LAUSR

Improving the calibration accuracy of infrared hyperspectral interferometers is a prerequisite for their quantitative application. Updating satellite in-orbit calibration parameters is an important way to keep calibration accuracy. However, how to separate the deviation of one or more calibration parameters from the coupling error of observation data is a key issue that needs to be solved. Therefore, we propose a variational-based calibration parameter optimization algorithm (VarCalPOA), which uses only observation data and reference data to optimize the key calibration parameters based on variational assimilation theory to obtain the optimized values of the calibration parameters, thereby improving the calibration accuracy. Based on the observation and calibration simulation model, we choose $T_{\mathrm {ict}}$ , $e_{\mathrm {ict}}$ , and $a_{2}$ as the key calibration parameters. Using the VarCalPOA method, the simulation results show that the optimized calibration parameters have a certain positive effect on the improvement of calibration accuracy. This study proves that the VarCalPOA method is potentially an effective method for monitoring the in-orbit state of infrared hyperspectral interferometers.