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• Soil depth affects the erosion-entrainment process of debris flow. • This study assesses and suggest soil depth mapping methods under limited data. • The uniformly set method, though efficient, risks unstable results. • Soil depth mapping affects the flow height and erosion in debris flow simulation. Soil depth is essential in studying natural disasters such as landslides and debris flow hazards. Despite the importance of soil depth in the mechanism of erosion-entrainment during the debris flow process, research on soil-depth data for analyzing debris flows is limited. Therefore, this study focused on the Gallam-ri area with a watershed of 0.9 km2 to evaluate the soil depth mapping under limited data and significance of these maps for debris flow simulations. Based on the knocking pole test data, two-dimensional distribution soil depth maps were constructed using the S and Z models, the Kriging method, and a method that applies some values uniformly as the soil depth (U model). The accuracy of soil depth mapping methods was quantitatively evaluated using R2 and root mean squared error analysis. Since soil depth demonstrated independent patterns with land-surface data, soil depth maps using S and Z models have structural limitations showing R2 of 0.0003 and 0.002, respectively. The debris flows were analyzed through numerical model Deb2D, and the soil depth most significantly influenced the erosion volume and the damaged area. Analyses using S and U models showed 94 % and 98 % high similarity to the simulation results through the Kriging method, respectively. However, considering overall analyses, the S model was analyzed to be the most stable in constructing soil depth maps and simulating debris flows for ungauged basins. [ABSTRACT FROM AUTHOR]

• A large diversity of soils is found in the postglacial landscape small area. • In the area of the morainic parent material, the largest area is occupied by Luvisol. • Solimovic material is the basic material for the development of Phaeozems. • As a result of the construction of the drainage system new anthropogenic soil is formed. • The use of qualifiers (WRB 2022) is problematic for large-scale soil mapping. In conditions of complex glacial topography, large-scale soil mapping (1:10 000) according to the World reference base for soil resources (WRB) 2022 classification faces significant challenges. High diversity of reference soil groups (RSG) and qualifiers can be found in a small area, and this diversity is not related to the influence of a single but rather to a set of soil-forming factors. As a part of the study, the classification of soils on postglacial landscape was conducted using the WRB 2022 classification system. The analysis was focused on examining relationships between RSG and qualifiers, and also with type of geological deposits, location on the terrain, human activities (drainage of agricultural land, soil tillage and erosion). Survey showed that, in a relatively small area, soil cover consisted of WRB RSG such as Luvisols, Retisols, Regosols, Stagnosols, Gleysols Phaeozems, Planosols, Podzols, Histosols and Anthrosols, with the respective sets of qualifiers. Soils corresponding to the Anthrosol RSG were formed during the construction of a drainage system by burying the former soil with deeper material and later by translocation of colluvium by soil erosion. In the complex glacial topography, the distribution of RSG and their qualifiers is specific, which poses a number of challenges for soil mapping. The study confirmed the possibility of WRB classification for large-scale mapping of agricultural soils on postglacial landscape at the RSG level. The use of qualifiers for definition of mapping units in similar conditions of complex terrain and geological deposits (when mapping on a scale of 1:10 000) is possible only by increasing sampling density. [ABSTRACT FROM AUTHOR]

• An updated map of IPCC major soil types map based on HWSD version 2.0. is presented. • This map can be used to calculate Tier 1 reference soil organic carbon (SOC) stocks. • We report changes in soil types and Tier 1 SOC over small islands and northern latitudes. • Global soil datasets are subject to high uncertainties, as is the presented IPCC map. • Strengthened data collection is necessary for more accurate national SOC stocks estimates. Soils are the largest carbon pool in terrestrial ecosystems and play a major role in regulating the Earth's climate. Detailed information on soil properties is necessary to evaluate current soil organic carbon (SOC) stocks, as well as past and projected carbon stock changes. Currently, the most complete dataset of global soils is the Harmonized World Soil Database (HWSD), which was updated to version 2.0 in 2023. From the HWSD, it is possible to derive a map of the Intergovernmental Panel on Climate Change (IPCC) major soil types (high activity clay (HAC), low activity clay (LAC), volcanic, sandy, spodic, wetland, and organic soils) that can be used to calculate country reference SOC stocks using the default reference SOC stock values from the IPCC guidelines for national greenhouse gas (GHG) inventories. Following the update of the HWSD, an updated 1-kilometer resolution global map of the distribution of the seven IPCC major soil types, derived from the HWSD v2.0, is presented. When comparing the IPCC major soil types maps derived from HWSD v2.0 and HWSD v1.2, it appears that 18 % of the land areas are differently classified. Changes are mostly located in northern latitudes and small islands states. They are attributable to a change in the classification of soils, from FAO90 and FAO74 classes in the HWSD v1.2 to WRB classes in the HWSD v2.0, and an extended coverage of coastal areas and small islands. These same changes lead to an average difference in country reference SOC stocks of around 3 % when comparing Tier 1 country reference SOC stocks derived from the HWSD v1.2 and v2.0. [ABSTRACT FROM AUTHOR]

• Existing methods do not solve the problem of global soil data integration. • Global soil data integration requires the use of the systems approach. • Global soil data integration requires the proper use of classification theories. • The basic classification system is the main outcome of soil data integration. • The soil data integration components must be implemented within a single project. The current state of the problem of global soil data integration, including data on conditions of soil formation, is analyzed. Existing methods for soil data integration are presented. To solve the problem, the author proposes to use the systems approach and contemporary theories of classification, but subject to overcoming their conceptual-terminological uncertainties for this purpose. A definition is given for the global soil data integration, which, first of all, involves the creation of a basic conceptual soil-landscape classification system and expert multiscale soil-landscape maps that systematically reflect soil-landscape relationships. The components of global soil data integration are identified, namely thematic, spatial, geographic, horizontal, vertical and temporal, and the initial stages of its implementation are described. The system concept of soils and landscapes, including their system definitions, has been developed. The advantages of the proposed approach to the classification and mapping of soils and landscapes in comparison with existing ones are considered. [ABSTRACT FROM AUTHOR]

• The Caspian Sea retreat strongly affected the geochemistry of soils. • The properties of geomorphic surfaces play important roles in soil formation. • Distribution patterns of soil characteristics are strongly affected by landforms and seawater. The objectives of this study are: 1- Determining geochemical characteristics and soil formation processes that lead to soil salinity. 2- To investigate the variability of soil properties across different geomorphic units. The coastal landforms of the Caspian Sea have experienced several seawater retreat and progress in recent decades, which has caused changes in terms of pedology and geomorphology. Routine soil properties, including soil cations and anions (Ca2+, Mg2+, Na+, K+, Cl-, SO 4 2-, CO 3 2–, and HCO 3 –), and total and available forms of some micro-elements (Fe, Mn, Zn, and Cu) of 136 samples collected from 0-20 cm of soils were analyzed according to standard methods. Mapping of the obtained results was done using inverse distance weighting (IDW) in ArcGIS 10.8. The results showed that although the main source of cations and anions in the soil solution is seawater, their concentration and changes in the soil solution are different depending on the processes carried out in soil. Mg2+, Na+, and Cl- were highly enriched in saturated soil extracts compared to seawater (261 %, 153 %, and 149 % respectively). While, K+ and Ca2+, increased by 81 % and 47 % accordingly, and HCO 3 – and SO 4 2-, decreased by −56 % and −25 % due to precipitation of carbonate and gypsum in the soil. Total and available forms of micro-nutrients were high across the study area. The distribution pattern of the total micro-nutrients follows the type of landforms (affecting drainage condition) and their clay contents (mineralogical composition and abundance), however, the available form of micro-nutrients depends on soil moisture conditions and proximity or distance to the coastline. The results showed that the chemistry of seawater is the main factor controlling the chemical and physical properties of soils and their distribution patterns. The geochemical processes of soil formation cause special interactions between the components of soil solution, which leads to the removal of carbonate, bicarbonate, and sulfate from the soil solution and precipitation with calcium as calcium carbonate and calcium sulfate. [ABSTRACT FROM AUTHOR]

• Usage of Monte Carlo simulations in soil thickness mapping. • Soil thickness is independent of the position index for the study area. • Site-specific customization for better predictions. • Comparative analysis of customized soil thickness models. • Machine learning improves accuracy in soil thickness mapping. Precisely determining the thickness of soil, which is an essential parameter in environmental modelling, presents difficulties when applied to heterogenic large-scale areas. Current prediction models primarily concentrate on shallow soil depths and lack comprehensive spatial coverage. This study addresses this limitation by presenting the results of soil thickness assessment along three important roads in the Joshimath region (Indian Himalaya). Three different methods were examined incorporating geological and geomorphological data as input to obtain soil thickness maps: (1) a customized version of the conventional geomorphologically indexed soil thickness (GIST) model, modified specifically for the peculiarities of the research area, (2) the GIST model enhanced by Monte Carlo simulations (GIST-MCS), and (3) the random forest (RF) algorithm integrated with the GIST model (GIST-RF). By quantifying their errors and conducting validation using geophysical tests, the effectiveness of the models was assessed. Moreover, a critical comparison of the results provided useful insights to understand the peculiarities of the test site and how to adapt the site-specific customization of the models to the local features. The results indicate that the GIST model inadequately accounted for the substantial spatial variations in soil thickness observed across the study area. This is evident from the root-mean-square error (RMSE) of 5.28 m and the mean absolute error of 3.94 m. In contrast, the GIST-MCS model showed improvements, achieving an RMSE of 4.48 m and a mean absolute error of 2.86 m. However, the GIST-RF model demonstrated superior performance, yielding an RMSE of 2.40 m and a mean absolute error of 1.64 m. From a practical perspective, the generated soil thickness maps are particularly significant because they may serve as a crucial input parameter for further studies, including geotechnical assessments, environmental modelling, and decision-making processes. [ABSTRACT FROM AUTHOR]

[Display omitted] • The first of its kind estimation of potential and net yearly soil loss over India. • The national mean annual potential soil loss over India is 21 tons per hectare per year (t/h/y). • Nine out of the twenty most susceptible districts to soil loss in India are in the state of Assam. • A novel impact-based erosion-severity classification system finds that 29.46% of the landmass is prone to minor erosion while 3.17% experiences catastrophic erosion. • The national mean values for Sediment Delivery Ratio (SDR) and Sediment Yield (SSY) in India are 0.11 and 2.61 tons per hectare per year (t/h/y), respectively. Soil erosion generally removes the topmost fertile layer of soil, affecting agricultural productivity on a larger scale. As a significant portion of the Indian economy depends on agricultural productivity, granular assessment of the impact of soil erosion becomes critical. However, a national-scale assessment of soil erosion and an impact classification system currently doesn't exist over India. Given the resource-intensive and time-consuming nature of field experiments required for the measurement of soil loss across a vast country, the Revised Universal Soil Loss Equation (RUSLE) was extensively utilized for soil erosion calculations due to its simplicity, streamlined data requirements, and accuracy. This study estimates the yearly potential soil loss throughout India at a spatial resolution of 250 m and quantifies its variability considering districts, soil texture, soil type, land use and land cover, and basins. The relative importance of individual and combined impact of multiple parameters on quantified soil loss has been assessed using a random forest model. Rainfall erosivity (R-factor) emerges as the most crucial feature in estimating soil erosion in Indian conditions while rainfall intensity, combined with the topographic factor, demonstrated the highest influence on soil erosion in Indian conditions when the combined impact was assessed. Further, we mapped the Sediment Delivery Ratio (SDR) and Specific Sediment Yield (SSY) to assess the actual soil loss reaching downstream of basins across the national boundary. The national mean values for Sediment Delivery Ratio (SDR) and Sediment Yield (SSY) stand at 0.11 and 2.61 tons per hectare per year, respectively. The yearly potential soil loss for India is calculated at 21 tons per hectare per year, and nine out of the twenty districts with the highest susceptibility to soil erosion are in the state of Assam. Finally, a novel impact-based erosion-severity classification system has been introduced which finds that 29.46% of the landmass is prone to minor erosion while 3.17% experiences catastrophic erosion. This is the first comprehensive national-scale assessment of both soil erosion and sediment yield mapping over India, and the consequent classification system will enable the planning and implementation of soil conservation strategies locally as well as nationally. [ABSTRACT FROM AUTHOR]

[Display omitted] • A 250 m resolution black soil distribution map of China was developed. • Random forest classifier can accurately identify black soil at the national scale. • Daily mean land surface temperature and vegetation greatly affect black soil distribution. • A large area of unused black soil with reclamation potential in China. Black soil (BS) is an important soil resource. However, there is a lack of accurate information on BS spatial distribution, which affects the sustainable use and protection of BS. In this study, we linked BS distribution and machine learning. Random forest (RF) classifier and recent soil survey data were used to produce a high–precision BS distribution map in China. We analyzed environmental control factors of BS distribution. The results showed that the RF classifier method can well estimate the spatial distribution of BS at the national scale with an overall accuracy of 0.63 ∼ 0.91. The hot spots of BS distribution were mainly located in the Songnen Plain, Sanjiang Plain, the eastern side of the Greater Khingan Mountains and the western side of Changbai Mountains in Northeast China, the northeastern foot of Qilian Mountains in Qinghai–Tibet Plateau and the northern foot of Tianshan Mountains in Northwest China. Nearly 45 % BS area was covered by cropland. The daily mean land surface temperature and vegetation coverage had important controling effects on the BS distribution. Excluding the alpine mountains, BS area with potential for conversion to cropland can increase by 18.12 % (3.27 × 104 km2). The mapped BS distribution of China can serve as a benchmark for BS resource monitoring and play an important role in its assessment and protection. [ABSTRACT FROM AUTHOR]

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

• A global database of measured soil erodibility factor (K) was established. • The global K distribution characteristics show some regularity with soil properties. • RUSLE2-K and USLE-K algorithms can meet the model applicability conditions. • The result of RUSLE2-K is closest to the measured K. The soil erodibility factor (K) is the main data required for regional soil erosion investigation and mapping using soil erosion models. Fine mapping of K and the study of the applicability of different K estimation methods at the global scale are important to improve the accuracy of global soil erosion evaluation. In this study, the USLE-K, RUSLE2-K, EPIC-K and Dg-K algorithms were used to calculate and compare the global K , and a global measured K database was established by using literature backtracking method and retrieval tool method. Spatial pattern and applicability analysis were carried out on the results of the above four algorithms. The four algorithms were corrected according to the measured K database. The results showed that (1) the global K spatial patterns obtained by the four algorithms were similar but slightly different, with the result of RUSLE2-K being the closest to the measured K , followed by the USLE-K and the EPIC-K, and the result of Dg-K differing significantly from the measured K. (2) The global K distribution characteristics showed some regularity with soil properties, such as soil silt content and sand content, with silt content having the greatest influence on K. (3) The results calculated by the corrected RUSLE2-K and USLE-K algorithms could meet the model applicability conditions and coincided with the results of local K mapping. The results of K mapping in this study on a global scale and the results of the comparative analysis of the applicability of different algorithms provide the necessary scientific basis for the selection of K algorithms globally and quantitative evaluation of soil erosion. [ABSTRACT FROM AUTHOR]

[Display omitted] • We produced a high resolution (30 m) SOC map in cropland of Southern China. • Our produced SOC map has high accuracy with R2 = 0.73 and RMSE = 2.91 g kg−1. • The map accuracy was enhanced by incorporating soil management information. • Soil properties and soil management measures are most relevant to SOC variation in cropland. An accurate and fine map of soil organic carbon (SOC) plays a vital role in understanding the global carbon cycle and achieving soil carbon sequestration potential. Although global and national maps of SOC are already available with various spatial resolutions, limited sample size, and relative coarse resolution hinder its accuracy and application in local regions. Here, we collected 13,424 soil samples and information of 45 environmental covariates from cropland in Jiangxi Province, Southern China during 2012 and 2013. Then, the optimal covariates were selected by a recursive feature elimination algorithm for mapping SOC. After that, we deployed the random forest (RF) to produce a fine map (30 m) of SOC in the cropland of Jiangxi Province and 100 times bootstrapping was performed to calculate the prediction uncertainty. Finally, we determined the impacts of various covariates on SOC variability using RF and partial least squares structural equation modeling. Our results showed that compared with the predictive model without soil management information, introducing soil management information improved the predictability of SOC with an increase in R2 by 7.35 % (0.73 vs 0.68) and decrease in RMSE by 7.03 % (2.91 vs 3.13 g kg−1). Our results well estimated the uncertainty of the predicted result with a PICP of 0.91 for a 90 % prediction interval. Soil properties and soil management activities make the largest contribution for modelling SOC. Specifically, the total nitrogen content, straw return amount, total potassium content, and multi-resolution valley bottom flatness were found as the most important factors for mapping SOC in the cropland of our study area. Overall, this study deepened our knowledge of the variation of SOC and also emphasizes that incorporating soil management information could help us to achieve more accurate predictions of SOC. [ABSTRACT FROM AUTHOR]

• Combing soil survey and literature soil sample data enhanced SOC stock prediction. • ANN and MGWR had their own merits and drawbacks in predicting SOC stock. • Fusing different predicting models improved prediction accuracy of SOC stock. • Fusion model with the spatial autocorrelation considered had stronger adaptability. Soil organic carbon (SOC) is a crucial soil property governing various agricultural, ecological, and environmental processes in the terrestrial ecosystem. However, acquiring accurate and high resolution SOC density (SOCD) map remained a challenge in the current researches. Based on soil samples collected from soil survey and published literatures (952 samples), this study tested different approaches for generating high accuracy dataset of 0–20 cm SOCD with the spatial resolution of 90-m in the Yangtze River Delta. Two spatial prediction methods, including artificial neural network (ANN, represented non-linear), multiscale geographically weighted regression (MGWR, represented linear) were applied to generated the SOCD maps. Then three fusion models, including multiple linear regression fusion (MLR_F), ANN fusion (ANN_F) and a self-developed model (multiscale geographically normalize weighted fusion, MGNW_F), were applied to fuse the prediction results of spatial interpolation methods. Among spatial interpolation methods, the ANN performed better in capturing the SOCD spatial pattern, while the MGWR performed better in accuracy measures of mean square error, mean absolute error and coefficient of determination. After data fusing by the three models, improved accuracy measures than the ANN and MGWR were achieved. The predictions of fusion models also showed better reliability than the soil dataset of high resolution National Soil Information Grids of China, mainly due to that more soil samples were included in the prediction. Among these fusion models, the MGNW_F performed slightly better than the other two and had the strongest adaptability to the overall environment of the study area. Findings of this study highlighted the potentials of acquiring additional soil samples from existing literatures and fusing different methods for enhancing the accuracy in mapping SOCD. [ABSTRACT FROM AUTHOR]

• New maps and estimates of soil carbon in seasonally frozen ground in Tibet. • Four causal pathways showing interactions among controls and SOC were identified. • C inputs was found to be the most influential control on SOC. Seasonally frozen ground (SFG) significantly contributes to global carbon sinks. Global warming and anthropogenic-induced disturbances threaten the carbon storage capacity of SFG. Challenges in evaluating the SFG carbon storage potential include the lack of understanding of the control mechanisms of soil organic carbon (SOC) variations and timely spatial estimates of SOC. In this study, we investigated SOC stocks in SFG in the Tibet Autonomous Region, China, in 2020 and 2021. We employed partial least squares structural equation modeling (PLS-SEM) to explore the effect of complex processes (interacting roles of climate, plant physiology and phenology, freeze–thaw cycle, soil environment, and C inputs) on SOC and mapped SOC stocks in the topmost 30 cm. We identified four causal pathways: (1) an indirect pathway representing the effect of climate on plant physiology and phenology through changes in freeze–thaw cycles and soil environment, (2) an indirect pathway representing the effect of climate on C inputs through changes in freeze–thaw cycles, soil environment and plant physiology and phenology, (3) an indirect pathway representing the effect of climate on freeze–thaw cycles, and (4) an indirect pathway representing the effect of climate on the soil environment through changes in freeze–thaw cycles. C inputs exerted the greatest control on SOC. The effect of these factors decreased with increasing soil depth. We used PLS-SEM to generate maps of SOC stocks in SFG at a 500 m resolution with a moderate accuracy. The estimated mean SOC stocks in the 0–30 cm layer reached 6.87 kg m−2, with a 95% confidence interval ranging from 6.2 to 7.5 kg m−2. Our results indicated that it is critical to consider the depth dependence of the direct and indirect effects of environmental factors when assessing the control mechanisms of SOC variations. In this work, we also demonstrated that spatially explicit SOC estimates based on timely investigations are important for assessing C stocks against the background of considerable environmental changes across the Tibetan Plateau. [ABSTRACT FROM AUTHOR]

• We compared different isometric log-ratio balances for soil particle-size fraction prediction. • The performance of linear model, machine-learning model and regression kriging revealed different accuracy. • Data distribution of ILR balance should be focused for soil PSF mapping. Mapping of soil particle-size fractions (PSFs) combined with log-ratio transformation has been widely employed, particularly for isometric log-ratio (ILR). Regression kriging (RK), as a hybrid interpolator, represents a method to enhance prediction accuracy. However, different choices of ILR balance yield distinct transformed data. It remains unclear and lacks a comparison as to whether these results exhibit robustness when employing RK modeling. In this study, we compared the performance of four modelling approaches–generalized linear model (GLM), random forest (RF), and their hybrid models (i.e., GLMRK and RFRK). These models were applied to three ILR transformed datasets based on different balances, resulting in a total of 12 models, in the upper reaches of the Heihe River Basin, China. The results indicated that RF tended to provide more accurate predictions of soil PSFs, while GLM was better at producing predictions with less bias. The study recommends the use of RK, as it was found to broaden the value ranges of predictions, adjust bias, and enhance accuracy, especially when applied in conjunction with RF models. Furthermore, prediction maps generated from RK unveiled finer details of soil sampling points. The choices of ILR balance resulted in varying data distributions for the components of sand, silt, and clay. These components tended to cluster at approximately 120° in three groups, thereby indicating that even components with small content also exert significant influence on soil compositions. Rather than solely focusing on the relative abundance of components, this study suggests that the alignment of ILR components with a normal distribution is crucial for better model performance, especially for the first ILR component. Opting for the most abundant component as the first permutation may not always lead to optimal results for soil PSF mapping. This study provides insights into the role of data distribution and ILR balance selection in soil PSF mapping with transformed data. [ABSTRACT FROM AUTHOR]

This research introduces a scientific methodology for gully erosion susceptibility mapping (GESM) that employs geography information system (GIS)-based multi-criteria decision analysis. The model was tested in Semnan Province, Iran, which has an arid and semi-arid climate with high susceptibility to gully erosion. The technique for order of preference by similarity to ideal solution (TOPSIS) and the analytic hierarchy process (AHP) multi-criteria decision-making (MCDM) models were integrated. The important aspect of this research is that it did not require gully erosion inventory maps for GESM. Therefore, the proposed methodology could be useful in areas with missing or incomplete data. Fifteen variables reflecting topographic, hydrologic, geologic, environmental and soil characteristics were selected as proxies for gully erosion conditioning factors (GECFs). The experiment was conducted using 200 sample points that were selected randomly in the study area, and the weights of criteria (GECFs) were obtained using the AHP model. In the next step, the TOPSIS model was applied, and the weight of each alternative (sample points) was obtained. Kriging and inverse distance-weighted (IDW) methods were used for interpolation and GESM. Natural break method was used for classifying gully erosion susceptibility into five classes, from very low to very high. The area under the ROC curve (AUC) was used for validation. AHP results showed that distance to stream (0.14), slope degree (0.13) and distance to road (0.12) played major roles in controlling gully erosion in the study area. The values of points obtained by using the TOPSIS model ranged from 0.321 to 0.808. Verification results showed that kriging had higher prediction accuracy than IDW. The GESM results obtained by this methodology can be used by decision makers and managers to plan preventive measures and reduce damages due to gully erosion. Unlabelled Image • GIS-based MCDM techniques were used for gully erosion susceptibility mapping. • Two popular models namely AHP and TOPSIS MCDM were employed. • The weight of alternatives was determined by TOPSIS model. • Finally, GESM was produced using AHP-TOPSIS model and Kriging interpolation. [ABSTRACT FROM AUTHOR]

Among the clay-illuviated soils, dominant in the loess belt in southern Poland, islands of chernozemic soils are distinguished by their spectacular agricultural productivity. However, those soils are not marked as Chernozems/Phaeozems in European and world soil maps due to their common excessive wetness and doubts regarding their origin. Well-preserved earthen barrows of the Funnel Beaker culture, discovered recently in Muszkowice (SW Poland) offer a unique possibility of pedo-archaeological reconstruction (the first of such in Poland) of the pre- and post-Neolithic development of soil cover in loess-dominated areas, and explanation of the existing differentiation of soil cover into neighbouring Chernozems and Retisols. The buried chernozemic soil (Phaeozem) in Muszkowice started to develop in the Late Pleistocene period (mean residence time of humus – 12,670–13,000 cal BP). Until the burial (ca. 5480–5310 cal BP), an at least 22-cm thick mollic horizon has developed, with secondary carbonates accumulation at ca 80 cm. The barrow mound soil, constructed entirely of initially humus-rich topsoil material, has transformed into Retisol with fully developed, thick eluvial and illuvial (argic) horizons, and still preserved a high content of humus in the barrow mound soil profile. Further, the surrounding soils are entirely altered into Retisols (with secondary carbonates starting at 135–160 cm). Taking into account the palynological reconstruction from the nearest pollen profile in the loess-covered area, a crucial importance of the open-canopy (mainly oak) forests is concluded for the persistence of Chernozems under a temperate (increasingly moist) climate from the Early Holocene to the Neolithic period. In turn, the close-canopy broadleaf forests (mainly beech) are considered to be responsible for the rapid transformation of Chernozems/Phaeozems into Retisols/Luvisols in the Late Holocene. Chernozems have persisted and probably even rejuvenated only in the areas of permanent settlement since the Neolithic until the present time, where human activity (farming, pasturing) has eliminated a continuous broadleaf/beech close-canopy forest cover. • Chernozems dominated in soil cover of loess belt in SW Poland since Late Pleistocene. • Chernozems under close-canopy forests transformed into Luvisols after Neolithic. • Chernozems continuously occupied by humans since Neolithic persist until present. • Farming/pasturing during the Neolithic allowed further development of Chernozems. [ABSTRACT FROM AUTHOR]

Abstract Legacy databases provide unique information on soil properties and act as a guide for the setup of monitoring processes. However, their use requires an evaluation of their drawbacks, especially when aiming to model the soil traits by depth. We set up a procedure for the integration and error correction of a soil legacy database. This database consisted of 6994 records in its original form and 6674 records after correction. These records were collected from 2886 locations in the south of Italy on a 25,711-km2 island (Sicily, Italy). Samples were taken in arable lands (5471 records), orchards, vineyards and seminatural lands (3010 records), and woodland and natural areas (1203 records). The procedure for the integration and error highlighting improved the prediction of soil organic carbon (SOC), and a general linear model with covariate selection by Least Absolute Shrinkage and Selection Operator (LASSO) tested the procedure. We focussed on exploring the amount of legacy information as georeferenced soil properties. SOC and fine earth fractions were analysed for each sample. Bulk density was provided for only 20% of the samples. These results will help to account for the legacy data available and propose an analysis to harmonize an SOC dataset; highlight missing or incorrect data; summarize data; and offer synthesis criteria for benchmarking SOC in different land uses and pedological areas. In addition, the results may stimulate funding bodies to support research in an open data frame, which can be turned into more sustainable use of resources, improved communication between governments and farmers, and the production of standard datasets that meet and facilitate the requirements for regional agro-environmental modelling. Highlights • SOC legacy database analysis, integration and correction by data clearing • The correction can drive data expansion without taking redundant data. • 4.5% uninformative records removed • Correction fosters the use of legacy soil data for Space 3D-time soil mapping [ABSTRACT FROM AUTHOR]

Abstract Soil total phosphorus (TP) is an essential indicator to reflect the soil fertility in agricultural ecosystems. The accurate prediction of the spatial heterogeneity of TP is crucial to evaluate the soil productivity and quality. In this study, the interpolation methods of inverse distance weighted (IDW), radial basis functions (RBF), ordinary kriging (OK), co-kriging (COK), multiple linear regression (MLR), geographically weighted regression (GWR), regression kriging (RK), and geographically weighted regression kriging (GWRK) were used to estimate the spatial patterns of TP in four Mollisol areas with different kinds of landscapes at diverse scales. The calculation method, accuracy (mean error, mean absolute error, root mean square error and relative improvement), cost (money, labour and time), and process by which the models were established were also compared in order to ascertain the best interpolation methods to analyse and predict the spatial heterogeneity of TP in Mollisol areas. The results showed that i) generally, compared with OK, the RK and GWRK methods typically increased the simulative accuracy of the spatial distribution of TP, while IDW, COK and MLR decreased the accuracy, and RBF and GWR were not consistent at improving the accuracy in the Mollisol area; ii) the slope steepness and brightness index could be introduced to regression models as superior auxiliary variables to improve the interpolation precision (0.8%–1.9%) of TP by RK and GWRK when the study area is relatively flat; iii) the GWRK and RK methods with smaller root mean square error and higher relative improvement outperformed other methods in the region with suitable sampling evenness (>45%), while the RBF method is an optional approach when the sampling evenness is low. In summary, the GWRK and RK could be regarded as the optimal interpolation methods, despite the fact that their improvement was limited in this study. Meanwhile, when considering the cost, time and process by which the models were established, OK can also be deemed an optional interpolation method with relatively acceptable accuracy. Furthermore, the evenness and density of sample should be considered when mapping soil TP in Mollisol areas. Graphical abstract Unlabelled Image Highlights • Eight interpolation methods were used to predict TP distribution in Mollisol area. • Both of GWRK and RK were the optimal methods to predict soil TP distribution. • Brightness index as auxiliary variable can improve the interpolation precision. • Slope steepness as the best auxiliary variable can improve interpolation precision. • Interpolation precision is influenced by the evenness and density of soil sampling. [ABSTRACT FROM AUTHOR]

• Soil classification into four hydrologic groups is important for hydrological processes. • Four machine learning algorithms were applied to classify soil into four hydrologic groups. • Soil physical parameters from field surveys and remote sensing data were used for soil category mapping. • Saturated hydraulic conductivity is the main contributor to modeling hydrologic soil groups. Soil and water conservation is one of the major tasks of hydrologic soil groups (HSGs), which play an increasing role in the hydrological process. This research attempts to produce maps of soil classification into four hydrologic groups in the Upper Oum Er-Rbia basin (Morocco), which has many environmental risks due to specific climatic conditions and anthropogenic activities. The study was based on soil physical parameters (texture, organic matter, and bulk density) identified by extensive field surveys and remote sensing data (Digital Elevation Model, Landsat-9, European Reanalysis-5, and Soil Moisture Active Passive). The soil category mapping was prepared using four machine learning (ML) algorithms, namely, random forest (RF), support vector machine (SVM), K-nearest neighbor (K-NN), and naïve Bayes (NB). From 202 hydrologic soil groups, inventory locations were constructed and then randomly divided into a ratio of 70/30% for the training and validating models. Twenty-nine causative factors and indices were prepared and subdivided into four dataset types: whole variables (A), important variables (B), noncollinear variables (C), and HSG-specific variables (D) for HSG classification. The ML models' performance and robustness were evaluated using six accuracy metrics: Overall Accuracy (OA), Precision, Recall, F1-Score, Kappa coefficient, and Area Under the Curve (AUC) value. The experimental results revealed that the performance of models varies according to the datasets used. Based on the AUC results, all models performed comparably well, with very high accuracies. However, RF had a better classification performance (OA = 0.788), followed by NB (OA = 0.757), SVM, and K-NN (OA = 0.742), especially when using datasets related to HSGs. In addition, according to maps, HSG D had the highest rate and dominated the study area. However, saturated hydraulic conductivity is the main contributor to modeling HSG. Hence, the developed approach and maps produced from this study provide a new contextual planning tool for decision-makers to devise adaptation strategies to avoid environmental risks and conserve soil water resources. [ABSTRACT FROM AUTHOR]

• DSOLMap is a new high-resolution global digital soil property map for the SWAT + model. • Soil data play a crucial role in the hydrological cycle and its modelling. • An impact on streamflow performance using different global soil maps was observed. • The use of DSOLMap allows for an improved daily flow simulation. • DSOLMap can be useful for hydrological modelling in regions with limited soil data. This research paper addresses the ongoing challenge of developing fine-resolution global digital soil property maps for hydrological modelling applications. Hydrological models are essential for understanding watershed dynamics and the impact of human activities on water resources. Soil data, which plays a crucial role in the hydrological cycle, is a requisite model input. Global digital soil property maps usually have coarse spatial resolutions, adding considerable uncertainty to hydrological models despite calibration efforts. To address this issue, a new global digital soil property map with 250 m spatial resolution, known as Digital Soil Open Land Map (DSOLMap), was developed and evaluated in this study. The DSOLMap has a finer spatial resolution than existing global soil maps and a more detailed soil profile divided into six soil horizons. This new high-resolution global digital soil property map was tailored to the SWAT + model format. SWAT + is the latest released version of the Soil and Water Assessment Tool (SWAT), one of the most comprehensive hydrological models, and is widely used worldwide. A hydrological evaluation was conducted with the DSOLMap and its results were compared to two other global soil databases using the SWAT + model in a basin located in the north of Spain. The findings showed that using more detailed, finer-resolution soil data, such as those that the DSOLMap offers, improved the hydrological performance of the SWAT + model on a daily scale before and after calibration and validation procedures. The DSOLMap represents a global step forward in hydrological modelling, notably for regions with scarce or unavailable soil information. This new digital soil property map can help decision-makers address global challenges related to water resources and environmental issues through hydrological modelling. [ABSTRACT FROM AUTHOR]

• We evaluated the potential of using a combination of multitemporal bare soil and crop growth images for SOM prediction. • Remote sensing images during the bare soil period are more suitable for SOM mapping. • The accuracy of SOM mapping can be improved by combining images with appropriate periods. • The use of appropriate multitemporal crop growth images combined with environmental covariates has great potential in SOM mapping. Mapping of soil organic matter (SOM) in cultivated land is one of the important aspects of digital soil mapping, and its results are of great significance for agricultural precision management and carbon cycle assessment. This study takes Youyi Farm, a typical black soil area in Northeast China, as the research area and utilizes all Landsat-8 images covering the study area from April to October during 2014–2022. After masking clouds, all images were synthesized monthly. According to the local crop phenology, the period from April to October was divided into the bare soil period (April to June), peak crop growth period (July to August), and late crop growth period (September to October). Based on the random forest regression algorithm, differences in the accuracy of SOM mapping using synthetic images from different periods were evaluated, and the impact of adding environmental covariates on the SOM mapping accuracy was analyzed. The results showed that (1) when using a single-temporal synthesized image for SOM mapping, the order of accuracy was bare soil period > peak crop growth period > late crop growth period, with the synthesized image in May exhibiting the highest accuracy, with an RMSE of 0.979 %; (2) when using a multitemporal image combination for SOM mapping, the combination of optimal months (April, May, June) in the same periods can obtain the best SOM mapping accuracy, with an RMSE of 0.919 %; and (3) adding environmental covariates can effectively improve the accuracy of SOM mapping, especially when using the growing season remote sensing images, RMSE decreased from 1.136 % to 0.909 %. This study expands the applicable areas and conditions of SOM remote sensing mapping. [ABSTRACT FROM AUTHOR]

[Display omitted] • VTFs contributed the most to soil salinity mapping in the YRD. • Principal component analysis of VI time series aids in soil salinity mapping. • VI time series provide soil salinity information not in single-temporal RS images. • Combining VI time series and single-temporal RS images improves soil salinity inversion accuracy. Accurate mapping of soil salinity is of great significance for agricultural safety. However, soil salinity mapping has proven difficult because of its complex causes. The vegetation index (VI) time series has great potential in soil salinity mapping on a larger scale, yet studies on the use of this index are limited. Therefore, two inversion models (Model A and Model B) are established to assess the effects of VI time series on soil salinity mapping in the Yellow River Delta (YRD). Model A is developed by combining VI time series and single-temporal remote sensing (RS) images, while Model B does not consider VI time series. The effects of VI time series are evaluated from several perspectives by comparing the performances of Model A and Model B. The results show that VI time series can improve the accuracy of soil salinity mapping. The accuracy of Model A (R2 = 0.80, root mean square error (RMSE) = 0.4 mS cm−1) is better than that of Model B (R2 = 0.64, RMSE = 0.68 mS cm−1). In addition, the involvement of VI time series alleviates the overestimation of low-salinity soils and the underestimation of high-salinity soils, eliminating the dividing lines caused by different image times in soil salinity mapping. This study provides a large-scale, high spatial resolution (10 m), accurate soil salinity mapping method. It may help to maintain agricultural and ecological security and promote regional sustainable development. [ABSTRACT FROM AUTHOR]

• First national-scale mapping of soil erodibility over India. • K-factor estimated using the widely used Nomograph and EPIC models. • The average soil erodibility factor for India was estimated to be 0.028 t-ha-h/ha/MJ/mm. • CLOM index highly correlated with soil erodibility in Indian condition. • A susceptibility to soil erosion map was also created based on CLOM index over India. Soil erosion is a major environmental problem worldwide, and almost half of India's total geographical area is susceptible to it. The Revised Universal Soil Loss Equation (RUSLE) has been widely used globally to estimate soil erosion, and the Soil erodibility factor, denoted by the K-factor, is an essential component of RUSLE. Although previous studies have assessed soil erodibility in India, they have been limited to small scales such as watersheds or districts. A national-scale assessment of soil erodibility doesn't exist and is critical to developing a systematic understanding of soil erosion over India. In this study, we estimated soil erodibility factors over India using RUSLE Nomograph and Environmental Policy Integrated Climate (EPIC) model approaches at a high resolution of 250 m. Our results showed that the K-factor estimated using the Nomograph approach was more accurate than the observed soil erodibility factors. Additionally, we developed erodibility indices such as CR (Clay Ratio), MCR (Modified Clay Ratio), and CLOM (Critical Level of Organic Matter) to assess their sensitivity with respect to soil erodibility factors. Finally, we created a susceptibility to erosion map over India using CLOM index classification. The national average soil erodibility factor for India is estimated to be 0.028 t-ha-h/ha/MJ/mm. Histosols soil type is the least susceptible to erosion, while the Xerosols soil type is most susceptible among the prevalent soil classes in India. This is the first national-scale mapping of soil erodibility over India, providing an essential asset for soil conservation and erosion management planning by experts. [ABSTRACT FROM AUTHOR]

Multi-scale geospatial and absolute variation of surface and near-surface soil physical and chemical properties can be mapped and quantified by coupling digital soil mapping techniques with high resolution remote sensing products. The goal of this research was to advance data-driven digital soil mapping techniques by developing an approach that can integrate multi-scale digital surface topography and reflectance-derived remote sensing products, and characterize multi-scale soil-landscape relations of Quaternary alluvial and eolian deposits. The study area spanned the arid landscape encompassed by the Barry M. Goldwater Range West (BMGRW), which is administered by the Marine Corps Air Station Yuma, in southwestern Arizona, USA. An iterative principal component analysis (iPCA) was implemented for LiDAR elevation- and Landsat ETM + -derived soil predictors, termed environmental covariates. Principal components that characterize >95% of covariate space variability were then integrated and classified using an ISODATA (Iterative Self-Organizing Data) unsupervised technique. The classified map was further segmented into polygons based on a region growing algorithm, yielding multi-scale maps of soil-landscape relations that were compared with maps of soil landforms identified from aerial photographs, satellite images and field observation. The approach identified and mapped the spatial variability of soil-landscape relationships in alluvial and eolian deposits and illustrated the applicability of coupling covariate selection and integration by iPCA, ISODATA classification of integrated data layers, and image segmentation for effective spatial prediction of soil–landscape characteristics. The approach developed here is data-driven, applicable for multi-scale mapping, allows incorporation of a wide variety of covariates, and maps spatially homogenous soil-landscape units that are necessary for hydrologic models, land and ecosystem management decisions, and hazard assessment. [ABSTRACT FROM AUTHOR]

Traditional soil maps have helped us to better understand soil, to form our concepts and to teach and transfer our ideas about it, and so they have been used for many purposes. Although, soil maps are available in many countries, there is a need for them to be updated because they are often deficient in that their spatial delineations and their descriptions are subjective and lack assessments of uncertainty. Updating them is a priority for federal soil surveys worldwide as well as for research, teaching and communication. New data from sensors and quantitative ‘digital’ methods provide us with the tools to do so. Here, we present an approach to update large scale, national soil maps with data derived from a combination of traditional soil profile classifications, classifications made with visible–near infrared (vis–NIR) spectroscopy, and digital soil class mapping (DSM). Our results present an update of the Australian Soil Classification (ASC) orders map. The overall error rate of the DSM model, tested on an independent validation set, was 55.6%, and a few of the orders were poorly classified. We discuss the possible reasons for these errors, but argue that compared to the previous ASC maps, our classification was derived objectively, using currently best available data sets and methods, the classification model was interpretable in terms of the factors of soil formation, the modelling produced a 1 × 1 km resolution soil map with estimates of spatial uncertainty for each soil order and our map has no artefacts at state and territory borders. [ABSTRACT FROM AUTHOR]

Electromagnetic induction (EMI) is an established method for mapping field-scale soil water content (SWC). However, the correlation between the recorded apparent electrical conductivity (ECa) and SWC is affected by several factors that can vary across test sites and with environmental conditions. As agricultural practices affect both, ECa and SWC, it is likely that the mismatch in SWC predictions using ECa can be directly or indirectly attributed to agronomic treatment effects. Hence, EMI based SWC predictions are often limited to sites with one soil amendment and strong ECa – SWC correlations. However, non-invasive SWC mapping is particularly desirable for larger agricultural fields, covering different soil amendments. We hypothesized that different agronomic treatments altered the ECa – SWC correlations and consequently the EMI based SWC prediction accuracy. We further hypothesized that a model established on areas with high positive ECa – SWC correlation could be used to predict SWC for areas with unsuitable ECa – SWC correlations. A field-scale experiment was conducted to investigate the effects of six agronomic treatments (including biochar, BC) on SWC, ECa, and ECa – SWC correlation in a silage corn field. We tested the accuracy of three models to predict the SWC of independent data sets using data from i) all treatments (T all ), ii) plots with lowest and iii) plots with highest ECa – SWC correlations. We found statistically significant treatment effects on both, ECa and SWC, although overlapping data ranges were given. Furthermore, the correlations between ECa and SWC were affected by the treatments. Correlations were found to be lowest on nutrient-rich dairy manure plots (T2) and highest on the control plots (T6), likely due to differences in the ionic strength of pore water. BC mitigated the effect of ionic strength for T2 while it showed no measurable effects on ECa on plots receiving inorganic fertilizers. Most accurate SWC predictions were reached by employing T all data (RMSEP 1.40–3.13% vol.). However, models based on T6 data provided similar accuracies (RMSEP 1.46–3.96% vol.) using only 12.5% of the area. The T2 based model performance failed (RMSEP 3.02–7.21% vol.). Results suggest that ECa – SWC models established on non-manured areas could provide best possible SWC predictions and are recommended as training areas if soil texture and mineralogical composition can be expected to be relatively homogeneous. [ABSTRACT FROM AUTHOR]

This study aimed to use spatial uncertainty of magnetic susceptibility (MS) and clay content to improve soil-mapping units. In an experimental area of 870 ha, a regular sampling grid containing 371 points was set, in which MS and clay content were assessed at a depth range of 0–0.25 m. Using a digital elevation model and field observations, a 4440-m transect was established on the study area from hilltop to the foothill, toward the gentlest slope. Standard deviation maps based on 200 realizations of the sequential gaussian simulation measured spatial estimate uncertainties. To limit transition zones along the transect, uncertainty isolines representative of the transition between soil-mapping units were selected. Both attributes presented peaks of uncertainties near the change of mapping units, previously known. Uncertainty zones, previously delineated, ranged from 45 to 210 m and from 60 to 170 m, for MS and clay content, respectively. However, after extrapolating the uncertainties to the side of the transect, amplitude changes of the uncertainty zones were observed, especially in the transition regions between landscape shape and geology. Delineation of mapping units, which incorporated the uncertainties of MS and clay content, was similar. However, due to lower cost and promptness, MS becomes the most feasible alternative. Knowing spatial uncertainties enables readjusting limits in maps of soil-mapping units and may support identification of most favorable regions for determining modal pedon representative of each unit. [ABSTRACT FROM AUTHOR]

Mapping soil nutrients can help smallholder farmers identify soil nutrient status and implement site-specific soil management schemes. In the past, Digital Soil Mapping has seldom been utilized to guide soil nutrient management in smallholder farm settings in South India. The objective of this research was to analyze the spatial resolution effects of different remote sensing images on soil total nitrogen (TN) prediction models in two smallholder villages, Kothapally and Masuti in South India. Regression kriging (RK) was used to characterize the spatial pattern of TN in the topsoil (0–15 cm) by incorporating spectral indices with different spatial resolutions. The results suggested that soil moisture, vegetation, and soil crusts can contribute to the conservation of soil TN in both study areas. Soil prediction models with different spatial resolutions showed a similar spatial pattern of soil TN. The results also demonstrated that the effect of very fine spatial remote sensing spectral data inputs does not always lead to an increase of soil prediction model performance. A RapidEye-based (5 m) soil TN prediction model had lower prediction accuracy than a Landsat 8-based (30 m) soil TN prediction model in Masuti. WorldView-2/GeoEye-1/Pleiades-1A-based (2 m) soil TN prediction models had the highest prediction accuracy in both study areas. The spectral indices based on new bands of WorldView-2 such as coastal, yellow, red edge, and new near infrared bands had relatively strong correlations with soil TN. The utilization of Very High Spatial resolution images such as WorldView-2 in Digital Soil Mapping could improve soil model performance and spatial characterization. Remote sensing-based soil prediction models have high potential to be widely applied in smallholder farm settings. [ABSTRACT FROM AUTHOR]

This study describes and classifies the soils of the foot and toe slopes of the Vukan Mountain, Central Serbia, the contact point of two distinct geomorphological units, with a focus on their spatial distribution and their main soil-forming processes. The Vukan Mountain is formed of Jurassic limestones and dolomites, whereas the foot and toe slopes are of fluviatile-colluvial origin. A total of 42 soil profiles were examined. Soil profile locations were determined with respect to elevation differences, accounting for a set of six profiles for every 5-m elevation increase between 175 and 210 m a.s.l. The area is characterized by the dominance of chernic and mollic topsoil horizons and the major part of the area is covered with Phaeozems. Five Reference Soil Groups were found in a very small area. Eleven soil profiles are Chernic Phaeozems, five are Cambic Phaeozems, and twelve are Rendzic Phaeozems. The central part of the study area is characterized by the accumulation of secondary carbonates and Chernozems were identified. The northern part of the study area is covered with Fluvisols, Calcisols, and Leptic Rendzic Phaeozems, whereas Chernic and Cambic Phaeozems and Eutric Cambisols cover the western part of the study area. The differences in the CaCO 3 content in the gravels, the differences in the gravel content, and the different lateral and vertical distribution of CaCO 3 are strong evidence of surface processes that occurred in the past. The presence of different geological layers and buried horizons suggests fluviatile processes. The spatial distribution of soils is related mainly to parent material occurrence, and colluvial and alluvial processes that occurred in the past. The soil map created in GIS has Reference Soil Group as central unit following Rule 1 for map legend creation, except in the case of Phaeozems, which are present as Phaeozems (Leptic) and Phaeozems (other) following Rule 5. This soil survey with approximately one soil profile per 4 ha has indicated considerable soil heterogeneity in the study area. Detailed surveys are therefore recommended for areas with pronounced heterogeneity of soil-forming factors. [ABSTRACT FROM AUTHOR]

Nowadays there is an increasing effort for raising global awareness for the importance of soils to ensure food security, to improve agricultural and environmental planning and monitoring, and to establish effective and sustainable land management policies to counteract soil degradation. This study presents the INFOSOLO legacy database as the first effort to develop a soil information system in Portugal, suitable to compile soil data produced in the country, and to support stakeholders and land managers in decision-making. The database currently includes soil data from a set of 9934 horizons/layers studied in 3461 soil profiles across the country between 1966 and 2014. Data was extracted from scattered soil surveys, research projects, and academic studies carried out by public Portuguese and other European institutions, with a series of validation tests and harmonization procedures being implemented in order to access and improve the quality of the data. The importance of the INFOSOLO legacy dataset was discussed and exemplified with the analysis of the spatial and temporal distribution of selected soil properties, namely the organic carbon content, pH, and cation exchange capacity. For these properties, 1 km grid maps were also developed for the topsoil horizons/layers in Portugal using different spatial modelling approaches. To highlight the importance of using INFOSOLO for soil characterization, the EU-wide soil database LUCAS was used to compare both datasets in terms of data distributions, spatial continuity and accuracy of modelling outputs. The comparison also included the digital soil maps provided by the SoilGrids product for Portugal. The comparison results highlighted specific areas in the country for which INFOSOLO is capable to deliver accurate but also more reliable predictions when compared with the LUCAS and SoilGrids results. Thus, INFOSOLO provides the basis for improving soil information in the country and for raising national awareness of the importance of soil resources to the country's development. [ABSTRACT FROM AUTHOR]

Several studies have demonstrated the great range of possibilities offered by remote sensing in identifying, estimating and mapping biological soil crust (BSC) patterns, i.e. a feature recognised to play major functions in drylands. However those techniques are suitable mainly where BSC patterns are abundant (> 30%) and vegetation cover low (< 10%), otherwise reflectance values matched different levels of BSCs mixed with vegetation and bare soil surfaces. This study developed an alternative methodology in mapping BSC presence in areas with a wide range of BSC cover associated with different mosaics encompassing vegetation and bare surfaces in the Sahel. Data were collected during intensive field surveys and remote sensing imagery of two typical Sahelian watersheds in western Niger (Banizoumbou and Tamou). Statistical methods were used to explore relationships between BSC occurrence and abundance and key environmental factors (rainfall, land use, land cover, vegetation, physical crusts). A predictive model of BSC spatial distribution was developed based on logistic regressions. This model allowed predicting and mapping BSC occurrence in areas where BSC cover ranged from 0 to 65% at Tamou (15% in average) and 1 to 48% at Banizoumbou (4% in average) and where vegetation cover ranged from < 1% to > 75%. Predicted values were obtained with an overall accuracy of 77.7% (kappa = 0.54), classifying the model as good and discriminant. This work is the first step in assessing the local scale ecological functions of BSC. Further work is needed for extrapolation at the regional scale in order to provide a useful tool for ecological surveys and for predictions of soil surface dynamics related to global changes in dryland areas. [ABSTRACT FROM AUTHOR]

Whereas contemporary land use and land management planning require specific and quantitative georeferenced soil information, often only general-purpose and qualitative soil maps are available. With a view to fill this gap for topsoil clay and organic carbon content in the central plateaus of Burundi, we tested for a representative 15 km 2 hilly landscape, six types of SCORPAN models. The SCORPAN models were first applied as standalone trend models and next extended with a component accounting for the spatial autocorrelation of the residuals from the trend. Various sets of predictors, including class variables derived from the available soil map and continuous derivatives from a Digital Elevation Model (DEM) and from Landsat-imagery were incorporated. For clay, the best prediction method was a Residual Kriging (RK) using a Generalized Additive Model (GAM) as trend built with only DEM derivatives and spectral normalized difference vegetation index (NDVI). Furthermore, the classical and simplest RK, i.e. using a Least Squares Linear Regression (LR) trend built with only continuous covariates, outperformed all standalone trend models. For organic carbon, residuals from the trend models were not significantly auto-correlated, making RK meaningless. In this case the best model was a GAM combining lithologic units with DEM derivatives and NDVI. Overall, the contribution of soil map–derived predictors to the model performance was rather weak. It was concluded that, for prediction of specific soil characteristics in the study area, a SCORPAN approach is preferred the more as the performance can be boosted by kriging of trend residuals if auto-correlated. [ABSTRACT FROM AUTHOR]

We studied an improved slope form system using a fuzzy logic method to assess and map soil fertility of a mountain region in northern Vietnam that has strong relief conditions. The lack of good soil mapping techniques in Vietnam has brought about insufficient soil information, which often leads to false recommendations for land use and crop planning. The reviewed literature describes soil-mapping techniques using fuzzy logic method, but all of them are applied for mapping areas that have gentle relief conditions that are unlikely to be applicable to the mountainous soils in our study area. In this paper, we introduce a detailed slope form system that significantly describes the complexity of terrain characteristics of the area to be mapped, and provides more detail about the variability of the soil fertility of the area. Nine basic slopeforms were used to characterize for each of upper-, middle-, and foot slope positions, making the list 27 slopeforms. Together with crest and valley, the total unit number is 29. We investigated soils of the area and classified them into the major soil groups and calculated soil property indices for all of them. We identified four major environmental parameters affecting soil formation and soil quality: geology, elevation, slope inclination and land use. The findings indicated that soil fertility differs at slope positions. Soils located at upper slope positions, where agricultural activity only started recently, are more fertile than those found at middle slope positions. Soils located at foot slope positions, where eroded sediments accumulate, also have high levels of fertility compared to those on the middle slope. The improved slope form system then became an important additional environmental parameter for this soil mapping work. At a same comparable category, i.e. slope position, geology, soil group, elevation, slope gradient, straight slopeforms are an indicator for better soil fertility compared to convex and concave forms. Although the findings could not specify soil fertility variability for all 29 slopeforms, they did emphasize the major differences in soil fertility and soil formation based on three major forms of convex, straight and concave, with other factors taken into account, such as slope inclination, geology and elevation. We expect our results to be used by scientists and local authorities in deriving more effective land use and crop options for land use management strategies for the northern Vietnam's mountain regions. [ABSTRACT FROM AUTHOR]

Soil varies from location to location as a result of complex combinations of soil forming factors and processes operating at different scales and intensities. The scale-dependent variability in soil properties causes challenges for the characterization and mapping of soils. With the advent of technologies, proximal sensors such as gamma radiometrics can characterize soil spatial variability and the digital mapping of soil properties. In this study we aim to characterize scale-dependent variability of proximal gamma radiometrics and its complex relationship with soil properties at different scales using wavelet transform and wavelet coherency. We measured gamma radiometrics (γ-ray potassium ( 40 K), uranium ( 238 U), thorium ( 232 Th) and Total Count (γ-TC)) using a hand-held device in the field every 25 m along a 3.0 km transect. At each point we measured elevation (m) and land use and collected soil samples to determine key soil properties (electrical conductivity EC 1:5 , pH (CaCl 2 ), soil organic carbon (SOC), cation exchange capacity (CEC), exchangeable potassium (exch. K), and Colwell P). Wavelet spectra identified significant ( P < 0.05) variations at different scales and locations for both γ-ray and soil properties. There were significant localized variations with changes in elevation and land use also important. In most cases a ‘global’ Pearson correlation between the γ-ray and soil properties was not significant. In contrast, wavelet coherency provided a more powerful scale- and location-specific relationship between γ-ray, elevation and land use with selected soil properties. For example, a strong correlation (18% of the area was significant) was detected between land use and soil EC 1:5 at all scales (< 100–800 m) in a low drainage depression. This correlation was due to the localized mobilization of salts and has implications for the identification of saline land. At large spatial scales (> 800 m) there was significant positive correlations between γ-TC and SOC (36% by area). At medium scales (~ 400 m) there was significant and positive correlations between 40 K and CEC (23% by area), but only along the first half of the transect which was underlain with Bushy Creek Granodiorite. These findings provide an improved understanding of the spatial variability of γ-ray-soil relationships. The approach used shows the scope and significant potential to use wavelet analysis of gamma-soil property relationships that are scale-specific and would be useful for digital soil mapping. Moreover, scale-specific spatial information of soil properties aids understanding of soil formation processes and it illustrates an approach to improve future soil sampling strategies. [ABSTRACT FROM AUTHOR]

Owing to conducive economic conditions and a suitable climate Andalucía has become the largest olive oil producing area in the world. However, extensive rill and severe gully erosion are threatening sustainability. To conserve the soil, land management strategies have to be implemented that minimize erosion and enhance at the same time rain water conservation to safeguard crop yields. An indicator of soil quality and the success of soil management will be organic matter content (OM—%). There is therefore a need to measure, map, manage and monitor its content. Proximal sensors such as electromagnetic induction instruments may be useful in mapping this because the apparent soil electrical conductivity (EC a —mS/m) is related to clay, salinity and mineralogy, which influence organic matter content. In this research we collect data from a single frequency and multiple-coil DUALEM-21 along a transect and across an olive grove in the “La Manga” catchment in Setenil de las Bodegas in the southwest of Spain. We inverted the data using EM4Soil software and developed calibrations between estimates of true electrical conductivity (σ—mS/m) with depth against measured OM % using the quasi-2d algorithm along a single transect. We did this by using a fitted linear regression model and by varying the forward modelling algorithm (cumulative function and full solution), inversion algorithm (S1 and S2) and damping factor (λ) to determine a suitable electromagnetic conductivity image for 2-d and 3-d mapping. Our results along a detailed transect showed promise and suggest differences in OM content could be mapped down a topographic sequence. We applied this calibration to a quasi-3d model across the entire olive grove and to predict OM at various depths. The results across the olive grove were compromised in some locations and within geomorphological complex locations in the landscape, such as near the erosion gully where frequent erosion and deposition occurs. We conclude that better results may have been achieved if more detailed EC a data collection was undertaken in and around the gully and also across a larger extent. [ABSTRACT FROM AUTHOR]

Detailed digital soil maps showing the spatial heterogeneity of soil properties consistent with the landscape are required for site-specific management of plant nutrients, land use planning and process-based environmental modeling. We characterized the short-scale spatial heterogeneity of soil properties in an Alfisol catena in a tropical landscape of Sri Lanka. The impact of different land-uses (paddy, vegetable and un-cultivated) was examined to assess the impact of anthropogenic activities on the variability of soil properties at the catenary level. Conditioned Latin hypercube sampling was used to collect 58 geo-referenced topsoil samples (0–30 cm) from the study area. Soil samples were analyzed for pH, electrical conductivity (EC), organic carbon (OC), cation exchange capacity (CEC) and texture. The spatial correlation between soil properties was analyzed by computing cross-variograms and subsequent fitting of theoretical model. Spatial distribution maps were developed using ordinary kriging. The range of soil properties, pH: 4.3–7.9; EC: 0.01–0.18 dS m − 1 ; OC: 0.1–1.37%; CEC: 0.44–11.51 cmol (+) kg − 1 ; clay: 1.5–25% and sand: 59.1–84.4% and their coefficient of variations indicated a large variability in the study area. Electrical conductivity and pH showed a strong spatial correlation which was reflected by the cross-variogram close to the hull of the perfect correlation. Moreover, cross-variograms calculated for EC and Clay, CEC and OC, CEC and clay and CEC and pH indicated weak positive spatial correlation between these properties. Relative nugget effect (RNE) calculated from variograms showed strongly structured spatial variability for pH, EC and sand content (RNE < 25%) while CEC, organic carbon and clay content showed moderately structured spatial variability (25% < RNE < 75%). Spatial dependencies for examined soil properties ranged from 48 to 984 m. The mixed effects model fitting followed by Tukey's post-hoc test showed significant effect of land use on the spatial variability of EC. Our study revealed a structured variability of topsoil properties in the selected tropical Alfisol catena . Except for EC, observed variability was not modified by the land uses. Investigated soil properties showed distinct spatial structures at different scales and magnitudes of strength. Our results will be useful for digital soil mapping, site specific management of soil properties, developing appropriate land use plans and quantifying anthropogenic impacts on the soil system. [ABSTRACT FROM AUTHOR]

• A framework for efficient soil architecture mapping using GPR was proposed. • A preliminary survey is used to provide two key parameters for regular survey design. • The new survey strategy can effectively improve the mapping quality and efficiency. Ground-penetrating radar (GPR) is more and more prevailing in soil survey as a geophysical method due to its non-invasive and high efficiency. For subsurface soil architecture mapping, its quality relies not upon only the accuracy of GPR evaluation itself but also on often overlooked survey strategy. This study focuses on the latter one and proposed a new survey strategy to promote the commonly-used survey strategy that subjectively employing multiple parallel two-dimensional GPR acquisition lines. A preliminary survey roughly capturing the spatial variability of soil architecture is suggested to provide two key parameters, the spacing and the direction of the parallel survey lines, for the subsequent commonly-used survey design. Thus, the final survey can not only take advantage of the dense space sampling rate of GPR itself along the survey direction with the highest variability of the subsurface soil architecture, but also well controls the mapping quality and efficiency. Field experiments and numerical tests demonstrate that the new survey strategy could effectively reduce mapping uncertainties originating from unmatching sampling rate and interpolation algorithm selection. Besides, a suitable mapping quality and efficiency can be guaranteed but preventing unnecessary too dense sampling rate, namely low mapping efficiency. This indicates the proposed strategy would be useful for relevant soil surveys using geophysical methods. [ABSTRACT FROM AUTHOR]

[Display omitted] • A strategy was proposed to improve SSC maps using remote sensing. • The bands ratio index effectively enhanced the spectral absorption signal of soil salt. • A spatial association function was developed to optimize the calibration sample set. • The RF algorithm was used to construct the estimation model for SSC mapping. Salt is a basic soil parameter that is especially sensitive to land degradation and global climate warming. Machine learning combined with satellite images offers the potential for effectively surveying soil salt contents (SSC) in the spatial and temporal dimensions. However, outlier spectral samples caused by environmental factors (e.g., particle size distribution, moisture, and vegetation) have interfered with the determination of the mapping relationship between ground-truth SSC and corresponding spectral features, resulting in a random forest (RF) mapping model with low generalization capability. Therefore, a spatial association module was introduced into the RF to remove the outlier spectral samples for building an optimal sample set in order to calibrate an improved random forest (IRF) model for SSC mapping. A total of 233 soil samples and a simultaneous Sentinel-2B multispectral image were acquired for the Weibei Plain of China. The spectral absorption features of SSC were characterized by three indices (band difference, band ratio, and band normalized). Then a spatial association function was developed to identify the outlier spectral samples for optimizing the calibration samples. Lastly, the SSC-responding bands and SSC-related indices were used as input variables to build the improved random forest (IRF) model for regional SSC mapping. Results showed that: (1) bands 3, 8, and 11 in the Sentinel-2B image were the SSC-responding bands, and band 11 had a significant correlation with SSC; (2) the band ratio index synthesized the multi-band spectral information, and effectively enhanced the spectral absorption signal of soil salt; the optimal SSC-related indices included RI 34 (reflectance ratio of band 3 and band 4), RI 711 , NDI 611 (normalized reflectance values of band 6 and band 11), and DI 45 (reflectance difference value of band 4 and band 5); (3) when the SSC-responding bands and optimal SSC-related indices were input into the IRF remote sensing estimation model, the accuracy parameters R2 v and RPIQ were greater (0.89 and 3.52, respectively) than the comparable values with the RF model (R2 v = 0.69; RPIQ = 2.90), with accuracy improved by 28.99 % and 21.38 %, respectively, indicating that satisfactory results for regional SSC mapping had been obtained; (4) SSC distribution showed that the values in the central part of the study area were slightly higher than in the southern and northern parts; the highest SSC area was mainly related to the distribution of saltern; additionally, agricultural activities and microtopography contributed significantly to the distribution of SSC at the farm scale. The IRF model based on the SSC-responding bands and optimal SSC-related indices provides a strategy for supporting regional mapping of SSC, and will be useful for environmental policy-making. [ABSTRACT FROM AUTHOR]

Relationships between parent material and soil are not well understood and generally only reported in qualitative form. We present a classification of parent material for pedologic purposes, which includes twelve lithology classes based on mineralogical and chemical composition. The relationships of these lithology classes with six key soil properties (soil organic carbon, pH, cation exchange capacity, sum of bases, total P and clay %) were examined in a case study over New South Wales, Australia. We used multiple linear regression, Random Forest and Cubist tree models based on a soil dataset of over 3200 points. Semi-quantitative estimates are derived of change in these soil properties with a change in lithology class, and an associated silica index , for example, a 22% relative decrease in soil organic carbon with each 10% rise in silica, broadly equivalent to a change from shale to granite, assuming other factors remain constant. Parent material covariates are essential for the effective modelling and mapping of soil properties. Widely available lithology data have the potential for greater use in digital soil modelling and mapping (DSMM) programs. We compared the performance of the classified lithology data with other continuous, geophysical parent material covariates such as gamma radiometrics in digital soil models and maps over NSW. The lithology covariate was demonstrated to exert the greatest influence on all six soil properties, coming well ahead of all geophysical parent material and other environmental covariates. Validation statistics demonstrated strong improvement in both model and map quality when the lithology covariate was included. For example, Lin's concordance for the Cubist sum of bases model rose from 0.46 with no parent material covariates to 0.58 with the continuous geophysical covariates to a high of 0.77 when lithology was also used. The improvement was typically slightly less marked in the final digital maps than for the calibration models, probably due to the lower reliability of the lithology grid derived from broad scale polygonal geological and soil data. A process is suggested for the application of lithology data into DSMM programs. Despite the potential drawbacks of using polygonal data, properly organised categorical lithology data can be a strong covariate to complement other continuous geophysical data sources in DSMM programs, particularly where reliable and fine scale geological and soil data are available. [ABSTRACT FROM AUTHOR]

Conventional apparent electrical conductivity ( σ a ) inversion approaches are generally oriented towards a smooth soil profile with gradual transitions between large stratigraphic entities. In a sedimentary environment, layered models with abrupt boundaries between the different soil units represent the soil profile accurately. Therefore, a 1D σ a depth modeling procedure was developed where vertical constraints were subjected by cone penetration testing (CPT). We employed the σ a measurements of a multi-receiver EMI instrument to map the stratigraphy of a 53 ha estuarine polder area up to a depth of approximately 6.0 to 6.5 m. From a limited number of CPT observations, a 4-layered soil stratigraphy was deduced with a variable topsoil containing small-scale features above a layer of either sandy or clayey estuarine deposits, a peat layer of approximately 1 m thickness and a Quaternary coarse sand substrate. The detailed vertical observations from the CPT logs allowed to estimate the inversion parameters to perform a step-wise 1D inversion of the EMI σ a data, as to direct the inversion towards a realistic outcome. The depth of the interfaces between either the estuarine sediments and the peat layer and between the peat and the sand were estimated with an average error of approximately 0.5 m, hereby assuming that the thickness each layer is at least 1 m. Moreover, the variable conductivity of the top 2.0 m revealed a wide range of small and/or subtle features. To conclude, integrating the laterally dense σ a measurements with a limited amount of detailed vertical observations from CPT logs allowed for a pseudo 3D representation of the soil stratigraphy within a complex sedimentary environment. This information could serve as a proxy for a range of applications which necessitate detailed information about the subsoil. [ABSTRACT FROM AUTHOR]

The beginning of the 20th century through the 1970s were good times for soil science in the USA, with relatively strong funding and overall growth in the profession. However, the soil science discipline in the USA hit hard times in the 1980s and 1990s. Federal funding for soil survey work began to decline as did student numbers in university programs and membership in the Soil Science Society of America (SSSA). Despite this, there were still many positive advances within soil science in the USA during these two decades. There was an increased use of geophysical instrumentation, remote sensing, geographic information systems (GIS), and global positioning systems (GPS), and research began in digital soil mapping, all of which lead to better understanding of the spatial distribution and variability of soils. Many NRCS soil products were put online, making them widely available to the general public, the use of soil knowledge was expanded into new areas such as archaeology and environmental work, and historic connections to geology were re-established. While expansion into new areas required soil science to evolve as a field, separating the discipline to an extent from its agricultural roots, it also helped reinvigorate the discipline. As we move through the early parts of the 21st century, student numbers are increasing in university soil science programs and membership in SSSA is at an all-time high. Digital soil mapping is being incorporated into the National Cooperative Soil Survey, and the impact of humans on the soil system is being fully recognized. The importance of soils is being recognized by events such as the United Nations declaration of 2015 as the “International Year of Soils”. The expansion of soils into new areas and widening recognition of the importance of soils gives the field hope for a bright future in the USA. [ABSTRACT FROM AUTHOR]

Loess slides are widespread and dangerous gravity-induced surface processes on the Loess Plateau of north-central China. The local slope height and slope morphology have been considered as important conditioning factors for landslide susceptibility mapping. However, the quantitative relationship between landslide size and local slope height within different slope morphologies is not very clear. In this study, an inventory of 155 loess slides was developed based on detailed field investigation and remote sensing image interpretation. Statistical analysis and GIS were applied to find the quantitative relationships between the size of these loess slides and local slope height in different types of slope morphologies. The analysis shows that the relationship between the loess slides volume and area exhibits a similar trend within different types of slope morphologies. In addition, the analysis demonstrated that loess slides are more frequent on local slope height from 40 to 100 m. Stepped slopes with higher pore water pressure are prone to sliding, and approximate 34.19% of total loess slides occur in these stepped slopes. Moreover, the analysis shows that the mean loess slide area and volume rapidly increase with increasing local slope height. Furthermore, the result underlines the fact that both the local slope height and the slope morphology are two fundamental factors that control the occurrence as well as size distribution of loess slides. The loess slide size remarkably increases with increasing local slope height within every type of slope morphologies. These findings indicate that local slope height can limit the loess slide size and their spatial extent. Moreover, we quantified the correlation between the loess slide size and local slope height using power law form, and the scaling exponents of the correlations are different within different slope morphologies. [ABSTRACT FROM AUTHOR]

A study was carried out to test the accuracy and utility of field probes as proximal sensing tools for site-specific and regional soil mapping in the heavily urbanized terrain of Detroit, MI, USA. The results suggest that surface scanning in situ with a magnetic susceptibility (MS) field probe is accurate, but limited by tall grass. Better results were obtained after removal of the turf layer. The accuracy of the electrical conductivity (EC) field probe was questionable. Hence, superior results were obtained ex situ using both EC and MS lab sensors. Lab EC and MS were well suited for regional soil mapping, whereas all of the methods provided at least some useful information for site-specific applications. Tall grass, excessive artifact content, and severe compaction were the greatest limitations of proximal sensing. Tall grass interfered with surface scanners (MS; metal detector), whereas excessive artifacts and compaction restricted use of the EC surface probe. Penetrability and pH were useful for discriminating between natural and anthropogenic soils, and moisture content for proximal sensing of soil ^A horizons. MS was especially useful for delineating soils impacted by fly ash. Although it is less rapid and more labor-intensive than field methods, the lab-based approach still provides a minimally invasive alternative to augering. Lab-based geophysical surveying can be carried out first, and the results used as a guide for an auger-based soil survey. An anthropogenic map index (AMI) is introduced which combines pH, penetrability, EC and MS into a single overall measure of soil quality. AMI-based maps are postulated to be a useful as a geographic representation of soil health. These results support our hypothesis that proximal sensing methods can facilitate soil mapping in urbanized terrain based on the degree of human disturbance, and differences in artifact assemblages related to land use history. [ABSTRACT FROM AUTHOR]

Google Earth™ (GE) has been used in several geomorphological contexts, especially in the study of river networks, glacial, coastal, landslides and desert landforms. In some cases, quantification is possible and landform-process relations can be inferred. GE has been little used in studies of contemporary soil erosion. This brief case study illustrates the potential for quick, free and accurate surveys that are particularly valuable as an aid to field work. The main drawback of GE is the arbitrary timing of imagery, in this case with only 3 years of complete coverage of the study area between 2001 and 2015. [ABSTRACT FROM AUTHOR]

Information on the profile characteristics of soil–water content (SWC) and its temporal stability is essential for an accurate understanding of hydrological processes. This study investigated changes of spatial variation and temporal stability of SWC in soil profiles and estimated mean SWC based on direct and indirect methods. SWCs were measured at 20-cm intervals in the soil profiles to a depth of 3 m using neutron probes at 135 locations along a 1340-m long transect on 18 sampling dates from 2012 to 2013 on the Loess Plateau in China. The coefficient of variation over space (CV S ) of SWC first decreased and then increased vertically. The coefficient of variation over time (CV T ) of SWC decreased along the soil profiles. The spatial patterns of SWC strongly persisted vertically and temporally. Mean Spearman's rank correlation coefficients decreased from a depth of 10 to a depth of 20 cm, fluctuated from 20 to 180 cm, and then increased below 200 cm. Temporal stability increased along the soil profiles based on the standard deviation of mean relative difference (SDRD) and the mean absolute bias error (MABE). The number of locations with an SDRD and/or MABE < 5% increased along the soil profile, and the number of locations with a mean relative difference within ± 5% and/or representative locations were variably dependent on depth. Both direct and indirect methods could accurately estimate the mean SWC for each depth based on the mean absolute relative errors and root mean square errors. The driest and wettest locations tended to remain representative for more depths than did locations with mean-moistures. The driest locations were more likely to be the most temporally stable. These findings should improve our understanding of soil–water dynamics in soil profiles. [ABSTRACT FROM AUTHOR]

Soil water storage is a critical variable controlling hydrological and biological processes. The precise estimation of soil water storage in diverse soil layers is fundamental to understanding hydro-biological processes and efficiently managing water resources. The objectives of this study were to evaluate the effects of topography (elevation) and soil properties (clay, silt, sand content, median grain size, and fractal dimension) on soil water storage and then to estimate soil water storage using a state-space approach. The soil water storage values of three soil layers (0–1, 1–2, and 2–3 m) were measured from May to December 2014 at 70 locations along two 187 m long transects on a hillslope of the Loess Plateau, China. Samples from various depths were also collected to determine soil properties. The best state-space approach explained 98.8% of the total variation in soil water storage, while the best classical linear regression equation only explained 64.2%. The state-space approach using any combination of variables described the spatial pattern of soil water storage much better than equivalent linear regression equations. Elevation and clay content were identified as the most effective combination for soil water storage estimation in the state-space approach, and were used to effectively predict the soil water storage spatial pattern along the second transect. The state-space approach is thus a useful tool that is recommended for predicting soil water storage spatial patterns at the hillslope scale using topography and soil properties. [ABSTRACT FROM AUTHOR]

Cryogenic patterned ground represents spectacular periglacial landscapes. On the Alps, sorted/nonsorted patterned ground features larger than 1 m, formed by deep seasonal cryoturbation with or without permafrost, occupy exposed, stable surfaces at high altitudes and represent a particularly harsh habitat for plant life. We analyzed soils across transects through typical active patterned ground features (sorted/nonsorted circles and stripes) on four common lithotypes (calcschists, serpentinite, gabbros and gneiss) in the Western Italian Alps, in order to observe the small-scale lateral and depth variability in physico-chemical properties, and their association with cryoturbation, plant cover and species distribution. Cryoturbation was correlated with lateral/vertical textural sorting across features, mostly visible on silt and coarse sand, but with opposite trends on sorted and nonsorted patterned ground types. A strong lateral variability in organic carbon was detected, with high values near the better vegetated rims and low contents in the centers. Exchangeable bases, heavy metals and nutrients followed the same distribution. However, the differences inherited from the parent materials were overwhelming. Climate is the main driver of high altitude ecosystems, reducing total plant cover and causing cryoturbation, which in turn creates strong edaphic gradients over small distances. Plant species and communities are well correlated with edaphic properties inherited from the parent materials, such as exchangeable Ca and heavy metals. [ABSTRACT FROM AUTHOR]

[Display omitted] • Ensemble machine learning increseased accuracy of NPK balance models. • NPK stocks, soil erosion, MAP, DEM are very important predictors of NPK models. • The R2 of NPK balance predictions were 62%, 58%, and 58%, respectively. • Mean annual balance of NPK per hectare was −33.2 kg N, +2.70 kg P, and −74.6 kg K. Soil nutrient depletion is one of the major causes of high yield gaps and nutrient deficiencies in East Africa highlands, including Rwanda. This research sought to determine the current soil nutrient balance and its spatial variation in 10 Rwandan agro-ecological zones. Soil nitrogen (N), phosphorus (P) and potassium (K) depletion in croplands were calculated using data from 455 field trials of the Optimizing Fertilizer Recommendations in Africa (OFRA) project in Rwanda. Calculated soil nutrient balances (NPK) and 15 environmental covariates were used to calibrate soil nutrient depletion models using ensemble machine learning (EML) and 10-fold cross-validation. In the 2019–2020 growing season, annual N and K depletions were 33.6 kg N ha−1 yr−1 and 71.0 kg K ha−1 yr−1, with a positive P balance of 2.30 kg P ha−1 yr−1. High soil nutrient uptake and high soil nutrient loss due to erosion and leaching were two main causes of NPK depletion. Spatial variations of NPK balance were influenced by soil nutrient stocks, soil erosion, elevation, rainfall, soil texture, and soil bulk density. The 10-fold cross-validation showed that coefficients of determination (R2) of NPK models were 62%, 58%, and 58%, respectively. Compared to single models, ensemble machine learning improved NPK model accuracy up to 5%. Our research revealed that soil nutrient depletion was highest in the northwest and lowest in the southeast of the study area. We conclude that increasing soil nutrient inputs without reducing soil nutrient loss due to soil degradation will not decrease soil nutrient depletion in Rwanda and ensemble machine learning outperforms single models in predicting soil nutrient balance. The solution to reduce high soil nutrient depletion in all agro-ecological zones of Rwanda would be to prioritize soil and water conservation measures and increase soil nutrient inputs. [ABSTRACT FROM AUTHOR]

• Surface soil moisture was mapped by using remote sensing data through machine learning. • Stacking reduced overfitting and individual model biases. • Backscatter coefficient, modified-NDWI and LST were most significant covariates in mapping. • High and low irrigation zones were identified from soil moisture maps. Soil moisture information is key to irrigation water management, drought monitoring, and yield prediction. It plays a vital role in the water cycle and energy budget between the earth's surface and atmosphere. Hence, its monitoring is crucial for both natural and anthropogenic environments. While the current remote sensing-based global SM products available at coarser resolution (3/15 km) are unsuitable for field-level operations, the most widely used microwave remote sensing suffers from model complexities and in-situ data requirements. Weather conditions limit the alternate approaches such as optical/thermal. This study aims to map surface soil moisture (SSM) at 30 m spatial resolution in a semi-arid region by fusing optical, thermal, and microwave remote sensing data using bagging, boosting, and stacking machine learning approaches. The reference data were collected using a soil moisture meter. The covariates included radar backscatter from Sentinel-1, visible, near-infrared, short-wave infrared, land surface temperature, and spectral indices derived from Landsat 8. Boruta algorithm was used for feature selection which identified radar backscatter, modified normalized difference water index, and land surface temperature as the most critical covariates impacting the SSM. The random forest (RF) showed the highest correlation coefficient (r = 0.71), and least root mean square error (RMSE = 5.17%). The cubist model had the least mean bias error (MBE = 0.21%) during independent validation. Stacking of cubist, gradient boosting machine (GBM), and RF using elastic net (ELNET) as meta -learner further reduced the MBE (0.18%) and RMSE (5.03%) during the validation. Overall, stacking multiple machine learning models improved model prediction and can be recommended to improve the digital soil moisture mapping. [ABSTRACT FROM AUTHOR]

• A multi-objective spatial sampling layout optimization method was presented. • It sets one annealing path for each objective function to vote and anneal separately. • Can synchronously optimize objective functions of different types and magnitudes. • Can generate the best compromise solutions for objectives with equal importance. • The performance was proved in two soil sampling cases. Spatial sampling is important for soil surveys and mapping, and the optimization of the sampling design is a hot topic. Most often in soil sampling, multiple purposes are usually involved and corresponding objectives need to be optimized as much as possible. In such cases, balanced optimization is needed to produce the best compromised solutions that reach the maximum common interest, but not to generate a well-spread Pareto front. To solve this problem, the multiple path spatial simulated annealing (MP-SSA) was developed by extending the classic SSA. It can synchronously optimize multiobjective functions of different types and magnitudes by setting one annealing path for each objective, and designing a voting and annealing mechanism. To illustrate the difference and performance for MP-SSA, it was compared with the archived multiobjective simulated annealing (AMOSA) and Non-dominated Sorting Genetic Algorithm II (NSGA-II), both aiming at generating well-spread Pareto front, in two case studies with hypothetical data and actual soil heavy metal data. The results show that the MP-SSA is more efficient in generating the best compromised solutions, and is an efficient and promising tool for balanced multiobjective optimization for spatial sampling design when all objectives need to be optimized as much as possible. [ABSTRACT FROM AUTHOR]