Soil Mapping Research Articles

Evaluation of soil quality of cultivated lands with classification and regression-based machine learning algorithms optimization under humid environmental condition

In soil science, machine learning algorithms are preferred for pedotransfer functions due to their rapid data acquisition and high prediction accuracy. The current study aims to evaluate the prediction of soil quality in agricultural lands dominated by the humid Black Sea climate using various algorithms. Both classification and regression-based algorithms (Random Forest-RF, Light Gradient Boosting-LGB, Extreme Gradient Boosting-XGBoost, k-nearest neighbors-kNN, Logistic Regression, multilayer perceptron-MLP, Linear Regression-LR and Bayesian Ridge- BR) were used in the method. The comparison of soil maps is also included. Furthermore, the present study evaluates the Grid Search optimization method with K-Fold Cross Validation (K = 5) for both classification and regression-based algorithms. The prediction of soil quality was performed using class-based and regression-based algorithms. As a result of the study, the RF and XGBoost algorithms achieved an approximate accuracy rate of 92 % in the class-based prediction. In regression-based predictions, the most successful algorithms were BR and LR, with an R2 Score of 0.84. The Grid Search optimization method was used to improve the R2 Score, resulting in an increase to 0.90 and 0.88 for BR and LR, respectively. The optimized hyperparameters showed improved performance in predicting the soil quality index. The present study found that Gaussian and Spherical models had the lowest prediction errors in spatial distribution maps. Tree-based algorithms were found to be suitable for class-based prediction of soil quality, while the linear regression method was appropriate for regression predictions. This study is characterized by a rainy climate resulting in acidic soils with high organic matter content. Planning of new studies in different climates and soil properties is recommended.

A Comprehensive Review of LiDAR Applications in Crop Management for Precision Agriculture.

Precision agriculture has revolutionized crop management and agricultural production, with LiDAR technology attracting significant interest among various technological advancements. This extensive review examines the various applications of LiDAR in precision agriculture, with a particular emphasis on its function in crop cultivation and harvests. The introduction provides an overview of precision agriculture, highlighting the need for effective agricultural management and the growing significance of LiDAR technology. The prospective advantages of LiDAR for increasing productivity, optimizing resource utilization, managing crop diseases and pesticides, and reducing environmental impact are discussed. The introduction comprehensively covers LiDAR technology in precision agriculture, detailing airborne, terrestrial, and mobile systems along with their specialized applications in the field. After that, the paper reviews the several uses of LiDAR in agricultural cultivation, including crop growth and yield estimate, disease detection, weed control, and plant health evaluation. The use of LiDAR for soil analysis and management, including soil mapping and categorization and the measurement of moisture content and nutrient levels, is reviewed. Additionally, the article examines how LiDAR is used for harvesting crops, including its use in autonomous harvesting systems, post-harvest quality evaluation, and the prediction of crop maturity and yield. Future perspectives, emergent trends, and innovative developments in LiDAR technology for precision agriculture are discussed, along with the critical challenges and research gaps that must be filled. The review concludes by emphasizing potential solutions and future directions for maximizing LiDAR's potential in precision agriculture. This in-depth review of the uses of LiDAR gives helpful insights for academics, practitioners, and stakeholders interested in using this technology for effective and environmentally friendly crop management, which will eventually contribute to the development of precision agricultural methods.

Soil cover on complex glacial terrain and challenges for large-scale soil mapping using the World Reference Base (WRB) classification system

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.

An improved digital soil mapping approach to predict total N by combining machine learning algorithms and open environmental data

Digital Soil Mapping (DSM) is fundamental for soil monitoring, as it is limited and strategic for human activities. The availability of high temporal and spatial resolution data and robust algorithms is essential to map and predict soil properties and characteristics with adequate accuracy, especially at a time when the scientific community, legislators and land managers are increasingly interested in the protection and rational management of soil.Proximity and remote sensing, efficient data sampling and open public environmental data allow the use of innovative tools to create spatial databases and digital soil maps with high spatial and temporal accuracy. Applying machine learning (ML) to soil data prediction can improve the accuracy of maps, especially at scales where geostatistics may be inefficient. The aim of this research was to map the nitrogen (N) levels in the soils of the Nurra sub-region (north-western Sardinia, Italy), testing the performance of the Ranger, Random Forest Regression (RFR) and Support Vector Regression (SVR) models, using only open source and open access data. According to the literature, the models include soil chemical-physical characteristics, environmental and topographic parameters as independent variables. Our results showed that predictive models are reliable tools for mapping N in soils, with an accuracy in line with the literature. The average accuracy of the models is high (R2 = 0.76) and the highest accuracy in predicting N content in surface horizons was obtained with RFR (R2 = 0.79; RMSE = 0.32; MAE = 0.18). Among the predictors, SOM has the highest importance. Our results show that predictive models are reliable tools in mapping N in soils, with an accuracy in line with the literature. The results obtained could encourage the integration of this type of approach in the policy and decision-making process carried out at regional scale for land management.

Estimates of soil taxonomic change due to near‐surface permafrost loss in Alaska

AbstractGelisols (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.

Remote Sensing and Sustainable Management of Soil Organic Carbon in the Sahelian Area, Senegal, West Africa

The mapping of soil organic carbon (SOC) variability was carried out in a Sahelian region of Senegal by testing the effectiveness to include the Sentinel 2 remote sensed data in the characterization of the soil properties. Ordinary kriging (OK) applied under ArcGIS is compared with multiple linear regression (MLR) calibrated under R software. The results showed a slight decrease of the root mean square error ranging from 0.18 with kriging to 0.16 for multiple linear regression. Carbon variability was also detailed at a finer scale with multiple linear regression at the pixel scale from 10 to 20 m. Spectral bands situated in the visible wavelength, NDWI and NDVI were the most discriminating explanatory variables in the spatial modeling of organic carbon by multiple linear regression. Specific locations that require inputs of manure or compost were also geo-localized with multiple linear regression in order to ensure sustainable management of soil organic carbon. The use of remote sensed data also puts into perspective the possibility of spatializing the physical and chemical properties of the soil on larger areas and correcting the lack of information on soil mapping in the Sahelian regions of Africa.

Remote Sensing and Sustainable Management of Soil Organic Carbon in the Sahelian Area, Senegal, West Africa

The mapping of soil organic carbon (SOC) variability was carried out in a Sahelian region of Senegal by testing the effectiveness to include the Sentinel 2 remote sensed data in the characterization of the soil properties. Ordinary kriging (OK) applied under ArcGIS is compared with multiple linear regression (MLR) calibrated under R software. The results showed a slight decrease of the root mean square error ranging from 0.18 with kriging to 0.16 for multiple linear regression. Carbon variability was also detailed at a finer scale with multiple linear regression at the pixel scale from 10 to 20 m. Spectral bands situated in the visible wavelength, NDWI and NDVI were the most discriminating explanatory variables in the spatial modeling of organic carbon by multiple linear regression. Specific locations that require inputs of manure or compost were also geo-localized with multiple linear regression in order to ensure sustainable management of soil organic carbon. The use of remote sensed data also puts into perspective the possibility of spatializing the physical and chemical properties of the soil on larger areas and correcting the lack of information on soil mapping in the Sahelian regions of Africa.

Spatial analysis and assessment of soil erosion in the southern Western Ghats region in India.

Soil erosion is expected to worsen in the future as a result of climate change, growing population demands, improper land use, and excessive exploitation of natural resources in India. Due to the growing population and changes in land use, it has become increasingly crucial to map and quantitatively assess soil for the purpose of sustainable agricultural usage and planning conservation efforts. The problem of soil erosion is mainly on steeper slopes with intense rainfall in parts of Western Ghats. The 20.17% of geographical area have been converted into wasteland due to soil erosion. The Revised Universal Soil Loss Equation (RUSLE) is a highly prevalent and effective technique utilized for estimating soil loss in order to facilitate the planning of erosion control measures. Despite the fact that RUSLE is accurately estimate sediment yields from gully erosion, it is an effective tool in estimating sheet and rill erosions losses from diverse land uses like agricultural to construction sites. The current study is mainly about combining the RUSLE model with GIS (Geographic Information System) to find out how much soil is being lost, particularly in Noyyal and Sanganur watersheds which is located in Coimbatore district of Tamil Nadu, India. This analysis is based on the soil order, with a significant proportion of alfisols and inceptisols being considered. The obtained outcome is contrasted with the established soil loss tolerance threshold, leading to the identification of the areas with the highest susceptibility to erosion. Within the narrower and more inclined section of the watershed, yearly soil loss scales from 0 to 5455 tonnes/ha/year, with an average annual loss of soil of 2.44 tonnes/ha. The severe soil erosion of 100 to 5455 tonnes/ha/year is found along the steep and greater slope length. The generated soil map was classified into six categories: very slight, slight, moderate, high, severe, and very severe. These classifications, respectively, occupied 6.23%, 14.88%, 10.56%, 15.70%, 7.73%, and 6.63% of the basin area. Based on the results of cross-validation, the estimated result of the present study was found to be very high compared to past studies conducted 0 to 368.12 tonnes/ha/year especially in very severe erosion zones. But very slight to severe erosion zones nearly matched with same level of soil loss. To protect the soil in the study area from erosion, more specific actions should be taken. These include micro-catchment, broad bed furrows, up-and-down farming, soil amendment with coconut coir pith composition, streambank stabilization with vegetation, and micro-water harvesting with abandoned well recharge. These actions should be carried out over time to make sure to work.

Digital mapping of soil quality and salt-affected soil indicators for sustainable agriculture in the Nile Delta region

The study addresses the challenge of sustainable land management, which is crucial for agricultural production and soil quality (SQ), in the face of land degradation that negatively impacts crop production and SQ. The goal of the current work is to assess SQ using digital soil mapping (DSM) in Kafr El-Sheikh province, Egypt, to develop a framework employing two methods for soil quality index (SQI) assessment: the total data set (SQI-TDS) and a selected minimum data set (SQI-MDS) to choose indicators, along with a weighted additive SQI (SQIw), and a Random Forest (RF) model to predict and map the SQI, as well as the salt-affected soil indicators (EC, pH, and ESP). This framework uses remote sensing data: time series of Sentinel-1 (S-1) and Sentinel-2 (S-2) greenest pixel composite. Additionally, we incorporated environmental covariates derived from S-1 and S-2 imagery to understand their influence on SQ, which in turn informs land management practices, land degradation assessment, and crop productivity. The findings reveal a clear negative impact of salinity and alkalinity on SQ. We demonstrate the importance of Variance Inflation Factor (VIF) and Sequential Feature Selection (SFS) techniques for improving the performance of the RF model used for prediction. Notably, the greenest pixel composite imagery proved promising for SQI assessment using DSM beneath vegetation cover, crop mapping, and land-use dynamics. The precise SQI obtained is essential for decision-makers to detect land degradation, develop sustainable agricultural management strategies, and assess their appropriateness for developing plans and strategies to increase agricultural productivity.

High-resolution operational soil moisture monitoring for forests in central Germany

Abstract. The forests of central Germany (Saxony, Saxony-Anhalt, and Thuringia) are vital components of the local ecosystems, the economy, and recreation. However, in recent years, these forests have faced significant challenges due to prolonged climate-change-induced droughts, causing water shortages, tree stress, and pest outbreaks. One of the key components of the forests' vitality and productivity is the availability of soil moisture. Given the anticipated increase in the frequency and severity of drought events, there is a growing demand for accurate and real-time soil moisture information. This underscores the need for development of an appropriate monitoring tool to make forest management strategies more effective. The article introduces an operational high-resolution soil moisture monitoring framework for the forests in central Germany. The key components of this system include the advanced LWF-BROOK90 1D water balance model, a large database of the National Federal Forest Inventory, high-resolution forest soil maps, real-time climate data from the German Meteorological Service, and a web information platform for the presentation of daily updated results. This system informs the public and empowers forest managers and other decision-makers to take targeted, local measures for sustainable forest management, aiding in both drought mitigation and long-term forest health in the face of climate change. The validation of the system using soil moisture measurements from 51 stations with various sensor depths (up to 100 cm) showed an overall good agreement (0.76 median Pearson correlation), which was found to be higher for deciduous rather than coniferous forests. Finally, the framework is discussed against the background of the main limitations of existing monitoring systems and how operational soil moisture measurements contribute to better interpretation of simulations.

Mapping of on-field soil nutrient variabilities as a guiding force for smart farming: a case study from FarmerZone sentinel-1 from three potato agroecological zones of India.

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.

Contribution of Sentinel-2 spring seedbed spectra to the digital mapping of soil organic carbon concentration

Soil organic carbon (SOC) is central to the functioning of terrestrial ecosystems, has climate mitigation potential and provides several benefits for soil health. Understanding the spatial distribution of SOC can help formulate sustainable soil management practices. Digital soil mapping (DSM) uses advanced statistical and geostatistical methods to estimate soil properties across large areas. DSM integrates climate data, topographic features, geology, legacy soil maps, land management and remote sensing data. Bare soil spectra may reflect the presence of particular soil components, making satellite derived spectra suitable predictors of SOC. Bare soil spectra derived from Sentinel-2 were used to estimate SOC concentration (SOC%) and granulometric fractions in the plough layer (0–30 cm) of agricultural parcels in northern Belgium. Thereafter, the estimation performance of SOC% was compared for three DSM models: one with bare soil spectra, one with environmental covariates (topography, granulometry and vegetation), and a combined model with bare soil spectra and environmental covariates. The estimation performance of sand, silt and clay fractions using bare soil spectra from the spring seedbed (R2: 0.53–0.74; RPD: 1.49–2.05; RPIQ: 1.52–2.39) was higher than that of SOC% (R2: 0.16; RPD: 1.08; RPIQ: 1.32). The highest estimation performance of SOC% was obtained for a DSM model including all covariates (R2: 0.28; RPD: 1.18; RPIQ: 1.44), but the contribution of spring seedbed spectra to a model containing environmental covariates was small. The results provide valuable insights for refining soil property estimation using DSM with spectral and environmental covariates.

GPS and GIS Based Soil Mapping of Rajkot City

Abstract Traditional (Classical) soil mapping is inefficient in illustrating continuous changes in soil types or forms. Before designing and constructing any construction, geotechnical engineers should be aware of changes in the index qualities of the soil. Sand, Gravel, Silt and Clay, Plastic Limit, and other soil index values may vary from location to location. This work aims to develop a system for mapping important soil index features using GPS and GIS in conjunction with previously published soil exploration data. This database will allow geotechnical engineers to run more searches on various combinations of soil properties. During this study, data from 112 boreholes in Rajkot was compiled into soil investigation reports by various geotechnical specialists. All 112 boreholes are included in a soil database. GPS has been used to figure out each location’s longitude, latitude & altitude for georeferencing. The ordinary kriging geospatial interpolation method was used for thematic map preparation. In this 10% of boreholes, data has been used for validation. Map accuracy is measured using the Mean Average Error (MAE) and the Root Mean Squared Error (RMSE). Where the difference in values were not more than 10% in all 12 cases. The value is only crossed in the plastic index case, where it is more than 10% in the 1-2-meter depth case.

Spatiotemporal variations and driving factors of farmland soil organic carbon in various landforms of a complex topography

Estimating the spatiotemporal variations and driving factors of farmland soil organic carbon density (SOCD) is of great significance for enhancing soil carbon sequestration capacity. Herein, a large region of complex topography was targeted, which includes hill–mountain, valley–basin, and plain–platform. Based on the massive amounts of sampling data (57,254 measured values) and a large-scale soil map of 1:10,000 (371,976 polygonal patches), the gravity center migration model and gray correlation model were used to quantify the spatiotemporal variations and driving factors of farmland SOCD. The results indicated that the farmland soils in the study area had dual functions of carbon source and sink during 1982–2018, of which 45.50 % and 54.50 % were identified as carbon source and sink, respectively. Specifically, the SOCD for the entire study area, its hill–mountain, and valley–basin increased from 2.79 kg m−2, 2.97 kg m−2, and 3.06 kg m−2 to 2.87 kg m−2, 3.06 kg m−2, and 3.14 kg m−2, respectively, with 0.08 kg m−2 carbon sequestrations and a northeast migration direction for the SOCD gravity center (angle: 21.94°, 23.56°, and 18.82°; distance: 1.56 km, 2.73 km, and 3.20 km). There was a smaller increase of 0.07 kg m−2 in SOCD for the plain–platform from 2.38 kg m−2 (1982) to 2.45 kg m−2 (2018), and the SOCD gravity center migrated to the southwest with an angle of −172.46° and a distance of 1.84 km. Thus, the spatiotemporal variations of farmland SOCD in various landforms varied greatly. Over the past 36 years, SOCD variations were driven by a combination of intrinsic soil factors and external factors such as human disturbance. However, the driving effects of these factors on the landforms of hill–mountain, valley–basin, and plain–platform were quite different in size and order. Therefore, we suggest that topography must be considered when formulating policies to improve soil carbon sequestration, and priority should be given to landform-specific SOCD variation and the factors contributing to them.

Exploiting Soil and Remote Sensing Data Archives for 3D Mapping of Multiple Soil Properties at the Swiss National Scale

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

Mapping of soil carbon balances changes in the dry tropical forest ecosystem in Pernambuco Brazil

Maps of soil and vegetation carbon stock dynamics resulting from changes in land use in tropical dry areas are still scarce and virtually absent for the Brazilian Northeast region. The few data available were built on a scale that does not allow their use for decision-making and precision farming applications. Based on soil and land use data, we developed a geographical information system to estimate and map carbon balances in the large (86.135 km2) semiarid region of Pernambuco state, Brazil. Maps of carbon stocks for soil and vegetation for the years 2000 and 2016 were created on the scale of 1: 100000, stratified by land use and soil types. In this period, 28% of the area had decreases in soil and vegetation C stocks, 57% had no significant changes and only 13% had increases. Most of the change was associated with converting the open native forest vegetation (Caatinga) into pastures. The net C loss was 291 million Mg, representing an average loss of 2 Mg C ha-1 year-1. Water bodies, urban areas, and other unclassified uses were not accounted for but amounted to only 2% of the area. Overall, the method proved to be a fast and feasible approach to monitoring carbon balances derived from land use changes on a regional scale.

Assessing the risk of subsoil compaction in croplands using the soil compaction stratification ratio as an indicator

AbstractAccurate prediction of subsoil compaction in cropland areas is important for food security and sustainable agricultural production development. This study evaluated the validity and advantages of the soil compaction stratification ratio (SR) as an indicator for assessing the risk of subsoil compaction and presented a theoretical framework for predicting and evaluating subsoil compaction risk. Geographic information system and remote sensing technologies were utilized in combination with a state‐of‐the‐art ensemble machine learning model to quantitatively predict the spatial distribution and uncertainty of subsoil compaction risk in croplands. The results showed that the subsoil bulk density (BD) was significantly greater than the topsoil BD in Shaanxi Province, China. Compared with that of the subsoil BD, the stratification ratio of the bulk density (SRBD) exhibited greater sensitivity to subsoil compaction, and it accurately reflected spatial variations in subsoil compactness. The SRBD exhibited obvious spatial heterogeneity, and the mean annual precipitation, elevation, and mean land surface temperature substantially influenced its spatial distribution. The critical thresholds of the SRBD for the low‐risk, medium‐risk, and high‐risk groups were 1.01, 1.21, and 1.32, respectively. Low subsoil compaction dominated in the mountainous and hilly areas, whereas the Guanzhong Plain and Hanzhong Basin exhibited medium levels of subsoil compaction. Some croplands in the central Guanzhong Plain experienced severe subsoil compaction issues. In conclusion, the soil compaction SR was found to be an effective indicator of subsoil compaction by effectively eliminating soil background differences. This approach has potential applications in cropland management, digital soil mapping, and early warning of soil compaction risk.

Mapping available water capacity as a soil production capital metric in Australia

This study aims to identify a key soil property influencing farmland prices as a soil production capital indicator for soil security. There is generally a strong relationship between farmland price and production capital, as well as with biomass production. From a soil-centric point of view, under dryland agriculture in Australia, we hypothesise that soil available water capacity (AWC) is a key soil property influencing farmland prices and consequently can act as a soil production capital indicator. Here, we relate soil AWC of Australia to median farmland prices using digital soil maps of AWC. The results revealed that AWC from 0 to 100 cm is significantly associated with farmland prices (r = 0.31, p-value<0.05), providing a linear regression to estimate the soil production capital. As long as the AWC (mm) is above 67.6, the coefficient suggests that a 1 mm increase in AWC (mm) results in an increment of 293.6 $/ha in farmland prices in Australia. Additionally, an assessment of the anthropological impact on soils was conducted through phenosoil-genosoil differentiation analysis. We concluded that despite the shared formation characteristics of each pedogenon, soils with the highest AWC were not necessarily chosen for agricultural purposes since European settlement. Furthermore, our findings indicate that phenosoils in Queensland and Tasmania have higher levels of insecurity, likely due to intensified and inappropriate farming that may result in AWC reductions. This analysis provides another perspective to quantify the capital dimension of soil security, valuing its critical role in sustainable food systems and ecosystem services.

Fluid upwelling and alluvial controls on spring localization: An example from Sri Lanka

In water-scarce regions, perennial springs can be a valuable source of drinking water. However, to identify unreported springs and shallow water upwelling zones, it is essential to understand the factors that control spring localization. In a crystalline basement, as in Sri Lanka, without a sedimentary cover, faults and fractures provide the only far-reaching fluid pathways and springs commonly emerge at fault/fracture intersections. While surveying cold and hot water springs in Sri Lanka, it was observed that all springs probed were located at the edge of alluvium. In order to gain insight into this relationship, we performed a topographic and geomorphological analysis was conducted utilizing remote sensing, geological and soil maps, and geological mapping in the field. The results of our analysis of 27 springs indicate that their localization is controlled by fault intersections, non-permeable clay in the alluvium and laterite, and the chemically weathered surface of the bedrock. Furthermore, the constant discharge rates observed over the years and isotope analysis suggest that the springs are part of a tens-of-kilometer-wide regional groundwater system. Based on these results, we propose a conceptual model in which water rises at fault intersections from depth until it reaches the base of the alluvium where up to several meters thick clay with low to zero permeability further inhibits vertical flow forcing the water to spread laterally. Along the alluvium clay boundary with the more permeable weathered bedrock, the water continues its path to the surface. The localization of springs differs from that of fault intersection by tens of meters, with the potential for mixing between shallow and deep groundwater. This observed effect of alluvium and their contact boundaries on spring localization has not been reported for Sri Lanka. Consequently, discharge rates may be significantly increased if the fault intersections are specifically targeted by shallow drilling.

Soil loss estimation using RUSLE model: Comparison of conventional and digital soil data at watershed scale in central Iran

Sustainable agriculture and eco-environment conservation face constant threats from soil erosion in mountainous regions. This study aimed to predict soil loss in the mountainous watershed by applying the Revise Universal Equation Soil Loss (RUSLE) and Sediment Delivery Ratio (SDR) models and to evaluate the efficacy of the RUSLE model through the observed data in a hydrometric station. Comparing a polygonal, and a digital soil map (prepared by the Kriging method) for deriving the K factor to estimate soil loss was another objective of the recent research. In this study, two scenarios were examined for the calculation of spatial variation of the K-factor; scenario I comprised soil data derived from a legacy soil map with eight soil units and the data from their representative soil profiles (i.e., traditional soil survey), and scenario II comprised K-factor derived from 100 studied sites using kriging technique (i.e., digital soil survey). The results of RUSLE modeling showed that there was no significant difference between scenario I (7.97 t/(ha. yr)) and scenario II (7.93 t/(ha. yr)) for predicting soil loss with the RUSLE model. This finding confirms that the RUSLE model with limited and legacy data can provide a reliable prediction of soil loss in the given watershed. Moreover, long and short-term periods were used to estimate soil loss, while in the long-term period, estimated soil loss had higher accordance with actual soil loss. Sediment delivery ratio was calculated using models of Vanoni, Boyce, USDA-SCS, and Slope-based models. The results indicated that the Slope-based model had the highest fitness (R2=0.946, ME=0.27, and RMSE=0.275 for scenario I and R2=0.9443, ME=0.26 and RMSE=0.40 for scenario II), with observed sedimentation rate at the hydrometric station at the outlet of the watershed. Overall, predicted soil loss severity map using the traditional soil map by RUSLE model could provide trustworthy information for decision makers and governors at the given and similar watersheds in semiarid regions.