A New Spatially and Temporally Continuous Antarctic Ice-Sheet Surface Temperature Retrieval Method From Passive Microwave Swath Data

Abstract

Ice surface temperature (IST) plays a fundamental role in the Antarctic ice sheet/shelf study. However, the production of spatially and temporally continuous Antarctic IST products remains a challenge. We proposed an instantaneous IST retrieval framework that can generate the spatially and temporally continuous Antarctic IST using Advanced Microwave Scanning Radiometer 2 data. To generate a temporally continuous IST product, we developed an innovative scheme, which was based on the acquisition time difference between input and output data. We considered the impact of terrain and sensor observation state. The corresponding parameters were used as the auxiliary variables to improve the model accuracy. We trained and validated nine machine learning models using the generated sample set. The Light Gradient Boosting Machine (LightGBM) model presents the best performance, and the root-mean-square error (RMSE) of the LightGBM model is only half of that of the typical linear models. The RMSE of the LightGBM model decreased with the training sample set size and stabilized at 1.67 K. Further validation using multisource data showed that the IST retrieved using the LightGBM model has RMSEs of 1.39–2.32 K (relative to IST from Landsat-8) and 3.7–5.9 K (relative to IST from Baseline Surface Radiation Network data). Compared to the commonly used ERA5 IST data, the retrieved IST in this study has higher accuracy. We retrieved Antarctic IST from 2013 to 2020. Antarctic IST decreased continuously from 2013 to 2015. After 2015, Antarctic IST increased with large fluctuations.

Keywords

• Antarctic ice surface temperature (IST)
• light gradient boosting machine (LightGBM) model
• machine learning
• passive microwave (PM)