It is known that changes in temperature could cause changes in the Antarctic Ice Sheet, which would have an immediate and long-term impact on the global mean sea level [1]. For this reason, the monitoring of air temperature (T_a) is of great interest to the scientific community. On the other hand, Antarctica constitutes an area of difficult access, which makes it difficult to obtain in-situ data. Because of this, land surface temperature (LST) remote sensing data have become an important alternative for estimating T_a. In this work we estimate T_a from daytime and nighttime LST data at maritime Antarctic sites in the South Shetland Archipelago using empirical models, based on the addition of spatiotemporal variables [2]. We have used T_a data from the Spanish Antarctic stations and from the PERMASNOW project stations [3]. MOD11A1 and MYD11A1 (Collection 6) MODIS LST products were downloaded from the Google Earth Engine platform [4] and only the highest quality data were selected. Outliers associated with clouds were removed with filters. Two different multilinear regression models were tested: models for each individual station and global models based on the data from all the stations. The simple regression analysis LST against T_a showed that a better fit is always achieved with daytime LST data (R² average = 0.73) than with nighttime LST data (R² average = 0.56). The performance of the models was improved with the addition of spatiotemporal variables as predictive variables, with which we obtained an average R² = 0.75 for daytime data and an average R² = 0.60 for nighttime data. The global models allowed to improve the correlation and reduce the errors with respect to the models obtained using individual stations. Global models provide a precise description of the behavior of the temperature in maritime Antarctica, where it is not possible to install and maintain a dense network of weather stations.

References:

1. Medley, B.; Thomas, E.R. Increased snowfall over the Antarctic Ice Sheet mitigated twentieth-century sea-level rise. Nat. Clim. Chang. 2019, 9, 34–39, doi:10.1038/s41558-018-0356-x.
2. Recondo, C.; Corbea-Pérez, A.; Peón, J.; Pendás, E.; Ramos, M.; Calleja, J.F.; de Pablo, M.Á.; Fernández, S.; Corrales, J.A. Empirical Models for Estimating Air Temperature Using MODIS Land Surface Temperature ( and Spatiotemporal. Remote Sens. 2022, 14, 3206, doi:10.3390/rs14133206.
3. De Pablo, M.A.; Jiménez, J.J.; Ramos, M.; Prieto, M.; Molina, A.; Vieira, G.; Hidalgo, M.A.; Fernández, S.; Recondo, C.; Calleja, J.F.; et al. Frozen Ground and Snow Cover Monitoring in Livingston and Deception Islands, Antarctica: Preliminary Results of the - PERMASNOW Project. Cuad. Investig. Geográfica 2020, 46, 187–222, doi:http://doi.org/10.18172/cig.4381.
4. Gorelick, N.; Hancher, M.; Dixon, M.; Ilyushchenko, S.; Thau, D.; Moore, R. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 2017, 202, 18–27, doi:10.1016/j.rse.2017.06.031.