Search Results (3,807)

Accurate soil organic carbon (SOC) estimation is critical for assessing ecosystem services, carbon budgets, and informing sustainable land management, particularly in ecologically sensitive mountainous regions. This study focuses on modelling the spatial distribution of SOC within the heterogeneous volcanic landscape of the Nevado de Toluca (NdT), central Mexico, an area spanning 535.9 km² and characterised by diverse land uses, altitudinal gradients, and climatic regimes. Using 29 machine learning algorithms, we evaluated the predictive capacity of three key variables: land use, elevation, and the Normalised Difference Vegetation Index (NDVI) derived from satellite imagery. Complementary analyses were performed using the Bare Soil Index (BSI) and the Modified Soil-Adjusted Vegetation Index 2 (MSAVI2) to assess their relative performance. Among the tested models, the Quadratic Support Vector Machine (SVM) using NDVI, elevation, and land use emerged as the top-performing model, achieving a coefficient of determination (R²) of 0.84, indicating excellent predictive accuracy. Notably, 14 models surpassed the R² threshold of 0.80 when using NDVI and BSI as predictor variables, whereas MSAVI2-based models consistently underperformed (R² < 0.78). Validation plots demonstrated strong agreement between observed and predicted SOC values, confirming the robustness of the best-performing models. This research highlights the effectiveness of integrating multispectral remote sensing indices with advanced machine learning frameworks for SOC estimation in mountainous volcanic ecosystems Full article

Precision agriculture applies data-driven strategies to manage spatial and temporal variability within fields, aiming to increase productivity while minimizing pressure on natural resources. As interest in smart tillage systems expands, this study explores a central question: Can tillage tools be used to measure soil compaction during regular field operations? To investigate this, vibration data measurements were collected from a cultivator shank in the northeast of Kansas using the AVDAQ system. The test field soils were Reading silt loam and Eudora–Bismarck Grove silt loams. The relationship between shank vibrations, soil moisture (measured by a Hydrosense II soil–water sensor), and soil compaction (measured by a cone penetrometer) was evaluated using machine learning models. Both XGBoost and Random Forest demonstrated strong predictive performance, with Random Forest achieving a slightly higher correlation of 93.8% compared to 93.7% for XGBoost. Statistical analysis confirmed no significant difference between predicted and measured values, validating the accuracy and reliability of both models. Overall, the results demonstrate that combining vibration data with soil moisture data as model inputs enables accurate estimation of soil compaction, providing a foundation for future in situ soil sensing, reduced tillage intensity, and more sustainable cultivation practices. Full article

The Gonghe–Yushu Expressway (GYE) traverses the degrading permafrost region of the Qinghai–Xizang Plateau, where climate warming has resulted in widespread water ponding, posing significant engineering challenges. However, the spatiotemporal dynamics of this water accumulation and its impacts on permafrost embankment stability remain inadequately understood. This study integrates high-resolution unmanned aerial vehicle (UAV) remote sensing with ground-penetrating radar (GPR) to characterize the spatial patterns of water ponding and to quantify the spatial distribution, seasonal dynamics, and hydrothermal effects of roadside water on permafrost sections of the GYE. UAV-derived point cloud models, optical 3D models, and thermal infrared imagery reveal that approximately one-third of the 228 km study section of GYE exhibits water accumulation, predominantly occurring near the embankment toe in flat terrain or poorly drained areas. Seasonal monitoring showed a nearly 90% reduction in waterlogged areas from summer to winter, closely corresponding to climatic variations. Statistical analysis demonstrated significantly higher embankment distress rates in waterlogged areas (14.3%) compared to non-waterlogged areas (5.7%), indicating a strong correlation between surface water and accelerated permafrost degradation. Thermal analysis confirmed that waterlogged zones act as persistent heat sources, intensifying permafrost thaw and consequent embankment instability. GPR surveys identified notable subsurface disturbances beneath waterlogged sections, including a significant lowering of the permafrost table under the embankment and evidence of soil loosening due to hydrothermal erosion. These findings provide valuable insights into the spatiotemporal evolution of water accumulation along transportation corridors and inform the development of climate-adaptive strategies to mitigate water-induced risks in degrading permafrost regions. Full article

Understanding soil moisture’s spatiotemporal variations and the factors influencing it is crucial for the restoration and growth of vegetation across the Loess Plateau, particularly in the Ziwuling region. This study employs soil moisture remote sensing data, complemented by information on soil properties, environmental conditions, and topography, to examine soil moisture variability within the Ziwuling region between 2001 and 2020. Using trend analysis, geographic detectors, and multi-scale geographic weighting techniques, this research aims to elucidate the effects of driving factors on soil moisture’s spatiotemporal patterns. The findings indicate the following: (1) Over the study period, the mean soil moisture in the Ziwuling region exhibited a relatively stable declining trend, with an annual decrease of −0.00047 m³/(m³·a). Spatially, higher soil moisture levels were observed in the south-central area, while lower levels occurred in the northern, western, and eastern peripheries. (2) Geoprobe analysis illustrated that the normalized difference vegetation index (NDVI) had the most notable effect on the spatial distribution of soil moisture in the region. As a direct indicator of vegetation cover, NDVI strongly affects soil moisture distribution through ecological and hydrological processes. Following NDVI, average annual potential evapotranspiration and annual precipitation were identified as the next most influential factors. The combined effect of these factors on soil moisture surpassed that of individual factors, with the interaction between NDVI and annual precipitation being particularly pronounced, predominantly controlling the spatial variability of soil moisture in the Ziwuling region. (3) Different factors exhibited varying effects on soil moisture levels. Notably, slope and elevation consistently had negative impacts, whereas variables such as soil texture (loam and sand), land use, temperature, precipitation, NDVI, and slope aspect showed bidirectional influences. This study offers a comprehensive analysis of the spatiotemporal variability of soil moisture and its controlling factors in the Ziwuling region, ultimately offering a scientific basis to support ecological restoration and sustainable development initiatives on the Loess Plateau. Full article

The increasing pressure on water resources caused by agricultural intensification, the rising food demand and climate change requires new irrigation strategies that improve the sustainability and efficiency of agricultural production. The objective of this study is to evaluate the performance of the digital twin (DT), Irri_DesK, in a 15-hectare commercial processing tomatoes plot in Extremadura (Spain) over two growing seasons (2023 and 2024). Three irrigation strategies were compared: conventional farmer management, management based on a remote-sensing platform (Smart4Crops) and automated scheduling using Irri_DesK DT-integrated soil moisture sensors, climate data and simulation models to adjust irrigation doses daily. Results showed that the DT-based strategy allowed for the application of regulated deficit irrigation strategies while maintaining productivity or fruit quality. In 2023, it achieved an economic water efficiency of 284.81 EUR/mm with a yield of 140 t/ha using 413 mm of water. In 2024, it maintained high production levels (126 t/ha) under more challenging conditions of spatial variability. These results support the potential of DTs for improving irrigation management in water-limited environments. Full article

Despite advances in precision irrigation, no systematic review has yet integrated the roles of remote sensing, agro-meteorological data, and wireless sensor networks in high-value, water-sensitive crops such as mango, avocado, and vineyards. Existing research often isolates technologies or crop types, overlooking their convergence and joint performance in the field. This review fills that gap by examining how these tools estimate crop water demand and support sustainable, site-specific irrigation under variable climate conditions. A structured search across major databases yielded 365 articles, of which 92 met the inclusion criteria. Studies were grouped into four categories: remote sensing, agro-meteorology, wireless sensor networks, and integrated approaches. Remote sensing techniques, including multispectral and thermal imaging, enable the spatial monitoring of vegetation indices and stress indicators, such as the Crop Water Stress Index. Agro-meteorological data feed evapotranspiration models using temperature, humidity, wind, and radiation inputs. Wireless sensor networks provide continuous, localized data on soil moisture and canopy temperature. Integrated approaches combine these sources to improve irrigation recommendations. Findings suggest that combining remote sensing, wireless sensor networks, and agro-meteorological inputs can reduce water use by up to 30% without yield loss. Challenges include sensor calibration, data integration complexity, and limited scalability. This review also compares methodologies and highlights future directions, including artificial intelligence systems, digital twins, and affordable Internet of Things platforms for irrigation optimization. Full article

Understanding spatial variability in crop yields across fields is critical for developing precision agricultural strategies that optimize productivity while reducing negative environmental impacts. This variability often arises from a complex interplay of topographic features, soil characteristics, and hydrological conditions. This study investigates the influence of hydro-topographic factors on corn and soybean yield variability from 2016 to 2023 at the well-managed experimental sites in Beltsville, Maryland. A high-resolution surface digital elevation model (DEM) and subsurface DEM derived from ground-penetrating radar (GPR) were used to quantify topographic factors (elevation, slope, and aspect) and hydrological factors (surface flow accumulation, depth from the surface to the subsurface-restricting layer, and distance from each crop pixel to the nearest subsurface flow pathway). Topographic variables alone explained yield variation, with a relative root mean square error (RRMSE) of 23.7% (r² = 0.38). Adding hydrological variables reduced the error to 15.3% (r² = 0.73), and further combining with remote sensing data improved the explanatory power to an RRMSE of 10.0% (r² = 0.87). Notably, even without subsurface data, incorporating surface-derived flow accumulation reduced the RRMSE to 18.4% (r² = 0.62), which is especially important for large-scale cropland applications where subsurface data are often unavailable. Annual spatial yield variation maps were generated using hydro-topographic variables, enabling the identification of long-term persistent yield regions (LTRs), which served as stable references to reduce spatial anomalies and enhance model robustness. In addition, by combining remote sensing data with interannual meteorological variables, prediction models were evaluated with and without hydro-topographic inputs. The inclusion of hydro-topographic variables improved spatial characterization and enhanced prediction accuracy, reducing error by an average of 4.5% across multiple model combinations. These findings highlight the critical role of hydro-topography in explaining spatial yield variation for corn and soybean and support the development of precise, site-specific management strategies to enhance productivity and resource efficiency. Full article

Soil erosion is a critical environmental issue that leads to land degradation, reduced agricultural productivity, and ecological imbalance. This study aims to assess soil loss under various land surface conditions by developing 11 distinct scenarios using the RUSLE (Revised Universal Soil Loss Equation) model integrated within the Google Earth Engine (GEE) platform. Remote sensing-derived indices including NDVI, EVI, NDWI, SAVI, and BSI were incorporated to represent vegetation cover, moisture, and bare/built-up surfaces. The K, LS, P, and R factors were held constant, allowing the C factor to vary based on each index, simulating real-world landscape differences. Soil loss maps were generated for each scenario, and spatial variability was analyzed using bubble charts, bar graphs, and C-map visualizations. The results show that vegetation-based indices such as NDVI and EVI lead to significantly lower soil loss estimations, while indices associated with built-up or bare surfaces like BSI predict higher erosion risks. These findings highlight the strong relationship between land cover characteristics and erosion intensity. This study demonstrates the utility of integrating satellite-based indices into erosion modeling and provides a scenario-based framework for supporting land management and soil conservation practices. The proposed approach can aid policymakers and land managers in prioritizing conservation efforts and mitigating erosion risk. Moreover, maintaining and enhancing vegetative cover is emphasized as a key strategy for promoting sustainable land use and long-term ecological resilience. Full article

Wetlands located in mid-to-high latitudes have undergone significant changes in recent years, compromising their patterns and functions. To understand these alterations in wetland functions, it is crucial to identify the patterns of wetland degradation and the mechanisms based on the conceptual framework of “pattern-process-function.” Our study developed a wetland damage index to analyze changes by calculating the wetland decline rate, remote sensing ecological index, and human pressure index from remote sensing images. We utilized the geographic detectors model to conduct a quantitative analysis of the driving mechanisms. Furthermore, we applied the coupling coordination model to evaluate the relationship between wetland damage and functional changes in the Greater Khingan region. The findings revealed that the wetland damage index increased by 9.86% during 2000–2023, with the damage concentrated in the central area of the study region. The primary explanatory factor for wetland damage was soil temperature during 2000–2010, but population density had become the dominant factor by 2023. The interactive explanatory power of soil temperature and population density on wetland damage was relatively high in the early stage, while the interactive explanatory power of surface temperature and population density on wetland damage was the highest in the later stage. The coupling coordination degree between the Wetland Damage Index (WDI) and Net Primary Productivity (NPP) significantly increased during 2010–2023, rising from 0.19 to 0.23. The increase in the coupling coordination degree between the WDI and Gross Primary Productivity (GPP) exhibited a trend of gradual diffusion from the center to the edge. Our research offers a scientific basis for implementing wetland protection and restoration strategies in mid-to-high latitudes wetlands. Full article

Driven by both natural and anthropogenic factors, farmland abandonment and recultivation constitute complex and widespread global phenomena that impact the ecological environment and society. In the Inner Mongolia Yellow River Basin (IMYRB), a critical tension lies between agricultural production and ecological conservation, characterized by dynamic bidirectional transitions that hold significant implications for the harmony of human–nature relations and the advancement of ecological civilization. With the development of remote sensing, it has become possible to rapidly and accurately extract farmland changes and monitor its vegetation restoration status. However, mapping abandoned farmland presents significant challenges due to its scattered and heterogeneous distribution across diverse landscapes. Furthermore, subjectivity in questionnaire-based data collection compromises the precision of farmland abandonment monitoring. This study aims to extract crop phenological metrics, map farmland abandonment, and recultivation dynamics in the IMYRB and assess post-transition vegetation changes. We used Landsat time-series data to detect the land-use changes and vegetation responses in the IMYRB. The Farmland Abandonment and Recultivation Extraction Index (FAREI) was developed using crop phenology spectral features. Key crop-specific phenological indicators, including sprout, peak, and wilting stages, were extracted from annual MODIS NDVI data for 2020. Based on these key nodes, the Landsat data from 1999 to 2022 was employed to map farmland abandonment and recultivation. Vegetation recovery trajectories were further analyzed by the Mann–Kendall test and the Theil–Sen estimator. The results showed rewarding accuracy for farmland conversion mapping, with overall precision exceeding 79%. Driven by ecological restoration programs, rural labor migration, and soil salinization, two distinct phases of farmland abandonment were identified, 87.9 kha during 2002–2004 and 105.14 kha during 2016–2019, representing an approximate 19.6% increase. Additionally, the post-2016 surge in farmland recultivation was primarily linked to national food security policies and localized soil amelioration initiatives. Vegetation restoration trends indicate significant greening over the past two decades, with particularly significant increases observed between 2011 and 2022. In the future, more attention should be paid to the trade-off between ecological protection and food security. Overall, this study developed a novel method for monitoring farmland dynamics, offering critical insights to inform adaptive ecosystem management and advance ecological conservation and sustainable development in ecologically fragile semi-arid regions. Full article

Potentially toxic element pollution caused by coal mining activities, especially the accumulation of cadmium, has become a major threat to the global environment and health. Long-term mining activities in China, a major coal consumer, caused a large accumulation of coal gangue. Gangue weathering and leaching release Cd, which threatens the ecological safety of the surrounding soil and water bodies. Although the government has implemented ecological restoration projects in the mining areas, there is still a lack of systematic evaluation of pollution control of downstream farmlands. For this study, remote sensing analyses of fractional vegetation coverage (FVC), geo-accumulation index (I_geo), and potential ecological risk index (EI) data, as well as the pollution characteristics and ecological risks of Cd, were evaluated for a coal mining area in Jiangxi Province. Coal gangue, restoration cover soil, downstream farmland soil, irrigation water, and sediment samples were used in the analyses. After restoration, the Cd concentration in the mining cover soil (0.23 mg/kg) was significantly lower than that of the coal gangue (1.18 mg/kg), while the Cd concentration in the downstream farmland soil (0.44 mg/kg) was roughly an average of the two. The geo-accumulation index indicates that the farmland soil is mainly unpolluted (with an average I_geo of −0.25). However, some points have reached the level of no pollution to moderate pollution. Coal gangue poses a relatively high ecological risk (with an average EI of 118), while cover soil and farmland soil pose low risks (with an average EI of 22.5 and 39.86, respectively). The restoration project significantly reduced the Cd input in the downstream farmlands. The study revealed the effective blocking of external soil cover on Cd migration, providing a key scientific basis for the optimization of ecological restoration strategy and risk prevention and control in similar mining areas worldwide. Full article

Land degradation is one of the significant ecological and environmental issues threatening regional sustainable development. Datong Coalfield is located in an arid and semi-arid ecologically fragile area and is also an important energy base, the mining of coal resources and natural factors have caused serious land degradation problems. Therefore, dynamic monitoring and influencing factor analysis of land degradation in the Datong Coalfield is particularly important for land degradation prevention and land reclamation in mining areas. This study focuses on the Datong Coalfield, using remote sensing technology to dynamically extract soil erosion, net primary productivity of vegetation, land desertification, soil moisture content. Based on the Analytic Hierarchy Process (AHP), a comprehensive assessment model for land degradation was constructed to analyze the spatiotemporal evolution of land degradation in the Datong Coalfield from 2000 to 2021, and the influencing factors of land degradation were explored using a geographic detector. The results indicate that (1) from 2000 to 2021, the land degradation level in Datong Coalfield changed to mild degradation and non degradation, with the mild degradation area increasing by 30.48% and the non degradation area increasing by 13.9%, and spatially expanding contiguously from localized areas outwards. (2) Over the past 21 years, the land degradation situation in Datong Coalfield predominantly showed an improving trend, accounting for 69.11%, indicating an overall positive trajectory. However, 0.54% of the area experienced significantly intensified land degradation, scattered in the eastern and southwestern parts of the Datong Coalfield, which are areas requiring focused governance efforts. (3) Vegetation and land use are the main factors affecting land degradation in Datong Coalfield. At the same time, the influence of land use has gradually increased over the years, and the influence of vegetation and land use interaction is the highest in the two-factor interaction. Full article

Drought is a complex and impactful natural hazard, with sometimes catastrophic impacts on small or subsistence agriculture and water security. In Pacific Island countries, there lacks an agreed approach for monitoring agricultural drought hazard with satellite-derived remote sensing data. This study addresses this gap through a framework for agricultural drought monitoring in the Pacific using freely available space-based observations. Applying World Meteorological Organization’s (WMO) recommendations and a set of objective selection criteria, three remotely sensed drought indicators were chosen and combined using fuzzy logic to form a composite drought hazard index: the Standardised Precipitation Index, Soil Water Index, and Normalised Difference Vegetation Index. Each indicator represents a subsequential flow-on effect of drought on agriculture. The index classes geographic areas as low, medium, high, or very high levels of drought hazard. To test the drought hazard index, two case studies for drought in the western Pacific, Papua New Guinea (PNG), and Vanuatu, are assessed for the 2015–2016 El Niño-related drought. Findings showed that at the height of the drought in October 2015, 58% of PNG and 72% of Vanuatu showed very high drought hazard, compared to 6% and 40%, respectively, at the beginning of the drought. The hazard levels calculated were consistent with conditions observed and events that were reported during the emergency drought period. Application of this framework to operational drought monitoring will promote adaptive capacity and improve resilience to future droughts for Pacific communities. Full article

Ecological drought in terrestrial systems is a vegetation-functional degradation phenomenon triggered by the long-term imbalance between ecosystem water supply and demand. This process involves nonlinear coupling of multiple climatic factors, ultimately forming a compound ecological stress mechanism characterized by spatiotemporal heterogeneity. Based on meteorological and remote sensing datasets from 1982 to 2022, this study identified the spatial distribution and temporal variability of ecological drought in China, elucidated the dynamic evolution and return periods of typical drought events, unveiled the scale-dependent effects of climatic factors under both univariate dominance and multivariate coupling, as well as deciphered the response mechanisms of ecological drought to meteorological drought. The results demonstrated that (1) terrestrial ecological drought in China exhibited a pronounced intensification trend during the study period, with the standardized ecological water deficit index (SEWDI) reaching its minimum value of −1.21 in February 2020. Notably, the Alpine Vegetation Region (AVR) displayed the most significant deterioration in ecological drought severity (−0.032/10a). (2) A seasonal abrupt change in SEWDI was detected in January 2003 (probability: 99.42%), while the trend component revealed two mutation points in January 2003 (probability: 96.35%) and November 2017 (probability: 43.67%). (3) The drought event with the maximum severity (6.28) occurred from September 2019 to April 2020, exhibiting a return period exceeding the 10-year return level. (4) The mean values of gridded trend eigenvalues ranged from −1.06 in winter to 0.19 in summer; 87.01% of the area exhibited aggravated ecological drought in winter, with the peak period (88.51%) occurring in January. (5) Evapotranspiration (ET) was identified as the dominant univariate driver, contributing a percentage of significant power (POSP) of 18.75%. Under multivariate driving factors, the synergistic effects of ET, soil moisture (SM), and air humidity (AH) exhibited the strongest explanatory power (POSP = 19.21%). (6) The response of ecological drought to meteorological drought exhibited regional asynchrony, with the maximum correlation coefficient averaging 0.48 and lag times spanning 1–6 months. Through systematic analysis of ecological drought dynamics and driving mechanisms, a dynamic assessment framework was constructed. These outcomes strengthen the scientific basis for regional drought risk early-warning systems and spatially tailored adaptive management strategies. Full article

As the largest carbon reservoir in terrestrial ecosystems, soil organic carbon (SOC) plays a critical role in the global carbon cycle and climate change mitigation. A promising approach to swiftly procuring geographically dispersed SOC data is the amalgamation of UAV-based multispectral imagery at the local scale and Sentinel-2 satellite imagery at the regional scale. This integrated approach is particularly well-suited for precision agriculture and real-time monitoring. In this study, we evaluated the performance of UAVs and Sentinel-2 imagery in predicting SOC using four machine-learning models: Multiple Linear Regression (MLR), Support Vector Regression (SVR), Random Forest (RF), and Artificial Neural Networks (ANNs). UAV imagery outperformed Sentinel-2, achieving more accurate detection of local SOC variability thanks to its finer spatial resolution (5–10 cm versus 10–20 m). Among the models tested, the Random Forest algorithm achieved the highest accuracy, with an R² of up to 0.85 using UAV data and 0.65 using Sentinel-2 data, along with low RMSE values. All models confirmed the superiority of UAV imagery based on key error metrics (SSE, MSE, RMSE, and NSE). Although Sentinel-2 remains valuable for regional assessments, UAV imagery combined with Random Forest provides the most reliable SOC estimates at local scales. The spatial SOC maps generated from both UAV and Sentinel-2 imagery showed more nuanced spatial variability than standard interpolation techniques. While prediction accuracy using UAV-based models was slightly lower in some cases, UAV imagery provided greater spatial detail in SOC distribution. However, this is associated with higher acquisition and processing costs compared to freely available Sentinel-2 imagery. Given their respective advantages, we recommend using UAV imagery for detailed, site-specific SOC estimations and Sentinel-2 data for broader regional-to-global SOC mapping efforts. Full article