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).

Radiative transfer models (RTMs) designed to reproduce the anisotropy of surface brightness temperature (BT) are particularly useful for applications on Earth's energy budget when using remote sensing (RS) datasets. Despite the fact that several thermal infrared (TIR) RTMs have been developed, a quantitative analysis comparing the benefits and limits of these models remains necessary. Herein, three modeling frameworks (physical hybrid, analytical parameterization, and kernel driven) have been evaluated comparatively for homogeneous vegetation, a row-planted crop, and a sparse forest. Airborne measurements and the discrete anisotropy radiative transfer (DART) model simulations were retained as the benchmark. Forward modeling and inverse fitting schemes were proposed for the sake of comparison. Results reveal that: 1) in the forward modeling scheme, from airborne measurements, the hybrid model performs better with root-mean-squared errors (RMSEs) of 0.17 degrees C, 1.57 degrees C, and 0.38 degrees C for homogenous, row-planted vineyard, and sparse forest scenes, respectively; the analytical model appears similar performant (0.17 degrees C, 0.40 degrees C) for the homogeneous and sparse forest scenes, but less performant (2.39 degrees C) for the row-planted scene and 2) in the inverse fitting scheme, the uncertainties (95% of probability) of model coefficients and predicted directional anisotropies were considered. The kernel-driven model has fewer modeling constraints and statistically performs better for the homogeneous and sparse forest scenes with RMSEs of 0.07 degrees C and 0.19 degrees C, respectively, whereas it is less efficient for the row-planted scene with RMSE of 0.80 degrees C. This study highlights the differences in accuracy between models of different complexity and provides reference information for researchers to improve existing models and for users to choose their best modeling solution.

Surface temperature is critical for the simulation of climate change impacts on the ecology, environment, and particularly permafrost in the cryosphere. Virtually, surface temperatures are different in the near-surface air temperature (T-a) measured at a screen-height of 1.5-2 m, the land surface temperature (LST) on the top canopy layer, and the ground surface temperature (GST) 0-5 cm beneath the surface cover. However, not enough attention has been concentrated on the difference in these surface temperatures. This study aims at quantifying the distinction of surface temperatures by the comparisons and numerical simulations of observational field data collected in a discontinuous permafrost region on the northeastern Qinghai-Tibet Plateau (QTP). We compared the hourly, seasonal and yearly differences between T omega, IST, GST, and ground temperatures, as well as the freezing and thawing indices, the N-factors, and the surface and thermal offsets derived from these temperatures. The results showed that the peak hourly LST was reached earliest, closely followed by the hourly T-a. Mean annual LST (MALST) was moderately comparable to mean annual T-a (MAAT), and both were lower than mean annual GST (MAGST). Surface offsets (MAGST-MAAT) were all within 3.5 degrees C, which are somewhat consistent with other parts of the QTP but smaller than those in the Arctic and Subarctic regions with dense vegetation and thick, long-duration snow cover. Thermal offsets, the mean annual differences between the ground surface and the permafrost surface, were within -0.3 degrees C, and one site was even reversed, which may be relevant to equally thawed to frozen thermal conductivities of the soils. Even with identical T-a (comparable to MAAT of -3.27 and -3.17 degrees C), the freezing and thawing processes of the active layer were distinctly different, due to the complex influence of surface characteristics and soil, textures. Furthermore, we employed the Geophysical Institute Permafrost Lab (GIPL) model to numerically simulate the dynamics of ground temperature driven by T-a, LST, and GST, respectively. Simulated results demonstrated that GST was a reliable driving indicator for the thermal regime of frozen ground, even if no thermal effects of surface characteristics were taken into account. However, great biases of mean annual ground temperatures, being as large as 3 degrees C, were induced on the basis of simulations with LST and T-a when the thermal effect of surface characteristics was neglected. We conclude that quantitative calculation of the thermal effect of surface characteristics on GST is indispensable for the permafrost simulations based on the T-a datasets and the LST products-that derived from thermal infrared remote sensing.