Search Results (641)

The high sensitivity of HgCdTe infrared detectors makes them highly vulnerable to laser irradiation, yet the influence of pulse width on damage behavior in the short-wave infrared (SWIR) band remains insufficiently understood. In this study, we experimentally and numerically investigate the damage effects of SWIR pulsed lasers on HgCdTe focal plane array detectors, focusing on the role of pulse width. Three lasers with pulse widths of 5.5 ns, 0.6 ms and 2 ms are used to irradiate the detector, and the damage thresholds for spot damage, line damage, and complete failure are measured. Damage morphologies are characterized by optical microscopy and scanning electron microscopy. A finite-element thermal model is also established to calculate transient temperature distributions and theoretical damage thresholds. For the 0.6 ms pulse, the measured thresholds for spot damage, line damage, and complete failure are 5.7 J/cm², 65.4 J/cm², and 157.3 J/cm², respectively; for the 2 ms pulse, these increase to 12.1 J/cm², 149.3 J/cm², and 405 J/cm² due to energy dispersion. Microscopic analysis reveals that spot damage arises from melting of HgCdTe and indium bumps, line damage from partial damage to the read-out integrated circuit (ROIC) layer, and complete failure from melt-through of the ROIC layer. The spot damage threshold of the 5.5 ns pulse is 1.2 J/cm², while neither line damage nor complete failure occurs even with a 352.5 J/cm² laser pulse, indicating different damage mechanisms due to a thermal confinement effect. The simulation results agree well with the experimental observations. These findings clarify the pulse-width dependence of damage thresholds and provide practical guidance for detector hardening and photoelectric countermeasure design. Full article

The Sea and Land Surface Temperature Radiometer (SLSTR) is a dual-view scanning radiometer onboard the Sentinel-3A and Sentinel-3B satellites. This sensor provides data from the visible to the thermal infrared, with a temporal resolution of approximately 12 h. In this work, we present an automated system using shortwave infrared (SWIR) bands at 500 m spatial resolution to monitor active volcanoes in near real time. The system implements a normalized hotspot index (NHI) to detect and characterize high-temperature volcanic features in daylight and nighttime conditions. During the first three months of operation (i.e., August–October 2025), the system successfully identified several eruptive activities, with a false positive rate around 2.0%. The latter includes also true hot pixels associated with vegetation fires and other high-temperature sources. Results were assessed through comparison with the Fire Information for Resource Management System (FIRMS), the Middle Infrared Observations of Volcanic Activity (MIROVA), MODVOLC, and the S3-L2 FRP product. The preliminary comparison with the MIROVA-MODIS dataset reveals a good correlation in the estimates of fire radiative power over Etna (Italy) and Kilauea (Hawaii, USA), although discrepancies in the magnitude of this parameter remain significant also because of the SWIR retrieval method, which was optimized for gas flares. Despite the impact of snow-covered surfaces and band co-registration on the accuracy of hotspot detection, this study shows that the NHI-SLSTR system may provide a relevant contribution to the surveillance of active volcanoes from space, integrating information from other systems performing globally. Full article

A biological/synthetic hybrid graft copolymer was obtained by grafting poly(acrylic acid) (PAA, synthesized via reversible addition-fragmentation chain transfer (RAFT) polymerization) to amylopectin (AMP). The novel graft copolymer presents amphiphilic properties due to the inherent insolubility of AMP in water and was further utilized as a mediator for the synthesis of gold nanoparticles (AuNPs) following an environmentally friendly in situ procedure. The AMP-g-PAA copolymer formation by the interaction of the PAA end groups with the C(6)-OH groups on an AMP backbone was confirmed by Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) and 1D (proton (¹H NMR) and carbon (¹³C NMR) nuclear magnetic resonance, and Distortionless Enhancement by Polarization Transfer (DEPT)) and 2D (correlation (COSY) and heteronuclear single quantum coherence (HSQC)) spectroscopies. The calculated degree of substitution of 1.17 suggests that the grafting was done at one OH from the three in an anhydroglycosidic unit (AGU) (preferably at that in C6 position), with a mean grafting efficiency of 76%. Additional information obtained using thermogravimetric analysis shows that the thermal decomposition of AMP-g-PAA occurs in two steps, with a residual mass of ~16 wt% at 700 °C, higher than AMP or PAA, indicating increased thermal stability of the copolymer. Dynamic and electrophoretic light scattering (DLS and ELS) measurements were used to determine the hydrodynamic size and ionic charge of the AMP-g-PAA self-assemblies in aqueous solution as well as their stability. The AMP-g-PAA was subsequently tested as a reducing agent in the environmentally friendly synthesis of AuNPs in aqueous solution, at different incubation temperatures, reaction duration, and inorganic/polymer weight ratios. The development of the surface plasmon resonance band of AuNPs, observed in UV–vis spectra, was consistently monitored over the reaction time. DLS analysis indicated time-dependent changes in the AuNPs’ particle size distributions, while scanning transmission electron microscopy confirmed that the AuNPs formed at the inorganic/polymer weight ratio of 0.36 and at 60 °C were predominantly well-dispersed, spherical-shaped nanoparticles. The AuNPs synthesized in situ within the copolymer matrix did not introduce additional cytotoxicity compared to the parent copolymer alone, with the composites representing a promising safety baseline for further investigation in biomedical applications. Full article

Conventional 8YSZ thermal barrier ceramics suffer from limited phase stability and insufficient infrared radiation regulation at high temperatures. Sc₂O₃ doping can reduce thermal conductivity and improve phase stability, but the improvement remains limited because the fixed-valence substitution of Sc³⁺ cannot effectively increase defect concentration or regulate carrier behavior. In this work, CeO₂ with tunable valence states was incorporated into the Sc-stabilized YSZ system to realize the synergistic modulation of lattice thermal conductivity and photon thermal conductivity. A series of Sc₂O₃–CeO₂ co-doped YSZ ceramics were fabricated via solid-state sintering, and the effects of co-doping on phase structure, defect evolution, thermal conductivity, infrared emissivity, and bandgap characteristics were systematically investigated. The results show that all co-doped samples maintained a stable tetragonal fluorite structure with relative densities higher than 96%. Among them, Sc_0.08Ce_0.005Y_0.005Zr_0.91O₂ exhibited the best comprehensive performance. Its thermal conductivity at 1000 °C reached 2.073 W·m⁻¹·K⁻¹, which was 11.9% lower than that of conventional 8YSZ. Meanwhile, the average infrared emissivity in the 3–5 μm band increased to 0.779. XPS analysis indicated that Ce incorporation promoted oxygen-vacancy formation, which enhanced phonon scattering and reduced lattice thermal conductivity. In addition, co-doping narrowed the band gap and facilitated carrier excitation, thereby strengthening infrared absorption and emission behavior. The enhanced infrared emissivity further contributed to the suppression of radiative thermal transport at elevated temperatures. This work demonstrates that Sc₂O₃–CeO₂ co-doping provides an effective strategy for simultaneously regulating phonon transport and photon transport in YSZ-based ceramics. The results provide new insight into the design of advanced thermal barrier materials with low thermal conductivity and enhanced high-temperature infrared radiation performance. Full article

Heavy and highly viscous oils account for a significant proportion of the world’s hydrocarbon reserves. The development of these reserves in harsh climates is associated with technological risks due to paraffin deposits and equipment corrosion. Ensuring reliable transportation requires operational monitoring of the physical and chemical properties of fluids directly at the wellhead. Traditional laboratory methods such as SARA fractionation and gas chromatography (GC) are time-consuming and can yield to distortions in the sample composition during transportation. Field optical methods, such as an infrared (IR) spectroscopy are complicated by the optical heterogeneity of crude oils due to emulsified water, supramolecular associations of resins, asphaltenes, and paraffins. In this paper, ultrasonic (US) sample preparation for high-viscosity oils is justified as a method for increasing the reliability and information content of field IR spectroscopic analysis by unmasking the diagnostic extrema of absorption bands that are initially distorted by emulsified water, baseline scattering, and radiation scattering from large resin–asphaltene–paraffin aggregates. The technique is based on cavitation-induced destruction of emulsion shells and disaggregation of the structural framework without volume thermal heating. Experimental data obtained from watered high-viscosity oil has shown that 9 min of the US exposure reduces the light scattering index $I_{\mathrm{t}\mathrm{r}\mathrm{s}}$ by 92.83%, bringing the system into a less heterogeneous state. Statistical correlation analysis confirmed that emulsions and aggregates are the main scattering centers, and their destruction correlates directly with the transparency of the medium. Stability of spectral indices $I_{{\mathrm{C}\mathrm{H}}_3/{\mathrm{C}\mathrm{H}}_2}$, $I_{\mathrm{f}\mathrm{o}\mathrm{c}}$ and $I_{\mathrm{C}=\mathrm{O}}$ indicates the absence of chemical degradation or oxidation at the US exposure intensity of 0.12 W/mL, confirming the physical nature of the effect. The proposed method makes it possible to implement automated monitoring of the properties of high-viscosity oil directly at the wellhead, minimizing logistic costs and risks of the sample degradation. The practical significance of the proposed method is to improve the reliability and information content of wellhead monitoring by reducing optical heterogeneity and making diagnostic significant IR absorption extremes more distinguishable for further interpretation. Full article

Enhancing the ampacity of existing overhead transmission conductors through surface heat-dissipation regulation is important for grid capacity expansion. Herein, a superhydrophobic radiative heat-dissipating conductor was fabricated by combining phosphoric acid anodization with low-surface-energy modification. Porous anodic aluminum oxide (AAO) layers were in situ constructed on ACSR conductors under different anodizing current densities and oxidation times, followed by modification with hexadecyltrimethoxysilane or 1H,1H,2H,2H-perfluorodecyltrimethoxysilane to obtain H-AAO and F-AAO conductors, respectively. The surface morphology, optical properties, wettability, electrical resistance, current-induced temperature rise, and aging stability were systematically evaluated. The porous AAO layer enhanced the broadband infrared emissivity of the conductor surface while maintaining relatively high solar-band reflectance. The F-AAO conductor exhibited a water contact angle of 164.9° and a sliding angle of 1.8°, confirming excellent super-hydrophobicity. At 450 A, the steady-state temperature of the F-AAO conductor decreased from 106.85 °C for the Bare conductor to 75.34 °C. Under a 70 °C temperature limit, the allowable current increased from 343.58 to 431.57 A, corresponding to a 25.6% enhancement. Moreover, the F-AAO conductor retained stable heat-dissipation performance after 28 days of thermal aging. These findings demonstrate that anodization-assisted surface engineering is a feasible strategy for improving radiative heat dissipation, environmental adaptability, and current-carrying performance of overhead transmission conductors. Full article

Background/Objectives: This review describes the advantages of new photonic-based approaches for assessing the activity of caries lesions. Many lesions have been arrested or are non-carious developmental defects, such as fluorosis, which do not require intervention. New methods are needed to assess lesion activity and avoid unnecessary removal of the tooth structure. Methods: At present, there are no reliable methods for assessing lesion activity in vivo. Nondestructive optical monitoring of lesion structure and the changes in light scattering that occur during drying offer the potential for lesion activity assessment during a single examination. Since optical diagnostic instruments exploit changes in the porosity and the permeability of the lesion, they have the potential to assess whether lesions are active and expanding or arrested and undergoing remineralization. Optical coherence tomography (OCT), Raman imaging and fluorescence loss, thermal and short-wavelength infrared (SWIR) reflectance measurements during lesion dehydration with forced air are presented. Results: Clinical studies have shown that optical coherence tomography is capable of showing distinct structural differences between active and arrested lesions on coronal and root surfaces. Differences in the kinetics of dehydration measured using reflectance measurements at SWIR wavelengths coincident with water absorption bands also show great potential. Conclusions: OCT and dehydration imaging at SWIR wavelengths have great potential for assessing lesion activity since they can also be used for caries screening, are safe for frequent monitoring and do not require the application of external agents. Full article

Land Surface Temperature (LST) is essential for climate monitoring, drought assessment, and urban heat analysis. Despite its importance, the Landsat-8 Collection 2 Level-2 (C2L2) LST product has not been rigorously validated using ground measurements—a critical gap this study addresses. We present the first comprehensive accuracy assessment using 382 coincident satellite–ground observations collected from seven Surface Radiation Budget Network (SURFRAD) stations distributed across diverse climatic regions of the United States during the period 2023–2025. The validation results indicate strong overall agreement between satellite-derived and ground-measured temperatures, yielding an RMSE of 4.20 °C, a coefficient of determination (R²) of 0.91, and a Pearson correlation coefficient (r) of 0.98. These statistics demonstrate the high reliability of the C2L2 LST product across a wide range of environmental conditions. Nevertheless, a systematic warm bias of 1.75 °C was observed, indicating a tendency toward temperature overestimation. Model performance exhibited pronounced seasonal variability. The highest accuracy was achieved during winter conditions (RMSE = 2.17 °C; r = 0.99), whereas performance declined considerably during summer months (RMSE = 5.84 °C; r = 0.91). Analysis of atmospheric water vapor content revealed significant associations with retrieval errors at high-elevation and arid locations, particularly at FPK (r = 0.78) and DRA (r = 0.75), based on 106 matched observations. These relationships provide important insight into the atmospheric factors contributing to seasonal variations in retrieval accuracy. Temperature-dependent analyses further demonstrated that retrieval uncertainty increases with surface temperature. Performance progressively deteriorated from cooler to warmer thermal regimes, with RMSE values increasing from approximately 2.05 °C for temperatures below 20 °C to 5.71 °C for temperatures exceeding 40 °C. Spatial evaluation also revealed substantial differences among stations. Relatively homogeneous, low-elevation sites exhibited superior performance (GWN: RMSE = 2.60 °C; SXF: RMSE = 2.55 °C), whereas stations located in mountainous or topographically complex environments showed reduced accuracy (TBL: RMSE = 5.14 °C; FPK: RMSE = 5.62 °C). These outcomes emphasize the influence of terrain complexity and atmospheric heterogeneity on LST retrieval performance. Overall, this study establishes the first comprehensive benchmark for evaluating the reliability of Landsat-8 C2L2 LST products. The results provide valuable guidance for their application in climate research, precision agriculture, hydrological modeling, and environmental monitoring. Furthermore, the findings identify specific environmental conditions requiring enhanced validation efforts and suggest opportunities for future algorithm refinement through improved atmospheric correction procedures and more accurate surface emissivity characterization. Full article

Reconfigurable photonic metasurfaces enable tunable thermal-emission engineering in the long-wave infrared (LWIR), particularly within the 8–13 μm atmospheric window. This work includes the investigation on a concentric-ring VO₂/SiO₂/Au metasurface for LWIR spectral-emissivity modulation. Full-wave simulations showed that, in the metallic phase (σ = 2 × 10⁵ S/m where σ is conductivity), the structure exhibited an absorption over 90% across the 9.3–15 μm sub-band, with two near-unity resonances near 10.2 and 13.3 μm. Control structures, gap-dependent spectra, E-field maps, and current-density Cartesian multipole decomposition supported a hybridized-ring mechanism in which both dominant resonances were predominantly electric-dipole-like ring branches whose spectral positions and field localizations were modified by inter-ring coupling. Across the conductivity sweep, the normal-incidence band-averaged 8–13 μm emissivity changed from 0.0184 to 0.8844, corresponding to a switching ratio of 48.06. The four-fold symmetry of unit cell also yielded polarization-insensitive and angularly robust LWIR absorption, while the simplified endpoint thermal-balance estimate indicated a metallic-state net cooling power of 49.3 W m⁻² at T = T_amb = 300 K, where T_amb was the ambient temperature, and an estimated equilibrium temperature drop of 4.4 K below the ambient for the metallic-state endpoint, whereas the insulating-state one suppressed this response. These results identify concentric VO₂ ring metasurfaces as promising candidates for switchable LWIR thermal-emission control. Full article

Cloud detection is a fundamental task in atmospheric science and satellite remote sensing. While numerous algorithms utilizing multiple visible and infrared channels have been developed, the absence of visible light at night forces most current methods to rely on multi-channel thermal infrared (TIR) observations. Consequently, detection accuracy is significantly reduced due to the minimal thermal contrast between low clouds and the ground. Furthermore, distinguishing clouds under strictly moonless conditions remains a critical challenge. Leveraging the low-light observation capability of the Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS/DNB), this study proposes a single-channel cloud detection algorithm. Based on the physical scattering of ground-based artificial lights by clouds, the algorithm integrates a feature-engineering layer with a Random Forest machine learning model. This moonlight-independent approach can rapidly determine cloudy conditions, offering a novel method for high-precision nighttime cloud detection. Validation experiments using a single fixed radar site in Longmen, China, with 97 rigorously synchronized satellite-radar sample pairs, demonstrate that the proposed algorithm achieves an overall accuracy of 86.6% (95% CI: 78.4–92.0%) against millimeter-wave cloud radar observations. While strictly reliant on stable artificial ground lights—making it primarily applicable to urban and artificially lit regions—this method provides a valuable supplementary tool for nighttime monitoring. Full article

The forthcoming joint NASA/ASI (National Aeronautics and Space Administration/Italian Space Agency) Surface Biology and Geology Thermal Infrared (SBG-TIR) mission will operate in a sun-synchronous polar orbit collecting data on a global scale. The mission will acquire thermal infrared observations together with limited visible and near-infrared (VNIR) observations, consisting of two spectral bands and one panchromatic channel. In this context, and particularly given the limited number of VNIR bands, accurate retrieval of Vegetation Fractional Cover (FC) and Leaf Area Index (LAI) is particularly relevant. This is because it enables the synergistic use of VNIR and TIR observations to support vegetation monitoring and surface energy flux estimation during the mission. This study evaluates different machine learning approaches under different configurations for the retrieval of FC and LAI using the VNIR observations expected from the SBG-TIR mission. Synthetic datasets generated with the Soil Canopy Observation, Photochemistry and Energy Fluxes (SCOPE) radiative transfer model were used for model training and validation. Different input configurations were tested, including VNIR bands, the panchromatic channel, vegetation indices, and observation geometry variables. Model performance was assessed on independent test data, including uncertainty quantification. The optimal configuration, using Gaussian Process Regression (GPR), achieved RMSE values of 0.046 for FC and 0.053 m²/m² for LAI using a seven-channel input set, while yielding R² values greater than 0.9 for both variables. These results are consistent with previous studies, supporting the validity of the proposed approach. The trained models were subsequently applied to Sentinel-2 and evaluated against GBOV (Ground-Based Observations for Validation) reference measurements and standard Sentinel-2 biophysical products. The results showed strong statistical agreement with the Biophysical Processor implemented in the ESA Sentinel Application Platform (SNAP) toolbox, confirming the robustness of the proposed framework for operational estimation and mapping of FC and LAI in the context of the SBG-TIR space mission. Full article

The incorporation of ground tire rubber (GTR) into elastomeric compounds offers a sustainable route for recycling end-of-life tires; however, its effect on the structure–property relationships governing mechanical and dielectric performance remains insufficiently understood. In this study, NR/SBR composites containing 0–50 phr of devulcanized GTR were prepared and characterized through Fourier-transform infrared spectroscopy (FTIR), swelling analysis, thermogravimetric analysis (TGA), mechanical testing, and broadband dielectric spectroscopy. FTIR and swelling results revealed enhanced matrix–GTR interaction at intermediate GTR loadings (10–20 phr), evidenced by an increased intensity of sulfur-related bands and reduced swelling degree, indicating partial chemical integration of the recycled phase into the elastomer network. Mechanical testing showed that increasing GTR content increased stiffness at high loadings, while tensile strength, elongation at break, and toughness progressively decreased due to interfacial debonding mechanisms. TGA demonstrated that the main degradation temperature of the NR/SBR matrix remained essentially unchanged (418–425 °C) across all formulations, confirming preservation of thermal stability despite increasing structural heterogeneity. Dielectric spectroscopy (10⁻²–3 × 10⁶ Hz, 40–120 °C) revealed pronounced Maxwell–Wagner–Sillars interfacial polarization and thermally activated charge transport, with conductivity increasing with GTR content without evidence of electrical percolation, even at 50 phr. The results demonstrate that the performance of NR/SBR/GTR/SiO₂ composites is primarily controlled by the interfacial structure generated by the recycled phase. Intermediate GTR contents (10–20 phr) provide the most effective matrix–GTR interaction, while higher loadings mainly affect mechanical integrity and dielectric response through increased structural heterogeneity. These findings provide practical guidelines for designing sustainable elastomeric compounds with high recycled content while maintaining thermal stability and controlled electrical insulation properties. Full article

Waterbody extraction is of great significance for water resource investigation and monitoring. In addition to RGB bands, most common satellite images have a near-infrared (NIR) band. By combining these RGB-NIR bands, certain water, vegetation, and shadow indices can be calculated. The near-infrared band and these indices are very similar to the X modality in RGB+X data (common examples include RGB-D and RGB-Thermal). However, at present, no studies have thoroughly examined multimodal feature fusion from the RGB+X perspective in order to extract waterbodies with high precision. As a result, existing algorithms do not fully utilize satellite image information and have limited generalization ability. To overcome this limitation, we propose a dual-complexity backbone for waterbody extraction from the perspective of RGB+X data semantic segmentation. Its complex Transformer branch is used to extract RGB modality features, while its simple CNN branch is used to extract X modality features. This network structure can effectively capture multimodal, global, and local features in remote sensing images. It can also fully leverage the fact that the scale of RGB image datasets in computer vision is significantly larger than that of remote sensing waterbody extraction datasets. If a large pretrained model is used in the RGB branch, it is unnecessary to freeze the weights. Instead, both branches can be trained jointly, allowing the RGB branch to better adapt to the remote sensing waterbody extraction task without raising concerns that fine-tuning might undermine the pretrained model’s strong representation capability. We also propose two X modality configurations with strong generalization performance. To fully fuse multimodal features, we design a hybrid fusion module combining a CNN and a cross-attention mechanism. To integrate the multi-scale features, we employ a multi-scale Transformer structure in the RGB branch and design a multi-scale decoder. Our algorithm achieves state-of-the-art performance on the GID-5 dataset and competitive performance on the S1S2-Water dataset. Furthermore, it significantly outperforms existing methods in cross-dataset zero-shot transfer between the two datasets, with IoU/F1-score gains of 26.08%/27.33% on GID-5 and 38.74%/31.37% on S1S2-Water over previous SOTA methods. Our processing paradigm of modeling RGB-NIR remote sensing images as RGB+X data shows potential for generalization to other multi-modal remote sensing tasks. The dual-complexity backbone we design also has potential to be extended to other tasks that transfer large pretrained RGB models to remote sensing imagery with RGB-NIR four bands or even more spectral bands. We have open-sourced the code and trained models used in this research. Full article

Wildfires are highly destructive natural disasters posing serious threats to ecosystems. Visible–infrared fusion is an effective paradigm for robust fire detection in complex scenarios. However, existing spatial-domain fusion methods inevitably suffer from cross-modal contamination: the strong thermal radiation of flames dilutes visible textures, while dense visible smoke suppresses infrared targets. To circumvent this bottleneck, we reveal the frequency-domain asymmetry of fire features and propose an Asymmetric Frequency-Decoupled Network (AFDNet). By shifting from symmetric spatial fusion to asymmetric frequency decoupling, AFDNet effectively isolates modal conflicts, enabling targeted feature enhancement without mutual interference. Specifically, a Modality-Specific Frequency Decoupling (MFD) module first employs the Discrete Wavelet Transform to decompose features into low- and high-frequency sub-bands, breaking spatial entanglement. Subsequently, for low-frequency energy, a Thermal-Guided Low-Frequency Aggregation (TLA) module leverages infrared local contrast as a physical prior to guide fusion, ensuring precise thermal localization while preserving visible scene semantics. For high-frequency details, a Smoke-Masked High-Frequency Restoration (SHR) module maps smoke-induced visible high-frequency collapse into a soft reliability gate. This gate introduces infrared details to compensate for smoke-weakened structural cues. Extensive experiments on the RGBT-3M dataset demonstrate that AFDNet achieves state-of-the-art performance, outperforming the second-best method by 3.37% in mAP, while exhibiting exceptional robustness in complex wildfire environments. Full article

Accurate and timely wildfire hotspot detection is essential for ecological sustainability and supporting climate resilience strategies. Although sensors such as MODIS and VIIRS have been widely used for wildfire detection, the potential of ocean color satellites for terrestrial wildfire monitoring remains largely unexplored. In this study, a Spectral–Spatial Attention U-Net (SSA-UNet) framework is proposed for wildfire hotspot detection using multispectral observations from the HY-1E Coastal Zone Color Scanner II (COCTS2) over the Three-North region of China. The proposed framework integrates spectral attention to enhance fire-sensitive bands and spatial attention to capture contextual wildfire patterns under complex environmental conditions. Experimental results show that SSA-UNet achieves a Precision of 0.8913, Recall of 0.7961, and F1-score of 0.8680, outperforming conventional threshold-based approaches and baseline deep learning models. Ablation experiments further demonstrate the effectiveness of the spectral–spatial attention mechanism, while band analysis highlights the important contributions of near-infrared, shortwave infrared, and thermal infrared observations for wildfire hotspot detection. The real wildfire case analysis further confirms the practical applicability of the proposed framework. The results demonstrate that HY-1E COCTS2 data have considerable potential for large-scale terrestrial wildfire monitoring when combined with deep learning techniques. Full article