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Liyun Dai
Laboratory of Remote Sensing and Geospatial Science, Cold and Arid Regions Environmental and Engineering Research Institute, Lanzhou, Gansu, China, 730000

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Journal article
Published: 30 August 2021 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Long-term surface soil moisture (SM) data are increasingly needed in water budget and energy balance analysis of watersheds. The performance of nine remotely sensed SM products from AMSR2, SMOS and SMAP missions, are evaluated based on observations collected from distributed observation networks in the Heihe River Basin (HRB) of China during 2013 to 2017. Results show that the SMAP Level 3 Dual Channel Algorithm (DCA) SM retrievals reflect the seasonal SM variations well with high temporal correlations of ~0.7 and high accuracy within 0.04 m3/m3 in terms of unbiased Root Mean Squared Error (ubRMSE) over the grassland in the HRB. The SMOS level 3 SM retrievals present increased underestimation and ubRMSE of ~0.10 m3/m3 as the vegetation increases. The newly published SMOS-IC product in version 2 outperforms the SMOS level 3 product with improved temporal correlation coefficient above 0.4 and lower ubRMSE of ~0.05 m3/m3. AMSR2 Land Parameter Retrieval Algorithm (LPRM) SM products show extremely large overestimation over the vegetated regions in HRB, especially the C-band products. Drastically high underestimation biases are observed in the Japan Aerospace Exploration Agency (JAXA) AMSR2 SM product. Parameter uncertainty analyses indicate that the different parameterization schemes of vegetation optical depth (VOD) inputs could be one of the main reasons resulting in the systematic overestimation/underestimation in the AMSR2/SMOS/SMAP SM retrievals. The findings aim to provide insights into studies on algorithms refinements and data fusions of SM products in HRB.

ACS Style

Zengyan Wang; Tao Che; Tianjie Zhao; Liyun Dai; Xiaojun Li; Jean Pierre Wigneron. Evaluation of SMAP, SMOS and AMSR2 soil moisture products based on distributed ground observation network in cold and arid regions of China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2021, PP, 1 -1.

AMA Style

Zengyan Wang, Tao Che, Tianjie Zhao, Liyun Dai, Xiaojun Li, Jean Pierre Wigneron. Evaluation of SMAP, SMOS and AMSR2 soil moisture products based on distributed ground observation network in cold and arid regions of China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021; PP (99):1-1.

Chicago/Turabian Style

Zengyan Wang; Tao Che; Tianjie Zhao; Liyun Dai; Xiaojun Li; Jean Pierre Wigneron. 2021. "Evaluation of SMAP, SMOS and AMSR2 soil moisture products based on distributed ground observation network in cold and arid regions of China." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing PP, no. 99: 1-1.

Journal article
Published: 04 April 2021 in Remote Sensing
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Ice phenology data of 22 large lakes of the Northern Hemisphere for 40 years (1979–2018) have been retrieved from passive microwave remote sensing brightness temperature (Tb). The results were compared with site-observation data and visual interpretation from Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectivity products images (MOD09GA). The mean absolute errors of four lake ice phenology parameters, including freeze-up start date (FUS), freeze-up end date (FUE), break-up start date (BUS), and break-up end date (BUE) against MODIS-derived ice phenology were 2.50, 2.33, 1.98, and 3.27 days, respectively. The long-term variation in lake ice phenology indicates that FUS and FUE are delayed; BUS and BUE are earlier; ice duration (ID) and complete ice duration (CID) have a general decreasing trend. The average change rates of FUS, FUE, BUS, BUE, ID, and CID of lakes in this study from 1979 to 2018 were 0.23, 0.23, −0.17, −0.33, −0.67, and −0.48 days/year, respectively. Air temperature and latitude are two dominant driving factors of lake ice phenology. Lake ice phenology for the period 2021–2100 was predicted by the relationship between ice phenology and air temperature for each lake. Compared with lake ice phenology changes from 1990 to 2010, FUS is projected to be delayed by 3.1 days and 11.8 days under Representative Concentration Pathways (RCPs) 2.6 and 8.5 scenarios, respectively; BUS is projected to be earlier by 3.3 days and 10.7 days, respectively; and ice duration from 2080 to 2100 will decrease by 6.5 days and 21.9 days, respectively.

ACS Style

Lei Su; Tao Che; Liyun Dai. Variation in Ice Phenology of Large Lakes over the Northern Hemisphere Based on Passive Microwave Remote Sensing Data. Remote Sensing 2021, 13, 1389 .

AMA Style

Lei Su, Tao Che, Liyun Dai. Variation in Ice Phenology of Large Lakes over the Northern Hemisphere Based on Passive Microwave Remote Sensing Data. Remote Sensing. 2021; 13 (7):1389.

Chicago/Turabian Style

Lei Su; Tao Che; Liyun Dai. 2021. "Variation in Ice Phenology of Large Lakes over the Northern Hemisphere Based on Passive Microwave Remote Sensing Data." Remote Sensing 13, no. 7: 1389.

Journal article
Published: 25 March 2021 in Remote Sensing
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In this study, a machine learning algorithm was introduced to fuse gridded snow depth datasets. The input variables of the machine learning method included geolocation (latitude and longitude), topographic data (elevation), gridded snow depth datasets and in situ observations. A total of 29,565 in situ observations were used to train and optimize the machine learning algorithm. A total of five gridded snow depth datasets—Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) snow depth, Global Snow Monitoring for Climate Research (GlobSnow) snow depth, Long time series of daily snow depth over the Northern Hemisphere (NHSD) snow depth, ERA-Interim snow depth and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) snow depth—were used as input variables. The first three snow depth datasets are retrieved from passive microwave brightness temperature or assimilation with in situ observations, while the last two are snow depth datasets obtained from meteorological reanalysis data with a land surface model and data assimilation system. Then, three machine learning methods, i.e., Artificial Neural Networks (ANN), Support Vector Regression (SVR), and Random Forest Regression (RFR), were used to produce a fused snow depth dataset from 2002 to 2004. The RFR model performed best and was thus used to produce a new snow depth product from the fusion of the five snow depth datasets and auxiliary data over the Northern Hemisphere from 2002 to 2011. The fused snow-depth product was verified at five well-known snow observation sites. The R2 of Sodankylä, Old Aspen, and Reynolds Mountains East were 0.88, 0.69, and 0.63, respectively. At the Swamp Angel Study Plot and Weissfluhjoch observation sites, which have an average snow depth exceeding 200 cm, the fused snow depth did not perform well. The spatial patterns of the average snow depth were analyzed seasonally, and the average snow depths of autumn, winter, and spring were 5.7, 25.8, and 21.5 cm, respectively. In the future, random forest regression will be used to produce a long time series of a fused snow depth dataset over the Northern Hemisphere or other specific regions.

ACS Style

Yanxing Hu; Tao Che; Liyun Dai; Lin Xiao. Snow Depth Fusion Based on Machine Learning Methods for the Northern Hemisphere. Remote Sensing 2021, 13, 1250 .

AMA Style

Yanxing Hu, Tao Che, Liyun Dai, Lin Xiao. Snow Depth Fusion Based on Machine Learning Methods for the Northern Hemisphere. Remote Sensing. 2021; 13 (7):1250.

Chicago/Turabian Style

Yanxing Hu; Tao Che; Liyun Dai; Lin Xiao. 2021. "Snow Depth Fusion Based on Machine Learning Methods for the Northern Hemisphere." Remote Sensing 13, no. 7: 1250.

Journal article
Published: 07 October 2020 in Remote Sensing
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Snow cover is a key parameter of the climate system and its significant seasonal and annual variability have significant impacts on the surface energy balance and global water circulation. However, current snow depth datasets show large inconsistencies and uncertainties, which limit their applications in climate change projections and hydrological processes simulations. In this study, a comprehensive assessment of five hemispheric snow depth datasets was carried out against ground observations from 43,391 stations. The five snow depth datasets included three remote sensing datasets, i.e., Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), Advanced Microwave Scanning Radiometer-2 (AMSR2), Global Snow Monitoring for Climate Research (GlobSnow), and two reanalysis datasets, i.e., ERA-Interim and the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). Assessment results imply that the spatial distribution of GlobSnow and ERA-Interim exhibit overall better agreements with ground observations than other datasets. GlobSnow and ERA-Interim exhibit less uncertainty during the snow accumulation and ablation periods, respectively. In plain and forested regions, GlobSnow, ERA-Interim and MERRA-2 show better performances, while in mountain and forested mountain areas, GlobSnow exhibits the best performance. AMSR-E and AMSR2 agree better with ground observations in shallow snow condition (0–10 cm), while MERRA-2 shows more satisfying performance when snow depth exceeds 50 cm. These systematic and integrated understanding of the five representative snow depth datasets provides information on data selection and data refinement, as well as data fusion, which is our next work of interest.

ACS Style

Lin Xiao; Tao Che; Liyun Dai. Evaluation of Remote Sensing and Reanalysis Snow Depth Datasets over the Northern Hemisphere during 1980–2016. Remote Sensing 2020, 12, 3253 .

AMA Style

Lin Xiao, Tao Che, Liyun Dai. Evaluation of Remote Sensing and Reanalysis Snow Depth Datasets over the Northern Hemisphere during 1980–2016. Remote Sensing. 2020; 12 (19):3253.

Chicago/Turabian Style

Lin Xiao; Tao Che; Liyun Dai. 2020. "Evaluation of Remote Sensing and Reanalysis Snow Depth Datasets over the Northern Hemisphere during 1980–2016." Remote Sensing 12, no. 19: 3253.

Journal article
Published: 07 May 2020 in Remote Sensing
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High sea ice production (SIP) generates high-salinity water, thus, influencing the global thermohaline circulation. Estimation from passive microwave data and heat flux models have indicated that the Ross Ice Shelf polynya (RISP) may be the highest SIP region in the Southern Oceans. However, the coarse spatial resolution of passive microwave data limited the accuracy of these estimates. The Sentinel-1 Synthetic Aperture Radar dataset with high spatial and temporal resolution provides an unprecedented opportunity to more accurately distinguish both polynya area/extent and occurrence. In this study, the SIPs of RISP and McMurdo Sound polynya (MSP) from 1 March–30 November 2017 and 2018 are calculated based on Sentinel-1 SAR data (for area/extent) and AMSR2 data (for ice thickness). The results show that the wind-driven polynyas in these two years occurred from the middle of March to the middle of November, and the occurrence frequency in 2017 was 90, less than 114 in 2018. However, the annual mean cumulative SIP area and volume in 2017 were similar to (or slightly larger than) those in 2018. The average annual cumulative polynya area and ice volume of these two years were 1,040,213 km2 and 184 km3 for the RSIP, and 90,505 km2 and 16 km3 for the MSP, respectively. This annual cumulative SIP (volume) is only 1/3–2/3 of those obtained using the previous methods, implying that ice production in the Ross Sea might have been significantly overestimated in the past and deserves further investigations.

ACS Style

Liyun Dai; Hongjie Xie; Stephen F. Ackley; Alberto M. Mestas-Nuñez. Ice Production in Ross Ice Shelf Polynyas during 2017–2018 from Sentinel–1 SAR Images. Remote Sensing 2020, 12, 1484 .

AMA Style

Liyun Dai, Hongjie Xie, Stephen F. Ackley, Alberto M. Mestas-Nuñez. Ice Production in Ross Ice Shelf Polynyas during 2017–2018 from Sentinel–1 SAR Images. Remote Sensing. 2020; 12 (9):1484.

Chicago/Turabian Style

Liyun Dai; Hongjie Xie; Stephen F. Ackley; Alberto M. Mestas-Nuñez. 2020. "Ice Production in Ross Ice Shelf Polynyas during 2017–2018 from Sentinel–1 SAR Images." Remote Sensing 12, no. 9: 1484.

Preprint content
Published: 23 March 2020
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The fast ice in the McMurdo Sound plays an important role in the coastal ecological systems and climate changes, but its seasonal and interannual variations are poorly understood. In this study, the fast ice phenology and extent variation are investigated using Sentinel-1 Synthetic Aperture Radar (SAR) images from 2017 to 2019, and the factors controlling the fast ice development are explored. The results showed that the fast ice edge presented obvious seasonal change. In 2017/2018 and 2018/2019 years it arrived at northernmost during May – July, and keeps north until the end of December or January, and then moves south, arriving at most south on February or March. However, there are some difference between these two years. The date the fast ice edge arrived at northernmost in 2018 was about two months later than in 2017, but the ending time at the northern edge was about one month earlier (31 Dec 2018 vs 30 Jan 2018). The time when it retreated to the southernmost in 2019 was about one month before that in 2017 or 2018. It seems the longer the edge stays in the northernmost, the later it retreats to the southernmost, and it may not completely disappear; the shorter the edge stays in the northernmost, the earlier it retreats to the southernmost, and it may completely disappear. The dominant factor controlling the beginning and end dates are air temperature. This statement still needs to be confirmed when more data will be processed and analyzed in near future.

ACS Style

Liyun Dai.  Seasonal variability of fast ice edge in the McMurdo Sound between 2017 and 2019 based on Sentinel-1 SAR. 2020, 1 .

AMA Style

Liyun Dai.  Seasonal variability of fast ice edge in the McMurdo Sound between 2017 and 2019 based on Sentinel-1 SAR. . 2020; ():1.

Chicago/Turabian Style

Liyun Dai. 2020. " Seasonal variability of fast ice edge in the McMurdo Sound between 2017 and 2019 based on Sentinel-1 SAR." , no. : 1.

Journal article
Published: 15 August 2019 in The Cryosphere
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The successful bid for the 2022 Winter Olympics (Beijing 2022, officially known as the XXIV Olympic Winter Games) has greatly stimulated Chinese enthusiasm towards winter sports participation. Consequently, the Chinese ski industry is rapidly booming due to enormous market demand and government support. However, investing in ski areas in unreasonable locations will cause problems from an economic perspective (in terms of operation and management) as well as geographical concerns (such as environmental degradation). Therefore, evaluating the suitability of a ski area based on scientific metrics has become a prerequisite for the sustainable development of the ski industry. In this study, we evaluate the locational suitability of ski areas in China by integrating their natural and socioeconomic conditions using a linearly weighted method based on geographic information system (GIS) spatial analysis combined with remote sensing, online, and field survey data. The key indexes for evaluating natural suitability include snow cover, air temperature, topographic conditions, water resources, and vegetation, whereas socioeconomic suitability is evaluated based on economic conditions, accessibility of transportation, distance to a tourist attraction, and distance to a city. As such, metrics ranging from 0 to 1 considering both natural and socioeconomic conditions are used to define a suitability threshold for each candidate region for ski area development. A ski area is considered to be a dismal prospect when the locational integrated index is less than 0.5. The results show that 84 % of existing ski areas are located in areas with an integrated index greater than 0.5. Finally, corresponding development strategies for decision-makers are proposed based on the multicriteria metrics, which will be extended to incorporate potential influences from future climate change and socioeconomic development. However, the snowmaking model with local data should to be used to further analyze the suitability for a specific ski area.

ACS Style

Jie Deng; Tao Che; Cunde Xiao; Shijin Wang; Liyun Dai; Akynbekkyzy Meerzhan. Suitability analysis of ski areas in China: an integrated study based on natural and socioeconomic conditions. The Cryosphere 2019, 13, 2149 -2167.

AMA Style

Jie Deng, Tao Che, Cunde Xiao, Shijin Wang, Liyun Dai, Akynbekkyzy Meerzhan. Suitability analysis of ski areas in China: an integrated study based on natural and socioeconomic conditions. The Cryosphere. 2019; 13 (8):2149-2167.

Chicago/Turabian Style

Jie Deng; Tao Che; Cunde Xiao; Shijin Wang; Liyun Dai; Akynbekkyzy Meerzhan. 2019. "Suitability analysis of ski areas in China: an integrated study based on natural and socioeconomic conditions." The Cryosphere 13, no. 8: 2149-2167.

Journal article
Published: 11 August 2019 in Remote Sensing
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The long-term variations in snow depth are important in hydrological, meteorological, and ecological implications and climatological studies. The series of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) instruments onboard the Defense Meteorological Satellite Program (DMSP) platforms has provided a consistent 30+ year data record of global observations that is well-suited for the estimation of snow cover, snow depth, and snow water equivalent (SWE). To maximize the use of this continuous microwave observation dataset in long-term snow analysis and obtain an objective result, consistency among the SSM/I and SSMIS sensors is required. In this paper, we evaluated the consistency between the SSM/I and SSMIS concerning the observed brightness temperature (Tb) and the retrieved snow cover area and snow depth from January 2007 to December 2008, where the F13 SSM/I and the F17 SSMIS overlapped. Results showed that Tb bias at 19 GHz spans from −2 to −3 K in snow winter seasons, and from −4 to −5 K in non-snow seasons. There is a slight Tb bias at 37 GHz from −2 to 2 K, regardless of season. For 85 (91) GHz, the bias presents some uncertainty from the scattering effect of the snowpack and atmospheric emission. The overall consistency between SSM/I and SSMIS with respect to snow cover detection is between 80% and 100%, which will result in a maximum snow cover area difference of 25 × 104 km2 in China. The inconsistency in Tb between SSM/I and SSMIS can result in a −2 and −0.67 cm snow depth bias for the dual-channel and multichannel algorithms, respectively. SSMIS tends to yield lower snow depth estimates than SSM/I. Moreover, there are notable bias differences between SSM/I- and SSMIS-estimated snow depths in the tundra and taiga snow classes. Our results indicate the importance of considering the Tb bias in microwave snow cover detection and snow depth retrieval and point out that, due to the sensitivity of bias to seasons, it is better to do the intercalibration with a focus on snow-covered winter seasons. Otherwise, the bias in summer will disturb the calibration coefficients and introduce more error into the snow retrievals if the seasonal difference is not carefully evaluated and separated.

ACS Style

Jianwei Yang; Lingmei Jiang; Liyun Dai; Jinmei Pan; Shengli Wu; Gongxue Wang. The Consistency of SSM/I vs. SSMIS and the Influence on Snow Cover Detection and Snow Depth Estimation over China. Remote Sensing 2019, 11, 1879 .

AMA Style

Jianwei Yang, Lingmei Jiang, Liyun Dai, Jinmei Pan, Shengli Wu, Gongxue Wang. The Consistency of SSM/I vs. SSMIS and the Influence on Snow Cover Detection and Snow Depth Estimation over China. Remote Sensing. 2019; 11 (16):1879.

Chicago/Turabian Style

Jianwei Yang; Lingmei Jiang; Liyun Dai; Jinmei Pan; Shengli Wu; Gongxue Wang. 2019. "The Consistency of SSM/I vs. SSMIS and the Influence on Snow Cover Detection and Snow Depth Estimation over China." Remote Sensing 11, no. 16: 1879.

Preprint content
Published: 15 March 2019
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The successful bidding of the 2022 Winter Olympics (Beijing 2022, officially known as the XXIV Olympic Winter Games) has greatly stimulated Chinese enthusiasm to participate in winter sports. Consequently, the Chinese ski industry is rapidly booming driven by enormous market demand and government support. However, investing in ski area at an unreasonable location will cause problems both from economic perspective (in terms of operation and management) as well as geographical concerns (such as environmental degradation). To evaluate the suitability of a ski area based on scientific metrics has since become a prerequisite to the sustainable development of ski industry. In this study, we evaluate the locational suitability of ski areas in China by integrating their natural and socioeconomic conditions using linear weighted method based on geographic information systems (GIS) spatial analysis combined with remote sensing, online and field survey data. Key indexes for evaluating the natural suitability include snow cover, air temperature, topographic conditions, groundwater, and vegetation, whereas socioeconomic suitability is evaluated based on economic conditions, accessibility of transportation, distance to tourist attractions, and distance to cities. As such, an integrated metrics considering both natural and socioeconomic suitability is defined to be a threshold and used to identify the suitability of a candidate region for ski area development. The results show that 92 % of existing ski areas are located in areas with an integrated index greater than 0.5. In contrary, a ski area is considered to be a dismal prospect when the locational integrated index is less than 0.5. Finally, corresponding development strategies for decision-makers are proposed based on the multi-criteria metrics, which will be extended to incorporate potential influences from future climate change and socioeconomic development.

ACS Style

Jie Deng; Tao Che; Cunde Xiao; Shijin Wang; Liyun Dai; Akynbekkyzy Meerzhan. Suitability Analysis of Ski Areas in China: An Integrated Study Based on Natural and Socioeconomic Conditions. 2019, 2019, 1 -32.

AMA Style

Jie Deng, Tao Che, Cunde Xiao, Shijin Wang, Liyun Dai, Akynbekkyzy Meerzhan. Suitability Analysis of Ski Areas in China: An Integrated Study Based on Natural and Socioeconomic Conditions. . 2019; 2019 ():1-32.

Chicago/Turabian Style

Jie Deng; Tao Che; Cunde Xiao; Shijin Wang; Liyun Dai; Akynbekkyzy Meerzhan. 2019. "Suitability Analysis of Ski Areas in China: An Integrated Study Based on Natural and Socioeconomic Conditions." 2019, no. : 1-32.

Journal article
Published: 08 December 2018 in Remote Sensing
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Snow cover over the Qinghai-Tibetan Plateau (QTP) plays an important role in climate, hydrological, and ecological systems. Currently, passive microwave remote sensing is the most efficient way to monitor snow depth on global and regional scales; however, it presents a serious overestimation of snow cover over the QTP and has difficulty describing patchy snow cover over the QTP because of its coarse spatial resolution. In this study, a new spatial dynamic method is developed by introducing ground emissivity and assimilating the snow cover fraction (SCF) and land surface temperature (LST) of the Moderate Resolution Imaging Spectroradiometer (MODIS) to derive snow depth at an enhanced spatial resolution. In this method, the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) brightness temperature and MODIS LST are used to calculate ground emissivity. Additionally, the microwave emission model of layered snowpacks (MEMLS) is applied to simulate brightness temperature with varying ground emissivities to determine the key coefficients in the snow depth retrieval algorithm. The results show that the frozen ground emissivity presents large spatial heterogeneity over the QTP, which leads to the variation of coefficients in the snow depth retrieval algorithm. The overestimation of snow depth is rectified by introducing the ground emissivity factor at 18 and 36 GHz. Compared with in situ observations, the snow cover accuracy of the new method is 93.9%, which is better than the 60.2% accuracy of the existing method (old method) which does not consider ground emissivity. The bias and root-mean-square error (RMSE) of snow depth are 1.03 cm and 7.05 cm, respectively, for the new method; these values are much lower than the values of 6.02 cm and 9.75 cm, respectively, for the old method. However, the snow cover accuracy with depths between 1 and 3 cm is below 60%, and snow depths greater than 25 cm are underestimated in Himalayan mountainous areas. In the future, the snow cover identification algorithm should be improved to identify shallow snow cover over the QTP, and topography should be considered in the snow depth retrieval algorithm to improve snow depth accuracy in mountainous areas.

ACS Style

Liyun Dai; Tao Che; Hongjie Xie; Xuejiao Wu. Estimation of Snow Depth over the Qinghai-Tibetan Plateau Based on AMSR-E and MODIS Data. Remote Sensing 2018, 10, 1989 .

AMA Style

Liyun Dai, Tao Che, Hongjie Xie, Xuejiao Wu. Estimation of Snow Depth over the Qinghai-Tibetan Plateau Based on AMSR-E and MODIS Data. Remote Sensing. 2018; 10 (12):1989.

Chicago/Turabian Style

Liyun Dai; Tao Che; Hongjie Xie; Xuejiao Wu. 2018. "Estimation of Snow Depth over the Qinghai-Tibetan Plateau Based on AMSR-E and MODIS Data." Remote Sensing 10, no. 12: 1989.

Article
Published: 23 December 2017 in Journal of Geophysical Research: Biogeosciences
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Vegetation phenology is a sensitive indicator of climate change, and has significant effects on the exchange of carbon, water and energy between the terrestrial biosphere and the atmosphere. The Tibetan Plateau, the Earth's “third pole”, is a unique region for studying the long-term trends in vegetation phenology in response to climate change because of the sensitivity of its alpine ecosystems to climate and its low-level human disturbance. There has been a debate whether the trends in spring phenology over the Tibetan Plateau have been continuously advancing over the last two to three decades. In this study, we examine the trends in the start of growing season (SOS) for alpine meadow and steppe using the GIMMS3g NDVI dataset (1982-2014), the GIMMS NDVI dataset (1982-2006), the MODIS NDVI dataset (2001-2014), the SPOT Vegetation NDVI dataset (1999-2013) and the SeaWiFS NDVI dataset (1998-2007). Both logistic and polynomial fitting methods are used to retrieve the SOS dates from the NDVI datasets. Our results show that the trends in spring phenology over the Tibetan Plateau depend on both the NDVI dataset used and the method for retrieving the SOS date. There are large discrepancies in the SOS trends among the different NDVI datasets and between the two different retrieval methods. There is no consistent evidence that spring phenology (“green-up” dates) has been advancing over the Tibetan Plateau during the last two to three decades. Ground-based budburst data also indicate no consistent trends in spring phenology. The responses of SOS to environmental factors (air temperature, precipitation, soil temperature and snow depth) also vary among NDVI datasets and phenology retrieval methods. The increases in winter and spring temperature had offsetting effects on spring phenology.

ACS Style

Xufeng Wang; Jingfeng Xiao; Xin Li; Guodong Cheng; Mingguo Ma; Tao Che; Liyun Dai; Shaoying Wang; Jinkui Wu. No Consistent Evidence for Advancing or Delaying Trends in Spring Phenology on the Tibetan Plateau. Journal of Geophysical Research: Biogeosciences 2017, 122, 3288 -3305.

AMA Style

Xufeng Wang, Jingfeng Xiao, Xin Li, Guodong Cheng, Mingguo Ma, Tao Che, Liyun Dai, Shaoying Wang, Jinkui Wu. No Consistent Evidence for Advancing or Delaying Trends in Spring Phenology on the Tibetan Plateau. Journal of Geophysical Research: Biogeosciences. 2017; 122 (12):3288-3305.

Chicago/Turabian Style

Xufeng Wang; Jingfeng Xiao; Xin Li; Guodong Cheng; Mingguo Ma; Tao Che; Liyun Dai; Shaoying Wang; Jinkui Wu. 2017. "No Consistent Evidence for Advancing or Delaying Trends in Spring Phenology on the Tibetan Plateau." Journal of Geophysical Research: Biogeosciences 122, no. 12: 3288-3305.

Journal article
Published: 25 August 2017 in Remote Sensing
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Snow albedo feedback is one of the most crucial feedback processes that control equilibrium climate sensitivity, which is a central parameter for better prediction of future climate change. However, persistent large discrepancies and uncertainties are found in snow albedo feedback estimations. Remotely sensed snow cover products, atmospheric reanalysis data and radiative kernel data are used in this study to quantify snow albedo radiative forcing and its feedback on both hemispheric and global scales during 2003–2016. The strongest snow albedo radiative forcing is located north of 30°N, apart from Antarctica. In general, it has large monthly variation and peaks in spring. Snow albedo feedback is estimated to be 0.18 ± 0.08 W∙m−2∙°C−1 and 0.04 ± 0.02 W∙m−2∙°C−1 on hemispheric and global scales, respectively. Compared to previous studies, this paper focuses specifically on quantifying snow albedo feedback and demonstrates three improvements: (1) used high spatial and temporal resolution satellite-based snow cover data to determine the areas of snow albedo radiative forcing and feedback; (2) provided detailed information for model parameterization by using the results from (1), together with accurate description of snow cover change and constrained snow albedo and snow-free albedo data; and (3) effectively reduced the uncertainty of snow albedo feedback and increased its confidence level through the block bootstrap test. Our results of snow albedo feedback agreed well with other partially observation-based studies and indicate that the 25 Coupled Model Intercomparison Project Phase 5 (CMIP5) models might have overestimated the snow albedo feedback, largely due to the overestimation of surface albedo change between snow-covered and snow-free surface in these models.

ACS Style

Lin Xiao; Tao Che; Linling Chen; Hongjie Xie; Liyun Dai. Quantifying Snow Albedo Radiative Forcing and Its Feedback during 2003–2016. Remote Sensing 2017, 9, 883 .

AMA Style

Lin Xiao, Tao Che, Linling Chen, Hongjie Xie, Liyun Dai. Quantifying Snow Albedo Radiative Forcing and Its Feedback during 2003–2016. Remote Sensing. 2017; 9 (9):883.

Chicago/Turabian Style

Lin Xiao; Tao Che; Linling Chen; Hongjie Xie; Liyun Dai. 2017. "Quantifying Snow Albedo Radiative Forcing and Its Feedback during 2003–2016." Remote Sensing 9, no. 9: 883.

Journal article
Published: 23 August 2017 in The Cryosphere
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Snow cover on the Qinghai–Tibetan Plateau (QTP) plays a significant role in the global climate system and is an important water resource for rivers in the high-elevation region of Asia. At present, passive microwave (PMW) remote sensing data are the only efficient way to monitor temporal and spatial variations in snow depth at large scale. However, existing snow depth products show the largest uncertainties across the QTP. In this study, MODIS fractional snow cover product, point, line and intense sampling data are synthesized to evaluate the accuracy of snow cover and snow depth derived from PMW remote sensing data and to analyze the possible causes of uncertainties. The results show that the accuracy of snow cover extents varies spatially and depends on the fraction of snow cover. Based on the assumption that grids with MODIS snow cover fraction > 10 % are regarded as snow cover, the overall accuracy in snow cover is 66.7 %, overestimation error is 56.1 %, underestimation error is 21.1 %, commission error is 27.6 % and omission error is 47.4 %. The commission and overestimation errors of snow cover primarily occur in the northwest and southeast areas with low ground temperature. Omission error primarily occurs in cold desert areas with shallow snow, and underestimation error mainly occurs in glacier and lake areas. With the increase of snow cover fraction, the overestimation error decreases and the omission error increases. A comparison between snow depths measured in field experiments, measured at meteorological stations and estimated across the QTP shows that agreement between observation and retrieval improves with an increasing number of observation points in a PMW grid. The misclassification and errors between observed and retrieved snow depth are associated with the relatively coarse resolution of PMW remote sensing, ground temperature, snow characteristics and topography. To accurately understand the variation in snow depth across the QTP, new algorithms should be developed to retrieve snow depth with higher spatial resolution and should consider the variation in brightness temperatures at different frequencies emitted from ground with changing ground features.

ACS Style

Liyun Dai; Tao Che; Yongjian Ding; Xiaohua Hao. Evaluation of snow cover and snow depth on the Qinghai–Tibetan Plateau derived from passive microwave remote sensing. The Cryosphere 2017, 11, 1933 -1948.

AMA Style

Liyun Dai, Tao Che, Yongjian Ding, Xiaohua Hao. Evaluation of snow cover and snow depth on the Qinghai–Tibetan Plateau derived from passive microwave remote sensing. The Cryosphere. 2017; 11 (4):1933-1948.

Chicago/Turabian Style

Liyun Dai; Tao Che; Yongjian Ding; Xiaohua Hao. 2017. "Evaluation of snow cover and snow depth on the Qinghai–Tibetan Plateau derived from passive microwave remote sensing." The Cryosphere 11, no. 4: 1933-1948.

Preprint content
Published: 19 December 2016
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Snow cover on the Qinghai-Tibetan plateau (QTP) plays a significant role in the global climate system and is an important water resource for rivers in the high elevation region of Asia. At present, passive microwave (PM) remote sensing data are the only efficient way to monitor temporal and spatial variations in snow depth at large scale. However, existing snow depth products show the largest uncertainties across the QTP. In this study, MODIS fractional snow cover product, in situ observations, and airborne observation data are synthesized to evaluate the accuracy of snow cover and snow depth derived from PM remote sensing data and to analyze the possible causes of uncertainties. The results show that the accuracy of snow cover extents varies spatially and depends on the fraction of snow cover. Based on the assumption that grids with MODIS snow cover fraction > 10 % are regarded as snow cover, the overall accuracy in snow cover is 66.7 %, overestimation error is 56.1 %, underestimation error is 21.1 %, commission error is 27.6 % and omission error is 47.4 %. The commission and overestimation errors of snow cover primarily occur in the northwest and southeast areas with low ground temperature. Omission error primarily occurs in cold desert areas with shallow snow, and underestimation error mainly occurs in glacier and lake areas. Comparison between snow depths measured in field experiments, measured at meteorological stations and estimated across the QTP shows that agreement between observation and retrieval improves with an increasing number of observation points in a PM grid. The misclassification and errors between observed and retrieved snow depth are associated with the relatively coarse resolution of PM remote sensing, ground temperature, snow characteristics and topography. To accurately understand the variation in snow depth across the QTP, new algorithms should be developed to retrieve snow depth with higher spatial resolution and should consider the variation in brightness temperatures at different frequencies emitted from ground with changing ground features.

ACS Style

Liyun Dai; Tao Che; Yongjian Ding; Xiaohua Hao. Evaluation of snow cover and snow depth on the Qinghai-Tibetan Plateau derived from passive microwave remote sensing. 2016, 1 -31.

AMA Style

Liyun Dai, Tao Che, Yongjian Ding, Xiaohua Hao. Evaluation of snow cover and snow depth on the Qinghai-Tibetan Plateau derived from passive microwave remote sensing. . 2016; ():1-31.

Chicago/Turabian Style

Liyun Dai; Tao Che; Yongjian Ding; Xiaohua Hao. 2016. "Evaluation of snow cover and snow depth on the Qinghai-Tibetan Plateau derived from passive microwave remote sensing." , no. : 1-31.

Journal article
Published: 01 December 2016 in Journal of Meteorological Research
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Snow cover plays an important role in the hydrological cycle and water management in Kazakhstan. However, traditional observations do not meet current needs. In this study, a snow depth retrieval equation was developed based on passive microwave remote sensing data. The average snow depth in winter (ASDW), snow cover duration (SCD), monthly maximum snow depth (MMSD), and annual average snow depth (AASD) were derived for each year to monitor the spatial and temporal snow distributions. The SCD exhibited significant spatial variations from 30 to 250 days. The longest SCD was found in the mountainous area in eastern Kazakhstan, reaching values between 200 and 250 days in 2005. The AASD increased from the south to the north and maintained latitudinal zonality. The MMSD in most areas ranged from 20 to 30 cm. The ASDW values ranged from 15 to 20 cm in the eastern region and were characterized by spatial regularity of latitudinal zonality. The ASDW in the mountainous area often exceeded 20 cm.

ACS Style

Shamshagul Mashtayeva; Liyun Dai; Tao Che; Jay Sagin; Saltanat Sadvakasova; Marzhan Kussainova; Danara Alimbayeva; Meerzhan Akynbekkyzy. Spatial and temporal variability of snow depth derived from passive microwave remote sensing data in Kazakhstan. Journal of Meteorological Research 2016, 30, 1033 -1043.

AMA Style

Shamshagul Mashtayeva, Liyun Dai, Tao Che, Jay Sagin, Saltanat Sadvakasova, Marzhan Kussainova, Danara Alimbayeva, Meerzhan Akynbekkyzy. Spatial and temporal variability of snow depth derived from passive microwave remote sensing data in Kazakhstan. Journal of Meteorological Research. 2016; 30 (6):1033-1043.

Chicago/Turabian Style

Shamshagul Mashtayeva; Liyun Dai; Tao Che; Jay Sagin; Saltanat Sadvakasova; Marzhan Kussainova; Danara Alimbayeva; Meerzhan Akynbekkyzy. 2016. "Spatial and temporal variability of snow depth derived from passive microwave remote sensing data in Kazakhstan." Journal of Meteorological Research 30, no. 6: 1033-1043.

Journal article
Published: 01 September 2016 in Remote Sensing of Environment
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Snow depth is an important factor in water resources management in Northeast China. Forest covers 40% of Northeast China, and the presence of forests influences the accuracy of snow depth retrievals from passive microwave remote sensing data. An optimal iteration method was used to retrieve the forest transmissivities at 18 and 36 GHz based on the snow and forest microwave radiative transfer models and the snow properties measured in field experiments. The transmissivities at 18 and 36 GHz are 0.895 and 0.656 in the horizontal polarization, and 0.821 and 0.615 in the vertical polarization, respectively. Furthermore, the forest transmissivity and snow properties were input into the Microwave Emission Model of Layered Snowpacks (MEMLS) to establish a dynamic look-up table (LUT). Snow depths were retrieved from satellite passive microwave remote sensing data based on the LUT method, and these retrievals were verified by snow depth observations at 103 meteorological stations. The results showed that the bias between the retrieved and measured snow depths is very small, with root mean square errors (RMSEs) of approximately 6 cm in forest regions and 4 cm in non-forest regions. When compared with the existing snow products, the snow depth retrieved in this work presented the highest level of accuracy. The regional snow depth product in China is superior to the GlobSnow and NASA AMSR-E standard SWE products in non-forest regions, whereas the GlobSnow estimate is superior to the regional snow depth product in China and NASA AMSR-E standard SWE product estimates in forest regions. Therefore, we conclude that 1) the influence of forest on snow depth retrieval is important, and the appropriate forest parameters should be considered in the estimation of snow depth from passive microwave brightness temperature data; and 2) the snow depth retrieval algorithm based on the dynamic LUT method proved to be efficient in Northeast China.

ACS Style

Tao Che; Liyun Dai; Xingming Zheng; Xiaofeng Li; Kai Zhao. Estimation of snow depth from passive microwave brightness temperature data in forest regions of northeast China. Remote Sensing of Environment 2016, 183, 334 -349.

AMA Style

Tao Che, Liyun Dai, Xingming Zheng, Xiaofeng Li, Kai Zhao. Estimation of snow depth from passive microwave brightness temperature data in forest regions of northeast China. Remote Sensing of Environment. 2016; 183 ():334-349.

Chicago/Turabian Style

Tao Che; Liyun Dai; Xingming Zheng; Xiaofeng Li; Kai Zhao. 2016. "Estimation of snow depth from passive microwave brightness temperature data in forest regions of northeast China." Remote Sensing of Environment 183, no. : 334-349.

Journal article
Published: 02 June 2015 in Remote Sensing
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Long-term snow depth/snow water equivalent (SWE) products derived from passive microwave remote sensing data are fundamental for climatological and hydrological studies. However, the temporal continuity of the products is affected by the updating or replacement of passive microwave sensors or satellite platforms. In this study, we inter-calibrated brightness temperature (Tb) data obtained from the Special Sensor Microwave Imager (SSM/I) and the Special Sensor Microwave Imager/Sounder (SSMI/S). Then, we evaluated the consistency of the snow cover area (SCA) and snow depth derived from the Scanning Multichannel Microwave Radiometer (SMMR), SSM/I and SSMI/S. The results indicated that (1) the spatial pattern of the SCA derived from the SMMR and SSM/I data was more consistent after calibration than before; (2) the relative biases in the SCA and snow depth in China between the SSM/I and SSMI/S data decreased from 42.42% to 1.65% and from 66.18% to −1.5%, respectively; and (3) the SCA and snow depth derived from the SSM/I data carried on F08, F11 and F13 were highly consistent. To obtain consistent snow depth and SCA products, inter-sensor calibrations between SMMR, SSM/I and SSMI/S are important. In consideration of the snow data product continuation, we suggest that the brightness temperature data from all sensors be calibrated based on SSMI/S.

ACS Style

Liyun Dai; Tao Che; Yongjian Ding. Inter-Calibrating SMMR, SSM/I and SSMI/S Data to Improve the Consistency of Snow-Depth Products in China. Remote Sensing 2015, 7, 7212 -7230.

AMA Style

Liyun Dai, Tao Che, Yongjian Ding. Inter-Calibrating SMMR, SSM/I and SSMI/S Data to Improve the Consistency of Snow-Depth Products in China. Remote Sensing. 2015; 7 (6):7212-7230.

Chicago/Turabian Style

Liyun Dai; Tao Che; Yongjian Ding. 2015. "Inter-Calibrating SMMR, SSM/I and SSMI/S Data to Improve the Consistency of Snow-Depth Products in China." Remote Sensing 7, no. 6: 7212-7230.

Journal article
Published: 01 January 2014 in Journal of Applied Remote Sensing
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The spatial and temporal distributions of snow cover were analyzed using daily snow depths derived from special sensor microwave imager and advanced microwave scanning radiometer-Earth observing system data between 1987 and 2011 in northern China. To monitor the annual and interannual snow cover variability, snow cover days (SCD), monthly mean snow depth (MMSD), and cumulative snowfall (CS) were derived, and their variation slopes and relative standard deviations were calculated. The results showed that the snow cover reached its maximum values of depth and area in January and February. The MMSD, SCD, and CS interannual variation presented spatial heterogeneity. The average snow depth exhibited insignificant changes in most areas. For the annual SCD change trend, an insignificant decrease, significant decrease, insignificant increase, and significant increase in coverage characterized approximately 37.6%, 19.3%, 41.0%, and 1.1% of the total area, respectively. For the annual CS variation trend, an insignificant decrease, significant decrease, insignificant increase, and significant increase characterized approximately 30.2%, 2.4%, 57.1%, and 9.2% of the area, respectively. According to the snow depth variation in different climate zones, the snow depth decreased in the early and late periods in the snow season in all climate regions, but the fluctuations and change trends in these climate zones were different. The decreasing trend was more obvious in plateau climate zone than in other zones. The fluctuation amplitude was greatest in the temperate monsoon climate zone.

ACS Style

Liyun Dai; Tao Che. Spatiotemporal variability in snow cover from 1987 to 2011 in northern China. Journal of Applied Remote Sensing 2014, 8, 084693 -084693.

AMA Style

Liyun Dai, Tao Che. Spatiotemporal variability in snow cover from 1987 to 2011 in northern China. Journal of Applied Remote Sensing. 2014; 8 (1):084693-084693.

Chicago/Turabian Style

Liyun Dai; Tao Che. 2014. "Spatiotemporal variability in snow cover from 1987 to 2011 in northern China." Journal of Applied Remote Sensing 8, no. 1: 084693-084693.

Journal article
Published: 01 January 2014 in Journal of Applied Remote Sensing
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Snow is one of the most important components of the cryosphere. Remote sensing of snow focuses on the retrieval of snow parameters and monitoring of variations in snow using satellite data. These parameters are key inputs for hydrological and atmospheric models. Over the past 30 years, the field of snow remote sensing has grown dramatically in China. The 30-year achievements of research in different aspects of snow remote sensing in China, especially in (1) methods of retrieving snow cover, snow depth/snow water equivalent, and grain size and (2) applications to snowmelt runoff modeling, snow response on climate change, and remote sensing monitoring of snow-caused disasters are reviewed/summarized. The importance of the first remote sensing experiment on snow parameters at the upper reaches of the Heihe River Basin, in 2008, is also highlighted. A series of experiments, referred to as the Cooperative Observation Series for Snow (COSS), focus on some key topics on remote sensing of snow. COSS has been implemented for 3 years and will continue in different snow pattern regions of China. The snow assimilation system has been established in some regions using advanced ensemble Kalman filters. Finally, an outlook for the future of remote sensing of snow in China is given.

ACS Style

Jian Wang; Hongxing Li; Xiaohua Hao; Xiaodong Huang; Jinliang Hou; Tao Che; Liyun Dai; Tiangang Liang; Chunlin Huang; Hongyi Li; Zhiguang Tang; Zengyan Wang. Remote sensing for snow hydrology in China: challenges and perspectives. Journal of Applied Remote Sensing 2014, 8, 084687 -084687.

AMA Style

Jian Wang, Hongxing Li, Xiaohua Hao, Xiaodong Huang, Jinliang Hou, Tao Che, Liyun Dai, Tiangang Liang, Chunlin Huang, Hongyi Li, Zhiguang Tang, Zengyan Wang. Remote sensing for snow hydrology in China: challenges and perspectives. Journal of Applied Remote Sensing. 2014; 8 (1):084687-084687.

Chicago/Turabian Style

Jian Wang; Hongxing Li; Xiaohua Hao; Xiaodong Huang; Jinliang Hou; Tao Che; Liyun Dai; Tiangang Liang; Chunlin Huang; Hongyi Li; Zhiguang Tang; Zengyan Wang. 2014. "Remote sensing for snow hydrology in China: challenges and perspectives." Journal of Applied Remote Sensing 8, no. 1: 084687-084687.

Journal article
Published: 01 December 2012 in Remote Sensing of Environment
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Liyun Dai; Tao Che; Jian Wang; Pu Zhang. Snow depth and snow water equivalent estimation from AMSR-E data based on a priori snow characteristics in Xinjiang, China. Remote Sensing of Environment 2012, 127, 14 -29.

AMA Style

Liyun Dai, Tao Che, Jian Wang, Pu Zhang. Snow depth and snow water equivalent estimation from AMSR-E data based on a priori snow characteristics in Xinjiang, China. Remote Sensing of Environment. 2012; 127 ():14-29.

Chicago/Turabian Style

Liyun Dai; Tao Che; Jian Wang; Pu Zhang. 2012. "Snow depth and snow water equivalent estimation from AMSR-E data based on a priori snow characteristics in Xinjiang, China." Remote Sensing of Environment 127, no. : 14-29.