Search Results (1,707)

Degradation and sealing are still frequent in soil management today despite intensive research. An unsatisfactory assessment of soil key components and soil health still limits sustainable land use. For the future evaluation of soil health, soils under productive use have been compared with natural and semi-natural soils using thermogravimetric fingerprinting of air-dried soil samples. This approach has led to a more precise quantification of known relationships and the discovery of several new ones between soil components that have evolved over thousands of years of soil formation without human intervention, each changing in a specific way due to land use. The use-related deviations from the natural soil condition allow a distinction between natural soils, disturbed soils, and soil-like carbon-containing mineral mixtures (e.g., compost, horticultural substrates). Carbon added to soils with fresh organic residues or from anthropogenic (soot, slag) or geological (coal) sources can be distinguished from soil organic matter (humus) accumulated during soil genesis, regardless of extreme chemical heterogeneity. The degree of carbon sequestration in soils is easy to quantify. Using near-natural soils as a reference, considering bound water seems to be a suitable starting point for the experimental definition of soil health. An elucidation of the causal relationships between the soil components used should accompany it. Full article

This study critically examines the applications of the Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) in hydrological research from 2000 to 2023, with a focus on its use in event-based and continuous simulations. A bibliometric analysis reveals a steady growth in research productivity and identifies key thematic areas, including hydrologic modeling, climate change impact assessment, and land use analysis. Event-based modeling, employing methods such as the SCS curve number (CN) and SCS unit hydrograph, demonstrates exceptional performance in simulating short-term hydrological responses, particularly in flood risk management and stormwater applications. In contrast, continuous modeling excels in capturing long-term processes, such as soil moisture dynamics and groundwater contributions, using methodologies like soil moisture accounting and linear reservoir baseflow approaches, which are critical for water resource planning and climate resilience studies. This review highlights the adaptability of HEC-HMS, showcasing its successful integration of event-based precision and continuous process modeling through hybrid approaches, enabling robust analyses across temporal scales. By synthesizing methodologies, performance metrics, and case studies, this study offers practical insights for selecting appropriate modeling techniques tailored to specific hydrological objectives. Moreover, it identifies critical research gaps, including the need for advanced calibration methods, enhanced parameter sensitivity analyses, and improved integration with hydraulic models. These findings highlight HEC-HMS’s critical role in improving hydrological research and give a thorough foundation for its use in addressing current water resource concerns. Full article

High-resolution water depth data are of great significance in island research and coastal ecosystem monitoring. However, the acquisition of high-resolution imagery has been a challenge due to the difficulties and high costs associated with obtaining such data. To address this issue, this study proposes a water depth inversion method based on Gaofen-1 (GF-1) satellite data, which integrates multi-source satellite data to obtain high-resolution bathymetric data. Specifically, the research utilizes bathymetric data derived from Sentinel-2 and Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) as prior information, combined with high-resolution imagery obtained from the GF-1 satellite constellation (GF-1B/C/D). Then, it employs a scale-invariant effect to map bathymetry with a spatial resolution of 2 m, applied to four study areas in the Pacific Islands. The results are further evaluated using ICESat-2 data, which demonstrate that the water depth inversion results from this study possess high accuracy, with $R^2$ values exceeding 0.85, root mean square error (RMSE) ranging from 0.56 to 0.90 m, with an average of 0.7125 m, and mean absolute error (MAE) ranging from 0.43 to 0.76 m, with an average of 0.55 m. Additionally, this paper discusses the applicability of the scale-invariant assumption in this research and the improvements of the quadratic polynomial ratio model (QPRM) method compared to the classical linear ratio model (CLRM) method. The findings indicate that the integration of multi-source satellite remote sensing data based on the scale-invariant effect can effectively obtain high-precision, high-resolution bathymetric data, providing significant reference value for the application of GF-1 satellites in high-resolution bathymetry mapping. Full article

Evapotranspiration (ET) plays a crucial role in the surface water cycle and energy balance, and accurate ET estimation is essential for study in various domains, including agricultural irrigation, drought monitoring, and water resource management. Remote sensing (RS) technology presents an efficient approach for estimating ET at regional scales; however, existing RS retrieval algorithms for ET are intricate and necessitate a multitude of parameters. The land surface temperature–vegetation index (LST-VI) space method and statistical regression by machine learning (ML) offer the benefits of simplicity and straightforward implementation. This study endeavors to identify the optimal long-term sequence LST-VI space method and ML for ET estimation under conditions of limited observed variables, (LST, VI, and near-surface air temperature). A comparative analysis of their performance is undertaken using ground-based flux observations and MOD16 ET data. The findings can be summarized as follows: (1) Long-term remote sensing data can furnish a more comprehensive background field for the LST-VI space, achieving superior fitting accuracy for wet and dry edges, thereby enabling precise ET estimation with the following metrics: correlation coefficient (r) = 0.68, root mean square error (RMSE) = 0.76 mm/d, mean absolute error (MAE) = 0.49 mm/d, and mean bias error (MBE) = −0.14 mm. (2) ML generally produces more accurate ET estimates, with the Random Forest Regressor (RFR) demonstrating the highest accuracy: r = 0.79, RMSE = 0.61 mm/d, MAE = 0.42 mm/d, and MBE = −0.02 mm. (3) Both ET estimates derived from the LST-VI space and ML exhibit spatial distribution characteristics comparable to those of MOD16 ET data, further attesting to the efficacy of these two algorithms. Nevertheless, when compared to MOD16 data, both approaches exhibit varying degrees of underestimation. The results of this study can contribute to water resource management and offer a fresh perspective on remote sensing estimation methods for ET. Full article

Rapid and accurate leaf area index (LAI) determination is important for monitoring daylily growth, yield estimation, and field management. Because of low estimation accuracy of empirical models based on single-source data, we proposed a machine-learning algorithm combining optical and microwave remote-sensing data as well as the random forest regression (RFR) importance score to select features. A high-precision LAI estimation model for daylilies was constructed by optimizing feature combinations. The RFR importance score screened the top five important features, including vegetation indices land surface water index (LSWI), generalized difference vegetation index (GDVI), normalized difference yellowness index (NDYI), and backscatter coefficients VV and VH. Vegetation index features characterized canopy moisture and the color of daylilies, and the backscatter coefficient reflected dielectric properties and geometric structure. The selected features were sensitive to daylily LAI. The RFR algorithm had good anti-noise performance and strong fitting ability; thus, its accuracy was better than the partial least squares regression and artificial neural network models. Synergistic optical and microwave data more comprehensively reflected the physical and chemical properties of daylilies, making the RFR-VI-BC05 model after feature selection better than the others ( r = 0.711, RMSE = 0.498, and NRMSE = 9.10%). This study expanded methods for estimating daylily LAI by combining optical and radar data, providing technical support for daylily management. Full article

In the global context, transportation contributes 26% of the total CO₂ emissions, with land transport responsible for 92% of the emissions within the sector. Given this significant contribution to climate change, it is crucial to quantify vehicular impacts to implement effective mitigation strategies. This study introduces an innovative method for predicting fuel consumption and emissions of carbon monoxide, hydrocarbons, and nitrogen oxides in vehicles, based on instantaneous vehicle-specific power (VSP) and mean accumulated power. VSP is a parameter that measures a vehicle’s power in relation to its mass, providing an indicator of the efficiency with which the vehicle converts fuel into motion. This indicator is particularly useful for assessing how vehicles utilize their energy under different driving conditions and how this affects their fuel consumption and emissions. Using data collected from 10 vehicles over 2000 h and covering altitudes from 0 to 4000 m above sea level in Ecuador, the method not only improved the accuracy of consumption predictions, reducing the margin of error by up to 10% at high altitudes, but also provided a detailed understanding of how altitude affects both consumption and emissions. The precision of the new method was notable, with a standard deviation of only 0.25 L per 100 km, allowing for reliable estimates under various operational conditions. Interestingly, the study revealed an average increase in fuel consumption of 0.43 L per 1000 m of altitude gain, while CO₂ emissions showed a significant reduction from 260.93 g/km to 215.90 g/km when ascending from 500 m to 4000 m. These findings underscore the relevance of considering altitude in route planning, especially in mountainous terrains, to optimize performance and environmental sustainability. However, the study also indicated an increase in CO and NOx emissions with altitude, a challenge that highlights the need for integrated strategies addressing both fuel consumption and air quality. Collectively, the results emphasized the complex interplay between altitude, energy efficiency, and vehicular emissions, underscoring the importance of a holistic approach to transportation management, to minimize adverse environmental impacts and promote sustainability. Full article

With the acceleration of urbanization and due to the impact of climate warming, economic losses caused by urban waterlogging have become increasingly severe. To reduce urban waterlogging losses under the constraints of limited economic and time resources, it is essential to identify key waterlogging-prone areas for focused governance. Previous studies have often overlooked the spatial heterogeneity in the distribution of value and risk. Therefore, identifying the spatial distribution of land value and risk, and analyzing their spatial overlay effects, is crucial. This study constructs a “Waterlogging-Value-Loss” spatial analysis framework based on the hydrological and value attributes of land use. By developing a 1D–2D coupled hydrodynamic model, the study determines waterlogging risk distributions for different return periods. Combining these results with disaster loss curves, it evaluates land-use values and employs the bivariate local Moran’s I index to comprehensively assess waterlogging risk and land value, thereby identifying key areas. Finally, the SHAP method is used to quantify the contribution of water depth and value to waterlogging losses, and a Birch-K-means combined clustering algorithm is applied to identify dominant factors at the street scale. Using the central urban area of Beijing as a case study, the results reveal significant spatial heterogeneity in the distribution of urban waterlogging risks and values. Compared to traditional assessment methods that only consider waterlogging risk, the bivariate spatial correlation analysis method places greater emphasis on high-value areas, while reducing excessive attention to low-value, high-risk areas, significantly improving the accuracy of identifying key waterlogging-prone areas. Furthermore, the Birch-K-means combined clustering algorithm classifies streets into three types based on dominant factors of loss: water depth-dominated (W), value-dominated (V), and combined-dominated (WV). The study finds that as the return period increases, the dominant factors for 22.23% of streets change, with the proportion of W-type streets rising from 29% to 38%. This study provides a novel analytical framework that enhances the precision of urban flood prevention and disaster mitigation efforts. It helps decision-makers formulate more effective measures to prevent and reduce urban waterlogging disasters. Full article

To enhance the safety of hybrid inspection robots (HIRs) landing on power transmission lines (PTLs) with inclination and flexibility, this research derives a coupled dynamic model for a developed flying–walking power transmission line inspection robot (FPTLIR) to analyze the dynamic behavior of the FPTLIR during the landing process. The model uses the absolute nodal coordinate formulation (ANCF) for the dynamics of the PTL and the Hunt–Crossley theory for the contact model, integrating these components with the Euler–Lagrange method. A modular simulation was conducted to evaluate the effects of different landing positions and robot masses. An experimental platform was designed to evaluate the landing performance and validate the model, which confirms the method’s accuracy, with a mean relative Z-displacement error of 0.004. Simulation results indicate that Z-displacement decreases with increased landing distance, with the farthest point showing only 34.4% of the Z-displacement observed at the closest point. Conversely, roll increases, with the closest point exhibiting 3.7% of the roll at the farthest point. Both Z-displacement and roll are directly correlated with the robot’s mass; the lightest robot’s Z-displacement and roll are 9.2% and 12.8% of those of the heaviest robot, highlighting the safety advantage of lighter robots. This research enables precise analysis and prediction of the system’s responses during the landing process, providing significant insights for safe landing and design. Full article

Machine learning (ML) has revolutionized resource management in agriculture by analyzing vast amounts of data and creating precise predictive models. Precision agriculture improves agricultural productivity and profitability while reducing costs and environmental impact. However, ML implementation faces challenges such as managing large volumes of data and adequate infrastructure. Despite significant advances in ML applications in sustainable agriculture, there is still a lack of deep and systematic understanding in several areas. Challenges include integrating data sources and adapting models to local conditions. This research aims to identify research trends and key players associated with ML use in sustainable agriculture. A systematic review was conducted using the PRISMA methodology by a bibliometric analysis to capture relevant studies from the Scopus and Web of Science databases. The study analyzed the ML literature in sustainable agriculture between 2007 and 2025, identifying 124 articles that meet the criteria for certainty assessment. The findings show a quadratic polynomial growth in the publication of articles on ML in sustainable agriculture, with a notable increase of up to 91% per year. The most productive years were 2024, 2022, and 2023, demonstrating a growing interest in the field. The study highlights the importance of integrating data from multiple sources for improved decision making, soil health monitoring, and understanding the interaction between climate, topography, and soil properties with agricultural land use and crop patterns. Furthermore, ML in sustainable agriculture has evolved from understanding weather data to integrating advanced technologies like the Internet of Things, remote sensing, and smart farming. Finally, the research agenda highlights the need for the deepening and expansion of predominant concepts, such as deep learning and smart farming, to develop more detailed and specialized studies and explore new applications to maximize the benefits of ML in agricultural sustainability. Full article

High-intensity coal mining significantly impacts the surrounding soil moisture (SM) through water seepage, artificial watering for dust suppression, and geomorphological changes, which will lead to ecological degradation. This study explores the impact of open-pit mines on surface SM in an arid–semiarid open-pit mine area of China over the period from 2000 to 2021. Using the temperature vegetation dryness index (TVDI), derived from the Land Surface Temperature–Normalized Difference Vegetation Index (LST-NDVI) feature space, this paper proposes a method—the TVDI of climate factor separation (TVDI-CFS)—to disentangle the influence of climate factors. The approach employs the Geographically and Temporally Weighted Regression (GTWR) model to isolate the influence of temperature and precipitation, allowing for a precise quantification of mining-induced disturbances. Additional techniques, such as buffer analysis and the Dynamic Time Warping (DTW) algorithm, are used to examine spatiotemporal variations and identify disturbance years. The results indicate that mining impacts on surface SM vary spatially, with disturbance distances of 420–660 m and strong distance decay patterns. Mining expansion has increased disturbance ranges and intensified cumulative effects. Inter-annual TVDI trends from 2015 to 2021 reveal clustered disturbances in alignment with mining directions, with the largest affected area in 2016. These findings provide a systematic valuable insights for ecological restoration and sustainable environmental management in mining-affected areas. Full article

Accurately and objectively evaluating the spatiotemporal dynamic changes in CO₂ emissions is significant for human sustainable development. However, traditional CO₂ emissions estimates, typically derived from national or provincial energy statistics, often lack spatial information. To develop a more accurate spatiotemporal model for estimating CO₂ emissions, this research innovatively incorporates nighttime light data, vegetation cover data, land use data, and geographic big data into the study of pixel-level urban CO₂ emissions estimation in China. The proposed method significantly improves the precision of CO₂ emissions estimation, achieving an average accuracy of 83.76%. This study reveals that the type of decoupling varies according to different scales, with more negative decoupling occurring in northern cities. Factors such as the per capita GDP and urbanization contribute to the increase in CO₂ emissions, while the structure of industry and energy consumption play a crucial role in reducing them. The findings in this study could potentially be used to develop tailored carbon reduction strategies for different spatial scales in China. Full article

Rapidly obtaining information on the content and spatial distribution of soil organic carbon (SOC) in farmland is crucial for evaluating regional soil quality, land degradation, and crop yield. This study focuses on mountain soils in various crop cultivation areas in Shangzhou District, Shangluo City, Southern Shaanxi, utilizing ZY1-02D hyperspectral satellite imagery, field-measured hyperspectral data, and field sampling data to achieve precise inversion and spatial mapping of the SOC content. First, to address spectral bias caused by environmental factors, the Spectral Space Transformation (SST) algorithm was employed to establish a transfer relationship between measured and satellite image spectra, enabling systematic correction of the image spectra. Subsequently, multiple spectral transformation methods, including continuous wavelet transform (CWT), reciprocal, first-order derivative, second-order derivative, and continuum removal, were applied to the corrected spectral data to enhance their spectral response characteristics. For feature band selection, three methods were utilized: Variable Importance Projection (VIP), Competitive Adaptive Reweighted Sampling (CARS), and Stepwise Projection Algorithm (SPA). SOC content prediction was conducted using three models: partial least squares regression (PLSR), stepwise multiple linear regression (Step-MLR), and random forest (RF). Finally, leave-one-out cross-validation was employed to optimize the L4-CARS-RF model, which was selected for SOC spatial distribution mapping. The model achieved a coefficient of determination (R²) of 0.81, a root mean square error of prediction (RMSEP) of 1.54 g kg⁻¹, and a mean absolute error (MAE) of 1.37 g kg⁻¹. The results indicate that (1) the Spectral Space Transformation (SST) algorithm effectively eliminates environmental interference on image spectra, enhancing SOC prediction accuracy; (2) continuous wavelet transform significantly reduces data noise compared to other spectral processing methods, further improving SOC prediction accuracy; and (3) among feature band selection methods, the CARS algorithm demonstrated the best performance, achieving the highest SOC prediction accuracy when combined with the random forest model. These findings provide scientific methods and technical support for SOC monitoring and management in mountainous areas and offer valuable insights for assessing the long-term impacts of different crops on soil ecosystems. Full article

Tropical cyclones (TCs) frequently encompass multiple hazards, including extreme winds, intense rainfall, storm surges, flooding, lightning, and tornadoes. Accurate methods for forecasting TC tracks are essential to mitigate the loss of life and property associated with these hazards. Despite significant advancements, accurately forecasting the paths of TCs remains a challenge, particularly when they interact with complex land features, weaken into remnants after landfall, or are influenced by abnormal satellite observations. To address these challenges, we propose a generative adversarial network (GAN) model with a multi-scale architecture that processes input data at four distinct resolution levels. The model is designed to handle diverse inputs, including satellite cloud imagery, vorticity, wind speed, and geopotential height, and it features an advanced center detection algorithm to ensure precise TC center identification. Our model demonstrates robustness during testing, accurately predicting TC paths over both ocean and land while also identifying weak TC remnants. Compared to other deep learning approaches, our method achieves superior detection accuracy with an average error of 41.0 km for all landfalling TCs in Australia from 2015 to 2020. Notably, for five TCs with abnormal satellite observations, our model maintains high accuracy with a prediction error of 35.2 km, which is a scenario often overlooked by other approaches. Full article

Soil sand content is an important characterization index of soil texture, which directly affects soil water regulation, nutrient cycling, and crop growth potential. Therefore, its high-precision spatial distribution information is of great importance for agricultural resource management and land use. In this study, a remote sensing prediction method based on the combination of time-phase optimization and spectral feature preference is innovatively proposed for improving the mapping accuracy of the sand content in the till layer of a planosol area. The study first analyzed the prediction performance of single-time-phase images, screened the optimal time-phase (May), and constructed a single-time-phase model, which achieved significant prediction accuracy, with a coefficient of determination (R²) of 0.70 and a root mean square error (RMSE) of 1.26%. Subsequently, the model was further optimized by combining multiple time phases, and the prediction accuracy was improved to R² = 0.77 and the RMSE decreased to 1.10%. At the feature level, the recursive feature elimination (RF-RFE) method was utilized to preferentially select 19 key spectral variables from the initial feature set, among which the short-wave infrared bands (b11, b12) and the visible bands (b2, b3, b4) contributed most significantly to the prediction. Finally, the prediction accuracy was further improved to R² = 0.79 and RMSE = 1.05% by multi-temporal-multi-feature fusion modeling. The spatial distribution map of sand content generated by the optimized model shows that areas with high sand content are primarily located in the northern and central regions of Shuguang Farm. This study not only provides a new technical path for accurate mapping of soil texture in the planosol area, but also provides a reference for the improvement of remote sensing monitoring methods in other typical soil areas. The research results can provide a reference for mapping high-resolution soil sand maps over a wider area in the future. Full article

As a mining country, China faces enormous challenges in the context of the global commitment to achieve carbon neutrality. In order to achieve this goal, the Chinese government is actively promoting the green and low-carbon transformation of the energy system. Consequently, an increasing number of mines with poor production capacity and depleted resources are being closed down or eliminated, leading to a large quantity of stranded land resources that are now idle. However, in the process of rapid economic development, China is facing serious problems, such as land shortage and land use conflicts. Abandoned mining land (AML), as a kind of reserve land resource, has an important regulating role in solving the dilemma of land resource tension faced by national land spatial planning. In order to realize the rational planning and utilization of AML, this study proposes a high-precision AML planning model and simulates the planning of AML in multiple policy scenarios, using Ningbo City as an example. The results show that AML has great economic and ecological potential; the economic development scenario (EDS) enhanced the economic benefits of the mine region by 396%, and the ecological protection scenario (EPS) enhanced the ecological benefits of the mine region by 74.61%, when compared with the baseline scenario (BAU). The overall level of optimization is as follows: EDS > EPS > BAU. In addition, the optimal utilization of AML in all three scenarios significantly enhanced the ecological quality of the mining region, and the enhancement effect was EPS > BAU > EDS. Therefore, AML, as a kind of free land resource, has an important supporting effect for the spatial planning of the national territory. Furthermore, it is of great significance to scientifically and reasonably guide the optimal utilization of AML, according to the policy planning for future development, in order to achieve efficient economic development and improve the quality of the ecological environment. Full article