Assessment of the Urban Three-dimensional Structural Influence on the Satellite Thermal Infrared Measurement

Land surface temperature (LST) is one of the key parameters to study the global warming, evaporation, urban heat island, among many others. After decades of development, numerous LST retrieval algorithms have been developed, by using which, the LST could be retrieved at high accuracy of about 1.0 K for most flat natural surfaces. But for rugged terrain such as urban areas, the satellite observed radiance, especially for high spatial resolution thermal infrared images, is influenced by the surface three-dimensional (3-D) structure, which deteriorates the LST retrieval accuracy. Therefore, only the 3-D structural influence is well corrected, can the urban LST retrieval accuracy be improved. However, only a few forward radiative transfer models are available to explore the urban 3-D structural influence nowadays. In this study, a modification is attempted to take the urban 3-D structural influence into the traditional thermal infrared radiation transfer model, and then the resulted effect on the satellite observed brightness temperature is evaluated. To be more specific, viewing factors between target pixel and surrounded buildings are introduced to calculate the energy exchange inside the urban 3-D structure, based on which, the environmental radiance arriving at the target pixel can then be determined. As to the required atmospheric parameters, they were obtained from the MODerate resolution atmospheric TRANsmission (MODTRAN) model. After specifying different atmospheric profiles and simulated scenario parameters, the urban 3-D structural influence on the satellite measurement under different circumstances can be quantitively assessed. Results showed that the satellite observed target radiance is greatly affected by the radiation from 3-D surroundings. Besides, as the land surface emissivity (LSE) of the target decreases and the LST of the adjacent buildings increases, more environmental radiance is reflected into the sensor from the target, indicating that the 3-D structure contributes more to the satellite measurement. For example, given a normal urban landscape with the target LSE of 0.97 and the building LST of 290.0 K, the influence of the 3-D structure on the satellite observed brightness temperature at 10 μm was about 0.42 K, compared with the observation under flat surface assumption. When the target LSE decreases to 0.91 and the building LST increases to 310.0 K, the 3-D structural influence can be up to 2.06 K, quite large. Overall, the urban 3-D structure significantly influences the satellite measurement. Numerical results show that using the method proposed in this study, the 3-D structural influence can be well calibrated out. Field campaigns are under planned to experimentally validate the proposed method.