Remote Sensing

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19 pages, 1856 KiB

Open AccessArticle

Mass Balance of the Antarctic Ice Sheet in the Early 21st Century

by Tian Yang, Qi Liang, Lei Zheng, Teng Li, Zhuoqi Chen, Fengming Hui and Xiao Cheng

Remote Sens. 2023, 15(6), 1677; https://doi.org/10.3390/rs15061677 - 20 Mar 2023

Cited by 1 | Viewed by 2467

Mass loss from the Antarctic Ice Sheet (AIS) is an important contributor to global sea level rise. To examine the recent ice loss, we estimated the mass budget of the AIS from 2000 to 2020 using multiple ice velocity datasets, state-of-the-art ice thickness [...] Read more.

Mass loss from the Antarctic Ice Sheet (AIS) is an important contributor to global sea level rise. To examine the recent ice loss, we estimated the mass budget of the AIS from 2000 to 2020 using multiple ice velocity datasets, state-of-the-art ice thickness datasets, and extended surface mass balance (SMB) records. The AIS lost mass at an average rate of −89 ± 99 Gt/yr over the study period. The East Antarctic Ice Sheet (EAIS) showed a slightly positive mass balance, while the West Antarctic Ice Sheet (WAIS) experienced a significant acceleration in mass loss. The ice discharge from the AIS increased from 1792 ± 47 Gt/yr in 2000 to 1940 ± 37 Gt/yr in 2017–2020, with the increase in the discharge from the WAIS being three to four times higher than that from the EAIS. Moreover, the average mass balance for 2017–2020 was −99 ± 93 Gt/yr, slightly more negative than the average for the early 21st Century. During this recent period, the ice discharge decreased in the East Indian Ocean sector, in contrast to its rapid increase from 2000 to 2013–2017. However, the discharge in the Amundsen Sea sector still greatly increased from 2013–2017 to 2017–2020. Overall, our results are in agreement with recent mass balance estimates for the AIS based on gravimetry and altimetry. Our assessments of the recent AIS mass balance with the mass budget method (input-output method) will contribute to the understanding of ice dynamic processes and provide insights into the stability of the AIS. Full article

(This article belongs to the Special Issue Remote Sensing of Ice Loss Tracking at the Poles)

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22 pages, 9729 KiB

Open AccessArticle

Multitemporal Glacier Mass Balance and Area Changes in the Puruogangri Ice Field during 1975–2021 Based on Multisource Satellite Observations

by Shanshan Ren, Xin Li, Yingzheng Wang, Donghai Zheng, Decai Jiang, Yanyun Nian and Yushan Zhou

Remote Sens. 2022, 14(16), 4078; https://doi.org/10.3390/rs14164078 - 20 Aug 2022

Cited by 2 | Viewed by 2193

Abstract

Due to climate warming, the glaciers of the Tibetan Plateau have experienced rapid mass loss and the patterns of glacier changes have exhibited high spatiotemporal heterogeneity, especially in interior areas. As the largest ice field within the Tibetan Plateau, the Puruogangri Ice Field [...] Read more.

Due to climate warming, the glaciers of the Tibetan Plateau have experienced rapid mass loss and the patterns of glacier changes have exhibited high spatiotemporal heterogeneity, especially in interior areas. As the largest ice field within the Tibetan Plateau, the Puruogangri Ice Field has attracted a lot of attention from the scientific community. However, relevant studies that are based on satellite data have mainly focused on a few periods from 2000–2016. Long-term and multiperiod observations remain to be conducted. To this end, we estimated the changes in the glacier area and mass balance of the Puruogangri Ice Field over five subperiods between 1975 and 2021, based on multisource remote sensing data. Specifically, we employed KH-9 and Landsat images to estimate the area change from 1975 to 2021 using the band ratio method. Subsequently, based on KH-9 DEM, SRTM DEM, Copernicus DEM and ZY-3 DEM data, we evaluated the glacier elevation changes and mass balance over five subperiods during 1975–2021. The results showed that the total glacier area decreased from 427.44 ± 12.43 km² to 387.87 ± 11.02 km² from 1975 to 2021, with a decrease rate of 0.86 km² a⁻¹. The rate of area change at a decade timescale was −0.74 km² a⁻¹ (2000–2012) and −1.00 km² a⁻¹ (2012–2021). Furthermore, the rates at a multiyear timescale were −1.23 km² a⁻¹, −1.83 km² a⁻¹ and −0.42 km² a⁻¹ for 2012–2015, 2015–2017 and 2017–2021, respectively. In terms of the glacier mass balance, the region-wide results at a two-decade timescale were −0.23 ± 0.02 m w.e. a⁻¹ for 1975–2000 and −0.29 ± 0.02 m w.e. a⁻¹ for 2000–2021, indicating a sustained and relatively stable mass loss over the past nearly five decades. After 2000, the loss rate at a decade timescale was −0.04 ± 0.01 m w.e. a⁻¹ for 2000–2012 and −0.17 ± 0.01 m w.e. a⁻¹ for 2012–2021, indicating an increasing loss rate over recent decades. It was further found that the mass loss rate was −0.12 ± 0.02 m w.e. a⁻¹ for 2012–2015, −0.03 ± 0.01 m w.e. a⁻¹ for 2015–2017 and −0.40 ± 0.03 m w.e. a⁻¹ for 2017–2021. These results indicated that a significant portion of the glacier mass loss mainly occurred after 2017. According to our analysis of the meteorological measurements in nearby regions, the trends of precipitation and the average annual air temperature both increased. Combining these findings with the results of the glacier changes implied that the glacier changes seemed to be more sensitive to temperature increase in this region. Overall, our results improved our understanding of the status of glacier changes and their reaction to climate change in the central Tibetan Plateau. Full article

(This article belongs to the Special Issue Remote Sensing of Ice Loss Tracking at the Poles)

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22 pages, 8348 KiB

Open AccessArticle

Arctic Multiyear Ice Areal Flux and Its Connection with Large-Scale Atmospheric Circulations in the Winters of 2002–2021

by Huiyan Kuang, Yanbing Luo, Yufang Ye, Mohammed Shokr, Zhuoqi Chen, Shaoyin Wang, Fengming Hui, Haibo Bi and Xiao Cheng

Remote Sens. 2022, 14(15), 3742; https://doi.org/10.3390/rs14153742 - 4 Aug 2022

Cited by 5 | Viewed by 1628

Abstract

Arctic sea ice, especially the multiyear ice (MYI), is decreasing rapidly, partly due to melting triggered by global warming, in turn partly due to the possible acceleration of ice export from the Arctic Ocean to southern latitudes through identifiable gates. In this study, [...] Read more.

Arctic sea ice, especially the multiyear ice (MYI), is decreasing rapidly, partly due to melting triggered by global warming, in turn partly due to the possible acceleration of ice export from the Arctic Ocean to southern latitudes through identifiable gates. In this study, MYI and total sea ice areal flux through six Arctic gateways over the winters (October–April) of 2002–2021 were estimated using daily sea ice motion and MYI/total sea ice concentration data. Inconsistencies caused by different data sources were considered for the estimate of MYI flux. Results showed that, there is a slight declining trend in the Arctic MYI areal flux over the past two decades, which is attributable to the decrease in MYI concentration. Overall speaking, MYI flux through Fram Strait accounts for ~87% of the Arctic MYI outflow, with an average of ~325.92 × 10³ km² for the winters of 2002–2021. The monthly MYI areal flux through Fram Strait is characterized with a peak in March (~55.56 × 10³ km²) and a trough in April (~40.97 × 10³ km²), with a major contribution from MYI concentration. The connections between sea ice outflow and large-scale atmospheric circulations such as Arctic Oscillation (AO), North Atlantic Oscillation (NAO) and Dipole Anomaly (DA) were investigated. High correlation coefficients (CCs) were found in winter months such as January and February. While AO and NAO (especially NAO) exhibited generally weak correlations with the MYI/total sea ice flux, DA presented strong correlations with the areal flux, especially for MYI (CC up to 0.90 in January). However, the atmospheric circulation patterns are sometimes not fully characterized by the specific indices, which could have different effects on sea ice flux and its correlation with the atmospheric indices. Full article

(This article belongs to the Special Issue Remote Sensing of Ice Loss Tracking at the Poles)

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16 pages, 18087 KiB

Open AccessArticle

Glacier Mass Balance Pattern and Its Variation Mechanism in the West Kunlun Mountains in Tibetan Plateau

by Le Gao, Xiaofeng Yang, Jifeng Qi and Wenfeng Chen

Remote Sens. 2022, 14(11), 2634; https://doi.org/10.3390/rs14112634 - 31 May 2022

Viewed by 1668

Abstract

Mass balance observations are beneficial for assessing climate change in different world regions. This study analyzed the glacier elevation change, ice flux divergence, and surface mass balance (SMB) in the West Kunlun Mountains (WKM) on the Tibetan Plateau using remote sensing data, including [...] Read more.

Mass balance observations are beneficial for assessing climate change in different world regions. This study analyzed the glacier elevation change, ice flux divergence, and surface mass balance (SMB) in the West Kunlun Mountains (WKM) on the Tibetan Plateau using remote sensing data, including satellite altimetry, glacier surface velocity, and thickness fields. Seventeen local glaciers were examined in detail and showed varying surface elevation changes from −0.39 ± 0.11 to 0.83 ± 0.10 m/a. Overall, we obtained a reasonably rapid elevation trend of 0.21 ± 0.14 m/a. By combining the ice flux divergence and surface mass balance, the overall thickness change of the WKM glacier over time is almost zero, and the WKM glacier shows a positive mass balance of 0.21 ± 0.98 m/a. Moreover, the ice flux divergence is more significant on the ice tongue than in the flat region due to the more considerable gradient of surface velocity and thickness fields. We found that glacier heterogeneity dynamics were associated with a surging dynamic mechanism concentrated in the glacier tongue and were induced by inner terrain instabilities. The glacier surging causes a drastic drop in glacier elevation but does not cause a glacier mass gain or loss, and it has an enhanced effect on the ice flux divergence. Therefore, glacier surging is the main reason for the decline of the two glaciers monitored. In addition, the long-term meteorological data analysis found that, since 2000, the air temperature warming hiatus may have balanced the three glaciers, and significantly increasing precipitation variation may cause the glacier to thicken the most. Full article

(This article belongs to the Special Issue Remote Sensing of Ice Loss Tracking at the Poles)

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19 pages, 7795 KiB

Open AccessArticle

Decadal Changes in Greenland Ice Sheet Firn Aquifers from Radar Scatterometer

by Xinyi Shang, Xiao Cheng, Lei Zheng, Qi Liang and Zhaohui Chi

Remote Sens. 2022, 14(9), 2134; https://doi.org/10.3390/rs14092134 - 29 Apr 2022

Cited by 3 | Viewed by 1928

Abstract

Surface meltwater runoff is believed to be the main cause of the alarming mass loss in the Greenland Ice Sheet (GrIS); however, recent research has shown that a large amount of meltwater is not directly drained or refrozen but stored in the form [...] Read more.

Surface meltwater runoff is believed to be the main cause of the alarming mass loss in the Greenland Ice Sheet (GrIS); however, recent research has shown that a large amount of meltwater is not directly drained or refrozen but stored in the form of firn aquifers (FAs) in the interior of the GrIS. Monitoring the changes in FAs over the GrIS is of great importance to evaluate the stability and mass balance of the ice sheet. This is challenging because FAs are not visible on the surface and the direct measurements are lacking. A new method is proposed to map FAs during the 2010–2020 period by using the C-band Advanced Scatterometer (ASCAT) data based on the Random Forests classification algorithm with the aid of measurements from the NASA Operation IceBridge (OIB) program. Melt days (MD), melt intensity (MI), and winter mean backscatter (WM) parameters derived from the ASCAT data are used as the input vectors for the Random Forests classification algorithm. The accuracy of the classification model is assessed by ten-fold cross-validation, and the overall accuracy and Kappa coefficient are 97.49% and 0.72 respectively. The results show that FAs reached the maximum in 2015, and the accumulative area of FAs from 2010 to 2020 is 56,477 km², which is 3.3% of the GrIS area. This study provides a way to investigate the long-term dynamics in FAs which have great significance for understanding the state of subsurface firn and subglacial hydrological systems. Full article

(This article belongs to the Special Issue Remote Sensing of Ice Loss Tracking at the Poles)

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22 pages, 16395 KiB

Open AccessArticle

A Sea Ice Concentration Estimation Methodology Utilizing ICESat-2 Photon-Counting Laser Altimeter in the Arctic

by Jun Liu, Huan Xie, Yalei Guo, Xiaohua Tong and Peinan Li

Remote Sens. 2022, 14(5), 1130; https://doi.org/10.3390/rs14051130 - 24 Feb 2022

Cited by 4 | Viewed by 1811

Abstract

NASA’s Ice, Cloud and land Elevation Satellite-2 (ICESat-2) mission was launched in September 2018. The sole instrument onboard ICESat-2 is ATLAS, a highly precise laser that now provides routine, very-high-resolution, surface height measurements across the globe, including over the Arctic. To further improve [...] Read more.

NASA’s Ice, Cloud and land Elevation Satellite-2 (ICESat-2) mission was launched in September 2018. The sole instrument onboard ICESat-2 is ATLAS, a highly precise laser that now provides routine, very-high-resolution, surface height measurements across the globe, including over the Arctic. To further improve the detection accuracy of the sea ice concentration (SIC), we demonstrate a new processing chain that can be used to convert the along-track sea ice freeboard products (ATL10) obtained by ICESat-2 into the SIC, with our initial efforts being focused on the Arctic. For this conversion, we primarily make use of the classification results from the type (sea ice or lead) and segment length data gathered from ATL10. The along-track SIC is the ratio of the area that is covered by sea ice segments to the area of all of the along-track segments. We generated a monthly gridded SIC product with a 25 km resolution and compared this to the NSIDC Climate Data Record (CDR) sea ice concentration. The highest correlation was determined to be 0.7690 in September at high latitudes and the lowest correlation was found to be 0.8595 in June at mid-latitudes. The regions with large standard deviations in summer and autumn are mainly distributed in the thin-ice areas at mid-latitudes. In the Laptev Sea and Kara Sea of east Siberia, the differences in the standard deviation were large; the maximum bias was −0.1566, in November, and the minimum bias was −0.0216, in June. ICESat-2 shows great potential for the accurate estimation of the SIC. Full article

(This article belongs to the Special Issue Remote Sensing of Ice Loss Tracking at the Poles)

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13 pages, 3263 KiB

Open AccessArticle

Monitoring the Hydrological Activities of Antarctic Subglacial Lakes Using CryoSat-2 and ICESat-2 Altimetry Data

by Yi Fan, Weifeng Hao, Baojun Zhang, Chao Ma, Shengjun Gao, Xiao Shen and Fei Li

Remote Sens. 2022, 14(4), 898; https://doi.org/10.3390/rs14040898 - 14 Feb 2022

Cited by 5 | Viewed by 2177

Abstract

Monitoring the hydrological activities of subglacial lakes is critical to understanding the subglacial hydrological system and evaluating the internal mass changes of the Antarctic ice sheet. Drainage or filling events of active lakes lead to elevation changes in the ice surface. These changes [...] Read more.

Monitoring the hydrological activities of subglacial lakes is critical to understanding the subglacial hydrological system and evaluating the internal mass changes of the Antarctic ice sheet. Drainage or filling events of active lakes lead to elevation changes in the ice surface. These changes can be observed by satellite altimetry, but the monitoring must be conducted continuously since the water movements in active subglacial lakes may occur frequently. We used CryoSat-2 Baseline-D and ICESat-2 data from 2010 to 2020 to obtain the time series of the ice surface elevation changes for 17 active lakes. We also evaluated the uncertainty of the time series derived from the CryoSat-2 data by cross-validation. The mean and RMS of the biases between the CryoSat-2-based and ICESat-2-based time series are generally less than 0.3 m and 1.0 m, respectively. However, the mean and RMS are greater over the lakes with rough ice surfaces, such as Whillans₆, KT1, Mac3, and Slessor₂₃. The drainage and filling events continue exhibiting in the extended period and the hydrological activities of SLW, L12, Whillans₆, L78, and Mac1 occurred periodically. Furthermore, we inferred the hydrological connections between the lakes combining simulated water pathways. Full article

(This article belongs to the Special Issue Remote Sensing of Ice Loss Tracking at the Poles)

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18 pages, 4859 KiB

Open AccessArticle

Ionospheric Correction of L-Band SAR Interferometry for Accurate Ice-Motion Measurements: A Case Study in the Grove Mountains Area, East Antarctica

by Yuanyuan Ma, Zemin Wang, Fei Li, Shunlun Liu, Jiachun An, Bing Li and Weifeng Ma

Remote Sens. 2022, 14(3), 556; https://doi.org/10.3390/rs14030556 - 25 Jan 2022

Viewed by 2775

Abstract

Ice motion is an essential element for accurately evaluating glacier mass balance. Interferometric synthetic aperture radar (InSAR) has been widely applied for monitoring ice motion with high precision and wide coverage in the Antarctic. However, the ionospheric effects can significantly impact InSAR-based ice-motion [...] Read more.

Ice motion is an essential element for accurately evaluating glacier mass balance. Interferometric synthetic aperture radar (InSAR) has been widely applied for monitoring ice motion with high precision and wide coverage in the Antarctic. However, the ionospheric effects can significantly impact InSAR-based ice-motion measurements. At low radar frequencies in particular, the ionospheric effects have been regarded as a serious source of noise in L-band SAR data. The split-spectrum method (SSM) is commonly used for correcting the ionospheric effects of the InSAR technique. However, it requires spatial filtering with the relatively large factors used to scale the sub-bands’ interferograms, which often results in an unwrapped phase error. In this paper, a reformulation of the split-spectrum method (RSSM) is introduced to correct the ionospheric effects in the Grove Mountains of East Antarctica, which have slow ice flow and frequent ionosphere changes. The results show that RSSM can effectively correct the ionospheric effects of InSAR-based ice-motion measurements. To evaluate the ability of ionospheric correction using RSSM, the result of ionospheric correction derived from SSM is compared with the results of RSSM. In addition, ionosphere-corrected ice motion is also compared with GPS and MEaSUREs. The results show that the ionosphere-corrected ice velocities are in good agreement with GPS observations and MEaSUREs. The average ice velocity from the InSAR time series is compared to that from MEaSUREs, and the average ionosphere-corrected ice velocity error reduces 43.9% in SSM and 51.1% in RSSM, respectively. The ionosphere-corrected ice velocity error is the most significant, reducing 86.9% in SSM and 90.4% in RSSM from 1 November 2007 to 19 December 2007. The results show that the ability of RSSM to correct ionospheric effects is slightly better than that of SSM. Therefore, we deduce that the RSSM offers a feasible way to correct ionospheric effects in InSAR-based ice-motion measurements in Antarctica. Full article

(This article belongs to the Special Issue Remote Sensing of Ice Loss Tracking at the Poles)

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