Search Results (193)

Land Surface Temperature (LST) is a critical variable for understanding land–atmosphere interactions and is widely applied in urban heat monitoring, evapotranspiration estimation, near-surface air temperature modeling, soil moisture assessment, and climate studies. MODIS LST products, with their global coverage, long-term consistency, and radiometric calibration, are a major source of LST data. However, frequent data gaps caused by cloud contamination and atmospheric interference severely limit their applicability in analyses requiring high spatiotemporal continuity. This study presents a seamless MODIS LST reconstruction framework that integrates multi-source data fusion and a multi-stage optimization strategy. The method consists of three key components: (1) topography- and land cover-constrained spatial interpolation, which preliminarily fills orbit-induced gaps using elevation and land cover similarity criteria; (2) pixel-level LST reconstruction via random forest (RF) modeling with multi-source predictors (e.g., NDVI, NDWI, surface reflectance, DEM, land cover), coupled with HANTS-based temporal smoothing to enhance temporal consistency and seasonal fidelity; and (3) Poisson-based image fusion, which ensures spatial continuity and smooth transitions without compromising temperature gradients. Experiments conducted over two representative regions—Huainan and Jining—demonstrate the superior performance of the proposed method under both daytime and nighttime scenarios. The integrated approach (Step 3) achieves high accuracy, with correlation coefficients (CCs) exceeding 0.95 and root mean square errors (RMSEs) below 2K, outperforming conventional HANTS and standalone interpolation methods. Cross-validation with high-resolution Landsat LST further confirms the method’s ability to retain spatial detail and cross-scale consistency. Overall, this study offers a robust and generalizable solution for reconstructing MODIS LST with high spatial and temporal fidelity. The framework holds strong potential for broad applications in land surface process modeling, regional climate studies, and urban thermal environment analysis. Full article

The quantitative assessment of acoustic rain enhancement technology is highly significant for improving the ecological environment. A scientific and accurate evaluation of its operational effects provides an important basis for continued government and public support and investment in artificial weather modification activities. To effectively analyze the effects of acoustic rain enhancement on the vegetation of grassland ecosystems in arid and semi-arid areas and to clarify its mechanism, this study constructed eight vegetation indices based on Sentinel-2 satellite data. A comprehensive assessment of the changes in vegetation within the grassland ecosystem of the experimental zone was conducted by analyzing spatiotemporal distribution patterns, double-ratio analysis, and difference value comparisons. The results showed that (1) following the acoustic rain enhancement experiment, vegetation growth improved significantly. The mean values of all eight vegetation indices increased more substantially than before the experiment, with kNDVI showing the most notable gain. The proportion of the zone with kNDVI values greater than 0.417 increased from 52.62% to 71.59%, representing a relative increase of 36.05%. (2) The rain enhancement experiment significantly raised the values of eight vegetation indices: kNDVI increased by 0.042 (18.68%), ARVI by 0.043 (18.67%), and the remaining indices also increased to varying degrees (9.51–12.34%). (3) Vegetation improvement was more pronounced in areas closer to the acoustic rain enhancement site. Under consistent climate conditions, vegetation growth in the experimental zone showed significant enhancement. This study demonstrates that acoustic rain enhancement technology can mitigate drought and low rainfall, improve grassland ecosystem services, and provide a valuable foundation for ecological restoration and aerial water resource utilization in arid and semi-arid regions. Full article

Grassland classification via remote sensing is essential for ecosystem monitoring and precision management, yet conventional satellite-based approaches are fundamentally constrained by coarse spatial resolution. To overcome this limitation, we harness high-resolution UAV multi-sensor data, integrating multi-scale image fusion with deep learning to achieve fine-scale grassland classification that satellites cannot provide. First, four categories of UAV data, including RGB, multispectral, thermal infrared, and LiDAR point cloud, were collected, and a fused image tensor consisting of 10 channels (NDVI, VCI, CHM, etc.) was constructed through orthorectification and resampling. For feature-level fusion, four deep fusion networks were designed. Among them, the MultiScale Pyramid Fusion Network, utilizing a pyramid pooling module, effectively integrated spectral and structural features, achieving optimal performance in all six image fusion evaluation metrics, including information entropy (6.84), spatial frequency (15.56), and mean gradient (12.54). Subsequently, training and validation datasets were constructed by integrating visual interpretation samples. Four backbone networks, including UNet++, DeepLabV3+, PSPNet, and FPN, were employed, and attention modules (SE, ECA, and CBAM) were introduced separately to form 12 model combinations. Results indicated that the UNet++ network combined with the SE attention module achieved the best segmentation performance on the validation set, with a mean Intersection over Union (mIoU) of 77.68%, overall accuracy (OA) of 86.98%, F1-score of 81.48%, and Kappa coefficient of 0.82. In the categories of Leymus chinensis and Puccinellia distans, producer’s accuracy (PA)/user’s accuracy (UA) reached 86.46%/82.30% and 82.40%/77.68%, respectively. Whole-image prediction validated the model’s coherent identification capability for patch boundaries. In conclusion, this study provides a systematic approach for integrating multi-source UAV remote sensing data and intelligent grassland interpretation, offering technical support for grassland ecological monitoring and resource assessment. Full article

Turfgrass provides significant functional, environmental, recreational and aesthetic benefits; however, its high management inputs raise sustainability concerns due to intensive irrigation, fertilization and mowing. The aim of this study is to evaluate whether adopting a new mowing technology can support or enhance current low-input strategies in turfgrass management, such as reducing synthetic fertilization and deficit irrigation. This study was conducted from September 2023 to October 2024 at the Centre for Research on Turfgrass for Environment and Sports (CeRTES) in Pisa, Italy. Two turf compositions, pure tall fescue and tall fescue–microclover mixture, were managed using an autonomous mower operating daily at three mowing heights, 20, 40 and 60 mm. Turf quality, color, the NDVI, weed cover, leaf morphology, and clover presence were assessed throughout the growing season, including a drought and recovery period. The experimental design consisted of a two-factor split-plot randomized complete block design with four replications, and the statistical approach used was two-way and one-way ANOVAs with Fisher’s LSD at p = 0.05. The results of the study indicated that, under conditions where an autonomous mower was set to operate on a daily basis, the selected mowing height had minimal influence on drought response or recovery when water availability was a limiting factor. Furthermore, when subjected to the lowest mowing heights, the legume species included in the turfgrass mix demonstrated strong resilience, maintaining its presence and performance. In addition, when mowing with a high mowing frequency and at low mowing heights, the overall quality of the turfgrass appeared enhanced. These results serve as an important starting point for considering autonomous mowing technology as an innovative strategy in advancing toward turf management systems that prioritize sustainability and efficient use of resources. Full article

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

Hilly areas serve as critical ecological barriers yet face developmental challenges, drawing increasing attention to how land use transformation affects eco-environmental quality (EEQ). Systematic studies on EEQ drivers in complex terrains remain limited, particularly regarding nonlinear and interactive effects. This study examines Zhejiang’s hilly area—typical of southern China’s hills—using land use data from 2000, 2010, and 2020. Methods including land use transfer matrix, EEQI, hotspot analysis, and XGBoost-SHAP were applied to assess impacts and quantify drivers. Results show a slight but consistent decline in EEQ index (EEQI) (0.7635 to 0.7472), driven primarily by rapid built-up land (BL) expansion (276.41% increase). NDVI was the most influential factor (SHAP: 0.1226, >59%), followed by GDP per unit area and temperature. NDVI showed a threshold effect (>0.65 strengthens benefit), and strong interaction with per capita GDP. A slope-vegetation coupling mechanism was identified: on slopes > 30°, high NDVI significantly amplifies EEQ improvement, highlighting the importance of vegetation conservation on steep slopes. These findings provide a scientific basis for targeted land management in hilly regions of southern China and similar areas. Full article

Landslides pose persistent threats to mountainous regions in Taiwan, particularly in areas such as Nanfeng Village, Nantou County, where steep terrain and concentrated rainfall contribute to chronic slope instability. This study investigates spatiotemporal patterns of vegetation change as a proxy for identifying potential landslide-prone zones, with a focus on the Tung-An tribal settlement in the eastern part of the village. Using high-resolution satellite imagery from SPOT 6/7 (2013–2023) and Pléiades (2019–2023), we derived annual NDVI layers to monitor vegetation dynamics across the landscape. Long-term vegetation trends were evaluated using the Mann–Kendall test, while spatiotemporal clustering was assessed through Emerging Hot Spot Analysis (EHSA) based on the Getis-Ord Gi* statistic within a space-time cube framework. The results revealed statistically significant NDVI increases in many valley-bottom and mid-slope regions, particularly where natural regeneration or reduced disturbance occurred. However, other valley-bottom zones—especially those affected by recurring debris flows—still exhibited declining or persistently low vegetation. In contrast, persistent low or declining NDVI values were observed along steep slopes and debris-flow-prone channels, such as the Nanshan and Mei Creeks. These zones consistently overlapped with known landslide paths and cold spot clusters, confirming their ecological vulnerability and geomorphic risk. This study demonstrates that integrating NDVI trend analysis with spatiotemporal hot spot classification provides a robust, scalable approach for identifying slope hazard areas in data-scarce mountainous regions. The methodology offers practical insights for ecological monitoring, early warning systems, and disaster risk management in Taiwan and other typhoon-affected environments. By highlighting specific locations where vegetation decline aligns with landslide risk, the findings can guide local authorities in prioritizing slope stabilization, habitat conservation, and land-use planning. Such targeted actions support the Sustainable Development Goals, particularly SDG 11 (Sustainable Cities and Communities), SDG 13 (Climate Action), and SDG 15 (Life on Land), by reducing disaster risk, enhancing community resilience, and promoting the long-term sustainability of mountain ecosystems. Full article

Crop classification plays a vital role in acquiring the spatial distribution of agricultural crops, enhancing agricultural management efficiency, and ensuring food security. With the continuous advancement of remote sensing technologies, achieving efficient and accurate crop classification using remote sensing imagery has become a prominent research focus. Conventional approaches largely rely on empirical rules or single-feature selection (e.g., NDVI or VV) for temporal feature extraction, lacking systematic optimization of multimodal feature combinations from optical and radar data. To address this limitation, this study proposes a crop classification method based on feature-level fusion of multimodal remote sensing data, integrating the complementary advantages of optical and SAR imagery to overcome the temporal and spatial representation constraints of single-sensor observations. The study was conducted in Story County, Iowa, USA, focusing on the growth cycles of corn and soybean. Eight vegetation indices (including NDVI and NDRE) and five polarimetric features (VV and VH) were constructed and analyzed. Using a random forest algorithm to assess feature importance, NDVI+NDRE and VV+VH were identified as the optimal feature combinations. Subsequently, 16 scenes of optical imagery (Sentinel-2) and 30 scenes of radar imagery (Sentinel-1) were fused at the feature level to generate a multimodal temporal feature image with 46 channels. Using Cropland Data Layer (CDL) samples as reference data, a U-Net deep neural network was employed for refined crop classification and compared with single-modal results. Experimental results demonstrated that the fusion model outperforms single-modal approaches in classification accuracy, boundary delineation, and consistency, achieving training, validation, and test accuracies of 95.83%, 91.99%, and 90.81% respectively. Furthermore, consistent improvements were observed across evaluation metrics, including F1-score, precision, and recall. Full article

Early-season sugarcane identification plays a pivotal role in precision agriculture, enabling timely yield forecasting and informed policy-making. Compared to post-season crop identification, early-season identification faces unique challenges, including incomplete temporal observations and spectral ambiguity among crop types in early seasons. Previous studies have not systematically investigated the capability of optical and synthetic aperture radar (SAR) data for early-season sugarcane identification, which may result in suboptimal accuracy and delayed identification timelines. Both the timing for reliable identification (≥90% accuracy) and the earliest achievable timepoint matching post-season level remain undetermined, and which features are effective in the early-season identification is still unknown. To address these questions, this study integrated Sentinel-1 and Sentinel-2 data, extracted 10 spectral indices and 8 SAR features, and employed a random forest classifier for early-season sugarcane identification by means of progressive temporal analysis. It was found that LSWI (Land Surface Water Index) performed best among 18 individual features. Through the feature set accumulation, the seven-dimensional feature set (LSWI, IRECI (Inverted Red-Edge Chlorophyll Index), EVI (Enhanced Vegetation Index), PSSRa (Pigment Specific Simple Ratio a), NDVI (Normalized Difference Vegetation Index), VH backscatter coefficient, and REIP (Red-Edge Inflection Point Index)) achieved the earliest attainment of 90% accuracy by 30 June (early-elongation stage), with peak accuracy (92.80% F1-score) comparable to post-season accuracy reached by 19 August (mid-elongation stage). The early-season sugarcane maps demonstrated high agreement with post-season maps. The 30 June map achieved 88.01% field-level and 90.22% area-level consistency, while the 19 August map reached 91.58% and 93.11%, respectively. The results demonstrate that sugarcane can be reliably identified with accuracy comparable to post-season mapping as early as six months prior to harvest through the integration of optical and SAR data. This study develops a robust approach for early-season sugarcane identification, which could fundamentally enhance precision agriculture operations through timely crop status assessment. Full article

Accurate delineation of within-field management zones (MZs) is essential for implementing precision agriculture, particularly in spatially heterogeneous environments. This study evaluates the spatiotemporal consistency and practical value of MZs derived from three complementary data sources: electromagnetic conductivity (EM38-MK2), basic soil chemical properties (pH, humus, P₂O₅, K₂O, nitrogen), and vegetation/surface indices (NDVI, SAVI, LCI, BSI) derived from Sentinel-2 imagery. Using kriging, fuzzy k-means clustering, percentile-based classification, and Weighted Overlay Analysis (WOA), MZs were generated for a five-year period (2018–2022), with 2–8 zone classes. Stability and agreement were assessed using the Cohen Kappa, Jaccard, and Dice coefficients on systematic grid samples. Results showed that EM38-MK2 and humus-weighted BSP data produced the most consistent zones (Kappa > 0.90). Sentinel-2 indices demonstrated strong alignment with subsurface data (r > 0.85), offering a low-cost alternative in data-scarce settings. Optimal zoning was achieved with 3–4 classes, balancing spatial coherence and interpretability. These findings underscore the importance of multi-source data integration for robust and scalable MZ delineation and offer actionable guidelines for both data-rich and resource-limited farming systems. This approach promotes sustainable agriculture by improving input efficiency and allowing for targeted, site-specific field management. Full article

Landslide susceptibility assessment is critical for hazard mitigation and land-use planning. This study evaluates the impact of two different non-landslide sampling methods—random sampling and sampling constrained by the Global Landslide Hazard Map (GLHM)—on the performance of various machine learning and deep learning models, including Naïve Bayes (NB), Support Vector Machine (SVM), SVM-Random Forest hybrid (SVM-RF), and XGBoost. The study area is a 2 km buffer zone along the Duku Highway in Xinjiang, China, with 102 landslide and 102 non-landslide points extracted by aforementioned sampling methods. Models were tested using ROC curves and non-parametric significance tests based on 20 repetitions of 5-fold spatial cross-validation data. GLHM sampling consistently improved AUROC and accuracy across all models (e.g., AUROC gains: NB +8.44, SVM +7.11, SVM–RF +3.45, XGBoost +3.04; accuracy gains: NB +11.30%, SVM +8.33%, SVM–RF +7.40%, XGBoost +8.31%). XGBoost delivered the best performance under both sampling strategies, reaching 94.61% AUROC and 84.30% accuracy with GLHM sampling. SHAP analysis showed that GLHM sampling stabilized feature importance rankings, highlighting STI, TWI, and NDVI as the main controlling factors for landslides in the study area. These results highlight the importance of hazard-informed sampling to enhance landslide susceptibility modeling accuracy and interpretability. Full article

Understanding the detailed spatiotemporal variations in soil organic carbon (SOC) stocks is essential for assessing soil carbon sequestration potential. However, most existing studies predominantly focus on topsoil SOC stocks, leaving significant knowledge gaps regarding critical zones, depth-dependent variations, and key influencing factors associated with deeper SOC stock dynamics. This study adopted a comprehensive methodology that integrates random forest modeling, equal-area soil profile analysis, and space-for-time substitution to predict depth-specific SOC stock dynamics under climate warming in Northeast China’s forest ecosystems. By combining these techniques, the approach effectively addresses existing research limitations and provides robust projections of soil carbon changes across various depth intervals. The analysis utilized 63 comprehensive soil profiles and 12 environmental predictors encompassing climatic, topographic, biological, and soil property variables. The model’s predictive accuracy was assessed using 10-fold cross-validation with four evaluation metrics: MAE, RMSE, R², and LCCC, ensuring comprehensive performance evaluation. Validation results demonstrated the model’s robust predictive capability across all soil layers, achieving high accuracy with minimized MAE and RMSE values while maintaining elevated R² and LCCC scores. Three-dimensional spatial projections revealed distinct SOC distribution patterns, with higher stocks concentrated in central regions and lower stocks prevalent in northern areas. Under simulated warming conditions (1.5 °C, 2 °C, and 4 °C increases), both topsoil (0–30 cm) and deep-layer (100 cm) SOC stocks exhibited consistent declining trends, with the most pronounced reductions observed under the 4 °C warming scenario. Additionally, the study identified mean annual temperature (MAT) and normalized difference vegetation index (NDVI) as dominant environmental drivers controlling three-dimensional SOC spatial variability. These findings underscore the importance of depth-resolved SOC stock assessments and suggest that precise three-dimensional mapping of SOC distribution under various climate change projections can inform more effective land management strategies, ultimately enhancing regional soil carbon storage capacity in forest ecosystems. Full article

Frequent wildfires in the eastern grasslands of Mongolia pose significant threats to the ecological environment and pastoral livelihoods, creating an urgent need for high-temporal-resolution and high-precision fire prediction. To address this, this study established a daily-scale grassland fire risk assessment framework integrating multi-source remote sensing data to enhance predictive capabilities in eastern Mongolia. Utilizing fire point data from eastern Mongolia (2012–2022), we fused multiple feature variables and developed and optimized three models: random forest (RF), XGBoost, and deep neural network (DNN). Model performance was enhanced using Bayesian hyperparameter optimization via Optuna. Results indicate that the Bayesian-optimized XGBoost model achieved the best generalization performance, with an overall accuracy of 92.3%. Shapley additive explanations (SHAP) interpretability analysis revealed that daily-scale meteorological factors—daily average relative humidity, daily average wind speed, daily maximum temperature—and the normalized difference vegetation index (NDVI) were consistently among the top four contributing variables across all three models, identifying them as key drivers of fire occurrence. Spatiotemporal validation using historical fire data from 2023 demonstrated that fire points recorded on 8 April and 1 May 2023 fell within areas predicted to have “extremely high” fire risk probability on those respective days. Moreover, points A (117.36° E, 46.70° N) and B (116.34° E, 49.57° N) exhibited the highest number of days classified as “high” or “extremely high” risk during the April/May and September/October periods, consistent with actual fire occurrences. In summary, the integration of multi-source data fusion and Bayesian-optimized machine learning has enabled the first high-precision daily-scale wildfire risk prediction for the eastern Mongolian grasslands, thus providing a scientific foundation and decision-making support for wildfire prevention and control in the region. Full article

Advanced Synthetic Aperture Radar (SAR) has become an essential modality in remote sensing, offering all-weather capabilities and sensitivity to vegetation biophysical parameters and surface conditions, while effectively complementing optical sensor data. This study evaluates the impact of precipitation on the Normalized Polarimetric Radar Vegetation Index (NPRVI) using dual-polarization Sentinel-1 C-band SAR data from agricultural fields at the Beltsville Agricultural Research Center (BARC). Field-measured precipitation and Global Precipitation Measurement (GPM) precipitation datasets were temporally aligned with Sentinel-1 acquisition times to assess the sensitivity of radar signals to precipitation events. NPRVI exhibited a strong sensitivity to precipitation, particularly within the 1 to 7 h prior to the satellite overpass, even for small amounts of precipitation. A quality assessment (QA) framework was developed to flag and correct precipitation-affected radar observations through interpolation. The adjusted NPRVI values, based on the QA framework using precipitation within a 6 h window, showed strong agreement between field- and GPM-derived data, with an RMSE of 0.09 and a relative RMSE of 19.8%, demonstrating that GPM data can serve as a viable alternative for quality adjustment despite its coarse spatial resolution. The adjusted NPRVI for both soybean and corn fields significantly improved the temporal consistency of the time series and closely followed NDVI trends, while also capturing crop-specific seasonal variations, especially during periods of NDVI saturation or limited variability. These findings underscore the value of the proposed radar-based QA framework in enhancing the interpretability of vegetation dynamics. NPRVI, when adjusted for precipitation effects, can serve as a reliable and complementary tool to optical vegetation indices in agricultural and environmental monitoring. Full article

With increasing threats to forest resources, there is a growing demand for accurate, timely, and quantitative information on their status, trends, and sustainability. Satellite remote sensing provides an effective means of consistently monitoring large forest areas. Vegetation Indices (VIs) are commonly used to assess forest conditions, but their effectiveness remains a key question. This study aimed to assess and map forest degradation status and trends in Lagawa locality, West Kordofan State, Sudan using the soil adjusted and atmospheric resistant vegetation index (SARVI) to quantify the relationship between SARVI and the Normalized Difference Vegetation Index (NDVI) and compare the efficiency of both indices in detecting and monitoring changes in forest conditions. The study utilized four free cloud images (TM 1988, TM 1998, TM 2008, and OLI 2018), which were processed using Google Earth Engine (GEE) to derive the indices. The study found significant forest degradation over time, with 63% of the area categorized as moderately to severely degraded. A strong, positive relationship between SARVI and NDVI (R² = 0.9085, p < 0.001) was identified, indicating that both are effective in detecting forest changes. Both indices proved efficacy, cost-effectiveness, and applicable for monitoring forest changes across Sudan’s drylands. The study recommends applying similar methods in other dryland forests in other regions. Full article