Deep Mixture Model-Based Land Surface Temperature Retrieval for Hyperspectral Thermal IASI Sensor

Abstract

A deep mixture model was developed to retrieve land surface temperatures (LSTs) from infrared atmospheric sounding interferometer (IASI) observations. The IASI brightness temperature (Tb) data and the Advanced Very High Resolution Radiometer onboard MetOp (AVHRR/MetOp) LST data were randomly divided into training and test datasets, and a deep mixture model was constructed to simulate radiation transmission in order to invert the LST. The constructed model could evaluate dataset characteristics that included global features, local features, and time-domain predictions, covering most of the features of the satellite dataset. For the test datasets, the root mean square error (RMSE) indicated that the LST in Algeria and South Africa could be retrieved with an error of less than 2 K and 2.5 K, respectively. Compared with the AVHRR/MetOp LST product in March and December 2019 for Algeria and South Africa, the LST could be retrieved with the maximum RMSE of 2.5 K. The LST retrievals at nighttime had an RMSE of less than 2.0 K, which was superior to those retrieved during daytime for Algeria. This deep mixture model can be applied to time-series temperature prediction.

Keywords

• Hyperspectral thermal infrared
• land surface temperature retrieval
• IASI
• deep learning