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Helical piles are widely used for their low installation noise, low disturbance, recyclability, and increased load-bearing capacity. This study investigated the effects of installing multiple-plate helical piles on the surrounding soil using the coupled Eulerian–Lagrangian finite-element method. The comprehensive parametric study was carried out using the advancement ratio, the spacing ratio, the ratio of helix plate diameter to shaft diameter, the embedment depth ratio, and the soil strain softening parameters. The influence on the uplift capacity was then investigated by mapping the remolded soil strength field, derived from the coupled Eulerian–Lagrangian computation, into a numerical model for finite-element limit analysis. The results reveals that the penetration of single- and multiple-plate helical piles affects the soil strength in a cylindrical region with a radius of 0.7 times the helix plate diameter and a height of the penetration depth surrounding the pile. Although strain softening parameters have a significant impact on soil strength, they do not alter the range of soil strength disturbance. The impact of embedment depth ratio on uplift bearing capacity is most notably significant. [ABSTRACT FROM AUTHOR]

Lack of site-specific nutrients information for different cropping systems has been a major challenge in addressing declining soil fertility levels and enhance crop productivity in Punjab, Pakistan. Therefore, the study was designed to assess and quantify soil physico-chemical characteristics, crop yield and economic feasibility of different cropping systems (CS), including groundnut-wheat (G-W), rice-wheat (R-W), fallow-gram/wheat (F-G/W), mix cropping (Mix C) and cotton-wheat (C-W). A total of 470 georeferenced soil samples were collected using a random survey approach, and the samples were analyzed for soil texture, pH, electrical conductivity (EC), organic matter percentage (OM), total nitrogen percentage (TN), accessible phosphorus (AvP) (mg kg−1) and extractable potassium (ExK) (mg kg−1). For crop yield and economic feasibility, the data collected for each crop were summed up and mean data for every cropping system were compared. Cotton-wheat cropping system had the highest mean value of EC (1.57 dS/m), while mix cropping showed the maximum level of OM (0.53%), TN (0.028%), AvP (5.16 mg kg−1), yield (10.09 t ha−1), gross revenue (PK Rs. 87,883) and benefit cost ratio (2.2). The R-W cropping system had the highest pH (8.37), ExK (127.39 mg kg−1) and total cost (PK Rs. 46,882.25). Curiously, the fallow-gram/wheat cropping system had lower value of OM, TN, AvP, ExK, yield, GR and BCR, highlighted its poor performance compared to mix cropping system. Deficiencies in OM were widespread across all cropping systems, with only 3.1% of samples under mix cropping system being in the medium range. Similarly, TN %, AvP and ExK were deficient in varying degrees across all cropping systems, particularly in the fallow-gram/wheat cropping system. Spatial variability maps showed that nutrient deficiencies were more pronounced from the southern to northern side of study area. Our findings indicate that mixed cropping can improve soil health and enhance crop productivity, supporting the need for targeted nutrient management in Punjab agriculture systems. [ABSTRACT FROM AUTHOR]

In vegetated areas, soil pH impacts plant growth, soil properties, and spectral characteristics. Remote sensing enables soil pH mapping by delivering detailed surface data, and while high-resolution satellite images show great potential in complex terrains, research in this area is still limited. This study evaluated PlanetScope (high-resolution) and Sentinel-2 (medium-resolution) images in estimating soil pH across diverse land use types in southwestern China's hilly areas. It examined how spectral variables from four seasonal images affect prediction accuracy. We integrated topographic and spectral variables at seven spatial resolutions (3 m, 10 m, 20 m, 30 m, 40 m, 50 m, and 60 m), using extreme gradient boosting (XGboost) for orchards, dry land, and paddy fields. We found that the models developed with PlanetScope images tended to achieve better prediction accuracy compared to those utilizing Sentinel-2 images. For each satellite, single-temporal images showed greater predictive power under each land use type. In particular, the spring spectral data showed desirable predictive performance for the orchards and the paddy fields, while the autumn spectral data contributed more effectively to the models for the dry land. Specifically, PlanetScope provided the best prediction accuracy for soil pH at 3 m resolution (orchard: R2 = 0.72, MAE = 0.24, RMSE = 0.30, RPD = 1.91; dry land: R2 = 0.77, MAE = 0.37, RMSE = 0.40, RPD = 2.09; paddy field: R2 = 0.66, MAE = 0.35, RMSE = 0.41, RPD = 1.71), while Sentinel-2 performed better at 10 m resolution (orchard: R2 = 0.67, MAE = 0.29, RMSE = 0.33, RPD = 1.75; dry land: R2 = 0.70, MAE = 0.39, RMSE = 0.47, RPD = 1.83; paddy field: R2 = 0.64, MAE = 0.34, RMSE = 0.42, RPD = 1.66). Our findings demonstrate that sensor selection, land use, temporal phases, and modeling resolution significantly impact outputs. High-resolution PlanetScope images prove effective for predicting soil pH in complex terrains. [ABSTRACT FROM AUTHOR]

The research investigates the advantages of real-time soil quality monitoring for various land management applications. We emphasize the crucial role of soil modeling and mapping by visualizing and understanding aridity trends across different regions. The primary objective is to develop an innovative soil monitoring system utilizing Internet of Things (IoT) technology. This system, equipped with intelligent sensors, will operate autonomously, collecting real-time data to identify key trends in soil conditions. Our system employs smart soil sensors to measure macronutrient values up to a depth of 80 cm. These sensors will transmit data wirelessly. Laboratory research involved a two-month evaluation of the system's performance across three distinct soil types collected from diverse geographical locations. Analysis of the three soil types yielded a model accuracy estimate of 0.01. A strong positive linear correlation (0.92) between moisture and macronutrients has been observed in two out of the three soil types. The results, particularly related to soil moisture, were averaged over the testing period. While precipitation values were not directly integrated into the modeling framework, they were calculated in l/m2 to ensure accurate real-time estimates. The need for such advanced monitoring systems is critical for optimizing key soil macronutrients and enabling spatiotemporal mapping. This information is essential for developing effective strategies to mitigate soil aridification and prevent desertification. [ABSTRACT FROM AUTHOR]

Remote sensing technologies continue to expand their role in environmental monitoring, providing invaluable advances in soil assessing and mapping. This study aimed to prove the need to apply spatial statistical models for processing data in remote sensing (RS), which appears to be an important source of spatial data at multiple scales. A crucial problem facing us is the fusion of multi-source spatial data of different natures and characteristics, among which there is the support size of measurement that unfortunately is little considered in RS. A data fusion approach of both sample (point) and grid (areal) data is proposed that explicitly takes into account spatial correlation and change of support in both increasing support (upscaling) and decreasing support (downscaling). The techniques of block cokriging and kriging downscaling were employed for the implementation of such an approach, respectively. The method is applied to soil sample data, jointly analysed with hyperspectral data measured in the laboratory, UAV, and satellite data (Planet and Sentinel 2) of an olive grove after filtering soil pixels. Each data type had its own support that was transformed to the same support as the soil sample data so that the data fusion approach could be applied. To demonstrate the statistical, as well as practical, effectiveness of such a method, it was compared by a cross-validation test with a univariate approach for predicting each soil property. The positive results obtained should stimulate advanced statistical techniques to be applied more and more widely to RS data. [ABSTRACT FROM AUTHOR]

Accurate soil maps are essential for soil protection, management, and digital agriculture. However, traditional soil maps often lack the detail required for local applications, while farm-scale surveys are often not economically viable. This study uses legacy soil data and digital soil mapping (DSM) to produce accurate, low-cost maps of key soil properties, namely clay, sand, total lime (CaCO3), organic carbon (SOC), total nitrogen (TN), and the cation-exchange capacity (CEC). The DSM procedure involved multivariate stepwise regression kriging that uses the terrain attributes and bare soil reflectance composite (SRC) from Sentinel-2 multitemporal images. The procedure to obtain the SRC was carried out following the Soil Composite Mapping Processor (SCMaP) methodology. The Sentinel-2 bands of the SRC showed strong correlations with soil features, making them very suitable explicative variables for regression kriging. In particular, the SWIR bands (b11 and b12) were important covariates in predicting clay, sand, and CEC maps. The accuracy of the regression models was very good for clay, sand, SOC, and CEC (R2 > 0.90), while CaCO3 showed lower accuracy (R2 = 0.67). Normalization of SOC, TN, and CaCO3 did not significantly improve the prediction accuracy, except for SOC, which showed a slight improvement. In addition, a supervised classification approach was applied to predict soil typological units (STUs) using the mapped soil attributes. This methodology demonstrates the potential of SRCs and regression kriging to produce detailed soil property maps to support precision agriculture and sustainable land management. [ABSTRACT FROM AUTHOR]

Evaluating high-throughput soil profile information is essential in safflower precision agriculture, as it facilitates efficient resource management and design of an experiment that promotes sustainable production. We collected soil from representative target environments (TE) of safflower cultivation and evaluated 14 soil physio-chemical features for constructing fine-resolution maps. The robustness, versatility, and predictive ability of two statistical learning models in correctly classifying the soil profile to clusters were tested. Calcium, sand, soil organic carbon, phosphorous, potassium, and sodium were found to be most influential in classifying the representative TE. Random Forest model was found to be the best performing with average prediction accuracy above 85% in all test settings which reached 100% in some. The optimal training population size for prediction was found to be 70–80%. The spatial distribution of sodium in Delhi was found to be aligned with the low yield of safflower emphasizing the importance of fine-resolution soil mapping to design a field experiment and optimize the nutrient supply. Fine-resolution mapping not only enhance soil management strategies but also support government initiatives such as soil health cards, delineation of cultivable land, and risk assessments in crop-growing areas. [ABSTRACT FROM AUTHOR]

In most tropical climates, one of the most serious natural dangers that negatively impacts agricultural operations in coastal regions is increasing sea levels because of climate alteration-induced soil salinity. This problem has become worse and has been happening more often in Vietnam's Mekong River Delta. Utilizing Sentinel-1 SAR C-band data in conjunction with 5 cutting-edge machine learning models—MLP-NN, RBF-NN, Gaussian Processes, SVR, and RF—the primary goal of the research is to map soil salinity invasion in Ben Tre region that is situated on the Mekong River Delta of Vietnam. In order to do this, 65 soil specimens were gathered in the grassland observation that took place between August 9 and 11, 2022, in accordance with the Sentinel-1 SAR images. The root-mean-square error, mean absolute error, and correlation coefficient were utilized to find and compare the performance of the 5 models. The GP model beat the other machine learning models and produced the best estimation performance (RMSE = 2.116, MAE = 1.247, and correlation coefficient = 0.904), according to the findings. We come to the conclusion that the latest machine learning models may be utilized to map the salinity of the soil in the Delta regions, offering a helpful tool to help farmers and policy makers choose more suitable crop varieties in light of climate change. [ABSTRACT FROM AUTHOR]

Recent improvements in soil moisture mapping using satellites provide estimates at higher spatial and temporal resolutions. The accuracy in alpine regions is, however, still not well understood. The main objective of this study is to evaluate the accuracy of the experimental ASCAT-DIREX soil moisture product in a small alpine catchment and to identify factors that control the soil moisture agreement between the satellite estimates and in situ observations in open and forest sites. The analysis is carried out in the experimental mountain catchment of Jalovecký Creek, situated in the Western Tatra Mountains (Slovakia). The satellite soil moisture estimates are derived by merging the ASCAT and Sentinel-1 retrievals (the ASCAT-DIREX dataset), providing relative daily soil moisture estimates at 500 m spatial resolution in the period 2012–2019. The soil water estimates represent four characteristic timescales of 1, 2, 5, and 10 days, which are compared with in situ topsoil moisture observations. The results show that the correlation between satellite-derived and in situ soil moisture is larger at the open site and for larger characteristic timescales (10 days). The correlations have a strong seasonal pattern, showing low (negative) correlations in winter and spring and larger (more than 0.5) correlations in summer and autumn. The main reason for low correlations in winter and spring is insufficient masking of the snowpack. Using local snow data masks and soil moisture retrieval in the period December–March, improves the soil moisture agreement in April was improved from negative correlations to 0.68 at the open site and 0.92 at the forest site. Low soil moisture correlations in the summer months may also be due to small-scale precipitation variability and vegetation dynamics mapping, which result in satellite soil moisture overestimation. [ABSTRACT FROM AUTHOR]

Context: This research investigates the advantages of real-time monitoring of soil quality for various land management practices. It also highlights the significance of spatio-temporal soil modeling and mapping in providing a clear and visual understanding of how aridity changes over time and across different locations. Aims: This paper aims to provide a comprehensive guide to the key processes required for the development of a laboratory-based soil quality monitoring system. Methods: The applied methodologies involved the processes of sensor deployment, data acquisition infrastructure establishment, and sensor calibration. These procedures culminated in the development of a soil quality assessment model that was subsequently subjected to two months of laboratory testing using three distinct soil types. The analysis yielded a strong positive linear correlation between the measured and predicted soil quality values. Key Results: As expected, the assimilation of prior soil quality estimates within the modeling framework demonstrated a significant enhancement in the accuracy of real-time soil quality estimations. Conclusions: This research promotes the importance of iterative improvements of the soil quality monitoring system. The need for a long-term perspective and a plan for maintenance and continuous improvement of such systems in the ecosystem is important to improve the ease of making predictions to avoid soil aridization. The results of this research will be useful for researchers and practitioners involved in the design and implementation of soil monitoring systems. [ABSTRACT FROM AUTHOR]

Abstract Accurate information on the distribution of regional black-soil resource is one of the important elements for the sustainable management of soils. And its results can provide decision makers with robust data that can be translated into better decision making. This study utilized all Sentinel-2 images covering the study area from April to July in 2022. After masking clouds, all images were synthesized monthly. Based on the revised random forest classification algorithm, model performance using different feature combination programs were evaluated to search for an efficient, high-precision method for mapping black-soil resource. The impact on model performance of adding data from temperature, precipitation and slope geographic covariates was analyzed. And the robustness of the model was verified using Landsat-8 data with lower spatial resolution. The results showed that (1) the model based on multi-temporal ensemble features for mapping black-soil resource shows the best performance, with an OA of 94.6%; (2) adding temperature covariate can effectively improve the accuracy of black-soil resource mapping; (3) compared to the sentinel data, the performance of the model based on Landsat-8 data is reduced but still plausible, verifying the robustness of the model. This study provides a robust method to improve model performance for rapid mapping of black-soil resource.

Strong human activities greatly challenge the high-accuracy spatial prediction of soil pollutants and their speciation. This study first determined three auxiliary variables of soil total arsenic (TA) in a typical strong human-affected area, namely in-situ portable X-ray fluorescence (PXRF) TA calibrated by robust geographically weighted regression (RGWR), atmospheric deposition information simulated by atmospheric diffusion model (AERMOD), and land-use types. Then, robust residual cokriging with the above three auxiliary variables (RRCoK-RCPXRF/AD/LUT) was proposed to spatially predict soil TA. Finally, RGWR-robust ordinary kriging (RGWR-ROK) with the RRCoK-predicted soil TA was proposed to spatially predict soil As(III). The results show that: (i) RGWR obtained a higher spatial calibration accuracy (RI = 64.78%) for in-situ PXRF TA than the basic geographically weighted regression and traditionally-used ordinary least squares; (ii) The effect of auxiliary variables and model robustness on the prediction accuracy of soil TA is significant (RI > 14.33%); (iii) RRCoK-RCPXRF/AD/LUT achieved a higher prediction accuracy (RI = 58.87%) for soil TA than the other six traditional models; and (iv) RGWR-ROK achieved a higher prediction accuracy (RI = 55.26%) for soil As(III) than the other three traditional models. Therefore, this study provided a cost-effective solution for high-accuracy spatial prediction of soil pollutants and their speciation in strong human-affected areas. [Display omitted] • Suitable auxiliary variables were explored to map soil total arsenic (TA). • RGWR was constructed for high-accuracy spatial calibration of in-situ PXRF TA. • Atmospheric deposition information was simulated by the atmospheric diffusion model. • RRCoK-RCPXRF/AD/LUT was proposed to map soil TA accurately. • RGWR-ROK with the predicted TA was proposed to map soil As(III) accurately. [ABSTRACT FROM AUTHOR]

The use of remote sensing techniques for mapping soil organic matter (SOM) in black soil regions is well established. However, in areas where Planosols are interspersed with non-Planosols, tilling impacts the soil spectra of tilled soils at varying times and to different extents. As a result, errors may arise when modeling Planosols and non-Planosols collectively using conventional methods. This study developed a probability hybrid model specifically designed for the interlayered zones of Planosol and non-Planosol soils to accurately reflect the content and spatial distribution of SOM. A total of 712 topsoil samples were collected from the 852 Farm, a typical area with the interlayered zones of Planosol and non-Planosol soils in northeastern China. Cloud-free Sentinel-2 images were obtained during the bare soil period from April to May between 2021 and 2023. The spatial distribution of Planosol was detected, and the probability of soil classification was calculated using a random forest model. Based on soil classification probabilities, global models, multi-temporal ordinary hybrid models, and multi-temporal probability hybrid models were developed respectively. The results of SOM mapping using these different strategies were compared. Under seasonal reductive leaching, Planosol exhibits a distinct eluvial horizon beneath the topsoil. Long-term tilling leads to the mixing of this eluvial horizon with the topsoil in Planosol, resulting in spectral characteristics that differ significantly from those of other soil types. Accordingly, we propose a new remote sensing index—the Normalized Difference Planosol Index (NDPI), to reflect the upturning degree of the eluvial horizon and get "whiteness degree" information. We evaluated the effect of adding this index as an input on the detection of Planosol and the accuracy of SOM mapping. The results of the study show that (1) May is the optimal time window for SOM mapping and Planosol detection in the typical interlayered area of Planosol and non-Planosol soils. (2) Based on the random forest model combined with multi-period May bare soil images can accurately detect the spatial distribution of Planosol with the highest accuracy, the overall accuracy is 97.66 %; (3) The hybrid models outperform the global model, with the probability hybrid model achieving the highest accuracy (R2=0.8056, RMSE=4.2869 g/kg) and the mapping is more continuous and smoother. (4) The inclusion of NDPI improves the accuracy of Planosol spatial distribution detection and SOM mapping in Planosol areas, resulting in an increase in the Kappa coefficient by 0.0168 and an improvement in R2 by 0.0122. The present study innovatively utilizes remote sensing imagery to monitor Planosol, thus expanding the application of remote sensing technology in digital soil mapping. • Understanding eluvial horizon upturning is key for monitoring Planosols. • Enhancing "whiteness degree" information optimizes Planosol monitoring accuracy. • May is optimal for Planosol detection and SOM mapping in typical Planosol regions of Northeast China. • Prior knowledge with a probability hybrid model boosts SOM mapping accuracy. [ABSTRACT FROM AUTHOR]

Topsoil compaction is a persistent problem in minesoils, jeopardizing the revegetation and ecological reclamation of the mined land. Evaluation of soil structural quality (Sq) through quantitative methods is usually labor-intensive and/or costly, especially if a large area has to be examined. Therefore, reconciling cost-effective and accurate diagnose of minesoil Sq is crucial. The Visual Evaluation of Soil Structure (VESS) is a spade-based method scoring the soil Sq from 1 (good) to 5 (poor), which has not yet been validated for minesoils, and this was exactly the aim of this study. We made use of our long-term field experiment where quantitative physical attributes differed between perennial grasses used for minesoil revegetation, creating a Sq range to be screened by VESS. The minesoil, located in Southern Brazil, was revegetated for 14.3 years with Hemarthria altissima , Paspalum notatum , Cynodon dactylon , and Urochloa brizantha. The Sq of the minesoil (0.00–0.10 and 0.10–0.20 m layer) was evaluated by VESS and tensile strength of aggregates (TS), soil macroaggregates and microaggregates (%), soil organic matter (SOM) content, bulk density (BD), macroporosity (MaP), microporosity, total porosity (TP), and soil penetration resistance (PR). Through significant correlations between VESS scores and TS, MaP, macroaggregates (%), microaggregates (%), TP, SOM and especially BD (r = 0.60) and PR (r = 0.56), we found VESS to be a suitable method for reliable assessment of minesoil Sq. VESS scores 2.0–3.1 confirmed improved Sq at 0.00–0.10 m compared to 0.10–0.20 m (2.7–3.5), and this was supported by the ordination of 0.00–0.10 m samples together with SOM, macroaggregates (%), MaP and TP by principal component analysis. Moreover, VESS confirmed improved Sq in H. altissima (2.7) compared to C. dactylon (3.6) at 0.10–0.20 m, likely due to gains in soil MaP, TP, macroaggregates (%) and SOM. In this pioneering study we validated VESS as a practical and science-grounded method to monitor the Sq of a clayey subtropical minesoil. • 14.3 years of minesoil revegetation with grasses enhances topsoil structural quality. • VESS correlates significantly with six minesoil physical variables and organic matter. • VESS detected less (0.00–0.10 m) and more (0.10–0.20 m) compacted layers. • VESS scores respond sensitively to grasses used for revegetation of clayey minesoil. • Validation of VESS to monitor the structural quality of revegetated minesoils. [ABSTRACT FROM AUTHOR]

[Display omitted] • Climate variables have dominant effects on mapping TN and TK. • Soil properties and climate variables made the largest contribution to map TP. • Introducing remote sensing images and soil management factors failed to improve prediction accuracy of soil nutrients. • Introducing CARS algorithm failed to improve prediction accuracy of soil nutrients. • Maps of location-specific primary covariate for different soil nutrients were produced. Detailed maps of soil nutrients are crucial for farmland management and agricultural production. However, soil nutrients are largely affected by various natural and anthropogenic factors, making it a challenging task to make clear its spatial distribution. To fill this gap, we produced the fine maps (30 m) of total content of nitrogen (TN), phosphorus (TP), and potassium (TK) in the farmland across Jiangxi Province in Southern China and quantified overall contribution of different covariates, as well as mapped the location-specific primary variable for predicting soil nutrients using an interpretable machine learning model. Our results reveal that random forest outperformed Cubist and XGBoost for mapping TN, TP and TK. The optimal models achieved R2 of 0.29, 0.29, 0.52 and RMSE of 0.43, 0.15 and 3.42 g kg−1 for TN, TP and TK, respectively. Moreover, we found both introducing competitive adaptive reweighted sampling algorithm and incorporating remote sensing images as well as soil management factors failed to clearly improve prediction accuracy of TN, TP and TK. In addition, climate variables had dominant overall effects on mapping TN (60.2 %) and TK (62.7 %), while soil properties made the largest contribution to mapping TP (34.3 %). The aridity index (46.90 %), mean annual solar radiation (34.94 %), and mean annual temperature (26.92 %) is the location-specific primary variable for mapping TN, TP, and TK in largest proportion of the study area, respectively. The soil nutrients maps we produced could function as baseline maps for monitoring spatio-temporal variation of soil nutrients, and our results could provide valuable implications for making more specific and efficient measures for soil management. [ABSTRACT FROM AUTHOR]

• We propose a hybrid model to predict soil depth. • Soil depth prediction by mechanistic, empirical, and hybrid models was compared. • The mechanistic model performed better for shallow soils. • The Empirical model was more suitable for deeper soils. • The hybrid model showed the best performance. Soil thickness is an important property as it influences the landscape dynamics, partakes part in hydrologic and geomorphologic processes, and controls water saturation and soil moisture, which are directly related to agricultural production. However, soil thickness data are difficult to obtain in situ , especially in areas with deep soils (>2 m). In this study, we developed and compared three models to predict soil thickness. First, we developed a mechanistic model which uses physical equations from a landscape evolution model applied to a digital elevation model (DEM) (30 m spatial resolution). We evaluated the inclusion of parameters derived from the soil parent material, including erosion and sediment deposition. Second, we developed an empirical model using terrain derivatives obtained from a 30-m DEM, using a Random Forest algorithm. This model was calibrated using 1,362 soil thickness data collected in field as right censored data. We implemented a hybrid model using the residual from the mechanistic model as a dependent variable in the empirical model. The models were validated with 214 soil observation points collected in field as right censored data and 12 data points with real data. The result was added back to predictions of the mechanistic model. For all models, we verified coherence with a soil map at 1:100,000. The models were also evaluated considering changes in the spatial resolution. The mechanistic model was improved when parent material parameters were added. The mechanistic models performed better in areas with shallow soils (<1 m), whereas the empirical model was better in predicting deeper soils and was more coherent with a soil class map. Model performance could be further improved when updating DEM data to a 5-m resolution. As expected, the hybrid model could combine the model performances and improve the predictions. However, the predictions remained poor for shallow soils. [ABSTRACT FROM AUTHOR]

The accuracy of soil heavy metal pollution mapping is heavily reliant on the sampling strategies utilized in both the preliminary and detailed survey stages of site investigations. This study introduces an entropy-informed multi-stage sampling design (EIMSD) method that leverages preliminary survey data as background information and utilizes relative entropy to progressively select sampling points in detailed surveys. Results indicate that the EIMSD method outperforms the grid sampling design (GSD) and conventional sampling design (CSD) methods across both hypothetical and real-world study areas. This superiority is evidenced by a notable rise in R2 values, ranging from 6.4% to 60.4% and a decrease in RMSE values from 16.7% to 54.0% relative to GSD, and a similar trend with an increase in R2 values from 6.7% to 44.1% and a reduction in RMSE values from 16.5% to 39.7% when compared to CSD. This study also investigates the optimal configurations for EIMSD, focusing on the number of detailed sampling points per stage (N add) and the ratio of preliminary-to-detailed survey sample sizes (N p / N d) when the sum of N p and N d is held constant. Our findings highlight that adding one detailed sampling point per stage (N add = 1) is the most effective. For areas with strong spatial variability, a larger N p / N d value of approximately 3/2 is recommended, whereas a ratio close to 1 is apt for areas with moderate variability. Conversely, for areas with weak variability, a smaller N p / N d value of about 2/3 is advised. EIMSD provides a more detailed and accurate map of soil heavy metal contamination, facilitating more targeted and effective remediation strategies. [Display omitted] • A novel entropy-informed multi-stage sampling design (EIMSD) method was proposed. • EIMSD's mapping accuracy outperforms that of conventional sampling methods. • The optimal configurations for EIMSD are explored to guide future applications. [ABSTRACT FROM AUTHOR]

Spatial information on the soil carbon-to-nitrogen (C:N) ratio is essential for sustainable soil use and management. The unprecedented availability of Sentinel optical and radar data on cloud computing platforms, such as the Google Earth Engine (GEE), has created new possibilities for developing soil prediction models from the local scale to the planetary scale. However, there is a paucity of literature on the effects of Sentinel sensor selection and integration and radar data utilization strategies on mapping the C:N ratio. In this study, we explored the use of multiyear Sentinel-1 radar and Sentinel-2 optical data obtained from the GEE platform combined with the digital soil mapping (DSM) technique to map the soil C:N ratio at the European scale. The performance of soil prediction models, which were constructed using two modeling techniques (random forest and support vector machine), derived under multiple scenarios based on optical, radar and commonly used auxiliary data (climatic and topographic variables) combined with the LUCAS 2018 soil dataset, was evaluated by a cross-validation technique. The results showed that the modeling performance varied with the selection and integration of Sentinel observations, as well as the configuration of the radar data. Models based on single polarization performed the worst across all scenarios related to Sentinel-1, with cross-polarization performing better than copolarization. Models that utilized Sentinel-1 data from ascending orbits outperformed those that utilized data from descending orbits. The application of Sentinel-1 backscatter information derived from different orbits and polarization modes resulted in improved prediction accuracy. Our study also demonstrated the potential of integrating multiyear Sentinel satellite data via the GEE to map the continental-scale C:N ratio. The model based on Sentinel-1 data outperformed the one built on Sentinel-2 data, whereas combining Sentinel-2 optical data with Sentinel-1 radar data led to more accurate predictions. The variable importance results indicated that optical data and backscattering information from Sentinel observations are the most important groups of variables for soil C:N ratio mapping compared to the other variable groups (terrain and climate data). The digital soil maps generated under the different scenarios exhibited detailed patterns with significant spatial variation, with similar overall trends but slightly different details. • Soil C:N ratios were mapped at the European scale using Sentinel radar and optical data on GEE. • The strategy for using Sentinel observations greatly impacted the prediction accuracy. • Predictions can benefit from information on different orbits and polarizations. • Optical and backscatter information are the most important groups of variables. • Our results indicated that the GEE platform is a promising tool for developing DSM products. [ABSTRACT FROM AUTHOR]

This study addresses the challenge of mapping gully erosion susceptibility, which is often hindered by limited observed data, the complexity of controlling factors, and the uncertainties associated with characterizing these factors. We utilized a semi-quantitative modeling approach that integrates field-based data and ten controlling factors in the Chemoga watershed of Ethiopia's Upper Blue Nile basin. The resulting gully erosion susceptibility map was compared with a random forest-based approach to assess the methodological applicability. Additionally, an independent dataset from adjacent watersheds was used to validate the approach. The findings revealed that certain landscape positions with specific elevation ranges and slope steepness were more susceptible to gully erosion due to factors such as rainfall, lithological formations, soil characteristics, and agricultural activities. Approximately 10% of the watershed area was affected by gully erosion, with varying susceptibility levels. The comparison between the semi-quantitative and random forest approaches demonstrates a total agreement of around 58%, with minimal differences in susceptibility classes. The study also highlights a strong agreement between simulated and observed susceptibility maps, with a 76% value for the simulation and a lower 48% agreement for the random forest approach. Furthermore, in the adjacent watershed, 65% of the area exhibits no discrepancies between observed and simulated maps. This suggests that the semi-quantitative approach is effective in extrapolating gully erosion susceptibility when detailed data is limited, offering a cost-effective and efficient solution. The study emphasizes the utility of the semi-quantitative modeling approach in mapping gully erosion susceptibility and its potential for practical applications in land management and intervention strategies. • Mapped gully erosion susceptibility in Chemoga watershed, Upper Blue Nile basin, Ethiopia. • Used a semi-quantitative modeling approach with field data and ten factors, compared to a random forest approach. • Certain landscape positions with specific elevation and slope ranges are more prone to gully erosion. • About 10% of the watershed is affected by gully erosion, with varying susceptibility levels. • 58% agreement between semi-quantitative and random forest approaches; 76% agreement with observed maps. [ABSTRACT FROM AUTHOR]