Land Surface Temperature Retrieval over Urban areas from simulated TRISHNA data

The future space joint-mission TRISHNA (Thermal infraRed Imaging Satellite for High-resolution Natural resource Assessment, CNES and ISRO) will allow to retrieve LST at 60 m spatial resolution every 3 days, improving the urban environment monitoring capacities. Two methods can be used to derive LST from remote sensing TIR data, Split-Window (SW) or TES (Temperature Emissivity Separation). Even if proven efficient, the complexity and heterogeneity of urban areas limit the performance of those methods. This paper focuses on the TES algorithm. For TES, errors can be generated by the impact of the surface roughness on the data. In order to investigate the efficiency of the future TRISHNA mission, research was conducted to evaluate the TES algorithm. To do so, the impacts of using a representative material database when using TES were explored and validated based on airborne or satellite data, and the impact of the surface geometry on the TES estimations was evaluated using 3D thermo-radiative model simulations. This study shows that TES can be used for TRISHNA images with a mean difference of 1.6 K between validation measurements and the retrieved LST for both daytime and nighttime images and that neglecting the impact of the surface geometry while performing TES could lead to errors up to 4°C in LST estimations.