Session CR5.2

CR5.2 | Remote Sensing of Cold Regions

Remote Sensing of Cold Regions

Convener: Helena Bergstedt | Co-conveners: Manu Tom, Rebecca Scholten, Claude Duguay, Katja Kuhwald, Xiao Yang, Laura Carrea

Orals

| Wed, 26 Apr, 08:30–10:15 (CEST)

Room 1.14

Posters on site

| Attendance Tue, 25 Apr, 14:00–15:45 (CEST)

Hall X5

Posters virtual

| Attendance Tue, 25 Apr, 14:00–15:45 (CEST)

vHall CR/OS

This session is merged from 'Remote Sensing of Permafrost Landscapes' and 'Cold Climate Lake Remote Sensing'

Recent studies show widespread warming of permafrost while simultaneously reports indicate that the Arctic has warmed nearly four times faster than the global average. Increasing temperatures initiate a wide range of landscape and environmental changes, including vegetation changes, changing hydrological and fire regimes as well as abrupt and gradual permafrost thaw features.
This session is intended as a forum for current research on remote sensing of permafrost-dominated landscapes. It addresses (1) recent and upcoming advances of remote sensing of permafrost-related phenomena and permafrost dominated landscapes; (2) the impact of permafrost changes on the natural and human environment; (3) advances and new developments in approaches and analysis techniques. It will bring together investigations of high-latitude and mountain permafrost regions.

We seek contributions that reflect diverse scientific fields, approaches, geographic locations, and data sources (such as satellite, airborne, UAV remote sensing). We particularly encourage contributions that (a) present novel approaches for analysis; (b) outline new strategies to improve process understanding; (c) address different spatial and temporal assessment scales; (d) integrate remote sensing data into earth system models; (e) discuss multi-platform data merging or integration of ground validation data, as well as cloud computing and processing of large data sets. We also encourage contributions focusing on historic satellite data and upcoming satellite missions.

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Lakes in cold regions of the Earth are sensitive to climate change and global warming. Declining lake ice duration, shifting ice timing, and warming of lake water, as shown by recent studies, can alter the hydrological, ecological, and climatological functions of these water bodies, having far-reaching influence beyond their regional context. The scientific study of these cold region lakes and their related observables is of great importance in various fields such as climatology, hydrology, geomorphology, and ecology. Remote sensing together with state-of-the-art data analysis techniques is a powerful tool for scientific observation and analysis of these cold region lakes.

In this session, we invite abstracts that focus on remote sensing of cold climate lake observables such as ice cover, ice thickness, water extent, water level, surface water temperature, and water colour (e.g. turbidity, chlorophyll-a and coloured dissolved organic matter). We encourage studies using either single or multi-source remote sensing platforms, with input data sources including (but not limited to) ground-based webcams, UAVs, and satellite-based optical, thermal and microwave sensors. Both data-driven (e.g., machine learning/deep learning) and physics-inspired approaches will be considered. We especially encourage contributions that aim at large-scale (both spatial and temporal) analysis and/or multi-sensor data fusion. The session offers the opportunity to present results from ongoing research projects.

Orals: Wed, 26 Apr | Room 1.14

Chairpersons: Helena Bergstedt, Manu Tom, Claude Duguay

08:30–08:35

5-minute convener introduction

08:35–08:55

EGU23-1675

solicited

Virtual presentation

Mapping Retrogressive Thaw Slumps Using Satellite Data With Deep Learning

Yili Yang, Brendan M. Rogers, Greg Fiske, Jennifer Watts, Stefano Potter, Tiffany Windholz, Andrew Mullen, Ingmar Nitze, and Sue Natali

Retrogressive thaw slumps (RTS) are thermokarst features in ice-rich hillslope permafrost terrain and can cause dynamic changes to the landscape. Their occurrence in the Arctic has become increasingly frequent. RTS can significantly impact permafrost stability and generate substantial carbon emissions. Understanding the spatial distribution of RTS is critical to understanding and modelling global warming factors from permafrost thaw. Mapping RTS using conventional Earth observation approaches is challenging due to the highly dynamic nature and often small scale of RTS in the Arctic. In this study, we trained deep neural network models to map RTS across several landscapes in Siberia and Canada. Convolutional neural networks were trained with 965 RTS features, where 509 were from the Yamal and Gydan peninsulas in Siberia, and 456 from six other pan-Arctic regions including Canada and Northeastern Siberia. We used 4-m Maxar commercial imagery as the base map, 10-m NDVI derived from Sentinel-2 as the vegetation feature and 2-m ArcticDEM as the elevation feature. The best-performing model reached a validation Intersection over Union (IoU) score of 0.74 and a test IoU score of 0.71. Compared to past efforts to map RTS features, this represents one of the best-performing models and generalises well for mapping RTS in different permafrost regions, representing a critical step towards pan-Arctic deployment. Our experiments shed light on the impact of within-class and between-class variances of RTS in different regions on the model performance and provided critical implications for our follow-up study. We propose this method as an effective, accurate and computationally undemanding approach for RTS mapping.

How to cite: Yang, Y., M. Rogers, B., Fiske, G., Watts, J., Potter, S., Windholz, T., Mullen, A., Nitze, I., and Natali, S.: Mapping Retrogressive Thaw Slumps Using Satellite Data With Deep Learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1675, https://doi.org/10.5194/egusphere-egu23-1675, 2023.

08:55–09:05

EGU23-9035

ECS

Highlight

On-site presentation

Unraveling fire-permafrost interactions in Northeastern Siberian tundra using InSAR and machine learning

Sonam Wangchuk, Kevin Schaefer, Roger Michaelides, Jorien Vonk, and Sander Veraverbeke

Permafrost soils in boreal forests and tundra store more than two atmospheres worth of carbon, yet the vigorous permafrost-carbon-climate feedback loop remains poorly understood. In addition to ongoing strong warming, fires can further accelerate permafrost degradation and trigger the release of ancient carbon into the atmosphere. Despite the urgency after the recent Arctic fire seasons of 2019, 2020 and 2021, fire-permafrost interactions are currently not included in Earth system models from the sixth assessment of the Intergovernmental Panel on Climate Change (IPCC). This is because large-scale observations of fire-induced permafrost degradation are lacking. Therefore, we studied fire-induced permafrost degradation using the Interferometric Synthetic Aperture Radar (InSAR) technique and time series of Sentinel-1 (S-1) imagery. In this pilot study, we tested our approach on fires from 2019 and 2020 in the Chokurdakh area, Northeastern Siberia. We processed time series S-1 SAR data from a snow-free season (June-October) where S-1 SAR image selection was automated by using the Moderate Resolution Imaging Spectroradiometer snow cover products. To understand the drivers of InSAR-derived subsidence, we applied the XGBoost regression algorithm using subsidence as a response variable and ten other environmental variables as predictor variables. First, we found that the time series InSAR technique is suitable for deriving subsidence over fire-affected permafrost terrain. Second, the fire-affected permafrost terrain exhibited four to five times greater subsidence compared to the surrounding unburned area. Third, the XGBoost regression model revealed land surface temperature (LST) and albedo (derived from Landsat data) as the primary predictor variables of surface subsidence, accounting for more than 50% of the predictive power. The permafrost degradation in many tundra areas is likely dominated by fire-induced changes in the surface energy balance. From this pilot study, we conclude that our approach has the potential to study fire-permafrost interaction and environmental drivers of surface subsidence at the northern circumpolar scale. Models can also use our data to parameterize subsidence and thermokarst processes associated with permafrost degradation due to fire.

How to cite: Wangchuk, S., Schaefer, K., Michaelides, R., Vonk, J., and Veraverbeke, S.: Unraveling fire-permafrost interactions in Northeastern Siberian tundra using InSAR and machine learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9035, https://doi.org/10.5194/egusphere-egu23-9035, 2023.

09:05–09:15

EGU23-6816

ECS

On-site presentation

Mapping and inventorying rock glaciers on the Tibetan Plateau from Planet Basemaps using deep learning

Zhangyu Sun, Yan Hu, Lin Liu, Adina Racoviteanu, and Stephan Harrison

Rock glaciers are geomorphologically valuable indicators of permafrost distribution and form potentially important hydrological resources in the context of future climate change. Despite the widespread distribution of permafrost on the Tibetan Plateau and its reputation as the "water tower of Asia", this region lacks a complete inventory and systematic investigation of rock glaciers. In this study, we develop a deep-learning-based approach for mapping rock glaciers on the Tibetan Plateau. A powerful deep learning network, DeepLabv3+, is trained using Planet Basemaps as training imagery and multi-source rock glacier inventories as training labels. The well-trained model is then used to map new rock glaciers. The visually consistent and cloud-free properties of Planet Basemaps are crucial for developing comprehensive maps of rock glacier distribution; and the rock glacier inventories from multiple regions can improve the volume and diversity of the training dataset. The deep learning mapped results present strong identification and acceptable boundary delineation of rock glaciers, indicating that the deep learning model could serve as a useful tool for facilitating the inventory of rock glaciers over vast regions. Based on the deep learning outputs, we compile 4233 rock glaciers on eight subregions of the Tibetan Plateau, which are widespread in the surrounding regions while being scarcely distributed in inner areas. Talus- and glacier-connected rock glaciers are two major classes, which are dominant on the southeastern and densely distributed on the northwestern Tibetan Plateau, respectively. The regions with steep slopes are favored by rock glacier clusters with high density, and glacier-abundant regions tend to breed large rock glaciers. The proposed rock glacier mapping method effectively speeds up inventorying efforts, which will be used to map and inventory rock glaciers on the entire Tibetan Plateau. The complete inventory will offer a significant contribution to the global catalog and serves as a benchmark dataset for modeling and monitoring the state of permafrost in a changing climate.

How to cite: Sun, Z., Hu, Y., Liu, L., Racoviteanu, A., and Harrison, S.: Mapping and inventorying rock glaciers on the Tibetan Plateau from Planet Basemaps using deep learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6816, https://doi.org/10.5194/egusphere-egu23-6816, 2023.

09:15–09:25

EGU23-9092

On-site presentation

Seasonal and annual kinematics of active rock glaciers under different climate regimes in the Western USA

Alexander L. Handwerger, Camryn Kluetmeier, George Brencher, and Jeffrey S. Munroe

Rock glaciers are common landforms in many alpine permaforst landscapes that play an important role in alpine hydrology and landscape evolution, principally through the release of seasonal meltwater and the downslope transport of coarse material. Here, we use satellite-based interferometric synthetic aperture radar (InSAR) to identify and monitor rock glaciers in the Western USA. We focus on the movement of active and transitional rock glaciers in Utah (Uinta, Wasatch, and La Sal Mountains), and Wyoming (Wind River Mountains) between 2015 and 2022. Using the new framework established by the International Permafrost Association (IPA) Action Group, we identified 255 active and transitional rock glaciers in the ~3500 km² Uinta Mountains, 45 rock glaciers in the ~200 km² La Sal Mountains, 55 rock glaciers in the ~135 km² Wasatch Mountains, and 120 rock glaciers in the ~3000 km² Wind River Mountains. These rock glaciers currently occur under different climatic regimes based on data from the 30 year (1991-2020) normal Parameter-elevation Relationships on Independent Slopes Model (PRISM). The La Sals and Wasatch are warmer and wetter with a mean annual air temperature (MAAT) of ~3.0± 1.9 ˚C and 2.7 ± 1.1 ˚C and a mean annual precipitation (MAP) of ~92 ± 13 cm and ~130 ± 17 cm, respectively, whereas the Uintas and Wind Rivers are cooler and drier with a MAAT of ~0.24 ± 1.4 ˚C and -0.87 ± 1.4 ˚C and a MAP of ~87 ± 11 cm and ~81 ± 10 cm. The mean line-of-sight (LOS) velocities for individual rock glaciers range from ~1 to 10 cm/yr. We also examined the time-dependent relationship between the motion of the rock glaciers and local climatic drivers such as temperature and precipitation. We found that rock glaciers exhibit seasonal and annual velocity changes, likely driven by liquid water availability (from snowmelt and rainfall), with accelerated motion during summers and during wetter years. Our findings demonstrate the ability to use satellite InSAR to monitor rock glaciers over large areas and provide insight into the environmental factors that control their kinematics.

How to cite: Handwerger, A. L., Kluetmeier, C., Brencher, G., and Munroe, J. S.: Seasonal and annual kinematics of active rock glaciers under different climate regimes in the Western USA, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9092, https://doi.org/10.5194/egusphere-egu23-9092, 2023.

09:25–09:35

EGU23-9630

ECS

On-site presentation

Comparison of land surface temperatures from Landsat with soil freeze/thaw measurements in permafrost peatlands

Aida Taghavi Bayat, Markus Gerke, and Björn Riedel

Permafrost is an important component of sub-Arctic environments and is extremely vulnerable to the impact of climate change. During the last decades, permafrost regions in northern high latitudes have been exposed to greater temperature changes than other regions worldwide. Increased temperatures cause rapid thawing of permafrost which can lead to changes in hydrological processes. Therefore, capturing dynamics of land surface temperature (LST) as one of the key factors affecting the thermal regime of permafrost landscapes at high spatial resolution is crucial for better monitoring these areas under drastic warming projected due to climate change.
Landsat imagery at 30 m resolution offers the potential to obtain a consistent coverage of near-surface temperature values. In this study LST values from Landsat were compared with in-situ based soil freeze/thaw (F/T) index, air and soil temperature measurements obtained at the Abisko peatland site in the permafrost areas of northern Sweden. The soil F/T index is an important proxy that describes the relationship between the unfrozen soil water content and the soil temperature in freezing soils. From 2017 to 2022, comparisons between Landsat LST and soil F/T index show high similarity between them in identifying frozen state, thawed state, and transition periods. In addition, Landsat LST values were found to be better correlated with air temperature (R² > 90%) than with soil temperature (R² > 80%) measurements. Overall, it is concluded that Landsat LST offers great potential for monitoring surface temperature changes in high-latitude permafrost regions and provides a promising source of input data for developing models to determine the spatial heterogeneity of freezing and thawing cycles.

How to cite: Taghavi Bayat, A., Gerke, M., and Riedel, B.: Comparison of land surface temperatures from Landsat with soil freeze/thaw measurements in permafrost peatlands, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9630, https://doi.org/10.5194/egusphere-egu23-9630, 2023.

09:35–09:45

EGU23-10182

ECS

Virtual presentation

Modelling SAR Backscatter from Lake Ice under Wet Conditions using the Snow Microwave Radiative Transfer (SMRT) model

Justin Murfitt, Claude Duguay, Ghislain Picard, and Juha juha.lemmetyinen@fmi.fi

Lake ice plays a critical role in local energy balances and provides crucial socio-economic services such as travel between communities and transportation of goods during winter months. However, over the past 40 years, the number of in situ observations has declined. Additionally, increasing temperatures lead to an increasing number of melt events throughout the season, resulting in the formation of more snow ice and slush layers. The increase in wet ice conditions poses a challenge in monitoring lake ice using active microwave technologies (e.g., synthetic aperture radar) and can be a risk to those who use ice cover as an essential travel route. This study focuses on Lake Oulujärvi in Finland during the 2020-2021 ice season. Using the snow microwave radiative transfer (SMRT) model, backscatter was modelled using observations of dry and wet conditions from the field. Snow density, snow depth, microstructure data, and ice thickness data collected during the field campaign helped parameterize the Snow Microwave Radiative Transfer (SMRT) model. Simulations under dry conditions showed that increasing roughness at the ice-water interface had the largest increase in backscatter. However, when the water content of the overlying snow layers increased, the roughness of the interface with the highest moisture content became the dominant interface impacting backscatter. Melt-freeze events throughout the ice season had a prolonged impact on backscatter resulting in increases of >3.69 dB. Larger increases in backscatter due to higher moisture were a result of larger dielectric contrasts created between overlying dry snow on slush layers. Improved understanding of the impact of wet conditions on backscatter can help to improve the monitoring of hazardous lake ice conditions and aid in the further development of inversion models for lake ice properties.

How to cite: Murfitt, J., Duguay, C., Picard, G., and juha.lemmetyinen@fmi.fi, J.: Modelling SAR Backscatter from Lake Ice under Wet Conditions using the Snow Microwave Radiative Transfer (SMRT) model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10182, https://doi.org/10.5194/egusphere-egu23-10182, 2023.

09:45–09:55

EGU23-16157

ECS

Virtual presentation

Estimation of persistence on glacial lakes in tropical Andes mountain-range with 2000-2020 period LANDSAT series images

Jesús Pozo, Gladis Celmi, Juan Fernandez, Yadira Curo, Mayra Mejía, Danny Robles, and Alberto Castañeda

Peruvian mountain-range glaciers are characterized by the presence of numerous lakes of glacial origin, whose dynamics show a great temporal-spatial variability due to factors such as glacial melting and precipitation of different types, seasons, and intensities, engaging also river flow, usually for the benefit of population settlements. Therefore, it is important to determine its continuity to consider them permanent resources of water. Previously, the evaluation of this parameter was made traditionally, by looking at optical satellite imagery. However, this process ends up being too long and complicated as there are up to 3000 lakes in some mountain ranges.

We propose a methodology with the objective of estimating the temporal persistence of glacial lakes mainly performed in Google Earth Engine, convenient for the flexibility, data-size issues, and quick computations of statistical approach. This process is based on LANDSAT 7 and 8 normalized difference water index (NDWI) time series data products, comprised of 252 images of the Ampato glacier mountain-range across the calendar years 2000-2020. Initially, we extract the NDWI values for each polygon -from the INAIGEM 2020 glacial lake Inventory- and image and apply different NDWI thresholds and ways to mean them. Finally, we get a representative conversion of the value to mark their existence and do the percentage calculations over the evaluation period.

NDWI threshold of 0.05 and median values were chosen to obey previous evaluations and have tight results. We observe that between 86 and 99% of images were available for the 518 polygons of this area, indicating suitability to support subsequent conclusions. The final persistence values vary between 50% and 99% for lakes greater than 5000 m², while lesser lakes present values between 25 and 75% of persistence during the evaluation period, corresponding to weather modulating factors in a shorter scale such as seasonality, ENSO events, extreme precipitation, etc. The presented investigation could have relevant applications from water management, ecology, tourism, to climate investigation, as a way to sophisticate the processes of a more exact and specific glacial lake Inventory in Peru or other parts of the sphere.

How to cite: Pozo, J., Celmi, G., Fernandez, J., Curo, Y., Mejía, M., Robles, D., and Castañeda, A.: Estimation of persistence on glacial lakes in tropical Andes mountain-range with 2000-2020 period LANDSAT series images, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16157, https://doi.org/10.5194/egusphere-egu23-16157, 2023.

09:55–10:05

EGU23-13288

ECS

On-site presentation

Aufeis in High Mountain Asia: Evidence from two endorheic basins (Tso Moriri and Pangong Tso)

Dagmar Brombierstäudl, Tobias Schmitt, Susanne Schmidt, and Marcus Nüsser

Aufeis is a common phenomenon in permafrost and cold regions of the Northern Hemisphere that develops during winter by successive water overflow and freezing on ice-covered surfaces. Most studies on the occurrence and hydrological importance of aufeis focus on North America and Siberia, while research in High Mountain Asia is still in an early phase. However, its widespread occurrence in the Upper Indus Basin, especially in the cold-arid regions of the Trans-Himalaya and the Tibetan Plateau indicates a need for comprehensive analysis.

Two endorheic basins, located at an elevation above 4500 m a.s.l. were selected for an in depth study: Pangong Tso and Tso Moriri covering an area of ~33500 km² and 2350 km², respectively. Based on a time-series analysis of Landsat and Sentinel-2 data for the period 2008–2021, aufeis fields were mapped and their spatial occurrence and temporal patterns were characterized. Derived parameters include the number, maximum area, and topographical parameters, such as elevation and slope. In addition, high altitude wetland areas were classified for both basins in order to explore potential interactions between hydrology and vegetation cover.

More than 1000 aufeis fields covering an area of 88 km² were detected in the Pangong basin. The largest individual aufeis field reached an area of 14 km². The size increases from south to north towards the Tibetan plateau. 50 % are located at an elevational range from 4800 and 5000 m a.s.l.. In the Tso Moriri basin 27 aufeis fields covering a maximum area of 9 km² spreading over an elevational range from 4600 up to 5000 m a.s.l. were detected. Here, the largest individual aufeis spreads over 1.7 km². The accumulation of aufeis fields starts with regular overflow of water between November until April, while aufeis is usually completely melted by the end of July. However, in the Pangong basin 28 aufeis fields remain until the onset of the next accumulation cycle. All of them are located in elevations above 5000 m a.s.l.. In contrast to the Pangong basin, aufeis fields in the Tso Moriri basin are mostly found in close proximity to wetlands, on areas with frequent aufeis accumulation vegetation is almost completely absent. Potential water sources for overflow events are often located close or within the wetland areas, suggesting close hydrological interactions. The study contributes to an improved understanding of aufeis development and distribution in cold-arid environments and will help further comprehensive cryosphere studies in High Mountain Asia and beyond.

How to cite: Brombierstäudl, D., Schmitt, T., Schmidt, S., and Nüsser, M.: Aufeis in High Mountain Asia: Evidence from two endorheic basins (Tso Moriri and Pangong Tso), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13288, https://doi.org/10.5194/egusphere-egu23-13288, 2023.

10:05–10:15

EGU23-4024

ECS

On-site presentation

Variation characteristics of mesoscale lakes in the Tibetan Plateau

Weiyao Ma, Lin Bai, Weiqiang Ma, Wei Hu, Zhipeng Xie, Rongmingzhu Su, Binbin Wang, and Yaoming Ma

Known as “Water Tower of Asia”, the Tibetan Plateau (TP) is widely distributed with numerous inflow lakes. Lakes on the TP are less affected by human actives and can be considered as a sensitive indicator of climate change, changes of lakes can well reflect the change of regional climate. However, due to the harsh environment, data acquisition is not easy, studies on the response of lake change to climate mainly focus on several typical lakes (Nam Co, Selin Co Ngoring lake, etc.), but less attention is paid to variation characteristics of mesoscale lake (~100km²). To compensate for this deficiency, we selected three typical mesoscale lakes (Bamu Co, Langa Co and Longmu Co) in different climate zones and studied the lake changes and their responses to climate change using in-situ observations data and remote sensing data. By using multisource remote sensing and water level observation data, this study systematically analyzed inter-annual changes from 1970 to 2021 and monthly changes from 2019 to 2021. The main conclusions are as follows: (1) The changes to lakes in different climatic regions are different: lakes in the monsoon-dominated region showed a significant trend of expansion from 2000 to 2014, but the trend slowed down and stabilized after 2014; lakes in the westerlies-dominated region showed a small expansion trend; lakes in the region affected by both westerlies and the monsoon showed an overall shrinking trend. (2) The monthly variation of lake water volume showed a periodical trend of first increasing and then decreasing, with the largest relative change of lake water volume in August and September. (3) Temperature and precipitation are dominant meteorological elements affecting the variation of lakes, and with the warming of the TP, temperature plays an increasingly important role. Combining observational data and remote sensing data, the study of mesoscale lakes changes can increase the understanding of relationship between lake change and climate change, provide help for further study of lake - atmosphere interaction and climate effect and climate change in the TP.

Key words: Tibetan Plateau; mesoscale lakes; change of lake water volume; multisource altimetry data; in-situ observation; climate zones

How to cite: Ma, W., Bai, L., Ma, W., Hu, W., Xie, Z., Su, R., Wang, B., and Ma, Y.: Variation characteristics of mesoscale lakes in the Tibetan Plateau, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4024, https://doi.org/10.5194/egusphere-egu23-4024, 2023.

Posters on site: Tue, 25 Apr, 14:00–15:45 | Hall X5

Chairpersons: Rebecca Scholten, Manu Tom, Helena Bergstedt

X5.275

EGU23-10950

ECS

A Deep Learning-based Toolbox for Automated Monitoring of Central Asian Glacial Lakes from Space

Manu Tom, Holger Frey, Simon Allen, Alessandro Cicoira, Laura Niggli, and Christian Huggel

In recent decades, climate change has intensified the melting of glaciers in high mountain regions around the world, leading to the formation of new glacial lakes. These lakes can cause damage up to several hundred kilometres downstream when an outburst flood occurs. While more and more glacier lake inventories are becoming available to the research community, high-frequency mapping and monitoring of these lakes are still essential to assess hazards and estimate Glacial Lake Outburst Flood (GLOF) risks, particularly for lakes with high seasonal variations. In Central Asia, new lakes have been known to develop quickly, and non-stationary lakes can expand or regrow within a matter of weeks to months. Monitoring these lakes is crucial to understanding and mitigating the risks they pose.

Detecting glacial lakes using satellite sensors is difficult due to their small size and the fact that they are often frozen for much of the year. Furthermore, optical satellite imagery can be hindered by clouds. Additionally, cast and cloud shadows, as well as increasing lake and atmospheric turbidity, make it challenging to accurately observe and monitor these lakes using optical satellite imagery. On the other hand, using a SAR satellite sensor to monitor these lakes is difficult during windy scenarios and changes in backscattering due to variations in turbidity and the presence of cast shadows.

We have developed a Python-based toolbox for mapping potentially dangerous glacial lakes in Central Asia and for monitoring the dynamics of these lakes over time and space. The proposed analytical toolbox uses a combination of Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical satellite data as input. Satellite data fusion allows high temporal resolution, while SAR can penetrate cloud cover and allow year-round monitoring. The user interface for the toolbox is designed to also accommodate users with a non-programming background.

The Convolutional Neural Network (CNN)-based approach fuses information from heterogeneous satellite input data by learning joint satellite embeddings (feature representations), that are equivariant to the type of satellite input data. The proposed network has separate encoder branches for each input sensor. The learned embeddings are then fused to guide the identification of glacial lakes. The ultimate goal of our data-driven methodology is to create geolocated maps of the target regions by classifying each pixel as either a lake or background in a supervised manner.

This work is part of the GLOFCA project which aims to lower the vulnerability of people in Kazakhstan, Kyrgyzstan, Tajikistan, and Uzbekistan to GLOFs. This project is implemented by UNESCO and funded by the UN Adaptation Fund, in collaboration with various international and national partners.

How to cite: Tom, M., Frey, H., Allen, S., Cicoira, A., Niggli, L., and Huggel, C.: A Deep Learning-based Toolbox for Automated Monitoring of Central Asian Glacial Lakes from Space, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10950, https://doi.org/10.5194/egusphere-egu23-10950, 2023.

X5.276

EGU23-1041

ECS

Highlight

Rock Glacier Surface Change Detection Based on UAV- and Tristereo Pléiades Data (Agua Negra, Argentina)

Melanie A. Stammler, Rainer Bell, Xavier Bodin, Jan Blöthe, and Lothar Schrott

Glacial and periglacial landforms in the semi-arid Andes represent an essential water storage and feed regional river runoff. Glacial and periglacial systems are undergoing change; with signs of permafrost degradation such as thermokarst being visible in the study area of the Agua Negra catchment in the Desert Andes of Argentina. Surface changes are often indicators of thawing and freezing processes and/or permafrost degradation. The analysis of surficial changes provides local patterns and indicates potential meltwater contribution to runoff. It is important to understand such changing processes to assess their future input to the hydrological system. Analyses that exceed landform scale are, however, rare due to limited accessibility and high demand on fieldwork and resources.

Glaciers and permafrost in the Agua Negra catchment exist within close proximity, suggesting (de)coupling effects. Glaciers and permafrost features can act as thermal and mechanical entity with water functioning as agent of transient glacier-permafrost interaction. Investigating (de)coupling is essential to better understand landscape (in)stability and changing water storages within the system. We hypothesize that periglacial systems directly interacting with glacial landscapes display diverging surface processes compared to non-glacially impacted periglacial systems. They differ in terms of magnitude and pattern, e.g. due to meltwater (re)routing.

We derive high-resolution digital elevation models (DEMs) for one talus-derived and one glacially impacted rock glacier and assess surface change based on repeated UAV flights in 2017, 2018, 2022 and 2023 for the talus-derived, and 2022 and 2023 for the glacially impacted rock glacier; both georeferenced by DGPS measurements. We increase the spatial scale of the analysis and use tristereo Pléiades data for Pléiades-based surface change detection of two glacially impacted rock glaciers between 2014 and 2022. Here, we use the UAV-based DEMs as validation datasets. We envision that combining the two data sources allows us to investigate change signals over larger spatial areas which might provide new insight in our process-response understanding of the high Andean (peri)glacial landscape and its hydrological significance.

First results from UAV based DEM comparison indicate net negative surface changes of the talus-derived rock glacier. Preliminary analysis of the Pléiades data shows a net negative mass balance of Agua Negra glacier and highlights the need for improved co-registering of the Pléiades data for rock glacier surface change detection.

How to cite: Stammler, M. A., Bell, R., Bodin, X., Blöthe, J., and Schrott, L.: Rock Glacier Surface Change Detection Based on UAV- and Tristereo Pléiades Data (Agua Negra, Argentina), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1041, https://doi.org/10.5194/egusphere-egu23-1041, 2023.

X5.277

EGU23-8999

ECS

Exploring the merging potential of high temporal resolution and high spatial resolution microwave remote sensing data

Yueli Chen and Ralf Ludwig

Microwave remote sensing can provide effective monitoring of landscape FT dynamics. Its sensitivity to surface permittivity, which is predominantly influenced by the phases of water, can be used to measure landscape freeze/thaw state information. The technique of Interferometric Synthetic Aperture Radar (InSAR) enables to map the ground movement through the use of Synthetic Aperture Radar (SAR). Compared to optical imagery, microwave data has advantages that it would not be affected by cloud cover, smoke or daylight and exhibits useful penetration depths of soil and vegetation.
Both active and passive microwave remote sensing with different wavelengths have shown their principal capacity in many studies and have complementary advantages to each other. While many passive sensors (such as SMAP and SMOS) are providing observations with high temporal resolution and good worldwide coverage at the deca-kilometer scale, there are a series of active sensors providing observations with worse temporal resolution but much better spatial resolution at the scale from a few meters to a few deca-meters, for example, the Sentinel-1 mission from the European Space Agency (ESA) with 5 x 5 m spatial resolution and 6-12 days repeat cycle. Hence, the combined use of different microwave data can be expected further to promote the monitoring of permafrost-related phenomena and permafrost-dominated landscapes.
An assumption of near-linear relation between the measurements from the passive and active sensors has been used in NASA’s Soil Moisture Active Passive (SMAP) active-passive baseline algorithm for downscaling coarse-resolution radiometer brightness temperature (TB) using high-resolution radar backscatter (σ 0). Recent research proved that a good linear relationship could be found at a global scale (Zeng et al., 2021). However, the relation is significantly affected by environmental factors, for example, the density of vegetation cover.
Based on the findings, we attempt to explore the possibility of merging microwave remote-sensing data from different platforms in this work. We are committed to exploring suitable data sources for merging, as well as the possibility of taking environmental factors into consideration. The capacity and limitation of the merging process will be discussed.

How to cite: Chen, Y. and Ludwig, R.: Exploring the merging potential of high temporal resolution and high spatial resolution microwave remote sensing data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8999, https://doi.org/10.5194/egusphere-egu23-8999, 2023.

X5.278

EGU23-1340

Monitoring lake ice phenology from CYGNSS: Algorithm development and assessment using Qinghai Lake, Tibet Plateau, as a case study

Yusof Ghiasi, Claude Duguay, Justin Murfitt, Milad Asgarimehr, and Yuhao Wu

This study introduces the first use of Global Navigation Satellite System Reflectometry (GNSS-R) for monitoring lake ice phenology. This is demonstrated using Qinghai Lake, Tibetan Plateau, as a case study. Signal-to-Noise Ratio (SNR) values obtained from the Cyclone GNSS (CYGNSS) constellation over four ice seasons (2018 to 2022) were used to examine the impact of lake surface conditions on reflected GNSS signals during open water and ice cover seasons. A moving t-test (MTT) algorithm was applied to time-varying SNR values allowing for the detection of lake ice at daily temporal resolution. Strong agreement is observed between ice phenology records derived from CYGNSS and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. Differences during freeze-up (i.e., the period starting with the first appearance of ice on the lake until the lake becomes fully ice covered) ranged from 3 to 21 days with a mean bias error (MBE) and mean absolute error (MAE) of 10 days, while those during breakup (i.e., the period beginning with the first pixel of open water and ending when the whole lake becomes ice-free) ranged from 3 to 18 days (MBE and MAE: 6 and 7 days, respectively). Observations during the breakup period revealed the sensitivity of GNSS reflected signals to the onset of surface (snow and ice) melt before the appearance of open water conditions as determined from MODIS. While the CYGNSS constellation is limited to the coverage of lakes between 38° S and 38° N, the approach presented herein will be applicable to data from other GNSS-R missions that provide opportunities for the monitoring of ice phenology from large lakes globally (e.g., Spire constellation of satellites).

How to cite: Ghiasi, Y., Duguay, C., Murfitt, J., Asgarimehr, M., and Wu, Y.: Monitoring lake ice phenology from CYGNSS: Algorithm development and assessment using Qinghai Lake, Tibet Plateau, as a case study, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1340, https://doi.org/10.5194/egusphere-egu23-1340, 2023.

X5.279

EGU23-6486

ECS

From pixels to charts – using remote sensing for the climate change indicator “lake ice” in alpine lakes

Katja Kuhwald, Marcel König, Kerstin Brembach, and Natascha Oppelt

Lake ice is an important feature for many limnic ecosystems. Periodic ice cover influences biological, chemical and physical processes in lakes during the cold climate period. Additionally, ice cover also affects processes outside the ice period, for instance, lake water temperature, timing of spring bloom, primary productivity and mixing regimes. In the face of climate change, many regions experience shifting lake ice cover. The observed and projected loss significantly affects lake ecology but also cultural ecosystem services. In the alpine region of Germany, people associate personal memories, sportive activities and many other aspects with lake ice. Lake ice is connected to society and also strongly affected by climate change. Therefore, it well suits as an indicator to communicate climate change. The region, however, lacks systematic measurements and data on lake ice cover.

In our project, we therefore aimed at developing a remote sensing approach to create a comparable data basis for a climate change indicator on lake ice. Our case study analysed six lakes between 700 and 2000 m AMSL. We generated a data set on lake surface characteristics (water, ice, snow, transparent ice etc.) using public webcam imagery as independent source. The data set was used to train and validate random forest classifiers for Setinel-1 A/B, Sentinel-2 A/B and Landsat 8 imagery. We excluded Sentinel-1 data, which were acquired at wind speeds > 1 m/s (ERA5-LAND). Thus, we prevented erroneous classification of rough waters. The validation revealed very high accuracies with balanced overall accuracies around 0.99, which is misleading. The high accuracies result from how we designed the ground the data since we only used data with labelling under high certainty. In this region, mapping lake ice faces the challenge of multiple freezing in thawing processes within the ice period. We therefore, implemented an air temperature (ERA5-LAND) filter to check the plausibility of classification results.

The final classification results differentiated binary between ice and no-ice pixels. From this data, we defined ice-days with at least 80 % ice cover on a lake. To build the indicator, we divided the monthly sum of ice days by the number of valid image acquisitions. Thus, the indicator also accounts for varyingly available satellite data.

With covering currently seven ice periods, the time series is relatively short for a climate change indicator. The approach may also be transferred to archived imagery whereas lacking ground truth data remain challenging. The small size of (0.2 - 3 km²) complicates the usage of large scale sensors such as MODIS. Thus, combining data from five satellites resolving at 10 – 30 m allowed to generate comparable and spatially explicit data on ice cover of these lakes for the first time.

How to cite: Kuhwald, K., König, M., Brembach, K., and Oppelt, N.: From pixels to charts – using remote sensing for the climate change indicator “lake ice” in alpine lakes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6486, https://doi.org/10.5194/egusphere-egu23-6486, 2023.

X5.280

EGU23-7162

ECS

Highlight

Greenland Lake Ice Breakup Detection from Sentinel-1 SAR

Christoph Posch and Jakob Abermann

The timing of lake ice formation and breakup are relevant climate indicators. In this study, we explore the potential of utilizing Sentinel-1 synthetic aperture radar (SAR) data for identifying the timing of lake ice breakup across Greenland between 2016 and 2022 and assess its latitudinal and vertical gradients. We retrieve average backscatter data of lakes in peripheral Greenland with a surface area > 1km² (n = 1842). Data with a low number of acquisitions for the entire study period (n < 1000) or exhibiting strong uniformal annual characteristics (backscatter difference between 95^th and 5^th quantile < 5dB) are excluded from the analysis. We apply a locally weighted scatterplot smoothing (LOWESS) filter to remove outliers. A dynamic numerical threshold (backscatter decline within 3 consecutive acquisitions > 25% of the annual backscatter range) is applied for each respective year to identify the timing of ice breakup. The study area is divided into 6 main regions of Greenland (N, NE, SE, S, SW, NW) to explore spatio-temporal statistics. The data exhibits a temporal resolution of about 2 days during the relevant period. We validate the breakup detection (n = 10) by utilizing daily time-lapse images of 3 lakes between 2016 and 2020. The detection of the timing from SAR data proves to be conservative (i.e., later) compared to time-lapse camera data and allows characterizing lake ice breakup with a mean error of 7 days. We find that only SAR data in West Greenland (S, SW, NW), i.e., > 43°W and < 70°N, exhibits characteristics for breakup detection (97%, 77% and 57% suitable) while coverage for North and East Greenland (N, NE, SE) lacks necessary radiometric and temporal characteristics (only 2%, 3% and 2% suitable). Our preliminary results indicate that no significant trend (α = 0.05) of breakup timing between 2016 and 2022 can be identified. Annual median DOYs range between June 8 (2019) and July 11 (2022). Ice breakup timing increases with latitude and elevation, however, strong correlations (up to r = 0.81) can only be identified for limited years. Correlations are in the order of 2 to 5 DOY/°lat. and 2 to 7 DOY/100m. Based on these preliminary results, we aim to explore statistical relations in greater detail to assess the role of extreme events and global climate change. Furthermore, we intend to apply this automated algorithm for an analysis of lake ice breakup timing on a global scale.

How to cite: Posch, C. and Abermann, J.: Greenland Lake Ice Breakup Detection from Sentinel-1 SAR, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7162, https://doi.org/10.5194/egusphere-egu23-7162, 2023.

X5.281

EGU23-4015

Observational studies of water surface Evaporation on inland lake over the classical Tibetan Plateau

Weiqiang Ma, Yaoming Ma, Binbin wang, Rongmingzhu Su, Weiyao Ma, Zhipeng Xie, Wei Hu, Jianan He, Nan Yao, Longtengfei Ma, and Ling Bai

To understand how the changing process of lake water level and area in Tibetan Plateau effects on the dynamic process of water resources in the surrounding area is very important. This project intends to focus on the study of surface evaporation observation of typical inland lake in Tibetan plateau and makes full use of the "water - ice - atmosphere -biology " multi-spheres comprehensive observation and various professional networks on the Tibetan plateau and surrounding regions which were establish by the leading of institute of the Tibetan plateau research, CAS, to carry out the research of synchronous comprehensive observation. Meanwhile, it collects the existing comprehensive observation data combined with the inversion method of satellite remote sensing to carry out multi-disciplinary comprehensive analysis and research. The aim is to reveal the longer time scale change of lake level by the lake expansion and withdrawal process in different areas of Tibetan plateau. By improving the method of remote sensing and observation, it will get the hourly and daily long-term data of the water level of inland lakes in different areas of Tibetan plateau which is lacking in previous studies. And by combining the analysis of meteorological factors and flow data in lake regions to research daily water cycle, it will help us to clearly understand the change rule of lake water level in different areas of Tibetan plateau. This time some general in-situ observation and model results will be show here.

How to cite: Ma, W., Ma, Y., wang, B., Su, R., Ma, W., Xie, Z., Hu, W., He, J., Yao, N., Ma, L., and Bai, L.: Observational studies of water surface Evaporation on inland lake over the classical Tibetan Plateau, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4015, https://doi.org/10.5194/egusphere-egu23-4015, 2023.

X5.282

EGU23-7720

ECS

Highlight

Generation of an improved land surface temperature time series to support permafrost modelling in the northern high latitudes

Sonia Dupuis, Frank-Michael Göttsche, and Stefan Wunderle

Northern high latitudes have experienced pronounced warming throughout the last decades with particularly high temperatures during winter and spring. Due to Arctic Amplification, the Arctic region is warming thrice as fast as anywhere else. The warming affects the sensible ecosystem, vegetation dynamics and the cryosphere (sea ice, snow and permafrost). Permafrost, which is a crucial component of arctic ecosystems, is particularly sensitive to increasing air temperatures and changes in the snow regime. Climate change has a high impact in these regions because thawing affects the stability of the bedrock, damages infrastructures and releases massive quantities of organic carbon. Permafrost cannot directly be observed from space, but permafrost models link physical surface variables such as land surface temperature (LST) to the thermal ground regime. Models are an important addition to boreholes to monitor the status of the permafrost at hemispheric scale. On a global scale, observation of LST is only available from very few in-situ stations or climate models with coarse spatial resolution. Both data sources are not sufficient to model fine-scaled features. In contrast, LST information retrieved from satellite data has high spatiotemporal coverage.

To compute LST on a hemispheric scale, we use the Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage (GAC) data set starting in 1981. The AVHRR on board the NOAA and MetOp satellite series now covers more than four decades. AVHRR’s two thermal infrared channels allow applying the split-window (SW) method to reduce the atmospheric effect and retrieve LST. Split-window algorithms (SWA) performances depend on the quality of SW coefficients. These are empirical coefficients, which are retrieved by fitting the SWA to a calibration database. Here, the calibration data is generated by running a radiative transfer (RT) model. The input profiles of the RT are selected to cover typical atmospheric conditions occurring in permafrost regions. The coefficients are adjusted for different water vapour and satellite viewing conditions. Cloud and water masks as well as fractional snow cover information from the ESA CCI snow project and emissivity data are included in the final LST retrieval algorithm. Besides, a machine learning algorithm was applied to improve the spatial resolution of the GAC data to generate a 40-year time series with a spatial resolution of 1km. The first validation results of the LST time series are shown.

How to cite: Dupuis, S., Göttsche, F.-M., and Wunderle, S.: Generation of an improved land surface temperature time series to support permafrost modelling in the northern high latitudes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7720, https://doi.org/10.5194/egusphere-egu23-7720, 2023.

Posters virtual: Tue, 25 Apr, 14:00–15:45 | vHall CR/OS

Chairperson: Helena Bergstedt

vCO.2

EGU23-3784

ECS

Highlight

Remotely Sensed Water Color as a Proxy for Monitoring Water Quality in Inland Lakes

Wondwosen Seyoum and Andrew Dooley

Constituents in water control the amount of light reflected from and absorbed by natural water bodies. This interaction is used as a basis for water quality monitoring using remotely sensed data. Recent studies have shown that water color derived from satellite data can be used to investigate water quality changes due to human and climate change impacts. However, how the change in satellite-based water color corresponds with specific water quality variables needs to be better understood. We analyzed timeseries (2013-2022) satellite-derived water color. We compared it with in-situ measured water quality variables (Secchi depth, turbidity, chlorophyll a, and total suspended solids) for lakes in the Midwest and Northeast regions of the USA. One of the focuses of this study, Lake Erie, observed for size, movement, and toxicity of harmful algal blooms (HABs) at multiple stations. Four bands (ultra blue, blue, green, and red) were extracted from harmonized Landsat and Sentinel-2 data to obtain the tristimulus values. These values are mapped on a chromaticity diagram to get the dominant color wavelength and the Forel–Ule Index (FUI). Results showed a strong relationship between in-situ water quality variables (e.g., Secchi depth and turbidity) and satellite-based FUI. Spatially, the relationship between in-situ water quality variables and water color is not consistent, as there is high variability in the concentration of the observed variables between the sampling locations. For example, measurement stations characterized by yellow to brown colors exhibited a strong relationship with TSS. However, generally, peak chl a concentration corresponds with yellowish green color. Typically, stations with blue to green water color are characterized by lower Chl a concentrations. This in-situ validation is used to infer the water quality of water bodies with no available in-situ monitoring.

How to cite: Seyoum, W. and Dooley, A.: Remotely Sensed Water Color as a Proxy for Monitoring Water Quality in Inland Lakes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3784, https://doi.org/10.5194/egusphere-egu23-3784, 2023.

vCO.3

EGU23-661

ECS

Morphometric parameters of retrogressive thaw slumps as of 2021 in West Siberia

Nina Nesterova, Maxim Altukhov, and Marina Leibman

Retrogressive thaw slumps (RTS, also referred to as thermocirques) are dynamic polycyclic landforms resulting from ground ice melting. Initiation of RTS causes organic carbon emissions into the atmosphere and hydrosphere, as well as changes in topography and vegetation. West Siberia's Arctic zone is characterized by continuous permafrost and the presence of tabular ground ice close to the surface. These factors result in widespread RTS occurrence over the region. Since the majority of RTS studies in West Siberia have been limited to fieldwork at a few key sites, there is still no understanding of true RTS distribution, as well as morphometric and topographical parameters in the region. Remote sensing approaches help gain more knowledge of RTS characteristics over vast areas. This research presents preliminary results of the actual morphometric characteristics of 97 lake-associated RTSs located on the Yamal and Gydan peninsulas. The area of each modern RTS that are possible to identify on Sentinel-2 satellite images taken in 2021 was obtained. Elevation profile for several transects over the digitized RTS were collected using ArcticDEM data. The largest RTS was found in the northern part of the Gydan peninsula with an area of 38 ha. The smallest identified RTS based on the 10 m spatial resolution of Sentinel-2 satellite images was located in central Yamal with an area of 6 ha. The median area was found to be 2,5 ha. Around 70% of RTS had elongated shapes along the coastline with a width larger than the length. This can be caused by either merging neighboring RTSs or by widthwise enlargement. Around 21% of the RTSs were found to have approximately equal width and length. And only 9% of RTS were found to expand inland with a width much less than length. According to our estimates, the average elevation of studied RTS edges was 26 meters above sea level. The smallest difference between the edge and front line heights of the RTS was evaluated at ~ 0,2 meters and the largest appeared to be ~ 5,6 meters. Data collected from the Yamal and Gydan peninsulas enable further analysis of the morphometric parameters of RTSs.

This research was funded by the Russian Science Foundation, grant number № 22-27-00644.

How to cite: Nesterova, N., Altukhov, M., and Leibman, M.: Morphometric parameters of retrogressive thaw slumps as of 2021 in West Siberia , EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-661, https://doi.org/10.5194/egusphere-egu23-661, 2023.