Search Results (2,075)

To meet the growing demand for quantitative remote sensing applications in GIIRS radiometric calibration, this paper proposes a temperature field-driven adaptive scan mirror thermal radiation interference correction method. Based on the on-orbit deep space observation data from the Fengyun-4B satellite, this paper systematically analyzes the thermal radiation interference characteristics caused by scan mirror deflection and constructs the first scan mirror thermal radiation response model suitable for GIIRS. On the basis of this model, this paper further introduces the dynamic variation characteristics of the internal thermal environment of the instrument, enabling adaptive response and compensation for radiation disturbances. This method overcomes the limitations of relying on static calibration parameters and improves the generality and robustness of the model. Independent validation results show that this method effectively suppresses the interference of scan mirror deflection on instrument background radiation and enhances the consistency of the deep space and blackbody spectral diurnal variation time series. After correction, the average system bias of the interference-sensitive channel decreased by 94%, and the standard deviation of radiance bias from 2.5 mW/m²·sr·cm⁻¹ to below 0.5 mW/m²·sr·cm⁻¹. In the O-B test, the maximum improvement in relative standard deviation reached 0.15 K. Full article

Environmental degradation from rapid urbanization significantly threatens ecological resilience (ER). Nevertheless, accurately evaluating ER remains a persistent challenge. Prior studies’ limited attention to resilience’s cross-scale complexity has hindered evidence-based management. This study, based on long-term time series remote sensing and multi-source data, developed a cross-scale spatiotemporal ER analysis framework integrating landscape ecology and panarchy perspectives. A local “resistance–adaptation–recovery” substrate resilience evaluation was combined with telecoupling-based global network resilience to quantify multi-scale ER from 2000 to 2020. Key drivers across time scales were identified using a hybrid XGBoost–SHAP and genetic algorithm (GA)–optimized dynamic Bayesian network (DBN), and spatial optimization scenarios were simulated with patch-generating land use simulation (PLUS) model. ER decreased slightly from 0.4856 in 2000 to 0.4503 in 2020, with dynamic fluctuations across periods. A clear spatial pattern emerged, with higher ER in the east and lower in the west. Forest land contributed strongly to ER, while construction and cropland reduced it. Spatial composition factors—especially the proportions of forest and construction land—were dominant drivers, outweighing structural factors such as landscape pattern. DBN backward inference revealed nonlinear threshold effects among socio–natural–spatial drivers. Scenario-based simulations confirmed that regulating spatial composition via our optimization pathway can enhance ER. This is particularly effective when expanding forestland in mountainous regions while restraining the growth of built-up areas. This study proposes an integrated framework of “resilience assessment—driver analysis—spatial optimization,” which not only advances the theoretical basis for nested ER assessment but also offers a transferable approach for optimizing spatial patterns and sustainable land management, thereby enhancing ecological resilience in rapidly urbanizing regions. Full article

Rangeland degradation poses a serious challenge for the sustainable management of free-ranging livestock in Mediterranean wetlands. In Doñana National Park, Spain, the endangered Marismeño horse depends exclusively on natural forage, making it essential to monitor vegetation productivity and grazing suitability under increasing climate variability. This study presents a satellite-based assessment of rangeland carrying capacity to support the adaptive management of this iconic breed. A six-year time series (2015–2020) of 1242 images from Landsat 8 OLI/TIRS and Sentinel-2 (L1C/L2A) was processed using ILWIS and Python-based workflows to derive vegetation indices (GNDVI, NDMI) and model aboveground biomass, forage energy, and grazing pressure across five grazing units. Results revealed strong seasonal cycles, with biomass and nutritive value peaking in spring and declining sharply in summer. Ecotonal zones such as La Vera y Sotos acted as crucial refuges during drought-induced resource shortages. The harmonized multi-sensor approach demonstrated high reliability for mapping forage dynamics and assessing carrying capacity at fine scales. This remote sensing framework offers an effective, scalable tool for sustainable livestock management in Doñana, directly supporting biodiversity conservation and the long-term resilience of Mediterranean rangeland ecosystems. Full article

Pine wilt disease (PWD) is one of the fastest-spreading invasive forest pathogens worldwide, causing rapid mortality of infected trees and posing a severe threat to global forest ecosystem security and carbon sink capacity. However, the spatial dynamics and diffusion characteristics of PWD at the stand scale remain poorly understood. In this study, we selected a typical epidemic area in Qingyuan County, Liaoning Province, China, as the study site. By integrating 23 phases of unmanned aerial vehicle (UAV) multispectral imagery, airborne LiDAR data, and field survey observations, we reconstructed the spatiotemporal diffusion process of PWD from 2023 to 2025 and developed a stand-scale, tree-level mortality risk prediction model. Our results show that 50% of transmission events occurred within 17.2 m, and the spatial autocorrelation range was approximately 28 m. The peak of the lethal latency period occurred 17 days after infection, with 40% of mortality events occurring within 11–22 days and 50% of infected trees dying within 40 days. The latency period was significantly shorter in spring and summer than in winter ($p<0.01$). Among tree-level mortality risk prediction approaches, the random forest model performed best, improving overall accuracy by more than 15% compared with other methods and correctly identifying 98.6% of high-risk individuals. The distance to the nearest infected or dead tree was identified as the dominant predictor, followed by tree height and vegetation parameters reflecting host physiological status. This study reveals the spatial diffusion characteristics of PWD at the stand scale and proposes a tree-level risk prediction framework, providing a theoretical foundation and technical support for dynamic monitoring, early warning, and precision management of PWD. Full article

Classical instrumental technologies still remain important among the geodetic methods of dam monitoring, but periodic observations are often insufficient for timely detection of hazardous deformations. Therefore, the integration of continuous and remote sensing technologies into a multi-level system of observation improves the assessment of a structural condition. This research work evaluates the integrated approach that combines the GNSS data, robotic total station measurements, and satellite radar data processed by the PSInSAR technique for detecting the cyclic thermal deformations of the Dniester HPP concrete dam. The dataset includes 185 ascending and 184 descending Sentinel-1A SAR images (2019–2025, 12-day repeat cycle). PSInSAR processing was performed using StaMPS, with validation through comparison of InSAR-derived vertical displacements and GNSS data from the stationary monitoring system of the dam. The GNSS and InSAR time series have revealed consistent seasonal patterns and a common long-term trend. Harmonic components with amplitudes of 4–5 mm, peaking in late summer and declining in winter, confirm the dominant influence of thermal processes. In order to reduce noise, Fourier-based filtering and approximation were applied, thus ensuring balance between accuracy and data retention. The combined use of GNSS, robotic total station, and InSAR has increased the density of reliable control points and improved the thermal deformation model. Maximum vertical displacements of 6–13 mm were observed on the horizontal sections most exposed to solar radiation. Full article

Forest productivity is a critical indicator of forest ecosystem vitality and carbon budget status. Understanding its historical trends and driving mechanisms is essential for assessing forest responses to climate change. Currently, widely used methods for productivity reconstruction, including forest inventories, eddy covariance observations, and remote sensing models, have temporal limitations and cannot adequately meet the demands of long-term ecological research. Tree-ring data, with their advantages of annual resolution and extended time series, have become an important tool for reconstructing historical forest productivity. Research has demonstrated that tree-ring width, stable isotopes, wood density, and anatomical properties are closely related to forest productivity. Mechanistic studies indicate that the climate–canopy–stem coupling relationship exhibits three key nonlinear characteristics: the bidirectional threshold effect of precipitation, the inverted U-shaped temperature response, and the carbon allocation “legacy effect”. Correlation analyses show that the optimal response period between tree rings and productivity is concentrated primarily in the growing season or summer, reflecting the critical regulatory role of temperature and moisture on tree growth. Based on this understanding, existing research has focused predominantly on mid- to high-latitude temperate forests in the Northern Hemisphere that are sensitive to climate, with tree-ring chronologies from arid regions showing stronger correlations with forest productivity. Given current progress and existing limitations, future research should address the impact of stand dynamics on reconstruction accuracy, strengthen linkages between vegetation indices and tree-ring data, integrate belowground productivity, and deepen understanding of the physiological mechanisms underlying forest productivity. Full article

Large-scale rainfed cropping systems (broadacre agriculture) face intensifying climate and resource stresses that undermine yield stability and farm livelihoods. Remote sensing (RS) offers critical tools for improving resilience by monitoring crop performance—productivity, phenology, and environmental stress—across large areas and timeframes. This review aims to synthesize methodological advances over the past two decades in applying RS for broadacre crop monitoring and to identify key challenges and integration opportunities. Peer-reviewed studies across diverse crops and regions were systematically examined to evaluate the strengths, limitations, and emerging trends across the three RS application themes. The review finds that (1) RS enables spatially explicit yield estimation from regional to paddock scales, with vegetation indices (VIs) and phenology-adjusted metrics closely correlated with yield. (2) Time-series analyses of RS data effectively capture phenological transitions critical for forecasting, supported by advances in curve fitting, sensor fusion, and machine learning. (3) Thermal and multispectral indices support the early detection of abiotic (drought, heat, salinity) and biotic (pests, disease) stresses, though specificity remains limited. Across themes, methodological silos and sensor integration barriers hinder holistic application. Emerging approaches, such as multi-sensor/scale fusion, RS–crop model data assimilation, and operational and big data integration, provide promising pathways toward resilience-focused decision support. Future research should define quantifiable resilience metrics, cross-theme predictive integration, and accessible tools to guide climate adaptation. Full article

Although surface soil moisture (SSM) is particularly important in crop yield prediction, irrigation scheduling optimization, and runoff generation mechanisms, accurate monitoring of time-series SSM is still challenging for agricultural and hydrological research. This study presented an improved approach integrating Sentinel-1 C-band SAR and MODIS optical data (2019–2020) to estimate surface soil moisture. To address vegetation effects, we developed a piecewise function using fractional vegetation coverage (FVC) to correct soil moisture and backscatter extrema and established the normalized difference enhanced vegetation index (NDEVI) to characterize backscatter-vegetation relationships across various land covers. Furthermore, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm identified anomalous surface changes, enabling segmentation of long-term series into invariant periods that satisfy the change detection method assumptions. Validation in the Shandian River Basin demonstrated significant improvement over traditional methods, achieving determination coefficients (R²) of 0.844 and root mean square errors (RMSE) of 0.030 m³/m³. The method effectively captured soil moisture dynamics from precipitation and irrigation events, providing reliable monitoring in heterogeneous landscapes. This integrated approach offers a robust technical framework for multi-source remote sensing of soil moisture in semi-arid areas, enhancing capability for agricultural water resource management. Full article

Understanding soybean cultivation in Northeast China is essential for informing policies related to national food security. However, long-term, high-resolution soybean maps are still lacking, largely due to persistent cloud cover, limited availability of high-quality field labels, and the difficulty of capturing crop phenological dynamics using traditional remote sensing methods. To address this gap, this study aims to develop a robust framework for generating decade-long soybean distribution maps by integrating medium-resolution Landsat imagery with advanced deep learning techniques. We mapped the soybean distribution across Northeast China from 2013 to 2022 by constructing a bi-monthly NDVI-based composite and applying a deep learning model that combines the Transformer architecture with fully connected neural networks. The model was trained using a large set of field-surveyed samples collected between 2017 and 2019. Validation results demonstrate strong classification performance, with a user accuracy of 89.77% and a producer accuracy of 88.59%, sufficient for reliable spatiotemporal analysis. When compared with prefecture-level statistical yearbook data, the predicted annual soybean areas show a high degree of agreement (R² = 0.9226). Overall, this study not only fills an important gap in long-term soybean mapping for Northeast China, but also provides a replicable methodological framework for large-scale, time-series crop mapping. The approach has strong potential for broader application in agricultural monitoring and food security assessment. Full article

Monitoring physiological activity and sleep states in real time is challenging, particularly for continuous assessment in daily life settings using wearable IoMT devices. We developed a 24 h wearable system that integrates electrocardiogram (ECG) electrodes for heart rate measurement and a glove-mounted flex sensor for motor responses, connected through an Internet of Medical Things (IoMT) platform. Flex signals were combined using principal component analysis (PCA) to generate a single kinematic channel, then standardized with heart rate. Time-series windows were embedded using TS2Vec and clustered with DBSCAN, while t-SNE was applied only for visualization. The framework identified four physiologically coherent states: (i) nocturnal sleep with the lowest heart rate and minimal motion, (ii) evening pre-sleep with low movement and moderately higher heart rate, (iii) daytime activity with variable motion and mid-range heart rate, and (iv) late-day high-intensity activity with the highest heart rate and increased motor responses. A few outliers were observed during transient body movements or sensor readjustments, which were identified and excluded during preprocessing to ensure stable clustering results. Across 24 h, heart rate ranged from 52 to 96 bpm (mean 77.4), while flexion spanned 0 to 165° (mean 52.5°), showing alignment between movement intensity and cardiac response. This integrated sensing and analytics pipeline provides an interpretable, subject-specific state map that enables continuous remote monitoring of physiological activity and sleep patterns. Full article

Satellite-based surface soil moisture (SSM) products often contain spatial gaps and reduced reliability due to variations in vegetation cover and type, complex surface conditions such as heterogeneous topography and soil texture, or inherent limitations of satellite microwave sensors. This study presents a residual autoencoder model named TsSMNet, which combines multi-source remote sensing inputs with statistical features derived from SSM time series, including central tendency, dispersion and variability, extremes and distribution, temporal dynamics, magnitude and energy, and count-based features, to reconstruct gap-free SSM estimates. The model incorporates one-dimensional convolutional layers to efficiently capture local continuity patterns within the flattened SSM representations while reducing parameter complexity. TsSMNet was used to generate seamless 9 km SSM data over China from 2016 to 2022, based on the SMAP product, and was evaluated using in situ observations from six networks in the International Soil Moisture Network. The results show that TsSMNet outperforms AutoResNet, Transformer, Random Forest and XGBoost models, reducing the root mean square error (RMSE) by an average of 17.1 percent and achieving a mean RMSE of 0.09 cm³/cm³. Feature importance analysis highlights the strong contribution of temporal predictors to model accuracy. Compared to its variant without time-series features, TsSMNet provides better spatial representation, improved consistency with in situ temporal observations, and enhanced evaluation metrics. The reconstructed product offers improved spatial coverage and continuity relative to the original SMAP data, supporting broader applications in regional-scale hydrological analysis and large-scale climate, ecological, and agricultural studies. Full article

Jinghe County, as a typical arid area unit in the Ebinur Lake Basin, has a fragile ecosystem background and prominent soil erosion problems which have posed a serious threat to regional ecological security. Therefore, this paper takes Jinghe County as the research area, sets up two scale landscape plots of 250 × 250 m and 500 × 500 m, and combines time-series remote sensing data to systematically analyze the correlation characteristics between landscape richness and ecosystem functions. The research results are as follows: (1) From 2008 to 2023, the landscape pattern of Jinghe County underwent phased changes, reflecting the dynamic response of the landscape ecosystem driven by natural disturbances, ecological restoration and human activities. (2) At the 250 × 250 m plot scale, landscape diversity has a stronger explanatory power for EVI_AVG, while under different spatial scale conditions, the impact of log(LR) on ecosystem productivity and phenological indicators shows significant differences. Overall, as the spatial scale increases, the positive effect of NE gradually strengthens, and its correlation with landscape patterns becomes more intimate. (3) At different sampling scales, there exist varying degrees of correlations between landscape pattern indices and environmental factors, as well as within the two types of indicators themselves. (4) The overall trend of ecological effects is consistent at different sampling scales, but there are local differences; in addition, scale changes can regulate the direction and significance level of the correlation of ecological processes. This study reveals the regulatory mechanism of landscape richness on ecosystem functions in Jinghe County at different spatial scales, providing a scientific basis for the optimization of landscape patterns in arid areas. Full article

Timely and accurate monitoring of lakes’ water quality is crucial for assessing regional ecological health and implementing targeted conservation activities. Compared with traditional in situ water quality measurement methods, satellite remote sensing technology is more cost-effective and convenient, and also enables long-term time-series monitoring. This study utilizes Sentinel-2 multispectral imagery, selects East Juyan Lake as the study area, and employs measured water quality data from 30 in situ sampling points as training and testing samples. Using the correlation coefficient, root mean square error, and mean absolute error as evaluation metrics, a Grid Search-based XGBoost machine-learning method is applied to invert the concentration of total phosphorus (TP), a key parameter for water quality assessment. The experiments demonstrate that: (1) The XGBoost model, after parameter tuning via Grid Search, achieved the highest inversion accuracy, with R², RMSE, and MRE values of 0.856, 0.017, and 7.20%, respectively; The average TP concentration retrieved for the lake was 0.231 mg/L. This method requires minimal manual setting of numerous training parameters, reducing human intervention. (2) The spatial distribution shows that TP is primarily enriched in the deeper central and eastern parts of the lake, while concentrations are relatively lower in the near-shore vegetation zones and the western shallow water areas. The findings provide a significant reference for remote sensing monitoring of lake water quality and can be used to predict and regulate salinity, eutrophication, and similar conditions in comparable lakes. Full article

Arundo donax L. (common reed), a highly invasive species in Mediterranean wetlands such as the Albufera Natural Park, poses significant ecological and management challenges. Using Landsat-5 and Sentinel-2 NDVI data, this study quantified changes in its coverage between 1996 and 2024. The results reveal a significant expansion, showing a decreasing trend (91.4 ha in 1996 to 62.5 ha in 2011; −31.6%) followed by a clear rebound (83.5 ha in 2024; +33.6%), especially in the southern shrublands of the lagoon. A Mann–Kendall analysis confirmed a significant decreasing trend during 1996–2011 and an increasing trend during 2011–2024 (p < 0.05). The results indicate that previous control efforts reduced A. donax cover but that the species has recolonised after 2011, likely due to discontinuous management. These dynamics emphasise that long-term monitoring is required. Management strategies must focus on targeting the rhizome and implementing long-term monitoring programmes spanning three to five years. The utilisation of remote sensing methodologies proved effective in the monitoring of coverage, thereby facilitating the development of remediation strategies. It is imperative that actions accord primacy to critical areas such as the south and canals, complemented by native restoration and enhanced inter-administrative coordination, with the communication of benefits such as flood risk reduction. A balanced approach is required that considers ecological objectives, risks, and socio-political aspects. Full article

Traffic safety is a critical issue in highway operation management, where accurate identification of accident hotspots enables proactive risk prevention and facility optimization. Traditional methods relying on historical statistics often fail to capture macro-level environmental patterns and micro-level dynamic variations. To address this challenge, we propose a Dual-Branch Feature Adaptive Gated Fusion Network (DFAGF-Net) that integrates satellite remote sensing imagery with traffic flow time-series data. The framework consists of three components: the Global Contextual Aggregation Network (GCA-Net) for capturing macro spatial layouts from remote sensing imagery, a Sequential Gated Recurrent Unit Attention Network (Seq-GRUAttNet) for modeling dynamic traffic flow with temporal attention, and a Hybrid Feature Adaptive Module (HFA-Module) for adaptive cross-modal feature fusion. Experimental results demonstrate that the DFAGF-Net achieves superior performance in accident hotspot recognition. Specifically, GCA-Net achieves an accuracy of 84.59% on satellite imagery, while Seq-GRUAttNet achieves an accuracy of 82.51% on traffic flow data. With the incorporation of the HFA-Module, the overall performance is further improved, reaching an accuracy of 90.21% and an F1-score of 0.92, which is significantly better than traditional concatenation or additive fusion methods. Ablation studies confirm the effectiveness of each component, while comparisons with state-of-the-art models demonstrate superior classification accuracy and generalization. Furthermore, model interpretability analysis reveals that curved highway alignments, roadside greenery, and varying traffic conditions across time are major contributors to accident hotspot formation. By accurately locating high-risk segments, DFAGF-Net provides valuable decision support for proactive traffic safety management and targeted infrastructure optimization. Full article