Articles | Volume 14, issue 9
https://doi.org/10.5194/essd-14-4445-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/essd-14-4445-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
29 Sep 2022
Data description paper |
| 29 Sep 2022
| 29 Sep 2022
HMRFS–TP: long-term daily gap-free snow cover products over the Tibetan Plateau from 2002 to 2021 based on hidden Markov random field model
Yan Huang
CORRESPONDING AUTHOR
Key Laboratory of Geographic Information Science, Ministry of
Education, East China Normal University, Shanghai 200241, China
School of Geographic Sciences, East China Normal University, Shanghai 200241, China
Jiahui Xu
Key Laboratory of Geographic Information Science, Ministry of
Education, East China Normal University, Shanghai 200241, China
School of Geographic Sciences, East China Normal University, Shanghai 200241, China
Jingyi Xu
Key Laboratory of Geographic Information Science, Ministry of
Education, East China Normal University, Shanghai 200241, China
School of Geographic Sciences, East China Normal University, Shanghai 200241, China
Yelei Zhao
Key Laboratory of Geographic Information Science, Ministry of
Education, East China Normal University, Shanghai 200241, China
School of Geographic Sciences, East China Normal University, Shanghai 200241, China
Bailang Yu
Key Laboratory of Geographic Information Science, Ministry of
Education, East China Normal University, Shanghai 200241, China
School of Geographic Sciences, East China Normal University, Shanghai 200241, China
Hongxing Liu
Department of Geography, the University of Alabama, Tuscaloosa, AL
35487, USA
Shujie Wang
Department of Geography, Earth and Environmental Systems Institute,
Pennsylvania State University, University Park, PA 16802, USA
Wanjia Xu
Key Laboratory of Geographic Information Science, Ministry of
Education, East China Normal University, Shanghai 200241, China
School of Geographic Sciences, East China Normal University, Shanghai 200241, China
Jianping Wu
Key Laboratory of Geographic Information Science, Ministry of
Education, East China Normal University, Shanghai 200241, China
School of Geographic Sciences, East China Normal University, Shanghai 200241, China
Zhaojun Zheng
National Satellite Meteorological Center, Beijing 100081, China
Related authors
Jingwen Ni, Jin Chen, Yao Tang, Jingyi Xu, Jiahui Xu, Linxin Dong, Qingyu Gu, Bailang Yu, Jianping Wu, and Yan Huang
EGUsphere, https://doi.org/10.5194/egusphere-2024-2885, https://doi.org/10.5194/egusphere-2024-2885, 2024
Short summary
Short summary
The average time differences (∆T) between green-up date and snowmelt onset date from 2001–2018 on the Tibetan Plateau were 36.7 days. With the increasing spring mean temperature, spring total precipitation and daily snowmelt, ∆T became shorter. Besides, in arid and low-vegetation areas, ∆T is primarily influenced by snowmelt, whereas in humid and high-vegetation areas, temperature plays a dominant role.
Jiahui Xu, Yao Tang, Linxin Dong, Shujie Wang, Bailang Yu, Jianping Wu, Zhaojun Zheng, and Yan Huang
The Cryosphere, 18, 1817–1834, https://doi.org/10.5194/tc-18-1817-2024, https://doi.org/10.5194/tc-18-1817-2024, 2024
Short summary
Short summary
Understanding snow phenology (SP) and its possible feedback are important. We reveal spatiotemporal heterogeneous SP on the Tibetan Plateau (TP) and the mediating effects from meteorological, topographic, and environmental factors on it. The direct effects of meteorology on SP are much greater than the indirect effects. Topography indirectly effects SP, while vegetation directly effects SP. This study contributes to understanding past global warming and predicting future trends on the TP.
Zhengyi Hu, Wei Jiang, Yuzhen Yan, Yan Huang, Xueyuan Tang, Lin Li, Florian Ritterbusch, Guo-Min Yang, Zheng-Tian Lu, and Guitao Shi
The Cryosphere, 18, 1647–1652, https://doi.org/10.5194/tc-18-1647-2024, https://doi.org/10.5194/tc-18-1647-2024, 2024
Short summary
Short summary
The age of the surface blue ice in the Grove Mountains area is dated to be about 140 000 years, and one meteorite found here is 260 000 years old. It is inferred that the Grove Mountains blue-ice area holds considerable potential for paleoclimate studies.
Anyao Jiang, Xin Meng, Yan Huang, and Guitao Shi
EGUsphere, https://doi.org/10.5194/egusphere-2023-1810, https://doi.org/10.5194/egusphere-2023-1810, 2023
Short summary
Short summary
Landlocked lakes are crucial in the Antarctic ecosystem and sensitive to climate change. Limited research on their distribution prompted us to develop an automated detection process using deep learning and multi-source satellite imagery. This allowed us to accurately determine the landlocked lakes’ open water (LLOW) area in Antarctica, generating high-resolution time series data. We find that the changes in positive degree days and air temperature predominantly drive variations in the LLOW area.
Song Shu, Hongxing Liu, Richard A. Beck, Frédéric Frappart, Johanna Korhonen, Minxuan Lan, Min Xu, Bo Yang, and Yan Huang
Hydrol. Earth Syst. Sci., 25, 1643–1670, https://doi.org/10.5194/hess-25-1643-2021, https://doi.org/10.5194/hess-25-1643-2021, 2021
Short summary
Short summary
This study comprehensively evaluated 11 satellite radar altimetry missions (including their official retrackers) for lake water level retrieval and developed a strategy for constructing consistent long-term water level records for inland lakes. It is a two-step bias correction and normalization procedure. First, we use Jason-2 as the initial reference to form a consistent TOPEX/Poseidon–Jason series. Then, we use this as the reference to remove the biases with other radar altimetry missions.
Jingwen Ni, Jin Chen, Yao Tang, Jingyi Xu, Jiahui Xu, Linxin Dong, Qingyu Gu, Bailang Yu, Jianping Wu, and Yan Huang
EGUsphere, https://doi.org/10.5194/egusphere-2024-2885, https://doi.org/10.5194/egusphere-2024-2885, 2024
Short summary
Short summary
The average time differences (∆T) between green-up date and snowmelt onset date from 2001–2018 on the Tibetan Plateau were 36.7 days. With the increasing spring mean temperature, spring total precipitation and daily snowmelt, ∆T became shorter. Besides, in arid and low-vegetation areas, ∆T is primarily influenced by snowmelt, whereas in humid and high-vegetation areas, temperature plays a dominant role.
Fangbo Pan, Lingmei Jiang, Gongxue Wang, Jinmei Pan, Jinyu Huang, Cheng Zhang, Huizhen Cui, Jianwei Yang, Zhaojun Zheng, Shengli Wu, and Jiancheng Shi
Earth Syst. Sci. Data, 16, 2501–2523, https://doi.org/10.5194/essd-16-2501-2024, https://doi.org/10.5194/essd-16-2501-2024, 2024
Short summary
Short summary
It is important to strengthen the continuous monitoring of snow cover as a key indicator of imbalance in the Asian Water Tower (AWT) region. We generate long-term daily gap-free fractional snow cover products over the AWT at 0.005° resolution from 2000 to 2022 based on the multiple-endmember spectral mixture analysis algorithm and the gap-filling algorithm. They can provide highly accurate, quantitative fractional snow cover information for subsequent studies on hydrology and climate.
Jiahui Xu, Yao Tang, Linxin Dong, Shujie Wang, Bailang Yu, Jianping Wu, Zhaojun Zheng, and Yan Huang
The Cryosphere, 18, 1817–1834, https://doi.org/10.5194/tc-18-1817-2024, https://doi.org/10.5194/tc-18-1817-2024, 2024
Short summary
Short summary
Understanding snow phenology (SP) and its possible feedback are important. We reveal spatiotemporal heterogeneous SP on the Tibetan Plateau (TP) and the mediating effects from meteorological, topographic, and environmental factors on it. The direct effects of meteorology on SP are much greater than the indirect effects. Topography indirectly effects SP, while vegetation directly effects SP. This study contributes to understanding past global warming and predicting future trends on the TP.
Zhengyi Hu, Wei Jiang, Yuzhen Yan, Yan Huang, Xueyuan Tang, Lin Li, Florian Ritterbusch, Guo-Min Yang, Zheng-Tian Lu, and Guitao Shi
The Cryosphere, 18, 1647–1652, https://doi.org/10.5194/tc-18-1647-2024, https://doi.org/10.5194/tc-18-1647-2024, 2024
Short summary
Short summary
The age of the surface blue ice in the Grove Mountains area is dated to be about 140 000 years, and one meteorite found here is 260 000 years old. It is inferred that the Grove Mountains blue-ice area holds considerable potential for paleoclimate studies.
Anyao Jiang, Xin Meng, Yan Huang, and Guitao Shi
EGUsphere, https://doi.org/10.5194/egusphere-2023-1810, https://doi.org/10.5194/egusphere-2023-1810, 2023
Short summary
Short summary
Landlocked lakes are crucial in the Antarctic ecosystem and sensitive to climate change. Limited research on their distribution prompted us to develop an automated detection process using deep learning and multi-source satellite imagery. This allowed us to accurately determine the landlocked lakes’ open water (LLOW) area in Antarctica, generating high-resolution time series data. We find that the changes in positive degree days and air temperature predominantly drive variations in the LLOW area.
Marion A. McKenzie, Lauren E. Miller, Jacob S. Slawson, Emma J. MacKie, and Shujie Wang
The Cryosphere, 17, 2477–2486, https://doi.org/10.5194/tc-17-2477-2023, https://doi.org/10.5194/tc-17-2477-2023, 2023
Short summary
Short summary
Topographic highs (“bumps”) across glaciated landscapes have the potential to affect glacial ice. Bumps in the deglaciated Puget Lowland are assessed for streamlined glacial features to provide insight on ice–bed interactions. We identify a general threshold in which bumps significantly disrupt ice flow and sedimentary processes in this location. However, not all bumps have the same degree of impact. The system assessed here has relevance to parts of the Greenland Ice Sheet and Thwaites Glacier.
Xiaohua Hao, Guanghui Huang, Zhaojun Zheng, Xingliang Sun, Wenzheng Ji, Hongyu Zhao, Jian Wang, Hongyi Li, and Xiaoyan Wang
Hydrol. Earth Syst. Sci., 26, 1937–1952, https://doi.org/10.5194/hess-26-1937-2022, https://doi.org/10.5194/hess-26-1937-2022, 2022
Short summary
Short summary
We develop and validate a new 20-year MODIS snow-cover-extent product over China, which is dedicated to addressing known problems of the standard snow products. As expected, the new product significantly outperforms the state-of-the-art MODIS C6.1 products; improvements are particularly clear in forests and for the daily cloud-free product. Our product has provided more reliable snow knowledge over China and can be accessible freely https://dx.doi.org/10.11888/Snow.tpdc.271387.
Song Shu, Hongxing Liu, Richard A. Beck, Frédéric Frappart, Johanna Korhonen, Minxuan Lan, Min Xu, Bo Yang, and Yan Huang
Hydrol. Earth Syst. Sci., 25, 1643–1670, https://doi.org/10.5194/hess-25-1643-2021, https://doi.org/10.5194/hess-25-1643-2021, 2021
Short summary
Short summary
This study comprehensively evaluated 11 satellite radar altimetry missions (including their official retrackers) for lake water level retrieval and developed a strategy for constructing consistent long-term water level records for inland lakes. It is a two-step bias correction and normalization procedure. First, we use Jason-2 as the initial reference to form a consistent TOPEX/Poseidon–Jason series. Then, we use this as the reference to remove the biases with other radar altimetry missions.
Zuoqi Chen, Bailang Yu, Chengshu Yang, Yuyu Zhou, Shenjun Yao, Xingjian Qian, Congxiao Wang, Bin Wu, and Jianping Wu
Earth Syst. Sci. Data, 13, 889–906, https://doi.org/10.5194/essd-13-889-2021, https://doi.org/10.5194/essd-13-889-2021, 2021
Short summary
Short summary
An extended time series (2000–2018) of NPP-VIIRS-like nighttime light (NTL) data was proposed through a cross-sensor calibration from DMSP-OLS NTL data (2000–2012) and NPP-VIIRS NTL data (2013–2018). Compared with the annual composited NPP-VIIRS NTL data, our extended NPP-VIIRS-like NTL data have a high accuracy and also show a good spatial pattern and temporal consistency. It could be a useful proxy to monitor the dynamics of urbanization for a longer time period compared to existing NTL data.
Shujie Wang, Marco Tedesco, Patrick Alexander, Min Xu, and Xavier Fettweis
The Cryosphere, 14, 2687–2713, https://doi.org/10.5194/tc-14-2687-2020, https://doi.org/10.5194/tc-14-2687-2020, 2020
Short summary
Short summary
Glacial algal blooms play a significant role in darkening the Greenland Ice Sheet during summertime. The dark pigments generated by glacial algae could substantially reduce the bare ice albedo and thereby enhance surface melt. We used satellite data to map the spatial distribution of glacial algae and characterized the seasonal growth pattern and interannual trends of glacial algae in southwestern Greenland. Our study is important for bridging microbial activities with ice sheet mass balance.
Xiaodong Huang, Changyu Liu, Zhaojun Zheng, Yunlong Wang, Xubing Li, and Tiangang Liang
The Cryosphere Discuss., https://doi.org/10.5194/tc-2020-202, https://doi.org/10.5194/tc-2020-202, 2020
Revised manuscript not accepted
Short summary
Short summary
In the study, the long-term snow cover variation and distribution characteristics are illustrated across China during the period of 1951–2018, using the snow depth dataset retrieve from the National Meteorological Information Center of the China Meteorological Administration. The geographical and meteorological factors were closely related to snow cover change, especially the change in temperature, which will lead to significant changes in snow depth and phenology in mainland China.
Related subject area
Domain: ESSD – Ice | Subject: Snow and Sea Ice
Time series of alpine snow surface radiative-temperature maps from high-precision thermal-infrared imaging
Operational and experimental snow observation systems in the upper Rofental: data from 2017 to 2023
SMOS-derived Antarctic thin sea ice thickness: data description and validation in the Weddell Sea
A 12-year climate record of wintertime wave-affected marginal ice zones in the Atlantic Arctic based on CryoSat-2
MODIS daily cloud-gap-filled fractional snow cover dataset of the Asian Water Tower region (2000–2022)
Mapping of sea ice concentration using the NASA NIMBUS 5 Electrically Scanning Microwave Radiometer data from 1972–1977
A climate data record of year-round global sea-ice drift from the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF)
Snow accumulation and ablation measurements in a midlatitude mountain coniferous forest (Col de Porte, France, 1325 m altitude): the Snow Under Forest (SnoUF) field campaign data set
A new sea ice concentration product in the polar regions derived from the FengYun-3 MWRI sensors
NH-SWE: Northern Hemisphere Snow Water Equivalent dataset based on in situ snow depth time series
IT-SNOW: a snow reanalysis for Italy blending modeling, in situ data, and satellite observations (2010–2021)
Sara Arioli, Ghislain Picard, Laurent Arnaud, Simon Gascoin, Esteban Alonso-González, Marine Poizat, and Mark Irvine
Earth Syst. Sci. Data, 16, 3913–3934, https://doi.org/10.5194/essd-16-3913-2024, https://doi.org/10.5194/essd-16-3913-2024, 2024
Short summary
Short summary
High-accuracy precision maps of the surface temperature of snow were acquired with an uncooled thermal-infrared camera during winter 2021–2022 and spring 2023. The accuracy – i.e., mean absolute error – improved from 1.28 K to 0.67 K between the seasons thanks to an improved camera setup and temperature stabilization. The dataset represents a major advance in the validation of satellite measurements and physical snow models over a complex topography.
Michael Warscher, Thomas Marke, Erwin Rottler, and Ulrich Strasser
Earth Syst. Sci. Data, 16, 3579–3599, https://doi.org/10.5194/essd-16-3579-2024, https://doi.org/10.5194/essd-16-3579-2024, 2024
Short summary
Short summary
Continuous observations of snow and climate at high altitudes are still sparse. We present a unique collection of weather and snow cover data from three automatic weather stations at remote locations in the Ötztal Alps (Austria) that include continuous recordings of snow cover properties. The data are available over multiple winter seasons and enable new insights for snow hydrological research. The data are also used in operational applications, i.e., for avalanche warning and flood forecasting.
Lars Kaleschke, Xiangshan Tian-Kunze, Stefan Hendricks, and Robert Ricker
Earth Syst. Sci. Data, 16, 3149–3170, https://doi.org/10.5194/essd-16-3149-2024, https://doi.org/10.5194/essd-16-3149-2024, 2024
Short summary
Short summary
We describe a sea ice thickness dataset based on SMOS satellite measurements, initially designed for the Arctic but adapted for Antarctica. We validated it using limited Antarctic measurements. Our findings show promising results, with a small difference in thickness estimation and a strong correlation with validation data within the valid thickness range. However, improvements and synergies with other sensors are needed, especially for sea ice thicker than 1 m.
Weixin Zhu, Siqi Liu, Shiming Xu, and Lu Zhou
Earth Syst. Sci. Data, 16, 2917–2940, https://doi.org/10.5194/essd-16-2917-2024, https://doi.org/10.5194/essd-16-2917-2024, 2024
Short summary
Short summary
In the polar ocean, wind waves generate and propagate into the sea ice cover, forming marginal ice zones (MIZs). Using ESA's CryoSat-2, we construct a 12-year dataset of the MIZ in the Atlantic Arctic, a key region for climate change and human activities. The dataset is validated with high-resolution observations by ICESat2 and Sentinel-1. MIZs over 300 km wide are found under storms in the Barents Sea. The new dataset serves as the basis for research areas, including wave–ice interactions.
Fangbo Pan, Lingmei Jiang, Gongxue Wang, Jinmei Pan, Jinyu Huang, Cheng Zhang, Huizhen Cui, Jianwei Yang, Zhaojun Zheng, Shengli Wu, and Jiancheng Shi
Earth Syst. Sci. Data, 16, 2501–2523, https://doi.org/10.5194/essd-16-2501-2024, https://doi.org/10.5194/essd-16-2501-2024, 2024
Short summary
Short summary
It is important to strengthen the continuous monitoring of snow cover as a key indicator of imbalance in the Asian Water Tower (AWT) region. We generate long-term daily gap-free fractional snow cover products over the AWT at 0.005° resolution from 2000 to 2022 based on the multiple-endmember spectral mixture analysis algorithm and the gap-filling algorithm. They can provide highly accurate, quantitative fractional snow cover information for subsequent studies on hydrology and climate.
Wiebke Margitta Kolbe, Rasmus T. Tonboe, and Julienne Stroeve
Earth Syst. Sci. Data, 16, 1247–1264, https://doi.org/10.5194/essd-16-1247-2024, https://doi.org/10.5194/essd-16-1247-2024, 2024
Short summary
Short summary
Current satellite-based sea-ice climate data records (CDRs) usually begin in October 1978 with the first multichannel microwave radiometer data. Here, we present a sea ice dataset based on the single-channel Electrical Scanning Microwave Radiometer (ESMR) that operated from 1972-1977 onboard NASA’s Nimbus 5 satellite. The data were processed using modern methods and include uncertainty estimations in order to provide an important, easy-to-use reference period of good quality for current CDRs.
Thomas Lavergne and Emily Down
Earth Syst. Sci. Data, 15, 5807–5834, https://doi.org/10.5194/essd-15-5807-2023, https://doi.org/10.5194/essd-15-5807-2023, 2023
Short summary
Short summary
Sea ice in the Arctic and Antarctic can move several tens of kilometers per day due to wind and ocean currents. By analysing thousands of satellite images, we measured how sea ice has been moving every single day from 1991 through to 2020. We compare our data to how buoys attached to the ice moved and find good agreement. Other scientists will now use our data to better understand if climate change has modified the way sea ice moves and in what way.
Jean Emmanuel Sicart, Victor Ramseyer, Ghislain Picard, Laurent Arnaud, Catherine Coulaud, Guilhem Freche, Damien Soubeyrand, Yves Lejeune, Marie Dumont, Isabelle Gouttevin, Erwan Le Gac, Frédéric Berger, Jean-Matthieu Monnet, Laurent Borgniet, Éric Mermin, Nick Rutter, Clare Webster, and Richard Essery
Earth Syst. Sci. Data, 15, 5121–5133, https://doi.org/10.5194/essd-15-5121-2023, https://doi.org/10.5194/essd-15-5121-2023, 2023
Short summary
Short summary
Forests strongly modify the accumulation, metamorphism and melting of snow in midlatitude and high-latitude regions. Two field campaigns during the winters 2016–17 and 2017–18 were conducted in a coniferous forest in the French Alps to study interactions between snow and vegetation. This paper presents the field site, instrumentation and collection methods. The observations include forest characteristics, meteorology, snow cover and snow interception by the canopy during precipitation events.
Ying Chen, Ruibo Lei, Xi Zhao, Shengli Wu, Yue Liu, Pei Fan, Qing Ji, Peng Zhang, and Xiaoping Pang
Earth Syst. Sci. Data, 15, 3223–3242, https://doi.org/10.5194/essd-15-3223-2023, https://doi.org/10.5194/essd-15-3223-2023, 2023
Short summary
Short summary
The sea ice concentration product derived from the Microwave Radiation Image sensors on board the FengYun-3 satellites can reasonably and independently identify the seasonal and long-term changes of sea ice, as well as extreme cases of annual maximum and minimum sea ice extent in polar regions. It is comparable with other sea ice concentration products and applied to the studies of climate and marine environment.
Adrià Fontrodona-Bach, Bettina Schaefli, Ross Woods, Adriaan J. Teuling, and Joshua R. Larsen
Earth Syst. Sci. Data, 15, 2577–2599, https://doi.org/10.5194/essd-15-2577-2023, https://doi.org/10.5194/essd-15-2577-2023, 2023
Short summary
Short summary
We provide a dataset of snow water equivalent, the depth of liquid water that results from melting a given depth of snow. The dataset contains 11 071 sites over the Northern Hemisphere, spans the period 1950–2022, and is based on daily observations of snow depth on the ground and a model. The dataset fills a lack of accessible historical ground snow data, and it can be used for a variety of applications such as the impact of climate change on global and regional snow and water resources.
Francesco Avanzi, Simone Gabellani, Fabio Delogu, Francesco Silvestro, Flavio Pignone, Giulia Bruno, Luca Pulvirenti, Giuseppe Squicciarino, Elisabetta Fiori, Lauro Rossi, Silvia Puca, Alexander Toniazzo, Pietro Giordano, Marco Falzacappa, Sara Ratto, Hervè Stevenin, Antonio Cardillo, Matteo Fioletti, Orietta Cazzuli, Edoardo Cremonese, Umberto Morra di Cella, and Luca Ferraris
Earth Syst. Sci. Data, 15, 639–660, https://doi.org/10.5194/essd-15-639-2023, https://doi.org/10.5194/essd-15-639-2023, 2023
Short summary
Short summary
Snow cover has profound implications for worldwide water supply and security, but knowledge of its amount and distribution across the landscape is still elusive. We present IT-SNOW, a reanalysis comprising daily maps of snow amount and distribution across Italy for 11 snow seasons from September 2010 to August 2021. The reanalysis was validated using satellite images and snow measurements and will provide highly needed data to manage snow water resources in a warming climate.
Cited articles
Antonic, O.: Modelling daily topographic solar radiation without
site-specific hourly radiation data, Ecol. Model., 113, 31–40, https://doi.org/10.1016/S0304-3800(98)00132-X, 1998.
Azizi, A. H. and Akhtar, F.: Analysis of spatiotemporal variation in the
snow cover in Western Hindukush-Himalaya region, Geocarto Int.,
1–23, https://doi.org/10.1080/10106049.2021.1939442, 2021.
Bormann, K. J., McCabe, M. F., and Evans, J. P.: Satellite based
observations for seasonal snow cover detection and characterisation in
Australia, Remote Sens. Environ., 123, 57–71, https://doi.org/10.1016/j.rse.2012.03.003, 2012.
Cereceda-Balic, F., Vidal, V., Ruggeri, M. F., and Gonzalez, H. E.: Black
carbon pollution in snow and its impact on albedo near the Chilean stations
on the Antarctic peninsula: First results, Sci. Total Environ.,
743, 140801, https://doi.org/10.1016/j.scitotenv.2020.140801, 2020.
Chen, S., Wang, X., Guo, H., Xie, P., Wang, J., and Hao, X.: A conditional
probability interpolation method based on a space-time cube for MODIS snow
cover products gap filling, Remote Sensing, 12, 3577,
https://doi.org/10.3390/rs12213577, 2020.
Chen, X., Long, D., Liang, S., He, L., Zeng, C., Hao, X., and Hong, Y.:
Developing a composite daily snow cover extent record over the Tibetan
Plateau from 1981 to 2016 using multisource data, Remote Sens.
Environ., 215, 284–299, https://doi.org/10.1016/j.rse.2018.06.021, 2018a.
Chen, X., Long, D., Hong, Y., Hao, X., and Hou, A.: Climatology of snow
phenology over the Tibetan plateau for the period 2001–2014 using
multisource data, Int. J. Climatol., 38, 2718–2729, https://doi.org/10.1002/joc.5455, 2018b.
Crawford, C. J.: MODIS Terra Collection 6 fractional snow cover validation
in mountainous terrain during spring snowmelt using Landsat TM and ETM+,
Hydrol. Process., 29, 128–138, https://doi.org/10.1002/hyp.10134, 2015.
Dariane, A. B., Khoramian, A., and Santi, E.: Investigating spatiotemporal
snow cover variability via cloud-free MODIS snow cover product in Central
Alborz Region, Remote Sens. Environ., 202, 152–165, https://doi.org/10.1016/j.rse.2017.05.042, 2017.
Dong, C.: Remote sensing, hydrological modeling and in situ observations in
snow cover research: A review, J. Hydrol., 561, 573–583, https://doi.org/10.1016/j.jhydrol.2018.04.027, 2018.
Dong, C. and Menzel, L.: Producing cloud-free MODIS snow cover products with
conditional probability interpolation and meteorological data, Remote
Sens. Environ., 186, 439–451, https://doi.org/10.1016/j.rse.2016.09.019, 2016.
Gao, J., Williams, M. W., Fu, X., Wang, G., and Gong, T.: Spatiotemporal
distribution of snow in eastern Tibet and the response to climate change,
Remote Sens. Environ., 121, 1–9, https://doi.org/10.1016/j.rse.2012.01.006,
2012.
Gul, M. S., Muneer, T., and Kambezidis, H. D.: Models for obtaining solar
radiation from other meteorological data, Sol. Energy, 64, 99–108, https://doi.org/10.1016/S0038-092x(98)00048-6, 1998.
Hall, D. K. and Riggs, G. A.: MODIS/Terra Snow Cover Daily L3 Global 500m SIN
Grid, Version 6, NASA [data set], https://doi.org/10.5067/MODIS/MOD10A1.006, 2016a.
Hall, D. K. and Riggs, G. A.: MODIS/Aqua Snow Cover Daily L3 Global 500m SIN
Grid, Version 6, NASA [data set], https://doi.org/10.5067/MODIS/MYD10A1.006, 2016b.
Hall, D. K. and Riggs, G. A.: MODIS/Terra Snow Cover 8-Day L3 Global 500m
SIN Grid, Version 61, NASA [data set], https://doi.org/10.5067/MODIS/MOD10A2.061, 2021.
Hall, D. K., Riggs, G., and Salomonson, V. V.: Development of methods for
mapping global snow cover using Moderate Resolution Imaging
Spectroradiometer (MODIS) data, Remote Sens. Environ., 54, 127–140,
https://doi.org/10.1016/0034-4257(95)00137-P, 1995.
Hou, J., Huang, C., Zhang, Y., Guo, J., and Gu, J.: Gap-filling of MODIS
fractional snow cover products via non-local spatio-temporal filtering based
on machine learning techniques, Remote Sensing, 11, 90, https://doi.org/10.3390/rs11010090,
2019.
Huang, G., Li, Z., Li, X., Liang, S., Yang, K., Wang, D., and Zhang, Y.:
Estimating surface solar irradiance from satellites: Past, present, and
future perspectives, Remote Sens. Environ., 233, 111371, https://doi.org/10.1016/j.rse.2019.111371, 2019.
Huang, K., Zhang, Y., Tagesson, T., Brandt, M., Wang, L., Chen, N., Zu, J.,
Jin, H., Cai, Z., Tong, X., Cong, N., and Fensholt, R.: The confounding
effect of snow cover on assessing spring phenology from space: A new look at
trends on the Tibetan Plateau, Sci. Total Environ., 756, 144011, https://doi.org/10.1016/j.scitotenv.2020.144011, 2021.
Huang, X., Liang, T., Zhang, X., and Guo, Z.: Validation of MODIS snow cover
products using Landsat and ground measurements during the 2001–2005 snow
seasons over northern Xinjiang, China, Int. J. Remote
Sens., 32, 133–152, https://doi.org/10.1080/01431160903439924, 2011.
Huang, Y. and Xu, J.: Daily cloud-free snow cover products for Tibetan
Plateau from 2002 to 2021, National Tibetan Plateau Data Center [data set], https://doi.org/10.11888/Cryos.tpdc.272204, 2022.
Huang, Y., Chen, Z., Wu, B., Chen, L., Mao, W., Zhao, F., Wu, J., Wu, J.,
and Yu, B.: Estimating roof solar energy potential in the downtown area
using a GPU-accelerated solar radiation model and airborne LiDAR data,
Remote Sensing, 7, 17212–17233, https://doi.org/10.3390/rs71215877, 2015.
Huang, Y., Liu, H., Yu, B., Wu, J., Kang, E. L., Xu, M., Wang, S., Klein,
A., and Chen, Y.: Improving MODIS snow products with a HMRF-based
spatio-temporal modeling technique in the Upper Rio Grande Basin, Remote
Sens. Environ., 204, 568–582, https://doi.org/10.1016/j.rse.2017.10.001, 2018.
Huang, Y., Song, Z. C., Yang, H. X., Yu, B. L., Liu, H. X., Che, T., Chen,
J., Wu, J. P., Shu, S., Peng, X. B., Zheng, Z. J., and Xu, J. H.: Snow cover
detection in mid-latitude mountainous and polar regions using nighttime
light data, Remote Sens. Environ., 268, 112766, https://doi.org/10.1016/j.rse.2021.112766, 2022.
Hussainzada, W., Lee, H. S., Vinayak, B., and Khpalwak, G. F.: Sensitivity
of snowmelt runoff modelling to the level of cloud coverage for snow cover
extent from daily MODIS product collection 6, J. Hydrol.: Reg Stud., 36, 100835, https://doi.org/10.1016/j.ejrh.2021.100835, 2021.
Immerzeel, W. W., van Beek, L. P. H., and Bierkens, M. F. P.: Climate change
will affect the asian water towers, Science, 328, 1382–1385, https://doi.org/10.1126/science.1183188, 2010.
Jing, Y. H., Shen, H. F., Li, X. H., and Guan, X. B.: A two-stage fusion
framework to generate a spatio–temporally continuous MODIS NDSI product
over the Tibetan Plateau, Remote Sensing, 11, 2261, https://doi.org/10.3390/rs11192261, 2019.
Kilpys, J., Pipiraitė-Januškienė, S., and Rimkus, E.: Snow
climatology in Lithuania based on the cloud-free moderate resolution imaging
spectroradiometer snow cover product, Int. J. Climatol.,
40, 4690–4706, https://doi.org/10.1002/joc.6483, 2020.
Klein, A.: Validation of daily MODIS snow cover maps of the Upper Rio Grande
River Basin for the 2000–2001 snow year, Remote Sens. Environ., 86,
162–176, https://doi.org/10.1016/s0034-4257(03)00097-x, 2003.
Kumar, L., Skidmore, A. K., and Knowles, E.: Modelling topographic variation
in solar radiation in a GIS environment, Int. J.
Geogr. Inf. Sci., 11, 475–497, https://doi.org/10.1080/136588197242266,
1997.
Li, C., Su, F., Yang, D., Tong, K., Meng, F., and Kan, B.: Spatiotemporal
variation of snow cover over the Tibetan Plateau based on MODIS snow
product, 2001-2014, Int. J. Climatol., 38, 708–728, https://doi.org/10.1002/joc.5204, 2018.
Li, M., Zhu, X., Li, N., and Pan, Y.: Gap-filling of a MODIS Normalized
Difference Snow Index product based on the similar pixel selecting
algorithm: a case study on the Qinghai–Tibetan Plateau, Remote Sensing, 12, 1077,
https://doi.org/10.3390/rs12071077, 2020.
Li, X., Jing, Y., Shen, H., and Zhang, L.: The recent developments in cloud removal approaches of MODIS snow cover product, Hydrol. Earth Syst. Sci., 23, 2401–2416, https://doi.org/10.5194/hess-23-2401-2019, 2019.
Li, Y., Chen, Y., and Li, Z.: Developing daily cloud-free snow composite
products from MODIS and IMS for the Tienshan mountains, Earth and Space
Science, 6, 266–275, https://doi.org/10.1029/2018ea000460, 2019.
Liang, H., Huang, X., Sun, Y., Wang, Y., and Liang, T.: Fractional
snow-cover mapping based on MODIS and UAV data over the Tibetan Plateau,
Remote Sensing, 9, 1332, https://doi.org/10.3390/rs9121332, 2017.
Liang, T., Huang, X., Wu, C., Liu, X., Li, W., Guo, Z., and Ren, J.: An
application of MODIS data to snow cover monitoring in a pastoral area: A
case study in Northern Xinjiang, China, Remote Sens. Environ., 112,
1514–1526, https://doi.org/10.1016/j.rse.2007.06.001, 2008.
Liu, C., Li, Z., Zhang, P., Zeng, J., Gao, S., and Zheng, Z.: An assessment
and error analysis of MOD10A1 snow product using Landsat and ground
observations over China during 2000–2016, IEEE J. Sel. Top.
Appl., 13, 1467–1478, https://doi.org/10.1109/jstars.2020.2983550, 2020.
Liu, Y., Chen, X., Hao, J.-S., and Li, L.-h.: Snow cover estimation from
MODIS and Sentinel-1 SAR data using machine learning algorithms in the
western part of the Tianshan Mountains, J. Mt. Sci., 17,
884–897, https://doi.org/10.1007/s11629-019-5723-1, 2020.
Muhammad, S. and Thapa, A.: An improved Terra–Aqua MODIS snow cover and Randolph Glacier Inventory 6.0 combined product (MOYDGL06*) for high-mountain Asia between 2002 and 2018, Earth Syst. Sci. Data, 12, 345–356, https://doi.org/10.5194/essd-12-345-2020, 2020.
Parajka, J. and Blöschl, G.: Spatio-temporal combination of MODIS images
– potential for snow cover mapping, Water Resour. Res., 44, W03406, https://doi.org/10.1029/2007wr006204, 2008.
Parajka, J., Holko, L., Kostka, Z., and Blöschl, G.: MODIS snow cover mapping accuracy in a small mountain catchment – comparison between open and forest sites, Hydrol. Earth Syst. Sci., 16, 2365–2377, https://doi.org/10.5194/hess-16-2365-2012, 2012.
Qiu, J.: China: The third pole, Nature, 454, 393–396, https://doi.org/10.1038/454393a,
2008.
Richiardi, C., Blonda, P., Rana, F. M., Santoro, M., Tarantino, C., Vicario,
S., and Adamo, M.: A revised snow cover algorithm to improve discrimination
between snow and clouds: a case study in Gran Paradiso National Park, Remote
Sensing, 13, 1957, https://doi.org/10.3390/rs13101957, 2021.
Riggs, G. A., Hall, D. K., and Román, M. O.: Overview of NASA's MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS) snow-cover Earth System Data Records, Earth Syst. Sci. Data, 9, 765–777, https://doi.org/10.5194/essd-9-765-2017, 2017.
Riggs, G. A., Hall, D. K., and Román, M. O.: MODIS Snow Products
Collection 6.1 User Guide, https://modis-snow-ice.gsfc.nasa.gov/uploads/snow_user_guide_C6.1_final_revised_april.pdf (last access: 1 March
2022), 2019.
Salomonson, V. V. and Appel, I.: Estimating fractional snow cover from MODIS
using the normalized difference snow index, Remote Sens. Environ.,
89, 351–360, https://doi.org/10.1016/j.rse.2003.10.016, 2004.
Tang, Z., Wang, J., Li, H., and Yan, L.: Accuracy validation and cloud
obscuration removal of MODIS fractional snow cover products over Tibetan
Plateau, Remote Sensing Technology and Application, 28, 423–430, https://doi.org/10.16089/j.cnki.1008-2786.000237, 2013.
Teilet, P. M., Guindon, B., and Goodenough, D. G.: On the slope-aspect
correction of multispectral scanner data, Can. J. Remote
Sens., 8, 1537–1540, https://doi.org/10.1080/07038992.1982.10855028, 1982.
Tran, H., Nguyen, P., Ombadi, M., Hsu, K. L., Sorooshian, S., and Qing, X.:
A cloud-free MODIS snow cover dataset for the contiguous United States from
2000 to 2017, Scientific Data, 6, 180300, https://doi.org/10.1038/sdata.2018.300, 2019.
Wang, G., Jiang, L., Shi, J., Liu, X., Yang, J., and Cui, H.: Snow-covered
area retrieval from Himawari–8 AHI imagery of the Tibetan Plateau, Remote
Sensing, 11, 2391, https://doi.org/10.3390/rs11202391, 2019.
Wang, X., Xie, H., Liang, T., and Huang, X.: Comparison and validation of
MODIS standard and new combination of Terra and Aqua snow cover products in
northern Xinjiang, China, Hydrol. Process., 23, 419–429, https://doi.org/10.1002/hyp.7151, 2009.
Wang, X., Wu, C., Peng, D., Gonsamo, A., and Liu, Z.: Snow cover phenology
affects alpine vegetation growth dynamics on the Tibetan Plateau: Satellite
observed evidence, impacts of different biomes, and climate drivers,
Agr. Forest Meteorol., 256–257, 61–74, https://doi.org/10.1016/j.agrformet.2018.03.004, 2018.
Wu, Z., Jiang, Z., Li, J., Zhong, S., and Wang, L.: Possible association of
the western Tibetan plateau snow cover with the decadal to interdecadal
variations of northern China heatwave frequency, Clim. Dynam.,
39, 2393–2402, 2012.
Xiao, X., Liang, S., He, T., Wu, D., Pei, C., and Gong, J.: Estimating fractional snow cover from passive microwave brightness temperature data using MODIS snow cover product over North America, The Cryosphere, 15, 835–861, https://doi.org/10.5194/tc-15-835-2021, 2021.
Xu, W. F., Ma, H. Q., Wu, D. H., and Yuan, W. P.: Assessment of the daily
cloud-free MODIS snow-cover product for monitoring the snow-cover phenology
over the Qinghai-Tibetan Plateau, Remote Sensing, 9, 585, https://doi.org/10.3390/rs9060585, 2017.
Yang, J., Jiang, L., Ménard, C. B., Luojus, K., Lemmetyinen, J., and
Pulliainen, J.: Evaluation of snow products over the Tibetan Plateau,
Hydrol. Process., 29, 3247–3260, https://doi.org/10.1002/hyp.10427, 2015.
Yang, K., Wu, H., Qin, J., Lin, C., Tang, W., and Chen, Y.: Recent climate
changes over the Tibetan Plateau and their impacts on energy and water
cycle: A review, Global Planet. Change, 112, 79–91, https://doi.org/10.1016/j.gloplacha.2013.12.001, 2014.
Yao, T., Xue, Y., Chen, D., Chen, F., Thompson, L., Cui, P., Koike, T., Lau,
W. K. M., Lettenmaier, D., Mosbrugger, V., Zhang, R., Xu, B., Dozier, J.,
Gillespie, T., Gu, Y., Kang, S., Piao, S., Sugimoto, S., Ueno, K., Wang, L.,
Wang, W., Zhang, F., Sheng, Y., Guo, W., Ailikun, Yang, X., Ma, Y., Shen, S.
S. P., Su, Z., Chen, F., Liang, S., Liu, Y., Singh, V. P., Yang, K., Yang,
D., Zhao, X., Qian, Y., Zhang, Y., and Li, Q.: Recent third pole's rapid
warming accompanies cryospheric melt and water cycle intensification and
interactions between monsoon and environment: multidisciplinary approach
with observations, Modeling, and Analysis, B. Am.
Meteorol. Soc., 100, 423–444, https://doi.org/10.1175/bams-d-17-0057.1, 2019.
You, Q., Wu, T., Shen, L., Pepin, N., Zhang, L., Jiang, Z., Wu, Z., Kang,
S., and AghaKouchak, A.: Review of snow cover variation over the Tibetan
Plateau and its influence on the broad climate system, Earth-Sci.
Rev., 201, 103043, https://doi.org/10.1016/j.earscirev.2019.103043, 2020.
Yu, J., Zhang, G., Yao, T., Xie, H., Zhang, H., Ke, C., and Yao, R.:
Developing daily cloud-free snow composite products from MODIS Terra–Aqua
and IMS for the Tibetan Plateau, IEEE T. Geosci. Remote, 54, 2171–2180, https://doi.org/10.1109/tgrs.2015.2496950, 2016.
Zhang, H., Zhang, F., Zhang, G., Yan, W., and Li, S.: Enhanced scaling
effects significantly lower the ability of MODIS normalized difference snow
index to estimate fractional and binary snow cover on the Tibetan Plateau,
J. Hydrol., 592, 125795, https://doi.org/10.1016/j.jhydrol.2020.125795, 2021.
Zheng, Z. and Cao, G.: Snow cover dataset based on multi-source remote
sensing products blended with 1km spatial resolution on the Qinghai-Tibet
Plateau (1995–2018), National Tibetan Plateau Data Center [data set], https://doi.org/10.11888/Snow.tpdc.270102, 2019.
Short summary
Reliable snow cover information is important for understating climate change and hydrological cycling. We generate long-term daily gap-free snow products over the Tibetan Plateau (TP) at 500 m resolution from 2002 to 2021 based on the hidden Markov random field model. The accuracy is 91.36 %, and is especially improved during snow transitional period and over complex terrains. This dataset has great potential to study climate change and to facilitate water resource management in the TP.
Reliable snow cover information is important for understating climate change and hydrological...
Special issue
Altmetrics
Final-revised paper
Preprint