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Accurate identification of glacier surges aids in promoting a better understanding of the mechanisms of rapid glacier movement and predicting possible surge-related hazards. Surges of 10 glaciers in the Kongur Tagh area of the Tibetan Plateau were examined based on 128 scenes of Sentinel-1 Synthetic Aperture Radar (SAR) imagery with 12-day and 24-day time intervals from 2015 to 2019. To identify the surges, a set of quantifiable multi-feature criteria was used, including higher glacier velocity, surge front position movement and continuous higher velocity at contiguous time intervals based on surge-type glacier characteristics. The results showed that the Karayaylak Glacier surge reported in May 2015 was more likely initiated in summer 2014, and an unreported pulse event occurred in 2019. Another new and complete surge in the Jangmanjiar Glacier from 2016 to 2018 was further identified. Another two pulse events in the Jangmanjiar Glacier and the Kokodak Glacier in 2015 were discovered. The elevation changes from ASTER images and glacier terminus evolution based on Landsat 8 images also provided positive evidence for the occurrence of two surges. In the Kongur Tagh area, the surge-type glaciers might have larger areas (above 40 km2), longer lengths (above 13 km), lower slopes (below 10°) and higher mean quiescent velocities (above 0.1 ± 0.05 m d−1) than non-surging glaciers. The temperature and precipitation data of ECMWF Re-Analysis 5 (ERA5) showed that the surges in the Kongur Tagh area were mainly induced by thermal and hydrological trigger mechanisms together.
Qinghui Zhu; Chang-Qing Ke; Haili Li. Monitoring glacier surges in the Kongur Tagh area of the Tibetan Plateau using Sentinel-1 SAR data. Geomorphology 2021, 390, 107869 .
AMA StyleQinghui Zhu, Chang-Qing Ke, Haili Li. Monitoring glacier surges in the Kongur Tagh area of the Tibetan Plateau using Sentinel-1 SAR data. Geomorphology. 2021; 390 ():107869.
Chicago/Turabian StyleQinghui Zhu; Chang-Qing Ke; Haili Li. 2021. "Monitoring glacier surges in the Kongur Tagh area of the Tibetan Plateau using Sentinel-1 SAR data." Geomorphology 390, no. : 107869.
In August 2018, a remarkable polynya was observed off the north coast of Greenland, a perennial ice zone where thick sea ice cover persists. In order to investigate the formation process of this polynya, satellite observations, a coupled ice-ocean model, ocean profiling data, and atmosphere reanalysis data were applied. We found that the thinnest sea ice cover in August since 1978 (mean value of 1.1 m, compared to the average value of 2.8 m during 1978−2017) and the modest southerly wind caused by a positive North Atlantic Oscillation (mean value of 0.82, compared to the climatological value of −0.02) were responsible for the formation and maintenance of this polynya. The opening mechanism of this polynya differs from the one formed in February 2018 in the same area caused by persistent anomalously high wind. Sea ice drift patterns have become more responsive to the atmospheric forcing due to thinning of sea ice cover in this region.
Xiaoyi Shen; Chang-Qing Ke; Bin Cheng; Wentao Xia; Mengmeng Li; Xuening Yu; Haili Li. Thinner Sea Ice Contribution to the Remarkable Polynya Formation North of Greenland in August 2018. Advances in Atmospheric Sciences 2021, 38, 1474 -1485.
AMA StyleXiaoyi Shen, Chang-Qing Ke, Bin Cheng, Wentao Xia, Mengmeng Li, Xuening Yu, Haili Li. Thinner Sea Ice Contribution to the Remarkable Polynya Formation North of Greenland in August 2018. Advances in Atmospheric Sciences. 2021; 38 (9):1474-1485.
Chicago/Turabian StyleXiaoyi Shen; Chang-Qing Ke; Bin Cheng; Wentao Xia; Mengmeng Li; Xuening Yu; Haili Li. 2021. "Thinner Sea Ice Contribution to the Remarkable Polynya Formation North of Greenland in August 2018." Advances in Atmospheric Sciences 38, no. 9: 1474-1485.
H Li; Q Zhu; Cq Ke; D Wang; X Shen. The influence of summer great cyclones on sea ice concentration and area in the Arctic Ocean. Climate Research 2021, 1 .
AMA StyleH Li, Q Zhu, Cq Ke, D Wang, X Shen. The influence of summer great cyclones on sea ice concentration and area in the Arctic Ocean. Climate Research. 2021; ():1.
Chicago/Turabian StyleH Li; Q Zhu; Cq Ke; D Wang; X Shen. 2021. "The influence of summer great cyclones on sea ice concentration and area in the Arctic Ocean." Climate Research , no. : 1.
The land ice surface heights derived from Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) were assessed by comparing to the near-coincident measurements from the Airborne Topographic Mapper (ATM) Light Detection and Ranging (LiDAR) system during the 2018 Operation IceBridge mission in Antarctica. Over the marginal and interior Antarctic Ice Sheet, the ICESat-2 ice surface height was accurate to less than 14 cm, and remarkable correlations (R 2 = 1) were found through the comparison to the ATM measurements. Centimetre-scale accuracy was seen for all ICESat-2 beams, ranging from −13 cm to −3 cm. The ICESat-2 land ice surface height performance was affected by the terrain conditions (i.e., surface roughness and slope); larger height differences could be seen when the surface roughness or slope values were higher as it becomes difficult for photons to accurately capture height distributions in areas with complex measurement geometry. No evident performance difference was found for the ICESat-2 beams; weak beams can still be used for height measurement. The fine spatial resolution and centimetre-scale accuracy of the ICESat-2 land ice elevation product make it one of the most suitable data sources for ice surface height measurements.
Xiaoyi Shen; Chang-Qing Ke; Xuening Yu; Yu Cai; Yubin Fan. Evaluation of Ice, Cloud, And Land Elevation Satellite-2 (ICESat-2) land ice surface heights using Airborne Topographic Mapper (ATM) data in Antarctica. International Journal of Remote Sensing 2020, 42, 2556 -2573.
AMA StyleXiaoyi Shen, Chang-Qing Ke, Xuening Yu, Yu Cai, Yubin Fan. Evaluation of Ice, Cloud, And Land Elevation Satellite-2 (ICESat-2) land ice surface heights using Airborne Topographic Mapper (ATM) data in Antarctica. International Journal of Remote Sensing. 2020; 42 (7):2556-2573.
Chicago/Turabian StyleXiaoyi Shen; Chang-Qing Ke; Xuening Yu; Yu Cai; Yubin Fan. 2020. "Evaluation of Ice, Cloud, And Land Elevation Satellite-2 (ICESat-2) land ice surface heights using Airborne Topographic Mapper (ATM) data in Antarctica." International Journal of Remote Sensing 42, no. 7: 2556-2573.
The Arctic sea ice volume (SIV) was investigated by applying sea ice concentration (SIC) and multi‐source sea ice thickness (SIT) products from the Pan‐Arctic Ice‐Ocean Modelling and Assimilation System (PIOMAS), Envisat and CryoSat‐2 (CS‐2) products. The SIV was estimated during the sea ice growth season (October–April) from October 2002 to December 2018. During the Envisat period (October 2002–April 2010), negative SIV trends were estimated by a hybrid Envisat and PIOMAS SIT dataset (defined as Envi‐PIO); the declining trends for both the maximum/minimum SIV were 360 and 177 km3⋅year−1, respectively; similar SIV trends were obtained by applying only the PIOMAS SIT data. During the CS‐2 period (October 2010–December 2018), no clear trends in the SIV were estimated by either CS‐2 or PIOMAS, except for clear increases in the SIV in northern Greenland and the Canadian Arctic Archipelago (CAA) using the CS‐2 SIT data. The age of sea ice plays an important role in SIV variability. For example, the SIV trend was found to be similar to the multi‐year ice trend between 2003 and 2007. The correlation coefficients between the monthly mean SIV and surface air temperature (SAT) and sea surface temperature (SST) were −0.60 and −0.82, respectively. The decreasing trend in the SIV during the Envisat period was influenced by the increase in the annual maximum SST and minimum SAT. The significant increase in the SIV in northern Greenland and the CAA during the CS‐2 period was related to ice deformation.
Mengmeng Li; Chang‐Qing Ke; Xiaoyi Shen; Bin Cheng; Haili Li. Investigation of the Arctic Sea ice volume from 2002 to 2018 using multi‐source data. International Journal of Climatology 2020, 41, 2509 -2527.
AMA StyleMengmeng Li, Chang‐Qing Ke, Xiaoyi Shen, Bin Cheng, Haili Li. Investigation of the Arctic Sea ice volume from 2002 to 2018 using multi‐source data. International Journal of Climatology. 2020; 41 (4):2509-2527.
Chicago/Turabian StyleMengmeng Li; Chang‐Qing Ke; Xiaoyi Shen; Bin Cheng; Haili Li. 2020. "Investigation of the Arctic Sea ice volume from 2002 to 2018 using multi‐source data." International Journal of Climatology 41, no. 4: 2509-2527.
The successful launch of the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) provides a new and advanced tool for sea ice thickness (SIT) estimations in the Arctic. However, the performance of ICESat-2 for SIT estimations still remains unknown. In the present study, SIT estimates derived from ICESat-2 are examined using three retrieval methods, namely, two buoyancy methods with the merged snow depth and empirical snow depth (BMA and BME, respectively) and one empirical estimation method (EEM), and these estimates are compared to near-simultaneous airborne measurements from the IceBird mission in April 2019. Overall, the ICESat-2 total freeboard registers quite well with that from the near-concurrent IceBird mission with a mean bias of 2.5 cm, which demonstrates the high reliability of ICESat-2 data for SIT estimation. However, the much more evident difference between SIT estimations than total freeboard from ICESat-2 and IceBird indicates that other parameters (e.g., snow depth and snow/ice densities) may bring increased uncertainties to the SIT estimation. Overall, BMA is the best method for SIT estimation and has the closest thickness distribution to that of IceBird data with a mean bias of 0.11 m, followed by the BME and EEM methods. The dominate error sources for SIT estimation using the buoyancy method are ice density and snow depth that require further investigation in future studies.
Xiaoyi Shen; Chang-Qing Ke; Qimao Wang; Jie Zhang; Lijian Shi; Xi Zhang. Assessment of Arctic Sea Ice Thickness Estimates From ICESat-2 Using IceBird Airborne Measurements. IEEE Transactions on Geoscience and Remote Sensing 2020, 59, 3764 -3775.
AMA StyleXiaoyi Shen, Chang-Qing Ke, Qimao Wang, Jie Zhang, Lijian Shi, Xi Zhang. Assessment of Arctic Sea Ice Thickness Estimates From ICESat-2 Using IceBird Airborne Measurements. IEEE Transactions on Geoscience and Remote Sensing. 2020; 59 (5):3764-3775.
Chicago/Turabian StyleXiaoyi Shen; Chang-Qing Ke; Qimao Wang; Jie Zhang; Lijian Shi; Xi Zhang. 2020. "Assessment of Arctic Sea Ice Thickness Estimates From ICESat-2 Using IceBird Airborne Measurements." IEEE Transactions on Geoscience and Remote Sensing 59, no. 5: 3764-3775.
Lake changes have great impacts on local and regional ecosystems and socioeconomic activities. To determine the recent changes in Hongze Lake, radar altimeter and Landsat data were used to examine the water level and area and estimate the water volume changes from 2003 to 2018. Generally, the annual water level results from radar altimeter data showed good agreement with in situ measurements (r2 = 0.62, bias = −0.02 m, mean absolute error = 0.10 m and root mean square error = 0.12 m). Although the results show that the water level and water volume of Hongze Lake had no trends during the 16 years, the water area decreased significantly at a rate of 19.76 km2 per year. Despite great fluctuations throughout 16 years, two shifts occurred in approximately 2007 and 2013. The water level, area and volume of Hongze Lake decreased from 2007 to 2012, but they all showed increasing trends after 2013. The interannual changes in the water level and volume of Hongze Lake were consistent with the changes in annual precipitation, while the change in area was mainly affected by human activities, especially land reclamation and aquaculture activities along the lakeshore. In addition, the intra-annual change in the water level was greatly affected by artificial activities such as irrigation and flood prevention.
Yu Cai; Chang-Qing Ke; Xiaoyi Shen. Variations in water level, area and volume of Hongze Lake, China from 2003 to 2018. Journal of Great Lakes Research 2020, 46, 1511 -1520.
AMA StyleYu Cai, Chang-Qing Ke, Xiaoyi Shen. Variations in water level, area and volume of Hongze Lake, China from 2003 to 2018. Journal of Great Lakes Research. 2020; 46 (6):1511-1520.
Chicago/Turabian StyleYu Cai; Chang-Qing Ke; Xiaoyi Shen. 2020. "Variations in water level, area and volume of Hongze Lake, China from 2003 to 2018." Journal of Great Lakes Research 46, no. 6: 1511-1520.
A number of satellite altimeters have been used to measure Arctic sea ice freeboard and to study its changes over the past decades (1992-present). In order to produce long-term time series of sea ice freeboard data set, it is essential to investigate the difference and consistency between different satellite-based sea ice freeboard data sets. Hence in this study, the comparison between ice freeboard products from altimeters on board Sentinel-3A and CryoSat-2 is constructed from February 2017 to January 2018 excluding summer months. The comparisons of echo waveform shapes and along-track radar freeboard estimates suggest that the freeboard difference between these two sensors is caused by the signal range bin number and the chosen retrackers for different surface types (leads and sea ice floes). Monthly gridded freeboard results show that mean values of two different satellite altimeters agree each other reasonably over the whole study period. In general, Sentinel-3A data set shows lower freeboard estimates than CryoSat-2 data set, this phenomenon is found in both First-Year ice (FYI) and Multi-Year ice (MYI) regions. No ice-type-related difference indicates the good consistency between Sentinel-3A and CryoSat-2 data sets. Over the whole period, mean freeboard estimates for the entire Arctic differs generally by not more than 0.07 m between Sentinel-3A and CryoSat-2. Compared to airborne Operation IceBridge (OIB) data, Sentinel-3A has closer sea ice freeboard estimates than CryoSat-2.
Xiaoyi Shen; Chang-Qing Ke; Hongjie Xie; Mengmeng Li; Wentao Xia. A comparison of Arctic sea ice freeboard products from Sentinel-3A and CryoSat-2 data. International Journal of Remote Sensing 2019, 41, 2789 -2806.
AMA StyleXiaoyi Shen, Chang-Qing Ke, Hongjie Xie, Mengmeng Li, Wentao Xia. A comparison of Arctic sea ice freeboard products from Sentinel-3A and CryoSat-2 data. International Journal of Remote Sensing. 2019; 41 (7):2789-2806.
Chicago/Turabian StyleXiaoyi Shen; Chang-Qing Ke; Hongjie Xie; Mengmeng Li; Wentao Xia. 2019. "A comparison of Arctic sea ice freeboard products from Sentinel-3A and CryoSat-2 data." International Journal of Remote Sensing 41, no. 7: 2789-2806.
Lakes sensitively respond to global and regional climate change, especially in arid areas. Using Moderate Resolution Imaging Spectroradiometer (MODIS) daily snow products, the lake ice phenology of 23 lakes in the Xinjiang Uygur Autonomous Region of China from 2001 to 2018 was extracted based on thresholds of ice/water pixel numbers, and their change trends over 18 years were calculated. The results of MODIS-derived lake ice phenology showed consistent variations with existing ice phenology data sets derived from passive microwave data. Generally, lakes in Xinjiang begin to freeze from October to December every year, and their ice cover periods end from March to June. The average ice cover duration for the 23 lakes is 167 days, of which 16 lakes have an average shortening rate of − 1.08 days/year and seven lakes have an average extending rate of 1.18 days/year. The majority of lakes experienced later freeze-up (17 lakes) and earlier break-up (18 lakes) from 2001 to 2018. Lake ice phenology is affected by both climatic factors and lake physicochemical characteristics, in which freeze-up dates are more easily affected by lake-specific factors such as lake area (r = 0.535), while climatic factors especially water surface temperature have greater impacts on lake break-up dates (r = − 0.874). Compared to air temperature, water surface temperature changes have a more direct influence on the variations in lake ice phenology, 1° increase in water surface temperature may cause the ice cover duration to decrease by 12 days, while precipitation changes have almost no effect on the lakes in Xinjiang. In some cases, lake changes such as changes to the area and mineralization may also have dominant impacts on lake ice phenology.
Yu Cai; Chang-Qing Ke; Guohui Yao; Xiaoyi Shen. MODIS-observed variations of lake ice phenology in Xinjiang, China. Climatic Change 2019, 158, 575 -592.
AMA StyleYu Cai, Chang-Qing Ke, Guohui Yao, Xiaoyi Shen. MODIS-observed variations of lake ice phenology in Xinjiang, China. Climatic Change. 2019; 158 (3-4):575-592.
Chicago/Turabian StyleYu Cai; Chang-Qing Ke; Guohui Yao; Xiaoyi Shen. 2019. "MODIS-observed variations of lake ice phenology in Xinjiang, China." Climatic Change 158, no. 3-4: 575-592.
The spatiotemporal changes in the sea ice region albedo over the entire Arctic region and in eight subregions (the Central Arctic Ocean (CAO), the Beaufort and Chukchi Seas (BC), the East Siberian and Laptev Seas (ESL), Baffin Bay and Labrador Sea (BL), the Canadian Archipelago (CA), the Greenland Sea (GS), Hudson Bay (HB) and the Kara and Barents Seas (KB)) in the summer of 1982‐2015 are analysed with CLARA‐A2‐SAL data. The results indicate that in the summer of 1982‐2015, the Arctic sea ice region albedo fluctuated with a downward trend of ‐1.6% per decade (significance level of 99%). The BC had the largest decline in the albedo trend of ‐2.7% per decade (significance level of 99%), and most other subregions had downward trends except the GS, which exhibited a slight upward trend. The mean Arctic sea ice region albedo was 44%. The high albedo areas were mainly concentrated in the CAO and the vicinity of Greenland. The albedo decreased with decreasing latitude, while the low‐value areas were mainly concentrated in the outer sea ice area. In the Arctic region, both the sea ice concentration (SIC) and the sea ice extent (SIE) showed a decreasing trend, while the near‐surface air temperature (NSAT) and the summer Central Arctic Index (CAI) showed an increasing trend. The Arctic sea ice region albedo was positively correlated with the SIC and the SIE (0.84, 0.78) and negatively correlated with the NSAT (‐0.72), all with a statistical significance level of 99%. The correlation between sea ice region albedo and the summer CAI revealed different relationships in these regions. The BC and ESL had a significant negative correlation, and the GS showed a significant positive correlation. These findings indicated that the decrease in albedo is closely related to the reduction in Arctic sea ice and the increase in air temperature. In addition, the sea ice region albedo variations in the BC, ESL and GS are also greatly influenced by atmospheric circulation. This article is protected by copyright. All rights reserved.
Hai‐Tao Peng; Chang‐Qing Ke; Xiaoyi Shen; Mengmeng Li; Zhu‐De Shao. Summer albedo variations in the Arctic Sea ice region from 1982 to 2015. International Journal of Climatology 2019, 40, 3008 -3020.
AMA StyleHai‐Tao Peng, Chang‐Qing Ke, Xiaoyi Shen, Mengmeng Li, Zhu‐De Shao. Summer albedo variations in the Arctic Sea ice region from 1982 to 2015. International Journal of Climatology. 2019; 40 (6):3008-3020.
Chicago/Turabian StyleHai‐Tao Peng; Chang‐Qing Ke; Xiaoyi Shen; Mengmeng Li; Zhu‐De Shao. 2019. "Summer albedo variations in the Arctic Sea ice region from 1982 to 2015." International Journal of Climatology 40, no. 6: 3008-3020.
The area of Arctic sea ice has dramatically decreased, and the length of the open water season has increased; these patterns have been observed by satellite remote sensing since the 1970s. In this paper, we calculate the net primary productivity (NPP, calculated by carbon) from 2003 to 2016 based on sea ice concentration products, chlorophyll a (Chl a) concentration, photosynthetically active radiation (PAR), sea surface temperature (SST), and sunshine duration data. We then analyse the spatiotemporal changes in the Chl a concentration and NPP and further investigate the relations among NPP, the open water area, and the length of the open water season. The results indicate that (1) the Chl a concentration increased by 0.025 mg/m3 per year; (2) the NPP increased by 4.29 mg/(m2·d) per year, reaching a maximum of 525.74 mg/(m2·d) in 2016; and (3) the Arctic open water area increased by 57.23×103 km2/a, with a growth rate of 1.53 d/a for the length of the open water season. The annual NPP was significantly positively related to the open water area, the length of the open water season and the SST. The daily NPP was also found to have a lag correlation with the open water area, with a lag time of two months. With global warming, NPP has maintained an increasing trend, with the most significant increase occurring in the Kara Sea. In summary, this study provides a macroscopic understanding of the distribution of phytoplankton in the Arctic, which is valuable information for the evaluation and management of marine ecological environments.
Haili Li; Changqing Ke; Qinghui Zhu; Su Shu. Spatial-temporal variations in net primary productivity in the Arctic from 2003 to 2016. Acta Oceanologica Sinica 2019, 38, 111 -121.
AMA StyleHaili Li, Changqing Ke, Qinghui Zhu, Su Shu. Spatial-temporal variations in net primary productivity in the Arctic from 2003 to 2016. Acta Oceanologica Sinica. 2019; 38 (8):111-121.
Chicago/Turabian StyleHaili Li; Changqing Ke; Qinghui Zhu; Su Shu. 2019. "Spatial-temporal variations in net primary productivity in the Arctic from 2003 to 2016." Acta Oceanologica Sinica 38, no. 8: 111-121.
As a fundamental climate state variable, sea ice thickness (SIT) has exhibited a declining trend over the past five decades. Here, we present a quantitative comparison of three CryoSat-2 (CS-2) SIT products from the Alfred-Wegener-Institute (AWI), the National Snow and Ice Data Centre (NSIDC), and the European Space Agency (ESA) during the growth season (October to April) from 2010 to 2018 with Operation IceBridge (OIB) data. The results show that the NSIDC SIT product is the closest to the OIB SIT, with ESA SIT exhibiting the highest bias. During each growth season, the SIT differences between AWI and NSIDC gradually decrease, while such differences between ESA and NSIDC increase for first-year ice (FYI) and decrease then increase for multiyear ice (MYI). The difference between ESA and NSIDC is larger than that between AWI and NSIDC. Moreover, the rather large differences between ESA and NSIDC are mainly located in thin ice areas. Consistent to SIT comparative results, sea ice freeboard for ESA is higher than that for OIB, AWI and NSIDC, especially FYI freeboard. Sea ice freeboard for NSIDC is the closest to that for OIB. The comparative results indicate that the sources of the differences in SIT between the products mainly originate from the sea ice density and freeboard retrieval methods. The choices of different waveform retrackers and threshold assignments significantly influence the MYI freeboard retrievals due to the relatively thick snow depth and high surface roughness over MYI.
Mengmeng Li; Chang-Qing Ke; Hongjie Xie; Xin Miao; Xiaoyi Shen; Wentao Xia. Arctic sea ice thickness retrievals from CryoSat-2: seasonal and interannual comparisons of three different products. International Journal of Remote Sensing 2019, 41, 152 -170.
AMA StyleMengmeng Li, Chang-Qing Ke, Hongjie Xie, Xin Miao, Xiaoyi Shen, Wentao Xia. Arctic sea ice thickness retrievals from CryoSat-2: seasonal and interannual comparisons of three different products. International Journal of Remote Sensing. 2019; 41 (1):152-170.
Chicago/Turabian StyleMengmeng Li; Chang-Qing Ke; Hongjie Xie; Xin Miao; Xiaoyi Shen; Wentao Xia. 2019. "Arctic sea ice thickness retrievals from CryoSat-2: seasonal and interannual comparisons of three different products." International Journal of Remote Sensing 41, no. 1: 152-170.
A new method called Bézier curve fitting (BCF) for approximating CryoSat-2 (CS-2) SAR-mode waveform is developed to optimize the retrieval of surface elevation of both sea ice and leads for the period of late winter/early spring. We found that the best results are achieved when the retracking points are fixed on positions at which the rise of the fitted Bézier curve reaches 70% of its peak in case of leads, and 50% in case of sea ice. In order to evaluate the proposed retracking algorithm, we compare it to other empirically-based methods currently reported in the literature, namely the threshold first-maximum retracker algorithm (TFMRA) and the European Space Agency (ESA) CS-2 in-depth Level-2 algorithm (L2I). The results of the retracking procedure for the different algorithms are validated using data of the Operation Ice Bridge (OIB) airborne mission. For two OIB campaign periods in March 2015 and April 2016, the mean absolute differences between freeboard values retrieved from CS-2 and OIB data were 9.22 and 7.79 cm when using the BCF method, 10.41 cm and 8.16 cm for TFMRA, and 10.01 cm and 8.42 cm for L2I. This suggests that the sea ice freeboard data can be obtained with a higher accuracy when using the proposed BCF method instead of the TFMRA or the CS-2 L2I algorithm.
Xiaoyi Shen; Markku Similä; Wolfgang Dierking; Xi Zhang; Changqing Ke; Meijie Liu; Manman Wang. A New Retracking Algorithm for Retrieving Sea Ice Freeboard from CryoSat-2 Radar Altimeter Data during Winter–Spring Transition. Remote Sensing 2019, 11, 1194 .
AMA StyleXiaoyi Shen, Markku Similä, Wolfgang Dierking, Xi Zhang, Changqing Ke, Meijie Liu, Manman Wang. A New Retracking Algorithm for Retrieving Sea Ice Freeboard from CryoSat-2 Radar Altimeter Data during Winter–Spring Transition. Remote Sensing. 2019; 11 (10):1194.
Chicago/Turabian StyleXiaoyi Shen; Markku Similä; Wolfgang Dierking; Xi Zhang; Changqing Ke; Meijie Liu; Manman Wang. 2019. "A New Retracking Algorithm for Retrieving Sea Ice Freeboard from CryoSat-2 Radar Altimeter Data during Winter–Spring Transition." Remote Sensing 11, no. 10: 1194.
In recent years, the intensity of Arctic cyclones has remarkably increased and the impact on the Arctic ecosystem has become more prominent. The Kara Sea is a Russian marginal sea with wide shelf, high productivity of phytoplankton, and abundant resources, and has a very important strategic position and research significance. We have used multiple datasets, including satellite remote sensing data and model reanalysis, to obtain the spatial–temporal distribution of Chlorophyll-a (Chl-a) concentration and sea surface temperature (SST) before and after the passage of the Arctic cyclone over the Kara Sea in summer, to explore basic processes and the ecosystem response to the cyclone. The results indicated that after the passage of the Arctic cyclone, the Chl-a concentration in different regions of the Kara Sea increased at different levels, and the SST decreased briefly. The increase of Chl-a concentration (0.49 mg/m3) caused by the Arctic cyclone happened in Kara Sea in July 2012. The nearshore Chl-a concentration increased more than that on the continental shelf, the continental shelf increases in turn being greater than the deep sea; however, the nearshore response time was shorter than that on the shelf and the deep sea. Compared with nearshore SST, which decreased more than 2 °C, the SST on the continental shelf and in the deep-sea area decreased slightly less. Besides, the left side of the cyclone cooled faster than the right side.
Haili Li; Delu Pan; Difeng Wang; Fang Gong; Yan Bai; Xianqiang He; Zengzhou Hao; Changqing Ke. The Impact of Summer Arctic Cyclones on Chlorophyll-a Concentration and Sea Surface Temperature in the Kara Sea. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2019, 12, 1396 -1408.
AMA StyleHaili Li, Delu Pan, Difeng Wang, Fang Gong, Yan Bai, Xianqiang He, Zengzhou Hao, Changqing Ke. The Impact of Summer Arctic Cyclones on Chlorophyll-a Concentration and Sea Surface Temperature in the Kara Sea. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2019; 12 (5):1396-1408.
Chicago/Turabian StyleHaili Li; Delu Pan; Difeng Wang; Fang Gong; Yan Bai; Xianqiang He; Zengzhou Hao; Changqing Ke. 2019. "The Impact of Summer Arctic Cyclones on Chlorophyll-a Concentration and Sea Surface Temperature in the Kara Sea." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 12, no. 5: 1396-1408.
During urbanization, different dimensions of the expansion of construction land causes different degrees of surface deformation. Based on the C-band ENVISAT ASAR data (December 2004 to September 2010) and Sentinel-1A data (March 2015 to April 2017), the small baseline subset interferometric synthetic aperture radar (SBAS InSAR) method was used to monitor the spatial and temporal variations of surface deformation in Shanghai, China. The results showed that widespread uneven subsidence occurred in Shanghai from December 2004 to April 2017. A transition from urban areas toward the suburbs appeared in the spatial distribution, in which the cumulative deformation in the urban areas has the characteristics of seasonal fluctuation, which shows the alternation of subsidence and rebound. In addition, the deformation characteristics of different types of construction land with the same geological conditions were compared, which showed that residential land had the least cumulative subsidence and clear seasonal fluctuations, industrial land had the greatest cumulative subsidence, and transportation land had greater subsidence during the construction period but tended to become stable after being put into use. This suggests that the deformation characteristics of Shanghai are changing, and the type of construction land is also an important factor in the deformation process.
Guohui Yao; Chang-Qing Ke; Jinhua Zhang; Yanyan Lu; Jiaman Zhao; Hoonyol Lee. Surface deformation monitoring of Shanghai based on ENVISAT ASAR and Sentinel-1A data. Environmental Earth Sciences 2019, 78, 225 .
AMA StyleGuohui Yao, Chang-Qing Ke, Jinhua Zhang, Yanyan Lu, Jiaman Zhao, Hoonyol Lee. Surface deformation monitoring of Shanghai based on ENVISAT ASAR and Sentinel-1A data. Environmental Earth Sciences. 2019; 78 (6):225.
Chicago/Turabian StyleGuohui Yao; Chang-Qing Ke; Jinhua Zhang; Yanyan Lu; Jiaman Zhao; Hoonyol Lee. 2019. "Surface deformation monitoring of Shanghai based on ENVISAT ASAR and Sentinel-1A data." Environmental Earth Sciences 78, no. 6: 225.
In this study, we applied the 1988–2017 monthly average sea ice concentration data from the Met Office Hadley Centre and the 1988–2017 monthly average reanalysis data from the National Centers for Environmental Prediction/Department of Energy (NCEP/DOE) Reanalysis II to analyze the relationship between the winter precipitation in the Barents and Kara Seas (BKS) and the previous autumn eastern Siberian Sea ice anomalies. Through the correlation analysis, we found that the correlation between eastern Siberian Sea ice and the BKS winter precipitation was strongest in September and weakest in November. The results indicated that, when the eastern Siberian Sea ice extent decreased in September–October, a significant positive geopotential height anomaly would occur in the coming winter (December–February) in the Norwegian–Barents region. This result in turn caused anomalies in the northward meridional wind. Consequently, the anomalous water vapor from the mid-latitude Atlantic to the Arctic passed through the Greenland Sea before finally reaching the BKS. The meridional wind also caused the temperature in said seas to increase and the BKS ice to melt, leading to an increase of winter precipitation. We also found that the increase of the Siberian high (SH) in winter was related to the decrease of autumn East Siberian Sea ice extent and the increase of the winter BKS precipitation anomaly. Further research still needs to be refined for this issue in future studies.
Jiajun Feng; Yuanzhi Zhang; Changqing Ke. Relationship between Winter Precipitation in Barents–Kara Seas and September–October Eastern Siberian Sea Ice Anomalies. Applied Sciences 2019, 9, 1091 .
AMA StyleJiajun Feng, Yuanzhi Zhang, Changqing Ke. Relationship between Winter Precipitation in Barents–Kara Seas and September–October Eastern Siberian Sea Ice Anomalies. Applied Sciences. 2019; 9 (6):1091.
Chicago/Turabian StyleJiajun Feng; Yuanzhi Zhang; Changqing Ke. 2019. "Relationship between Winter Precipitation in Barents–Kara Seas and September–October Eastern Siberian Sea Ice Anomalies." Applied Sciences 9, no. 6: 1091.
Lake ice is a robust indicator of climate change. The availability of information contained in Moderate Resolution Imaging Spectroradiometer (MODIS) daily snow products from 2000 to 2017 could be greatly improved after cloud removal by gap filling. Thresholds based on open water pixel numbers are used to extract the freeze‐up start and break‐up end dates for 58 lakes on the Tibetan Plateau (TP), 18 lakes are also selected to extract the freeze‐up end and break‐up start dates. The lake ice durations are further calculated based on freeze‐up and break‐up dates. Lakes on the TP begin to freeze‐up in late October and all the lakes start the ice cover period in mid‐January of the following year. In late March, some lakes begin to break‐up, and all the lakes end the ice cover period in early July. Generally, the lakes in the northern Inner‐TP have earlier freeze‐up dates and later break‐up dates (i.e. longer ice cover durations) than those in the southern Inner‐TP. Over 17 years, the mean ice cover duration of 58 lakes is 157.78 days, 18 (31%) lakes have a mean extending rate of 1.11 d yr‐1 and 40 (69%) lakes have a mean shortening rate of 0.80 d yr‐1. Geographical location and climate conditions determine the spatial heterogeneity of the lake ice phenology, especially the ones of break‐up dates, while the physico‐chemical characteristics mainly affect the freeze‐up dates of the lake ice in this study. Ice cover duration is affected by both climatic and lake specific physico‐chemical factors, which can reflect the climatic and environmental change for lakes on the TP.
Yu Cai; Chang‐Qing Ke; Xingong Li; Guoqing Zhang; Zheng Duan; Hoonyol Lee. Variations of Lake Ice Phenology on the Tibetan Plateau From 2001 to 2017 Based on MODIS Data. Journal of Geophysical Research: Atmospheres 2019, 124, 825 -843.
AMA StyleYu Cai, Chang‐Qing Ke, Xingong Li, Guoqing Zhang, Zheng Duan, Hoonyol Lee. Variations of Lake Ice Phenology on the Tibetan Plateau From 2001 to 2017 Based on MODIS Data. Journal of Geophysical Research: Atmospheres. 2019; 124 (2):825-843.
Chicago/Turabian StyleYu Cai; Chang‐Qing Ke; Xingong Li; Guoqing Zhang; Zheng Duan; Hoonyol Lee. 2019. "Variations of Lake Ice Phenology on the Tibetan Plateau From 2001 to 2017 Based on MODIS Data." Journal of Geophysical Research: Atmospheres 124, no. 2: 825-843.
Lake Tana is the largest lake in Ethiopia, and its lake outflow is the source of the Blue Nile River that provides vital water resources for many livelihoods and downstream/international stakeholders. Therefore, it is essential to quantify and monitor the water balance of Lake Tana. However, Lake Tana is poorly gauged, with more than 50% of Lake Tana Basin being ungauged from in-situ measurements, making it difficult to quantify the lake inflow from surrounding basins. The lack of in-situ measurements highlights the need for the innovative application of satellite remote sensing. This study explores how freely accessible satellite remote sensing can be used to complement routine weather data to quantify the water balance of Lake Tana and its surrounding catchments. This study particularly investigates whether the outflow from Lake Tana can be estimated with sufficient accuracy as the residual of the lake water balance. Monthly inflow into lake was computed as the total runoff from the surrounding catchments; the runoff was estimated as the residual of the land-based catchment water balance using satellite precipitation improved with an integrated downscaling-calibration procedure, satellite evapotranspiration, and a correction term for changes in land total storage (soil moisture storage and deep percolation). The outflow from Lake Tana was estimated as the residual of lake water balance by combining satellite-based lake precipitation, changes in water storage, and lake inflow with estimated lake evaporation. Evaluation using limited available measurements showed that estimated annual runoff for two gauged subbasins agreed well with measurements, with differences within 4%. The estimated annual outflow from Lake Tana was also close to measured outflow, with a difference of 12%. However, the estimated monthly runoff from catchments and monthly lake outflow were unsatisfactory, with large errors.
Zheng Duan; Hongkai Gao; Changqing Ke. Estimation of Lake Outflow from the Poorly Gauged Lake Tana (Ethiopia) Using Satellite Remote Sensing Data. Remote Sensing 2018, 10, 1060 .
AMA StyleZheng Duan, Hongkai Gao, Changqing Ke. Estimation of Lake Outflow from the Poorly Gauged Lake Tana (Ethiopia) Using Satellite Remote Sensing Data. Remote Sensing. 2018; 10 (7):1060.
Chicago/Turabian StyleZheng Duan; Hongkai Gao; Changqing Ke. 2018. "Estimation of Lake Outflow from the Poorly Gauged Lake Tana (Ethiopia) Using Satellite Remote Sensing Data." Remote Sensing 10, no. 7: 1060.
Lake ice is a sensitive indicator of climate change. Based on the disparities between the brightness temperatures of lake ice and water, passive microwave data can be used to monitor the ice variations of a lake. With focus on the analysis of long time series variability of lake ice, this study extracts four characteristic dates related to lake ice (the annual freeze start, freeze completion, ablation start and ablation completion dates) for Qinghai Lake from 1979 to 2016 using Scanning Multichannel Microwave Radiometer (SMMR) and Special Sensor Microwave Imager (SSM/I) passive microwave brightness temperature data. The corresponding freezing duration, ablation duration, complete freezing duration and ice coverage duration are calculated. Applying Moderate Resolution Imaging Spectroradiometer (MODIS) daily snow products, the accuracy of the results derived from passive microwave data is validated. The validation analysis shows a strong agreement (R(2) ranges from 0.70 to 0.85, mean absolute error (MAE) ranges from 2.25 to 3.94days) in the freeze start, ablation start, and ablation completion dates derived from the MODIS data and passive microwave data; the ice coverage duration also has a small error (relative error (RE)=2.95%, MAE=3.13days), suggesting that the results obtained from passive microwave data are reliable. The results show that the freezing dates of Qinghai Lake have been delayed and the ablation dates have advanced. Over 38years, the freeze start date and freeze completion date have been pushed back by 6.16days and 2.27days, respectively, while the ablation start date and ablation completion date have advanced by 11.24days and 14.09days, respectively. The freezing duration and ablation duration have shortened by 3.89days and 2.85days, respectively, and the complete freezing duration and ice coverage duration have shortened by 14.84days and 21.21days, respectively. There is a significant negative correlation between the ice coverage duration and the mean air temperature in winter.
Yu Cai; Chang-Qing Ke; Zheng Duan. Monitoring ice variations in Qinghai Lake from 1979 to 2016 using passive microwave remote sensing data. Science of The Total Environment 2017, 607-608, 120 -131.
AMA StyleYu Cai, Chang-Qing Ke, Zheng Duan. Monitoring ice variations in Qinghai Lake from 1979 to 2016 using passive microwave remote sensing data. Science of The Total Environment. 2017; 607-608 ():120-131.
Chicago/Turabian StyleYu Cai; Chang-Qing Ke; Zheng Duan. 2017. "Monitoring ice variations in Qinghai Lake from 1979 to 2016 using passive microwave remote sensing data." Science of The Total Environment 607-608, no. : 120-131.
Sea ice type is one of the most sensitive variables in Arctic ice monitoring and detailed information about it is essential for ice situation evaluation, vessel navigation, and climate prediction. Many machine-learning methods including deep learning can be employed for ice-type detection, and most classifiers tend to prefer different feature combinations. In order to find the optimal classifier-feature assembly (OCF) for sea ice classification, it is necessary to assess their performance differences. The objective of this letter is to make a recommendation for the OCF for sea ice classification using Cryosat-2 (CS-2) data. Six classifiers including convolutional neural network (CNN), Bayesian, K nearest-neighbor (KNN), support vector machine (SVM), random forest (RF), and back propagation neural network (BPNN) were studied. CS-2 altimeter data of November 2015 and May 2016 in the whole Arctic were used. The overall accuracy was estimated using multivalidation to evaluate the performances of individual classifiers with different feature combinations. Overall, RF achieved a mean accuracy of 89.15%, followed by Bayesian, SVM, and BPNN (~86%), outperforming the worst (CNN and KNN) by 7%. Trailing-edge width (TeW) and leading-edge width (LeW) were the most important features, and feature combination of TeW, LeW, Sigma0, maximum of the returned power waveform (MAX), and pulse peakiness (PP) was the best choice. RF with feature combination of TeW, LeW, Sigma0, MAX, and PP was finally selected as the OCF for sea ice classification and the results that demonstrated this method achieved a mean accuracy of 91.45%, which outperformed the other state-of-art methods by 9%.
Xiaoyi Shen; Jie Zhang; Xi Zhang; Junmin Meng; Changqing Ke. Sea Ice Classification Using Cryosat-2 Altimeter Data by Optimal Classifier–Feature Assembly. IEEE Geoscience and Remote Sensing Letters 2017, 14, 1948 -1952.
AMA StyleXiaoyi Shen, Jie Zhang, Xi Zhang, Junmin Meng, Changqing Ke. Sea Ice Classification Using Cryosat-2 Altimeter Data by Optimal Classifier–Feature Assembly. IEEE Geoscience and Remote Sensing Letters. 2017; 14 (11):1948-1952.
Chicago/Turabian StyleXiaoyi Shen; Jie Zhang; Xi Zhang; Junmin Meng; Changqing Ke. 2017. "Sea Ice Classification Using Cryosat-2 Altimeter Data by Optimal Classifier–Feature Assembly." IEEE Geoscience and Remote Sensing Letters 14, no. 11: 1948-1952.