Absolute radiometric calibration evaluation of the thermal infrared spectrometer onboard SDGSAT-1

A thermal infrared spectrometer is an important sensor onboard the SDGSAT-1 dedicated to serving the 2030 Agenda for Sustainable Development of the United Nations. Field campaigns were conducted from January to December 2022 to evaluate its radiometric calibrations. A radiance-based calibration method was used to derive the spectral radiance of the lake surface around the satellite overpass using a boat-deployed TIR spectrometer or radiometer. The radiometric calibration conditions were quantified by examining the temperature bias between the satellite-based brightness temperature and the predicted at-sensor brightness temperature. Our results indicated that the average brightness temperature bias reached 0.772 K, -1.032 K, and -0.300 K, with RMSEs of 1.00, 1.10, and 0.43 K in bands 1, 2 and 3, respectively. Eighty percent of the field experiments achieved a high calibration accuracy with temperature biases of less than 1 K, and the calibration accuracy of band 3 was better than that of other bands. Atmospheric conditions and surface characterization play important roles in determining the calibration accuracy through surface observations and atmospheric transfer simulations. With the increasing need for SDGSAT-1 products, periodic field experiments are needed to monitor and improve the operational radiometric calibration of satellite instruments to create high-resolution and reasonable scientific datasets.

Automated summary

A thermal infrared spectrometer is a crucial sensor on SDGSAT-1 for sustainable development. Field campaigns were conducted to evaluate radiometric calibrations by deriving spectral radiance of lake surface. Results showed varying calibration accuracy among different bands, with band 3 having the best accuracy. Atmospheric conditions and surface characterization affect the calibration accuracy. Regular field experiments are necessary to monitor and improve calibration, creating high-resolution and reasonable datasets.