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Surface longwave radiation (SLR) plays a pivotal role in the Earth's energy balance, influencing a range of environmental processes and climate dynamics. As the demand for high spatial resolution remote sensing products grows, there is an increasing need for accurate SLR retrieval with enhanced spatial detail. This study focuses on the development and validation of models to estimate SLR using measurements from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor. Given the limitations posed by fewer spectral bands and data products in ASTER compared to moderate-resolution sensors, the proposed approach combines an atmospheric radiative transfer model MODerate resolution atmospheric TRANsmission (MODTRAN) with the Light Gradient Boosting Machine algorithm to estimate SLR. The MODTRAN simulations were performed to construct a representative training dataset based on comprehensive global atmospheric profiles and surface emissivity spectra data. Global sensitivity analyses reveal that key inputs influencing the accuracy of SLR retrievals should reflect surface thermal radiative signals and near-surface atmospheric conditions. Validated against ground-based measurements, surface upward longwave radiation (SULR) and surface downward longwave radiation (SDLR) using ASTER thermal infrared bands and surface elevation estimations resulted in root mean square errors of 17.76 W/m2 and 25.36 W/m2, with biases of 3.42 W/m2 and 3.92 W/m2, respectively. Retrievals show systematic biases related to extreme temperature and moisture conditions, e.g., causing overestimation of SULR in hot humid conditions and underestimation of SDLR in arid conditions. While challenges persist, particularly in addressing atmospheric variables and cloud masking, this work lays a foundation for accurate SLR retrieval from high spatial resolution sensors like ASTER. The potential applications extend to upcoming satellite missions, such as the Landsat Next, and contribute to advancing high-resolution remote sensing capabilities for an improved understanding of Earth's energy dynamics. [ABSTRACT FROM AUTHOR]

During an earthquake, crustal deformation, fluid flow, and temperature variation are coupled; however, earthquake-related land surface temperature (LST) variations remain unclear. To determine whether post-seismic fluid migration can cause changes in LST, and taking the Mw 7.3 2017 Iran earthquake as an example, we modeled surface cooling (CA) and warming (WA) areas induced by co-seismic slip and fluid migration using a thermo-hydro-mechanical (THM) coupled numerical simulation. Moreover, using nighttime LST data with 15-min resolution, the daily attenuation coefficient k of nighttime LST was extracted by attenuation function fitting, and the trend of the k time series was analyzed using the Mann–Kendall and Sen's methods. Based on the comparison of k trends between the post-seismic and 2010–2016 periods, we obtained cooling and warming trends for the modeled CA and WA. The numerical simulation and observational data show good consistency, and both indicate that fluid migration caused by crustal deformation can lead to changes in LST. The numerical simulations show that after the Iran earthquake, the surface projection area of co-seismic slip correlated with a cooling area (CA), while the surrounding area correlated with a warming area (WA). For the LST observational data, the post-seismic k trends of the calculated CA and WA are positive and negative, indicating sustained cooling and warming processes, respectively. This study provides evidence that LST variation is caused by co-seismic crustal deformation and fluid migration and reveals the coupled evolution of deformation, fluid, and temperature fields. The results provide new insights into the mechanisms of seismic thermal anomalies. [ABSTRACT FROM AUTHOR]