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Accurate radiation thermometry of real objects critically depends on knowledge of surface emissivity, which is rarely known a priori and often varies with surface condition, temperature, and environment. Although theoretical models for spectral emissivity evaluation exist, their practical validation under application-relevant conditions remains limited. In this study, spectral and radiometric emissivity evaluation methods are compared on metallic samples up to 350 °C. The spectral method derives effective emissivity from spectroscopy-measured spectral emissivity using instrument-specific spectral sensitivity (responsivity), while the radiometric method evaluates emissivity directly from radiance measurements using a radiation thermometer and a reference contact temperature. The radiometric method is treated as an application-level reference. Stable and homogeneous chromium nitride (CrN)-coated samples show good agreement between the two methods, whereas raw metals and polysiloxane-coated samples highlight practical limitations related to sample surface instability and inhomogeneity. The results demonstrate that spectral emissivity evaluation is valid in practice when its underlying method assumptions are fulfilled, while radiometric evaluation remains preferable for in situ infrared thermometry.

This study explores the estimation of land surface temperature (LST) for the globe from Landsat 5, 7 and 8 thermal infrared sensors, using different surface emissivity sources. A single channel algorithm is used for consistency among the estimated LST products, whereas the option of using emissivity from different sources provides flexibility for the algorithm’s implementation to any area of interest. The Google Earth Engine (GEE), an advanced earth science data and analysis platform, allows the estimation of LST products for the globe, covering the time period from 1984 to present. To evaluate the method, the estimated LST products were compared against two reference datasets: (a) LST products derived from ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer), as higher-level products based on the temperature-emissivity separation approach; (b) Landsat LST data that have been independently produced, using different approaches. An overall RMSE (root mean square error) of 1.52 °C was observed and it was confirmed that the accuracy of the LST product is dependent on the emissivity; different emissivity sources provided different LST accuracies, depending on the surface cover. The LST products, for the full Landsat 5, 7 and 8 archives, are estimated “on-the-fly” and are available on-line via a web application.

The temperature and emissivity separation (TES) algorithm is currently adopted to retrieve the land surface temperature (LST) and emissivity (LSE) from Moderate Resolution Imaging Spectroradiometer (MODIS) images (i.e., the MOD/MYD21 product). Unfortunately, the TES algorithm often yields anomalous LSE spectra for graybodies with low spectral contrast. The MODIS TES algorithm does not effectively address this issue. To overcome this limitation, refined TES algorithms, including the optimized smoothing for temperature emissivity separation (OSTES) and the temperature and emissivity separation with nonlinear constraint (TESNC), have been proposed. Although these algorithms offer theoretical improvements, their performance has not been systematically validated using real MODIS data. This study evaluates the performance of three TES algorithms (MODIS TES, OSTES, and TESNC) in retrieving LST&E from MODIS data over six lakes on the Qinghai–Tibet Plateau, which serve as representative examples of low-spectral-contrast surfaces. Three years (2018–2020) of MODIS data from six lakes on the Qinghai–Tibet Plateau were collected to retrieve LST&E using three TES algorithms. Using the quality-controlled MODIS LST product (MOD11) as a benchmark, the TESNC algorithm achieved the highest accuracy, with bias and RMSE values of 0.18 K and 0.22 K, respectively, compared with the bias and RMSE values of 0.51 K and 0.53 K for the MODIS TES algorithm and 0.58 K and 0.60 K for the OSTES algorithm, respectively. In terms of LSE, the TESNC algorithm achieved an RMSE within 0.005 for all bands, demonstrating superior accuracy over the other algorithms. Overall, the TESNC algorithm significantly improved the accuracy of LST&E retrieval from MODIS for graybodies with low spectral contrast. This study is the first to systematically evaluate refined TES algorithms using real MODIS data over graybodies. The findings provide valuable insights for improving the MODIS LST&E product and advancing the retrieval of LST&E for low-spectral-contrast surfaces.

Land Surface Temperature (LST) is an important parameter for many scientific disciplines since it affects the interaction between the land and the atmosphere. Many LST retrieval algorithms based on remotely sensed images have been introduced so far, where the Land Surface Emissivity (LSE) is one of the main factors affecting the accuracy of the LST estimation. The aim of this study is to evaluate the performance of LST retrieval methods using different LSE models and data of old and current Landsat missions. Mono Window Algorithm (MWA), Radiative Transfer Equation (RTE) method, Single Channel Algorithm (SCA) and Split Window Algorithm (SWA) were assessed as LST retrieval methods processing data of Landsat missions (Landsat 5, 7 and 8) over rural pixels. Considering the LSE models introduced in the literature, different Normalized Difference Vegetation Index (NDVI)-based LSE models were investigated in this study. Specifically, three LSE models were considered for the LST estimation from Landsat 5 Thematic Mapper (TM) and seven Enhanced Thematic Mapper Plus (ETM+), and six for Landsat 8. For the accurate evaluation of the estimated LST, in-situ LST data were obtained from the Surface Radiation Budget Network (SURFRAD) stations. In total, forty-five daytime Landsat images; fifteen images for each Landsat mission, acquired in the Spring-Summer-Autumn period in the mid-latitude region in the Northern Hemisphere were acquired over five SURFRAD rural sites. After determining the best LSE model for the study case, firstly, the LST retrieval accuracy was evaluated considering the sensor type: when using Landsat 5 TM, 7 ETM+, and 8 Operational Land Imager (OLI), and Thermal Infrared Sensor (TIRS) data separately, RTE, MWA, and MWA presented the best results, respectively. Then, the performance was evaluated independently of the sensor types. In this case, all LST methods provided satisfying results, with MWA having a slightly better accuracy with a Root Mean Square Error (RMSE) equals to 2.39 K and a lower bias error. In addition, the spatio-temporal and seasonal analyses indicated that RTE and SCA presented similar results regardless of the season, while MWA differed from RTE and SCA for all seasons, especially in summer. To efficiently perform this work, an ArcGIS toolbox, including all the methods and models analyzed here, was implemented and provided as a user facility for the LST retrieval from Landsat data.

Thermal infrared (TIR) multi-/hyperspectral and sun-induced fluorescence (SIF) approaches together with classic solar-reflective (visible, near-, and shortwave infrared reflectance (VNIR)/SWIR) hyperspectral remote sensing form the latest state-of-the-art techniques for the detection of crop water stress. Each of these three domains requires dedicated sensor technology currently in place for ground and airborne applications and either have satellite concepts under development (e.g., HySPIRI/SBG (Surface Biology and Geology), Sentinel-8, HiTeSEM in the TIR) or are subject to satellite missions recently launched or scheduled within the next years (i.e., EnMAP and PRISMA (PRecursore IperSpettrale della Missione Applicativa, launched on March 2019) in the VNIR/SWIR, Fluorescence Explorer (FLEX) in the SIF). Identification of plant water stress or drought is of utmost importance to guarantee global water and food supply. Therefore, knowledge of crop water status over large farmland areas bears large potential for optimizing agricultural water use. As plant responses to water stress are numerous and complex, their physiological consequences affect the electromagnetic signal in different spectral domains. This review paper summarizes the importance of water stress-related applications and the plant responses to water stress, followed by a concise review of water-stress detection through remote sensing, focusing on TIR without neglecting the comparison to other spectral domains (i.e., VNIR/SWIR and SIF) and multi-sensor approaches. Current and planned sensors at ground, airborne, and satellite level for the TIR as well as a selection of commonly used indices and approaches for water-stress detection using the main multi-/hyperspectral remote sensing imaging techniques are reviewed. Several important challenges are discussed that occur when using spectral emissivity, temperature-based indices, and physically-based approaches for water-stress detection in the TIR spectral domain. Furthermore, challenges with data processing and the perspectives for future satellite missions in the TIR are critically examined. In conclusion, information from multi-/hyperspectral TIR together with those from VNIR/SWIR and SIF sensors within a multi-sensor approach can provide profound insights to actual plant (water) status and the rationale of physiological and biochemical changes. Synergistic sensor use will open new avenues for scientists to study plant functioning and the response to environmental stress in a wide range of ecosystems.

HighlightsLightweight dual-functional segregated nanocomposite foams are developed via the supercritical CO2 (SC-CO2) foaming combined with hydrogen bonding assembly and compression molding strategyThe segregated nanocomposite foams exhibit superior infrared stealth performances benefitting from the synergistic effect of highly effective thermal insulation and low infrared emissivity.Excellent absorption-dominant electromagnetic interference shielding performances are achieved owing to the synchronous construction of microcellular structures and segregated structuresLightweight infrared stealth and absorption-dominant electromagnetic interference (EMI) shielding materials are highly desirable in areas of aerospace, weapons, military and wearable electronics. Herein, lightweight and high-efficiency dual-functional segregated nanocomposite foams with microcellular structures are developed for integrated infrared stealth and absorption-dominant EMI shielding via the efficient and scalable supercritical CO2 (SC-CO2) foaming combined with hydrogen bonding assembly and compression molding strategy. The obtained lightweight segregated nanocomposite foams exhibit superior infrared stealth performances benefitting from the synergistic effect of highly effective thermal insulation and low infrared emissivity, and outstanding absorption-dominant EMI shielding performances attributed to the synchronous construction of microcellular structures and segregated structures. Particularly, the segregated nanocomposite foams present a large radiation temperature reduction of 70.2 °C at the object temperature of 100 °C, and a significantly improved EM wave absorptivity/reflectivity (A/R) ratio of 2.15 at an ultralow Ti3C2Tx content of 1.7 vol%. Moreover, the segregated nanocomposite foams exhibit outstanding working reliability and stability upon dynamic compression cycles. The results demonstrate that the lightweight and high-efficiency dual-functional segregated nanocomposite foams have excellent potentials for infrared stealth and absorption-dominant EMI shielding applications in aerospace, weapons, military and wearable electronics.

The identification of sources driving cosmic reionization, a major phase transition from neutral hydrogen to ionized plasma around 600–800 Myr after the Big Bang 1 – 3 , has been a matter of debate 4 . Some models suggest that high ionizing emissivity and escape fractions ( f esc ) from quasars support their role in driving cosmic reionization 5 , 6 . Others propose that the high f esc values from bright galaxies generate sufficient ionizing radiation to drive this process 7 . Finally, a few studies suggest that the number density of faint galaxies, when combined with a stellar-mass-dependent model of ionizing efficiency and f esc , can effectively dominate cosmic reionization 8 , 9 . However, so far, comprehensive spectroscopic studies of low-mass galaxies have not been done because of their extreme faintness. Here we report an analysis of eight ultra-faint galaxies (in a very small field) during the epoch of reionization with absolute magnitudes between M UV  ≈ −17 mag and −15 mag (down to 0.005 L ⋆ (refs.  10 , 11 )). We find that faint galaxies during the first thousand million years of the Universe produce ionizing photons with log[ ξ ion  (Hz erg −1 )] = 25.80 ± 0.14, a factor of 4 higher than commonly assumed values 12 . If this field is representative of the large-scale distribution of faint galaxies, the rate of ionizing photons exceeds that needed for reionization, even for escape fractions of the order of 5%. An analysis of eight ultra-faint galaxies during the epoch of reionization with absolute magnitudes between −17 mag and −15  mag shows that most of the photons that reionized the Universe come from dwarf galaxies.

While surface microstructures of butterfly wings have been extensively studied for their structural coloration or optical properties within the visible spectrum, their properties in infrared wavelengths with potential ties to thermoregulation are relatively unknown. The midinfrared wavelengths of 7.5 to 14 μm are particularly important for radiative heat transfer in the ambient environment, because of the overlap with the atmospheric transmission window. For instance, a high midinfrared emissivity can facilitate surface cooling, whereas a low midinfrared emissivity can minimize heat loss to surroundings. Here we find that the midinfrared emissivity of butterfly wings from warmer climates such as Archaeoprepona demophoon (Oaxaca, Mexico) and Heliconius sara (Pichincha, Ecuador) is up to 2 times higher than that of butterfly wings from cooler climates such as Celastrina echo (Colorado) and Limenitis arthemis (Florida), using Fourier-transform infrared (FTIR) spectroscopy and infrared thermography. Our optical computations using a unit cell approach reproduce the spectroscopy data and explain how periodic microstructures play a critical role in the midinfrared. The emissivity spectrum governs the temperature of butterfly wings, and we demonstrate that C. echo wings heat up to 8 °C more than A. demophoon wings under the same sunlight in the clear sky of Irvine, CA. Furthermore, our thermal computations show that butterfly wings in their respective habitats can maintain a moderate temperature range through a balance of solar absorption and infrared emission. These findings suggest that the surface microstructures of butterfly wings potentially contribute to thermoregulation and provide an insight into butterflies' survival.

We study the relation between angular spectral absorptivity and emissivity for any thermal emitter, which consists of any linear media that can be dispersive, inhomogeneous, bianisotropic, or nonreciprocal. First, we establish an adjoint Kirchhoff’s law for mutually adjoint emitters. This law is based on generalized reciprocity and is a natural generalization of conventional Kirchhoff’s law for reciprocal emitters. Using this law, we derive all the relations between absorptivity and emissivity for an arbitrary thermal emitter We reveal that such relations are determined by the symmetries of the system, which are characterized by a Shubnikov point group. We classify all thermal emitters based on their symmetries using the known list of all three-dimensional Shubnikov point groups. Each class possesses its own set of laws that relates the absorptivity and emissivity. We numerically verify our theory for all three types of Shubnikov point groups: Gray groups, colorless groups, and black and white groups. We also verify the theory for both planar and nonplanar structures with single or multiple diffraction channels. Our theory provides a theoretical foundation for further exploration of thermal radiation in general media.

A visible transparent metamaterial absorber was designed and fabricated with ultrabroadband microwave absorption and low infrared emissivity to meet the increasing demand for multispectral compatible camouflage. The absorber was fabricated with a low-infrared emissive layer at the top, a microwave-absorbing layer in the middle, and a reflective layer at the bottom, which were separated by polymethyl methacrylate plates. The absorber showed an average visible transmittance of 55%, infrared emissivity of ∼0.37, and effective microwave absorption bandwidth of 32.1 GHz with a total thickness of 3.0 mm. Furthermore, microwave absorption exhibited wide-angle stability and polarization insensitivity characteristics. The mechanism of microwave attenuation was further explored through effective electromagnetic parameters as well as surface current, electric field, magnetic field, and energy loss density distributions. The experimental results were consistent with those of the simulations and calculations, indicating the potential of the designed metamaterial absorber for future applications in multispectral compatible camouflage.