Surface Emissivity Research Articles

Effects of high temperature wettability and thermal properties of glass with different Al/Ga ratios on p+ emitter metallization

Ag-Al paste is a key material of n-type silicon-based solar cells. The uneven Ag/Al spike on the transmitter surface after metallization is easy to cause local short circuit, which is a bottleneck that limits its application. The glass play an indispensable role in the surface metallization of the emitter. The influence of high temperature wettability and thermal properties of glass on the metallization of p+ emitter was studied from the point of view of ionic radius by adjusting the ratio of Al/Ga. The results indicated that when the Al/Ga ratio is 2:3, an abundance of free oxygen improved the O/Si ratio within the glass network structure, facilitating the depolymerization of siloxane anion clusters into simpler structural units in the silicate network structure. Al ions preferentially combine with free oxygen to formed [AlO4]. The entrapment of free oxygen by [BO3] units tended to form more [BO4] units with increased thermal stability. The glass sample demonstrated minimal difference between left and right contact angles. The glass wet the Si wafer steadily and evenly throughout the melting process. The formation of a continuous dense glass layer increased the contacts between the metal electrode and the emitter. Not only a mass of silver crystals were deposited on the emitter surface, but also small and dense Ag/Al spikes were distributed. Excellent metallization contacts were formed with the p+ emitter, which improved the solar cell's photoelectric conversion efficiency.

Using Google Earth Engine open-source code for land surface temperature estimation from Landsat data in Chuong My district, Hanoi city, Vietnam

Land surface temperature (LST) in association with urbanization has significantly influenced on local climates and terrestrial surface processes. By using multi-temporal Landsat data in the GEE platform, the study has found that there have been changes in land covers due to the main drivers of industrial development, urban and built-up expansion. This study estimated land surface temperature (LST) in Chuong My district from Landsat 5, 7, 8 and 9 thermal infrared sensors, using different surface emissivity sources. The Google Earth Engine (GEE), an advanced earth science data and analysis platform, offers the estimation of LST products, covering the time period from 2000 to 2024 in study site. RF algorithm for land cover classification and mapping is suggested to be applied in Chuong My district, with overall accuracy of land cover mapping in 2024 being 91.9% with Kappa coefficient of 0.89. The period 2000-2010 showed that 327.0 ha of land were converted to land for industrial development, urban and residential land, while the period 2010-2024 continued to witness a continued conversion of land to other land use purposes, with a decrease in agricultural land area (65.7 ha) and an increase in industrial land use (65.2 ha), but the rate of conversion was much lower than the previous period (2000-2024). The periods of 2000-2010 and 2010-2024 showed that there have been significant changes in land surface temperature. Most of the surface temperature in the entire region increased by over 20C, especially in the period 2003-2015 when a total area of 1424 ha had the temperature increased by above 30C. This is a consequence of the process of urbanization and industrialization, which began to take place in 2008, along with the process of changing the land cover status of the study area. Further studies are needed to better understand the thermal conductivity of surface materials and to plan how to reduce LST in such areas.

Enhanced surface emission of bismuth-ion doped glass by adding metal nanoparticles

Enhancing the luminescent properties of doped silica glass has garnered significant interest due to its potential applications in photonics and optoelectronics. In this study, we enhance the surface emission of bismuth(Bi)-doped silica glass by incorporating metal nanoparticles. Utilizing finite-difference time-domain (FDTD) simulations, we observed a significant increase in surface emission following population inversion. We developed a novel algorithm to achieve a uniform distribution of nanoparticles in a two-dimensional computational model, ensuring that the distribution is physically accurate. Through systematic investigation, we explored the effects of the number, distribution, and size of silver nanoparticles on the surface emission enhancement of Bi-doped glass. Our results demonstrate that under optimal conditions, the surface emission enhancement can reach nearly 72%. Additionally, we evaluated the impact of various metal nanoparticles, finding that gold, silver, copper, and platinum positively influence surface emission enhancement, while titanium has an inhibitory effect. This study underscores the potential of metal nanoparticles to significantly improve the luminescent properties of doped glass, paving the way for advanced applications in photonic devices.

TRUST-DART: Land Surfaces Temperature and Emissivity Nonlinear Mapping from Nonisothermal Mixed Pixels of Satellite Images with the DART 3D Radiative Transfer Model

Abstract. Coarse spatial resolution of Thermal Infrared (TIR) satellites hampers measuring the temperature and emissivity of scene elements from space that improves our understanding of land surfaces' thermal behavior. The stringent conditions of current TIR unmixing methods hinder the production of extensive component temperature and emissivity products. To address this, we designed a gradient-based multi-pixel physical model, TRUST-DART, to derive the temperature and emissivity of urban features from non-isothermal mixed pixels of satellite images using the DART 3D radiative transfer model. Unlike traditional TIR unmixing methods, TRUSTDART is not constrained by issues related to spatial, spectral, temporal resolution, angular, scene, field measurement requirements, or manual operations. Its inputs include an at-surface radiance image, downwelling sky irradiance, a 3D urban mock-up with feature information, and DART input parameters such as spatial resolution. It generates maps of emissivity and temperature per urban feature. Its accuracy is validated for two vegetation and urban scenes and two types of images (DART simulated pseudo satellite and ASTER observed images). The accuracy of the TRUST-DART depends heavily on the fraction of components. TRUST-DART proves robust for high-fraction components. However, its accuracy decreases with decreasing fractions. TRUST-DART is distributed with DART and is available for education and research via Toulouse III University (https://dart.omp.eu).

Measuring and modelling directional effects in the frame of TIRAMISU (Thermal InfraRed Anisotropy Measurements in India and Southern eUrope)

Abstract. TRISHNA (Thermal infraRed Imaging Satellite for High-Resolution Natural resource Assessment) is a cross-purpose thermal infrared (TIR) Earth Observation (EO) mission designed to deliver images at high spatial (60 m) and temporal (3 days) resolutions. Its launch is foreseen in 2026 with a nominal mission duration of 5 years. This Indo-French polar-orbiting mission will overcome the limitations of thermal-optical observations from Landsat series and ASTER: low revisit, morning observations. TRISHNA scenes will be fully harnessed to pioneer the first cutting-edge high-resolution global maps of the land surface temperature (LST) and land surface emissivity (LSE) of natural and managed agroecosystems, man-made structures, water bodies, bare soils, rocks, snow, ice and sea. The quality of the preprocessing (radiometric calibration, atmospheric and directional corrections) is mandatory to satisfy the specifications with an expected precision of 1 K on LST in order to meet the targeted objectives. For such, it will be necessary to correct LST from directional effects with emphasis on the hot spot effect that can have an impact on LST up to several K. This is the goal of the TIRAMISU project to analyze TIR multiangular signatures over various biomes thanks to a pivoting system of cameras and a multi-model approach (1D, 3D, paramaterization). The modeling tools include 3 categories of models: SCOPE 1D, DART 3D and parametric BRDF models that are computationally efficient to perform inversion at global scale.

Experimental study on surface temperature and emissivity of rotating turbine blades of a micro turbojet engine

The temperature of the rotating turbine blades of a turbojet engine must be known to assess the combustion state and improve turbine blade quality. Obtaining online temperature measurements of high-speed rotating turbine blades in high-temperature gas flow remains challenging. A multispectral radiation thermometry method without emissivity assumption is proposed to measure the temperature of high-speed rotating turbine blades of a micro turbojet engine. The relative radiative intensity is calculated by accounting for smooth changes in the wavelength using the moving narrowband method to determine the influence of emissivity on multispectral radiation thermometry results. The method’s performance in measuring the surface temperature of high-speed rotating objects is tested. The wavelength range of 970 nm to 1700 nm is selected because the radiation from gases is weaker in comparison to the radiation emitted by solid surfaces within this band. The spectral radiation of the turbine blade surface is measured at three rotational speeds: 38,000 r/min, 48,000 r/min, and 58,000 r/min. The results show that, unlike thermocouples that measure the temperature of the hot gas flow near the blade, multispectral radiation thermometry provides the average surface temperature with high consistency (with a deviation smaller than 50 K). Multispectral radiation thermometry can be applied to measure the surface temperature and emissivity of high-speed rotating objects, such as the blades of turbojet engines.

Quantification and spatial analysis of gridded (0.1° × 0.1°) emission of indirect GHGs/Air pollutants from anthropogenic sources in India

Indirect greenhouse gases (GHGs) play a key role in modulating both regional and global atmospheric chemistry and act as air pollutants that degrade air quality. The rising number of premature deaths and notable health hazards could be linked to increasing concentration of these air pollutants in the atmosphere. As a developing nation, India is often regarded as one of the most polluting countries due to increasing anthropogenic activities. Therefore, identifying the sources of these indirect GHGs gases such as CO, NOx, VOC and SO2 and quantifying their emissions has become a critical requirement for understanding regional atmospheric chemistry. In this study, we developed a comprehensive, technology-driven gridded emission inventory for India at a high resolution (0.1°×0.1°) for the base year 2020. Using the IPCC bottom-up approach, the study estimated the total emissions of CO (45 Tg/yr), NOx (22.8 Tg/yr), VOC (10.8 Tg/yr) and SO2 (15.1 Tg/yr) from all the dominating sources. Vehicle exhaust contributes the most NOx and VOC whereas the residential and power sectors are the highest emitters of CO and SO2, respectively. This newly reported surface emission data would be an essential tool for policymakers in formulating mitigation strategies and will also serve as a vital input for atmospheric chemistry and climate studies.

Experimental study on high-precision measurement of surface temperature in friction stir welding aluminium alloy 2219 based on infrared thermometry

In friction stir welding (FSW) of aluminium alloy 2219, the welding temperature critically influences the weld quality. Accurate measurement of the welding temperature is essential for effective welding process control. However, the conventional approach of non-contact infrared thermometry faces significant challenges in FSW due to the inherently low and variable emissivity of aluminium alloys, making it susceptible to environmental radiation interference. This paper proposes a novel, high-precision method for measuring the surface temperature of FSW aluminium alloy 2219 based on infrared thermometry principles. Initially, the study delves into these principles, analysing factors that affect the accuracy of infrared thermometry in the context of FSW in industrial settings. To enhance the surface emissivity of the weldments, a high-emissivity coating is applied to the aluminium alloy surface. Additionally, strategic adjustments in the positioning of the thermal imager and careful selection of the measuring area effectively mitigate interference from the FSW tool’s radiation. Conventional methods report a temperature measurement error greater than 2%, while the proposed method reduces this error to 1.2%. The effectiveness and practical utility of the method are validated through FSW experiments on flat plates and rocket tank rings, corroborated by comparative analyses with surface thermocouple temperature measurements. This confirms the viability of the proposed method for high-precision temperature measurement in aluminium alloy FSW applications.

Validation of heat transfer models and optimization of heat shielding performance of high-temperature multilayer insulations for hypersonic vehicles

Multilayer insulation (MLI) is widely used in hypersonic vehicles because of its low density and excellent thermal insulation performance. However, the insulation mechanism of MLI remains poorly understood, leading to conflicting views on how to enhance its thermal insulation capabilities. In this study, two different numerical models were built and validated with experiment data to investigate key factors influencing MLI performance. The analysis focused on the effects of reflective screen positioning, the surface emissivity of reflective screens and the radiation properties parameters of fibrous materials on the thermal insulation performance of the MLI. The results show that the thermal insulation performance is better when the reflective screens are placed close to the thermal boundary. Moreover, insulation materials with lower absorption coefficients enhance the effectiveness of the reflective screens, further improving the thermal insulation performance of MLI. In addition, the study reveals that the thermal insulation mechanisms differ between the upper and lower surfaces of the reflective screens. Lower emissivity on the upper surface combined with higher emissivity on the lower surface optimizes the thermal insulation performance of MLI. These findings offer valuable insights for advancing MLI designs and improving its application in future high-speed vehicles.

Calcination-Controlled Synthesis of Carbon Dots@MgF2 Composites with Yellow, White, and Ultraviolet-Blue Thermally Activated Delayed Fluorescence for Multilevel Information Encryption.

It is attractive but challenging to develop carbon dot (CD) based materials with tunable thermally activated delayed fluorescence (TADF), especially in the long wavelength region. Here, by simply calcinating the mixture of m-phenylenediamine and MgF2 at 300-500 °C, a series of CDs@MgF2 composites exhibiting yellow, white, and ultraviolet-blue TADF with high photoluminescence quantum yields of up to 37.6% are prepared. Photophysical studies reveal that the yellow TADF with long lifetimes of 810-1106ms originates from the surface emission centers of CDs, while the ultraviolet-blue TADF with short lifetimes of 266-379ms is related to the carbon core emission centers. The combination of yellow and ultraviolet-blue TADF in a single material triggers dynamic afterglow with time-dependent colors from white to yellow. The MgF2 matrix offers multiple confinement of CDs through a rigid network, strong space constraint, and robust covalent/hydrogen bonding, thus preventing the triplet excitations from non-radiative transitions. The electron-withdrawing fluorine atoms induce substantial spin-orbit coupling and reduce the singlet-triplet energy gap, consequently facilitating the reverse intersystem crossing to enhance TADF efficiency. Importantly, the CDs@MgF2 composites possess outstanding optical stability against water, organic solvents, strong acids, bases, and oxidants. The fascinating TADF features enable the successful demonstration of multilevel information encryption using CDs@MgF2.

Observed response of microwave land surface emissivity to antecedent rainfall in Hainan Island

ABSTRACT Microwave emissivity is an important parameter for over-land retrieval of atmospheric variables such as water vapour, rainfall, and snowfall. However, it remains unclear for the dynamic response of multi-frequency microwave emissivity to prior rainfall over heterogeneous land surfaces. This study combined multi-frequency Microwave Land Surface Emissivity (MLSE) under all-weather conditions with hourly in-situ rainfall to investigate the response of MLSE to rainfall over different vegetation types in Hainan Island in China. Specifically, we explored the change of MLSE at 6.925, 10.65, 18.7, 23.8 and 36.5 Ghz to rainfall intensity in four rainfall cases, followed with statistical characteristics of MLSE to rainfall and dry duration (DD) across different vegetation types during 2003–2010. We found that the magnitude of decline in MLSE (0.004–0.06) is dependent on the rainfall intensity and duration, and the reduction caused by short-lasting heavy rain (daily rainfall >100 mm) was 2–6 times higher than that by light long-lasting rain (daily rainfall <80 mm). Among different microwave frequencies, the MLSE at 23.8 Ghz was most reduced by rainfall, while less reduction was found at low microwave frequencies such as at 6.925 and 10.65 Ghz. More importantly, the change of MLSE after rainfall was significantly different among vegetation types. Over woody savannas and forested lands, MLSE slightly decreased (about 0.004) in the first two hours of DD, whereas a rapid increase was observed in the first hour of DD over areas dominated with woody savanna, grassland, and cropland. These findings improve our understanding of the multi-frequency MLSE in response to rainfall over complex land surfaces, and benefit the microwave retrieval of rainfall over land.

Toward an advanced physics-based scheme for retrieving land surface emissivity and temperature based on FengYun-3D MERSI-II daytime mid-infrared data

Toward an advanced physics-based scheme for retrieving land surface emissivity and temperature based on FengYun-3D MERSI-II daytime mid-infrared data

Development of 1 ×1 km gridded emission inventory for air quality assessment and mitigation strategies from open biomass burning in Karnataka, India

In this work, we have developed a high-resolution (1 ×1 km grid) emission inventory of greenhouse gases and air pollutants from open biomass burning (OBB) in Karnataka for 2000, 2005, 2010, 2015, 2020, and 2022. Secondary and field datasets were used to validate the emission inventories. The GIS-based statistical model and activity data were used to estimate greenhouse and air pollutant gas emissions from OBB (Forest, grassland, and crop fires). The uncertainty analysis of the emissions were done using the Monte Carlo Simulation (MCS) model and Low Emission Analysis Platform (LEAP) tool was used for projecting the emissions to 2050 based on the annual average growth rate. According to our results, the total OBB burned areas (%) in Karnataka during the aforementioned years were 24.43, 15.69, 15.95, 11.41, 26.73, and 5.78. The results showed that total annual average OBB emissions in Karnataka for 2000–2022 in Gg were SO2 (6.67), NOx (9.48), NH3(9.80), CO (670.12), OC (59.78), BC (5.09), CO2 (11071 .11), CH4(33.68), PM10(84.29), PM2.5(81.08), NMVOC (74.13), and NO2 (4.80) respectively. The districts of Kalburgi, Raichur, Bagalkot, Uttara Kannada, and Shivamogga have the highest emissions from OBB in Karnataka from 2000 to 2022. The emission uncertainty results showed ±26.92, ±28.17, and ±30.27 % for the crop, grassland, and forest fires, respectively. Under the business-as-usual (BAU) scenarios, the CH4 emissions from forest, grassland, and crop fires are projected at 8823, 2.64, and 5262 metric tonnes by 2030 and 58046, 10, and 30700 metric tonnes by 2050, respectively. The developed high-resolution inventories are the most up-to-date surface emission datasets with hotspots in Karnataka from OBB emission. National Clean Air Programme (NCAP) and India’s Net-Zero Emission target by 2070, our high-resolution emission inventories and mitigation strategies may be helpful for air quality monitoring, health benefits analysis, and policy-making studies in Karnataka, India.

Temperature measurement in laser–metal processing using optical sensing: Radiometric calibration and validation of a multispectral camera

In laser–metal processing such as Laser-based Powder Bed Fusion of Metals (PBF-LB/M), the quality of the final product is significantly influenced by the thermal behaviour of the melt pool. Accurate temperature measurements are challenging due to high temperature gradients and rapid cooling. This study introduces Multispectral Imaging (MSI) as a highly effective thermometry technique to address these demanding conditions. MSI captures multiple bands of radiation, enabling the simultaneous determination of absolute temperature and surface emissivity. This capability is crucial for ensuring high-quality outcomes in laser–metal processing such as PBF-LB/M, contingent upon robust radiometric calibration.To enhance reliability, two radiometric calibration models were developed: an empirical model based on linear sensor data correlations and an analytical model leveraging sensor behaviour. An efficient calibration was found for both models across a range of signal-to-noise ratios in the black body calibrator, where the analytical model even performs robust under a high noise level.Additionally, the temperature accuracy in the solid-state case was tested with a thermo-mechanical simulator. Results showed that the empirical and analytical models achieved mean relative errors of 2.0% and 1.6%, respectively, outperforming the state-of-the-art. Further, laser irradiation experiments highlighted the analytical model’s ability to accurately determine the emissivity decrease during the solid-to-liquid transition, allowing for accurate melt pool temperature estimations. Conversely, the empirical model struggled to estimate temperature effectively in the liquid phase.This study not only proposed a robust methodology of MSI in temperature measurement but also confirmed the accuracy and reliability of the system, broadening the scope for further investigation into the intricate thermal dynamics involved in laser–metal interactions.

Metal Nanowire‐Induced Enhancement of Surface Emission in Luminescent Glass

AbstractLuminescent glass, as an important component of photonic materials, is extensively utilized in applications such as lasers and optical fiber amplifiers, which demand high quantum efficiency. To further enhance the surface emission performance of luminescent glass, this study introduces a novel approach by employing custom‐made metal nanowires, applying them as coatings on the surface of Erbium‐doped glass, rather than embedding traditional metal nanoparticles. Utilizing experimental techniques and Finite‐Difference Time‐Domain (FDTD) simulations, it is demonstrated that the surface‐emission from Erbium‐doped glass coated with silver nanowires or copper nanowire‐graphene hybrids is significantly increased, showing an enhancement rate of up to 117.27% compared to intrinsic glass. Extended FDTD analysis reveals that variations in the size and density of the nanowires can optimize emission characteristics, thereby improving the emission performance of luminescent glass. The enhancement of the local electromagnetic field at the interface underscores the efficacy of metal nanowires in boosting photonic materials. This study not only highlights the practicality of metal nanowires in photonics but also introduces a rapid deployment pathway for customizing photonic interactions through nano‐engineering, potentially extendable to other photonic materials.

Analysis of radiative heat flux using ASTER thermal images: Climatological and volcanological factors on Java Island, Indonesia

This study focuses on analysing natural Radiative Heat Flux (RHF) anomalies to map out the heat distribution across the Java Island. Leveraging remote sensing techniques, we calculated natural RHF anomalies using Land Surface Temperature (LST) and Land Surface Emissivity (LSE) data obtained from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery. A key aspect of our approach was distinguishing between natural and anthropogenic heat sources by cross-referencing the LST Map with the Land Use Land Cover (LULC) map of Java Island. The study interprets natural RHF anomalies by examining regional trends in non-volcanic areas and local trends within volcanic regions, considering climatological and volcanological factors. Relation with climatological factors involves assessing soil moisture parameters from Soil Moisture Active Passive (SMAP) data, precipitation from monthly Global Precipitation Measurement (GPM) data, and classifications according to the Köppen-Geiger climate schema. Our regional analysis reveals high natural RHF anomalies in the northern regions of West Java, parts of Central Java, and most of East Java, attributed to low soil moisture and low precipitation in savanna and monsoon climates. On a more localised scale, RHF values are significantly high in volcanic areas, particularly around Central and East Java's volcanoes, such as Mt. Merapi, Mt. Slamet, Mt. Semeru, the Sidoarjo Mud Volcano, and Mt. Ijen. The Natural RHF anomalies at volcanoes in West Java were identified as not being high except at Mt Patuha. These areas exhibit average natural RHF anomalies ranging between 32.22 W/m2 and 115.13 W/m2, indicating strong and intense volcanic activity. The insights obtained from these findings explain the overall thermal characteristics of Java Island and highlight the presence of subsurface thermal zones associated with volcanic activity and geothermal potential.

A 40-Year Time Series of Land Surface Emissivity Derived from AVHRR Sensors: A Fennoscandian Perspective

Accurate land surface temperature (LST) retrieval depends on precise knowledge of the land surface emissivity (LSE). Neglecting or inaccurately estimating the emissivity introduces substantial errors and uncertainty in LST measurements. The emissivity, which varies across different surfaces and land uses, reflects material composition and surface roughness. Satellite data offer a robust means to determine LSE at large scales. This study utilises the Normalised Difference Vegetation Index Threshold Method (NDVITHM) to produce a novel emissivity dataset spanning the last 40 years, specifically tailored for the Fennoscandian region, including Norway, Sweden, and Finland. Leveraging the long and continuous data series from the Advanced Very High Resolution Radiometer (AVHRR) sensors aboard the NOAA and MetOp satellites, an emissivity dataset is generated for 1981–2022. This dataset incorporates snow-cover information, enabling the creation of annual emissivity time series that account for winter conditions. LSE time series were extracted for six 15 × 15 km study sites and compared against the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD11A2 LSE product. The intercomparison reveals that, while both datasets generally align, significant seasonal differences exist. These disparities are attributable to differences in spectral response functions and temporal resolutions, as well as the method considering fixed values employed to calculate the emissivity. This study presents, for the first time, a 40-year time series of the emissivity for AVHRR channels 4 and 5 in Fennoscandia, highlighting the seasonal variability, land-cover influences, and wavelength-dependent emissivity differences. This dataset provides a valuable resource for future research on long-term land surface temperature and emissivity (LST&amp;E) trends, as well as land-cover changes in the region, particularly with the use of Sentinel-3 data and upcoming missions such as EUMETSAT’s MetOp Second Generation, scheduled for launch in 2025.

A novel approach to estimate land surface temperature from landsat top-of-atmosphere reflective and emissive data using transfer-learning neural network

Land Surface Temperature (LST) is a crucial parameter in studies of urban heat islands, climate change, evapotranspiration, hydrological cycles, and vegetation monitoring. However, conventional satellite-based approaches for LST retrieval often require additional data like land surface emissivity (LSE). Meanwhile, traditional machine learning (ML) techniques face challenges in acquiring representative training data and leveraging data from varied sources effectively. To address these issues, we introduce a novel transfer-learning (TL) neural network approach for LST retrieval using top-of-atmosphere (TOA) reflective and emissive data from Landsat. This method not only improves LST retrieval by integrating various data types but also demonstrates the potential of shortwave data in surrogating LSE information, thereby reducing dependence on explicit LSE data. Our TL approach utilized extensive simulations from the radiative transfer model (RTM) and measurements from the real world. The simulations are comprehensive, covering a wide range of atmospheric and surface scenarios, and the inclusion of real-world data mitigates the discrepancy between simulations and actual observations. When applied to a decade of Landsat-8 observations and ground measurements from 241 stations across diverse regions, our TL method significantly outperforms ML, single-channel (SC), and split-window (SW) algorithms in terms of root mean square error (RMSE), with improvements of 0.46 K, 0.84 K, and 0.57 K, respectively. This superiority underscores the advantage of integrating simulated and observed data, as well as the benefit of utilizing both reflective and emissive data without relying on uncertain LSE inputs. Our findings present a promising new TL framework for estimating LST directly from TOA data, offering a robust approach that we have made publicly available through Google Earth Engine (GEE) for broader use. The LST data retrieved by our proposed method can provide valuable insights for environmental research.

Assessment and Validation of Land Surface Temperature Retrieval Algorithms Using Landsat 8 TIRS Data in Antarctic Ice-Free Areas

This study evaluates the effectiveness of different Land Surface Temperature (LST) retrieval algorithms applied to Landsat 8 Thermal Infrared Sensor (TIRS) data in the ice-free regions of the Antarctic Peninsula. The primary objective is to determine the most accurate algorithm for LST estimation in these environments. Three algorithms, namely radiative transfer equation (RTE), single channel (SC), and mono window (MW), were utilised and compared to in-situ measurements at two locations in the northern part of James Ross Island (JRI), Antarctic Peninsula. The study considered various factors influencing LST accuracy, including land surface emissivity, atmospheric conditions, and sun elevation angles. The findings reveal that all three algorithms demonstrate significant sensitivity to emissivity. The MW algorithm emerged as the most suitable, showing the lowest root mean square error (RMSE) of 3.06 °C, followed by the SC and RTE algorithms with RMSE values of 3.68 and 3.98 °C, respectively. The study also underscores a strong positive correlation between LST retrieval accuracy and sun elevation angle, with more accurate results obtained from satellite images acquired in February, characterised by lower sun elevation angles. No significant relationship with water vapour content in the atmosphere was identified during the investigated period.

THEORETICAL APPROACH FOR DETERMINING AN EMISSIVITY OF SOLID MATERIALS AND ITS COMPARISON WITH EXPERIMENTAL STUDIES ON THE EXAMPLE OF 316L POWDER STEEL

The work used Maxwell's electromagnetic theory to quantitatively describe the emissivity of solid materials through electrical resistivity and temperature. An equation is proposed for recalculating the emissivity of smooth surfaces into powdery or rough surfaces. The obtained theoretical characteristics of the change in the emissivity of 316L powder steel were compared with experimental ones. As a result of the comparison, it was established that the experimental results obtained correlate with theoretical calculations and do not go beyond the limits of the expanded uncertainty of measurement.