Although extensive research of Moderate Resolution Imaging Spectroradiometer (MODIS) albedo data is available on the Greenland Ice Sheet, there is a lack of studies evaluating MODIS albedo products over Antarctica. In this paper, MOD10A1, MYD10A1, and MCD43 (C6) daily albedo products were compared with the in situ albedo data on Livingston Island, South Shetland Islands (SSI), Antarctica, from 2006 to 2015, for both all-sky and clear-sky conditions, and for the entire study period and only the southern summer months. This is the first evaluation in which MYD10A1 and MCD43 are also included, which can be used to improve the accuracy of the snow BRDF/albedo modeling. The best correlation was obtained with MOD10A1 in clear-sky conditions (r = 0.7 and RMSE = 0.042). With MCD43, only data from the backup algorithm could be used, so the correlations obtained were lower (r = 0.6). However, it was found that there was no significant difference between the values obtained for all-sky and for clear-sky data. In addition, the MODIS products were found to describe the in situ data trend, with increasing albedo values in the range between 0.04 decade⁻¹ and 0.16 decade⁻¹. We conclude that MODIS daily albedo products can be applied to study the albedo in the study area.

The aim of this work is to investigate whether snow albedo seasonality and trend under all sky conditions at Johnsons Glacier (Livingston Island, Antarctica) can be tracked using the Moderate Resolution Imaging Spectroradiometer (MODIS) snow albedo daily product MOD10A1. The time span is from December 2006 to February 2015. As the MOD10A1 snow albedo product has never been used in Antarctica before, we also assess the performance for the MOD10A1 cloud mask. The motivation for this work is the need for a description of snow albedo under all sky conditions (including overcast days) using satellite data with mid-spatial resolution. In-situ albedo was filtered with a 5-day windowed moving average, while the MOD10A1 data were filtered using a maximum filter. Both in-situ and MOD10A1 data follow an exponential decay during the melting season, with a maximum decay of 0.049/0.094 day⁻¹ (in-situ/MOD10A1) for the 2006–2007 season and a minimum of 0.016/0.016 day⁻¹ for the 2009–2010 season. The duration of the decay varies from 85 days (2007–2008) to 167 days (2013–2014). Regarding the albedo trend, both data sets exhibit a slight increase of albedo, which may be explained by an increase of snowfall along with a decrease of snowmelt in the study area. Annual albedo increases of 0.2% and 0.7% are obtained for in-situ and MOD10A1 data, respectively, which amount to respective increases of 2% and 6% in the period 2006–2015. We conclude that MOD10A1 can be used to characterize snow albedo seasonality and trend on Livingston Island when filtered with a maximum filter.

In this article, we present empirical models for estimating daily mean air temperature (T_a) in the Hurd Peninsula of Livingston Island (Antarctica) using Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) data and spatiotemporal variables. The models were obtained and validated using the daily mean T_a from three Spanish in situ meteorological stations (AEMET stations), Juan Carlos I (JCI), Johnsons Glacier (JG), and Hurd Glacier (HG), and three stations in our team’s monitoring sites, Incinerador (INC), Reina Sofía (SOF), and Collado Ramos (CR), as well as daytime and nighttime Terra-MODIS LST and Aqua-MODIS LST data between 2000 and 2016. Two types of multiple linear regression (MLR) models were obtained: models for each individual station (for JCI, INC, SOF, and CR—not for JG and HG due to a lack of data) and global models using all stations. In the study period, the JCI and INC stations were relocated, so we analyzed the data from both locations separately (JCI1 and JCI2; INC1 and INC2). In general, the best individual T_a models were obtained using daytime Terra LST data, the best results for CR being followed by JCI2, SOF, and INC2 (R² = 0.5–0.7 and RSE = 2 °C). Model cross validation (CV) yielded results similar to those of the models (for the daytime Terra LST data: R²_CV = 0.4–0.6, RMSE_CV = 2.5–2.7 °C, and bias = −0.1 to 0.1 °C). The best global T_a model was also obtained using daytime Terra LST data (R² = 0.6 and RSE = 2 °C; in its validation: R²_CV = 0.5, RMSE_CV = 3, and bias = −0.03), along with the significant (p < 0.05) variables: linear time (t) and two time harmonics (sine-cosine), distance to the coast (d), slope (s), curvature (c), and hour of LST observation (H). T_a and LST data were carefully corrected and filtered, respectively, prior to its analysis and comparison. The analysis of the T_a time series revealed different cooling/warming trends in the locations, indicating a complex climatic variability at a spatial scale in the Hurd Peninsula. The variation of T_a in each station was obtained by the Locally Weighted Regression (LOESS) method. LST data that was not “good quality” usually underestimated T_a and were filtered, which drastically reduced the LST data (<5% of the studied days). Despite the shortage of “good” MODIS LST data in these cold environments, all months were represented in the final dataset, demonstrating that the MODIS LST data, through the models obtained in this article, are useful for estimating long-term trends in T_a and generating mean T_a maps at a global level (1 km² spatial resolution) in the Hurd Peninsula of Livingston Island.

Temperature analysis is of special interest in polar areas because temperature is an essential variable in the energy exchange between the Earth’s surface and atmosphere. Although land surface temperature (LST) obtained using satellites and air temperature (T_a) have different physical meanings and are measured with different techniques, LST has often been successfully employed to estimate T_a. For this reason, in this work, we estimated T_a from LST MODIS collection 6 (C6) and used other predictor variables. Daily mean T_a was calculated from Spanish State Meteorological Agency (AEMET) stations data on the Livingston and Deception Islands, and from the PERMASNOW project stations on Livingston Island; both islands being part of the South Shetland Islands (SSI) archipelago. In relation to our previous work carried out in the study area with collection 5 (C5) data, we obtained higher R² values (R²_CV = 0.8, in the unique model with Terra daytime data) and lower errors (RMSE_CV = 2.2 °C, MAE_CV = 1.6 °C). We corroborated significant improvements in MODIS C6 LST data. We analyzed emissivity as a possible factor of discrepancies between C5 and C6, but we did not find conclusive results, therefore we could not affirm that emissivity is the factor that causes differences between one collection and another. The results obtained with the applied filters indicated that MODIS data can be used to study T_a in the area, as these filters contribute to the reduction of uncertainties in the modeling of T_a from satellites.

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. 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 estimated 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. We used T_a data from the Spanish Antarctic stations and from the PERMASNOW project stations. MOD11A1 and MYD11A1 (Collection 6) Moderate Resolution Imaging Spectroradiometer (MODIS) LST products were downloaded from the Google Earth Engine platform 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 for improving the correlation and reducing 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.