A new high-resolution elevation model of Greenland derived from TanDEM-X

In this paper we present for the first time the new digital elevation model (DEM) for Greenland produced by the TanDEM-X (TerraSAR add-on for digital elevation measurement) mission. The new, full coverage DEM of Greenland has a resolution of 0.4 arc seconds corresponding to 12 m. It is composed of more than 7.000 interferometric synthetic aperture radar (InSAR) DEM scenes. X- Band SAR penetrates the snow and ice pack by several meters depending on the structures within the snow, the acquisition parameters, and the dielectricity constant of the medium. Hence, the resulting SAR measurements do not represent the surface but the elevation of the mean phase center of the backscattered signal. Special adaptations on the nominal TanDEM-X DEM generation are conducted to maintain these characteristics and not to raise or even deform the DEM to surface reference data. For the block adjustment, only on the outer coastal regions ICESat (Ice, Cloud, and land Elevation Satellite) elevations as ground control points (GCPs) are used where mostly rock and surface scattering predominates. Comparisons with ICESat data and snow facies are performed. In the inner ice and snow pack, the final X-Band InSAR DEM of Greenland lies up to 10 m below the ICESat measurements. At the outer coastal regions it corresponds well with the GCPs. The resulting DEM is outstanding due to its resolution, accuracy and full coverage. It provides a high resolution dataset as basis for research on climate change in the arctic

Mapping blue-ice areas in Antarctica using ETM+ and MODIS data

AbstractBlue-ice areas (BIAs) and their geographical distribution in Antarctica were mapped using Landsat-7 ETM+ images with 15 m spatial resolution obtained during the 1999–2003 austral summers and covering the area north of 82.5° S, and a snow grain-size image of the MODIS-based Mosaic of Antarctica (MOA) dataset with 125 m grid spacing acquired during the 2003/04 austral summer from 82.5°S to the South Pole. A map of BIAs was created with algorithms of thresholds based on band ratio and reflectance for ETM+ data and thresholds based on snow grain size for the MOA dataset. The underlying principle is that blue ice can be separated from snow or rock by their spectral discrepancies and by different grain sizes of snow and ice. We estimate the total area of BIAs in Antarctica during the data acquisition period is 234 549 km2, or 1.67% of the area of the continent. Blue ice is scattered widely over the continent but is generally located in coastal or mountainous regions. The BIA dataset presented in this study is the first map covering the entire Antarctic continent sourced solely from ETM+ and MODIS data. This dataset can potentially benefit other studies in glaciology, meteorology, climatology and paleoclimate, meteorite collection and airstrip site selection.</jats:p

Snowmelt Detection on Alpine Glaciers using Synthetic Aperture Radar Time Series

Hindu Kush Himalayan (HKH) glaciers serve as some of the most sensitive indicators of changes in global climate. These glaciers shape the hydrologic dynamics of river systems supplying freshwater to over 2 billion people throughout Asia and regulate the geochemistry of sensitive aquatic alpine ecosystems. As snowmelt onsets sooner, lasts longer, and snowfields retreat due to increases in global temperature, the hydrologic dynamics of catchments draining HKH threaten to change the availability of surface freshwater resources for nearly one fifth of the global population, disturb sensitive aquatic habitat, and precipitate hazards associated with glacier wasting. Informed planning and decision-making around adaption to a changing climate requires operational monitoring of glacier melt dynamics to improve study of predicted disturbances to HKH hydrologic systems. This research presents a method for spatially resolved alpine glacier melt detection using synthetic aperture radar (SAR) time series. Building on research into melt detection from passive microwave scatterometers over large ice sheets, this study detects melt characteristics from Sentinel-1 SAR backscatter intensity time series over glacier surfaces using a classification threshold based on a decrease in backscatter intensity relative to average values across the frozen season. Statistical analysis of the radiometric response to dielectric loss on glaciated area within the study region (70,789 km2) shows that cross-polarized melt classification accounts for 24% more of glacier surface area than co-polarized observations. Illustrative comparison of melt classification results to optical imagery captured near the end of seasonal melt reveals that dual polarized melt measurements are concentrated within areas of apparent glacier accumulation yet cross-polarized melt detection occurs more homogeneously across glacier surfaces relative to co-polarized observations. The results of this study suggest that physical characteristics of the glacier surface may be radiometrically distinct across positive and negative zones of glacier mass balance. Improvements to radiometric terrain correction of SAR data in complex high mountain terrain would improve the accuracy of temporal thresholding algorithms for melt detection

Automatic Features Extraction From Time Series Of Passive Microwave Images For Snowmelt Detection Using Deep-Learning – A Bidirectional Long-Short Term Memory Autoencoder (Bi-Lstm-Ae) Approach.

The Antarctic surface snowmelt is prone to the polar climate and is common in its coastal regions. With about 90 percent of the planet\u27s glaciers, if all of the Antarctica glaciers melted, sea levels will rise about 58 meters around the planet. The development of an effective automated ice-sheet snowmelt monitoring system is therefore crucial. Microwave remote sensing instruments, on the one hand, are very sensitive to snowmelt and can see day and night through clouds, allowing us to distinguish melting from dry snow and to better understand when, where, and for how long melting has taken place. On the other hand, deep-learning (DL) algorithms, which can learn from linear and non-linear data in a hierarchical way robust representations and discriminative features, have recently become a hotspot in the field of machine learning and have been implemented with success in the geospatial and remote sensing field. This study demonstrates that deep learning, particularly long-short memory autoencoder architecture (LSTM-AE) is capable of fully exploiting archives of passive microwave time series data. In this thesis, An LSTM-AE algorithm was used to reduce and capture essential relationships between attributes stored as brightness temperature within pixel time series and k-means clustering is applied to cluster the leaned representations. The final output map highlights the melt extent in Antarctica

FABIAN: A daily product of fractional austral-summer blue ice over Antarctica during 2000-2021 based on MODIS imagery using Google Earth Engine

Antarctic blue ice areas are exposed due to erosion and sublimation of snow. At the same time, surface melt can form surface types that are spectrally similar to blue ice, especially at low elevations. These are termed melt-induced blue ice areas. Both types of blue ice are sensitive indicators of climate change. Satellite remote sensing is a powerful technique to retrieve the spatial extent of blue ice areas and their variation in time. Yet, existing satellite-derived blue ice area products are either mono-temporal for the entire Antarctic ice sheet, or multi-temporal for a limited area. Here, we present FABIAN, a product of blue ice fraction over Antarctica, derived from the moderate resolution imaging spectroradiometer (MODIS) archive covering the period 2000–2021. A spectral mixture analysis (SMA) in Google Earth Engine, based on a careful selection of endmember spectra, accurately reconstructs the reflectance observed by MODIS in blue ice areas. Based on a validation with contemporaneous Sentinel-2 images, FABIAN has a root mean square error in blue ice fraction of approximately 10% ∼ 20% in wind-induced blue ice areas, and 20% ∼ 30% in melt-induced blue ice areas across six selected test sites in the coastal East Antarctic ice sheet. FABIAN is challenged in regions with shallow melt streams and lakes, since their spectral profiles are similar to those from blue ice areas in MODIS bands. For further analyses and applications, FABIAN holds the potential for (1) deriving annual blue ice area maps, (2) distinguishing between wind-and melt-induced blue ice types, (3) evaluating and correcting (regional) climate models, and (4) analyzing temporal variations in blue ice abundance and exposure

Monitoring Snow Cover and Snowmelt Dynamics and Assessing their Influences on Inland Water Resources

Snow is one of the most vital cryospheric components owing to its wide coverage as well as its unique physical characteristics. It not only affects the balance of numerous natural systems but also influences various socio-economic activities of human beings. Notably, the importance of snowmelt water to global water resources is outstanding, as millions of populations rely on snowmelt water for daily consumption and agricultural use. Nevertheless, due to the unprecedented temperature rise resulting from the deterioration of climate change, global snow cover extent (SCE) has been shrinking significantly, which endangers the sustainability and availability of inland water resources. Therefore, in order to understand cryo-hydrosphere interactions under a warming climate, (1) monitoring SCE dynamics and snowmelt conditions, (2) tracking the dynamics of snowmelt-influenced waterbodies, and (3) assessing the causal effect of snowmelt conditions on inland water resources are indispensable. However, for each point, there exist many research questions that need to be answered. Consequently, in this thesis, five objectives are proposed accordingly. Objective 1: Reviewing the characteristics of SAR and its interactions with snow, and exploring the trends, difficulties, and opportunities of existing SAR-based SCE mapping studies; Objective 2: Proposing a novel total and wet SCE mapping strategy based on freely accessible SAR imagery with all land cover classes applicability and global transferability; Objective 3: Enhancing total SCE mapping accuracy by fusing SAR- and multi-spectral sensor-based information, and providing total SCE mapping reliability map information; Objective 4: Proposing a cloud-free and illumination-independent inland waterbody dynamics tracking strategy using freely accessible datasets and services; Objective 5: Assessing the influence of snowmelt conditions on inland water resources