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This study analyzes the dynamics of surface melting in Antarctica, which are crucial for understanding glacier and ice sheet behavior and monitoring polar climate change. Specifically, we focus on the Nivlisen ice shelf in East Antarctica, examining melt ponds, supra glacial lakes (SGLs), seasonal surface melt extent, and surface ice flow velocity. Spatial and temporal analysis is based on Landsat and Sentinel-1 data from the austral summers of 2000 to 2023. Between 2000 and 2014, melt ponds and SGLs on the ice shelf covered roughly 1 km2. However, from 2015 to 2023, surface melting increased consistently, leading to more extensive melt ponds and SGLs. Significant SGL depths were observed in 2016, 2017, 2019, and 2020, with 2008, 2016, and 2020 showing the highest volumes and progressive SGL area growth. We also examined the relationship between seasonal surface melt extent and ice flow velocity. Validation efforts involved ground truth data from a melt pond in central Dronning Maud Land (cDML) during the 2022–2023 austral summer, along with model-based results. The observed increase in melt pond depth and volume may significantly impact ice shelf stability, potentially accelerating ice flow and ice shelf destabilization. Continuous monitoring is essential for accurately assessing climate change's ongoing impact on Antarctic ice shelves. [ABSTRACT FROM AUTHOR]

The present study focuses on using remote sensing techniques to estimate the Karakoram glacier velocity that emulates glacier's reaction to climate warming. Proposed study is essential and critical due to the vast spatial and temporal variability of the Karakoram Glaciers, fieldwork difficulties, and the lack of in situ data in the Karakoram. It focuses on assessing the robustness in extracting the velocity products of the Rimo Glacier, Karakoram using DInSAR, Intensity tracking, and Normalized Cross-Correlation. The results indicate that the average velocity of the Rimo glacier estimated from DInSAR and correlation techniques are around 22.4cm/day and 5.2 cm/day respectively which is similar to earlier studies. Intensity tracking based average velocity is found to be around 18cm/day. Results obtained are also compared with field-based measurements for corroboration. Continuous monitoring of the velocity of Karakoram glaciers is necessary to understand the complex and evolving trends of surging in the future. [ABSTRACT FROM AUTHOR]

Glacier velocity, the glacier's health indicator, is an emerging research interest in global warming among the scientific community over the current decade. As per scientific reports, due to their stable and advanced nature, the glaciers of Karakoram are exclusive. The present study focuses on understanding the temporal velocity variability of the Siachen Glacier, Shyok Basin, Karakoram using SAR datasets, in the perspective of the difficulties in estimating the glacier velocity over the inaccessible Karakoram Mountain. For the hydrological year 2017-18, 30 sentinel-1 derived interferograms using the DInSAR technique were analyzed to obtain Line Of Sight velocity. The transient velocity of the glacier reflects separate patterns within the glacier for different seasons due to the thermodynamic and orographic system over the glacier surface. In azimuth and range direction, the projected annual LOS velocity to a two-dimensional surface velocity is estimated to be around 103.8 ± 3.1 m/year. [ABSTRACT FROM AUTHOR]

Monitoring of Earth’s surface processes is efficiently performed by Differential SAR interferometry (DInSAR) techniques with the availability of open access Copernicus Sentinel-1A and 1B SAR data products having 6 days temporal resolution. With over 750 landslides triggered in Kodagu district, Karnataka, India, interferometric wide swath (IW) mode Sentinel-1 datasets are used to identify landslide locations. Interferograms from DInSAR obtained using Sentinel-1 VV polarization data are corrected using zenith total delay (ZTD) maps obtained from GACOS, which successfully assisted to identify major landslides in the study area. Sentinel-1 DInSAR analysis has limitations over dense vegetative regions and terrain conditions (particularly in mountainous regions) due to spatial and temporal decorrelation. With the aid of InSAR time series analysis, integration of deformation recognized and land use/land cover pattern helps experts in providing near real-time mitigation measures during a disaster event. Research highlights: Due to heavy rainfall, Kodagu District recorded 104 landslides between 15th and 17th August 2018. Advanced InSAR approach is adopted to identify the location of landslides in Kodagu district, Karnataka to explore the advantages of utilising SAR Datasets during an event. GACOS corrected Sentinel-1 derived InSAR results assists in identification of larger landslides. Limitations are experienced due to the dense vegetation and loss of coherence between the InSAR pairs. Research suggested to use L and S band datasets along with corner reflectors installed in the mountainous regions to obtain results at higher accuracy. [ABSTRACT FROM AUTHOR]