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25 pages, 20390 KiB

Open AccessArticle

A New and Robust Index for Water Body Extraction from Sentinel-2 Imagery

by Zhenfeng Su, Longwei Xiang, Holger Steffen, Lulu Jia, Fan Deng, Wenliang Wang, Keyu Hu, Jingjing Guo, Aile Nong, Haifu Cui and Peng Gao

Remote Sens. 2024, 16(15), 2749; https://doi.org/10.3390/rs16152749 - 27 Jul 2024

Viewed by 1329

Abstract

Land surface water is a key part in the global ecosystem balance and hydrological cycle. Remote sensing has become an effective tool for its spatio-temporal monitoring. However, remote sensing results exemplified in so-called water indices are subject to several limitations. This paper proposes [...] Read more.

Land surface water is a key part in the global ecosystem balance and hydrological cycle. Remote sensing has become an effective tool for its spatio-temporal monitoring. However, remote sensing results exemplified in so-called water indices are subject to several limitations. This paper proposes a new and effective water index called the Sentinel Multi-Band Water Index (SMBWI) to extract water bodies in complex environments from Sentinel-2 satellite imagery. Individual tests explore the effectiveness of the SMBWI in eliminating interference of various special interfering cover features. The Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA) method and confusion matrix along with the derived accuracy evaluation indicators are used to provide a threshold reference when extracting water bodies and evaluate the accuracy of the water body extraction results, respectively. The SMBWI and eight other commonly used water indices are qualitatively and quantitatively compared through vision and accuracy evaluation indicators, respectively. Here, the SMBWI is proven to be the most effective at suppressing interference of buildings and their shadows, cultivated lands, vegetation, clouds and their shadows, alpine terrain with bare ground and glaciers when extracting water bodies. The overall accuracy in all tests was consistently greater than 96.5%. The SMBWI is proven to have a high ability to identify mixed pixels of water and non-water, with the lowest total error among nine water indices. Most notably, better results are obtained when extracting water bodies under interfering environments of cover features. Therefore, we propose that our novel and robust water index, the SMBWI, is ready to be used for mapping land surface water with high accuracy. Full article

(This article belongs to the Special Issue Remote Sensing for Surface Water Monitoring)

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21 pages, 14776 KiB

Open AccessArticle

Monitoring Spatio-Temporal Variations of Ponds in Typical Rural Area in the Huai River Basin of China

by Zhonglin Ji, Hongyan Ren, Chenfeng Zha and Eshetu Shifaw Adem

Remote Sens. 2024, 16(1), 39; https://doi.org/10.3390/rs16010039 - 21 Dec 2023

Viewed by 1017

Abstract

Ponds constitute a pivotal component of aquatic ecosystems. The aquatic ecosystem of the Huai River Basin (HRB) in China was once damaged by severe pollution, and numerous ponds in the basin have not been secured. In this paper, Shenqiu County, a typical county [...] Read more.

Ponds constitute a pivotal component of aquatic ecosystems. The aquatic ecosystem of the Huai River Basin (HRB) in China was once damaged by severe pollution, and numerous ponds in the basin have not been secured. In this paper, Shenqiu County, a typical county in HRB with many ponds, is selected. Based on high-resolution images with ALOS in 2010, GF-2 in 2016, and GF-1 in 2022, we employed discriminant analysis (DA), classification and regression tree, support vector machine, and random forest to extract the ponds based on object-oriented and further analyzed the spatial-temporal variations of the ponds in this county. The results of the DA in these three years exhibited a higher kappa coefficient (>0.7), and overall accuracy (>75%), signifying superior performance when compared to the other three methods. There were 4625, 5315, and 4748 ponds in 2010, 2016, and 2022, with a total area of 12.87, 11.99, and 9.37 km², respectively. The number of ponds had a trend of rising in the initial period (2010–2016) and falling later (2016–2022), while the total area revealed a continuous decline. Meanwhile, these ponds showed a clustering phenomenon with three main clustering areas, and the scope of the clustering areas also changed to a certain extent from 2010 to 2022. Our study offers valuable methodological support for the ecological monitoring and management of water environments in regions characterized by a dense concentration of ponds. The crucial data related to ponds in this study will help inform both environmental and social development initiatives within the area. Full article

(This article belongs to the Special Issue Remote Sensing for Surface Water Monitoring)

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24 pages, 29480 KiB

Open AccessArticle

Associating Anomaly Detection Strategy Based on Kittler’s Taxonomy with Image Editing to Extend the Mapping of Polluted Water Bodies

by Giovanna Carreira Marinho, Wilson Estécio Marcílio Júnior, Mauricio Araujo Dias, Danilo Medeiros Eler, Almir Olivette Artero, Wallace Casaca and Rogério Galante Negri

Remote Sens. 2023, 15(24), 5760; https://doi.org/10.3390/rs15245760 - 16 Dec 2023

Viewed by 1308

Abstract

Anomaly detection based on Kittler’s Taxonomy (ADS-KT) has emerged as a powerful strategy for identifying and categorizing patterns that exhibit unexpected behaviors, being useful for monitoring environmental disasters and mapping their consequences in satellite images. However, the presence of clouds in images limits [...] Read more.

Anomaly detection based on Kittler’s Taxonomy (ADS-KT) has emerged as a powerful strategy for identifying and categorizing patterns that exhibit unexpected behaviors, being useful for monitoring environmental disasters and mapping their consequences in satellite images. However, the presence of clouds in images limits the analysis process. This article investigates the impact of associating ADS-KT with image editing, mainly to help machines learn how to extend the mapping of polluted water bodies to areas occluded by clouds. Our methodology starts by applying ADS-KT to two images from the same geographic region, where one image has meaningfully more overlay contamination by cloud cover than the other. Ultimately, the methodology applies an image editing technique to reconstruct areas occluded by clouds in one image based on non-occluded areas from the other image. The results of 99.62% accuracy, 74.53% precision, 94.05% recall, and 83.16% F-measure indicate that this study stands out among the best of the state-of-the-art approaches. Therefore, we conclude that the association of ADS-KT with image editing showed promising results in extending the mapping of polluted water bodies by a machine to occluded areas. Future work should compare our methodology to ADS-KT associated with other cloud removal methods. Full article

(This article belongs to the Special Issue Remote Sensing for Surface Water Monitoring)

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24 pages, 30958 KiB

Open AccessArticle

Dimensionality Reduction and Anomaly Detection Based on Kittler’s Taxonomy: Analyzing Water Bodies in Two Dimensional Spaces

by Giovanna Carreira Marinho, Wilson Estécio Marcílio Júnior, Mauricio Araujo Dias, Danilo Medeiros Eler, Rogério Galante Negri and Wallace Casaca

Remote Sens. 2023, 15(16), 4085; https://doi.org/10.3390/rs15164085 - 19 Aug 2023

Cited by 1 | Viewed by 1465

Abstract

Dimensionality reduction is one of the most used transformations of data and plays a critical role in maintaining meaningful properties while transforming data from high- to low-dimensional spaces. Previous studies, e.g., on image analysis, comparing data from these two spaces have found that, [...] Read more.

Dimensionality reduction is one of the most used transformations of data and plays a critical role in maintaining meaningful properties while transforming data from high- to low-dimensional spaces. Previous studies, e.g., on image analysis, comparing data from these two spaces have found that, generally, any study related to anomaly detection can achieve the same or similar results when applied to both dimensional spaces. However, there have been no studies that compare differences in these spaces related to anomaly detection strategy based on Kittler’s Taxonomy (ADS-KT). This study aims to investigate the differences between both spaces when dimensionality reduction is associated with ADS-KT while analyzing a satellite image. Our methodology starts applying the pre-processing phase of the ADS-KT to create the high-dimensional space. Next, a dimensionality reduction technique generates the low-dimensional space. Then, we analyze extracted features from both spaces based on visualizations. Finally, machine-learning approaches, in accordance with the ADS-KT, produce results for both spaces. In the results section, metrics assessing transformed data present values close to zero contrasting with the high-dimensional space. Therefore, we conclude that dimensionality reduction directly impacts the application of the ADS-KT. Future work should investigate whether dimensionality reduction impacts the ADS-KT for any set of attributes. Full article

(This article belongs to the Special Issue Remote Sensing for Surface Water Monitoring)

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15 pages, 3860 KiB

Open AccessArticle

Developing a High-Resolution Seamless Surface Water Extent Time-Series over Lake Victoria by Integrating MODIS and Landsat Data

by Guiping Wu, Chuang Chen, Yongwei Liu, Xingwang Fan, Huilin Niu and Yuanbo Liu

Remote Sens. 2023, 15(14), 3500; https://doi.org/10.3390/rs15143500 - 12 Jul 2023

Cited by 2 | Viewed by 1614

Abstract

To effectively monitor the spatio–temporal dynamics of the surface water extent (SWE) in Lake Victoria, this study introduced a novel methodology for generating a seamless SWE time series with fine resolution by integrating daily a Moderate-resolution Imaging Spectroradiometer (MODIS) and Landsat imagery. In [...] Read more.

To effectively monitor the spatio–temporal dynamics of the surface water extent (SWE) in Lake Victoria, this study introduced a novel methodology for generating a seamless SWE time series with fine resolution by integrating daily a Moderate-resolution Imaging Spectroradiometer (MODIS) and Landsat imagery. In the proposed methodology, daily normalized difference vegetation index (NDVI) time series data with 30 m resolution were first generated based on the constructed pixel-by-pixel downscaling models between the simultaneously acquired MODIS-NDVI and Landsat-NDVI data. In the compositing process, a Minimum Value Composite (MinVC) algorithm was used to generate monthly minimum NDVI time series, which were then segmented into a seamless SWE time series of the years 2000–2020 with 30 m resolution from the cloud background. A comparison with the existing Landsat-derived JRC (European Joint Research Centre) monthly surface water products and altimetry-derived water level series revealed that the proposed methodology effectively provides reliable descriptions of spatio–temporal SWE dynamics. Over Lake Victoria, the average percentage of valid observations made using the JRC’s products was only about 70% due to persistent cloud cover or linear strips, and the correlation with the water level series was poor (R² = 0.13). In contrast, our derived results strongly correlated with the water level series (R² = 0.54) and efficiently outperformed the JRC’s surface water products in terms of both space and time. Using the derived SWE data, the long-term and seasonal characteristics of lake area dynamics were studied. During the past 20 years, a significant changing pattern of an initial decline followed by an increase was found for the annual mean SWE, with the lowest area of 66,386.57 km² in 2006. A general seasonal variation in the monthly mean lake area was also observed, with the largest SWE obtained during June–August and the smallest SWE observed during September–November. Particularly in the spring of 2006 and the autumn of 2020, Lake Victoria experienced intense episodes of drought and flooding, respectively. These results demonstrate that our proposed methodology is more robust with respect to capturing spatially and temporally continuous SWE data in cloudy conditions, which could also be further extended to other regions for the optimal management of water resources. Full article

(This article belongs to the Special Issue Remote Sensing for Surface Water Monitoring)

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20 pages, 8737 KiB

Open AccessArticle

Evaluation of SST Data Products from Multi-Source Satellite Infrared Sensors in the Bohai-Yellow-East China Sea

by Changlong Feng, Wenbin Yin, Shuangyan He, Mingjun He and Xiaoxia Li

Remote Sens. 2023, 15(10), 2493; https://doi.org/10.3390/rs15102493 - 9 May 2023

Cited by 3 | Viewed by 2443

Abstract

The measurement of sea surface temperature (SST) is of utmost importance in the realm of oceanography. The increasing utilization of satellite data in SST research has highlighted the crucial need to compare and evaluate various satellite data sources. Using iQuam2 in situ SST [...] Read more.

The measurement of sea surface temperature (SST) is of utmost importance in the realm of oceanography. The increasing utilization of satellite data in SST research has highlighted the crucial need to compare and evaluate various satellite data sources. Using iQuam2 in situ SST data, this study aims to assess the accuracy of SST datasets obtained from three polar-orbiting satellites (AVHRR, Modis-Aqua, and Modis-Terra) and one geostationary satellite (Himawari-8) in the Bohai-Yellow-East China Sea (BYECS) throughout 2019. The results showed a strong correlation between satellite and in situ data, with R correlation coefficients exceeding 0.99. However, the accuracy of the satellite datasets exhibited some variability, with Himawari-8 showing the highest deviation error and MODIS-Aqua showing the least. Subsequently, the Modis-Aqua data were used as a benchmark to evaluate the SST data of the other three satellites over the previous six years (July 2015–June 2021). The results indicate that, in addition to intricate temporal variations, the deviations of the three satellites from Modis-Aqua also show significant spatial disparities due to the effect of seawater temperature. Compared to Modis-Aqua, the deviation of Himawari-8 generally displayed a negative trend in BYECS and showed pronounced seasonal variation. The deviation of AVHRR showed a negative trend across all regions except for a substantial positive value in the coastal region, with the time variation exhibiting intricate features. The SST values obtained from MODIS-Terra exhibited only marginal disparities from MODIS-Aqua, with positive values during the day and negative values at night. All three satellites showed significantly abnormal bias values after December 2020, indicating that the MODIS-Aqua-derived SST reference dataset may contain outliers beyond this period. In conclusion, the accuracy of the four satellite datasets varies across different regions and time periods. However, they could be effectively utilized and integrated with relevant fusion algorithms to synthesize high-precision datasets in the future. Full article

(This article belongs to the Special Issue Remote Sensing for Surface Water Monitoring)

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16 pages, 9669 KiB

Open AccessArticle

Water Temperature Reconstruction via Station Position Correction Method Based on Coastal Acoustic Tomography Systems

by Pan Xu, Shijie Xu, Fenyuan Yu, Yixin Gao, Guangming Li, Zhengliang Hu and Haocai Huang

Remote Sens. 2023, 15(8), 1965; https://doi.org/10.3390/rs15081965 - 7 Apr 2023

Cited by 3 | Viewed by 1589

Abstract

Underwater acoustic tomography is an advanced technology in water environment observation. Sound propagation duration between transceivers is used for underwater parameter distribution profile reconstruction in the inverse problem. The key points of acoustic tomography are accurate station distance and time synchronization. Two methods [...] Read more.

Underwater acoustic tomography is an advanced technology in water environment observation. Sound propagation duration between transceivers is used for underwater parameter distribution profile reconstruction in the inverse problem. The key points of acoustic tomography are accurate station distance and time synchronization. Two methods are introduced in this study for sound station position correction. The direct signal transmission correction (DSC) method corrects the multi-peak (expect direct ray) travel time via the travel time difference between different sound rays and reference direct ray. The ray-model position correction (RMC) method calculates exact station position by the station drift distance obtained from transceiver depth variations to correct direct ray travel time; the other multi-peak travel time is revised by the corrected direct ray travel time. A water temperature observation experiment was carried out in a reservoir using coastal acoustic tomography (CAT) systems to verify the flexibility of these two methods. Multi-ray arrival peaks are corrected using DSC and RMC methods; water temperature inversion results in a 2D vertical profile are obtained. The reliability of the method is proved by comparison with temperature depth sensor (TD) data. The methods improve the quality of initial data and can be attempted for further water environment observation in acoustic tomography observation studies. Full article

(This article belongs to the Special Issue Remote Sensing for Surface Water Monitoring)

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31 pages, 31986 KiB

Open AccessArticle

Comparing Water Indices for Landsat Data for Automated Surface Water Body Extraction under Complex Ground Background: A Case Study in Jilin Province

by Shu Liu, Yanfeng Wu, Guangxin Zhang, Nan Lin and Zihao Liu

Remote Sens. 2023, 15(6), 1678; https://doi.org/10.3390/rs15061678 - 20 Mar 2023

Cited by 11 | Viewed by 3953

Abstract

Derived from Landsat imagery and capable of enhancing the contrast between surface water bodies and the background, water indices are widely used in surface water body extraction. Whether one index and its optimal threshold can maintain the best all year round is a [...] Read more.

Derived from Landsat imagery and capable of enhancing the contrast between surface water bodies and the background, water indices are widely used in surface water body extraction. Whether one index and its optimal threshold can maintain the best all year round is a question. At present, however, little research has considered the effect of time or conducted experiments with data from different months. To identify the best index for surface water body extraction, two regions in Jilin Province were selected for the case study and a comprehensive comparative analysis considering the imagery acquisition time was conducted. Ten water indices were selected and calculated based on the 30 m spatial resolution Landsat TM/OLI imagery acquired in 1999 and 2001 and 2019 and 2021 from May to October. The indices included the Modified Normalized Difference Water Index (NDWI3 and MNDWI), Automated Water Extraction Index (AWEI) for images with and without shadow, Multi-Band Water Index (MBWI), New Water Index (NWI), Water Ratio Index (WRI), Sentinel-2 Water Index (SWI) originally calculated based on the Sentinel-2 imagery, New Comprehensive Water Index (NCIWI), Index of Water Surfaces (IWS), and Enhanced Water Index (EWI). The OTSU algorism was adopted to adaptively determine the optimal segmentation threshold for each index and the indices were compared in terms of inter-class separability, threshold sensitivity, optimal threshold robustness, and water extraction accuracy. The result showed that NWI and EWI performed the best in different months and years, with the best water enhancement effect that could suppress background information, especially for the water-related land use types and cloud pollution. Their optimal segmentation thresholds throughout the study period were more stable than others, with the ranges of −0.342 to −0.038 and −0.539 to −0.223, respectively. Based on the optimal thresholds, they achieved overall accuracies of 0.952 to 0.981 and 0.964 to 0.981, commission errors of 0 to 28.2% and 0 to 7.7%, and omission errors of 0 to 15% and 0 to 8%, with a kappa coefficient above 0.8 indicating good extraction results. The results demonstrated the effectiveness of NWI and EWI combined with the OTSU algorithm in better monitoring surface water during different water periods and offering reliable results. Even though this study only focuses on the lakes within two regions, the indices have the potential for accurately monitoring the surface water over other regions. Full article

(This article belongs to the Special Issue Remote Sensing for Surface Water Monitoring)

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24 pages, 4014 KiB

Open AccessArticle

Multiple Spatial and Temporal Scales Evaluation of Eight Satellite Precipitation Products in a Mountainous Catchment of South China

by Binbin Guo, Tingbao Xu, Qin Yang, Jing Zhang, Zhong Dai, Yunyuan Deng and Jun Zou

Remote Sens. 2023, 15(5), 1373; https://doi.org/10.3390/rs15051373 - 28 Feb 2023

Cited by 15 | Viewed by 2409

Abstract

Satellite precipitation products (SPPs) have emerged as an important information source of precipitation with high spatio-temporal resolutions, with great potential to improve catchment water resource management and hydrologic modelling, especially in data-sparse regions. As an indirect precipitation measurement, satellite-derived precipitation accuracy is of [...] Read more.

Satellite precipitation products (SPPs) have emerged as an important information source of precipitation with high spatio-temporal resolutions, with great potential to improve catchment water resource management and hydrologic modelling, especially in data-sparse regions. As an indirect precipitation measurement, satellite-derived precipitation accuracy is of major concern. There have been numerous evaluation/validation studies worldwide. However, a convincing systematic evaluation/validation of satellite precipitation remains unrealized. In particular, there are still only a limited number of hydrologic evaluations/validations with a long temporal period. Here we present a systematic evaluation of eight popular SPPs (CHIRPS, CMORPH, GPCP, GPM, GSMaP, MSWEP, PERSIANN, and SM2RAIN). The evaluation area used, using daily data from 2007 to 2020, is the Xiangjiang River basin, a mountainous catchment with a humid sub-tropical monsoon climate situated in south China. The evaluation was conducted at various spatial scales (both grid-gauge scale and watershed scale) and temporal scales (annual and seasonal scales). The evaluation paid particular attention to precipitation intensity and especially its impact on hydrologic modelling. In the evaluation of the results, the overall statistical metrics show that GSMaP and MSWEP rank as the two best-performing SPPs, with KGE_Grid ≥ 0.48 and KGE_Watershed ≥ 0.67, while CHIRPS and SM2RAIN were the two worst-performing SPPs with KGE_Grid ≤ 0.25 and KGE_Watershed ≤ 0.42. GSMaP gave the closest agreement with the observations. The GSMaP-driven model also was superior in depicting the rainfall-runoff relationship compared to the hydrologic models driven by other SPPs. This study further demonstrated that satellite remote sensing still has difficulty accurately estimating precipitation over a mountainous region. This study provides helpful information to optimize the generation of algorithms for satellite precipitation products, and valuable guidance for local communities to select suitable alternative precipitation datasets. Full article

(This article belongs to the Special Issue Remote Sensing for Surface Water Monitoring)

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20 pages, 15265 KiB

Open AccessArticle

Monitoring Surface Water Inundation of Poyang Lake and Dongting Lake in China Using Sentinel-1 SAR Images

by Zirui Wang, Fei Xie, Feng Ling and Yun Du

Remote Sens. 2022, 14(14), 3473; https://doi.org/10.3390/rs14143473 - 19 Jul 2022

Cited by 9 | Viewed by 2802

Abstract

High-temporal-resolution inundation maps play an important role in surface water monitoring, especially in lake sites where water bodies change tremendously. Synthetic Aperture Radar (SAR) that guarantees a full time-series in monitoring surface water due to its cloud-penetrating capability is preferred in practice. To [...] Read more.

High-temporal-resolution inundation maps play an important role in surface water monitoring, especially in lake sites where water bodies change tremendously. Synthetic Aperture Radar (SAR) that guarantees a full time-series in monitoring surface water due to its cloud-penetrating capability is preferred in practice. To date, the methods of extracting and analyzing inundation maps of lake sites have been widely discussed, but the method of extracting surface water maps refined by inundation frequency map and the distinction of inundation frequency map from different datasets have not been fully explored. In this study, we leveraged the Google Earth Engine platform to compare and evaluate the effects of a method combining a histogram-based algorithm with a temporal-filtering algorithm in order to obtain high-quality surface water maps. Both algorithms were conducted on Sentinel-1 images over Poyang Lake and Dongting Lake, the two largest lakes in China, respectively. High spatiotemporal time-series analyses of both lakes were implemented between 2017 and 2021, while the inundation frequency maps extracted from Sentinel-1 data were compared with those extracted from Landsat images. It was found that Sentinel-1 can monitor water inundation with a substantially higher accuracy, although minor differences were found between the two sites, with the overall accuracy for Poyang Lake (95.38–98.69%) being higher than that of Dongting Lake (95.05–97.5%). The minimum and maximum water areas for five years were 1232.96 km² and 3828.36 km² in Poyang Lake, and 624.7 km² and 2189.17 km² in Dongting Lake. Poyang Lake was frequently inundated with 553.03 km² of permanent water and 3361.39 km² of seasonal water while Dongting Lake was less frequently inundated with 320.09 km² of permanent water and 2224.53 km² of seasonal water. The inundation frequency maps from different data sources had R² values higher than 0.8, but there were still significant differences between them. The overall inundation frequency values of the Sentinel-1 inundation frequency maps were lower than those of the Landsat inundation frequency maps due to the severe contamination from cloud cover in Landsat imagery, which should be paid attention in practical application. Full article

(This article belongs to the Special Issue Remote Sensing for Surface Water Monitoring)

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15 pages, 11429 KiB

Open AccessTechnical Note

The Formation of an Ice-Contact Proglacial Lake and Its Impact on Glacier Change: A Case Study of the Tanymas Lake and Fedchenko Glacier

by Zhijie Li, Ninglian Wang, Jiawen Chang and Quan Zhang

Remote Sens. 2023, 15(11), 2745; https://doi.org/10.3390/rs15112745 - 25 May 2023

Cited by 2 | Viewed by 1990

Abstract

Lake-terminating glaciers have some peculiar behaviors compared to land-terminating glaciers, but in-depth observation is still limited regarding their formation, which is crucial for understanding the glacier–lake interaction. Here, the long-term evolutions of Tanymas Lake and the Fedchenko Glacier were investigated based on Landsat [...] Read more.

Lake-terminating glaciers have some peculiar behaviors compared to land-terminating glaciers, but in-depth observation is still limited regarding their formation, which is crucial for understanding the glacier–lake interaction. Here, the long-term evolutions of Tanymas Lake and the Fedchenko Glacier were investigated based on Landsat images, Google Earth imagery, KH-9 images, glacier surface elevation and velocity change datasets, and meteorological records. The results indicate that Tanymas Lake is both an ice-contact proglacial lake and an ice-dammed lake. It covered an area of 1.10 km² in September 2022, and it is one of the largest glacial lakes in Pamir and even in HMA. The initial basin of Tanymas Lake is a moraine depression in Tanymas Pass, and the blocked dam is the Tanymas-5 Glacier and its terminal moraine. Tanymas Lake was in an embryonic stage before August 2005, in a formation and expansion stage from August 2005 to September 2018, and in a new expansion stage after September 2018. In this process, the Tanymas terminus of the Fedchenko Glacier also transformed from a land terminus to a partial lake terminus, and then to a complete lake terminus. The formation of Tanymas Lake is associated with the accumulation of glacial meltwater and the blockage of drainage, while the slow expansion of Tanymas Lake is related to the cold climate and slight glacier mass loss of Central Pamir. In the coming decades, with the accelerated mass loss of the Tanymas terminus of the Fedchenko Glacier, the area, depth, and water storage of Tanymas Lake will continue to increase, accompanied by the growing GLOF risk. Full article

(This article belongs to the Special Issue Remote Sensing for Surface Water Monitoring)

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