Soil Mapping Methods Research Articles

Quantification of Soil Erosion Using Digital Soil Mapping and RUSLE Method for Coimbatore District, Tamil Nadu, India

Quantification of Soil Erosion Using Digital Soil Mapping and RUSLE Method for Coimbatore District, Tamil Nadu, India

Prediction of soil texture using remote sensing data. A systematic review

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 time-consuming 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.

Mapping soil organic carbon density via geographically weighted regression with smooth terms: A case study in Shanxi Province

Soil, as the largest organic carbon reservoir in terrestrial ecosystems, plays a crucial role in the global carbon cycle. Accurately predicting the spatial distribution of soil organic carbon density (SOCD) is highly important for estimating organic carbon stocks and guiding agricultural production. Due to the complex relationship between the spatial distribution of SOCD and environmental factors, its relationship with a specific region is not exclusively linear or nonlinear; both types of relationships may coexist simultaneously. In this study, a new model, geographically weighted regression with smooth terms (SGWR), was proposed. This model can simultaneously consider both global spatial nonlinear relationships and local spatial nonstationary relationships. It was utilized to predict SOCD in Shanxi Province and was compared to multiple linear regression (MLR), generalized additive models (GAM) and geographically weighted regression (GWR) in terms of spatial prediction performance. The results showed that SGWR was highly competitive in predicting the spatial distribution of SOCD in Shanxi Province. Of factors influencing SOCD in Shanxi Province, elevation and slope were considered local linear variables, while the normalized difference vegetation index (NDVI), gross primary productivity (GPP), annual precipitation (AP), and soil moisture (SM) were considered global nonlinear variables. Among these factors, the NDVI and GPP had the most significant impacts on the distribution of SOCD, while elevation and SM had the least significant impacts on it. The results imply that integrating global linear, local linear, and global nonlinear relationships concurrently, as implemented in SGWR, improves predictive accuracy compared to approaches that solely address either local or global linear relationships based on environmental variable changes in SGWR. The results of this study indicate that the SGWR model is potentially a competitive method for digital soil mapping.

Sensitivity analysis of SWAT streamflow and water quality to the uncertainty in soil properties generated by the SoLIM model

Hydrological models are essential tools for understanding natural processes, yet they often encounter with uncertainties due to limited input data and simplified assumptions. Soil data is a crucial input parameter for hydrological models, but it faces cartographic challenges due to lack of standardized soil mapping methods. To explore the applicability of soil datasets derived from the Third Law of Geography in hydrological models, the Soil Landscape Inference Model (SoLIM) based on this principle is used to generate a soil dataset. This dataset is then utilized as input data for a semi-distributed hydrological model (Soil and Water Assessment Tool, SWAT) to assess the impact of soil dataset uncertainty on performance of the hydrological model. This study, utilized the Fengyu River Watershed in Yunnan Province, China as an example, showed that: 1) Although the integration of SoLIM-generated soil data into the SWAT model introduces sensitivity in sediment yield, the overall results remain reliable. This suggests that soil datasets based on the Third Law of Geography can serve as input for semi-distributed hydrological models. 2) Uncertainty introduced by SoLIM-generated soil data affects parameters such as evaporation, total porosity of the soil layer (Φd), and groundwater infiltration coefficient (Ksat). These variations influence the sensitivity of SWAT’s predictions regarding streamflow and water quality. 3) Factors influencing model outcomes include steeper slopes in certain areas, which enhance streamflow velocity and flow rates, resulting in more dynamic and non-linear responses. Additionally, soil water dynamics exhibit higher temporal and spatial variability in forests compared to pasture and rice paddy fields, where vegetation tends to be more uniform, maintaining stable soil water loss pathways and reducing uncertainty in streamflow predictions. The study provides a foundation for studies addressing the difficulties in acquiring high-precision soil datasets for such models.

Soil Salinity Mapping of Croplands in Arid Areas Based on the Soil–Land Inference Model

Soil salinization can decrease soil productivity and is a significant factor in causing land degradation. Precision mapping of salinization in agricultural fields would improve farmland management. This study focuses on the cropland in the Manas River Basin, located in the arid region of northwest China. It explores the potential of a soil mapping method, the Soil–Land Inference Model (SoLIM), which only requires a small number of soil samples to infer soil salinization of farmlands in arid areas. The model was utilized to create spatial distribution maps of soil salinity for the years 2009 and 2017, and changes in the distribution were analyzed. The research results indicate: (1) Through the analysis of sample point data, it was observed that soil salinity in the study area tends to accumulate in the surface layer (0–30 cm) in spring and in the subsoil layer (60–90 cm) during the crop growing season, with significant spatial variability. Therefore, it is necessary to conduct detailed salinity mapping. (2) Using field measurements as validation data, the simulation results of the SoLIM were compared with spatial interpolation methods and regression models. The SoLIM showed higher inference accuracy, with R2 values for the simulation results of the three soil layers all exceeding 0.5. (3) The SoLIM spatial inference showed salt accumulation in the northern part and desalination in the southern part. The findings of this study suggest that the SoLIM has the potential to effectively map soil salinization of croplands in arid areas, offering an efficient solution for monitoring soil salinity in arid oasis croplands.

A novel local-global dependency deep learning model for soil mapping

The accurate and cost-effective mapping of soil texture is essential for agricultural development and environmental activities. Soil texture exhibits high spatial heterogeneity which poses challenges for recent Digital Soil Mapping (DSM) methods in achieving accurate predictions. Feature engineering methods, extensively used to capture complex soil-forming relationships and enhance prediction accuracy, often involve labor-intensive processes. Additionally, the engineered “discrete” feature cannot reflect interactions between environmental covariates or dependencies. To address the challenges, this study proposes a novel Local-Global Dependency Long Short-Term Memory model (LGD-LSTM) to enhance soil texture predictions at various soil depths. Firstly,a covariate reorganization method hasbeen devised to generate multiple sets of input. Subsequently, several Long Short-Term Memory models (LSTM)have beenemployed to extract the interdependencies among the covariates.Finally, predictions are generated using a fully-connected layer. Cross-validation was conducted within this experiment to analyze prediction accuracy: the average explained variation (R2) ranged from 0.66 to 0.73, and the root mean square error (RMSE) ranged from 6.52% to 10.89%. The results indicated that the LGD-LSTM model offers distinct advantages over other digital soil mapping methods, including Random Forests (RF), Convolutional Neural Network (CNN), and the standard Long Short-Term Memory model (LSTM). In summary, this LGD-LSTM method demonstrates superior performance with relatively high accuracy, ensuring its applicability in effectively representing spatial variations in soil texture. Furthermore, it presents a novel option for DSM applications, enhancing the field's methodology and potential impact.

Digital soil mapping enables informed decision-making to conserve soils within protected areas

Protected areas are regarded as pristine land, but often they require rehabilitation and effective management to prevent increased land degradation. Soil management should be based on soil maps, which are difficult to create in protected areas due to their large size, restricted accessibility, limited available soil data and low budgets for such projects. The objective of this paper is to showcase a novel hybrid expert knowledge and machine learning digital soil mapping (DSM) method to map soils covering large areas with limited accessibility and available soil data, and on a small budget. The study is situated at Benfontein, a 9 900 ha protected area in South Africa. Soil landscape rules were used to determine virtual soil observation locations which were added to the training dataset used by a machine learning algorithm to create an acceptable soil associations map (validation Kappa = 0.69). Soil properties and interpreted soil indices were assigned to each soil association at 0.1, 0.5 and 0.9 percentile levels, to indicate the range of properties at an 80% certainty. Results show that Benfontein has large carbon sequestration potential, the soils are relatively stable against water erosion, and off-road driving should be prohibited on approximately half of the area. The approach of percentile mapping of soil property ranges at high confidence levels optimises limited data. The hybrid DSM method is viable for creating useful soil maps in data-scarce environments to inform management decisions in the unique settings of protected areas.

Cause-effect relationships using structural equation modeling for soil properties in arid and semi-arid regions

Predicting soil properties and evaluating their functions along with their related driving factors is useful for providing useful geographical information for soil management, which is especially important in arid and semi-arid regions. This study investigates the use of a structural equation modeling (SEM) approach for assessing the effects of soil forming factors through environ-mental proxies on three key soil properties, namely soil organic carbon (SOC), calcium carbonate equivalent (CCE) and clay content (clay) in an arid and semi-arid region of Iran. Using a set of 259 soil profiles collected over years 2016–2020 in the Qazvin plain, the cause-effect relationships were estimated between these soil properties and nine environmental factors derived from a digital elevation model and from satellite images. Focusing on two main horizons A and B, it was shown that normalized difference vegetation index, midslope position, elevation, multi-resolution valley bottom flatness, and saga wetness index are impacting these soil properties. Inside each horizon, the effect of CCE and clay on SOC was also evidenced, but to an extent that depends on the horizon. For each soil property, we were able to clearly identify the relationships between the two horizons. Although our SEM approach proved to be useful for identifying and estimating the cause-effect relationships, it failed to provide a good predictive model as required for a relevant digital soil mapping of these soil properties. However, as the SEM approach allows combining soil science knowledge inside a model that accounts for soil forming factors, external factors, and soil system at the same time, it permits an investigation of their potential cause-effect relationships in a rich theoretical framework. The SEM methodology is thus potentially useful for soil scientists that are studying various soil properties in other parts of the world, even if it cannot be advocated as an efficient digital soil mapping method in general.

Comparative Analysis of the Results of Traditional and Digital Large-Scale Soil Mapping on the Example of a Site at the National Park “Smolenskoe Lakeland”

For the territory of the Smolensk Lakeland with a complex and understudied soil cover and considerable influence of the anthropogenic factor, it is relevant to create soil maps using the comparative geographic method. For an area of 8.8 km2 in the southwestern part of the Smolenskoye Lakeland National Park, soil maps (scale 1 : 25 000) were created using the methods of traditional and digital soil mapping (DSM). And a comparison of the results obtained was made. The territory under consideration is dominated by gray-humus soils (Umbric Cambisol (Loamic) and Arenosols (Ochric)) on two maps. These soils are associated with past agricultural use and regeneration of old arable soils. Smaller areas are occupied by alpha-humus and texture-differentiated soils (Podzols (Arenic) and Retisols (Loamic), respectively). In addition, isolated areas of psammozems (Arenosols) have been found and identified on the map using traditional approach. The overall accuracy of the map compiled by the DSM methods was 55%. The leading factors were identified: the position in the relief, the nature of the substrate and vegetation. The distribution of texturally differentiated soils associated with loamic parent materials and humus-peat soils formed in local depressions and on the floodplain of Lake Baklanovskoye is predicted with an accuracy of 87% and 60%, respectively. The location of podzol soils (Entic Rustic Podzols (Ochric)) is predicted with an accuracy of only 29%. The traditional mapping method was able to better reflect the diversity of the soil cover on a large scale compared to the digital method.

Soil assessment in Denmark: Towards soil functional mapping and beyond

Soil provides essential ecosystem services sustaining and improving human life, but mapping soil functions is an ongoing challenge. Denmark has a long history of carrying out soil assessments − originally in order to determine tax revenues for the king, and, more recently, for aiding policymakers and farmers. This knowledge has supported the development of intensive agricultural systems while maintaining the provision of ecosystem services (e.g., clean water). Getting an overview of historical soil surveys and pedological mapping approaches can generate useful information for mapping soil, identifying gaps and proposing directions for future research. In this review, we explore the evolution of soil and environmental inventories, the historical development of soil mapping methods, and how these factors contributed to a better spatial understanding of soil functions. Specifically, we discuss soil functions related to water regulation (e.g., drainage, groundwater and water surface interactions, water table), water filtering (e.g., nitrogen leaching), carbon sequestration (e.g., peatlands), agricultural production (e.g., land suitability, wheat yields), and threats related to soil degradation (e.g., soil erosion). Denmark has benefitted from a government-coordinated approach, promoting detailed and systematic national soil surveys and environmental monitoring programmes. The large databases produced in the surveys formed the basis for mapping several soil properties and functions at increasingly high resolutions over the last many years based on developments in machine learning. In contrast to methodological advances in soil mapping and relevant contributions to pedometric research, we identified a lack of spatial information on soil biodiversity. Detailed spatial information about soil functions is essential to address global issues, such as climate change, food security and water security, and the experience of mapping soil functions in Denmark can be a source of inspiration to other parts of the world.

Cropland carbon stocks driven by soil characteristics, rainfall and elevation

Soil organic carbon (SOC) can influence atmospheric CO2 concentration and then the extent to which the climate emergency is mitigated globally. It follows the elucidation of the driving factors of cropland SOC stocks, which is fundamental to reducing soil carbon loss and promoting soil carbon sequestration. Here, we examined the influence of 16 environmental variables on SOC stocks and sequestration based on three machine learning soil mapping methods, i.e. multiple linear regression (MLR), random forest (RF) and extreme gradient boosting (XGBOOST), with 2875 observed soil samples from cropland topsoil across Hunan Province, China in 2010. We employed a structural equation model (SEM) to extricate the driving mechanisms of environmental variables on SOC stocks at the regional scale. Our results show that XGBOOST had the most reliable performance in predicting SOC stocks, explaining 66 % of the total SOC stock variation. Croplands with high SOC stocks were distributed in low-altitude and water-sufficient areas. The partial dependence of SOC on precipitation showed a trend of increasing and then slowly decreasing. In addition, the grid-based SEM results clearly presented the direct and indirect routes of environmental variables' impacts on cropland SOC stocks. Soil properties regulated by elevation, were the most influential natural factor on SOC stocks. Precipitation and elevation drove SOC stocks through direct and indirect effects respectively. Our SEM combined with machine learning approach can provide an effective explanation of the driving mechanism for SOC accumulation. We expect our proposed modelling approach can be applied to other regions and offer new insights, as a reference for mitigating cropland soil carbon loss under climate emergency conditions.

Applications and Analytical Methods of Ground Penetrating Radar for Soil Characterization in a Silvopastoral System

The use of ground penetrating radar (GPR) for soil characterization has grown rapidly in recent years due to substantial increases in computer processing power and advances in GPR methodologies. However, few studies have focused on applied GPR analysis for soil characterization and decision making in agricultural systems. In this study, we explored applications of some common qualitative and quantitative methods for GPR analysis and characterization of subsurface conditions in a silvopasture system. We analyzed GPR results using traditional visual interpretation methods to delineate depth to bedrock, clay layers, and other important soil features. Estimates of depth to bedrock correlated well with values measured in the field ([Formula: see text]), and estimates of depth to clay layers were marginally correlated with observed values ([Formula: see text]). We also extracted attributes from GPR images to train a random forest regression model to predict coarse fragment percentage and percent clay content. GPR attributes were found to be good predictors of soil coarse fragments, with an R2 value of 0.81 and root mean square error (RMSE) of 18.82 for test data. Our results demonstrate GPR can provide valuable information on subsurface features in silvopastoral systems. These results also suggest a strong potential for machine learning algorithms in GPR data analytics. Data generated using these methods could be integrated with or used to validate existing digital soil mapping methods and contribute to better understanding of subsurface characteristics for optimized soil management in silvopastoral systems.

Mapping of Soil pH Based on SVM-RFE Feature Selection Algorithm

The explicit mapping of spatial soil pH is beneficial to evaluate the effects of land-use changes in soil quality. Digital soil mapping methods based on machine learning have been considered one effective way to predict the spatial distribution of soil parameters. However, selecting optimal environmental variables with an appropriate feature selection method is key work in digital mapping. In this study, we evaluated the performance of the support vector machine recursive feature elimination (SVM-RFE) feature selection methods with four common performance machine learning methods in predicting and mapping the spatial soil pH of one urban area in Fuzhou, China. Thirty environmental variables were collected from the 134 samples that covered the entire study area for the SVM-RFE feature selection. The results identified the five most critical environmental variables for soil pH value: mean annual temperature (MAT), slope, Topographic Wetness Index (TWI), modified soil-adjusted vegetation index (MSAVI), and Band5. Further, the SVM-RFE feature selection algorithm could effectively improve the model accuracy, and the extreme gradient boosting (XGBoost) model after SVM-RFE feature selection had the best prediction results (R2 = 0.68, MAE = 0.16, RMSE = 0.26). This paper combines the RFE-SVM feature selection with machine learning models to enable the fast and inexpensive mapping of soil pH, providing new ideas for predicting soil pH at small and medium scales, which will help with soil conservation and management in the region.

Using soil erosion as an indicator for integrated water resources management: a case study of Ruiru drinking water reservoir, Kenya

Functions and services provided by soils play an important role for numerous sustainable development goals involving mainly food supply and environmental health. In many regions of the Earth, water erosion is a major threat to soil functions and is mostly related to land-use change or poor agricultural management. Selecting proper soil management practices requires site-specific indicators such as water erosion, which follow a spatio-temporal variation. The aim of this study was to develop monthly soil erosion risk maps for the data-scarce catchment of Ruiru drinking water reservoir located in Kenya. Therefore, the Revised Universal Soil Loss Equation complemented with the cubist–kriging interpolation method was applied. The erodibility map created with digital soil mapping methods (R2 = 0.63) revealed that 46% of the soils in the catchment have medium to high erodibility. The monthly erosion rates showed two distinct potential peaks of soil loss over the course of the year, which are consistent with the bimodal rainy season experienced in central Kenya. A higher soil loss of 2.24 t/ha was estimated for long rains (March–May) as compared to 1.68 t/ha for short rains (October–December). Bare land and cropland are the major contributors to soil loss. Furthermore, spatial maps reveal that areas around the indigenous forest on the western and southern parts of the catchment have the highest erosion risk. These detected erosion risks give the potential to develop efficient and timely soil management strategies, thus allowing continued multi-functional use of land within the soil–food–water nexus.

A Multiscale Cost–Benefit Analysis of Digital Soil Mapping Methods for Sustainable Land Management

With the emergence of machine learning methods during the past decade, alternatives to conventional geostatistical methods for soil mapping are becoming increasingly more sophisticated. To provide a complete overview of their performance, this study performed cost–benefit analysis of four soil mapping methods based on five criteria: accuracy, processing time, robustness, scalability and applicability. The evaluated methods were ordinary kriging (OK), regression kriging (RK), random forest (RF) and ensemble machine learning (EML) for the prediction of total soil carbon and nitrogen. The results of these mechanisms were objectively standardized using the linear scaling method, and their relative importance was quantified using the analytic hierarchy process (AHP). EML resulted in the highest cost–benefit score of the tested methods, with maximum values of accuracy, robustness and scalability, achieving a 55.6% higher score than the second-ranked RF method. The two geostatistical methods ranked last in the cost–benefit analysis. Despite that, OK could retain its place as the most frequent method for soil mapping in recent studies due to its widespread, user-friendly implementation in GIS software and its univariate character. Further improvement of machine learning methods with regards to computational efficiency could additionally improve their cost–benefit advantage and establish them as the universal standard for soil mapping.

Mapping Soil Properties at a Regional Scale: Assessing Deterministic vs. Geostatistical Interpolation Methods at Different Soil Depths

To determine which interpolation technique is the most suitable for each case study is an essential task for a correct soil mapping, particularly in studies performed at a regional scale. So, our main goal was to identify the most accurate method for mapping 12 soil variables at three different depth intervals: 0–5, 5–10 and >10 cm. For doing that, we have compared nine interpolation methods (deterministic and geostatistical), drawing soil maps of the Spanish region of Extremadura (41,635 km2 in size) from more than 400 sampling sites in total (e.g., more than 500 for pH for the depth of 0–5 cm). We used the coefficient of determination (R2), the mean error (ME) and the root mean square error (RMSE) as statistical parameters to assess the accuracy of each interpolation method. The results indicated that the most accurate method varied depending on the property and depth of study. In soil properties such as clay, EBK (Empirical Bayesian Kriging) was the most accurate for 0–5 cm layer (R2 = 0.767 and RMSE = 3.318). However, for 5–10 cm in depth, it was the IDW (Inverse Distance Weighted) method with R2 and RMSE values of 0.689 and 5.131, respectively. In other properties such as pH, the CRS (Completely Regularized Spline) method was the best for 0–5 cm in depth (R2 = 0.834 and RMSE = 0.333), while EBK was the best for predicting values below 10 cm (R2 = 0.825 and RMSE = 0.399). According to our findings, we concluded that it is necessary to choose the most accurate interpolation method for a proper soil mapping.

A new digital soil mapping method with temporal-spatial-spectral information derived from multi-source satellite images

Detailed soil maps are essential for effective agricultural practices and environmental protection. Yet, despite the increasing accuracy of digital soil mapping (DSM) in recent years, generating regional-scale, high-accuracy soil maps remains a challenging task. Our study objective was to propose a new DSM method based on temporal-spatial-spectral (TSS) data and to compare the DSM result with a legacy soil map of China. In this study, 13 Landsat multispectral data from 2000 to 2019 were fused by discrete wavelet transform (DWT) to obtain temporal information, a shuttle radar topography mission digital elevation model (SRTM-DEM) was used as spatial information, and Gaofen-5 satellite hyperspectral data was used as spectral information. TSS information was obtained after the DWT and spectral band segmentation methods were used to fuse the temporal and spectral information, combined it with the spatial information. Then, the TSS information and random forest model were used for DSM. The results indicated that 1) The mapping result based on TSS information was highly correlated with the legacy soil map. In different soil classes, there were minute differences in the core area and large differences in adjacent soil classes between the two maps. The overall accuracy and kappa coefficient of DSM based on TSS information were 88.11% and 0.82, respectively. 2) With the same values of the soil moisture, the overall accuracy and kappa coefficient of DSM based on hyperspectral data were 6.80% and 0.02, respectively, higher than those based on multispectral data. 3) With increasing temporal information, the DSM accuracy continuously increased, and the mapping accuracy based on multi-temporal multispectral data was higher than that based on mono-temporal hyperspectral data when the number of multi-temporal multispectral images reached 6. 4) The DSM accuracy was effectively improved when terrain factors were considered, and the terrain factors featuring a strong separability for various soil classes differed. The DSM method proposed in this study based on TSS information from multi-sources remote sensing data greatly improved the DSM accuracy and provided new insight for future DSM research.

Д.Г. Віленський — відомий український ґрунтознавець і геоботанік, організатор галузевого науково-освітнього процесу

Goal. To reveal the main milestones of the life and creative path, to summarize the most important scientific achievements of the talented agricultural scientist, one of the organizers of higher branch education and research in the Ukrainian SSR D.G. Vilenskyi. Methods. General scientific — analysis, synthesis, classification; historical — problem-chronological, comparative-historical, retrospective, biographical. Results. It is proved that D.G. Vilenskyi contributed to the establishment of leading institutions of higher agricultural education — Kharkiv Agricultural Institute named after H.G. Rakovsky and Kyiv Engineering and Reclamation Institute. He published the first textbook in the Ukrainian SSR «Botany» (1928) in the state language for higher and secondary educational institutions and the first textbook «History of soil science in Russia» (1958). On the initiative of the scientist, a large-scale interuniversity work was started — the natural zoning of the territory of the USSR for agriculture. It was established that D.G. Vilenskyi made efforts to organize the All-Ukrainian Research Institute of Agro-Soil Science and Chemical Agriculture, as well as the Ukrainian Institute of Applied Botany. For his great achievements in the field of soil science, he was elected president of the International Commission for Classification of Geography and Soil Mapping. Conclusions. The most significant scientific assets of D.G. Vilenskyi is the establishment of the phenomenon of clogging, the development of new indices of genetic horizons of soils — H, E, I, R, etc., preparation of the first soil maps of Ukraine, pioneering geobotanical studies of reclaimed drained soils, authorship of soil maps of the USA, Canada, individual regions and districts of the USSR, development of methods of soil research and large-scale soil mapping. It was established that D.G. Vilenskyi contributed to the popularization of the achievements of domestic science as a member of the editorial board (1930–1949), and editor (1934–1939) of the journal «Soil Science».

Improved parent material map disaggregation methods in the Saskatchewan prairies using historical bare soil composite imagery

The major drivers of soil variation in Saskatchewan at scales finer than the existing soil maps are parent material variance, slope position, and salinity. There is therefore a need to generate finer-scale parent material maps as part of updating soil maps in Saskatchewan. As spatially referenced soil point data are lacking in Saskatchewan, predictive soil mapping methods that disaggregate existing soil parent material maps are required. This study focused on investigating important environmental covariates to use in parent material disaggregation, particularly bare soil composite imagery (BSCI). Synthetic point observations were generated using an area-proportional approach based on existing soil survey polygons and a random forest model was trained with those synthetic observations to predict parent material classes. Including BSCI as environmental covariates increased model accuracy from 0.38 to 0.52 and the model Kappa score from 0.19 to 0.35 compared with models where it was not included. Models that included training points from all locations, regardless of whether BSCI was available, and included BSCI as environmental covariates had similar results to the BSCI model with an accuracy of 0.48 and a Kappa value of 0.30. Based on these results, BSCI is an important covariate for parent material disaggregation in the Saskatchewan Prairies. Future work to disaggregate soil classes based on slope position and salinity, and to combine those methods with parent material disaggregation is needed to generate detailed soil maps for the Canadian Prairies.

Farm-scale digital soil mapping of soil classes in South Africa

This study involved the evaluation of farm-scale digital soil classification in the Sandspruit catchment of the Western Cape Province, South Africa. The study aimed to evaluate a digital soil mapping (DSM) method, from feature selection, spatial predictions and sample design. The results showed that feature selection with the least absolute shrinkage and selection operator (LASSO) technique is a robust method as it had a high relative efficiency and achieved the highest accuracy for three out of the four soil classes predicted. This implies that covariate selection is the most notable aspect in DSM at the farm-scale. The top-performing predictive models achieved satisfactory results for soil associations (kappa = 0.64, accuracy = 74%), presence of a bleached topsoil (kappa = 0.64, accuracy = 74%) and soil depth (kappa = 0.48, accuracy = 74%), whereas only moderate results were achieved for soil texture (kappa = 0.43, accuracy = 66%). Lastly, the expert sampling locations had a higher average probability of occurrence (geographic and feature space distribution coverage) yet achieved similar performance to conditioned Latin hypercube sampling (cLHS).