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Bare soil mapping is inevitable for the monitoring of soil resources, as it acts as a major indicator of urban development. Although remote sensing based indices are largely used for the delineation of bare soil, their effectiveness has always been a challenge due to overlapping spectral characteristics of the bare soil and built up areas. Recently, built up indices have been employed to increase the accuracy of bare soil mapping, which can induce discrepancies due to lesser sensitivity in smaller patches of bare soil areas in high density urban regions. In this context, modified normalized difference bare soil index (MNDBSI) is developed using a logical combination of index with additional constraint for the precise delineation of bare soil area using Sentinel 2 images. The qualitative and quantitative analyses adopted to evaluate the performance of the MNDBSI revealed an overall agreement value of above 95% and the improvement percentage of MNDBSI over the other indices compared in terms of spectral discrimination index and transformed divergence varied from 8% to 166% and 15% to 97%, respectively, across all study areas. The comparative analyses of results indicate that MNDBSI is an effective alternative to the existing indices for precise bare soil mapping which can be used as a promising indicator of urban expansion. [ABSTRACT FROM AUTHOR]

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

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

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

The amount of high‐resolution agricultural data has increased rapidly in the current decade. Integration of satellite multispectral imagery, combine harvester yield monitoring data, and soil moisture mapping allows managing for within‐field variation and better interpreting on‐farm experimentation. In this study, we investigated the effect of cover crops on yield in Finland by integrating Sentinel‐2 satellite imagery (normalized difference vegetation index), topographic soil moisture indexes, and high‐resolution yield data. The experiment was run by three farmers over 4 years and serves as an example for low‐cost on‐farm experimentation. Our results confirmed earlier findings that undersown cover crops result in approximately 5% yield loss. We also found that the effect is highly variable across farms and within fields. The highest yield losses were found in areas of the field, which were wetter in the spring seeding time. The competition between crop and cover crop could be observed in the vegetation maps for autumn and early summer. Combining NDVI and soil moisture maps allows delineating field zones, which require extra management to reduce the risk of yield loss from cover crop resource competition. Evaluating the overall effect of cover crops on yield would require replication on more farms. The within‐field variation results and workflow investigated in this study can guide placement of sampling areas within those fields. Core Ideas: Cover crop effect on yield varies within fields.Multispectral imaging and topography can find areas where yield loss is likely.Managing for field variability is critical also for cover crops. [ABSTRACT FROM AUTHOR]

This article provides personal and professional assessments from disciples, colleagues and friends of Jaume Porta Casanellas (Barcelona, 1944; Lleida, 2023), a prominent soil scientist. He began his agricultural engineering studies at the Polytechnic University of Madrid (UPM) where he met Marta López-Acevedo, his wife and outstanding collaborator. At UPM, he started his early work in soil science under the guidance of Professor Carlos Roquero who became his mentor and friend. Jaume Porta was a dedicated, passionate soil scientist who engaged extensively in teaching and research in Soil Science, while also excelling as a manager. He emerged as a leader due to his initiatives in promoting Soil Science in Catalonia and Spain, and for his forward-thinking vision, evident in his decisions as Rector of the University of Lleida, which have significantly contributed to the city's development. From the beginning, he advocated for detailed (1:25,000) soil mapping of Catalonia to enhance territorial planning and agricultural progress. His primary research focus was on salt-affected soils and soils with gypsum, alongside soil erosion and conservation. Porta devoted a lot of effort to improve soil field descriptions with his Agenda de campo. He played a key role in standardizing soil analytical methods, establishing large series laboratories in Spain, notably the LAF in Sidamon (Lleida), and aligning Spanish soil labs with the international GLOSOLAN network. As president of the SECS, he energized activities and encouraged member participation. His educational publications, mainly his comprehensive textbook Edafología, are considered fundamental in Soil Science across Spanish-speaking countries, as is the Multilingual Dictionary of Soil Science, representing the pinnacle of his efforts to rigorously disseminate soil science concepts and terms in Spanish, Catalan, Galician, and Portuguese. He contributed significantly to international Soil Science courses in Mexico and played a key role in establishing the JADE postgraduate training program. He facilitated the creation and international visibility of the Spanish Journal of Soil Science. Additionally, he advocated for the establishment of the Soil Sciences Documentation Centre (Ce.SECS) to preserve historical publications and the legacy of soil scientists. Jaume Porta's enduring impact, both professionally and personally, will be felt for years to come. [ABSTRACT FROM AUTHOR]

A key challenge in helium (He) exploration is determining the efficacy of surficial soil gas surveys. While soil gas surveys can detect helium, the mechanisms leading to these signals are often poorly understood, hindering reliable interpretation for exploration purposes. Here we present the results of seven new He soil gas surveys (n = 1974) at the Akah Nez Field, Beautiful Mountain Field, Porcupine Dome area, Rattlesnake Field, Tom area, Tohache Wash area, and White Rock area, on the Colorado Plateau, Four Corners area, United States. Utilizing 2D seismic, well logs, and geophysical potential field data, structural maps were constructed of potential He reservoirs at depth and relationships were examined. Given geospatial relationships are being examined using the soil gas survey data, it is important to understand the mechanism that allows subsurface He to migrate upwards into the soil. In several fields interpreted basement faults act as migration conduits from the basement to the surface (i.e., leaky reservoir seals), and in other cases there is evidence for reservoir flank/crest fracturing likely due to differential compaction. Based on the regional geologic history, advective systems are likely responsible for the observed He soil gas signatures. Additionally, based on the Tohache Wash data (most prospective He area) an effective and risk-reducing novel technique is presented that constructs a predictive He exploration model utilizing soil gas geochemistry, high-resolution geophysical data, well data and seismic data using Bayesian ANOVA techniques, which may be translated to areas outside of the Four Corners area, United States. [ABSTRACT FROM AUTHOR]

Electromagnetic (EM) imaging aims to produce large-scale, high-resolution soil conductivity maps that provide essential information for Earth subsurface exploration. To rigorously generate EM subsurface models, one must address both the forward problem and the inverse problem. From these subsurface resistivity maps, also referred to as volumes of resistivity distribution, it is possible to extract useful information (lithology, temperature, porosity, permeability, among others) to improve our knowledge about geo-resources on which modern society depends (e.g., energy, groundwater, and raw materials, among others). However, this ability to detect electrical resistivity contrasts also makes EM imaging techniques sensitive to metallic structures whose EM footprint often exceeds their diminutive stature compared to surrounding materials. Depending on target applications, this behavior can be advantageous or disadvantageous. In this work, we review EM modeling and inverse solutions in the presence of metallic structures, emphasizing how these structures affect EM data acquisition and interpretation. By addressing the challenges posed by metallic structures, our aim is to enhance the accuracy and reliability of subsurface EM characterization, ultimately leading to improved management of geo-resources and environmental monitoring. Here, we consider the latter through the lens of a triple helix approach: physics behind metallic structures in EM modeling and imaging, development of computational tools (conventional strategies and artificial intelligence schemes), and configurations and applications. The literature review shows that, despite recent scientific advancements, EM imaging techniques are still being developed, as are software-based data processing and interpretation tools. Such progress must address geological complexities and metallic casing measurements integrity in increasing detail setups. We hope this review will provide inspiration for researchers to study the fascinating EM problem, as well as establishing a robust technological ecosystem to those interested in studying EM fields affected by metallic artifacts. [ABSTRACT FROM AUTHOR]

Currently, the Internet of Things (IoT) is in a premature phase. Although it is growing at a steady pace, there is still a need for further research in the field of security. In this work, the Fujian Province was selected as the study area. The climate, parent material and topographic information of the area were obtained, and the soil-landscape quantitative model was used to quantitatively obtain the relationship between the attributes of coastal sand and gravel soil. On the basis of soil type map, according to the difference of soil type elevation distribution, further predict the soil type distribution and make a map. The results show that the method can achieve more than 80% coincidence with the survey results on the scale of soil digital mapping and can make up for the missing areas of the survey. [ABSTRACT FROM AUTHOR]

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

ONE OF EGYPT'S top targets for future agricultural development is the Northern El Bahrya Oases region. The current study aims at digitally mapping the soils of the area under consideration and producing land capability and suitability digital maps. The considered region lies between longitudes 29° 9' 28.433'', 29° 22' 32.785'' East and latitudes 28° 57' 38.309'', 28° 58' 41.324" N. The region is covering roughly 46228 Fed. The Digital Elevation Model (DEM) is utilized along with the thematic maps of soil properties to produce digital land capability and suitability maps. The spatial analysis tool in the Arc-GIS 10.8 software generated thematic layers. Physiographic mapping units were created and showed that the studied area has one landform which is plateau. The land capability was evaluated by the method of Storie index where the studied area was classified into three capability grades, namely; grade 2, 3 and 4. The soils of grade 2 (66.41 % of the whole region) have moderate limitations for agricultural crops and texture is the main limiting factor. The area of grade 3 (25.59%), has soils with texture and salinity limiting factors, while the soils of grade 4 (7.99%), have texture, depth and salinity main limiting factors. Land suitability was done according to Sys and others for some crops and transferred to GIS to produce digital maps. The fertility maps of soil nutrients for N, P, K, Fe, Mn, Zn, and Cu are included. The study established a detailed database to serve decision-makers and help in sustainable development. [ABSTRACT FROM AUTHOR]

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

Soil moisture (SM) is an essential climate variable, governing land‐atmosphere interactions, runoff generation, and vegetation growth and productivity. Timely forecasts of SM spatial distribution and vertical profiles are needed for early detection and prediction of potential droughts. However, previous studies have primarily concentrated on historical or near real‐time soil moisture mapping, with less effort devoted to the development and integration of soil moisture forecast components within drought assessment systems. A satellite‐driven machine‐learning approach was developed in this study to build complex relationships between diversified predictor data sets and in situ multi‐layer SM measurements from the Montana Mesonet, a regionally dense environmental station network in the US upper Missouri and Columbia basins. The resulting 30‐m daily SM predictions showed strong performance against in situ SM measurements from 4‐, 8‐ and 20‐inch soil layers, and with 1‐ to 2‐week forecast lead times (R > 0.91; RMSE ≤ 0.047 cm3/cm3). The machine‐learning model was subsequently applied to the entire Montana region, and the SM deficit forecasts with both 1‐ and 2‐week lead times successfully depicted onset, progression, and termination phases of the 2017 Montana flash drought, which was not effectively identified from prevailing operational systems. The resulting system is capable of delineating local scale SM heterogeneity, and could be extended to predict other critical water cycle variables, potentially enhancing future drought forecasts through multivariate assessments and benefiting water resource management, agricultural practices, and the provision of ecosystem services. Plain Language Summary: Flash drought events are characterized by their rapid onset and fast development. Despite the substantial agricultural and economic losses that flash droughts can cause, operational forecasts of these types of events are not currently available. This study developed a machine‐learning approach linking satellite and geo‐spatial data sets with in situ observations for forecasting soil moisture at different depths with 1‐ and 2‐week lead times. The relative shortage of soil moisture was further estimated and used to pre‐assess drought severity. The resulting soil moisture deficit maps successfully depicted various phases of the 2017 Montana flash drought, which were not effectively identified from prevailing operational systems. The method may be extended to predict other critical variables related to drought, potentially enhancing future drought forecasts, improving drought mitigation and preparedness efforts, and benefiting water resource management, agricultural practices, and the provision of ecosystem services. Key Points: A satellite‐driven machine‐learning approach was developed for multi‐layer soil moisture forecasts with 1‐ and 2‐week lead timesThe soil moisture deficit forecasts successfully depicted the onset, progression, and termination of the 2017 Montana flash droughtThe method potentially enhances future drought forecasts and improves drought mitigation and preparedness efforts [ABSTRACT FROM AUTHOR]

This study assesses the relative performance of Sentinel-1 and -2 and their combination with topographic information for plow agricultural land soil salinity mapping. A learning database made of 255 soil samples' electrical conductivity (EC) along with corresponding radar (R), optical (O), and topographic (T) information derived from Sentinel-2 (S2), Sentinel-1 (S1), and the SRTM digital elevation model, respectively, was used to train four machine learning models (Decision tree—DT, Random Forest—RF, Gradient Boosting—GB, Extreme Gradient Boosting—XGB). Each model was separately trained/validated for four scenarios based on four combinations of R, O, and T (R, O, R+O, R+O+T), with and without feature selection. The Recursive Feature Elimination with k-fold cross validation (RFEcv 10-fold) and the Variance Inflation Factor (VIF) were used for the feature selection process to minimize multicollinearity by selecting the most relevant features. The most reliable salinity estimates are obtained for the R+O+T scenario, considering the feature selection process, with R2 of 0.73, 0.74, 0.75, and 0.76 for DT, GB, RF, and XGB, respectively. Conversely, models based on R information led to unreliable soil salinity estimates due to the saturation of the C-band signal in plowed lands. [ABSTRACT FROM AUTHOR]

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

Relief, climate and soils in the lands of the villages of Edrevo and Panicherevo, Stara Zagora region, Bulgaria were examined. A geographic information system (GIS), including the collected information, and spatial visualization of soil data and main soil characteristics was carried out in the form of a soil map and thematic maps of soil texture (physical clay content in %), humus content (%) and soil acidity (pH). A greater part of the agricultural land is occupied by Cinnamon forest soils, a total of 1720.56 ha, of which 1012.43 ha are strongly leached to slightly podzolized with varying degrees of erosion and 708.12 ha are undeveloped and shallow soils. The rest of the agricultural lands are from Alluvial (Deluvial) - meadow soils - 930.29 ha. The detailed description of the soil-climatic conditions was applied in determining the credit rating and category of the lands in the investigated lands (part II). [ABSTRACT FROM AUTHOR]

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

Yingfeng Area is located within Shiquan Xunyang National-level Gold Deposit Exploration Area in Shaanxi Province, characterized by favorable metallogenic geological conditions and promising prospecting potential. A 1:25 000 soil geochemical survey was conducted, analyzing the variation coefficients, correlations, and cluster analysis of 10 elements. The results indicate that the distribution of Au is uneven, with a high degree of dispersion, identifying it as the primary ore-forming element. By studying Au anomalies and comprehensive anomalies in conjunction with metallogenic geological conditions and mineralization characteristics, 5 gold alteration zones were delineated. Verification through geochemical profiles, trench exploration, and drilling projects resulted in the delineation of 17 gold ore (mineralized) bodies, further confirming the area's promising prospecting potential. [ABSTRACT FROM AUTHOR]

Gelisols (permafrost-affected soils in US Soil Taxonomy) are extensive in Alaska, currently occurring on ∼45% of the land area of the state. Gelisol taxonomic criteria rely on the presence of near-surface (less than 2 m deep) permafrost, but ongoing climatic and environmental change has the potential to affect the presence of near-surface permafrost across much of Alaska throughout the 21st century. In this study, we utilized scenarios of near-surface permafrost loss and active layer deepening through the 21st century under low (SRES B1, RCP 4.5), mid- (SRES A1B), and high (SRES A2, RCP 8.5) emissions scenarios, in conjunction with the statewide STATSGO soil map, to generate spatially explicit predictions of the susceptibility of Gelisols and Gelisol suborders to taxonomic change in Alaska. We find that 15%– 53% of Alaskan Gelisols are susceptible to taxonomic change by mid-century and that 41%–69% of Alaskan Gelisols are susceptible to taxonomic change by the end of the century. The extent of potential change varies between suborders and geographic regions, with Gelisols in Northern Alaska being the most resilient to taxonomic change and Western and Interior Alaskan Gelisols most susceptible to taxonomic change. The Orthel suborder is likely to be highly restricted by the late 21st century, while Histels and Tubels are more likely to be of greater extent. These results should be taken into consideration when designing initial survey and re-mapping efforts in Alaska and suggest that Alaskan Gelisol taxa should be considered threatened soil taxa due to the proportional extent of likely loss. [ABSTRACT FROM AUTHOR]

Local terrain or microsite conditions influence the development of trees, particularly at early ages. These conditions might be described by edaphic or topographic variables. We mapped soil and topographic variables from four even-aged and even-spaced cork oak plantations located in two climatically distinct Portuguese regions. The major goal of this research was to understand the relation between soil and topographic fine-scale conditions and tree growth expressed by diameter without cork annual growth (idu). The methodology consisted in (1) analysing the spatial variability and autocorrelation of idu; (2) modelling idu with ordinary least squares (OLS) regressions; (3) comparing with spatial modelling of idu, incorporating spatial autocorrelation. The driest stands A and B, exhibited weaker spatial autocorrelation, distributed in smaller clusters (R2 < 0.03, OLS models), while stands C (R2 = 0.18, OLS models) and D (R2 = 0.11, OLS models) showed higher predictive capacity. Spatial models increased R2 scores, keeping most variables from OLS models and accounting for spatial autocorrelation. A + B + C + D OLS model obtained an R2 = 0.34 and respective spatial model R2 = 0.58. Apparent electrical conductivity at 0.5 (ECa0.5) and 1 m of soil depth, slope, elevation and topography position index were included as predictors (OLS), but only ECa0.5, slope and elevation were selected in the spatial model. Models were fitted using average to high productivity stands and should be used cautiously outside this range. Local terrain conditions determine the growth of young cork oak trees. Mapping soil and topographic variables before establishing new plantations may identify limiting microsite conditions where using cork oak species is not suitable due to low growth rates expectations. [ABSTRACT FROM AUTHOR]

Soil erosion poses a severe threat to water quality and soil health in semi-arid regions. Assessing soil erosion and sedimentation at the watershed-scale is crucial for managing water resources and soil. In Iran, empirical models, e.g., the erosion potential method (EPM), modified Pacific Southwest Inter-Agency Committee (MPSIAC), and universal soil loss equation (USLE), have drawn more attention than other models, due to the availability of input data and lack of reliable hydrometric station, especially for a long time span. In the present article, we evaluated the soil erosion intensity and sediment yield in the Chehelgazi mountainous watershed (western Iran) utilizing the EPM, MPSIAC, and USLE, combined with the sediment delivery ratio (SDR) module, and then compared them with the observations. The required input data for EPM, MPSIAC, and USLE models all were created in ArcMap 10.8 and ENVI 5.5. The sediment yield amount and soil erosion zonation map were then obtained by the models mentioned and validated by the hydrometric data. According to the results, most portions of the watershed were subject to moderate erosion risk. Moreover, the annual average sediment yield of 196.86, 99.30, and 84.84 kt year−1 assessed by EPM, MPSIAC, and USLE combined with SDR, respectively, suggested the superiority of USLE-SDR and MPSIAC. As compared with the observed amounts of 86.46 kt year −1, the efficiency of USLE-SDR was astonishing. Overall, we conclude that the USLE model coupled with the Boyce (1975) SDR formula is the best for estimating sediment yield at Chehelgazi mountainous watersheds, while MPSIAC is better suited for mapping soil erosion state. [ABSTRACT FROM AUTHOR]

Mapping of soil nutrient parameters using experimental measurements and geostatistical approaches to assist site-specific fertiliser advisories is anticipated to play a significant role in Smart Agriculture. FarmerZone is a cloud service envisioned by the Department of Biotechnology, Government of India, to provide advisories to assist smallholder farmers in India in enhancing their overall farm production. As a part of the project, we evaluated the soil spatial variability of three potato agroecological zones in India and provided soil health cards along with field-specific fertiliser recommendations for potato cultivation to farmers. Specifically, 705 surface samples were collected from three representative potato-growing districts of Indian states (Meerut, UP; Jalandhar, Punjab and Lahaul and Spiti, HP) and analysed for soil parameters such as organic carbon, macronutrients (NPK), micronutrients (Zn, Fe, Mn, and Cu), pH, and EC. The soil parameters were integrated into a geodatabase and subjected to kriging interpolation to create spatial soil maps of the targeted potato agroecological zones through best-fit experimental semivariograms. The spatial distribution showed a deficiency of soil organic carbon in two studied zones and available nitrogen among all studied zones. The available phosphorus and potassium varied among the agroecological zones. The micronutrient levels were largely sufficient in all the zones except at a few specific sites where nutrient advisories are recommended to replenish. The general management strategies were recommended based on the nutrient status in the studied area. This study clearly supports the significance of site-specific soil analytics and interpolated spatial soil mapping over any targeted agroecological zones as a promising strategy to deliver reliable advisories of fertiliser recommendations for smart farming. [ABSTRACT FROM AUTHOR]

Arsenic (As) pollution in soil poses a significant risk to human health and sustainable development goals. Accurate measurement of the degree of As contamination and mapping in soil are important for identifying high-risk areas and developing remediation strategies. In this study, 201 soil samples (0–15 cm) were collected from As contaminated rice growing regions in Nadia, West Bengal, India. Various pollution indices (geoaccumulation index, Igeo; contamination factor, CF; ecological risk index, Eri) were computed to understand the contamination level and its health implications. Geostatistical analysis using ArcGIS mapped the spatial distribution of As contamination in soil. The accuracy of different interpolation techniques were evaluated using root mean square error (RMSE) and mean CV (cross-validation) parameters. Results showed that total As content in soil ranged from 3.47 to 53.1 mg kg‒1 (mean-19.3 mg kg‒1). The pollution indices such as Igeo, CF, and Eri varied from ‒0.22 to 3.72 (mean-2.11 mg kg‒1), 0.23 to 3.54 (mean-1.29 mg kg‒1), and 2.32 to 35.4 (mean-12.9 mg kg‒1), respectively. The northern side of Nadia showed higher As contamination, while comparatively lower levels were observed in southern side. Two interpolation techniques, viz. ordinary kriging (OK) and inverse distance weighting (IDW) were identified to precisely predict the spatial distribution of As in soil based on RMSE and mean CV values. Health risk assessment showed that children were more vulnerable to As toxicity in the study region. The findings support the development of location-specific remediation strategies and policies to minimize health risks associated with As-contaminated soils. [ABSTRACT FROM AUTHOR]

Global Hydrological and Land Surface Models (GHM/LSMs) embody numerous interacting predictors and equations, complicating the understanding of primary hydrological relationships. We propose a model diagnostic approach based on Random Forest (RF) feature importance to detect the input variables that most influence simulated hydrological fluxes. We analyzed the JULES, ORCHIDEE, HTESSEL, SURFEX, and PCR‐GLOBWB models for the relative importance of precipitation, climate, soil, land cover and topographic slope as predictors of simulated average evaporation, runoff, and surface and subsurface runoff. RF models functioned as a metamodel and could reproduce GHM/LSMs outputs with a coefficient of determination (R2) over 0.85 in all cases and often considerably better. The GHM/LSMs agreed that precipitation, climate and land cover share equal importance for evaporation prediction, and mean precipitation is the most important predictor of runoff, while topographic slope and soil texture have no influence on the total variance of the water balance. However, the GHM/LSMs disagreed on which features determine surface and subsurface runoff processes, especially with regard to the relative importance of soil texture and topographic slope. Finally, the selection of soil maps was only important for target variables of which soil is a relevant predictor. We conclude that estimating feature importance is a useful diagnostic approach for model intercomparison projects. Plain Language Summary: Simulations of hydrological fluxes such as evaporation and runoff at a global scale are uncertain. This happens because the models that produce global simulations are different in terms of structure, parametrization and meteorological data. So, several model intercomparison projects (MIP) have tried to identify where the hydrological fluxes estimates are most discrepant. In order to make MIPs even more useful, we are proposing an additional method focusing on understanding why the models disagree. This method consists of replacing the original global model with a random forest model and then identifying which input variables are more relevant using the feature importance functionality. More specifically, we detected how important meteorological variables, soil properties, land cover and topography are for each global model. We observed that the models agree that precipitation, climate and land cover are equally important for evaporation and that precipitation is the most important feature for estimating runoff. When partitioning runoff into quick and slow flow, we observed that the models disagree on the importance of features, especially topographic slope and soil. Key Points: Detecting the predictors importance can be an additional approach for Model Intercomparison ProjectsGlobal models agree about the features importance for water balance components but disagree for surface and subsurface runoffSelecting the soil database only matters when soil is a relevant predictor, which is not the case for all models and target variables [ABSTRACT FROM AUTHOR]

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

The spatial prediction of soil CO2 flux is of great significance for assessing regional climate change and high-quality agricultural development. Using a single satellite to predict soil CO2 flux is limited by climatic conditions and land cover, resulting in low prediction accuracy. To this end, this study proposed a strategy of multi-source spectral satellite coordination and selected seven optical satellite remote sensing data sources (i.e., GF1-WFV, GF6-WFV, GF4-PMI, CB04-MUX, HJ2A-CCD, Sentinel 2-L2A, and Landsat 8-OLI) to extract auxiliary variables (i.e., vegetation indices and soil texture features). We developed a tree-structured Parzen estimator (TPE)-optimized extreme gradient boosting (XGBoost) model for the prediction and spatial mapping of soil CO2 flux. SHapley additive explanation (SHAP) was used to analyze the driving effects of auxiliary variables on soil CO2 flux. A scatter matrix correlation analysis showed that the distributions of auxiliary variables and soil CO2 flux were skewed, and the linear correlations between them (r < 0.2) were generally weak. Compared with single-satellite variables, the TPE-XGBoost model based on multiple-satellite variables significantly improved the prediction accuracy (RMSE = 3.23 kg C ha−1 d−1, R2 = 0.73), showing a stronger fitting ability for the spatial variability of soil CO2 flux. The spatial mapping results of soil CO2 flux based on the TPE-XGBoost model revealed that the high-flux areas were mainly concentrated in eastern and northern farmlands. The SHAP analysis revealed that PC2 and the TCARI of Sentinel 2-L2A and the TVI of HJ2A-CCD had significant positive driving effects on the prediction accuracy of soil CO2 flux. The above results indicate that the integration of multiple-satellite data can enhance the reliability and accuracy of spatial predictions of soil CO2 flux, thereby supporting regional agricultural sustainable development and climate change response strategies. [ABSTRACT FROM AUTHOR]

Novel soil datasets and the application of pedotransfer functions provide soil hydraulic input data for modelling hydrological processes at different scales. We aimed to evaluate the reliability of soil hydraulic parameters derived by indirect methods in simulation of soil moisture time series and water budgets at profile level of three sites (Forest, Orchard and Grassland) from a Central European catchment (Lake Balaton, Hungary). Five soil-vegetation-atmosphere model variants were set up with the Hydrus-1D model for each site, differing only in the parametrization of input soil data: i) a calibrated reference, ii) measured values, iii) values predicted from measured basic soil properties, iv) values predicted from national soil map information, v) values derived from the 3D soil hydraulic dataset of Europe. Calibrated soil parameters led to Nash-Sutcliffe efficiency 0.50, 0.54 and 0.71 for the Forest, Orchard and Grassland Site respectively. The outcomes for model efficiency of soil moisture underline the superiority of local databases over regional ones and the need for more detailed vertical discretization during modelling. The model performance according to soil moisture and water budget accuracy led to different rank order of model variants. Water budget comparisons indicated moderate differences between the hydrologic fluxes simulated by the different model variants, emphasizing the uncertainties associated with soil hydraulic parametrization either at local or at watershed scale. [ABSTRACT FROM AUTHOR]

Bare soil mapping is inevitable for the monitoring of soil resources as it acts as a major indicator of urban development. Although remote sensing based indices are largely used for the delineation of bare soil, their effectiveness has always been a challenge due to overlapping spectral characteristics of the bare soil and built up areas. Recently, built up indices have been employed to increase the accuracy of bare soil mapping, which can induce discrepancies due to lesser sensitivity in smaller patches of bare soil areas in high density urban regions. In this context, Modified Normalized Difference Bare Soil Index (MNDBSI) is developed using a logical combination of index with additional constraint for the precise delineation of bare soil area using Sentinel 2 images. Qualitative and quantitative analyses adopted to evaluate the performance of the MNDBSI revealed an overall agreement value of above 95% and the improvement percentage of MNDBSI over the other indices compared in terms of Spectral Discrimination Index and Transformed Divergence varied from 8% to 166% and 15% to 97%, respectively across all study areas. The comparative analyses of results indicate that MNDBSI is an effective alternative to the existing indices for precise bare soil mapping which can be used as a promising indicator of urban expansion.

This article reviews in depth life, works and spirit of the eminent Greek agriculturist Ioannis Papadakis. Primarily, it shows the way Papadakis, working mainly as a plant breeder from 1923, tackled the major problem of Greek agricultural history, namely the accomplishment of Greece’s wheat self-sufficiency, by inventing new, high-yielding, varieties of great adaptability to the adverse soil and climatic conditions of the country. The solution to this problem is regarded as an outstanding achievement of agricultural research and policy in Greece. However, today, judging in the comfort of time-distance from the events of the Mid-war period and in an effort to interpret the outcome of this exercise and its implications on the development of Greece’s agricultural economy, one could say that the pursued single-cultivation of wheat with a simultaneous decrease in the area devoted to growing of certain crops destined for forage or other animal feed resources, this had a negative effect as regards the imbalanced evolution of the various branches of agricultural production in favor of wheat. Therefore, this partial approach brought about a dramatic delay in the development of the animal production sector, contributing to the huge exchange deficit for importing animal products in contemporary Greece. Nevertheless, Ioannis Papadakis had received the mandate by the Hellenic State for achieving the country’s wheat self-sufficiency and in this respect was successful and worked for it impeccably. Furthermore, in 1947, after having been invited by Argentina to construct its ecological map, he emigrated permanently to that country. Papadakis had published (1938) in French the first of its kind book worldwide “Agricultural Ecology”. He was also invited to write the entry “Soils” in Encyclopaedia Britannica. He worked for Food and Agriculture Organization of the United Nations and studied agricultural questions in South America, West Africa, South-East Asia and elsewhere. He wrote research and review papers on several aspects of agriculture, including field crops, plant breeding, biometry, soil science, agricultural ecology as well as rural economy and policy. This article concludes with some of Papadakis’ thoughts, stemming from his own wisdom of experience, accumulated from his long service in Greece, Argentina and elsewhere in the world.

The precise evaluation of subsurface soil information is paramount for effective infrastructure design and planning. Geotechnical soil maps (GSMs) play a pivotal role in estimating subsurface properties. As Kabul City, Afghanistan's largest metropolitan area, undergoes rapid expansion and the demand for safer infrastructure rises, the necessity for precise geotechnical information becomes increasingly urgent. Nevertheless, there is a lack of comprehensive studies on urban geotechnical zoning maps in Afghanistan. Therefore, to this end, this study utilizes inverse distance weighting (IDW) interpolation approaches for the construction and characterization of GSMs using a traditional geographic information system (GIS). Data about soil types and standard penetration (N‐value) up to 30 m depth were extracted from 130 locations in New Kabul City, Afghanistan. GSMs incorporating N‐value and soil types were constructed utilizing the IDW technique within the ArcGIS platform. The outcomes reveal that the subjected region was dominated by cohesive soil with N‐values varying from 7 to 45 in three different sections. The findings exhibit strong empirical correlations between N‐values and depth, with an R2 value of 0.95 and an RMSD value of 0.71. Moreover, the correlation coefficient to predict soil type classification is 97%, and it stands at 95% for N‐value prediction. These results facilitate the rapid evaluation of subsoil strength and stiffness, offering valuable insights for project planners and feasibility researchers. [ABSTRACT FROM AUTHOR]

Soil electrical conductivity has been extensively investigated in the fields of precision agriculture, geophysical mapping, and soil property mapping. However, the understanding of soil electro-osmotic conductivity is currently limited. Comprehensive insight into soil electro-osmotic conductivity is crucial for an accurately designing the electro-osmosis technique, which is widely accepted as a highly promising method for disposing soft soils with high water content. To fill this knowledge gap, a series of laboratory experiments was conducted using Hangzhou sludge. Four factors, including electrode material, potential gradient, soil water content, and pore water salinity, were investigated in terms of their influence on electrical conductivity of the electro-osmotically treated soil. Results show that during the electro-osmosis process, the soil electro-osmotic conductivity initially slightly increases and then decreases. The impact of electrode materials on soil electro-osmotic conductivity was minimal, whereas a high potential gradient was observed to generate low soil electro-osmotic conductivity. Furthermore, this conductivity was found to be linearly correlated with real-time water content. The slope of the correlation was also significantly influenced by the initial water content of the soil. Under different levels of pore water salinity, the soil electro-osmotic conductivity first increases to a peak and then decreases to a residual value. The soil electro-osmotic conductivity are observed to have a linear relationship with pore water salinity. These implications are closely linked to the soil conduction mechanism. Based on the foregoing findings, limiting the calculations to the pore water-induced conduction during the main stages of the electro-osmosis process is recommended. Furthermore, linear predictive relationships between soil electro-osmotic conductivity and the corresponding level of soil water content as well as between the soil electro-osmotic conductivity and the pore water salinity should be used in simplified calculation of the electro-osmosis process. The parameters involved in these relationships can be accurately determined through laboratory experiments. [ABSTRACT FROM AUTHOR]

Around 70% of surface in Extremadura, Spain, faces a critical risk of degradation processes, highlighting the necessity for regional-scale soil property mapping to monitor degradation trends. This study aimed to generate the most reliable soil property maps, employing the most accurate methods for each case. To achieve this, six different machine learning (ML) techniques were tested to map nine soil properties across three depth intervals (0–5, 5–10 and > 10 cm). Additionally, 22 environmental covariates were utilized as inputs for model performance. Results revealed that the Random Forest (RF) model exhibited the highest precision, followed by Cubist, while Support Vector Machine showed effectiveness with limited data availability. Moreover, the study highlighted the influence of sample size on model performance. Concerning environmental covariates, vegetation indices along with selected topographic indices proved optimal for explaining the spatial distribution of soil physical properties, whereas climatic variables emerged as crucial for mapping the spatial distribution of chemical properties and key nutrients at a regional scale. Despite providing an initial insight into the regional soil property distribution using ML, future work is warranted to ensure a robust, up-to-date, and equitable database for accurate monitoring of soil degradation processes arising from various land uses.Highlights: Overall, the Random Forest algorithm was the most accurate in mapping soil properties in Extremadura. Chemical properties and key nutrients exhibit more variability than soil physical properties. The number of soil samples determines the performance of the methods used for soil property mapping. Vegetation indices and topographic attributes emerge as the most relevant variables for mapping soil physical properties. Climatic variables are more important in mapping chemical properties and key soil nutrients. [ABSTRACT FROM AUTHOR]

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

Methods for soil water conditions assessment are often highly localized or data demanding. In this study we propose a new scalable approach to assess soil water conditions. The main goal is to test whether the approach can be used to provide information about local conditions, without the need of extensive data sets. The approach utilizes a combination of normalized topography derived from the HAND terrain model (Height Above the Nearest Drainage) and hypsometric curves to identify wet and saturated areas for any given geographical extent. The study was conducted through a case study in the Lagan River catchment in the southwest of Sweden. To analyze the performance of the approach, a non‐linear regression analysis was performed to assess the relationship between the fraction of wet area and the normalized terrain. This was followed by a correlation analysis, in which the correspondence of the derived output was validated against the national Soil Moisture Map provided by the Swedish University of Agricultural Sciences. The results show a strong, and statistically significant, negative exponential relationship between the fraction of wet area, and the maximum heights within the studied area. The approach also corresponds well with the spatial variations highlighted in the Soil Moisture Map, although better in predicted wetter areas than under dry conditions. Going forward, we believe the integration of hypsometric curves and the HAND model could not only improve water balance calculations but assist in the assessments of flood and drought‐prone areas. Key Points: Hypsometric curves in combination with normalized terrain data can be used to assess soil moisture with limited data requirementsThe inclusion of flexible and scalable parameters facilitates local adaptation and allow a broad application of the proposed approachThe proposed approach could prove useful for water balance calculations, flood management and agriculture [ABSTRACT FROM AUTHOR]

Introduction: Modeling and mapping of soil organic carbon concentration and distribution at the pedon scale is a current knowledge gap that can be addressed by laboratory-based hyperspectral imaging and chemometric analysis of soil cores. Despite the advancements in soil organic carbon models based on hyperspectral images, it is not clear how these models will perform upon input with images at higher resolutions than those of their training sets. This study aims to measure the generalizability of a soil organic carbon model based on a test set with higher resolution hyperspectral images than that of its training set. Methods: Organic carbon contents were measured at 10 cm intervals on eight soil cores for use as the training set and at 1 cm intervals on a single core for use as the test set. Three regression models, namely, multilayer perceptron, partial least-squares, and support vector regressions, were trained and tested with the median of each hyperspectral image for each of these intervals as the training and test predictors. Permutation importance analysis was performed to explain the models. Results: The results show that although all three models had the same validation R2 of 0.92 for cross-validation on the 10 cm data, multilayer perceptron regression allowed the best generalization with a test R2 of 0.96 compared to the partial least-squares regression (0.81) and support vector regression (0.86). It was demonstrated that the multilayer perceptron model is more robust to soil surface anomalies and that it predicts soil organic carbon on the test set by learning the spectral features related to soil organic matter chromophore activity in the 950--1,150 nm region along with clay mineralogy derived from peaks at 1,400, 1,900, 2,200, 2,250, and 2,350 nm. Conclusions: This study shows that while the regression models based on hyperspectral images perform well at the 10-cm-resolution cross validation, multilayer perceptron regression shows superior generalization and robustness for a higher 1-cm-resolution test set without much loss of prediction power. [ABSTRACT FROM AUTHOR]

Phosphorus (P) is found in the soil in organic (Po) and inorganic (Pi) forms and in compartments with different lability, normally being separated into labile, moderately labile, restricted lability and non-labile forms. The different sources of fertilizers used in the long term may change the form and availability of P over time, increasing or not its availability to plants, which can positively influence coffee productivity. This work aimed to evaluate in the field the effects of phosphate fertilizers with associated technology on the availability of P in soil with high P adsorption capacity after successive fertilizations in Coffea canephora Pierre ex A.Froehner crop. In the experiment, three phosphate sources (conventional MAP - MAP, polymer-coated MAP - MAPPOL and MAP + filter cake - MAPTF); two doses (100 and 150 % of the recommendation); and two nitrogen sources (conventional urea - UC and urea coated with polymer and sulfur - UPS) were used. After 3 years of field experimentation, the soil was collected at a depth of 0-10 cm and then, the labile, moderately labile, and restricted lability fractions were determined. Phosphate fertilizers with associated technology obtained better results in relation to MAP in terms of Pi and Po contents for the labile compartment over time, with an average increase of 66.02 mg dm-3 P in the soil. For the moderately labile fraction, fertilizers with associated technology increased the Pi content (HCl 1M) in the soil in relation to MAP, by 2.56 mg dm-3, representing an increase of 37.92%. These compartments are considered an important reserve of available P for crops grown in soils with high adsorption capacity. Phosphate fertilizers with integrated protection (MAPPOL + MAPTF) compared to MAP increased productivity by 6.79 and 11.62 % in the 2017/2018 and 2018/2019 harvests, respectively. Phosphate fertilizers with associated technology promote P dynamics in different soil compartments, increasing Coffea canephora Pierre ex A. Froehner productivity and sustainability [ABSTRACT FROM AUTHOR]

Introduction: Fast and accurate estimation and spatial mapping of soil total nitrogen (TN) content is important for the development of modern precision agriculture, such as soil fertility monitoring and land reclamation decision-making. Hyperspectral remote sensing has been demonstrated to be an accurate real-time technique for rapid estimation and mapping of soil TN content. Methods: To solve the problem of poor accuracy and generalization of estimation models caused by soil environmental heterogeneity in estimating and mapping soil TN content using hyperspectral images, 502 soil samples were collected from a typical black soil area in Yushu City, Jilin Province, China, as a test area, and three sample grouping strategies were established by soil environmental variables (soil type, thickness of the black soil layer, and topographic factors), and Pearson correlation coefficient and competitive adaptive reweighted sampling algorithm were used to determine the TN characteristic bands of each sample set under different strategies. Based on the data characteristics of the sub-sample set, the local regression estimation model based on sample grouping was constructed using the CatBoost algorithm, and the estimation and distribution mapping of soil TN content was carried out. Results and Discussion: The results showed that after dividing the samples according to the differences in soil environmental factors, the characteristic information of the samples is more targeted, with more abundant numbers and distribution ranges of TN characteristic bands. Compared to the global regression estimation with all samples, the local regression based on the grouping of soil environment differences showed improved accuracy, with the local regression estimation model constructed with the ST-G strategy exhibiting the highest estimation accuracy (R²p = 0.839). The results can provide a reference for large-area soil properties mapping, and technical support for soil quality digitization and precision fertilization. [ABSTRACT FROM AUTHOR]

Peat makes up approximately a quarter of Scotland's soil by area. Healthy, undisturbed, peatland habitats are critical to providing resilient biodiversity and habitat support, water management, and carbon sequestration. A high and stable water table is a prerequisite to maintain carbon sink function; any drainage turns this major terrestrial carbon store into a source that feeds back further to global climate change. Drainage and erosion features are crucial indicators of peatland condition and are key for estimating national greenhouse gas emissions. Previous work on mapping peat depth and condition in Scotland has provided maps with reasonable accuracy at 100‐m resolution, allowing land managers and policymakers to both plan and manage these soils and to work towards identifying priority peat sites for restoration. However, the spatial variability of the surface condition is much finer than this scale, limiting the ability to inventory greenhouse gas emissions or develop site‐specific restoration and management plans. This work involves an updated set of mapping using high‐resolution (25 cm) aerial imagery, which provides the ability to identify and segment individual drainage channels and erosion features. Combining this imagery with a classical deep learning‐based segmentation model enables high spatial resolution, national scale mapping to be carried out allowing for a deeper understanding of Scotland's peatland resource and which will enable various future analyses using these data. [ABSTRACT FROM AUTHOR]

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

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

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

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

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

THIS RESEARCH aims to create and analyze thematic soil maps for promising areas for agricultural expansion along the northwest coast of Egypt. The study area is 3151.671 km² and consists of a plateau and elevated areas merging into coastal lowlands. Remote sensing data were used with a digital elevation model to map the physiographic units and characterize land surface features. Five different physiographic entities are recognized; each mostly associated with the soil sub-great groups Typic Haplocalcids and Typic Torripsamments. Data collected through land survey and laboratory analysis were used to create thematic soil layers that were analyzed to determine land factors that limit agricultural development. The results showed that the land surface factors hinder agricultural development due to the steep slope and undulation of the land surface. Analysis of thematic soil maps showed that agricultural development in the region is mainly constrained by coarse soil texture, shallow soil depth, high CaCO3 content, and low CEC. To achieve agricultural sustainability in the region, improved drainage, organic fertilization, and crop selection are required. [ABSTRACT FROM AUTHOR]

Soil loss by water erosion is projected to increase by 13 – 22.5% in the European Union (EU) and United Kingdom (UK) by 2050, leading to loss of cultivable land and soil structure degradation. Accurate mapping of soil erosion is crucial for identifying vulnerable areas and implementing sustainable land management practices. In this study, we introduce machine learning (ML) models to map soil erosion, leveraging their capabilities in categorical mapping. Unlike previous applications that primarily mapped the absence or presence of a soil erosion class, we propose an ensemble strategy using three ML ensemble models (CatBoost, LightGBM, XGBoost) with remote sensing data to map four classes of soil erosion (i.e No Gully/badland, Gully, Badland, Land-slides). The proposed model effectively captures spatiotemporal variations over Europe in the period of 2000 – 2022, with particular precision in mapping Land-slides. The proposed method advances soil erosion mapping across different spatial and temporal scales particularly in the EU, contributing to the development of targeted conservation strategies and sustainable land management practices. [ABSTRACT FROM AUTHOR]

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

Soil is a very important resource for protecting productive agricultural lands and for ensuring their sustainable use. In this context, it is important to take the necessary measures to prevent the losses of soil by erosion and to determine amount of soil losses with relevant models. In this study, by using the Geo-Spatial Interface for Water Erosion Prediction Project (GeoWEEP) model, both surface (hillslope) and channel erosions were estimated to evaluate the total soil loss from the Olur Micro-catchment (37517 ha) within the Coruh River Basin. Additionally, for the conservation purposes, areas with high erosion potential in the micro-catchment were also identified and mapped by the model. The land uses of Olur Micro-catchment, which has a slope of 28.07%, consist of approximately 45% grassland, 19% forest and 14% agriculture. In this study, GeoWEPP model was run with 2 different simulation programs. In the first run, only one-year climate data from the year 2018 was utilized for the soil loss estimation. The total precipitation amount for the 2018 was 561 mm/yr. In the second run, 20-year climate data were utilized and the estimated soil loss values were obtained for 20 years. The recorded average precipitation over 20-year period was 426 mm/yr, which was much lower than in 2018. Based on this information, the total soil loss (sediment yield) in 2018 determined by the GeoWEPP model from the catchment was found to be 89123 tons/yr (2.8 tons/ha/yr). As for the 20-year simulation, a soil loss of 9501 tons/yr (0.27 tons/ha/yr) was estimated by the model. Results revealed that the higher amount of precipitation in 2018 caused soil loss to be 10.4 times higher than the 20 years average soil loss. The surface erosion (136525.2 ton/yr) was found to be almost 7 times higher than the channel erosion (19989.4 ton/yr) indicating the dominant pathway of erosion in the Olur micro-catchment. Additionally, the areas with high erosion risk in the study area were predicted very accurately and showed in the produced soil loss map. In conclusion, an idea with the study was obtained about the amount of soil loss from the Olur Micro-catchment to Ayvalı Dam, and the recommendations were made for soil protection measures to reduce the amount of soil loss in the erosion-sensitive areas that were identified over the the Olur micro-catchment. [ABSTRACT FROM AUTHOR]

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Erosion is a disaster on agricultural land. Due to extreme weather, flash floods, erosion, and landslides occurred in several areas of Minahasa Regency. The Kamenti Tulap watershed is one of the areas on the East Coast of Minahasa that experienced the flash flood disaster and was affected by material losses and damaged agricultural land. The Kamenti Tulap watershed needs to be managed well because it is a source of drinking water and domestic needs and a source of livelihood for the local community as farmers. The erosion hazard index is needed to determine the condition of soil damage in the Kamenti Tulap watershed. This research aims to calculate the potential erosion, actual erosion, and erosion hazard index using the USLE-RUSLE-MUSLE method integrated with spatial technology to obtain its distribution in the Kamanti Tulap watershed. The materials used are base maps such as research area, watershed, slope, land use, soil map, and rainfall data. The result analysis is Kamenti Tulap Watershed with an 891.53 ha spread across the potential and actual erosion hazard index classes. The high class of potential erosion hazard index of 30.52% spread over 118 land units is generally moderately steep (25-40%). The very high class of actual erosion hazard index spread over 146 land units with a coverage area of about 13.81% and generally on mixed garden land. The government needs information on erosion hazard index classification to determine priority areas for land rehabilitation. [ABSTRACT FROM AUTHOR]

Tritium present in deep vadose zones is a useful tracer for estimating groundwater recharge, but its full utility is constrained by not knowing where and for how long the tritium tracing method remains applicable. We obtained 44 tritium profiles from 17 globally distributed sites with vadose zone thicknesses of 12–624 m and used transport models to estimate the number of years that tritium may still be useful. Results show that the method may still be usable for 26 of 44 soil profiles surveyed, mainly in China, Australia, the United States, South Africa, and Senegal, with a remaining useful period of between 6 and 83 years. We also developed a statistical model that uses outputs from a hydrological model to predict the applicability of the tritium tracing method. Global implementation of the statistical model showed that the method remains usable at 20% of Earth's land mass (excluding Antarctica and Greenland) over the next few decades. Core Ideas: Soil tritium profiles were compiled to estimate current 1963 bomb peak distributions in deep vadose zones.Bomb‐peak tritium had migrated by an average of 20 m, depending on each site's soil and climate conditions.Climate and vadose zone thickness suggest a narrowing window of time for tritium tracing applicability. [ABSTRACT FROM AUTHOR]