Search Results (971)

Precisely predicting vegetation backscatter involves various challenges, such as complex vegetation structure, soil–vegetation interaction, and data availability. Deep learning (DL) works as a powerful tool to analyze complex data and approximate the nonlinear relationship between variables, thus exhibiting potential applications in microwave scattering problems. However, few DL-based approaches have been developed to reproduce vegetation backscatters owing to the lack of acquiring a large amount of training data. Motivated by a relatively accurate single-scattering radiative transfer model (SS-RTM) and radar measurements, we, for the first time to our knowledge, introduce a transfer learning (TL)-based approach to estimate the radar backscatter of vegetation canopy in the case of soybean fields. The proposed approach consists of two steps. In the first step, a simulated dataset was generated by the SS-RTM. Then, we pre-trained two baseline networks, namely, a deep neural network (DNN) and long short-term memory network (LSTM), using the simulated dataset. In the second step, limited measured data were utilized to fine-tune the previously pre-trained networks on the basis of TL strategy. Extensive experiments, conducted on both simulated data and in situ measurements, revealed that the proposed two-step TL-based approach yields a significantly better and more robust performance than SS-RTM and other DL schemes, indicating the feasibility of such an approach in estimating vegetation backscatters. All these outcomes provide a new path for addressing complex microwave scattering problems. Full article

Leaf chlorophyll content (LCC) and leaf area index (LAI) are crucial for rice growth and development, serving as key parameters for assessing nutritional status, growth, water management, and yield prediction. This study introduces a novel canopy radiative transfer model (RTM) by coupling the radiation transfer model for rice leaves (RPIOSL) and unified BRDF model (UBM) models, comparing its simulated canopy hyperspectra with those from the PROSAIL model. Characteristic wavelengths were extracted using Sobol sensitivity analysis and competitive adaptive reweighted sampling methods. Using these wavelengths, rice phenotype estimation models were constructed with back propagation neural network (BPNN), extreme learning machine (ELM), and broad learning system (BLS) methods. The results indicate that the RPIOSL-UBM model’s hyperspectra closely match measured data in the 500–650 nm and 750–1000 nm ranges, reducing the root mean square error (RMSE) by 0.0359 compared to the PROSAIL model. The ELM-based models using the RPIOSL-UBM dataset proved most effective for estimating the LAI and LCC, with RMSE values of 0.6357 and 6.0101 μg · cm⁻², respectively. These values show significant improvements over the PROSAIL dataset models, with RMSE reductions of 0.1076 and 6.3297 μg · cm⁻², respectively. The findings demonstrate that the proposed model can effectively estimate rice phenotypic parameters from UAV-measured hyperspectral data, offering a new approach to assess rice nutritional status and enhance cultivation efficiency and yield. This study underscores the potential of advanced modeling techniques in precision agriculture. Full article

In the optical satellite on-orbit imaging quality estimation system, the calculation of Modulation Transfer Function (MTF) is not fully standardized, and the influence of atmosphere is often simplified, making it difficult to obtain completely consistent on-orbit MTF measurements and comparisons. This study investigates the effects of various factors—such as edge angle, edge detection methods, oversampling rate, and interpolation techniques—on the accuracy of MTF calculations in the commonly used slanted-edge method for on-orbit MTF assessment, informed by simulation experiments. A relatively optimal MTF calculation process is proposed, which employs the Gaussian fitting method for edge detection, the adaptive oversampling rate, and the Lanczos (a = 3) interpolation method, minimizing the absolute deviation in the MTF results. A method to quantitatively analyze the atmospheric scattering and absorption MTF is proposed that employs a radiative transfer model. Based on the edge images of GF-2 satellite, images with various atmospheric conditions and imaging parameters are simulated, and their atmospheric scattering and absorption MTF is obtained through comparing the MTFs of the ground and top atmosphere radiance. The findings reveal that aerosol optical depth (AOD), viewing zenith angle (VZA), and altitude (ALT) are the primary factors influencing the accuracy of GF-2 satellite on-orbit MTF measurements in complex scenarios. The on-orbit MTF decreases with the increase in AOD and VZA and increases with the increase in ALT. Furthermore, a collaborative analysis of the main influencing factors of atmospheric scattering and absorption MTF indicates that, taking the PAN band of the GF-2 satellite as an example, the atmospheric MTF values are consistently below 0.7905. Among these, 90% of the data are less than 0.7520, with corresponding AOD conditions ranging from 0 to 0.08, a VZA ranging from 0 to 50°, and an ALT ranging from 0 to 5 km. The results can provide directional guidance for the selection of meteorological conditions, satellite attitude, and geographical location during satellite on-orbit testing, thereby enhancing the ability to accurately measure satellite MTF. Full article

Building a reliable and optimum underwater optical wireless communication (UOWC) system requires identifying all potential factors that cause the attenuation and dispersion of the optical signal. The radiative transfer equation (RTE) solution can be utilised to conclude these essential design parameters to build an optimum UOWC system. RTE has various numerical and simplified analytical solutions with varying reliability and capability scope. Many scientists consider the Monte Carlo simulation (MCS) method to be a consistent and widely accepted approach to formulating an RTE solution, which models the propagation of photons through various underwater channel environments. MCS recently attracted attention because we can build a reliable model for underwater environments. Based on such a model, this report demonstrates the resulting received optical power distribution as an output for an array of emulation inputs, including transmitted light’s spatial and temporal distribution, channel link regimes, and associated impairments. This study includes a survey component, which presents the required framework’s foundation to establish a valid RTE model, which leads to solutions with different scopes and depths that can be drawn for practical UOWC use cases. Hence, this work shows how underlying modelling elements can influence a solution technique, including inherent optical properties (IOPs), apparent optical properties (AOPs), and the potential limitations of various photon scattering function formats. The work introduces a novel derivation of mathematical equations for single- and multiple-light-pulse propagation in homogeneous and inhomogeneous channels, forming the basis for MCS-based UOWC studies. The reliability of MCS implementation is assessed using compliance with the Central Limit Theorem (CLT) and leveraging the Henyey–Greenstein phase function with full-scale random selection. As part of the tutorial component in this work, the MCS inner working is manifested using an object-oriented design method. Therefore, this work targets researchers interested in using MCS for UOWC research in general and UOWC photon propagation in seawater channel modelling in general. Full article

Solar-induced chlorophyll fluorescence (SIF) offers significant potential as a novel approach for quantifying carbon and water cycling in coastal wetland ecosystems across multiple spatial scales. However, the mechanisms governing these biogeochemical processes remain insufficiently understood, largely due to the periodic influence of tidal inundation. In this study, we investigated the effects and underlying mechanisms of meteorological and tidal factors on the relationships between canopy-level solar-induced chlorophyll fluorescence at 760 nm (SIF₇₆₀) and key ecosystem processes, including gross primary productivity (GPP) and evapotranspiration (ET), in coastal wetlands. These processes are critical components of the ecosystem carbon and water cycles. Our approach involved a comparative analysis of simulations from the Soil Canopy Observation, Photochemistry and Energy Fluxes (SCOPE) model with field measurements. The results showed that: (1) simulations of SIF₇₆₀ improved following observation-based calibration of the fluorescence photosynthesis module in the SCOPE model; (2) under optimal moisture and temperature conditions (VPD 1.2–1.4 kPa and temperatures of 20–23 °C for air, soil, and water), the simulations of GPP, ET, and SIF₇₆₀ were most accurate, although salinity stress reduced performance. GPP simulations tended to overestimate under drought stress but improved at higher air temperatures (30–32 °C); (3) during tidal inundation, the SIF₇₆₀-GPP relationship weakened while the SIF₇₆₀-ET strengthened. The range of significant correlations between SIF₇₆₀, water levels, and temperature narrowed, with both relationships becoming more complex due to salinity stress. These findings suggest that tidal inundation can alleviate temperature stress on photosynthesis and transpiration; however, it also decreases photosynthetic efficiency and alters radiative transfer processes due to elevated salinity and water levels. These factors are critical considerations when using SIF to monitor GPP and ET dynamics in coastal wetlands. This study demonstrated that the tidal dynamics significantly affected the SIF₇₆₀-GPP and SIF₇₆₀-ET relationships, underscoring the necessity of incorporating tidal influences in the application of SIF remote sensing for monitoring GPP and ET dynamics. The results of this study not only contribute to a deeper understanding of the mechanisms linking SIF₇₆₀ with GPP and ET but also provide new insights into the development and refinement of SIF-based remote sensing for carbon quantification in coastal blue-carbon ecosystems on a large-scale domain. Full article

This study aimed to determine whether canopy and air temperature difference (ΔT) as an existing simple normalizing index can be used to detect an increase in canopy temperature induced by soil drought in urban parks, regardless of the unique energy balance and three-dimensional (3D) structure of urban trees. Specifically, we used a thermal infrared camera to measure the canopy temperature of Zelkova serrata trees and compared the temporal variation of ΔT to that of environmental factors, including solar radiation, wind speed, vapor pressure deficit, and soil water content. Normalization based on a 3D energy-balance model was also performed and used for comparison with ΔT. To represent the 3D structure, a terrestrial light detection and ranging-derived 3D tree model was used as the input spatial data. The temporal variation in ΔT was similar to that of the index derived using the energy-balance model, which considered the 3D structure of trees and 3D radiative transfer, with a correlation coefficient of 0.85. In conclusion, the thermal-image-based ΔT performed comparably to an index based on the 3D energy-balance model and detected the increase in canopy temperature because of the reduction in soil water content for Z. serrata trees in an urban environment. Full article

A jet flame occurs when the release of flammable gas or liquid ignites, resulting in a long, intense, and highly directional flame. This type of fire is commonly associated with industrial incidents involving pipelines, storage tanks, and other pressurized equipment. Jet fires are a significant concern in the oil and gas industry due to the handling and processing of large volumes of flammable hydrocarbons under pressure. The new computational method presented here includes several aspects of hydrogen jet flame accidents and their mitigation: the CFD simulation of a hydrogen jet flame using the HyRAM code and Fire Dynamics Simulator (FDS) software 5.0 using a large eddy simulation (LES) turbulence model; the calculation of the gaseous mixture’s thermo-physical properties using the GASEQ thermochemical code; the calculation of convective and radiative heat fluxes using empirical correlation; and a heat transfer simulation on the pipe thermal barrier coating (TBC) using COMSOL Multiphysics software 4.2a during the heating phase. This method developed for the ceramic blanket was validated successfully against the previous experimental results obtained by Gravit et al. It was shown that a jet fire’s maximum temperature obtained using FDS software was similar to that obtained using GASEQ thermochemical software 0.79 and HyRAM software. The TBC’s surface temperature reached 1945 °C. The stainless steel’s maximal temperature reached 165.5 °C. There was a slight decrease in UTS at this temperature. Full article

This research develops a numerical fire model for a converter transformer utilizing the Fire Dynamics Simulator (FDS). The model’s accuracy was validated through comprehensive evaluations of temperature distribution, radiative heat transfer, and mass burning rate. Additionally, the cooling efficacy of fire-resistant coating and fine water mist with varying droplet sizes was investigated. The results indicate that fireproof coating significantly reduces the surface temperature of the transformer, thereby enhancing its fire resistance. Specifically, temperature reductions of 57.68%, 45.63%, 37.78%, and 36.78% were recorded at different facade heights. Furthermore, the cooling performance of fine water mist is strongly influenced by droplet size, primarily due to thermal buoyancy effects. Larger droplets (400 μm) exhibited the most efficient cooling effect directly beneath the spray, achieving temperature reductions of up to 67%. In contrast, smaller droplets (100 μm) showed diminished cooling performance in certain regions, owing to the compensatory buoyancy of hot air, even resulting in an 11% temperature increase in some cases. During the flame stabilization phase, the mass burning rate stabilized between 0.056 kg/(m²·s) and 0.070 kg/(m²·s), with the inhibitory effect of small particle mist becoming pronounced only after 450 s. These findings offer critical insights for optimizing fire protection strategies for converter transformers, highlighting the significance of cooling mechanisms and material properties. Full article

The fraction of absorbed photosynthetically active radiation (FAPAR) is one of the bio-geophysical Essential Climate Variables assessed through remote sensing observations and distributed globally by space and environmental agencies. Any reliable remote sensing product should be benchmarked against a reference, which is normally determined by means of ground-based measurements. They should generally be aggregated spatially to be compared with remote sensing products at different resolutions. In this work, the effectiveness of various in situ sampling methods proposed to assess FAPAR from flux measurements was evaluated using a three-dimensional radiative transfer framework over eight virtual vegetated landscapes, including dense forests (leaf-on and leaf-off models), open canopies, sparse vegetation, and agricultural fields with a nominal extension of 1 hectare. The reference FAPAR value was determined by summing the absorbed PAR-equivalent photons by either all canopy components, both branches and leaves, or by only the leaves. The incoming and upwelling PAR fluxes were simulated in different illumination conditions and at a high spatial resolution (50 cm). They served to replicate in situ virtual FAPAR measurements, which were carried out using either stationary sensor networks or transects. The focus was on examining the inherent advantages and drawbacks of in situ measurement protocols against GCOS requirements. Consequently, the proficiency of each sampling technique in reflecting the distribution of incident and reflected PAR fluxes—essential for calculating FAPAR—was assessed. This study aims to support activities related to the validation of remote sensing FAPAR products by assessing the potential uncertainty associated with in situ determination of the reference values. Among the sampling schemes considered in our work, the cross shaped sampling schemes showed a particular efficiency in properly representing the pixel scale FAPAR over most of the scenario considered. Full article

Grasslands are one of the most abundant and biodiverse ecosystems in the world. However, in southern European countries, the abandonment of traditional management activities, such as extensive grazing, has caused many semi-natural grasslands to be invaded by shrubs. Therefore, there is a need to characterize semi-natural grasslands to determine their aboveground primary production and livestock-carrying capacity. Nevertheless, current methods lack a realistic identification of vegetation assemblages where grassland biophysical parameters can be accurately retrieved by the inversion of turbid-medium radiative transfer models (RTMs) in fine-grained landscapes. To this end, in this study we proposed a novel framework in which multiple endmember spectral mixture analysis (MESMA) was implemented to realistically identify grassland-dominated pixels from Sentinel-2 imagery in heterogeneous mountain landscapes. Then, the inversion of PROSAIL RTM (coupled PROSPECT and SAIL leaf and canopy models) was implemented separately for retrieving grassland biophysical parameters, including the leaf area index (LAI), fractional vegetation cover (FCOVER), and aboveground biomass (AGB), from grassland-dominated Sentinel-2 pixels while accounting for non-vegetated areas at the subpixel level. The study region was the southern slope of the Cantabrian Mountains (Spain), with a high spatial variability of fine-grained land covers. The MESMA grassland fraction image had a high accuracy based on validation results using centimetric resolution aerial orthophotographs (R² = 0.74, and RMSE = 0.18). The validation with field reference data from several mountain passes of the southern slope of the Cantabrian Mountains featured a high accuracy for LAI (R² = 0.74, and RMSE = 0.56 m²·m⁻²), FCOVER (R² = 0.78 and RMSE = 0.07), and AGB (R² = 0.67, and RMSE = 43.44 g·m⁻²). This study provides a reliable method to accurately identify and estimate grassland biophysical variables in highly diverse landscapes at a regional scale, with important implications for the management and conservation of threatened semi-natural grasslands. Future studies should investigate the PROSAIL inversion over the endmember signatures and subpixel fractions depicted by MESMA to adequately address the parametrization of the underlying background reflectance by using prior information and should also explore the scalability of this approach to other heterogeneous landscapes. Full article

The Ozone Mapping and Profiler Suites (OMPS) Nadir Mapper (NM) is a grating spectrometer within the OMPS nadir instruments onboard the SNPP, NOAA-20, and NOAA-21 satellites. It is designed to measure Earth radiance and solar irradiance spectra in wavelengths from 300 nm to 380 nm for operational retrievals of the nadir total column ozone. This study presents calibration and validation analysis results for the NOAA-21 OMPS NM SDR data to meet the JPSS scientific requirements. The NOAA-21 OMPS SDR calibration derives updates of several previous OMPS algorithms, including the dark current correction algorithm, one-time wavelength registration from ground to on-orbit, daily intra-orbit wavelength shift correction, and stray light correction. Additionally, this study derives an empirical scale factor to remove 2.2% of systematic biases in solar flux data, which were caused by pre-launch solar calibration errors of the OMPS nadir instruments. The validation of the NOAA-21 OMPS SDR data is conducted using various methods. For example, the 32-day average method and radiative transfer model are employed to estimate inter-sensor radiometric calibration differences from either the SNPP or NOAA-20 data. The quality of the NOAA-21 OMPS NM SDR data is largely consistent with that of the SNPP and NOAA-20 OMPS data, with differences generally within ±2%. This meets the scientific requirements, except for some deviations mainly in the dichroic range between 300 nm and 303 nm. The deep convective cloud target approach is used to monitor the stability of NOAA-21 OMPS reflectance above 330 nm, showing a variation of 0.5% over the observed period. Data from the NOAA-21 VIIRS M1 band are used to estimate OMPS NM data geolocation errors, revealing that along-track errors can reach up to 3 km, while cross-track errors are generally within ±1 km. Full article

This study proposes parabolic dish-based, toroidal-structured fractal solar collectors. The potential of fractal geometry to increase heat transfer and the ability of the parabolic dish to concentrate solar rays form the basis of the proposed design for increasing efficiency. In this study, the thermal and hydrodynamic behaviors of the proposed 3-row, 4-row, and 5-row parabolic collectors were investigated comprehensively. Using theoretical modeling and experimental results, the performances of the proposed parabolic dish-based toroidal fractal solar collectors were evaluated and compared via numerical simulation methods. After the experimental studies of the 3-row toroidal fractal collector, the analysis studies were completed using the ANSYS-Fluent program. Then, simulations were carried out for other toroidal solar collectors using the results of these experimental studies. As a result of the converging numerical analyses, the radiative, hydrodynamic, and thermal analysis results of the toroidal absorbers in 3-row, 4-row, and 5-row structures integrated with the parabolic dish were compared. In the temperature distribution analysis, it was observed that the parabolic dish effectively focuses on the sun rays and provides a gradual temperature increase of approximately 21 K for the fractal collector. It is observed that 96.84% convergence was achieved between the experimental and numerical results. Full article

In this work, selected numerical simulation aspects are analyzed in terms of their effect on predictions of the m-c interface. The fixed-grid enthalpy porosity phase change model, which is highly attractive in the field of modeling sapphire crystallization processes, is examined for its sensitivity to the mushy zone parameter as well as the grid resolution. A further focus is set to the simulation of thermal transport including internal radiation in the crystal and the melt via the finite volume method. Depending on the purpose of the investigation, different requirements on the angular resolutions are relevant. While most of the m-c interface as well as the temperature distribution remain practically unchanged at reasonable resolutions, a high sensitivity of the m-c interface in the near-wall region is demonstrated. This sensitivity is also observed in terms of radiative transport and, hence, the total heat transfer. Full article

The impact of the mushy zone parameter ($A_{mushy}$) and the chosen numerical model during the solidification of a commercial paraffin-type phase change material (PCM) in a vertical cylinder under T-history conditions was examined through numerical simulations. The cooling process was modeled using three methods implemented in the CFD software ANSYS Fluent 2020 R2: the enthalpy–porosity method, the apparent heat capacity (AHC) method, and a new model proposed by the authors which incorporates heat capacity directly into ANSYS Fluent. To accurately define the boundary conditions, radiative heat transfer between surfaces was taken into account. Furthermore, the influence of the mushy zone parameter on the simulation accuracy and solidification rate was investigated, with the parameter being treated as a function of the liquid fraction. The results indicate that the proposed model aligns closely with experimental data regarding cooling temperature, offering better predictions compared to the other models. It was observed that temperature varies with time but not with position, suggesting that this model more effectively satisfies the lumped system condition—an essential characteristic of the T-history experiment—compared to the other methods. Additionally, the analysis showed that a higher mushy zone parameter enhances the accuracy of simulations and predicts a shorter solidification time; approximately 11% for the E-p and 7% for the AHC model. Using a variable mushy zone parameter based on the liquid fraction also produced similar results, resulting in an increased solidification rate. Full article

Accurate and rapid estimation of the leaf area index (LAI) is essential for assessing crop growth and nutritional status, guiding farm management, and providing valuable phenotyping data for plant breeding. Compared to the tedious and time-consuming manual measurements of the LAI, remote sensing has emerged as a valuable tool for rapid and accurate estimation of the LAI; however, the empirical inversion modeling methods face challenges of low efficiency for actual LAI measurements and poor model interpretability. The integration of radiative transfer models (RTMs) can overcome these problems to some extent. The aim of this study was to explore the potential of combining the PROSAIL model with high-resolution unmanned aerial vehicle (UAV) multispectral imaging to estimate the LAI across different growth stages at the plot scale. In this study, four inversion strategies for estimating the LAI were tested. Firstly, two types of lookup tables (LUTs) were built to estimate potato LAI of different varieties across different growth stages. Specifically, LUT1 was based on band reflectance, and LUT2 was based on vegetation index. Secondly, the hybrid models combining LUTs generated by PROSAIL and two machine learning algorithms (random forest (RF), Partial Least Squares Regression (PLSR)) are built to estimate potato LAI. The determination of coefficient (R²) of models for estimating LAI by LUTs ranged from 0.24 to 0.64. The hybrid method that integrates UAV multispectral, PROSAIL, and machine learning significantly improved the accuracy of LAI estimation. Compared to the results based on LUT2, the hybrid model achieved higher accuracy with the R² of the inversion model improved by 30% to 263%. The LAI retrieval model using the PROSAIL model and PLSR achieved an R² as high as 0.87, while the R² using the RF algorithm ranged from 0.33 to 0.81. The proposed hybrid model, integrated with UAV multispectral data, PROSAIL, and PLSR can achieve approximate accuracy compared with the empirical inversion models, which can alleviate the labor-intensive process of handheld LAI measurements for building inversion models. Thus, the hybrid approach provides a feasible and efficient strategy for estimating the LAI of potato varieties across different growth stages at the plot scale. Full article