Application of CBERS-04 IRS data to land surface temperature inversion: A case study based on Minqin arid area

Abstract Inversion based on remote sensing data with high spatial resolution is an important way to obtain high-precision surface temperature. The CBERS-04 infrared sensor (IRS) can provide thermal infrared data with high spatial resolution. Owing to the lack of band response curves, research on land surface temperature (LST) inversion methods based on CBERS-04 IRS data has been very limited. Considering the consistency of the thermal infrared bands of CBERS-04 and Landsat 7 in band range and central wavelength, the key parameters of inversion, such as atmospheric upward radiance brightness, atmospheric downward radiance brightness, and atmospheric transmittance, can be obtained based on the spectral response curve of Landsat 7 thermal infrared band. Three methods such as the radiative transfer equation method (RTE), the mono-window algorithm (MWA), and the single channel algorithm (SC) were used to retrieve land surface temperature in the Minqin arid area in this research. Inversion results of the MWA algorithm based on TIRS 10/Landsat 8 and the measured land surface temperature in the same period of 2020 were utilized to verify the reliability of the inversion method. The results showed that the method proposed in the research can be used to retrieve LST from thermal infrared data of CBERS-04. The linear fitting correlation between the inversion results of the MWA method based on CBERS-04 IRS data and the inversion results of the MWA algorithm based on TIRS 10/Landsat8 was the best, with R 2 of 0.9541. This approach provides a practical solution for LST inversion using CBERS-04 IRS data.