Sea ice leads are produced from deformational forces, which break apart the ice surface and expose open water areas or leads. Leads are the primary regulators of heat in Arctic sea ice during the polar night. Partially-frozen and re-frozen leads produce smaller heat exchanges than open leads due to the absence of a warm water surface. Thus detecting leads is important because they may be used as an indirect way of estimating air-sea heat fluxes. To quantify winter-time leads, we utilize Sentinel-1 C-Band Synthetic Aperture Radar (SAR) data to examine sea ice images through heavy cloud cover. We employ a support vector machine learning technique in a cloud computation environment (Google Earth Engine) to detect and quantify lead areas. With the use of dual-polarization data, we improve the separation of leads from other elongated features (e.g., ridges) in the Sentinel-1 dataset by adding altimetry information from ICESat-2. In addition to typical texture analysis to assess surface roughness, the ICESat-2 ATL-10 data allows us to train the algorithm by discretizing leads and ridges by their freeboard values. Performing this method in a cloud environment allows processing of a large volume of satellite data and converting it into a time series of leads properties. Overall, our method improves lead detection with dual-polarization SAR data while simultaneously providing a big data solution for SAR image processing. The interannual variability of leads and newly formed ice fractions were found for the winters of 2017-2020. Finally, we compare the results from previous studies to validate our cloud-derived sea ice lead detection maps.

The COVID-19 virus was first detected in Wuhan, China and its genesis was traced to infected bats, passed through an intermediate host to humans. COVID-19 or “Coronavirus” reached approximately 4 million confirmed cases in the United States by July 2020. The spread of this virus has affected both social and economic affairs on a global scale with more than 15 million confirmed cases worldwide in July 2020. The pandemic has proven a global public health and socio-economic crisis. In addition, the shelter-in-place orders provide an unprecedented opportunity for examining the resulting reduction in greenhouse gases and aerosols on the atmosphere. The Sentinel-5P satellite has shown distinct changes in atmospheric aerosols over the COVID-19 epicenter in Wuhan. After Texas, U.S. established similar shelter-in-place orders to prevent the spread of the virus, the state’s industrial activity experienced an economic slowdown. This paper quantifies the changes in atmospheric aerosols associated with the COVID-19 slowdown over major cities in Texas and compares it to similar changes in Wuhan, China using satellite imagery.

Ice shelves play an important role in Antarctic mass balance by buttressing grounded ice. Supraglacial lakes can threaten the stability of ice shelves through the mechanisms of hydrofracture and flexure. Supraglacial lakes also lower the albedo of the surface which can increase surface melting. Thus, it is important to understand and accurately measure variations in lake characteristics, particularly lake depth. Many studies have used optical satellite imagery such as Landsat and Sentinel-2 to measure lake depth. Since 2018, studies have also used ICESat-2 data to estimate lake depth focusing on Amery Ice Shelf. In this study, we use ICESat-2 over the Amery and Nansen Ice Shelves in East Antarctica and develop a new approach to estimate lake depth. We examine the ATL03 product, which is the sole source of the photon data used by higher level products of ICESat-2, and bin them into histograms. ATL06, a land ice product, gives the height information for the surface, hence it is used to detect the surface of lakes along with ATL03. The peaks in histogram are observed in areas with surface photons and histogram values smaller than the peak are identified to be lake bottom. We find that histogram values smaller than the peak are only observed in areas where lakes are present, while in other locations where there are no lakes, histograms show a single peak depicting surface heights. We examined the lakes on Amery and Nansen Ice Shelves and found the depth of these lakes to range between 1-5 m. These results are compared with previous studies over Amery Ice Shelf using ICESat-2.

Geophysical observations collected by The University of Texas at San Antonio’s (UTSA) SeaExplorer, an autonomous, buoyancy-driven, underwater glider equipped with an Acoustic Doppler Current Profiler (ADCP) sensor are described. This dataset was collected in the northwestern Mediterranean Sea off the coast of France in April 2018 during the glider production phase by manufacturer Alseamar to ensure proper functioning of all sensors and equipment. The complete, raw dataset is being used by UTSA to aid in the development of in-house processing code to compute water-column current velocity profiles. The SeaExplorer glider, SEA039, is the first ocean glider acquired by UTSA’s Department of Geological Sciences as it expands departmental research into ocean sciences by incorporating new observational technologies and data science. The SeaExplorer glider collects high-resolution vertical profiles of physical and biochemical observations including ADCP current velocities in the water column from the surface to 700 meters depth. The data is processed and visualized primarily using MathWorks® MATLAB and described in conjunction with other regionally-relevant environmental datasets.