The apparent effect of orbital drift on time series of MODIS MOD10A1 albedo on the Greenland ice sheet

The NASA MODIS MOD10A1 snow albedo product has enabled numerous glaciological applications. The temporal consistency of MODIS albedo is critical to obtaining reliable results from this 22-year time series. The orbit of Terra began to drift toward earlier acquisition times after the final inclination adjustment maneuver to maintain its nominal orbit by NASA on 27 February 2020, which may introduce biases that compromise the accuracy of quantitative time series analysis as the drift continues. Here, we evaluate the impact of Terra's orbital drift by comparing the differences between the Terra MODIS albedo and albedo products derived from Aqua MODIS, harmonized Landsat and Sentinel 2, Sentinel 3, and PROMICE (Programme for Monitoring of the Greenland Ice Sheet) ground measurements over the Greenland ice sheet. Our results suggest that the influence of orbital drift on albedo is small (+0.01 in 2020), but potentially biased for time series analysis. Our analysis also finds that the drift effect that causes earlier image acquisition time may lead to more apparently cloudy pixels and thus effectively reduce the Terra MODIS temporal resolution over Greenland

Understanding spatial and temporal variability in Supraglacial Lakes on an Antarctic Ice Shelf:A 31-year study of George VI

Floating ice shelves cover ~1.5million km2 of Antarctica’s area, and are important as they buttress land ice, which limits sea level rise. In recent years, several such Antarctic ice shelves have collapsed or retreated. Supraglacial lakes are linked to warm periods and influence the stability of ice shelves through hydrofracture. Climate change induced temperature increases may increase lake presence, thus decreasing stability. Monitoring ‘at risk’ ice shelves is therefore important to understand their likelihood of fracture. George VI is located on the western Antarctic Peninsula, covering ~23200 km2, and has had high lake densities in its northern sector. This study analyses 31 years of imagery to understand the long-term and seasonal dynamics of lake evolution. This is the first study to characterise supraglacial lake variability in the long- and short-term on George VI, thus producing a comprehensive picture of lake evolution. Here I use a semi-automated technique to map lakes in >160 satellite images from Sentinel 2 and Landsat 4-8. Additional influences on stability and lake presence are gauged using climatic and glaciological data. Analysis of recent imagery shows that the apparent high lake density in summer 2020 is not unique on George VI, with 1989-90 having similar densities persisting over ≥3 years. Decadal average temperature and annual summer snowfall are found to be primary controls on lake density at their respective timescales, the former being influenced by changes in the southern annular mode. Climatic controls on an intra-annual scale are complex, with melt, snowfall and temperature influencing lake density. Although lakes are widespread in several years, little evidence has been found of the unstable drainage which preceded neighbouring ice shelf collapse. This study demonstrates the value of frequent monitoring by current generations of optical satellites in lake analysis; and provides the first long-term catalogue of lakes on George VI

pySICE: A python package for the retrieval of snow surface properties from Sentinel 3 OLCI reflectances

<p>B. Vandecrux (1), A. Kokhanovsky (2), G. Picard (3) and J. Box (1)</p> <p>(1) Geological Survey of Denmark and Greenland (GEUS) Øster Voldgade 10, 1350 Copenhagen, Denmark (2) Max Planck Institute for Chemistry, Mainz 55128, Federal Republic of Germany (3) Institut des Géosciences de l'Environnement, Université de Grenoble, France</p> <p>This algorithm retrieves the snow surface properties using Sentinel-3's OLCI 865nm and 1020 nm bands and the theory developed by A. Kokhanovsky in:</p> <ul> <li><a href="https://tc.copernicus.org/articles/12/2371/2018/">Kokhanovsky et al. (2018) On the reflectance spectroscopy of snow</a></li> <li><a href="http://dx.doi.org/10.3390/rs11192280">Kokhanovsky et al. (2019) Retrieval of Snow Properties from the Sentinel-3 Ocean and Land Colour Instrument</a></li> <li><a href="http://dx.doi.org/10.3390/rs12020234">Kokhanovsky et al. (2020) The Determination of Snow Albedo from Satellite Measurements Using Fast Atmospheric Correction Technique</a></li> </ul> <p>It includes the ozone retrieval from the OLCI 620 nm band developed in: <a href="https://doi.org/10.1016/j.jqsrt.2020.107045">Kokhanovsky et al. (2020) Retrieval of the total ozone over Antarctica using Sentinel-3 ocean and land colour instrument</a></p> <p>And the retrieval of impurity load and characteristics from OLCI 400nm and 490nm bands developed in: <a href="https://www.frontiersin.org/article/10.3389/fenvs.2021.644551">Kokhanovsky, A. A., et al., 2021a: Retrieval of dust properties from spectral snow reflectance measurements</a></p> <p>It is also the version that will be converted into a <a href="https://step.esa.int/main/download/snap-download/">SNAP</a> plug-in.</p&gt