Search Results (930)

We propose a novel mono-temporal framework with a physical basis and ecological consistency to retrieve fire severity at very high spatial resolution. First, we sampled the Composite Burn Index (CBI) in 108 field plots that were subsequently surveyed through unmanned aerial vehicle (UAV) flights. Then, we mimicked the field methodology for CBI assessment in the remote sensing framework. CBI strata were identified through individual tree segmentation and geographic object-based image analysis (GEOBIA). In each stratum, wildfire ecological effects were estimated through the following methods: (i) the vertical structural complexity of vegetation legacies was computed from 3D-point clouds, as a proxy for biomass consumption; and (ii) the vegetation biophysical variables were retrieved from multispectral data by the inversion of the PROSAIL radiative transfer model, with a direct physical link with the vegetation legacies remaining after canopy scorch and torch. The CBI scores predicted from UAV ecologically related metrics at the strata level featured high fit with respect to the field-measured CBI scores (R² > 0.81 and RMSE < 0.26). Conversely, the conventional retrieval of fire effects using a battery of UAV structural and spectral predictors (point height distribution metrics and spectral indices) computed at the plot level provided a much worse performance (R² = 0.677 and RMSE = 0.349). Full article

The Multi-Scale Multi-Group Wide-Band (MSMGWB) model was used to calculate radiative transfer in strongly non-isothermal and inhomogeneous media such as the remote infrared sensing of aircraft exhaust system and jet plume scenario. In this work, the reference temperature was introduced into the model as an independent variable for each spectral subinterval group. Then, to deal with the exceedingly vast parameter sample space (i.e., the combination of spectral subinterval grouping results, reference temperatures and Gaussian quadrature schemes), an MSMGWB model’s parameter optimization process superior to the exhaustive approach employed in previous studies was established, which was consisted of the Non-dominated Sorting Genetic Algorithm II method (NSGA2) and an iterative scan method. Through a series of 0-D test cases and two real 3-D remote infrared imaging results of an aircraft exhaust system, it was observed that the MSMGWB model established and optimiazed in current work demonstrated notable improvements in both accuracy and computational efficiency. Full article

This study numerically investigated the effects of various contact strategies on the thermal hydraulic behavior within a structured bed of 100 explicitly modeled pebbles. Four contact strategies and two thermal hydraulic conditions were considered. The strategies to avoid contact singularities include decreasing the pebble diameter, increasing the pebble diameter, bridging the pebble surfaces near the contact region, and capping the pebble surfaces near the contact region. One strategy, Strategy 3a, which involves bridging with a cylinder equal to 10% of the pebble diameter, was selected as the baseline strategy because it addressed the contact singularity while minimizing the geometric changes that affect the bed porosity. The two thermal hydraulic conditions were full-power operation (Case 1) and pressurized loss of forced cooling or PLOFC (Case 2). Simulations of the conjugate heat transfer within the structured bed were performed using the Reynolds-averaged Navier–Stokes approach with the realizable k-ϵ turbulence model and two-layer all y+ wall treatment. The thermal-fluid quantities of interest were compared between the contact strategies for each case. In Case 1, the hydraulic behavior was sensitive to the contact strategy, with large differences in the pressure drop (30%) and volume-average velocity (4%). The thermal behavior was not sensitive, with less than a 0.5% difference across the strategies. To better understand the separate effects of each heat transfer mode, Case 2 was divided into the following subcases: conduction (Case 2a); conduction/radiation (Case 2b); and conduction/radiation/convection (Case 2c). Case 2a represents an early phase of the PLOFC transient. Case 2b represents an intermediate phase of the PLOFC transient, with the pebble temperatures sufficiently high for the radiative heat transfer to be non-negligible. Case 2c represents a late phase of the PLOFC transient after the establishment of the natural circulation of the heat transfer fluid. For Case 2, large differences in the contact strategy were observed only in Case 2a with only conduction. The difference in the maximum pebble temperature was 23% in Case 2a, 2% in Case 2b, and 0.3% in Case 2c. Full article

The precise estimation of above-ground biomass (AGB) is imperative for the advancement of breeding programs. Optical variables, such as vegetation indices (VI), have been extensively employed in monitoring AGB. However, the limited robustness of inversion models remains a significant impediment to the widespread application of UAV-based multispectral remote sensing in AGB inversion. In this study, a novel stem–leaf separation strategy for AGB estimation is delineated. Convolutional neural network (CNN) and transfer learning (TL) methodologies are integrated to estimate leaf biomass (LGB) across multiple growth stages, followed by the development of an allometric growth model for estimating stem biomass (SGB). To enhance the precision of LGB inversion, the large-scale remote sensing data and image simulation framework over heterogeneous scenes (LESS) model, which is a three-dimensional (3D) radiative transfer model (RTM), was utilized to simulate a more extensive canopy spectral dataset, characterized by a broad distribution of canopy spectra. The CNN model was pre-trained in order to gain prior knowledge, and this knowledge was transferred to a re-trained model with a subset of field-observed samples. Finally, the allometric growth model was utilized to estimate SGB across various growth stages. To further validate the generalizability, transferability, and predictive capability of the proposed method, field samples from 2022 and 2023 were employed as target tasks. The results demonstrated that the 3D RTM + CNN + TL method outperformed best in LGB estimation, achieving an R² of 0.73 and an RMSE of 72.5 g/m² for the 2022 dataset, and an R² of 0.84 and an RMSE of 56.4 g/m² for the 2023 dataset. In contrast, the PROSAIL method yielded an R² of 0.45 and an RMSE of 134.55 g/m² for the 2022 dataset, and an R² of 0.74 and an RMSE of 61.84 g/m² for the 2023 dataset. The accuracy of LGB inversion was poor when using only field-measured samples to train a CNN model without simulated data, with R² values of 0.30 and 0.74. Overall, learning prior knowledge from the simulated dataset and transferring it to a new model significantly enhanced LGB estimation accuracy and model generalization. Additionally, the allometric growth model’s estimation of SGB resulted in an accuracy of 0.87 and 120.87 g/m² for the 2022 dataset, and 0.74 and 86.87 g/m² for the 2023 dataset, exhibiting satisfactory results. Separate estimation of both LGB and SGB based on stem and leaf separation strategies yielded promising results. This method can be extended to the monitor and inversion of other critical variables. Full article

As a signature phenomenon of massive hypervelocity impacts (HVIs) in space, debris clouds provide critical optical information for satellite remote sensing and the assessment of large-scale impacts. However, studies of the optical scattering properties of debris clouds remain limited, and existing vector radiative transfer (VRT) methods struggle to accurately simulate the optical characteristics of these complex scatterers. To address this gap, this paper presents an improved Monte Carlo VRT program (PGS–MC) for multicomponent polydisperse scatterers to precisely evaluate the radiation and polarization characteristics of complex scatterers. Based on the Monte Carlo ray tracing (MCRT) method, our program introduces a particle grouping strategy (PGS) to further emphasize the importance of accounting for optical property discrepancies between different materials and particle sizes, thus significantly improving the fidelity of VRT simulations. Moreover, our program, developed using the compute unified device architecture (CUDA), can be run parallelly on graphics processing units (GPUs), which effectively reduces the computational time. The validation results indicated that the developed PGS–MC program can accurately and efficiently simulate the polarization of complex 3D scatterers. A further investigation showed that the polarization characteristics of debris clouds are highly sensitive to parameters such as the angle between the incident and detection directions, number density, particle size distribution, debris material, and wavelength. In addition, the polarization imaging of debris clouds offers distinct advantages over intensity imaging. This study offers guidance for analyzing the VRT properties of massive HVI debris clouds. Additionally, it provides a practical tool and concrete ideas for modeling the polarization characteristics of various complex scatterers, such as aircraft contrails and clouds, etc. Full article

In the context of autonomous driving, the augmentation of existing data through simulations provides an elegant solution to the challenge of capturing the full range of adverse weather conditions in training datasets. However, existing physics-based augmentation models typically rely on single scattering approximations to predict light propagation under unfavorable conditions, such as fog. This can prevent the reproduction of important signal characteristics encountered in a real-world environment. Consequently, in this work, Monte Carlo simulations are employed to assess the relevance of multiple-scattered light to the detected LiDAR signal in different types of fog, with scattering phase functions calculated from Mie theory considering real particle size distributions. Bidirectional path tracing is used within the self-developed GPU-accelerated Monte Carlo software to compensate for the unfavorable photon statistics associated with the limited detection aperture of the LiDAR geometry. To validate the Monte Carlo software, an analytical solution of the radiative transfer equation for the time-resolved radiance in terms of scattering orders is derived, thereby providing an explicit representation of the double-scattered contributions. The results of the simulations demonstrate that the shape of the detected signal can be significantly impacted by multiple-scattered light, depending on LiDAR geometry and visibility. In particular, double-scattered light can dominate the overall signal at low visibilities. This indicates that considering higher scattering orders is essential for improving AI-based perception models. Full article

The radiative heat transfer in arc plasma models is considered from the point of view of its description in terms of a net emission coefficient, the method of spherical harmonics in its lowest order, and the discrete ordinate method. Net emission coefficients are computed, applying approximate analytical and numerical approaches and a multi-band representation of the spectral absorption coefficient with three kinds of its averaging and two datasets. Self-consistent access to the radiative heat transfer is applied to a two-dimensional axisymmetric model of a free-burning arc in argon at atmospheric pressure. The results obtained from the models employing the net emission coefficient, the method of spherical harmonics, and the discrete ordinate method are compared. Full article

Preplanetary nebulae (PPNe) are formed from mass-ejecting late-stage AGB stars. Much of the light from the star gets scattered or absorbed by dust particles, giving rise to the observed reflection nebula seen at visible and near-IR wavelengths. Precursors to planetary nebulae (PNe), PPNe generally have not yet undergone any ionization by UV radiation from the still-buried stellar core. Bipolar PPNe are a common form of observed PPNe. This study lays the groundwork for future dynamical studies by reconstructing the dust density distribution of a particularly symmetric bipolar PPN, M1-92 (Minkowski’s Footprint, IRAS 19343+2926). For this purpose, we develop an efficient single-scattering radiative transfer model with corrections for double-scattering. Using a V-band image from the Hubble Space Telescope (HST), we infer the dust density profile and orientation of M1-92. These results indicate that M1-92’s slowly expanding equatorial torus exhibits an outer radial cutoff in its density, which implicates the influence of a binary companion during the formation of the nebula. Full article

This comprehensive study explores the influence of aerosols and clouds on solar radiation in the urban environments of nine of Greece’s largest cities over the decade from 2014 to 2023. Utilizing a combination of Earth Observation data, radiative transfer models, and geographic information systems, the research undertook digital surface modeling and photovoltaic simulations. The study meticulously calculated the optimal rooftop areas for photovoltaic installation in these cities, contributing significantly to their energy adequacy and achieving a balance between daily electricity production and demand. Moreover, the research provides an in-depth analysis of energy and economic losses, while also highlighting the environmental benefits. These include a reduction in pollutant emissions and a decrease in the carbon footprint, aligning with the global shift towards local energy security and the transformation of urban areas into green, smart cities. The innovative methodology of this study, which leverages open access data, sets a strong foundation for future research in this field. It opens up possibilities for similar studies and has the potential to contribute to the creation of an updated, comprehensive solar potential map for continental Greece. This could be instrumental in climate change mitigation and adaptation strategies, thereby promoting sustainable urban development and environmental preservation. Full article

The canopy of perennial evergreen fruit trees in southern China has a unique Bidirectional Reflectance Factor (BRF) due to its complex multi-branch structure and density changes. This study aimed to address the lack of clarity regarding the changes in BRF of evergreen fruit trees in southern China. Litchi, a typical fruit tree in this region, was chosen as the subject for establishing a three-dimensional (3D) real structure model. The canopy BRF of litchi was simulated under different leaf components, illumination geometry, observed geometry, and leaf area index (LAI) using a 3D radiation transfer model. The corresponding changes in characteristics were subsequently analyzed. The findings indicate that the chlorophyll content and equivalent water thickness of leaves exert significant influences on canopy BRF, whereas the protein content exhibit relatively weak effects. Variation in illumination and observation geometry results in the displacement of hotspots, with the solar zenith angle and view zenith angle exerting significant influence on the BRF. As the LAI of the litchi orchard increases, the distribution of hotspots becomes more concentrated, and the differences in angle information are relatively smaller when observed from multiple angles. With the increase in LAI in litchi orchards, the BRF on the principal plane would be saturated, but observation at hotspots could alleviate this phenomenon. The above analysis provides a reference for quantitative inversion of vegetation parameters using remote sensing monitoring information of typical perennial evergreen fruit trees. Full article

Quantifying the effect of maize tassel on canopy reflectance is essential for creating a tasseling progress monitoring index, aiding precision agriculture monitoring, and understanding vegetation canopy radiative transfer. Traditional field measurements often struggle to detect the subtle reflectance differences caused by tassels due to complex environmental factors and challenges in controlling variables. The three-dimensional (3D) radiative transfer model offers a reliable method to study this relationship by accurately simulating interactions between solar radiation and canopy structure. This study used the LESS (large-scale remote sensing data and image simulation framework) model to analyze the impact of maize tassels on visible and near-infrared reflectance in heterogeneous 3D scenes by modifying the structural and optical properties of canopy components. We also examined the anisotropic characteristics of tassel effects on canopy reflectance and explored the mechanisms behind these effects based on the quantified contributions of the optical properties of canopy components. The results showed that (1) the effect of tassels under different planting densities mainly manifests in the near-infrared band of the canopy spectrum, with a variation magnitude of ±0.04. In contrast, the impact of tassels on different leaf area index (LAI) shows a smaller response difference, with a magnitude of ±0.01. As tassels change from green to gray during growth, their effect on reducing canopy reflectance increases. (2) The effect of maize tassel on canopy reflectance varied with spectral bands and showed an obvious directional effect. In the red band at the same sun position, the difference in tassel effect caused by the observed zenith angle on canopy reflectance reaches 200%, while in the near-infrared band, the difference is as high as 400%. The hotspot effect of the canopy has a significant weakening effect on the shadow effect of the tassel. (3) The non-transmittance optical properties of maize tassels reduce canopy reflectance, while their high reflectance increases it. Thus, the dual effects of tassels create a game in canopy reflectance, with the final outcome mainly depending on the sensitivity of the canopy spectrum to transmittance. This study demonstrates the potential of using 3D radiative transfer models to quantify the effects of crop fine structure on canopy reflectance and provides some insights for optimizing crop structure and implementing precision agriculture management (such as selective breeding of crop optimal plant type). Full article

This work investigates the inverse coupled radiation–conduction problem for estimating thermophysical parameters and source terms by an improved grey wolf optimization (GWO). The transient coupled radiation–conduction heat transfer problem in participating slab media is solved by the finite volume method. The radiative intensities on both boundaries are adopted as known measurement information in the inverse model. To overcome the disadvantages of the original GWO algorithm, an improved grey wolf algorithm (IGWO) is developed by introducing the weight strategy and nonlinear factors. Three benchmark functions are adopted to prove that the IGWO has a faster convergence speed and higher estimation accuracy than the original one. The IGWO is applied to inverse the thermophysical parameters and source terms based on the coupled radiation–conduction model; the results indicate that the IGWO is accurate and effective for estimating refractive index, absorption coefficient, and source terms. Full article

Surface longwave radiation (SLR) plays a pivotal role in the Earth’s energy balance, influencing a range of environmental processes and climate dynamics. As the demand for high spatial resolution remote sensing products grows, there is an increasing need for accurate SLR retrieval with enhanced spatial detail. This study focuses on the development and validation of models to estimate SLR using measurements from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor. Given the limitations posed by fewer spectral bands and data products in ASTER compared to moderate-resolution sensors, the proposed approach combines an atmospheric radiative transfer model MODerate resolution atmospheric TRANsmission (MODTRAN) with the Light Gradient Boosting Machine algorithm to estimate SLR. The MODTRAN simulations were performed to construct a representative training dataset based on comprehensive global atmospheric profiles and surface emissivity spectra data. Global sensitivity analyses reveal that key inputs influencing the accuracy of SLR retrievals should reflect surface thermal radiative signals and near-surface atmospheric conditions. Validated against ground-based measurements, surface upward longwave radiation (SULR) and surface downward longwave radiation (SDLR) using ASTER thermal infrared bands and surface elevation estimations resulted in root mean square errors of 17.76 W/m² and 25.36 W/m², with biases of 3.42 W/m² and 3.92 W/m², respectively. Retrievals show systematic biases related to extreme temperature and moisture conditions, e.g., causing overestimation of SULR in hot humid conditions and underestimation of SDLR in arid conditions. While challenges persist, particularly in addressing atmospheric variables and cloud masking, this work lays a foundation for accurate SLR retrieval from high spatial resolution sensors like ASTER. The potential applications extend to upcoming satellite missions, such as the Landsat Next, and contribute to advancing high-resolution remote sensing capabilities for an improved understanding of Earth’s energy dynamics. Full article

Aerosols exert a significant influence on the brightness temperature observed in the thermal infrared (IR) channels, yet the specific contributions of various aerosol types remain underexplored. This study integrated the Copernicus Atmosphere Monitoring Service (CAMS) atmospheric composition reanalysis data into the Radiative Transfer for TOVS (RTTOV) model to quantify the aerosol effects on brightness temperature (BT) simulations for the Advanced Himawari Imager (AHI) aboard the Himawari-8 geostationary satellite. Two distinct experiments were conducted: the aerosol-aware experiment (AER), which accounted for aerosol radiative effects, and the control experiment (CTL), in which aerosol radiative effects were omitted. The CTL experiment results reveal uniform negative bias (observation minus background (O-B)) across all six IR channels of the AHI, with a maximum deviation of approximately −1 K. Conversely, the AER experiment showed a pronounced reduction in innovation, which was especially notable in the 10.4 μm channel, where the bias decreased by 0.7 K. The study evaluated the radiative effects of eleven aerosol species, all of which demonstrated cooling effects in the AHI’s six IR channels, with dust aerosols contributing the most significantly (approximately 86%). In scenarios dominated by dust, incorporating the radiative effect of dust aerosols could correct the brightness temperature bias by up to 2 K, underscoring the substantial enhancement in the BT simulation for the 10.4 μm channel during dust events. Jacobians were calculated to further examine the RTTOV simulations’ sensitivity to aerosol presence. A clear temporal and spatial correlation between the dust concentration and BT simulation bias corroborated the critical role of the infrared channel data assimilation on geostationary satellites in capturing small-scale, rapidly developing pollution processes. Full article

This paper proposes a temperature-adaptive radiative cooling (TARC) coating with simple preparation, cost effectiveness, and large-scale application based on a thermochromic powder. To determine the energy efficiency of the proposed TARC coating, the heat transfer on the surface of the TARC coating was analyzed. Then, a typical two-story residential building with a roof area of 258.43 m² was modeled using EnergyPlus. Finally, the energy-saving potential and carbon emission reduction resulting from the application of the proposed TARC roof in buildings under different climates in China were discussed. The results showed that the average solar reflectivity under visible light wavelengths (0.38–0.78 μm) decreases from 0.71 to 0.37 when the TARC coating changes from cooling mode to heating mode. Furthermore, energy consumption can be reduced by approximately 17.8–43.0 MJ/m² and 2.0–32.6 MJ/m² for buildings with TARC roofs compared to those with asphalt shingle roofs and passive daytime radiative cooling (PDRC) roofs, respectively. This also leads to reductions in carbon emissions of 9.4–38.0 kgCO₂/m² and 1.0–28.9 kgCO₂/m² for the buildings located in the selected cities. To enhance building energy efficiency, TARC roofs and PDRC roofs are more suitable for use on buildings located in zones with high heating demands and high cooling demands, respectively. Full article