Novel method to remotely measure air temperature distribution for indoor environments with heat sources using thermal infrared spectroradiometer

Abstract

Air temperature is an important physical indicator of a built space. A novel method for remotely measuring the air temperature distribution using a thermal infrared spectroradiometer is evaluated, and an experiment using the maximum a posteriori method, which uses a priori information, is conducted with a heat source. The a priori information is the probabilistic information that the observer knows about the air temperature prior to the observation. Assuming an indoor scale (10 m), the air temperature distribution is estimated (i.e., retrieved) by discretizing the space into four layers. Accordingly, our method reduces the error in the air temperature estimation compared to the a priori information. When the layer of the heat source is known, the air temperature estimation is sensitive to the air temperature of the heated layer, even when the heat source is away from the sensor (2nd to 4th layers). The root-mean-square error (RMSE) of the heated layer decreased from 10 K (a priori) to 4.8 K. Although the RMSE of the temperature distribution was approximately the same regardless of the number of spectral channels used in the retrieval, the RMSE of the heated layer was smaller when ten spectral channels were used. In the case of a target space and spectrometer, as in this study, ten spectral channels were sufficient. These findings indicate the possibility of remotely measuring the air temperature distribution in a space with a local heat source (e.g., data centers), which is a relatively difficult case for remote sensing methods.