Classification of Martian rocks and soils at Tianwen-1 landing site based on Laser-Induced Breakdown Spectroscopy data of MarSCoDe

Soil formation conditions, processes and classification on the Baltic Sea coast of Latvia

Accounting for soil class variability in Mediterranean Europe using legacy soil maps and the topsoil LUCAS survey

Understanding how land use and land cover changes (LULCCs) affect ecosystem services (ES) is essential for guiding sustainable management strategies in protected areas (PAs), particularly in small island contexts where ecological processes, historical land uses and socio-economic dynamics are tightly interwoven. This study explores the medium-term dynamics of ES supply using LULCCs as a spatial indicator on the island of Asinara (Italy). Spatially modelled results show that the transition from agricultural and semi-natural uses to uncultivated areas, driven by land abandonment and soft nature-restoration actions, has reshaped the balance between provisioning, regulating and cultural services, generating reflections on opportunities and land management challenges. These outcomes require careful contextualization to reflect underlying ecological processes and land capabilities, highlighting the need for governance frameworks oriented towards conservation-driven landscape management rather than passive rewilding, especially in landscapes long shaped by human activities. More generally, sustainability in PAs is not an automatic outcome of protection or abandonment, but a contingent, spatially differentiated and governed process, in which indicators and maps serve to guide decisions, not to certify their success.

Soil erosion poses a significant challenge to environmental sustainability, especially in regions with varying land-use patterns and topography. Soil erosion is a major environmental threat affecting soil quality, reservoir sedimentation, agricultural land, and watershed hydrology. This study aims to identify and classify homogeneous sub-watersheds in a mountainous watershed in Iran using GIS. Forty years of climate data, a high-resolution DEM, land-use maps, soil texture, and NDVI were applied to derive the main factors, while the P factor was determined based on slope classes and land-use types. The RUSLE results showed that annual soil erosion in the watershed had an average of about 7-ton ha⁻¹ year⁻¹, with more than 65% of the watershed area falling into the moderate to very high erosion classes. Average key factors were R = 78.08MJ·mm/ha·hr·year, K = 0.28 t·ha·h/MJ·mm·ha, LS = 1.62, and C = 0.39. The highest erosion occurred in areas with heavy rainfall, steep and long slopes, fine-textured soils, and sparse vegetation. Spatial autocorrelation analysis using Moran's I and the Getis-Ord Gi* statistic showed a clustered spatial pattern of erosion. High-high (HH) clusters, indicating severe erosion hotspots, were found in the southwest, while low-low (LL) clusters, representing minimal erosion coldspots, occurred in the north and northeast. These results support sub-watershed prioritization and indicate the need for targeted erosion control in high-rate zones. These results contribute to the development of more targeted and sustainable land management practices to mitigate soil erosion rates and improve watershed conservation efforts.

ABSTRACT The activities of people who lived on the Brazilian coast since the Holocene have mainly determined the formation of Sambaqui (= shell mound) soils. Residues from shells and other animals and plants used as food by these people were deposited, resulting in soils with high levels of phosphorus (P) and organic carbon, as well as high base saturation. This study aimed to evaluate P fractions, establish possible relationships between P measurement methods in the soil profiles of Sambaqui formations, and contribute to the definition of a soil class in the Brazilian Soil Classification System (SiBCS) that describes anthropogenic pedogenesis. Three soil profiles with records of anthropogenic activity were selected and sampled for this study, all in São José de Ribamar, Maranhão, Brazil. Soil morphology was described, the physical and chemical properties of the horizons were analyzed, and the profiles were classified according to the SiBCS. Available P was extracted by different methods, and P was fractionated. Surface horizons of profiles P2 and P3 were identified as anthropogenic A, with dark colors, presence of shells, high calcium and organic carbon concentrations, high base saturation, and P content greater than 30 mg kg -1 of soil. Of the available P extractants, Mehlich-3 withdrew the highest P concentrations. Olsen method was positively correlated with pH values, shell presence, and calcium carbonate levels. Phosphorus fractionation of the studied profiles indicated highest representation of the occluded and available P fractions (Mehlich-3). According to the SiBCS, the profiles were classified as Cambissolo Háplico Carbonático (P1 and P3) and Cambissolo Háplico Ta Eutrófico (P2), and an anthropogenic subgroup was proposed for P2 and P3. Based on the World Reference Base (WRB), the profiles were classified as Pretic Calcaric Cambisol (P1) and Pretic Anthrosols (P2 and P3). In other words, the WRB classifies these soils at a high hierarchical level (as Anthrosols or by the main qualifier Pretic), in contrast with the SiBCS.

The Kendal Industrial Park (KIK), Central Java, is characterized by soft clay soil with low bearing capacity, requiring improvement before being used as a construction foundation. This study aims to determine soil classification, analyze the effect of ferronickel slag and alkali (NaOH and KOH) mixtures, and evaluate the potential of these mixtures as soil stabilization materials. The research was conducted experimentally in the laboratory through specific gravity tests, grain size distribution analysis, Atterberg limits, Standard Proctor compaction test, direct shear test, and California Bearing Ratio (CBR) test. The mixture variations used consisted of 10% ferronickel slag combined with alkali solutions of NaOH and KOH at concentrations of 6%, 8%, and 10%. The results showed that the original soil was classified as fine-grained soil with high plasticity and a CBR value of 5.6%, indicating low bearing capacity. The addition of ferronickel slag and alkali reduced soil plasticity and increased soil strength and bearing capacity. Therefore, the mixture of ferronickel slag and alkali has the potential to be used as an environmentally friendly alternative soil stabilization material.

Türkiye is a country at high seismic risk due to its location on active tectonic zones. Therefore, regional-scale studies to reduce earthquake risk are of great importance. In this study, the soil-geotechnical properties, existing building stock, settlement patterns, and historical earthquake records of the Saraydüzü district of Sinop province, located close to the North Anatolian Fault Zone, were examined in detail. Seismic hazard assessment, Vs(30)-based soil classification, building inventory analysis, and earthquake scenario modeling were performed during the analysis. ELER v3.0 software was used in scenario development, and the Erdik and Eren (1983) model was applied as an empirical attenuation relation specific to Turkish conditions. In the scenario developed based on the February 6, 2023, Kahramanmaraş-centered earthquake, calculations based on 14588 residences projected to suffer severe or collapsed damage indicate that approximately 1508 people could lose their lives, with a loss of life rate of approximately 10335 for every 100 structures. Using an injury/death ratio of 2.019 for the same earthquake, it was estimated that approximately 3044 people could be injured. Multiplying the total of 17197 moderately and severely damaged residences by the 2024 average household size of 2.61 people in Turkey and subtracting the number of deaths, it was determined that approximately 43377 people would be in need of shelter. In the second scenario, using the parameters of the August 17, 1999 Izmit Gulf Earthquake, the number of residences with severe/collapsed damage was calculated as 322. Applying a 26% loss of life ratio, it was estimated that approximately 84 people could lose their lives. Similarly, using an injury/death ratio of 2.515, it was determined that 211 people could be injured, and based on household size, approximately 3155 people would need shelter. The results demonstrate that medium-sized settlements can be as high a seismic risk as large cities, clearly demonstrating the importance of disaster preparedness in such regions. It is evaluated that these analyses, specifically conducted in Saraydüzü, will be instructive for other settlement areas with similar characteristics and will contribute to Türkiye's earthquake response processes at a local scale.

This study presents the study of aerodynamic properties of a blended wing body (BWB) unmanned aerial system (UAS) with vertical takeoff and landing (VTOL) capabilities. The UAS's innovative design allows for VTOL and hovering capabilities similar to multi-copter drones, as well as cruise flying efficiency comparable to fixed-wing aircraft. The generation of lift by the complete airframe and the decrease of drag in the wing-body attachment are the major benefits of the BWB concept. Based on the calculated wing parameters, a baseline design was developed using XFLR5, and trade studies were performed to obtain the optimum tradeoff between aerodynamic performance and overall stability. The final CAD model was developed using CATIA and Plane Maker, followed by flight simulations in X-Plane 11. After evaluating performance and stability characteristics, the prototype was fabricated and integrated with electronic subsystems. The flight tests were carried out at an altitude of 2480 meter at Lete village of Mustang district, Nepal, and flight data were analyzed to study the UAS's response to a planned mission. Analysis of flight data confirmed successful VTOL-to-fixed-wing transitions exhibiting satisfactory performance and stability characteristics, demonstrating the potential of the UAS for aerial experiments, reconnaissance, and environmental monitoring missions.

The study characterised soil and mapped agricultural lands in north-central Nigeria to address the lack of detailed, geospatial soil data and classification within the agricultural environment of the Federal University of Technology, Minna, as it has been identified as obstructing sustainable land management, agricultural productivity, educational support, and research development. This process systematically involved field surveys, examinations, and laboratory analyses. This comprehensive study evaluated the soil quality, drainage, morphology, and physicochemical properties across the Federal University of Technology, Minna (FUT) Main Campus Teaching and Research Farm. By advanced techniques like gridding, soil examination, profile pit excavation, and laboratory analysis, the study delineated the farm into five distinct mapping units (FUT1, FUT2, FUT3, FUT4, and FUT5). It evaluated various soil attributes such as effective soil depth, texture, colour, drainage conditions, erosion evidence, bulk density, saturated hydraulic conductivity (Ks), and soil organic carbon content. Results showed notable variations among these units. FUT2, FUT3, and FUT5, characterised by higher slope gradients, exhibited the most susceptibility to erosion due to their soil properties and evident erosion features, whereas FUT1 and FUT4 displayed lower erosion potential with relatively stable soil properties. Soil conservation and proper land management practices should have been prioritised in the past and continue to be prioritised at present. Therefore, the soil mapping units are produced to achieve these targets and serve as a basis for further research at the study site. Ridging across the slopes at intervals will minimise soil erosion by water, and conservation tillage is encouraged.

Geospatial analysis for land capability assessment and sustainable land use planning in Baro Akobo Basin, South-west Ethiopia

Abstract. There is increasing interest in global dynamic soil information with changes in soil properties mapped over time and at high spatial resolution. Thanks to long-term, multi-temporal, and fine- and medium-resolution satellite missions such as Landsat, MODIS, Copernicus Sentinel and similar, it is possible to produce globally consistent predictions of key soil variables that match other 10–30 m spatial resolution global data sets. This paper describes data preparation, modeling, and production of OpenLandMap-soildb: global dynamic predictions of soil organic carbon content, soil organic carbon density, bulk density, soil pH in H2O, soil texture fractions (clay, sand and silt) and USDA subgroup soil types (USDA soil taxonomy subgroups) at 30 m spatial resolution based on spatiotemporal Machine Learning (Quantile Regression Random Forest with output predictions showing the mean plus the 68 % probability lower and upper prediction intervals). To train the models, a large compilation of soil samples imported from legacy soil projects was used: 216 000 soil samples with soil carbon density (kg m−3), 408 000 soil samples with soil carbon content (g kg−1), 272 000 soil samples with soil pH in H2O, 363 000 soil samples with clay, silt and sand content (%) and 134 000 samples with bulk density oven dry (t m−3). Soil carbon and soil pH were mapped with 5-year time-intervals; soil texture fractions, bulk density, and soil types were mapped for recent years only. The cross-validation results indicate Root Mean Square Error (RMSE) of 17.7 (kg m−3; 0.486 in log-scale) and Concordance Correlation Coefficient (CCC) of 0.88 for SOC density, RMSE of 51.3 (g kg−1; 0.574 in log-scale) and CCC of 0.87 for SOC content, RMSE of 0.15 (t m−3) and CCC of 0.92 for bulk density of fine-earth, RMSE of 0.51 and CCC of 0.91 for soil pH, RMSE of 8.4 % and CCC of 0.87 for soil clay content, and RMSE of 12.6 % and CCC of 0.84 for soil sand content respectively. The most important variables for predicting soil organic carbon density (kg m−3) were: soil depth, Landsat-based uncalibrated Gross Primary Productivity (GPP), Normalized Difference Vegetation Index (NDVI) and CHELSA bioclimatic indices. The global distribution of soil pH can be primarily explained by the CHELSA Aridity Index (long-term), annual precipitation, and salinity grade. The global stocks for 2020–2022+ period for 0–30 cm depth interval are estimated at 461 Pg (Peta grams); the results further indicate that, in the last 25 years, the world has lost at least 11 Pg of SOC in the top soil. Suggestions are made on how to set up global permanent monitoring stations to accurately track land degradation and enable land restoration projects. The training data set is available at https://doi.org/10.5281/zenodo.4748499 (Hengl and Gupta, 2025), while the resulting data products can be accessed at https://doi.org/10.5281/zenodo.15470431 (Consoli et al., 2025) and https://world.soils.app (OpenGeoHub Foundation, 2026). Both datasets are released under a CC-BY license.

Integrated assessment of ecological land capability and land suitability for irrigated agriculture in Alborz Province Iran.

A comprehensive approach for Bayesian soil classification using Cone Penetration Test data

Soil class mapping with severely limited sample data: an explicit rule of soil-landscape relationships in complex terrain

Abstract The accuracy of seismic damage scenarios is of paramount relevance for various objectives inherent to disaster risk management and particularly critical to support effective emergency response and to address seismic mitigation policies. When transitioning from large-scale studies – such as national ones – to urban scale applications, the assessment does not often reflect a downscaling in the detail level of exposure, vulnerability, and hazard data, leading to inaccurate evaluations of damage and loss, as well as of the related uncertainty. While the value of local, high-resolution data is widely acknowledged, its quantitative impact on final damage predictions remains poorly constrained. This study addresses this gap by quantifying the sensitivity of urban damage predictions to varying levels of detail in hazard, vulnerability, and exposure data. Specifically, the study compares estimates derived from large-scale datasets against those based on refined, local information acquired on-site. To this aim, a multi-level comparative framework is applied to the Sanremo Municipality (Northwestern Italy), simulating ground-motion scenarios consistent with the 1887 M6.3 Ligurian Sea earthquake. Within this framework, ground shaking is estimated using ground-motion prediction equations, which are amended to account for site-specific amplification effects. This critical step compares results derived from national soil classification maps against detailed seismic microzonation studies. Building damage and consequences are then assessed using fragility curves. Outcomes from vulnerability models based on standard aggregated census-level data are compared to those derived from refined inventories and field inspections. The results show substantial discrepancies between the predicted scenarios. The use of local data, particularly site-specific amplification effects and building characteristics, leads to significant differences in damage intensity and, especially, its spatial distribution. This study underscores the critical importance of improving knowledge through acquisition of local data and provides a robust general framework to improve decision-making for disaster risk management.

The Percut Watershed is vital in supporting the region’s ecological and socio-economic systems. However, the watershed has been increasingly impacted by rapid urbanization, deforestation, pollution, and unregulated land use, leading to environmental degradation and decreased water quality. This study aims to assess the current state of the Percut Watershed and evaluate the effectiveness of ongoing management practices. The research utilizes a combination of water quality analysis, land-use mapping through Geographic Information Systems (GIS), and stakeholder engagement to monitor key environmental indicators. The research results show that the Percut Watershed has a restored classification where the total value of the watershed carrying capacity reaches 101.75 (including the “moderate” criteria). Criteria that need attention are critical land and flood vulnerability. The land parameters in the Percut Watershed are considered quite good, with the erosion index in the Percut Watershed also having a moderate value; this is because, apart from natural topographic factors, there is also a mismatch in land use with existing land capabilities. The condition of the water system in the Percut Watershed is considered quite good because the flow regime coefficient value is low, which indicates the land’s ability to hold and store water is quite good high annual flow coefficient value. The use of regional space in the Percut Watershed is still good. Attention needs to be paid, especially to cultivated areas that are topographically less suitable for agricultural cultivation. Effective monitoring and evaluation are crucial for addressing these challenges and ensuring the sustainable management of the watershed.

Abstract Accurate classification of geographic regions of soil is imperative for precision agriculture, targeted land management, and other related applications. Often, soil samples for such analyses exhibit high similarity in features, especially from geographically neighboring regions, which poses a challenge for accurate classification of soil geographic regions based on laser-induced breakdown spectroscopy (LIBS) technology. To address this issue, we propose a hybrid classification framework that integrates Random Forest-based Importance Measurement (VIM-RF) with a Backpropagation Neural Network (BPNN). The LIBS spectral data used in this study were collected from soil samples with close geographical origins and high inherent similarities. The VIM-RF algorithm was employed to identify and retain the most discriminative spectral variables, which are essential for distinguishing these similar soil classes. Based on the value of VIM，30 spectral variables were retained , which covered these trace elements, such as Fe, Mn, Ti, as well as major elements like K and N in the soil. Subsequently, the screened variables were used to train the BPNN model for classification, and after 10 fold-cross validation optimization, achieved a classification accuracy of 99.6%±1.21%. The experimental results demonstrate that the proposed VIM-RF-BPNN framework has superior classification performance, and the effectiveness of the VIM-RF method in screening highly relevant features from complex and highly similar LIBS data. When combined with BPNN, it can significantly improve the classification accuracy. This study highlights the potential of VIM-RF as a powerful feature selection strategy and confirms the effectiveness of the VIM-RF-BPNN approach for accurate soil classification based on LIBS, particularly in scenarios involving spectrally analogous or overlapping soil profiles.

This research aims to identify the most appropriate predictor for the Vertical‑to‑Horizontal (VH) ratio of the Peak Ground Velocity (PGV) from the five existing models in the literature useful for regional or site‑specific probabilistic seismic hazard assessment, and practical applications in the Bay of Algiers. Firstly, dataset of 285 observed VH ratios of PGV was compiled from nine seismic stations within the study area, installed on different flat and irregular surfaces. This dataset was derived from earthquakes with moment magnitude (Mw) ranging from 3.3 to 5.3. Next, the dataset was categorized into two groups based on the soil type at the station locations: rock (S1) (Vs30 above 800 m/s) and stiff (S2) (Vs30 360‑800 m/s) soils. After that, a preliminary linear regression analysis was performed for each group of observed VH ratios of PGV as a function of the Joyner‑Boore distance (RJB) of near field and compared with three selected candidature predictors: Akkar et al. (2014), Bozorgnia and Campbell (2016), and Ramadan et al. (2021) (RA2021). For an extensive evaluation, the Euclidean Distance‑based ranking method (EDR) was applied on the three mentioned candidature predictors. For rocky soil, the results indicate that all models closely align with the linear regression fit, around a VH ratio of PGV of 0.6. However, RA2021 appears to provide a reasonable fit, with a VH ratio of PGV of 0.4 for stiff soil, despite the significant site‑effects at the respective stations. The EDR showed that RA2021 gives the lowest sigma with the observed ratios for S1 and S2 soil classes. For the far field, estimates of the VH ratio of PGV are provided for three strong earthquake magnitudes (6.5, 7.0, and 7.5) and different soil classes, using the existing models.

This study investigates the seismic response of steel moment-resisting frames (MRFs) using linear time-history analysis (LTHA) to evaluate the influence of spectrum matching techniques. Three archetype buildings—3-, 9-, and 20-story—were modeled according to the SAC Steel Project (FEMA 355-C Appendix B) and analyzed using SAP2000®. The analysis employed three types of seismic spectra: the Elastic Spectrum (ES), Reduced Turkish Earthquake Code (TEC-2018) Spectrum, and Reduced ASCE Spectrum across four local soil classes (ZA–ZD). A total of 252 earthquake ground motions, including real and artificial records, were used. The results revealed that artificial records achieved smoother spectral compatibility and more stable base shear predictions, whereas real records induced higher variability owing to their natural frequency content and phase characteristics. This divergence was most significant in softer soils (ZC and ZD), with base shear variations of up to ±35%. The findings highlight the need to integrate both record types into seismic assessments and contribute to the advancement of performance-based earthquake engineering approaches.