualitative relationships between soil and ES to quantitative ones. This could also lead to the discovery of new soil–ES relationships. For this potential to be realized, progress should be made in mapping the most crucial soil parameters with greater precision and in promoting the use of soil parameters in ES assessment. Core Ideas: Soil properties are major determinants of forest ecosystem services (ES).Advances in digital soil mapping are generating better spatial estimates of soil properties.Soil properties are rarely used to evaluate ES.A literature review identifies how soil property maps could help improve the assessment of four ES.The ES considered are timber production, soil carbon storage, water quality/quantity, and habitat. [ABSTRACT FROM AUTHOR]

Local morphometric variables (LMVs) are frequently found as weaker predictors than other environmental covariates in digital soil mapping. This study tested and evaluated the performance of a hybrid approach combining gradient boosted regression trees (GBRT) and regularized regression (RR) algorithms in predicting soil textural fractions using a set of LMVs in Khuzestan province, Iran. Here five LMVs (slope gradient, slope aspect, horizontal curvature, vertical curvature, and contour geodesic torsion) were derived from a spheroidal equal-angular DEM as original predictors. The results demonstrated that the hybrid approach improved prediction accuracy for sand, clay, and silt contents by an average of 56% compared to the GBRT models. The importance analysis revealed the significant contribution of tree-based variables obtained from decomposing GBRT models in predicting soil textural fractions. This approach could be recommended for digital soil mapping, particularly in situations of limited environmental covariates or geomorphometric techniques that cannot be easily applied. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Soil compaction is one of the most important and readily mitigated threats to soil health. Digital Soil Mapping (DSM) has emerged as an efficient method to provide broad-scale maps by combining soil information with environmental covariates. Until now, soil information input to DSM has been mainly composed of point-based quantitative measurements of soil properties and/or of soil type/horizon classes derived from laboratory analysis, point observations, or soil maps. In this study, we used field estimates of soil compaction to map soil behavior to compaction at a national scale. The results from a previous study enabled clustering of six different behaviors using the in situ field observations. Mapping potential responses to soil compaction is an effective land management tool for preventing future compaction. Random forest was used to make spatial predictions of soil behavior to compaction over cultivated soils of mainland France (about 210,000 km2). Modeling was performed at 90 m resolution. The map enabled us to spatially identify clusters of possible responses to compaction. Most clusters were consistent with known geographic distributions of some soil types and properties. This consistency was checked by comparing maps with both national and local-scale external sources of soil information. The best spatial predictors were available digital maps of soil properties (clay, silt, sand, organic carbon (SOC) content, and pH), some indicators of soil structural quality using SOC and clay content, and environmental covariates (T °C and relief-related covariates). Predicted maps were interpretable to support management recommendations to mitigate soil compactness at the soil–scape scale. Simple observational field data that are usually collected by soil surveyors, then stored and available in soil databases, provide valuable input data for digital mapping of soil behavior to compaction and assessment of inherent soil sensitivity to compaction. [ABSTRACT FROM AUTHOR]

Intensive agricultural practices lead to a deterioration in soil quality, causing a decline in farm productivity and quality, and disturbing the ecosystem balance in command areas. To achieve sustainable production and implement effective soil management strategies, understanding the state and spatial variability of soil quality is essential. The study aims to enhance the understanding of soil quality variability and provide actionable insights for sustainable soil management. In this regard, principal component analysis (PCA) and digital soil mapping were used to assess and map the spatial variability of the soil quality index (SQI) in the Cauvery command area, Mandya district, Karnataka, India. A total of 145 georeferenced soil samples were drawn at 0–15 cm depth and analyzed for physico-chemical properties. PCA was used to reduce the dataset into a minimum dataset as eight important soil indicators and to determine relative weightage factors, which were used for assessing SQI with linear and non-linear scoring methods. For spatial assessment of SQI, the random forest algorithm with environmental covariates was used to map eight soil indicators selected in the minimum dataset. The soil property maps were subjected to linear and non-linear scoring, followed by multiplying with corresponding weightage factors and summation to produce SQI maps. Results reveal that values of SQI calculated using linear scoring, range from 0.10 to 0.64, with a mean of 0.39, while non-linear scoring exhibits a wider range of 0.12 to 0.78 and a mean of 0.48. With a slight higher sensitivity index of 6.5, non-linear scoring proved to be the better scoring method compared to linear scoring. Spatial assessment shows that the R2 and LCC between the calculated and predicted SQI were higher for non-linear scoring (0.66 and 0.66) compared to linear scoring (0.60 and 0.65). The SQI maps reveal high spatial variability with more than 40 percent of soils classified as moderate-to-low index. The soils with low SQI were distributed in eastern parts, whereas western parts exhibited high-to-very-high soil quality. To achieve production goals and improve soil quality in the eastern region, sustainable soil and crop management strategies must be developed, and their effects on soil quality should be assessed. [ABSTRACT FROM AUTHOR]

Synthesizing bare soil pictures in regions with complex vegetation is challenging, which hinders the accuracy of predicting soil organic carbon (SOC) in specific areas. An SOC prediction model was developed in this study by integrating the convolutional neural network and long and short-term memory network (CNN-LSTM) algorithms, taking into consideration soil-forming factors such as climate, vegetation, and topography in Hainan. Compared with common algorithmic models (random forest, CNN, LSTM), the SOC prediction model based on the CNN-LSTM algorithm achieved high accuracy (R2 = 0.69, RMSE = 6.06 g kg−1, RPIQ = 1.96). The model predicted that the SOC content ranged from 5.49 to 36.68 g kg−1, with Hainan in the central and southern parts of the region with high SOC values and the surrounding areas with low SOC values, and that the SOC was roughly distributed as follows: high in the mountainous areas and low in the flat areas. Among the four models, CNN-LSTM outperformed LSTM, CNN, and random forest models in terms of R2 accuracy by 11.3%, 23.2%, and 53.3%, respectively. The CNN-LSTM model demonstrates its applicability in predicting SOC content and shows great potential in complex areas where obtaining sample data is challenging and where SOC is influenced by multiple interacting factors. Furthermore, it shows significant potential for advancing the broader field of digital soil mapping. [ABSTRACT FROM AUTHOR]