Search Results (2,052)

Understanding land surface fluxes is essential for sustaining dryland ecosystem functioning and services. However, the scarcity of in situ measurements poses a significant challenge to dryland monitoring. Satellite optical and thermal remote sensing data can provide the instantaneous estimates of land surface fluxes, such as surface temperature (LST), net radiation (Rn), sensible heat flux (H), evapotranspiration (latent heat flux, LE), and gross primary productivity (GPP). However, satellite-based estimates are often limited by sensor revisit frequencies and cloud-cover conditions. To facilitate temporally continuous estimation, process-based land surface models are often used to integrate sparse remote sensing observations and meteorological inputs, thereby generating continuous estimates of energy, water, and carbon fluxes. However, the impact of satellite thermal data accuracy and temporal resolutions on simulating land surface fluxes is under-explored, particularly in dryland ecosystems. Therefore, this study assessed the accuracy of Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared data in a dryland tussock grassland ecosystem in southern Spain. We also assessed the incorporation and various temporal frequencies of thermal data into process-based modelling for simulating land surface fluxes. The model simulations were validated against in situ measurements from eddy covariance towers. Results show that MODIS LST has a high correlation but large bias with in situ measurements (R² = 0.81, RMSE = 4.34 °C). After a linear correction of MODIS LST with in situ measurements, we found that the adjusted MODIS LST can effectively improve the half-hourly simulation of LST, Rn, H, LE, SWC, and GPP with relative RMSEs of 7.84, 5.67, 7.81, 11.32, 6.59, and 13.09%, respectively. Such performance is close to the flux simulations driven by in situ LST. We also found that by adjusting the revisit frequency of the satellite sensor to 8 days, the model performance of simulating surface fluxes did not change significantly. This study provides insights into how satellite thermal remote sensing can be integrated with the process-based model to understand dryland ecosystem functioning, which is critical for ecological management and climate adaptation strategies. Full article

Forest height is a key structural parameter for evaluating ecological functions, biodiversity, and carbon dynamics. While LiDAR and Synthetic Aperture Radar (SAR) provide vertical structure information, their large-scale use is restricted by sparse sampling (LiDAR) and temporal decorrelation (SAR). Optical remote sensing offers complementary spectral information but lacks direct height retrieval. To address these limitations, we developed a multi-modal framework integrating GEDI waveform LiDAR, Sentinel-1 SAR (InSAR and PolSAR), and Sentinel-2 multispectral data, combined with machine learning, to estimate forest canopy height across Jiangsu Province, China. GEDI L2A footprints were used as training labels, and a suite of structural and spectral features was extracted from SAR, GEDI, and Sentinel-2 data as input variables for canopy height estimation. The performance of two ensemble algorithms, Random Forest (RF) and Gradient Tree Boosting (GTB) for canopy height estimation, was evaluated through stratified five-fold cross-validation. RF consistently outperformed GTB, with the integration of SAR, GEDI, and optical features achieving the best accuracy (R² = 0.708, RMSE = 2.564 m). The results demonstrate that InSAR features substantially enhance sensitivity to vertical heterogeneity, improving forest height estimation accuracy. These findings highlight the advantage of incorporating SAR, particularly InSAR with optical data, in enhancing sensitivity to vertical heterogeneity and improving the performance of RF and GTB in estimating forest height. The framework we proposed is scalable to other regions and has the potential to contribute to global sustainable forest monitoring initiatives. Full article

While recent industrial automation trends emphasize the importance of non-destructive inspection by material-identifying millimeter-wave, terahertz-wave, and infrared (MMW, THz, IR) monitoring, fundamental tools in these wavelength bands (such as sensors) are still immature. Although inorganic semiconductors serve as diverse sensors with well-established large-scale fine-processing fabrication, the use of those devices is insufficient for non-destructive monitoring due to the lack of photo-absorbent properties for such major materials in partial regions across MMW–IR wavelengths. To satisfy the inherent advantageous non-destructive MMW–IR material identification, ultrabroadband operation is indispensable for photo-sensors under compact structure, flexible designability, and sensitive performances. This review then introduces the recent advances of carbon nanotube film-based photo-thermoelectric imagers regarding usable and high-yield device fabrication techniques and scientific synergy among computer vision to collectively satisfy material identification with three-dimensional (3D) structure reconstruction. This review synergizes material science, printable electronics, high-yield fabrication, sensor devices, optical measurements, and imaging into guidelines as functional non-destructive inspection platforms. The motivation of this review is to introduce the recent scientific fusion of MMW–IR sensors with visible-light computer vision, and emphasize its significance (non-invasive material-identifying sub-millimeter-resolution 3D-reconstruction with 660 nm–1.15 mm-wavelength imagers at noise equivalent power within 100 pWHz^−1/2) among the existing testing methods. Full article

Flexible upconversion (UC) devices, owing to their unique combination of high–efficiency optical energy conversion and mechanical flexibility, have attracted increasing attention in the fields of optoelectronics, wearable devices, flexible displays, and biomedical applications. However, significant challenges remain in balancing optical performance, mechanical adaptability, long–term stability, and scalable fabrication, which limit their practical deployment. This review systematically introduces five representative upconversion mechanisms—excited–state absorption (ESA), energy transfer upconversion (ETU), energy migration upconversion (EMU), triplet–triplet annihilation upconversion (TTA–UC), and photon avalanche (PA)—highlighting their energy conversion principles, performance characteristics, and applicable scenarios. The article further delves into the flexible transition of upconversion devices, detailing not only the evolution of the luminescent layer from bulk crystals and nanoparticles to polymer composites and hybrid systems, but also the optimization of electrodes from rigid metal films to metal grids, carbon–based materials, and stretchable polymers. These developments significantly enhance the stability and reliability of flexible upconversion devices under bending, stretching, and complex mechanical deformation. Finally, emerging research directions are outlined, including multi–mechanism synergistic design, precise nanostructure engineering, interface optimization, and the construction of high–performance composite systems, emphasizing the broad potential of flexible UC devices in flexible displays, wearable health monitoring, solar energy harvesting, flexible optical communications, and biomedical photonic applications. This work provides critical insights for the design and application of high–performance flexible optoelectronic devices. Full article

Background/Objectives: To evaluate the modality-specific impact of cataracts on the detection of hydroxychloroquine retinopathy. Methods: In this retrospective cohort study, 202 eyes (101 patients) with confirmed HCQ retinopathy were included; analyses focused on 141 cataractous eyes from 72 patients. At each visit, the severity of cataracts in 141 eyes was graded using the Lens Opacities Classification System III (LOCS III), with clinically significant cataracts defined as a LOCS III grade ≥ 3. Screening was performed using swept source optical coherence tomography (OCT), ultrawide field fundus autofluorescence (FAF), and Humphrey visual field (HVF) tests. The detection rates of abnormalities on OCT, FAF, and HVF were compared between minimal (at the time of diagnosis or after cataract surgery) and maximal cataract severity as well as between eyes with clinically significant cataracts and others. Multivariate logistic regression was performed to identify the factors associated with the detection of retinopathy-associated abnormalities across each screening modality. Results: Of the 141 eyes with cataracts, 52 (36.9%) developed clinically significant opacities during the monitoring period, and 23 (16.3%) underwent cataract surgery. OCT detected ellipsoid zone disruptions in 100% of cataractous eyes, while visual fields revealed characteristic paracentral scotomas with comparable sensitivity regardless of cataract severity. In contrast, FAF sensitivity was significantly lower in eyes with clinically significant cataracts (61.5%) compared to those with mild cataracts (92.1%, p < 0.001). Sensitivities were also reduced at maximal versus minimal severity in eyes with clinically significant cortical opacities and nuclear opalescence (both p < 0.05). Multivariate analysis demonstrated that higher cortical opacity (odds ratio [OR] 0.43 per grade increase, 95% CI 0.22–0.85) and nuclear opalescence (OR 0.21, 95% CI 0.07–0.66) independently decreased FAF detection, whereas greater retinopathy severity was positively associated with detection on both FAF (OR 4.85, 95% CI 1.40–16.9) and HVF (OR 3.37, 95% CI 1.17–9.71). Conclusions: Cataracts impaired the FAF-based detection of hydroxychloroquine retinopathy, while OCT and HVF remained reliable despite significant lens opacities. Therefore, clinicians should consider cataract severity when interpreting FAF results and prioritize OCT and HVF assessments in patients with clinically significant cataracts. Full article

A sensor network based on 10 stationary nodes distributed in Bari (Southern Italy) has been deployed for urban air quality (AQ) monitoring. The low-cost sensor systems have been installed in specific sites (e.g., buildings, offices, schools, streets, ports, and airports) to enhance environmental awareness of the citizens and to supplement the expensive official air-monitoring stations with cost-effective sensor nodes at high spatial and temporal resolution. Continuous measurements were performed by low-cost electrochemical gas sensors (CO, NO₂, O₃), optical particle counter (PM₁₀), and NDIR infrared sensor (CO₂), including micro-sensors for temperature and relative humidity. The sensors are operated to assess the performance during a campaign (July 2015–December 2017) of several months for citizen science in sustainable smart cities. Typical values of CO₂, measured by distributed nodes, varied from 312 to 494 ppm (2016), and from 371 to 527 ppm (2017), depending on seasonal micro-climate change and site-specific conditions. The results of the AQ-monitoring long-term campaign for selected sensor nodes are presented with a relative error of 26.2% (PM₁₀), 21.7% (O₃), 25.5% (NO₂), and 79.4% (CO). These interesting results suggest a partial compliance, excluding CO, with Data Quality Objectives (DQO) by the European Air Quality Directive (2008/50/EC) for Indicative (Informative) Measurements. Full article

This study evaluates the cosmetic degradation of polyethylene (PE) and polypropylene (PP) automotive components under four exposure scenarios—no exposure, outdoor exposure with and without glass shielding, and accelerated UV chamber weathering (ASTM G154)—through the evolution of visible (VIS) reflectance. Thirty-two samples (16 PE, 16 PP) were monitored over five time points; surface reflectance was recorded at 21 wavelengths and summarized into seven VIS bands, and hardness (Shore D) was measured pre/post-exposure. Repeated-measures univariate and multivariate analyses consistently revealed significant effects of Condition, Time, and their interaction on reflectance, with initial-reflectance adjustment improving inference stability across bands. PE exhibited more gradual and coherent reflectance decay, whereas PP showed greater band-to-band variability—most notably under UV chamber exposure. Additionally, hardness decreased in most exposed groups, aligning with optical changes. To place spectral trajectories in a kinetic context, a family of exponential models with small-sample information criterion selection was fitted, yielding $\eta \left(t\right)$—a dimensionless degradation efficiency summarizing spectral change. The contribution of this work is a multi-statistical framework—combining VIS-band-aware summaries with covariate-adjusted univariate/multivariate testing—that supports comparisons across materials and exposure conditions, underscoring the practical value of UV chamber protocols as surrogates for outdoor weathering. In sum, the study demonstrates the effectiveness of multivariate and covariate-adjusted models in quantifying optical degradation of polyolefins, offering pragmatic guidance for assessing mid- to long-term performance in automotive applications. Full article

ZnO nanostructures have attracted attention as transducer materials in optical biosensing platforms due to their wide bandgap, defect-mediated photoluminescence, high surface-to-volume ratio, and tunable morphology. This review examines how the dimensionality of ZnO nanostructures affects biosensor performance, particularly in terms of charge transport, signal transduction, and biomolecule immobilization. The synthesis approaches are discussed, highlighting how they influence crystallinity, defect density, and surface functionalization potential. The impact of immobilization strategies on sensor stability and sensitivity is also assessed. The role of ZnO in various optical detection schemes, including photoluminescence, surface plasmon resonance (SPR), localized (LSPR), fluorescence, and surface-enhanced Raman scattering (SERS), is reviewed, with emphasis on label-free and real-time detection. Representative case studies demonstrate the detection of clinically and environmentally relevant targets, such as glucose, dopamine, cancer biomarkers, and SARS-CoV-2 antigens, with limits of detection in the pico- to femtomolar range. Recent developments in ZnO-based hybrid systems and their integration into fiber-optic and microfluidic platforms are explored as scalable solutions for portable, multiplexed diagnostics. The review concludes by outlining current challenges related to reproducibility, long-term operational stability, and surface modification standardization. This work provides a framework for understanding structure–function relationships in ZnO-based biosensors and highlights future directions for their development in biomedical and environmental monitoring applications. Full article

Monitoring large-gradient surface displacement caused by underground mining remains a significant challenge for conventional Synthetic Aperture Radar (SAR)-based techniques. This study introduces optical flow methods to monitor large-gradient displacement in mining areas and conducts a comprehensive comparison with Small Baseline Subset Interferometric SAR (SBAS-InSAR) and Pixel Offset Tracking (POT) methods. Using 12 high-resolution TerraSAR-X (TSX) SAR images over the Daliuta mining area in Yulin, China, we evaluate the performance of each method in terms of sensitivity to displacement gradients, computational efficiency, and monitoring accuracy. Results indicate that SBAS-InSAR is only capable of detecting displacement at the decimeter level in the Dalinta mining area and is unable to monitor rapid, large-gradient displacement exceeding the meter scale. While POT can detect meter-scale displacements, it suffers from low efficiency and low precision. In contrast, the proposed optical flow method (OFM) achieves sub-pixel accuracy with root mean square errors of 0.17 m (compared to 0.26 m for POT) when validated against Global Navigation Satellite System (GNSS) data while improving computational efficiency by nearly 30 times compared to POT. Furthermore, based on the optical flow results, mining parameters and three-dimensional (3D) displacement fields were successfully inverted, revealing maximum vertical subsidence exceeding 4.4 m and horizontal displacement over 1.5 m. These findings demonstrate that the OFM is a reliable and efficient tool for large-gradient displacement monitoring in mining areas, offering valuable support for hazard assessment and mining management. Full article

The rapid spread of unmanned aerial vehicles (UAVs) has created new challenges for airspace security, as drones are increasingly used for surveillance, smuggling, and potentially for attacks near critical infrastructure. A key difficulty lies in reliably distinguishing UAVs from visually similar birds in electro-optical surveillance channels, where complex backgrounds and visual noise often increase false alarms. To address this, we investigated recent YOLO architectures and developed an enhanced model named YOLOv12-ADBC, incorporating an adaptive hierarchical feature integration mechanism to strengthen multi-scale spatial fusion. This architectural refinement improves sensitivity to subtle inter-class differences between drones and birds. A dedicated dataset of 7291 images was used to train and evaluate five YOLO versions (v8–v12), together with the proposed YOLOv12-ADBC. Comparative experiments demonstrated that YOLOv12-ADBC achieved the best overall performance, with precision = 0.892, recall = 0.864, mAP50 = 0.881, mAP50–95 = 0.633, and per-class accuracy reaching 96.4% for drones and 80% for birds. In inference tests on three video sequences simulating realistic monitoring conditions, YOLOv12-ADBC consistently outperformed baselines, achieving a detection accuracy of 92.1–95.5% and confidence levels up to 88.6%, while maintaining real-time processing at 118–135 frames per second (FPS). These results demonstrate that YOLOv12-ADBC not only surpasses previous YOLO models but also offers strong potential as the optical module in multi-sensor fusion frameworks. Its integration with radar, RF, and acoustic channels is expected to further enhance system-level robustness, providing a practical pathway toward reliable UAV detection in modern airspace protection systems. 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

Turbidity is a crucial and reliable indicator that is extensively utilized in water quality monitoring through remote sensing technology. The development of accurate and applicable models for turbidity estimation is essential. While many existing studies rely on uniform models based on statistical regression or traditional machine learning techniques, the application of deep learning models for turbidity estimation remains limited. This study proposed deep learning models for turbidity estimation based on optical classification of inland waters using Sentinel-2 data. Specifically, the fuzzy c-means (FCM) clustering method was employed to classify optical water types (OWTs) based on their spectral reflectance characteristics. A weighted sum of the turbidity prediction results was generated by the OWT-based convolutional neural network-random forest (CNN-RF) model, with weights derived from the FCM membership degrees. Turbidity for four typical waters was mapped by the proposed method using Sentinel-2 images. The FCM method efficiently classified waters into three OWTs. The OWT-based weighted CNN-RF model demonstrated strong robustness and generalization performance, achieving a high prediction accuracy (R² = 0.900, RMSE = 11.698 NTU). The turbidity maps preserved the spatial continuity of the turbidity distribution and accurately reflected water quality conditions. These findings facilitate the application of deep learning models based on optical classification in turbidity estimation and enhance the capabilities of remote sensing for water quality monitoring. Full article

Accurate bathymetric information is crucial for coastal management, navigation, and ecosystem monitoring, yet conventional hydrographic surveys are costly and logistically demanding. This study introduces a two-stage semiempirical model for satellite-derived bathymetry (SDB) based on log-ratio reflectance indices from atmospherically corrected Landsat 8 imagery. The approach combines the optical sensitivity of the green/blue band ratio and the attenuation properties of the red/blue ratio within a parametric regression framework, enhancing both stability and interpretability. The methodology was evaluated in two contrasting coastal environments: the turbid Magdalena-Almejas Lagoon System (Mexico) and the clear-water coral reef setting of Buck Island (U.S. Virgin Islands). Results demonstrated that the proposed model outperformed traditional semiempirical approaches (Lyzenga, Stumpf, Hashim), achieving $R^2=0.8155$ (RMSE = 1.16 m) in Magdalena-Almejas and $R^2=0.9157$ (RMSE = 1.38 m) in Buck Island. Performance was statistically superior to benchmark methods according to cross-validated confidence intervals and was comparable to an artificial neural network, while avoiding overfitting in data-scarce environments. These findings highlight the model’s suitability as a transparent, cost-efficient, and scalable alternative for SDB, particularly valuable in regions where in situ data are limited. Full article

Geohazards pose significant dangers to human safety, infrastructures, and the environment, highlighting the need for advanced monitoring techniques for early damage detection and structure management. The distributed optical fiber sensors (DFOS) are strain, temperature, and vibration monitoring tools characterized by minimal intrusiveness, accuracy, ease of deployment, and the ability to perform measurements with high spatial resolution. Although these sensors rely on well-established measurement techniques, available for over 40 years, their diffusion within monitoring and early warning systems is still limited, and there is a certain mistrust towards them. In this regard, based on several case studies, the implementation of DFOS for early warning of various geotechnical hazards, such as landslides, earthquakes and subsidence, is discussed, providing a comparative analysis of the typical advantages and limitations of the different systems. The results show that real-time monitoring systems based on well-established distributed fiber-optic sensing techniques are now mature enough to enable reliable and long-term geotechnical applications, identifying a market segment that is only minimally saturated by using other monitoring techniques. More challenging remains the application of the technique for vibration detection that still requires improved interrogation technologies and standardized practices before it can be used in large-scale, real-time early warning systems. Full article

In this work, we used a label-free biosensor that provides optical readouts to perform continuous detection of human interleukin 8 (IL-8), which is especially overexpressed in certain cancers and, thus, could be an effective biomarker for cancer prognosis estimation and therapy evaluation. For this purpose, we engineered a compact, portable, and easy-to-assemble biosensing module device. It combines a fluidic chip for reagent flow, a biosensing chip for signal transduction, and an optical readout head based on fiber optics in a single module. The biosensing chip is based on independent arrays of resonant nanopillar transducer (RNP) networks. We integrated the biosensing chip with the RNPs facing down in a simple and rapidly fabricated polydimethyl siloxane (PDMS) microfluidic chip, with inlet and outlet channels for the sample flowing through the RNPs. The RNPs were vertically oriented from the backside through an optical fiber mounted on a holder head fabricated ad hoc on polytetrafluoroethylene (PTFE). The optical fiber was connected to a visible spectrometer for optical response analysis and consecutive biomolecule detection. We obtained a sensogram showing anti-IL-8 immobilization and the specific recognition of IL-8. This unique portable and easy-to-handle module can be used for biomolecule detection within minutes and is particularly suitable for in-line sensing of physiological and biomimetic organ-on-a-chip systems. Cancer biomarkers’ continuous monitoring arises as an efficient and non-invasive alternative to classical tools (imaging, immunohistology) for determining clinical prognostic factors and therapeutic responses to anticancer drugs. In addition, the multiplexed layout of the optical transducers and the simplicity of the monolithic sensing module yield potential high-throughput screening of a combination of different biomarkers, which, together with other medical exams (such as imaging and/or patient history), could become a cutting-edge technology for further and more accurate diagnosis and prediction of cancer and similar diseases. Full article