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The Sentinel-1 mission has now reached its maturity, and is acquiring high-quality images with a high revisit time, allowing for effective continuous monitoring of our rapidly changing planet. The purpose of this work is to assess the performance of the different synthetic aperture radar products made available by the European Space Agency through the Sentinels Data Hub against glacier displacement monitoring with offset tracking methodology. In particular, four classes of products have been tested: the medium resolution ground range detected, the high-resolution ground range detected, acquired in both interferometric wide and extra-wide swath, and the single look complex. The first are detected pre-processed images with about 40, 25, and 10-m pixel spacing, respectively. The last category, the most commonly adopted for the application at issue, represents the standard coherent synthetic aperture radar product, delivered in unprocessed focused complex format with pixel spacing ranging from 14 to 20 m in azimuth and from approximately 2 to 6 m in range, depending on the acquisition area and mode. Tests have been performed on data acquired over four glaciers, i.e., the Petermann Glacier, the Nioghalvfjerdsfjorden, the Jackobshavn Isbræ and the Thwaites Glacier. They revealed that the displacements estimated using interferometric wide swath single look complex and high-resolution ground range detected products are fully comparable, even at computational level. As a result, considering the differences in memory consumption and pre-processing requirements presented by these two kinds of product, detected formats should be preferred for facing the application. [ABSTRACT FROM AUTHOR]

The Ili River Basin, a crucial transboundary river in the arid region of Central Asia, plays a significant role in the region's ecology and water resources. However, current methods for monitoring glacier area changes in this region face challenges in automation and accuracy due to the complex terrain and climatic conditions. This study aims to evaluate the effectiveness of the Google Earth Engine (GEE) platform for monitoring glacier area changes in the Ili River Basin from 1992 to 2020, with a focus on improving data accuracy and processing efficiency. Utilizing the Landsat data series, we employed the random forest (RF) classification algorithm within the GEE platform to extract glacier areas, optimizing a multidimensional feature set using the Jeffries–Matusita (JM) distance method, and applied visual interpretation for data refinement. Our results demonstrated that the GEE platform, combined with the RF algorithm, provided high accuracy in glacier monitoring, achieving an overall accuracy of 89% and a kappa coefficient of 0.85. During the study period, the glacier area in the Ili River Basin decreased by 184.76 km2, with an average annual retreat rate of 6.84 km2, most notably between 3800 and 4400 m in elevation. The analysis revealed that temperature changes had a more pronounced impact on glacier dynamics than precipitation. This approach significantly enhances image utilization efficiency and data processing speed, offering a reliable tool for monitoring glacier dynamics. Future research should focus on integrating additional environmental variables and extending the temporal scope to further refine glacier dynamics modeling and predictions. [ABSTRACT FROM AUTHOR]

The increasing global warming trend has resulted in the mass loss of most glaciers. The Urumqi Vally, located in the dry and cold zone of China, and its widely dispersed glaciers are significant to the regional ecological environment, oasis economic development, and industrial and agricultural production. This is representative of glaciers in Middle Asia and represents one of the world's longest observed time series of glaciers, beginning in 1959. The Urumqi Headwater Glacier No. 1 (UHG-1) has a dominant presence in the World Glacier Monitoring Service (WGMS). This paper supplies a comprehensive analysis of past studies and future modeling of glacier changes in the Urumqi Valley. It has received insufficient attention in the past, and the mass balance of UHG-1 was used to verify that the geodetic results and the OGGM model simulation results are convincing. The main conclusions are: The area of 48.68 ± 4.59 km2 delineated by 150 glaciers in 1958 decreased to 21.61 ± 0.27 km2 delineated by 108 glaciers in 2022, with a reduction of 0.47 ± 0.04 km2·a−1 (0.96% a−1 in 1958–2022). The glacier mass balance by geodesy is −0.69 ± 0.11 m w.e.a−1 in 2000–2022, which is just deviating from the measured result (−0.66 m w.e.a−1), but the geodetic result in this paper can be enough to reflect the glacier changes (−0.65 ± 0.11 m w.e.a−1) of the URB in 2000–2022. The future loss rate of area and volume will undergo a rapid and then decelerating process, with the fastest and slowest inflection points occurring around 2035 and 2070, respectively. High temperatures and large precipitation in summer accelerate glacier loss, and the corresponding lag period of glacier change to climate is about 2–3 years. [ABSTRACT FROM AUTHOR]

Geosynchronous spaceborne–airborne (GEO-SA) ultra-high-frequency ultra-wideband bistatic synthetic aperture radar (UHF UWB BiSAR) provides high-precision images for marine and polar environments, which are pivotal in glacier monitoring and sea ice thickness measurement for polar ocean mapping and navigation. Contrasting with traditional high-frequency BiSAR, it faces unique challenges, such as the considerable spatial variability, significant range–azimuth coupling, and vast volumes of echo data, which impede high-resolution image reconstruction. This paper presents an improved bistatic nonlinear chirp scaling (NLCS) algorithm for imaging oceanic scenes with GEO-SA UHF UWB BiSAR. This methodology extends the two-dimensional (2-D) spectrum up to the sixth order via the method of series reversion (MSR) to meet accuracy demands and then employs an elliptical model to elucidate the alterations in the azimuth frequency modulation (FM) rate mismatch. Initially, the imaging geometry and signal model are introduced, and then a separation of bistatic slant ranges based on the configuration is proposed. In addition, during range processing, after eliminating linear range cell migration (RCM), the derivation process for the sixth-order 2-D spectrum is detailed and an improved filter is applied to correct the high-order RCM. Finally, during azimuth processing, the causes of the FM rate mismatch are analyzed, a cubic perturbation function derived from the elliptical model is used for FM rate equalization, and a unified sixth-order filter is applied to complete the azimuth compression. Experimental results with point targets and natural oceanic scenes validate the outstanding efficacy of the proposed NLCS algorithm, particularly in imaging quality enhancements for GEO-SA UHF UWB BiSAR. [ABSTRACT FROM AUTHOR]

InSARTrac is an innovative method for 3D displacement monitoring that combines terrestrial interferometric synthetic aperture radar (InSAR) and computer vision-based feature tracking. The 3D measurements obtained are considered far superior to 1D or 2D data and facilitate evaluations concerning the mechanisms controlling kinematics. This study presents the results of InSARTrac measurements at the Mölltal Glacier in Carinthia, Austria. The duration of glacier monitoring was four weeks and involved two instrument setup positions to obtain comparative measurements of supraglacial rock debris from different angles without utilizing retroreflectors. The mean displacement rate of the resultant vector is 22 mm/day and includes ~11 mm/day in the downgradient ice surface direction and 6 to 18 mm/day vertically downward. Additionally, the entire glacier surface was measured three times using a LIDAR-equipped UAV, revealing mean vertical displacements of 16 mm/day. The measurements indicate an InSARTrac accuracy of 4.2 ppm, which is 27% lower than in the initial controlled tests utilizing retroreflectors. The field test demonstrates the capability of InSARTrac to provide meaningful 3D displacement measurements of supraglacial rock debris. The material monitored has texture and reflectivity similar to certain classes of landslides, rock glaciers, and other alpine processes, indicating that InSARTrac has promising applications for monitoring a variety of geologic phenomena. [ABSTRACT FROM AUTHOR]

The significant retreat of glaciers in terms of climate change compels researchers to increase the frequency of studies regarding the transformations occurring in glacier boundaries. In this study, we provided glacier area delineation of Ala-Archa valley glaciers by using a Sentinel-1 SAR dataset and the InSAR Coherence technique. Since glaciers have specific patterns of movement, the low coherence method signals the presence of ice. The analysis used the pair of Sentinel-1 datasets for the summer, to ensure the lowest coherence and provide an areal estimation during the peak of ablation. The independence of the SAR images from cloud and light conditions permits us to acquire the images in a timely manner, which highly affects the results of glacier monitoring. This method has shown high potential in the mapping of debris-covered ice and the indication of dead ice. To identify and separate areas of low coherence, such as glacier lakes and unstable slopes, we used object-based mapping by using the geomorphological features of the ice. In this study, we defined a coherence value of less than 0.3 in the glacier area. Our research identified a number of 56 glaciers within the study area of 31.45 km2 and obtained highly accurate glacier maps for the glaciers with a smooth terminus. The analysis shows that automatic and manual delineation of the glaciers' boundaries have certain limitations, but using the advantages of both scientific approaches, further studies will generate more accurate results. [ABSTRACT FROM AUTHOR]

Albedo/reflectance is of great importance for glaciers’ mass balance and energy budget. Elevation could be a major factor of influence for glacier reflectance, and therefore when studying glacier reflectance, the altitude ranges should be considered. However, due to the limitations of traditional earth observation systems, conventional analyses usually consider the spatial and temporal patterns of the reflectance average, which is severely restricted. The launch of Landsat-8 gives us the opportunity to study the seasonal glacier reflectance–elevation relationship. We have obtained the monthly near-nadir reflectance per 100 m for twelve glaciers in western China based on 372 scenes of Landsat 8 images acquired from April 2013 to December 2015. Variations of monthly broadband reflectance, reflectance–elevation relationships and reflectance gradients are analyzed and discussed. The results show that the linear trend of the reflectance–elevation relationship (when the altitude is less than 6100 m) is very significant; elevation has greater influence than location on seasonal reflectance variations; and the level of glacier reflectance gradient may relate with its climate. This may be the first work that has used remote-sensing data to analyze seasonal glacier reflectance–elevation patterns. [ABSTRACT FROM AUTHOR]

Microwave remote sensing is one of the main approaches to glacier monitoring. This paper provides a comparative analysis of how different types of radar information differ in identifying debris-covered alpine glaciers using machine learning algorithms. Based on Sentinel-1A data, three data suites were designed: A backscattering coefficient (BC)-based data suite, a polarization decomposition parameter (PDP)-based data suite, and an interference coherence coefficient (ICC)-based data suite. Four glaciers with very different orientations in different climatic zones of the Tibetan Plateau were selected and classified using an integrated machine learning classification approach. The results showed that: (1) The boosted trees and subspace k-nearest neighbor algorithms were optimal and robust; and (2) the PDP suite (63.41–99.57%) and BC suite (55.85–99.94%) both had good recognition accuracy for all glaciers; notably, the PDP suite exhibited better rock and debris recognition accuracy. We also analyzed the influence of the distribution of glacier surface aspect on the classification accuracy and found that the more asymmetric it was about the sensor orbital plane, the more difficult it was for the BC and PDP suites to recognize the glacier, and a large slope could further reduce the accuracy. Our results suggested that during the inventory or classification of large-scale debris-covered alpine glaciers, priority should be given to polarization decomposition features and elevation information, and it is best to divide the glaciers into multiple subregions based on the spatial relationship between glacier surface aspect and radar beams. [ABSTRACT FROM AUTHOR]

In the context of global warming, the melting of glaciers in the Tien Shan Mountains as the important "solid reservoir" in the arid area of Central Asia is accelerating in recent decades, leading to profound changes in regional water resources. Based on the simulated glaciological data from the Python Glacier Evolution Model (PyGEM) and the measured glaciological data from the World Glacier Monitoring Service (WGMS), this paper analyzed the applicability of simulated data, the changes in glacier mass balance, and the responses of the glacier to climate change and its impacts on glacier runoff in the Tien Shan Mountains. The results show that (1) the PyGEM simulation dataset is in good agreement with the measurements, which can effectively reproduce the change in the glacier mass balance in the Tien Shan Mountains glaciers and is suitable for studying the regional scale glacier change. (2) From 1980 to 2016, the decadal average mass balance change rate of glaciers in the Tien Shan Mountains was −0.012 m w.e. yr−1. The regional mass balance showed an overall negative increasing trend (the area with increasingly negative accounted for 80.13% of the entire area), with a positive increase that only occurred in the West Tien Shan Mountains and western North Tien Shan Mountains (19.87%). (3) The correlation between the temperature and mass balance is much higher than that between the precipitation and mass balance. Temperature dominates the change and development of regional glaciers. The increase in negative glacier mass balance that was observed in the study area is mainly affected by the rising temperature, the decreasing solid precipitation in the accumulation period, and the rapid melting in the ablation period. (4) The glacier runoff in the six representative rivers showed an increasing trend. The contribution rate of glacier runoff to river runoff changed significantly after 2000 but differed among rivers. Overall, the larger the glacier area in the source region is, the greater the contribution rate of glacier runoff is, and the more the contribution rate continuously increases or fluctuates; otherwise, the contribution rate keeps declining, which means the runoff peak may have passed and future runoff may decrease. [ABSTRACT FROM AUTHOR]

Constructing high-precision subsidence basins is of paramount importance for mining subsidence monitoring. Traditional unmanned aerial vehicle (UAV) photogrammetry techniques typically construct subsidence basins by directly differencing digital elevation models (DEMs) from different monitoring periods. However, this method often neglects the influence of horizontal displacement on the accuracy of the subsidence basin. Taking a mining area in Ordos, Inner Mongolia, as an example, this study employed the normalized cross-correlation (NCC) matching algorithm to extract horizontal displacement information between two epochs of a digital orthophoto map (DOM) and subsequently corrected the horizontal position of the second-epoch DEM. This ensured that the planar positions of ground feature points remained consistent in the DEM before and after subsidence. Based on this, the vertical displacement in the subsidence area (the subsidence basin) was obtained via DEM differencing, and the parameters of the post-correction subsidence basin were inverted using the probability integral method (PIM). The experimental results indicate that (1) the horizontal displacement was influenced by the gully topography, causing the displacement within the working face to be segmented on both sides of the gully; (2) the influence of the terrain on the subsidence basin was significantly reduced after correction; (3) the post-correction surface subsidence curve was smoother than the pre-correction curve, with abrupt error effects markedly diminished; (4) the accuracy of the post-correction subsidence basin increased by 43.12% compared with the total station data; and (5) comparing the measured horizontal displacement curve with that derived using the probability integral method revealed that the horizontal displacement on the side of an old goaf adjacent to the newly excavated working face shifted toward the advancing direction of the new working face as mining progressed. This study provides a novel approach and insights for using low-cost UAVs to construct high-precision subsidence basins. [ABSTRACT FROM AUTHOR]

The Eastern Tianshan region, influenced by the far-field effect of northward compression and expansion of the Qinghai-Xizang block, features highly developed Late Quaternary active faults that exhibit significant neotectonic activity. Historically, the Barkol-Yiwu Basin, located to the north of the Eastern Tianshan, experienced two major earthquakes in 1842 and 1914, each with a magnitude of M71/2. In contrast, the Hami Basin on the southern margin of the Eastern Tianshan has no historical records of any major earthquakes, and its seismic potential, mechanisms, and future earthquake hazards remain unclear. Based on satellite image interpretation and field surveys, this study identified a relatively recent and well-preserved seismic surface rupture zone with good continuity in the Liushugou area of the western segment of the Northern Margin Fault of the Hami Basin (HMNF), which is the seismogenic structure responsible for the rupture. The surface rupture zone originates at Kekejin in the east, extends intermittently westward through Daipuseke Bulake and Liushugou, and terminates at Wuzun Bulake, with a total length of approximately 21 km. The rupture zone traverses the youngest geomorphic surface units, such as river beds or floodplains and first-order terraces (platforms), and is characterized by a series of single or multiple reverse fault scarps. The morphology of fault scarps is clear, presenting a light soil color with heights ranging from 0.15 m to 2.13 m and an average displacement of 0.56 m, suggesting that this surface rupture zone likely represents the most recent seismic event. Comparison with historical earthquake records in the Eastern Tianshan region suggests that the rupture zone may have been formed simultaneously with the Xiongkuer rupture zone by the 1842 M71/2 earthquake along the boundary faults on both sides of the Barkol Mountains, exhibiting a flower-like structural pattern. Alternatively, it might represent a separate, unrecorded seismic event occurring shortly after the 1842 earthquake. The estimated magnitude of the associated earthquake is about 6.6~6.9. Given that surface-rupturing earthquakes have already occurred in the western segment, the study indicates that the Erdaogou–Nanshankou section of the HMNF has surpassed the average recurrence interval for major earthquakes, indicating a potential future earthquake hazard. [ABSTRACT FROM AUTHOR]

Ice avalanche (IA) has a strong concealment and sudden characteristics, which can cause severe disasters. The early identification of IA hidden danger is of great value for disaster prevention and mitigation. However, it is very difficult, and there is poor efficiency in identifying it by site investigation or manual remote sensing. So, an artificial intelligence method for the identification of IA hidden dangers using a deep learning model has been proposed, with the glacier area of the Yarlung Tsangpo River Gorge in Nyingchi selected for identification and validation. First, through engineering geological investigations, three key identification indices for IA hidden dangers are established, glacier source, slope angle, and cracks. Sentinel-2A satellite data, Google Earth, and ArcGIS are used to extract these indices and construct a feature dataset for the study and validation area. Next, key performance metrics, such as training accuracy, validation accuracy, test accuracy, and loss rates, are compared to assess the performance of the ResNet50 (Residual Neural Network 50) and VGG16 (Visual Geometry Group 16) models. The VGG16 model (96.09% training accuracy) is selected and optimized, using Early Stopping (ES) to prevent overfitting and L2 regularization techniques (L2) to add weight penalties, which constrained model complexity and enhanced simplicity and generalization, ultimately developing the ES-L2-VGG16 (Early Stopping—L2 Norm Regularization Techniques—Visual Geometry Group 16) model (98.61% training accuracy). Lastly, during the validation phase, the model is applied to the Yarlung Tsangpo River Gorge glacier area on the Tibetan Plateau (TP), identifying a total of 100 IA hidden danger areas, with average slopes ranging between 34° and 48°. The ES-L2-VGG16 model achieves an accuracy of 96% in identifying these hidden danger areas, ensuring the precise identification of IA dangers. This study offers a new intelligent technical method for identifying IA hidden danger, with clear advantages and promising application prospects. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau (TP) is the largest glacier reserve outside the Antarctic and Arctic regions. Climate warming has affected the reserve of freshwater resources and led to frequent glacier disasters. However, due to its extreme environment of hypoxia and low pressure, it is extremely difficult to obtain data. Compared with other traditional monitoring methods such as makers and satellite remote sensing technology, Ground-Based (GB) radar systems have the advantages of convenient carrying and installation, sub-second level sampling, and sub-millimeter measurement accuracy. They can be used as an effective way to study the short-term rapid movement changes in glaciers. Based on a self-built GB radar system, monitoring experiments were conducted on two glacier termini on the TP. The movement speed of the Rongbuk glacier terminus on Mount Qomolangma was obtained through time-series interferometric measurement as 4.10 cm/day. When the altitude was about 5200 m, the glacier movement speed was 7.74 cm/day, indicating the spatial differences with altitude changes. And in another region, the movement speed of the Yangbulake glacier terminus on Mount Muztag Ata was 198.96 cm/day, indicating significant changes in glacier movement. The cross-validation of Sentinel-1 data during the same period proved the effectiveness of GB radar system interferometry in measuring glacier movement speed and also provided field validation data for remote sensing inversion. [ABSTRACT FROM AUTHOR]

The current emphasis in the advancement of space-based synthetic-aperture radar (SAR) is on lightweight payloads under 100 kg with resolutions surpassing 1 m. This focus is directed toward meeting the launch criteria for multiple satellites on a single rocket and cutting costs. This article discusses the creation and progress of a Ku-band SAR payload for the Taijing-4(03) satellite, launched on 23 January 2024 and accompanied by four other satellites. The SAR payload design was customized to meet the demands of a micro-nano satellite platform, resulting in a lightweight, flat design weighing less than 80 kg, seamlessly integrated with the plate-shaped satellite platform. The article also introduces a beam optimization strategy for the phased array SAR antenna, significantly boosting the SAR system's performance. The SAR payload provides various operating modes like slide-spot, strip, Scan 1, Scan 2, and others, with a maximum achievable resolution exceeding 1 m. Extensive in-orbit testing of the payload produced numerous high-quality SAR images with potential uses in emergency disaster mitigation, safeguarding ecosystems, monitoring forests, managing crops, tracking sea ice, and more. [ABSTRACT FROM AUTHOR]

The Vilcanota is the second-largest snow-capped mountain range in Peru, featuring 380 individual glaciers, each with its own unique characteristics that must be studied independently. However, few studies have been conducted in the Vilcanota range to monitor and track the area and volume changes of the Suyuparina and Quisoquipina glaciers. Notably, there are only a few studies that have approached this issue using LIDAR technology. Our methodology is based on a combination of optical, radar and LIDAR data sources, which allowed for constructing coherent temporal series for the both the perimeter and volume changes of the Suyuparina and Quisoquipina glaciers while accounting for the uncertainty in the perimeter detection procedure. Our results indicated that, from 1990 to 2013, there was a reduction in snow cover of 12,694.35 m2 per year for Quisoquipina and 16,599.2 m2 per year for Suyuparina. This represents a loss of 12.18% for Quisoquipina and 22.45% for Suyuparina. From 2006 to 2013, the volume of the Quisoquipina glacier decreased from 11.73 km3 in 2006 to 11.04 km3 in 2010, while the Suyuparina glacier decreased from 6.26 km3 to 5.93 km3. Likewise, when analyzing the correlation between glacier area and precipitation, a moderate inverse correlation (R = −0.52, p < 0.05) was found for Quisoquipina. In contrast, the correlation for Suyuparina was low and nonsignificant, showing inconsistency in the effect of precipitation. Additionally, the correlation between the snow cover area and the annual mean air temperature (R = −0.34, p > 0.05) and annual minimum air temperature (R = −0.36, p > 0.05) was low, inverse, and not significant for Quisoquipina. Meanwhile, snow cover on Suyuparina had a low nonsignificant correlation (R = −0.31, p > 0.05) with the annual maximum air temperature, indicating a minimal influence of the measured climatic variables near this glacier on its retreat. In general, it was possible to establish a reduction in both the area and volume of the Suyuparina and Quisoquipina glaciers based on freely accessible remote sensing data. [ABSTRACT FROM AUTHOR]

Glacier meltwater is an indispensable water supply for billions of people living in the catchments of major Asian rivers. However, the role of glaciers on river runoff regulation is seldom investigated due to the lack of annual glacier mass balance observation. In this study, we employed an albedo-based model with a daily land surface albedo dataset to derive the annual glacier mass balance over the Tuotuo River Basin (TRB). During 2000–2022, an annual glacier mass balance range of −0.89 ± 0.08 to 0.11 ± 0.11 m w.e. was estimated. By comparing with river runoff records from the hydrometric station, the contribution of glacier mass change to river runoff was calculated to be 0.00–31.14% for the studied period, with a mean value of 9.97%. Moreover, we found that the mean contribution in drought years is 20.07%, which is approximately five times that in wet years (4.30%) and twice that in average years (9.49%). Therefore, our results verify that mountain glaciers act as a significant buffer against drought in the TRB, at least during the 2000–2022 period. [ABSTRACT FROM AUTHOR]

Since the 1970s, extensive halite extraction in Maceió, Brazil, has resulted in significant geological risks, including ground collapses, sinkholes, and infrastructure damage. These risks became particularly evident in 2018, following an earthquake, which prompted the cessation of mining activities in 2019. This study investigates subsidence deformation resulting from these mining operations, focusing on the collapse of Mine 18 on 10 December 2023. We utilized the Quasi-Persistent Scatterer Interferometric Synthetic Aperture Radar (QPS-InSAR) technique to analyze a dataset of 145 Sentinel-1A images acquired between June 2019 and April 2024. Our approach enabled the analysis of cumulative displacement, the loss of amplitude stability, the evolution of amplitude time series, and the amplitude change matrix of targets near Mine 18. The study introduces an innovative QPS-InSAR approach that integrates phase and amplitude information using amplitude time series to assess the lifecycle of radar scattering targets throughout the monitoring period. This method allows for effective change detection following sudden events, enabling the identification of affected areas. Our findings indicate a maximum cumulative displacement of −1750 mm, with significant amplitude changes detected between late November and early December 2023, coinciding with the mine collapse. This research provides a comprehensive assessment of deformation trends and ground stability in the affected mining areas, providing valuable insights for future monitoring and risk mitigation efforts. [ABSTRACT FROM AUTHOR]

This article provides a comprehensive overview of all currently available datasets of the Long-term Ecosystem Research (LTER) site Oberes Stubachtal. The site is located in the Hohe Tauern mountain range (Eastern Alps, Austria) and includes both protected areas (Hohe Tauern National Park) and unprotected areas (Stubach valley). While the main research focus of the site is on high mountains, glaciology, glacial hydrology, and biodiversity, the eLTER Whole-System Approach (WAILS) was used for data selection. This approach involves a systematic screening of all available data to assess their suitability as eLTER Standard Observations (SOs). This includes the geosphere, atmosphere, hydrosphere, biosphere, and sociosphere. These SOs are fundamental to the development of a comprehensive long-term ecosystem research framework. In total, more than 40 datasets have been collated for the LTER site Oberes Stubachtal and included in the Dynamic Ecological Information Management System—Site and Data Registry (DEIMS-SDR), the eLTER's data platform. This paper provides a detailed inventory of the datasets and their primary attributes, evaluates them against the WAILS-required observation data, and offers insights into strategies for future initiatives. All datasets are made available through dedicated repositories for FAIR (findable, accessible, interoperable, reusable) use. [ABSTRACT FROM AUTHOR]

This paper investigates the ability of a relatively low cost, commercially available uncrewed aerial vehicle (UAV), the DJI Mavic 3 Multispectral, to perform cryospheric research. The performance of this UAV, where applicable, is compared to a similar but higher cost system, the DJI Matrice 350, equipped with a Micasense RedEdge-MX Multispectral dual-camera system. The Mavic 3 Multispectral was tested at three field sites: the Lemon Creek Glacier, Juneau Icefield, AK; the Easton Glacier, Mt. Baker, WA; and Bagley Basin, Mt. Baker, WA. This UAV proved capable of mapping the spatial distribution of red snow algae on the surface of the Lemon Creek Glacier using both spectral indices and a random forest supervised classification method. The UAV was able to assess the timing of snowmelt and changes in suncup morphology on snow-covered areas within the Bagley Basin. Finally, the UAV was able to classify glacier surface features using a random forest algorithm with an overall accuracy of 68%. The major advantages of this UAV are its low weight, which allows it to be easily transported into the field, its low cost compared to other alternatives, and its ease of use. One limitation would be the omission of a blue multispectral band, which would have allowed it to more easily classify glacial ice and snow features. [ABSTRACT FROM AUTHOR]

The interest in the enhancement of innovative solutions in the geospatial data classification domain from integrated aerial methods is rapidly growing. The transition from unstructured to structured information is essential to set up and arrange geodatabases and cognitive systems such as digital twins capable of monitoring territorial, urban, and general conditions of natural and/or anthropized space, predicting future developments, and considering risk prevention. This research is based on the study of classification methods and the consequent segmentation of low-altitude airborne LiDAR data in highly forested areas. In particular, the proposed approaches investigate integrating unsupervised classification methods and supervised Neural Network strategies, starting from unstructured point-based data formats. Furthermore, the research adopts Machine Learning classification methods for geo-morphological analyses derived from DTM datasets. This paper also discusses the results from a comparative perspective, suggesting possible generalization capabilities concerning the case study investigated. [ABSTRACT FROM AUTHOR]

Glacier, snow, and ice are the essential components of the Himalayan cryosphere and provide a sustainable water source for different applications. Continuous and accurate monitoring of glaciers allows the forecasting analysis of natural hazards and water resource management. In past literature, different methodologies such as spectral unmixing, object-based detection, and a combination of various spectral indices are commonly utilized for mapping snow, ice, and glaciers. Most of these methods require human intervention in feature extraction, training of the models, and validation procedures, which may create bias in the implementation approaches. In this study, the deep learning classifier based on ENVINet5 (U-Net) architecture is demonstrated in the delineation of glacier boundaries along with snow/ice over the Bara Shigri glacier (Western Himalayas), Himachal Pradesh, India. Glacier monitoring with Landsat data takes the advantage of a long coverage period and finer spectral/spatial resolution with wide coverage on a larger scale. Moreover, deep learning utilizes the semantic segmentation network to extract glacier boundaries. Experimental outcomes confirm the effectiveness of deep learning (overall accuracy, 91.89% and Cohen's kappa coefficient, 0.8778) compared to the existing artificial neural network (ANN) model (overall accuracy, 88.38% and kappa coefficient, 0.8241) in generating accurate classified maps. This study is vital in the study of the cryosphere, hydrology, agriculture, climatology, and land-use/land-cover analysis. [ABSTRACT FROM AUTHOR]

We use data from the Gravity Recovery and Climate Experiment and its Follow-On mission (GRACE/GRACE-FO) from April 2002 to December 2022 to analyze interannual glacial mass changes in High Mountain Asia (HMA) and its subregions and their driving factors. Glacial mass changes in the HMA subregions show clear regional characteristics. Interannual glacial mass changes in the HMA region are closely related to interannual oscillations of precipitation and temperature, and are also correlated with El Niño–Southern Oscillation (ENSO). Glacial mass changes in the regions (R1–R6) are dominated by precipitation, and ENSO affects interannual glacial mass changes mainly by affecting precipitation. In region (R7) and region (R8), the glacial mass changes are mainly controlled by temperature. ENSO also affects the interannual glacial mass changes by affecting interannual changes in temperature. The interannual glacial mass changes in regions (R9–R11) are jointly dominated by temperature and precipitation, and also related to ENSO. Another interesting finding of this study is that glacial mass changes in the western part of HMA (R1–R6) show a clear 6–7-year oscillation, strongly correlated with a similar oscillation in precipitation, while in the eastern part (R9–R11), a 2–3-year oscillation was found in both glacial mass change and precipitation, as well as temperature. These results verify the response of interannual HMA glacial mass changes to climate processes, crucial for understanding regional climate dynamics and sustainable water resource management. [ABSTRACT FROM AUTHOR]

The geological environment damage caused by coal mining subsidence has become an important factor affecting the sustainable development of mining areas. Reconstruction of the Coal Mining Subsidence Field (CMSF) is the key to preventing geological disasters, and the needs of CMSF reconstruction cannot be met by solely relying on a single remote sensing technology. The combination of Unmanned Aerial Vehicle (UAV) and Synthetic Aperture Radar (SAR) has complementary advantages; however, the data fusion strategy by refining the SAR deformation field through UAV still needs to be updated constantly. This paper proposed a Prior Weighting (PW) method based on Satellite Aerial (SA) heterogeneous remote sensing. The method can be used to fuse SAR and UAV Light Detection and Ranging (LiDAR) data for ground subsidence parameter inversion. Firstly, the subsidence boundary of Differential Interferometric SAR (DInSAR) combined with the large gradient subsidence of Pixel Offset Tracking (POT) was developed to initialize the SAR preliminary CMSF. Secondly, the SAR preliminary CMSF was refined by UAV LiDAR data; the weights of SAR and UAV LiDAR data are 0.4 and 0.6 iteratively. After the data fusion, the subsidence field was reconstructed. The results showed that the overall CMSF accuracy improved from ±144 mm to ±51 mm. The relative errors of the surface subsidence factor and main influence angle tangent calculated by the physical model and in situ measured data are 1.3% and 1.7%. It shows that the proposed SAR/UAV fusion method has significant advantages in the reconstruction of CMSF, and the PW method contributes to the prevention and control of mining subsidence. [ABSTRACT FROM AUTHOR]

Forest inspection is a crucial component of forest monitoring in China. The current methods for detecting changes in forest patches primarily rely on remote sensing imagery and manual visual interpretation, which are time-consuming and labor-intensive approaches. This study aims to automate the extraction of changed forest patches using UAVs and artificial intelligence technologies, thereby saving time while ensuring detection accuracy. The research first utilizes position and orientation system (POS) data to perform geometric correction on the acquired UAV imagery. Then, a convolutional neural network (CNN) is used to extract forest boundaries and compare them with the previous vector data of forest boundaries to initially detect patches of forest reduction. The average boundary distance algorithm (ABDA) is applied to eliminate misclassified patches, ultimately generating precise maps of reduced forest patches. The results indicate that using POS data with RTK positioning for correcting UAV imagery results in a central area correction error of approximately 4 m and an edge area error of approximately 12 m. The TernausNet model achieved a maximum accuracy of 0.98 in identifying forest areas, effectively eliminating the influence of shrubs and grasslands. When the UAV flying height is 380 m and the distance threshold is set to 8 m, the ABDA successfully filters out misclassified patches, achieving an identification accuracy of 0.95 for reduced forest patches, a precision of 0.91, and a kappa coefficient of 0.89, fully meeting the needs of forest inspection work in China. Select urban forests with complex scenarios in the research area can be used to better promote them to other regions. This study ultimately developed a fully automated forest change detection system. [ABSTRACT FROM AUTHOR]

The glacier surface is composed not only of ice or snow but also of a heterogeneous mixture of various materials. The presence of light-absorbing impurities darkens the glacier surface, reducing local reflectance and thereby accelerating the glacier melting process. However, our understanding of the spatial distribution of these impurities remains limited, and there is a lack of studies on quantifying the dirty degree of glacier surfaces. During the Sentinel satellite overpass on 21 August 2023, we used an ASD FieldSpec3 spectrometer to measure the reflectance spectra of glacier surfaces with varying degrees of dirtiness on the Qiyi glacier, Qinghai–Tibet Plateau. Using Multiple Endmember Spectral Mixture Analysis (MESMA), the Sentinel imagery was decomposed to generate fraction images of five primary ice surface materials as follows: coarse-grained snow, slightly dirty ice, moderately dirty ice, extremely dirty ice, and debris. Using unmanned aerial vehicle (UAV) imagery with a 0.05 m resolution, the primary ice surface was delineated and utilized as reference data to validate the fraction images. The findings revealed a strong correlation between the fraction images and the reference data (R2 ≥ 0.66, RMSE ≤ 0.21). Based on pixel-based classification from the UAV imagery, approximately 80% of the glacier surface is covered by slightly dirty ice (19.2%), moderately dirty ice (33.3%), extremely dirty ice (26.3%), and debris (1.2%), which significantly contributes to its darkening. Our study demonstrates the effectiveness of using Sentinel imagery in conjunction with MESMA to map the degree of glacier surface dirtiness accurately. [ABSTRACT FROM AUTHOR]

The glaciers in southeastern Tibet Plateau (SETP) influenced by oceanic climate are sensitive to global warming, and there remains a notable deficiency in accurate multitemporal change analyses of these glaciers. We conduct glacier inventories in the Yigong Zangbo River Basin (YZRB) in SETP for the years 1988, 2015, and 2023 utilizing Landsat and Sentinel-2 imagery, and analyze the glacier spatiotemporal variation incorporating the existing glacier inventory data. Since the 1970s until 2023, the glaciers significantly retreated at a rate of 0.76 ± 0.11%·a−1, with the area decreasing from 2583.09 ± 88.80 km2 to 1635.89 ± 71.74 km2, and the ice volume reducing from 221.7017 ± 7.9618 km3 to 152.7429 ± 6.1747 km3. The most significant retreat occurred in glaciers smaller than 1 km2. Additionally, glaciers on southern aspects retreated slower than the northern counterparts. The glaciers in the western YZRB witnessed a significantly greater shrinkage rate than those in the eastern section, with the most pronounced changes occurring in Aso Longbu River Basin. Furthermore, severe glacier mass deficits were observed from 2000 to 2019, averaging a loss rate of 0.57 ± 0.06 m w.e. a−1. The continuous rise in air temperature has primarily induced a general widespread glacier change in the YZRB. However, diverse topography led to spatial variability in glacier changes with discrepancies as large as several times. The features of individual glaciers, such as glacier size, debris cover, and the development of ice-contact glacial lakes enhanced the local complexity of glacier change and elusive response behaviors to climate warming led by the different topographic conditions. [ABSTRACT FROM AUTHOR]

Assessing glaciers using recent and historical data and predicting the future impacts on them due to climate change are crucial for understanding global glacier mass balance, regional water resources, and downstream hydrology. Computational methods are crucial for analyzing current conditions and forecasting glacier changes using remote sensing and other data sources. Due to the complexity and large data volumes, there is a strong demand for accelerated computing. AI-based approaches are increasingly being adopted for their efficiency and accuracy in these tasks. Thus, in the current state-of-the-art review work, available research results on the application of AI methods for glacier studies are addressed. Using selected search terms, AI-based publications are collected from research databases. They are further classified in terms of their geographical locations and glacier-related research purposes. It was found that the majority of AI-based glacier studies focused on inventorying and mapping glaciers worldwide. AI techniques like U-Net, Random forest, CNN, and DeepLab are mostly utilized in glacier mapping, demonstrating their adaptability and scalability. Other AI-based glacier studies such as glacier evolution, snow/ice differentiation, and ice dynamic modeling are reviewed and classified, Overall, AI methods are predominantly based on supervised learning and deep learning approaches, and these methods have been used almost evenly in glacier publications over the years since the beginning of this research area. Thus, the integration of AI in glacier research is advancing, promising to enhance our comprehension of glaciers amid climate change and aiding environmental conservation and resource management. [ABSTRACT FROM AUTHOR]

The development of satellite altimetry has significantly advanced the application of satellite Earth observation technologies in surface water monitoring, resulting in a substantial body of research. Although numerous reviews have summarized progress in this field, their analyses are often limited in scope and fail to provide a systematic, quantitative assessment of the current research prospects and trends. To address this gap, we utilize CiteSpace and VOSviewer bibliometric software to analyze 13,500 publications from the WOS database, spanning the years from 1988 to 2022. Our analysis focused on publication volume, authorship, collaboration networks, and content. We also compare data from Google Scholar and Scopus to validate the reliability of our dataset. Our findings indicate a steadily growing research potential in this field, as evidenced by trends in publication volume, authorship, journal influence, and disciplinary focus. Notably, the leading journals are primarily in the realm of remote sensing, while key disciplines include geology, remote sensing science, and oceanography. Keyword analysis revealed current research hotspots such as sea-level rise, snow depth, and machine learning applications. Among various water body types, research on glaciers ranks second only to ocean studies. Furthermore, research focus areas are shifting from large oceanic regions like the Pacific and Atlantic Oceans to significant inland water bodies, notably the Tibetan Plateau and the Amazon basin. This study combines qualitative and quantitative methods to analyze vast amounts of information in the field of surface water monitoring by satellite altimetry. The resulting visualizations provide researchers with clear insights into the development trends and patterns within this domain, offering valuable support for identifying future research priorities and directions. [ABSTRACT FROM AUTHOR]

Maritime glaciers in the southeastern Tibetan Plateau (SETP) are particularly sensitive to changes in climate, and their changes directly and severely affect regional water security and glacier-related hazards. Given their large societal importance, a better understanding of the mass balance of maritime glaciers in the SETP, a key variable for characterizing the state of glacier health, is of great scientific interest. In this review, we synthesize in situ, satellite-based observations and simulations that present an overall accelerating negative mass balance of maritime glaciers in the SETP in recent decades. We hereby highlight a significant spatiotemporal difference in the mass balance of maritime glaciers across the SETP and investigate the drivers of the accelerated mass loss of these glaciers in recent years. We find that accelerated glacier mass loss agrees with the variabilities in temperatures rising and precipitation decreasing at regional scales, as well as the spatial patterns of widespread melt hotspots (e.g., thin debris, ice cliffs, supraglacial ponds, and surface streams), the expansion of glacial lakes, enlarged ice crevasses, and frequent ice avalanches. Finally, the challenges of the mass balance study of maritime glaciers and future perspectives are proposed. Our review confirms the urgent need to improve the existing glacier inventory and establish comprehensive monitoring networks in data-scarce glacierized catchments, and it suggests paying particular attention to the development of glacier mass-balance models that coupe multiple physical processes at different interfaces to predict the status of maritime glaciers and their responses to climate change. This study can inform the sustainable management of water resources and the assessment of socio-economic vulnerability due to glacier-related hazards in the SETP and its surroundings in the context of marked atmospheric warming. [ABSTRACT FROM AUTHOR]

Many debris-covered glaciers are widely distributed on the Qinghai–Tibet Plateau. Glaciers are important freshwater resources and cause disasters such as glacier collapse and landslides. Therefore, it is of great significance to monitor the movement characteristics of large active glaciers and analyze the process of mass migration, which may cause serious threats and damage to roads and people living in surrounding areas. In this study, we chose a glacier with strong activity in Lulang County, Tibet, as the study area. The complete 4-year time series deformation of the glacier was estimated by using an improved small-baseline subset InSAR (SBAS-InSAR) technique based on the ascending and descending Sentinel-1 datasets. Then, the three-dimensional time series deformation field of the glacier was obtained by using the 3D decomposition technique. Furthermore, the three-dimensional movement of the glacier and its material migration process were analyzed. The results showed that the velocities of the Lulang glacier in horizontal and vertical directions were up to 8.0 m/year and 0.45 m/year, and these were basically consistent with the movement rate calculated from the historical optical images. Debris on both sides of the slope accumulated in the channel after slipping, and the material loss of the three provenances reached 6–9 × 103 m3/year, while the volume of the glacier also decreased by about 76 × 103 m3/year due to snow melting and evaporation. The correlation between the precipitation, temperature, and surface velocity suggests that glacier velocity has a clear association with them, and the activity of glaciers is linked to climate change. Therefore, in the context of global warming, the glacier movement speed will gradually increase with the annual increase in temperature, resulting in debris flow disasters in the future summer high-temperature period. [ABSTRACT FROM AUTHOR]

In this study, we utilized a feature optimization method combining texture and topographical factors with the random forest (RF) approach to identify changes in the extent of the debris cover around the Tianshan Tomur Peak between 1989 and 2022. Based on Sentinel-1 image data, we extracted glacier flow velocities using an offset tracking method and conducted a long-term analysis of flow velocities in combination with existing datasets. The debris identification results for 2022 showed that the debris-covered area in the study region was 409.2 km2, constituting 22.8% of the total glacier area. Over 34 years, the area of debris cover expanded by 69.4 km2, reflecting a growth rate of 20.0%. Analysis revealed that glaciers in the Tomur Peak area have been decelerating at an overall rate of −4.0% per decade, with the complexity of the glacier bed environment and the instability of the glacier's internal structure contributing to significant seasonal and interannual variability in the movement speeds of individual glaciers. [ABSTRACT FROM AUTHOR]

When using ground-based synthetic aperture radar (GB-SAR) for monitoring open-pit mines, dynamic atmospheric conditions can interfere with the propagation speed of electromagnetic waves, resulting in atmospheric phase errors. These errors are particularly complex in rapidly changing weather conditions or steep terrain, significantly impacting monitoring accuracy. In such scenarios, traditional regression model-based atmospheric phase correction (APC) methods often become unsuitable. To address this issue, this paper proposes a clustering method based on the spatial autocorrelation function. First, the interferogram is uniformly divided into multiple blocks, and the phase consistency of each block is evaluated using the spatial autocorrelation function. Then, a region growing algorithm is employed to classify each block according to its phase pattern, followed by merging adjacent blocks based on statistical data. To verify the feasibility of the proposed method, both the traditional regression model-based method and the proposed method were applied to deformation monitoring of an open-pit mine in Northwest China. The experimental results show that for complex atmospheric phase scenarios, the proposed method significantly outperformed traditional methods, demonstrating its superiority. [ABSTRACT FROM AUTHOR]

A'nyemaqen Snow Mountain (ASM) is the largest glacier area in the Yellow River source area and has been experiencing significant ablation in recent years. To investigate spatial–temporal elevation changes in ASM, a 21–year Digital Elevation Model (DEM) time series was obtained using the MicMac ASTER (MMASTER) algorithm and ASTER L1A V003 data. It covers the period from January 2002 to January 2023. The mean elevation of ASM decreased by −7.88 ± 3.37 m during this period, with highly spatial variation. The elevation decrease occurred mainly in the lower elevations and opposite in the higher elevations. The corresponding elevation decrease was −12.99 ± 11.29 and −4.45 ± 11.36 m at the southern Yehelong Glacier and the northern Weigeledangxiong Glacier, respectively. Moreover, there exists a temporal variation in ASM. The maximum elevation was observed in February for both ASM and the southern Yehelong Glacier but March for Weigeledangxiong Glacier, with about 1 month lagged. With the elevation time series and climate data from ERA5 datasets, we applied the random forest technique and found that the temperature is the main factor to elevation change in ASM. Furthermore, the response of elevation changes to temperature appeared with a lag and varied with the location. Based on the elevation time series, the ARIMA model was further used to forecast the elevation changes in the next 5 years. All regions will experience the elevation decrease, with a mean decline −1.74 ± 0.39 m and a corresponding rate −0.35 ± 0.08 m/a in ASM. This is similar to that of −0.38 ± 0.16 m/a between 2002 and 2003, showing its stability in the near future. [ABSTRACT FROM AUTHOR]

Artificial corner reflectors are widely used in the vegetated landslide for time series InSAR monitoring due to their permanent scattering features. This paper investigated the RCS features of a novel dihedral CR under multi-resource SAR datasets. An RCS reduction model for the novel dihedral corner reflector has been proposed to evaluate the energy loss caused by the deviation between the SAR incident angle and the CR's axis. On the Huangtupo slope, Badong county, Hubei province, tens of dihedral CRs had been installed and the TSX–spotlight and Sentinel-TOPS data had been collected. Based on the observation results of CRs with more than ten deviation angles, the proposed reduction model was tested with preferable consistency under a real dataset, while 2 dBsm of systematic bias was verified in those datasets. The maximum incident angle deviation in the Sentinel data overlapping area is over 12°, which leads to a 2.4 dBsm RCS decrease for horizontally placed dihedral CRs estimated by the proposed model, which has also been testified by the observed results. The testing results from the Sentinel data show that in high, vegetation-covered mountain areas like the Huangtupo slope, the dihedral CRs with a 0.4 m slide length can be achieve 1 mm precision accuracy, while a side length of 0.2 m can achieve the same accuracy under TSX–spotlight data. [ABSTRACT FROM AUTHOR]

Small intermountain river basins are most suitable for developing new methods to estimate water balance due to their well-defined catchment boundaries, relatively rapid runoff processes, and accessible landscapes for study. In general terms, dissecting the hydrograph of a small mountain river requires calibration of the flow model against multi-year data sets, including (a) glacier mass balance and snow water content, (b) radiation balance calculation, (c) estimation of the groundwater contribution, and (d) water discharge measurements. The minimum primary data set is limited to the precipitation and temperature distributions at the catchment. This approach postulates that the conditions for the formation of all components of river flow are known in advance. It is reduced to calculating the dynamic balance between precipitation (input part) and runoff, ablation, and evaporation (output part). In practice, accurately accounting for the inflow and outflow components of the balance, as well as the impact of regulating reservoirs, can be a challenging task that requires significant effort and expense, even for the extensively researched catchments. Our studies indicate the potential benefits of an approach based on one-time, but detailed, observations of stable isotope composition, temperature, and water chemistry, in addition to standard datasets. This paper presents the results of the 2022–2023 work conducted in the basin of the small mountain river Ala-Archa, located on the northern slope of the Kyrgyz Range in Tien-Shan, which was chosen as an example due to its well-studied nature. Our approach could identify previously unknown factors of flow formation and assess the time and effectiveness of work in similar conditions. [ABSTRACT FROM AUTHOR]

Assessing the regional mass balance of European glaciers presents significant challenges due to limited measurements. While various albedo methods have been explored for individual glaciers, a comprehensive analysis of aggregated albedo methods is lacking. Addressing this gap, in our study, we examined five MODIS aggregated albedos (raw average, minimum average, average minimum, interpolated average, and cumulative) versus the annual mass balance for 12 Austrian and Norwegian glaciers from 2001 to 2020 to establish connections between them. We find that the raw average albedo is strongly correlated with the annual mass balance of Austrian glaciers (r = 0.91), while the interpolated average albedo is significantly correlated with the annual mass balance of Norwegian glaciers (r = 0.90). Moreover, we observe that high-elevation glaciers experience fewer cloud cover days, allowing the raw average albedo to reliably estimate the annual mass balance, whereas low-elevation glaciers are often obscured by clouds, potentially masking the true minimum albedo. Additionally, traditional indicators, such as the equilibrium-line altitude and accumulation area ratio, exhibit significant correlations with the annual mass balance of Norwegian and Austrian glaciers (r = 0.90 and 0.87, respectively), yet albedo demonstrates higher robustness. These findings provide a reference for selecting appropriate aggregation methods to reconstruct glacier mass balance from albedo observations. [ABSTRACT FROM AUTHOR]

Over the past decades, the cryosphere has changed significantly in High Mountain Asia (HMA), leading to multiple natural hazards such as rock–ice avalanches, glacier collapse, debris flows, landslides, and glacial lake outburst floods (GLOFs). Monitoring cryosphere change and evaluating its hydrological effects are essential for studying climate change, the hydrological cycle, water resource management, and natural disaster mitigation and prevention. However, knowledge gaps, data uncertainties, and other substantial challenges limit comprehensive research in climate–cryosphere–hydrology–hazard systems. To address this, we provide an up-to-date, comprehensive, multidisciplinary review of remote sensing techniques in cryosphere studies, demonstrating primary methodologies for delineating glaciers and measuring geodetic glacier mass balance change, glacier thickness, glacier motion or ice velocity, snow extent and water equivalent, frozen ground or frozen soil, lake ice, and glacier-related hazards. The principal results and data achievements are summarized, including URL links for available products and related data platforms. We then describe the main challenges for cryosphere monitoring using satellite-based datasets. Among these challenges, the most significant limitations in accurate data inversion from remotely sensed data are attributed to the high uncertainties and inconsistent estimations due to rough terrain, the various techniques employed, data variability across the same regions (e.g., glacier mass balance change, snow depth retrieval, and the active layer thickness of frozen ground), and poor-quality optical images due to cloudy weather. The paucity of ground observations and validations with few long-term, continuous datasets also limits the utilization of satellite-based cryosphere studies and large-scale hydrological models. Lastly, we address potential breakthroughs in future studies, i.e., (1) outlining debris-covered glacier margins explicitly involving glacier areas in rough mountain shadows, (2) developing highly accurate snow depth retrieval methods by establishing a microwave emission model of snowpack in mountainous regions, (3) advancing techniques for subsurface complex freeze–thaw process observations from space, (4) filling knowledge gaps on scattering mechanisms varying with surface features (e.g., lake ice thickness and varying snow features on lake ice), and (5) improving and cross-verifying the data retrieval accuracy by combining different remote sensing techniques and physical models using machine learning methods and assimilation of multiple high-temporal-resolution datasets from multiple platforms. This comprehensive, multidisciplinary review highlights cryospheric studies incorporating spaceborne observations and hydrological models from diversified techniques/methodologies (e.g., multi-spectral optical data with thermal bands, SAR, InSAR, passive microwave, and altimetry), providing a valuable reference for what scientists have achieved in cryosphere change research and its hydrological effects on the Third Pole. [ABSTRACT FROM AUTHOR]

Mountain glaciers are considered natural indicators of warming and a device for climatic change. In addition, it is also a solid reservoir of freshwater resources. Along with climate change, clarifying the dynamic changes of glacier in the Aksu River Basin (ARB) are important for hydrological processes. The study examined the variations in glacier area, elevation, and their reaction to climate change in the ARB between 1990 and 2022. The glacier melt on the runoff is explored from 2003 to 2020. This investigation utilized Landsat and Sentinal-2 images, ICESat, CryoSat, meteorological and hydrological data. The findings suggest that: (1) The glacier area in the ARB retreated by 309.40 km2 (9.37%, 0.29%·a−1) from 1990 to 2022. From 2003 to 2021, the ARB glacier surface elevation retreat rate of 0.38 ± 0.12 m·a−1 (0.32 ± 0.10 m w.e.a−1). Comparison with 2003–2009, the retreat rate is faster from 2010 to 2021. (2) From 1990 to 2022, the Toxkan and the Kumalak River Basin's glacier area decreases between 61.28 km2 (0.28%·a−1) and 248.13 km2 (0.30%·a−1). Additionally, the rate of glacier surface elevation declined by −0.34 ± 0.11 m·a−1, −0.42 ± 0.14 m·a−1 from 2003 to 2021. (3) The mass balance sensitivities to cold season precipitation and ablation-phase accumulated temperatures are +0.27 ± 0.08 m w.e.a−1(10%)−1 and −0.33 ± 0.10 m w.e.a−1 °C−1, respectively. The mass loss is (962.55 ± 0.57) × 106 m3 w.e.a−1, (1087.50 ± 0.68) × 106 m3 w.e.a−1 during 2003–2009, 2010–2021 respectively. Warmer ablation-phase accumulated temperatures dominate glacier retreat in the ARB. (4) Glacier meltwater accounted for 34.57% and 41.56% of the Aksu River's runoff during the ablation-phase of 2003–2009 and 2010–2020, respectively. The research has important implications for maintaining the stability of water resource systems based on glacier meltwater. [ABSTRACT FROM AUTHOR]

The impact of global climate change on glaciers has drawn significant attention; however, limited research has been conducted to comprehend the consequences of glacier melting on the associated formation and evolution of glacial lakes. This study presents a semi-automated methodology developed on the cloud platforms Google Earth Engine and Google Colab to effectively detect dynamic changes in the glaciers as well as glacial and non-glacial lakes of the Cordillera Real, Bolivia, using over 200 Landsat images from 1984 to 2021. We found that the study area experienced a rise in temperature and precipitation, resulting in a substantial decline in glacier coverage and a simultaneous increase in both the total number and total area of lakes. A strong correlation between glacier area and the extent of natural glacier-fed lakes highlights the significant downstream impact of glacier recession on water bodies. Over the study period, glaciers reduced their total area by 42%, with recent years showing a deceleration in glacier recession, aligning with the recent stabilization observed in the area of natural glacier-fed lakes. Despite these overall trends, many smaller lakes, especially non-glacier-fed ones, decreased in size, attributed to seasonal and inter-annual variations in lake inflow caused by climate variability. These findings suggest the potential decline of natural lakes amid ongoing climate changes, prompting alterations in natural landscapes and local water resources. The study reveals the response of glaciers and lakes to climate variations, including the contribution of human-constructed water reservoirs, providing valuable insights into crucial aspects of future water resources in the Cordillera Real. [ABSTRACT FROM AUTHOR]

The southeastern Tibetan Plateau (SETP), which hosts the most extensive marine glaciers on the Tibetan Plateau (TP), exhibits enhanced sensitivity to climatic fluctuations. Under global warming, persistent glacier mass depletion within the SETP poses a risk to water resource security and sustainability in adjacent nations and regions. This study deployed a high-precision ICESat-2 satellite altimetry technique to evaluate SETP glacier thickness changes from 2018 to 2022. Our results show that the average change rate in glacier thickness in the SETP is −0.91 ± 0.18 m/yr, and the corresponding glacier mass change is −7.61 ± 1.52 Gt/yr. In the SETP, the glacier mass loss obtained via ICESat-2 data is larger than the mass change in total land water storage observed by the Gravity Recovery and Climate Experiment follow-on satellite (GRACE-FO), −5.13 ± 2.55 Gt/yr, which underscores the changes occurring in other land water components, including snow (−0.44 ± 0.09 Gt/yr), lakes (−0.06 ± 0.02 Gt/yr), soil moisture (1.88 ± 1.83 Gt/yr), and groundwater (1.45 ± 0.70 Gt/yr), with a closure error of −0.35 Gt/yr. This demonstrates that this dramatic glacier mass loss is the main reason for the decrease in total land water storage in the SETP. Generally, there are decreasing trends in solid water storage (glacier and snow) against stable or increasing trends in liquid water storage (lakes, soil moisture, and groundwater) in the SETP. This persistent decrease in solid water is linked to the enhanced melting induced by rising temperatures. Given the decreasing trend in summer precipitation, the surge in liquid water in the SETP should be principally ascribed to the increased melting of solid water. [ABSTRACT FROM AUTHOR]

For the challenges of high-precision mapping in complex terrain, a novel airborne Interferometric Synthetic Aperture Radar (InSAR) system is designed. This system, named ASMIS (Airborne Short-Baseline Millimeter-Wave InSAR System), adopts the coplanar antenna and a pod-type structure. This design makes the system lightweight and highly integrated. It can be compatible with small general aviation flight platforms. The baseline is millimeters in size, which greatly simplifies the unwrapping process. The coplanar antennas have two advantages: they maximize the baseline utilization and minimize the Doppler decorrelation and the motion error inconsistency. Acquisition campaigns of the system have been carried out in Boao, Bayannur, and Chengde, China. In the Chengde experimental area, we designed an antiparallel flight experiment to account for the topographic relief. High-precision Digital Orthophoto Maps (DOMs) and Digital Surface Models (DSMs) at a scale of 1:5000 were obtained. The coordinate Root Mean Square Error (RMSE) of the checkpoints within the obtained DSM is less than 0.82 m in altitude and 3 m horizontally. The RMSE of the Sparse Ground Control Points (GCPs) within the obtained DSM is less than 0.3 m in altitude. Experimental results from different areas, including plains, mountains, and coastlines, demonstrate the system's performance. [ABSTRACT FROM AUTHOR]

Recent satellite measurements of glacier mass balances show mountain glaciers all over the world had generally negative mass balances in the first decades of the 21st century. Mean summer temperatures all over the world rose from the 1961–1990 period to the 1991–2020 period, implying increasingly negative mass balances. We studied archived annual balances for 38 northern hemisphere glaciers to assess changes within the 1961–2020 period. We used a modified double-mass curve to visualize mass balance changes occurring around 1990. Mean balances in 1961–1990 were already small negative for many of the studied glaciers and became even more negative in 1991–2020 for glaciers in the Alps, at high latitudes and in western North America. The largest mass balance changes were for some glaciers in the Alps. We are unable to explain the lack of change in mean balance for one glacier in High Mountain Asia. We found complex changes for eight glaciers in Scandinavia, even including one glacier with a positive balance. We explain these changes by visualizing the deviations in winter and summer balances from their respective 1961–1990 mean values. High winter balances in the 1990s for Scandinavia partly obscured the emerging trend of increasingly negative summer balances, which we expect to continue in the future. [ABSTRACT FROM AUTHOR]

Glaciers play a vital role in the Asian mountain water towers and have significant downstream impacts on domestic, agricultural, and industrial water usage. The rate of glacier mass loss in the Southeastern Tibetan Plateau (SETP) is among the highest in Asia and has intensified in recent decades. However, a comprehensive quantification that considers both spatial and temporal aspects of glacier mass loss across the entire SETP is still insufficient. This study aimed to address this gap by utilizing geodetic datasets specific to each glacier by calibrating the Open Global Glacier Model (OGGM) driven by HAR v2 and reconstructing the glacier mass balance of 7756 glaciers in the SETP from 1980 to 2019 while examining their spatial variability. The findings reveal that the average mass balance during this period was −0.50 ± 0.28 m w.e. a−1, with an accelerated loss observed in the 2000s (average: 0.62 ± 0.24 m w.e. a−1). Notably, central glaciers in the SETP exhibited relatively smaller mass loss, indicating a gradient effect of increased loss from the central region toward the eastern and western sides. By the end of this century, the area, length, and volume of glaciers in the entire SETP region are projected to decrease by 83.57 ± 4.91%, 90.25 ± 4.23%, and 88.04 ± 4.52%, respectively. Moreover, the SETP glacier melt runoff is estimated to decrease by 62.63 ± 6.16% toward the end of the century, with the "peak water" point of glacier melt runoff predicted to occur in 2023 under the SSP585 scenario. Sensitivity experiments demonstrated that the SETP glaciers are more than three times more sensitive to temperature changes than to precipitation variations, and the observed decrease in monsoon precipitation indicates the weakening magnitude of the Indian summer monsoon in recent years. The spatially refined and high-temporal-resolution characteristics of glacier mass loss presented in this study contribute to a better understanding of specific glacier changes in the SETP. Additionally, the prediction results provide valuable references for future water resources management and policy formulation in the SETP region. [ABSTRACT FROM AUTHOR]

Glaciers play an important role in understanding the climate, water resources, and surrounding natural change. Baishui River Glacier No. 1, a temperate glacier in the monsoon-influenced Southeastern Qinghai–Tibet Plateau, has experienced significant ablation due to regional warming during the past few decades. However, little is known about the yearly changes in Baishui River Glacier No. 1. To investigate how Baishui River Glacier No. 1 has changed in recent years, digital orthophoto maps and digital elevation models were obtained from an unmanned aerial vehicle on 20 October 2018 and 22 July 2021, covering 84% and 47% of the total area, respectively. The results of the Baishui River Glacier No. 1 changes were obtained by differencing the digital elevation models, manual tracking, and terminus-retreat calculation methods. Our results showed that the surveyed area had a mean elevation change of −4.26 m during 2018 and 2021, and the lower area lost more ice than other areas. The terminus of Baishui River Glacier No. 1 has retreated by 16.35 m/a on average, exhibiting spatial variation with latitude. Moreover, we initially found that there was a high correlation between surface velocity and elevation gradient in this high-speed glacier. The surface velocity of Baishui River Glacier No. 1 was derived with the manual feature tracking method and ranged from 10.48 to 32.00 m/a, which is slightly smaller than the seasonal average. However, the snow coverage and ice melting of the two epochs led to the underestimation of our elevation change and velocity results, which need further investigation. [ABSTRACT FROM AUTHOR]

Headwater in the Indus River in Pakistan is largely dependent on the glaciers located in the northern part of the country, along with other sources such as direct precipitation. Glaciers are a major source of freshwater that provides agriculture and livelihood to millions of people. The hydro-climatic variations in the Gilgit watershed of the Upper Indus basin are poorly investigated scientifically due to high topographical differences, geography, remoteness of the region, and larger variations in climatic conditions. These glaciers are continuously changing due to melting as a consequence of global warming or accumulation due to snowfall/precipitation at higher altitude regions. The study is carried out using remote sensing data to quantify glacier changes in spatiotemporal variability in the past three decades. Five glaciers in the Gilgit region (near the junction of the Hindukush and Karakoram Mountains) with an area of more than 5 square kilometers were selected, namely Phakor, Karamber, East Gammu, Bhort, and Bad-e-Swat glaciers. These glaciers were monitored for changes in their sizes through a cloud-free continuous series of Landsat satellite imagery. The annual climatic trends were studied through spatially interpolated gridded climate data WοrldClim version-1 climate database for 1970–2000, utilized for assessment of meteorological condition by analyzing the variations of minimum and maximum temperature, solar radiation, and precipitation. The temporal variations in five glaciers in the Gilgit watershed are found to be minimal and, thus, are rather stable and show no sign of rapid melting or diminishing. The little variability of glaciers' extent may be attributed to their geographic condition, altitude, topography, and orientation. The mapped glacier classes have been validated to check the accuracy assessment through an error matrix method. The kappa coefficient from the error matrix has been calculated as 84%, which shows a good agreement. The study makes a critical input towards understanding the dynamics of the glacier in the upper Indus catchment's Gilgit watershed. [ABSTRACT FROM AUTHOR]

Landslides represent a significant natural hazard with wide-reaching impacts. Addressing the challenge of accurately detecting and monitoring landslides, this research introduces a novel approach that combines feature tracking with histogram analysis for efficient outlier removal. Distinct from existing methods, our approach leverages advanced histogram techniques to significantly enhance the accuracy of landslide detection, setting a new standard in the field. Furthermore, when tested on three different data sets, this method demonstrated a notable reduction in outliers by approximately 15 to 25 percent of all displacement vectors, exemplifying its effectiveness. Key to our methodology is a refined feature tracking process utilizing terrestrial laser scanners, renowned for their precision and detail in capturing surface information. This enhanced feature tracking method allows for more accurate and reliable landslide monitoring, representing a significant advancement in geospatial analysis techniques. [ABSTRACT FROM AUTHOR]

Glaciers continue to erode and transport material, forming an accumulation area at the front of the glacier. The trunk glacier, which has many tributary glaciers upstream and converges on the main channel, deposits vast amounts of material in the main channel. It blocks the main channel, forming barrier lakes, and eventually turns into mountain disasters, such as debris flows or outburst floods. Therefore, the accumulation rate of the material is a major parameter in such disasters and can determine the frequency of disasters. The material usually comes from bedrock erosion by glaciers, weathering of bedrock walls, and upstream landslides, and the material loss depends on river erosion. Based on this, we set up a method to calculate the material accumulation rate in the glacier front based on satellite images. Then, the Peilong catchment was taken as an example to validate the proposed method. The results indicate that climatic fluctuations may increase landslides, resulting in more actual accumulation than the calculated value according to the average rate of bedrock retreat. The material provided by the retreat of bedrock accounts for 92% of the total volume. Our method provides a practical reference for the mid- and long-term prediction of glacial catastrophic mass movement, as global warming seriously threatens glacier instability and downstream communities. [ABSTRACT FROM AUTHOR]

The integration of Artificial Intelligence (AI) and Satellite Remote Sensing in Land Cover Change Detection (LCCD) has gained increasing significance in scientific discovery and research. This collaboration accelerates research efforts, aiding in hypothesis generation, experiment design, and large dataset interpretation, providing insights beyond traditional scientific methods. Mapping land cover patterns at global, regional, and local scales is crucial for monitoring the dynamic world, given the significant impact of land cover distribution on climate and environment. Satellite remote sensing is an efficient tool for monitoring land cover across vast spatial extents. Detection of land cover change through satellite remote sensing images is critical in influencing ecological balance, climate change mitigation, and urban development guidance. This paper conducts a comprehensive review of LCCD using remote sensing images, encompassing exhaustive examination of satellite remote sensing data types and contemporary methods, with a specific focus on advanced AI technology applications. Furthermore, the study delves into the challenges and potential solutions in the field of LCCD, providing a comprehensive overview of the state of the art, offering insights for future research and practical applications in this domain. [ABSTRACT FROM AUTHOR]