Remote Sensing

Research

14 pages, 6100 KiB

Open AccessCommunication

Quantifying Basal Roughness and Internal Layer Continuity Index of Ice Sheets by an Integrated Means with Radar Data and Deep Learning

by Xueyuan Tang, Kun Luo, Sheng Dong, Zidong Zhang and Bo Sun

Remote Sens. 2022, 14(18), 4507; https://doi.org/10.3390/rs14184507 - 9 Sep 2022

Cited by 2 | Viewed by 1894

Abstract

Understanding englacial and subglacial structures is a fundamental method of inferring ice sheets’ historical evolution and surface mass balance. The internal layer continuity index and the basal roughness are key parameters and indicators for the speculation of the relationship between the ice sheet’s [...] Read more.

Understanding englacial and subglacial structures is a fundamental method of inferring ice sheets’ historical evolution and surface mass balance. The internal layer continuity index and the basal roughness are key parameters and indicators for the speculation of the relationship between the ice sheet’s internal structure or bottom and ice flow. Several methods have been proposed in the past two decades to quantitatively calculate the continuity index of ice layer geometry and the roughness of the ice–bedrock interface based on radar echo signals. These methods are mainly based on the average of the absolute value of the vertical gradient of the echo signal amplitude and the standard deviation of the horizontal fluctuation of the bedrock interface. However, these methods are limited by the amount and quality of unprocessed radar datasets and have not been widely used, which also hinders further research, such as the analysis of the englacial reflectivity, the subglacial conditions, and the history of the ice sheets. In this paper, based on geophysical processing methods for radar image denoising and deep learning for ice layer and bedrock interface extraction, we propose a new method for calculating the layer continuity index and basal roughness. Using this method, we demonstrate the ice-penetrating radar data processing and compare the imaging and calculation of the radar profiles from Dome A to Zhongshan Station, East Antarctica. We removed the noise from the processed radar data, extracted ice layer continuity features, and used other techniques to verify the calculation. The potential application of this method in the future is illustrated by several examples. We believe that this method can become an effective approach for future Antarctic geophysical and glaciological research and for obtaining more information about the history and dynamics of ice sheets from their radar-extracted internal structure. Full article

(This article belongs to the Special Issue Remote Sensing in Glaciology and Cryosphere Research)

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21 pages, 18547 KiB

Open AccessArticle

A Self-Trained Model for Cloud, Shadow and Snow Detection in Sentinel-2 Images of Snow- and Ice-Covered Regions

by Kamal Gopikrishnan Nambiar, Veniamin I. Morgenshtern, Philipp Hochreuther, Thorsten Seehaus and Matthias Holger Braun

Remote Sens. 2022, 14(8), 1825; https://doi.org/10.3390/rs14081825 - 10 Apr 2022

Cited by 9 | Viewed by 3823

Abstract

Screening clouds, shadows, and snow is a critical pre-processing step in many remote-sensing data processing pipelines that operate on satellite image data from polar and high mountain regions. We observe that the results of the state-of-the-art Fmask algorithm are not very accurate in [...] Read more.

Screening clouds, shadows, and snow is a critical pre-processing step in many remote-sensing data processing pipelines that operate on satellite image data from polar and high mountain regions. We observe that the results of the state-of-the-art Fmask algorithm are not very accurate in polar and high mountain regions. Given the unavailability of large, labeled Sentinel-2 training datasets, we present a multi-stage self-training approach that trains a model to perform semantic segmentation on Sentinel-2 L1C images using the noisy Fmask labels for training and a small human-labeled dataset for validation. At each stage of the proposed iterative framework, we use a larger network architecture in comparison to the previous stage and train a new model. The trained model at each stage is then used to generate new training labels for a bigger dataset, which are used for training the model in the next stage. We select the best model during training in each stage by evaluating the multi-class segmentation metric, mean Intersection over Union (mIoU), on the small human-labeled validation dataset. This effectively helps to correct the noisy labels. Our model achieved an overall accuracy of 93% compared to the Fmask 4 and Sen2Cor 2.8, which achieved 75% and 76%, respectively. We believe our approach can also be adapted for other remote-sensing applications for training deep-learning models with imprecise labels. Full article

(This article belongs to the Special Issue Remote Sensing in Glaciology and Cryosphere Research)

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21 pages, 8690 KiB

Open AccessArticle

Influence of Topographic Shading on the Mass Balance of the High Mountain Asia Glaciers

by Rongjun Wang, Yongjian Ding, Donghui Shangguan, Wanqin Guo, Qiudong Zhao, Yaojun Li and Miao Song

Remote Sens. 2022, 14(7), 1576; https://doi.org/10.3390/rs14071576 - 24 Mar 2022

Cited by 7 | Viewed by 2417

Abstract

Most studies attribute the glacier mass balance within High Mountain Asia (HMA) to climate change, ignoring the influence of its complex terrain. Knowledge of the influence of this complex terrain is crucial for understanding the spatial variability in its mass balance. However, there [...] Read more.

Most studies attribute the glacier mass balance within High Mountain Asia (HMA) to climate change, ignoring the influence of its complex terrain. Knowledge of the influence of this complex terrain is crucial for understanding the spatial variability in its mass balance. However, there is a lack of any systematic assessment of this influence across HMA. Therefore, in this study, we used the glacier outlines and raster data (SRTM DEM, slope and aspect) to calculate the topographic shading of all 97,965 glaciers within HMA during the ablation period, which is regarded as a major index of the influence of complex terrain on the mass balance. The results showed that 27.19% of HMA glacier area was subjected to topographic shading, and regional differences were significant with respect to both their altitudinal and spatial distributions. The topographic shading contributed to the protection of the smallest glaciers from solar illumination. Furthermore, we found a significant correlation between the topographic shading and mass balance in these small north-facing glaciers. However, these small glaciers were most prevalent in the north-facing orientation, especially in West Kunlun, East Kunlun, Inner Tibet Plateau and Qilian Shan, where shading was found to increase with decreases in the glacier area. This indicates that complex terrain can affect the spatial distribution of the mass balance by altering the solar illumination pattern. Full article

(This article belongs to the Special Issue Remote Sensing in Glaciology and Cryosphere Research)

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19 pages, 7049 KiB

Open AccessArticle

The Evolution of the Glacier Surges in the Tuanjie Peak, the Qilian Mountains

by Yongpeng Gao, Shiyin Liu, Miaomiao Qi, Xiaojun Yao, Yu Zhu, Fuming Xie, Kunpeng Wu and Muhammad Saifullah

Remote Sens. 2022, 14(4), 852; https://doi.org/10.3390/rs14040852 - 11 Feb 2022

Cited by 8 | Viewed by 2245

Abstract

Glacier surges (GSs) are a manifestation of glacier instability and one of the most striking phenomena in the mountain cryosphere. Here, we utilize optical images acquired between 1973 and 2021 to map changes in glacier surface velocity and morphology and characterize differences in [...] Read more.

Glacier surges (GSs) are a manifestation of glacier instability and one of the most striking phenomena in the mountain cryosphere. Here, we utilize optical images acquired between 1973 and 2021 to map changes in glacier surface velocity and morphology and characterize differences in surface elevation using multi-source DEMs in the Tuanjie Peak (TJP), located in the Qilian Mountains (QLMs). These data provide valuable insights into the recent dynamic evolution of glaciers and hint at how they might evolve in the next few years. We identified a confirmed surge-type glacier (STG), three likely STGs, and three possible STGs. Our observations show that TJP GSs are generally long-term, although they are shorter in some cases. During the active phase, all glaciers exhibit thickened reservoir areas and thinned receiving areas, or vice-versa. The ice volume transfer was between 0.11 ± 0.13 × 10⁷ m³ to 5.71 ± 0.69 × 10⁷ m³. Although it was impossible to obtain integrated velocity profiles throughout the glacier surge process due to the limitations of available satellite imagery, our recent observations show that winter velocities were much higher than summer velocities, suggesting an obvious correlation between surge dynamics and glacial hydrology. However, the initiation and termination phase of GSs in this region was slow, which is similar to Svalbard-type STGs. We hypothesize that both thermal and hydrological controls are crucial. Moreover, we suggest that the regional warming trend may potentially increase glacier instability and the possibility of surge occurrence in this region. Full article

(This article belongs to the Special Issue Remote Sensing in Glaciology and Cryosphere Research)

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18 pages, 5358 KiB

Open AccessArticle

Decadal Changes in Glacier Area, Surface Elevation and Mass Balance for 2000–2020 in the Eastern Tanggula Mountains Using Optical Images and TanDEM-X Radar Data

by Yushan Zhou, Xin Li, Donghai Zheng, Xiaolong Zhang, Yingzheng Wang, Shanshan Ren and Yanlong Guo

Remote Sens. 2022, 14(3), 506; https://doi.org/10.3390/rs14030506 - 21 Jan 2022

Cited by 6 | Viewed by 2433

Abstract

The response of lake-terminating glaciers to climate change is complex, and their rapid changes are often closely linked to glacial-lake outburst floods. However, the eastern Tanggula Mountains, which are the only area where lake-terminating glaciers are found within the Tibetan Plateau, have received [...] Read more.

The response of lake-terminating glaciers to climate change is complex, and their rapid changes are often closely linked to glacial-lake outburst floods. However, the eastern Tanggula Mountains, which are the only area where lake-terminating glaciers are found within the Tibetan Plateau, have received little attention to date. In this study, to address this gap, we generated updated glacier boundaries and estimated the interdecadal area changes for 2000–2020 based on the interpretation of Landsat-5/8 and Sentinel-2 images. In addition, based on the method of digital elevation model (DEM) differencing, we quantified the changes in glacier thickness and mass balance using TanDEM-X radar data and SRTM DEM over almost the same periods. The final results show that the glaciers in the eastern Tanggula Mountains, as a whole, have experienced accelerated area shrinkage (with a rate of area loss increasing from −0.34 ± 0.83 km² a⁻¹ to −0.93 ± 0.81 km² a⁻¹ for 2000–2013 and 2013–2020, respectively) and accelerated ice thinning (changing from −0.19 ± 0.05 m a⁻¹ and −0.53 ± 0.08 m a⁻¹ for 2000−2012 and 2012–2020, respectively). Furthermore, the region-wide glacier mass balance was −0.16 ± 0.04 m w.e. a⁻¹ and −0.45 ± 0.07 m w.e. a⁻¹ for these two sub-periods, corresponding to a 1.8 times acceleration of mass loss rate. The average mass balance during 2000–2020 was −0.23 ± 0.04 m w.e. a⁻¹, which is equivalent to a rate of mass loss of −0.04 Gt a⁻¹. More specifically, within the region, the lake-terminating glaciers have exhibited more significant acceleration of area loss and mass loss, compared to the land-terminating glaciers. However, interestingly, the average thinning rate of the lake-terminating glaciers is always lower than that of the land-terminating glaciers over all study periods, which is in contrast with previous findings in other high mountain areas (e.g., the Himalaya Mountains). Field study and proglacial lakes monitoring suggest that the local topography plays a vital role in the evolution of the glacial lakes in this region, which further affects the glacier changes. Furthermore, the present status of the glacier changes in this region can be attributed to the long-term increase in air temperature. Our findings provide a comprehensive overview of the current state of glacier changes across the eastern Tanggula Mountains and will help to improve the understanding of the heterogeneous response of glaciers to climate change. Full article

(This article belongs to the Special Issue Remote Sensing in Glaciology and Cryosphere Research)

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24 pages, 9625 KiB

Open AccessArticle

Glacier Area and Snow Cover Changes in the Range System Surrounding Tarim from 2000 to 2020 Using Google Earth Engine

by Jing Zhang, Li Jia, Massimo Menenti, Jie Zhou and Shaoting Ren

Remote Sens. 2021, 13(24), 5117; https://doi.org/10.3390/rs13245117 - 16 Dec 2021

Cited by 12 | Viewed by 4896

Abstract

Glacier and snow are sensitive indicators of regional climate variability. In the early 21st century, glaciers in the West Kunlun and Pamir regions showed stable or even slightly positive mass budgets, and this is anomalous in a worldwide context of glacier recession. We [...] Read more.

Glacier and snow are sensitive indicators of regional climate variability. In the early 21st century, glaciers in the West Kunlun and Pamir regions showed stable or even slightly positive mass budgets, and this is anomalous in a worldwide context of glacier recession. We studied the evolution of snow cover to understand whether it could explain the evolution of glacier area. In this study, we used the thresholding of the NDSI (Normalized Difference Snow Index) retrieved with MODIS data to extract annual glacier area and snow cover. We evaluated how the glacier trends related to snow cover area in five subregions in the Tarim Basin. The uncertainty in our retrievals was assessed by comparing MODIS results with the Landsat-5 TM in 2000 and Landsat-8 OLI in 2020 glacier delineation in five subregions. The glacier area in the Tarim Basin decreased by 1.32%/a during 2000–2020. The fastest reductions were in the East Tien Shan region, while the slowest relative reduction rate was observed in the West Tien Shan and Pamir, i.e., 0.69%/a and 1.08%/a, respectively, during 2000–2020. The relative glacier stability in Pamir may be related to the westerlies weather system, which dominates climate in this region. We studied the temporal variability of snow cover on different temporal scales. The analysis of the monthly snow cover showed that permanent snow can be reliably delineated in the months from July to September. During the summer months, the sequence of multiple snowfall and snowmelt events leads to intermittent snow cover, which was the key feature applied to discriminate snow and glacier. Full article

(This article belongs to the Special Issue Remote Sensing in Glaciology and Cryosphere Research)

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18 pages, 3936 KiB

Open AccessArticle

ACFNet: A Feature Fusion Network for Glacial Lake Extraction Based on Optical and Synthetic Aperture Radar Images

by Jinxiao Wang, Fang Chen, Meimei Zhang and Bo Yu

Remote Sens. 2021, 13(24), 5091; https://doi.org/10.3390/rs13245091 - 15 Dec 2021

Cited by 14 | Viewed by 3305

Abstract

Glacial lake extraction is essential for studying the response of glacial lakes to climate change and assessing the risks of glacial lake outburst floods. Most methods for glacial lake extraction are based on either optical images or synthetic aperture radar (SAR) images. Although [...] Read more.

Glacial lake extraction is essential for studying the response of glacial lakes to climate change and assessing the risks of glacial lake outburst floods. Most methods for glacial lake extraction are based on either optical images or synthetic aperture radar (SAR) images. Although deep learning methods can extract features of optical and SAR images well, efficiently fusing two modality features for glacial lake extraction with high accuracy is challenging. In this study, to make full use of the spectral characteristics of optical images and the geometric characteristics of SAR images, we propose an atrous convolution fusion network (ACFNet) to extract glacial lakes based on Landsat 8 optical images and Sentinel-1 SAR images. ACFNet adequately fuses high-level features of optical and SAR data in different receptive fields using atrous convolution. Compared with four fusion models in which data fusion occurs at the input, encoder, decoder, and output stages, two classical semantic segmentation models (SegNet and DeepLabV3+), and a recently proposed model based on U-Net, our model achieves the best results with an intersection-over-union of 0.8278. The experiments show that fully extracting the characteristics of optical and SAR data and appropriately fusing them are vital steps in a network’s performance of glacial lake extraction. Full article

(This article belongs to the Special Issue Remote Sensing in Glaciology and Cryosphere Research)

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19 pages, 8841 KiB

Open AccessArticle

Recent Changes of Glacial Lakes in the High Mountain Asia and Its Potential Controlling Factors Analysis

by Meimei Zhang, Fang Chen, Hang Zhao, Jinxiao Wang and Ning Wang

Remote Sens. 2021, 13(18), 3757; https://doi.org/10.3390/rs13183757 - 19 Sep 2021

Cited by 25 | Viewed by 5156

Abstract

The current glacial lake datasets in the High Mountain Asia (HMA) region still need to be improved because their boundary divisions in the land–water transition zone are not precisely delineate, and also some very small glacial lakes have been lost due to their [...] Read more.

The current glacial lake datasets in the High Mountain Asia (HMA) region still need to be improved because their boundary divisions in the land–water transition zone are not precisely delineate, and also some very small glacial lakes have been lost due to their mixed reflectance with backgrounds. In addition, most studies have only focused on the changes in the area of a glacial lake as a whole, but do not involve the actual changes of per pixel on its boundary and the potential controlling factors. In this research, we produced more accurate and complete maps of glacial lake extent in the HMA in 2008, 2012, and 2016 with consistent time intervals using Landsat satellite images and the Google Earth Engine (GEE) cloud computing platform, and further studied the formation, distribution, and dynamics of the glacial lakes. In total, 17,016 and 21,249 glacial lakes were detected in 2008 and 2016, respectively, covering an area of 1420.15 ± 232.76 km² and 1577.38 ± 288.82 km²; the lakes were mainly located at altitudes between 4400 m and 5600 m. The annual areal expansion rate was approximately 1.38% from 2008 to 2016. To explore the cause of the rapid expansion of individual glacial lakes, we investigated their long-term expansion rates by measuring changes in shoreline positions. The results show that glacial lakes are expanding rapidly in areas close to glaciers and had a high expansion rate of larger than 20 m/yr from 2008 to 2016. Glacial lakes in the Himalayas showed the highest expansion rate of more than 2 m/yr, followed by the Karakoram Mountains (1.61 m/yr) and the Tianshan Mountains (1.52 m/yr). The accelerating rate of glacier ice and snow melting caused by global warming is the primary contributor to glacial lake growth. These results may provide information that will help in the understanding of detailed lake dynamics and the mechanism, and also facilitate the scientific recognition of the potential hazards associated with glacial lakes in this region. Full article

(This article belongs to the Special Issue Remote Sensing in Glaciology and Cryosphere Research)

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21 pages, 6879 KiB

Open AccessArticle

Deep Learning on Airborne Radar Echograms for Tracing Snow Accumulation Layers of the Greenland Ice Sheet

by Debvrat Varshney, Maryam Rahnemoonfar, Masoud Yari, John Paden, Oluwanisola Ibikunle and Jilu Li

Remote Sens. 2021, 13(14), 2707; https://doi.org/10.3390/rs13142707 - 9 Jul 2021

Cited by 7 | Viewed by 3118

Abstract

Climate change is extensively affecting ice sheets resulting in accelerating mass loss in recent decades. Assessment of this reduction and its causes is required to project future ice mass loss. Annual snow accumulation is an important component of the surface mass balance of [...] Read more.

Climate change is extensively affecting ice sheets resulting in accelerating mass loss in recent decades. Assessment of this reduction and its causes is required to project future ice mass loss. Annual snow accumulation is an important component of the surface mass balance of ice sheets. While in situ snow accumulation measurements are temporally and spatially limited due to their high cost, airborne radar sounders can achieve ice sheet wide coverage by capturing and tracking annual snow layers in the radar images or echograms. In this paper, we use deep learning to uniquely identify the position of each annual snow layer in the Snow Radar echograms taken across different regions over the Greenland ice sheet. We train with more than 15,000 images generated from radar echograms and estimate the thickness of each snow layer within a mean absolute error of 0.54 to 7.28 pixels, depending on dataset. A highly precise snow layer thickness can help improve weather models and, thus, support glaciological studies. Such a well-trained deep learning model can be used with ever-growing datasets to aid in the accurate assessment of snow accumulation on the dynamically changing ice sheets. Full article

(This article belongs to the Special Issue Remote Sensing in Glaciology and Cryosphere Research)

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17 pages, 6157 KiB

Open AccessArticle

Measurement of Snow Water Equivalent Using Drone-Mounted Ultra-Wide-Band Radar

by Rolf Ole R. Jenssen and Svein K. Jacobsen

Remote Sens. 2021, 13(13), 2610; https://doi.org/10.3390/rs13132610 - 2 Jul 2021

Cited by 19 | Viewed by 3614

Abstract

The use of unmanned aerial vehicle (UAV)-mounted radar for obtaining snowpack parameters has seen considerable advances over recent years. However, a robust method of snow density estimation still needs further development. The objective of this work is to develop a method to reliably [...] Read more.

The use of unmanned aerial vehicle (UAV)-mounted radar for obtaining snowpack parameters has seen considerable advances over recent years. However, a robust method of snow density estimation still needs further development. The objective of this work is to develop a method to reliably and remotely estimate snow water equivalent (SWE) using UAV-mounted radar and to perform initial field experiments. In this paper, we present an improved scheme for measuring SWE using ultra-wide-band (UWB) (0.7 to 4.5 GHz) pseudo-noise radar on a moving UAV, which is based on airborne snow depth and density measurements from the same platform. The scheme involves autofocusing procedures with the frequency–wavenumber (F–K) migration algorithm combined with the Dix equation for layered media in addition to altitude correction of the flying platform. Initial results from field experiments show high repeatability (R > 0.92) for depth measurements up to

5.5

m, and good agreement with Monte Carlo simulations for the statistical spread of snow density estimates with standard deviation of

0.108

g/cm³. This paper also outlines needed system improvements to increase the accuracy of a snow density estimator based on an F–K migration technique. Full article

(This article belongs to the Special Issue Remote Sensing in Glaciology and Cryosphere Research)

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23 pages, 25613 KiB

Open AccessArticle

Remote Sensing Monitoring of Advancing and Surging Glaciers in the Tien Shan, 1990–2019

by Sugang Zhou, Xiaojun Yao, Dahong Zhang, Yuan Zhang, Shiyin Liu and Yufang Min

Remote Sens. 2021, 13(10), 1973; https://doi.org/10.3390/rs13101973 - 19 May 2021

Cited by 21 | Viewed by 3541

Abstract

The advancing of glaciers is a manifestation of dynamic glacial instability. Glaciers in the Tien Shan region, especially in the Central Tien Shan, show instability, and advancing glaciers have been recently detected. In this study, we used Landsat TM/ETM+/OLI remote sensing images to [...] Read more.

The advancing of glaciers is a manifestation of dynamic glacial instability. Glaciers in the Tien Shan region, especially in the Central Tien Shan, show instability, and advancing glaciers have been recently detected. In this study, we used Landsat TM/ETM+/OLI remote sensing images to identify glaciers in the Tien Shan region from 1990 to 2019 and found that 48 glaciers advanced. Among them, thirty-four glaciers exhibited terminal advances, and 14 glaciers experienced advances on the tributary or trunk. Ten of the glaciers experiencing terminal advances have been identified as surging glaciers. These 48 glaciers are distributed in the western part of the Halik and Kungey Mountain Ranges in the Central Tien Shan, and Fergana Mountains in the Western Tien Shan, indicating that the Tien Shan is also one of the regions where advancing and surging glaciers are active. From 1990 to 2019, a total of 169 times advances occurred on 34 terminal advancing glaciers in the Tien Shan region; the highest number of advancing and surging of glaciers occurred in July (26 and 14 times, respectively). With reference to the existing literature and the present study, the surge cycle in the Tien Shan is longer than that in other regions at high latitudes in Asia, lasting about 35–60 years. Surging glaciers in the Tien Shan region may be affected by a combination of thermal and hydrological control. An increase in temperature and precipitation drives surging glaciers, but the change mechanism is still difficult to explain based on changes in a single climate variable, such as temperature or precipitation. Full article

(This article belongs to the Special Issue Remote Sensing in Glaciology and Cryosphere Research)

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28 pages, 12332 KiB

Open AccessArticle

Anisotropy Parameterization Development and Evaluation for Glacier Surface Albedo Retrieval from Satellite Observations

by Shaoting Ren, Evan S. Miles, Li Jia, Massimo Menenti, Marin Kneib, Pascal Buri, Michael J. McCarthy, Thomas E. Shaw, Wei Yang and Francesca Pellicciotti

Remote Sens. 2021, 13(9), 1714; https://doi.org/10.3390/rs13091714 - 28 Apr 2021

Cited by 12 | Viewed by 3354

Abstract

Glacier albedo determines the net shortwave radiation absorbed at the glacier surface and plays a crucial role in glacier energy and mass balance. Remote sensing techniques are efficient means to retrieve glacier surface albedo over large and inaccessible areas and to study its [...] Read more.

Glacier albedo determines the net shortwave radiation absorbed at the glacier surface and plays a crucial role in glacier energy and mass balance. Remote sensing techniques are efficient means to retrieve glacier surface albedo over large and inaccessible areas and to study its variability. However, corrections of anisotropic reflectance of glacier surface have been established for specific shortwave bands only, such as Landsat 5 Thematic Mapper (L5/TM) band 2 and band 4, which is a major limitation of current retrievals of glacier broadband albedo. In this study, we calibrated and evaluated four anisotropy correction models for glacier snow and ice, applicable to visible, near-infrared and shortwave-infrared wavelengths using airborne datasets of Bidirectional Reflectance Distribution Function (BRDF). We then tested the ability of the best-performing anisotropy correction model, referred to from here on as the ‘updated model’, to retrieve albedo from L5/TM, Landsat 8 Operational Land Imager (L8/OLI) and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery, and evaluated these results with field measurements collected on eight glaciers around the world. Our results show that the updated model: (1) can accurately estimate anisotropic factors of reflectance for snow and ice surfaces; (2) generally performs better than prior approaches for L8/OLI albedo retrieval but is not appropriate for L5/TM; (3) generally retrieves MODIS albedo better than the MODIS standard albedo product (MCD43A3) in both absolute values and glacier albedo temporal evolution, i.e., exhibiting both fewer gaps and better agreement with field observations. As the updated model enables anisotropy correction of a maximum of 10 multispectral bands and is implemented in Google Earth Engine (GEE), it is promising for observing and analyzing glacier albedo at large spatial scales. Full article

(This article belongs to the Special Issue Remote Sensing in Glaciology and Cryosphere Research)

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22 pages, 5195 KiB

Open AccessArticle

Remote Detection of Surge-Related Glacier Terminus Change across High Mountain Asia

by Amelia B. Vale, Neil S. Arnold, W. Gareth Rees and James M. Lea

Remote Sens. 2021, 13(7), 1309; https://doi.org/10.3390/rs13071309 - 30 Mar 2021

Cited by 17 | Viewed by 4235

Abstract

High Mountain Asia (HMA) hosts the largest glacier concentration outside of polar regions. It is also distinct glaciologically as it forms one of two major surge clusters globally, and many glaciers there contradict the globally observed glacier recession trend. Surging glaciers are critical [...] Read more.

High Mountain Asia (HMA) hosts the largest glacier concentration outside of polar regions. It is also distinct glaciologically as it forms one of two major surge clusters globally, and many glaciers there contradict the globally observed glacier recession trend. Surging glaciers are critical to our understanding of HMA glacier dynamics, threshold behaviour and flow instability, and hence have been the subject of extensive research, yet many dynamical uncertainties remain. Using the cloud-based geospatial data platform, Google Earth Engine (GEE) and GEE-developed tool, GEEDiT, to identify and quantify trends in the distribution and phenomenological characteristics of surging glaciers synoptically across HMA, we identified 137 glaciers as surging between 1987–2019. Of these, 55 were newly identified, 15 glaciers underwent repeat surges, and 18 were identified with enhanced glaciological hazard potential, most notably from Glacier Lake Outburst Floods (GLOFs). Terminus position time series analysis from 1987–2019 facilitated the development of a six-part phenomenological classification of glacier behaviour, as well as quantification of surge variables including active phase duration, terminus advance distance and rate, and surge periodicity. This research demonstrates the application of remote sensing techniques and the GEE platform to develop our understanding of surging glacier distribution and terminus phenomenology across large areas, as well as their ability to highlight potential geohazard locations, which can subsequently be used to focus monitoring efforts. Full article

(This article belongs to the Special Issue Remote Sensing in Glaciology and Cryosphere Research)

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22 pages, 4546 KiB

Open AccessArticle

A Response of Snow Cover to the Climate in the Northwest Himalaya (NWH) Using Satellite Products

by Animesh Choudhury, Avinash Chand Yadav and Stefania Bonafoni

Remote Sens. 2021, 13(4), 655; https://doi.org/10.3390/rs13040655 - 11 Feb 2021

Cited by 14 | Viewed by 4127

Abstract

The Himalayan region is one of the most crucial mountain systems across the globe, which has significant importance in terms of the largest depository of snow and glaciers for fresh water supply, river runoff, hydropower, rich biodiversity, climate, and many more socioeconomic developments. [...] Read more.

The Himalayan region is one of the most crucial mountain systems across the globe, which has significant importance in terms of the largest depository of snow and glaciers for fresh water supply, river runoff, hydropower, rich biodiversity, climate, and many more socioeconomic developments. This region directly or indirectly affects millions of lives and their livelihoods but has been considered one of the most climatically sensitive parts of the world. This study investigates the spatiotemporal variation in maximum extent of snow cover area (SCA) and its response to temperature, precipitation, and elevation over the northwest Himalaya (NWH) during 2000–2019. The analysis uses Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra 8-day composite snow Cover product (MOD10A2), MODIS/Terra/V6 daily land surface temperature product (MOD11A1), Climate Hazards Infrared Precipitation with Station data (CHIRPS) precipitation product, and Shuttle Radar Topography Mission (SRTM) DEM product for the investigation. Modified Mann-Kendall (mMK) test and Spearman’s correlation methods were employed to examine the trends and the interrelationships between SCA and climatic parameters. Results indicate a significant increasing trend in annual mean SCA (663.88 km²/year) between 2000 and 2019. The seasonal and monthly analyses were also carried out for the study region. The Zone-wise analysis showed that the lower Himalaya (184.5 km²/year) and the middle Himalaya (232.1 km²/year) revealed significant increasing mean annual SCA trends. In contrast, the upper Himalaya showed no trend during the study period over the NWH region. Statistically significant negative correlation (−0.81) was observed between annual SCA and temperature, whereas a nonsignificant positive correlation (0.47) existed between annual SCA and precipitation in the past 20 years. It was also noticed that the SCA variability over the past 20 years has mainly been driven by temperature, whereas the influence of precipitation has been limited. A decline in average annual temperature (−0.039 °C/year) and a rise in precipitation (24.56 mm/year) was detected over the region. The results indicate that climate plays a vital role in controlling the SCA over the NWH region. The maximum and minimum snow cover frequency (SCF) was observed during the winter (74.42%) and monsoon (46.01%) season, respectively, while the average SCF was recorded to be 59.11% during the study period. Of the SCA, 54.81% had a SCF above 60% and could be considered as the perennial snow. The elevation-based analysis showed that 84% of the upper Himalaya (UH) experienced perennial snow, while the seasonal snow mostly dominated over the lower Himalaya (LH) and the middle Himalaya (MH). Full article

(This article belongs to the Special Issue Remote Sensing in Glaciology and Cryosphere Research)

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