Component temperature and emissivity are crucial for understanding plant physiology and urban thermal dynamics. However, existing thermal infrared unmixing methods face challenges in simultaneous retrieval and multicomponent analysis. We propose Thermal Remote sensing Unmixing for Subpixel Temperature and emissivity with the Discrete Anisotropic Radiative Transfer model (TRUST-DART), a gradient-based multi-pixel physical method that simultaneously separates component temperature and emissivity from non-isothermal mixed pixels over urban areas. TRUST-DART utilizes the DART model and requires inputs including at-surface radiance imagery, downwelling sky irradiance, a 3D mock-up with component classification, and standard DART parameters (e.g., spatial resolution and skylight ratio). This method produces maps of component emissivity and temperature. The accuracy of TRUST-DART is evaluated using both vegetation and urban scenes, employing Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images and DART-simulated pseudo-ASTER images. Results show a residual radiance error is approximately 0.05 W/(m2 & sdot;sr). In absence of the co-registration and sensor noise errors, the median residual error of emissivity is approximately 0.02, and the median residual error of temperature is within 1 K. This novel approach significantly advances our ability to analyze thermal properties of urban areas, offering potential breakthroughs in urban environmental monitoring and planning. The source code of TRUSTDART is distributed together with DART (https://dart.omp.eu).

The Imaging InfraRed Spectrometer (IIRS) on board Chandrayaan-2 has been providing high spatial and spectral resolution observations of the lunar surface in 256 spectral bands (0.7-5 mu m) since September 2019. It is primarily intended for mineral mapping and identifying hydration features on the lunar surface using reflectance spectra in the range of 0.7-3.2 mu m. Here, we have used the IIRS observations in the 3-5 mu m range to retrieve daytime lunar surface temperature and spectral emissivity using an optimal estimation theory -based retrieval algorithm. The surface temperature is retrieved at every pixel, while spectral emissivity is retrieved at every third pixel of the hyperspectral image. The mean uncertainty of the retrieved spectral emissivity varies from 0.04 to 0.08, while for surface temperature, it is about 3.5 K. The retrieved spectral emissivity is found to be in close agreement with the emissivity of the Apollo -16 return soil samples.

Soil directional emissivity plays a crucial role in canopy thermal-infrared (TIR) emissivity modeling over sparsely vegetated solo slopes. To our knowledge, the canopy emissivity model explicitly considers soil emissivity directionality, and topography does not exist. This study proposes a new canopy emissivity model under the framework of the four-stream approximation theory employed in the well-known 4SAIL model by incorporating soil directional emissivity and topography. The new model was validated by the discrete anisotropic radiative transfer (DART) model. The new model-simulated canopy emissivity data exhibited excellent consistency with the DART simulation data, and the bias, root mean square error (RMSE), and determination coefficient ( R-2 ) were -0.001, 0.003, and 0.97, respectively, under the different leaf area indices (LAIs), slopes, and view zenith angles (VZAs). Sensitivity analysis revealed that LAI and soil nadir emissivity explained most of the variance, with total sensitivity indices of 52.9% and 30.3%, respectively. The effects of soil directional emissivity, topography, and leaf angle distribution (LAD) on canopy emissivity were subsequently investigated, and the results indicated that the differences could reach more than 0.02 when soil directional emissivity and/or topography were neglected; moreover, the influence of LAD functions is not significant. The model proposed in this article provides a practical method for modeling mountainous area canopy emissivity and can improve estimates of surface broadband emissivity (BBE) and land surface temperature (LST).

The northern floor and wall of Amundsen crater, near the lunar south pole, is a permanently shaded region (PSR). Previous study of this area using data from the Lunar Orbiter Laser Altimeter (LOLA), Diviner and LAMP instruments aboard Lunar Reconnaissance Orbiter (LRO) shows a spatial correlation between brighter 1064 nm albedo, annual maximum surface temperatures low enough to enable persistence of surface water ice (< 110 K), and anomalous ultraviolet radiation. We present results using data from Diviner that quantify the differential emissivities observed in the far-IR (near the Planck peak for PSR-relevant temperatures) between the PSR and a nearby non-PSR target in Amundsen Crater. We find features in far-IR emissivity (50-400 mu m) could be attributed to either, or a combination, of two effects (i) differential regolith emissive behavior between permanently-shadowed temperature regimes and those of normally illuminated polar terrain, perhaps related to presence of water frost (as indicated in other studies), or (ii) high degrees of anisothermality within observation fields of view caused by doubly-shaded areas within the PSR target that are colder than observed brightness temperatures. The implications in both cases are compelling: The far-IR emissivity curve of lunar cold traps may provide a metric for the abundance of micro cold traps that are ultra-cool, i.e. shadowed also from secondary and higher order radiation (absorption and re-radiation or scattering by surrounding terrain), or for emissive properties consistent with the presence of surface water ice.