Search Results (420)

Coastal lagoons are recent geological formations, crucial biodiversity hot-spots, and fragile ecosystems which provide several ecosystem services. These areas are strongly affected by nutrient inputs, which can lead to eutrophication and algal blooms. We identified nine Italian coastal lagoons with a surface area greater than 10 km². Most of them were previously classified in a poor ecological condition. Therefore, we used remote sensing, in particular Sentinel-2 images, to assess the trophic state of these areas over time from 2015 until 2025. Automatic products of chlorophyll-a (Chl-a), total suspended matter (TSM), and water transparency (kd_z90max) were derived. Chl-a concentrations indicated predominantly eutrophic conditions, ranging from 0.44 (Mare Piccolo) to 80.81 mg·m⁻³ (Comacchio). Comacchio and Cabras showed persistently high Chl-a values and low transparency, while Mare Piccolo was characterized by high transparency and oligotrophic conditions. Varano and Cabras showed a significant increase in Chl-a (p < 0.05) coupled with an increase in TSM (p < 0.01) and decline in transparency in Varano (p < 0.05). Most other lagoons showed no long-term trends but remained in eutrophic–hypereutrophic states. Therefore, the Italian coastal lagoons studied are vulnerable areas to environmental degradation. Many of the lagoons showed persistent eutrophic conditions and no long-term recovery trends. However, among the lagoons, there were heterogeneous ecological conditions, ranging from oligotrophic (Mare Piccolo) to chronically hypereutrophic (Comacchio, Cabras). Water clarity was mainly affected by suspended solids; however, in some cases, there was a key role in primary production (algal blooms). Sentinel-2 data proved effective for monitoring spatial and temporal variability in coastal lagoon water quality, offering a valuable tool for environmental management and early detection of degradation trends. Full article

Radar backscatter from large-scale scenarios plays a crucial role in remote sensing applications. However, due to the diversity and heterogeneity of the natural environment, traditional empirical methods which rely on simplified physics and a limited set of parameters, fail to adequately model land backscatter, thus exhibiting significant limitations. While purely data-driven deep learning (DL) methods offer flexibility, they often struggle to ensure physical consistency and effectively generalize to unseen scenarios. To address these issues, we propose a novel knowledge-aided (KA) DL-based method (called KADL) in this paper for predicting the radar backscatter from large-scale scenarios. The proposed KADL is implemented in three parts. First, based on multi-source remote sensing data, the dielectric properties of land surface, i.e., soil moisture and leaf area index (LAI) are incorporated as priori physical knowledge into the Multi-Feature Clutter Dataset (MFCD) to obtain initialized input. Second, a knowledge perception module (KPM) is introduced into the cascaded deep neural network (DNN) solver to exploit the representative features within the inputs. Third, an efficient knowledge-weighted fusion (KWF) strategy is developed to further enhance the discriminative features and simultaneously suppress the non-informative features. For better comparison, we refitted the specific empirical models based on the measured data and introduced an advanced nonhomogeneous terrain clutter model (termed ANTCM) derived from our previous work. Extensive experiments conducted on the measured data demonstrate that KADL achieves a root mean square error (RMSE) of 4.74 dB and a mean absolute percentage error (MAPE) of 8.7% on independent test data. Furthermore, KADL exhibits superior robustness, with a standard deviation of RMSE as low as 0.18 dB across multiple trials. All these results validate the superior accuracy, robustness, and generalization ability of KADL for large-scale backscatter prediction. Full article

Food safety and quality are paramount global concerns, with the complexities of the modern supply chain demanding advanced technologies for monitoring, preservation, and decontamination. Conventional methods often fall short due to limitations in speed, sensitivity, cost, and functionality. Metal–organic frameworks (MOFs), a class of crystalline porous materials, have emerged as a highly universal platform to address these challenges, owing to their unprecedented structural tunability, ultrahigh surface areas, and tailorable chemical functionalities. This comprehensive review details the state-of-the-art applications of multifunctional MOFs across the entire spectrum of food safety and quality enhancement. First, the review details the application of MOFs in advanced food analysis, covering their transformative roles as sorbents in sample preparation (e.g., solid-phase extraction and microextraction), as novel stationary phases in chromatography, and as the core components of highly sensitive sensing platforms, including luminescent, colorimetric, electrochemical, and SERS-based sensors for contaminant detection. Subsequently, the role of MOFs in food preservation and packaging is explored, highlighting their use in active packaging systems for ethylene scavenging and controlled antimicrobial release, in intelligent packaging for visual spoilage indication, and as functional fillers for enhancing the barrier properties of packaging materials. Furthermore, the review examines the direct application of MOFs in food processing for the selective adsorptive removal of contaminants from complex food matrices (such as oils and beverages) and as robust, recyclable heterogeneous catalysts. Finally, a critical discussion is presented on the significant challenges that impede widespread adoption. These include concerns regarding biocompatibility and toxicology, issues of long-term stability in complex food matrices, and the hurdles of achieving cost-effective, scalable synthesis. This review not only summarizes recent progress but also provides a forward-looking perspective on the interdisciplinary efforts required to translate these promising nanomaterials from laboratory research into practical, real-world solutions for a safer and higher-quality global food supply. Full article

Debris-covered glaciers, with their intricate thermal dynamics and significant spatial heterogeneity, play a pivotal role in elucidating glacier ablation processes and their responses to climate change. However, existing research on their energy balance predominantly focuses on short-term or localized processes, while the long-term evolution of energy fluxes and the combined effects of debris cover and ice cliffs remain underexplored. This study, focused on the Lirung glacier in the Central Himalaya, leverages multi-source remote sensing data (Landsat 8, MODIS, Planet) in conjunction with meteorological observations and an energy balance model to investigate the spatiotemporal variations in the glacier’s surface energy balance from October 2017 to August 2019. Key findings are as follows: (1) Net radiation flux emerges as the predominant energy driver for ablation, reaching its peak during May–June and substantially outpacing both sensible and latent heat fluxes in magnitude; (2) The energy balance exhibits pronounced spatial heterogeneity, with lower-altitude regions receiving enhanced energy inputs and displaying reduced albedo, thereby magnifying the local ablation flux; (3) The average debris thickness is quantified at 0.55 ± 0.02 m, with thicker debris layers mitigating ablation, while thinner layers exacerbate it; (4) Ice cliffs are characterized by significantly elevated ablation fluxes, with certain areas recording values as high as 1.73 times the glacier-wide mean; (5) The proglacial lake has expanded by 21.1 ± 11.4%, with its temporal variations closely tracking the fluctuations in net radiation flux. These findings provide crucial insights into the energy balance and climate responses of debris-covered glaciers in the Central Himalaya. 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

Accurate quantification of regional ET is essential for agricultural water management. Upscaling methods based on flux tower observations have been widely applied in large-scale ET estimation. However, the coarse spatial resolution of existing upscaling approaches limits their utility in field-scale management. Therefore, this study proposes an integrated upscaling framework that combines data fusion and machine learning, enabling spatiotemporally continuous ET estimation at the field scale (30 m × 30 m). First, daily 30 m resolution land surface temperature (LST) and vegetation indices were generated by fusing MODIS, Landsat, and China Land Data Assimilation System (CLDAS) datasets. These variables, along with meteorological data and the footprint model, were used as inputs for machine learning. The upscaled ET was evaluated under varying surface heterogeneity using optical-microwave scintillometers (OMS). The results show that a one-dimensional convolutional neural network (1D CNN) using both remote sensing and meteorological data performed best in relatively homogeneous croplands, achieving a correlation coefficient (R) of 0.90, a bias of −0.14 mm/d, a mean absolute error (MAE) of 0.46 mm/d, and a root mean square error (RMSE) of 0.66 mm/d. In contrast, for heterogeneous urban-agricultural landscapes, the 1D CNN using only remote sensing data outperformed other models, with R, bias, MAE, and RMSE of 0.93, −0.14 mm/d, 0.66 mm/d, and 0.88 mm/d, respectively. Furthermore, SHapley Additive exPlanations (SHAP) revealed that LST and the two-band enhanced vegetation index (EVI2) were the most influential drivers in the models. The framework successfully enables ET modeling and spatial extrapolation in heterogeneous regions, providing a foundation for precision water resource management. Full article

The clumping index (CI) describes the level of foliage grouping relative to the random distribution within the canopy. It plays a vital role in the derivation of other important parameters (e.g., the leaf area index, (LAI)) that are usually employed in hydrological, ecological and climatological modeling. In recent years, several satellite-based CI products have been developed using multi-angle reflectance data. However, these products have been validated through the use of a “point-to-point” comparison, which rarely involves a quantitative analysis of spatial representativeness for field-measured CIs in most cases. In this study, we developed a methodological framework to validate the MODIS CI at three different data scales on the basis of intense field measurements, high-resolution unmanned aerial vehicle (UAV) observations and Landsat 8 data. This framework was used to understand the impacts of the scale issue and subpixel variance of the CI in the validation of the MODIS CI for a case study of 12 gridded 500 m pixels in Saihanba National Forest Park, Hebei, China. The results revealed that the MODIS CIs in the study area were in good agreement with the upscaled field CIs (R = 0.75, RMSE = 0.05, bias = 0.02) and UAV CIs. Through a comparison of the observed CIs along the 30 m transects with the 500 m MODIS CIs, we gained insight into the uncertainty caused by the direct “point-to-pixel” evaluation method, which ranged from −0.21~+0.27 for the 10th and 90th percentiles of the observed-MODIS CI error distribution for the twelve pixels. Moreover, semivariogram analysis revealed that the representativeness assessments based on high-resolution albedo and CI maps could reflect the spatial heterogeneity within pixels, whereas the CI map provided more information on the variation in vegetation structures. The average observational footprint needed for a spatially representative sample is approximately 209 m according to an analysis of the high-resolution CI map. The uncertainty of mismatched MODIS land cover types can lead to a deviation of 0.33 in CI estimates, and compared with the CLX method, the scaled-up CI method based on simple arithmetic averages tends to overestimate CIs. In summary, various validation efforts in this case study reveal that the accuracy of the MODIS CIs is generally reliable and in good agreement with that of the upscaled field CIs and UAV CIs; however, with the development of surface process modeling and remote sensing technology, substantial measurements of field CIs in conjunction with high-resolution remotely sensed CI maps derived from single-angle advanced methods are urgently needed for further validation and potential applications. Certainly, such a validation effort will help to improve the understanding of MODIS CI products, which, in turn, will further support the methods and applications of global geospatial information. Full article

The study aims to evaluate the impact of climate change on pasture ecosystems in Central Kazakhstan, particularly within the Karaganda and Ulytau regions. The assessment combines remote sensing indicators (NDVI, LST) with long-term climatic datasets (CRU TS v4.09 and national meteorological records) for the period 2000–2024. Non-parametric statistical methods, including the Mann–Kendall trend test, Sen’s slope estimator, and Pettitt’s test, were applied to identify the direction, intensity, and structural shifts in temperature and precipitation trends. The results indicate significant regional warming, especially during summer and spring, alongside spatially inconsistent precipitation changes. The southern and southwestern areas (Zhezkazgan and Satpayev) show intensified aridization, manifested in rising land surface temperatures, decreasing rainfall, and declining vegetation productivity and exacerbated by anthropogenic pressures. Conversely, the eastern and northeastern regions exhibit stable or increasing NDVI values and moderate precipitation growth, suggesting potential for natural recovery. The study concludes that pasture degradation in Central Kazakhstan is driven by combined climatic and human factors, with pronounced spatial heterogeneity. The integrated approach enhances the reliability of climate impact assessments and provides a scientific basis for developing adaptive and region-specific strategies for sustainable pasture management under ongoing climate change. Full article

This study addresses the observation of floating plastic debris in freshwater environments using an Unmanned Aerial Vehicle (UAV) multi-sensor strategy. An experimental campaign is described where an heterogeneous plastic assemblage, namely a plastic target, and a naturally occurring leaf-litter mat are observed by a UAV platform in the Lake Calore (Avellino, Southern Italy) within the framework of the “multi-layEr approaCh to detect and analyze cOastal aggregation of MAcRo-plastic littEr” (ECOMARE) Italian Ministry of Research (MUR)-funded project. Three UAV platforms, equipped with optical, multispectral, and thermal sensors, are adopted, which overpass the two targets with the objective of analyzing the sensitivity of optical radiation to plastic and the possibility of discriminating the plastic target from the natural one. Georeferenced orthomosaics are generated across the visible, multispectral (Green, Red, Red Edge, Near-Infrared—NIR), and thermal bands. Two novel indices, the Plastic Detection Index (PDI) and the Heterogeneity Plastic Index (HPI), are proposed to discriminate between the detection of plastic litter and natural targets. The experimental results highlight that plastics exhibit heterogeneous spectral and thermal responses, whereas natural debris showed more homogeneous signatures. Green and Red bands outperform NIR for plastic detection under freshwater conditions, while thermal imagery reveals distinct emissivity variations among plastic items. This outcome is mainly explained by the strong NIR absorption of water, the wetting of plastic surfaces, and the lower sensitivity of the Mavic 3′s NIR sensor under high-irradiance conditions. The integration of optical, multispectral, and thermal data demonstrate the robustness of UAV-based approaches for distinguishing anthropogenic litter from natural materials. Overall, the findings underscore the potential of UAV-mounted remote sensing as a cost-effective and scalable tool for the high-resolution monitoring of plastic pollution over inland waters. 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

Planetary remote sensing super-resolution aims to enhance the spatial resolution and fine details from low-resolution images. In practice, planetary remote sensing is inherently constrained by sensor payload limitations and communication bandwidth, resulting in restricted spatial resolution and inconsistent scale factors across observations. These constraints make it impractical to acquire uniform high-resolution images, thereby motivating the need for arbitrary-scale super-resolution capable of dynamically adapting to diverse imaging conditions and mission design restrictions. Despite extensive progress in general SR, such constraints remain under-addressed in planetary remote sensing. To address those challenges, this article proposes an arbitrary-scale super-resolution (SR) model, the Adaptive Frequency–Spatial Neural Operator (AFSNO), designed to address the regional context homogeneity and heterogeneous surface features of planetary remote sensing images through frequency separation and non-local receptive field. The AFSNO integrates Frequency–Spatial Hierarchical Encoder (FSHE) and Fusion Neural Operator in a unified framework, achieving arbitrary-scale SR tailored for planetary image characteristics. To evaluate the performance of AFSNO in planetary remote sensing, we introduce Ceres-1K, the planetary remote sensing dataset. Experiments on Ceres-1K demonstrate that AFSNO achieves competitive performance in both objective assessment and perceptual quality while preserving fewer parameters. Beyond pixel metrics, sharper edges and high-frequency detail enable downstream planetary analyses. The lightweight, arbitrary-scale design also suits onboard processing and efficient data management for future missions. Full article

Agricultural drought poses a critical constraint to food security and regional sustainable development, particularly in the Huang-Huai-Hai Plain, a major grain-producing region characterized by high spatial heterogeneity in drought risk. Previous studies have demonstrated that the Crop Water Stress Index (CWSI) outperforms traditional meteorological indices in detecting agricultural droughts in various regions. However, there is limited research specifically focusing on its spatiotemporal dynamics and the complex relationships with environmental factors, particularly in the Huang-Huai-Hai Plain. To fill this gap, this study first estimated CWSI using remote sensing evapotranspiration data and systematically assessed the spatiotemporal dynamics of agricultural drought in the Huang-Huai-Hai Plain from 2005 to 2020. Then, an integrated analytical framework that combines Local Indicators of Spatial Association (LISA) with Random Forest (RF) modeling has been proposed to identify primary environmental drivers. Results revealed a general downward trend in CWSI over the study period, with drought hotpots primarily concentrated in the central plains and along the eastern foothills of the Taihang Mountains. LISA identified four distinct spatial cluster types and revealed significant spatial associations between CWSI and six environmental variables. The major driving factors of CWSI included vegetation conditions (NDVI), land surface temperature (LST), rainfall, and temperature-related factors (SAT, DSR), with LST and SAT exhibiting the strongest correlations with CWSI in multiple regions. Among these, LST and SAT exhibited strong positive correlations with CWSI in multiple regions. By integrating spatial clustering and variable importance analysis, we found that agricultural drought patterns are shaped by interacting environmental factors, with region-specific dominant mechanisms. This study provides a novel analytical framework that bridges remote sensing, spatial statistics, and machine learning, offering valuable insights and tools for drought monitoring and attribution at regional scales. Full article

Pavement surface defects such as potholes pose significant safety risks and accelerate infrastructure deterioration. Accurate and automated detection of such defects requires both advanced sensing technologies and robust deep learning models. In this study, we propose PoFormer, a Transformer–CNN hybrid framework designed for precise segmentation of pavement potholes from heterogeneous image datasets. The architecture leverages the global feature extraction ability of Transformers and the fine-grained localization capability of CNNs, achieving superior segmentation accuracy compared to state-of-the-art models. To construct a representative dataset, we combined open source images with high-resolution field data acquired using a multi-sensor pavement inspection vehicle equipped with a line-scan camera and infrared/laser-assisted lighting. This sensing system provides millimeter-level resolution and continuous 3D surface imaging under diverse environmental conditions, ensuring robust training inputs for deep learning. Experimental results demonstrate that PoFormer achieves a mean IoU of 77.23% and a mean pixel accuracy of 84.48%, outperforming existing CNN-based models. By integrating multi-sensor data acquisition with advanced hybrid neural networks, this work highlights the potential of 3D imaging and sensing technologies for intelligent pavement condition monitoring and automated infrastructure maintenance. Full article

Optical sensing technologies are revolutionizing global food safety surveillance through exceptional sensitivity, rapid response, and high portability. This review systematically evaluates five major platforms, revealing unprecedented detection capabilities from sub-picomolar to single-cell resolution. Surface plasmon resonance achieves $0.021 \mathrm{ng}/\mathrm{mL}$ detection limits for veterinary drugs with superior molecular recognition. Quantum dot fluorescence sensors reach $0.17 \mathrm{nM}$ sensitivity for pesticides, enabling rapid on-site screening. Surface-enhanced Raman scattering attains $0.2 \mathrm{pM}$ sensitivity for heavy metals, ideal for trace contaminants. Laser-induced breakdown spectroscopy delivers multi-elemental analysis within seconds at $0.0011 \mathrm{mg}/\mathrm{L}$ detection limits. Colorimetric assays provide cost-effective preliminary screening in resource-limited settings. We propose a stratified detection framework that strategically allocates differentiated sensing technologies across food supply chain nodes, addressing heterogeneous demands while eliminating resource inefficiencies from deploying high-precision instruments for routine screening. Integration of microfluidics, artificial intelligence, and mobile platforms accelerates evolution toward multimodal fusion and decentralized deployment. Despite advances, critical challenges persist: matrix interference, environmental robustness, and standardized protocols. Future breakthroughs require interdisciplinary innovation in materials science, intelligent data processing, and system integration, transforming laboratory prototypes into intelligent early warning networks spanning the entire food supply chain. Full article

Methane (CH₄) emissions are a major contributor to greenhouse gases and pose significant challenges to global climate mitigation efforts. The accurate determination of CH₄ concentrations via remote sensing is crucial for emission monitoring but remains impeded by surface spectral heterogeneity—notably albedo variations and land cover diversity. This study systematically assessed the sensitivity of three mainstream algorithms, namely, matched filter (MF), albedo-corrected reweighted-L1-matched filter (ACRWL1MF), and differential optical absorption spectroscopy (DOAS), to surface type, albedo, and emission rate through high-fidelity simulation experiments, and proposed a dynamic regularized adaptive matched filter (DRAMF) algorithm. The experiments simulated airborne hyperspectral imagery from the Airborne Visible/InfraRed Imaging Spectrometer-Next Generation (AVIRIS-NG) with known CH₄ concentrations over diverse surfaces (including vegetation, soil, and water) and controlled variations in albedo through the large-eddy simulation (LES) mode of the Weather Research and Forecasting (WRF) model and the MODTRAN radiative transfer model. The results show the following: (1) MF and DOAS have higher true positive rates (TP > 90%) in high-reflectivity scenarios, but the problem of false positives is prominent (TN < 52%); ACRWL1MF significantly improves the true negative rate (TN = 95.9%) through albedo correction but lacks the ability to detect low concentrations of CH₄ (TP = 63.8%). (2) All algorithms perform better at high emission rates (1000 kg/h) than at low emission rates (500 kg/h), but ACRWL1MF performs more robustly in low-albedo scenarios. (3) The proposed DRAMF algorithm improves the F1 score (0.129) by about 180% compared to the MF and DOAS algorithms and improves TP value (81.4%) by about 128% compared to the ACRWL1MF algorithm through dynamic background updates and an iterative reweighting mechanism. In practical applications, the DRAMF algorithm can also effectively monitor plumes. This research indicates that algorithms should be selected considering the specific application scenario and provides a direction for technical improvements (e.g., deep learning model) for monitoring gas emission. Full article