Articles | Volume 16, issue 5
https://doi.org/10.5194/essd-16-2501-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-16-2501-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
29 May 2024
Data description paper |
| 29 May 2024
| 29 May 2024
MODIS daily cloud-gap-filled fractional snow cover dataset of the Asian Water Tower region (2000–2022)
Fangbo Pan
State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Aerospace Information Research Institute of Chinese Academy of Sciences, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Aerospace Information Research Institute of Chinese Academy of Sciences, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Gongxue Wang
College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China
Jinmei Pan
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Jinyu Huang
State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Aerospace Information Research Institute of Chinese Academy of Sciences, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Cheng Zhang
State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Aerospace Information Research Institute of Chinese Academy of Sciences, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Huizhen Cui
State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Aerospace Information Research Institute of Chinese Academy of Sciences, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Jianwei Yang
State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Aerospace Information Research Institute of Chinese Academy of Sciences, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Zhaojun Zheng
Satellite Meteorological Institute, National Satellite Meteorological Center, China Meteorological Administration, Beijing 100081, China
Shengli Wu
Satellite Meteorological Institute, National Satellite Meteorological Center, China Meteorological Administration, Beijing 100081, China
Jiancheng Shi
National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China
Related authors
No articles found.
Xinran Xia, Rubin Jiang, Min Min, Shengli Wu, Peng Zhang, and Xiangao Xia
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-395, https://doi.org/10.5194/essd-2024-395, 2024
Preprint under review for ESSD
Short summary
Short summary
Based on the MicroWave Radiation Imager aboard FY-3 series satellites, we developed a global terrestrial precipitable water vapor dataset from 2012 to 2020. This dataset overcomes the limitations of infrared observations and provides accurate, all-weather PWV data ,spanning all types of land surface. Researchers are expected to leverage it to explore the role of water vapor in weather patterns, refine precipitation forecasting, and validate climate simulations.
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.
Jinmei Pan, Michael Durand, Juha Lemmetyinen, Desheng Liu, and Jiancheng Shi
The Cryosphere, 18, 1561–1578, https://doi.org/10.5194/tc-18-1561-2024, https://doi.org/10.5194/tc-18-1561-2024, 2024
Short summary
Short summary
We developed an algorithm to estimate snow mass using X- and dual Ku-band radar, and tested it in a ground-based experiment. The algorithm, the Bayesian-based Algorithm for SWE Estimation (BASE) using active microwaves, achieved an RMSE of 30 mm for snow water equivalent. These results demonstrate the potential of radar, a highly promising sensor, to map snow mass at high spatial resolution.
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.
Yan Huang, Jiahui Xu, Jingyi Xu, Yelei Zhao, Bailang Yu, Hongxing Liu, Shujie Wang, Wanjia Xu, Jianping Wu, and Zhaojun Zheng
Earth Syst. Sci. Data, 14, 4445–4462, https://doi.org/10.5194/essd-14-4445-2022, https://doi.org/10.5194/essd-14-4445-2022, 2022
Short summary
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.
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.
Shu Fang, Kebiao Mao, Xueqi Xia, Ping Wang, Jiancheng Shi, Sayed M. Bateni, Tongren Xu, Mengmeng Cao, Essam Heggy, and Zhihao Qin
Earth Syst. Sci. Data, 14, 1413–1432, https://doi.org/10.5194/essd-14-1413-2022, https://doi.org/10.5194/essd-14-1413-2022, 2022
Short summary
Short summary
Air temperature is an important parameter reflecting climate change, and the current method of obtaining daily temperature is affected by many factors. In this study, we constructed a temperature model based on weather conditions and established a correction equation. The dataset of daily air temperature (Tmax, Tmin, and Tavg) in China from 1979 to 2018 was obtained with a spatial resolution of 0.1°. Accuracy verification shows that the dataset has reliable accuracy and high spatial resolution.
Xiangjin Meng, Kebiao Mao, Fei Meng, Jiancheng Shi, Jiangyuan Zeng, Xinyi Shen, Yaokui Cui, Lingmei Jiang, and Zhonghua Guo
Earth Syst. Sci. Data, 13, 3239–3261, https://doi.org/10.5194/essd-13-3239-2021, https://doi.org/10.5194/essd-13-3239-2021, 2021
Short summary
Short summary
In order to improve the accuracy of China's regional agricultural drought monitoring and climate change research, we produced a long-term series of soil moisture products by constructing a time and depth correction model for three soil moisture products with the help of ground observation data. The spatial resolution is improved by building a spatial weight decomposition model, and validation indicates that the new product can meet application needs.
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.
Jianwei Yang, Lingmei Jiang, Kari Luojus, Jinmei Pan, Juha Lemmetyinen, Matias Takala, and Shengli Wu
The Cryosphere, 14, 1763–1778, https://doi.org/10.5194/tc-14-1763-2020, https://doi.org/10.5194/tc-14-1763-2020, 2020
Short summary
Short summary
There are many challenges for accurate snow depth estimation using passive microwave data. Machine learning (ML) techniques are deemed to be powerful tools for establishing nonlinear relations between independent variables and a given target variable. In this study, we investigate the potential capability of the random forest (RF) model on snow depth estimation at temporal and spatial scales. The result indicates that the fitted RF algorithms perform better on temporal than spatial scales.
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
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)
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
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.
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.
Yan Huang, Jiahui Xu, Jingyi Xu, Yelei Zhao, Bailang Yu, Hongxing Liu, Shujie Wang, Wanjia Xu, Jianping Wu, and Zhaojun Zheng
Earth Syst. Sci. Data, 14, 4445–4462, https://doi.org/10.5194/essd-14-4445-2022, https://doi.org/10.5194/essd-14-4445-2022, 2022
Short summary
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.
Cited articles
Ault, T. W., Czajkowski, K. P., Benko, T., Coss, J., Struble, J., Spongberg, A., Templin, M., and Gross, C.: Validation of the MODIS snow product and cloud mask using student and NWS cooperative station observations in the Lower Great Lakes Region, Remote Sens. Environ., 105, 341–353, https://doi.org/10.1016/j.rse.2006.07.004, 2006.
Bair, E. H., Stillinger, T., and Dozier, J.: Snow Property Inversion From Remote Sensing (SPIReS): A Generalized Multispectral Unmixing Approach With Examples From MODIS and Landsat 8 OLI, IEEE T. Geosci. Remote, 59, 7270–7284, https://doi.org/10.1109/TGRS.2020.3040328, 2021.
Barnett, T. P., Adam, J. C., and Lettenmaier, D. P.: Potential impacts of a warming climate on water availability in snow-dominated regions, Nature, 438, 303–309, https://doi.org/10.1038/nature04141, 2005.
Czyzowska-Wisniewski, E. H., van Leeuwen, W. J. D., Hirschboeck, K. K., Marsh, S. E., and Wisniewski, W. T.: Fractional snow cover estimation in complex alpine-forested environments using an artificial neural network, Remote Sens. Environ., 156, 403–417, https://doi.org/10.1016/j.rse.2014.09.026, 2015.
Dai, L., Che, T., Ding, Y., and Hao, X.: Evaluation of snow cover and snow depth on the Qinghai–Tibetan Plateau derived from passive microwave remote sensing, The Cryosphere, 11, 1933–1948, https://doi.org/10.5194/tc-11-1933-2017, 2017.
Dobreva, I. D. and Klein, A. G.: Fractional snow cover mapping through artificial neural network analysis of MODIS surface reflectance, Remote Sens. Environ., 115, 3355–3366, https://doi.org/10.1016/j.rse.2011.07.018, 2011.
Dong, C. and Menzel, L.: Improving the accuracy of MODIS 8-day snow products with in situ temperature and precipitation data, J. Hydrol., 534, 466–477, https://doi.org/10.1016/j.jhydrol.2015.12.065, 2016a.
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, 2016b.
Dozier, J., Painter, T. H., Rittger, K., and Frew, J. E.: Time–space continuity of daily maps of fractional snow cover and albedo from MODIS, Adv. Water Resour., 31, 1515–1526, https://doi.org/10.1016/j.advwatres.2008.08.011, 2008.
Elguindi, N., Hanson, B., and Leathers, D.: The Effects of Snow Cover on Midlatitude Cyclones in the Great Plains, J. Hydrometeorol., 6, 263–279, https://doi.org/10.1175/JHM415.1, 2005.
Fritsch, F. N. and Carlson, R. E.: Monotone Piecewise Cubic Interpolation, SIAM J. Numer. Anal., 17, 238–246, https://doi.org/10.1137/0717021, 1980.
Gafurov, A. and Bárdossy, A.: Cloud removal methodology from MODIS snow cover product, Hydrol. Earth Syst. Sci., 13, 1361–1373, https://doi.org/10.5194/hess-13-1361-2009, 2009.
Hall, D. K. and Riggs, G. A.: Accuracy assessment of the MODIS snow products, Hydrol. Process., 21, 1534–1547, https://doi.org/10.1002/hyp.6715, 2007.
Hall, D. K. and Riggs, G. A.: MODIS/Terra Snow Cover Daily L3 Global 500m SIN Grid, Version 6, Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/MODIS/MOD10A1.006, 2016.
Hall, D. K., Riggs, G. A., and Salomonson, V. V.: Development of Methods for Mapping Global Snow Cover Using Moderate Resolution Imaging Spectroradiometer Data, Remote Sens. Environ., 54, 127–140, https://doi.org/10.1016/0034-4257(95)00137-P, 1995.
Hao, S., Jiang, L., Shi, J., Wang, G., and Liu, X.: Assessment of MODIS-Based Fractional Snow Cover Products Over the Tibetan Plateau, IEEE J. Sel. Top. Appl., 12, 533–548, https://doi.org/10.1109/JSTARS.2018.2879666, 2019.
Hao, X., Huang, G., Che, T., Ji, W., Sun, X., Zhao, Q., Zhao, H., Wang, J., Li, H., and Yang, Q.: The NIEER AVHRR snow cover extent product over China – a long-term daily snow record for regional climate research, Earth Syst. Sci. Data, 13, 4711–4726, https://doi.org/10.5194/essd-13-4711-2021, 2021.
Hao, X., Huang, G., Zheng, Z., Sun, X., Ji, W., Zhao, H., Wang, J., Li, H., and Wang, X.: Development and validation of a new MODIS snow-cover-extent product over China, Hydrol. Earth Syst. Sci., 26, 1937–1952, https://doi.org/10.5194/hess-26-1937-2022, 2022.
Hori, M., Sugiura, K., Kobayashi, K., Aoki, T., Tanikawa, T., Kuchiki, K., Niwano, M., and Enomoto, H.: A 38-year (1978–2015) Northern Hemisphere daily snow cover extent product derived using consistent objective criteria from satellite-borne optical sensors, Remote Sens. Environ., 191, 402–418, https://doi.org/10.1016/j.rse.2017.01.023, 2017.
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 Sens., 11, 90, https://doi.org/10.3390/rs11010090, 2019.
Huang, X., Deng, J., Ma, X., Wang, Y., Feng, Q., Hao, X., and Liang, T.: Spatiotemporal dynamics of snow cover based on multi-source remote sensing data in China, The Cryosphere, 10, 2453–2463, https://doi.org/10.5194/tc-10-2453-2016, 2016.
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., Xu, J., Xu, J., Zhao, Y., Yu, B., Liu, H., Wang, S., Xu, W., Wu, J., and Zheng, Z.: 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, Earth Syst. Sci. Data, 14, 4445–4462, https://doi.org/10.5194/essd-14-4445-2022, 2022a.
Huang, Y., Song, Z., Yang, H., Yu, B., Liu, H., Che, T., Chen, J., Wu, J., Shu, S., Peng, X., Zheng, Z., and Xu, J.: 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, 2022b.
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.
Immerzeel, W. W., Lutz, A. F., Andrade, M., Bahl, A., Biemans, H., Bolch, T., Hyde, S., Brumby, S., Davies, B. J., Elmore, A. C., Emmer, A., Feng, M., Fernández, A., Haritashya, U., Kargel, J. S., Koppes, M., Kraaijenbrink, P. D. A., Kulkarni, A. V., Mayewski, P. A., Nepal, S., Pacheco, P., Painter, T. H., Pellicciotti, F., Rajaram, H., Rupper, S., Sinisalo, A., Shrestha, A. B., Viviroli, D., Wada, Y., Xiao, C., Yao, T., and Baillie, J. E. M.: Importance and vulnerability of the world's water towers, Nature, 577, 364–369, https://doi.org/10.1038/s41586-019-1822-y, 2020.
Jiang, L., Pan, F., Wang, G., Pan, J., Shi, J., and Zhang, C.: MODIS daily cloud-free factional snow cover data set for Asian water tower area (2000–2022), National Snow and Ice Data Center [data set], https://doi.org/10.11888/Cryos.tpdc.272503, 2022.
Jiang, L., Pan, F., Wang, G., Pan, J., Shi, J., Zhang, C., Huang, and jinyu: MODIS Daily Cloud-gap-filled Fractional Snow Cover Dataset of the Asian Water Tower Region (2000–2022) (V1.0), Zenodo [data set], https://doi.org/10.5281/zenodo.10005827, 2023a.
Jiang, L., Pan, F., Wang, G., Huang, J., Zhang, C., and Shi, J.: Validation dataset of 30 m resolution Landsat-8 fractional snow cover in the Asian Water Tower region (2013–2022) (V1.0), Zenodo [data set], https://doi.org/10.5281/zenodo.10008227, 2023b.
Ke, C.-Q., Li, X.-C., Xie, H., Ma, D.-H., Liu, X., and Kou, C.: Variability in snow cover phenology in China from 1952 to 2010, Hydrol. Earth Syst. Sci., 20, 755–770, https://doi.org/10.5194/hess-20-755-2016, 2016.
Key, J., Liu, Y., Wang, X., Letterly, A., and Painter, T.: Snow and Ice Products from ABI on the GOES-R Series, in: A New Generation of Geostationary Environmental Satellites, 165–177, https://doi.org/10.1016/B978-0-12-814327-8.00014-7, 2020.
Kraaijenbrink, P. D. A., Bierkens, M. F. P., Lutz, A. F., and Immerzeel, W. W.: Impact of a global temperature rise of 1.5 degrees Celsius on Asia's glaciers, Nature, 549, 257–260, https://doi.org/10.1038/nature23878, 2017.
Kuter, S.: Completing the machine learning saga in fractional snow cover estimation from MODIS Terra reflectance data: Random forests versus support vector regression, Remote Sens. Environ., 255, 112294, https://doi.org/10.1016/j.rse.2021.112294, 2021.
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 Sens., 12, 1077, https://doi.org/10.3390/rs12071077, 2020.
Li, W., Guo, W., Qiu, B., Xue, Y., Hsu, P.-C., and Wei, J.: Influence of Tibetan Plateau snow cover on East Asian atmospheric circulation at medium-range time scales, Nat. Commun., 9, 4243, https://doi.org/10.1038/s41467-018-06762-5, 2018.
Li, X., Shen, H., Zhang, L., Zhang, H., and Yuan, Q.: Dead Pixel Completion of Aqua MODIS Band 6 Using a Robust M-Estimator Multiregression, IEEE Geosci. Remote Sens. Lett., 11, 768–772, https://doi.org/10.1109/LGRS.2013.2278626, 2014.
Li, X., Fu, W., Shen, H., Huang, C., and Zhang, L.: Monitoring snow cover variability (2000–2014) in the Hengduan Mountains based on cloud-removed MODIS products with an adaptive spatio-temporal weighted method, J. Hydrol., 551, 314–327, https://doi.org/10.1016/j.jhydrol.2017.05.049, 2017.
Li, X., Long, D., Scanlon, B. R., Mann, M. E., Li, X., Tian, F., Sun, Z., and Wang, G.: Climate change threatens terrestrial water storage over the Tibetan Plateau, Nat. Clim. Change, 12, 801–807, https://doi.org/10.1038/s41558-022-01443-0, 2022.
Lindsay, C., Zhu, J., Miller, A., Kirchner, P., and Wilson, T.: Deriving snow cover metrics for Alaska from MODIS, Remote Sens., 7, 12961–12985, https://doi.org/10.3390/rs71012961, 2015.
Liu, T., Chen, D., Yang, L., Meng, J., Wang, Z., Ludescher, J., Fan, J., Yang, S., Chen, D., Kurths, J., Chen, X., Havlin, S., and Schellnhuber, H. J.: Teleconnections among tipping elements in the Earth system, Nat. Clim. Change, 13, 67–74, https://doi.org/10.1038/s41558-022-01558-4, 2023.
Liu, X. and Chen, B.: Climatic warming in the Tibetan Plateau during recent decades, Int. J. Climatol., 20, 1729–1742, https://doi.org/10.1002/1097-0088(20001130)20:14<1729::AID-JOC556>3.0.CO;2-Y, 2000.
López-Burgos, V., Gupta, H. V., and Clark, M.: Reducing cloud obscuration of MODIS snow cover area products by combining spatio-temporal techniques with a probability of snow approach, Hydrol. Earth Syst. Sci., 17, 1809–1823, https://doi.org/10.5194/hess-17-1809-2013, 2013.
Martinec, J.: Snowmelt – runoff model for stream flow forecasts, Hydrol. Res., 6, 145–154, https://doi.org/10.2166/nh.1975.0010, 1975.
Mazari, N., Tekeli, A. E., Xie, H., Sharif, H. I., and Hassan, A. A. E.: Assessment of ice mapping system and moderate resolution imaging spectroradiometer snow cover maps over Colorado Plateau, J. Appl. Remote Sens., 7, 073540, https://doi.org/10.1117/1.JRS.7.073540, 2013.
Metsamaki, S. J., Anttila, S. T., Markus, H. J., and Vepsalainen, J. M.: A feasible method for fractional snow cover mapping in boreal zone based on a reflectance model, Remote Sens. Environ., 95, 77–95, https://doi.org/10.1016/j.rse.2004.11.013, 2005.
Metsämäki, S., Mattila, O.-P., Pulliainen, J., Niemi, K., Luojus, K., and Böttcher, K.: An optical reflectance model-based method for fractional snow cover mapping applicable to continental scale, Remote Sens. Environ., 123, 508–521, https://doi.org/10.1016/j.rse.2012.04.010, 2012.
Naegeli, K., Neuhaus, C., Salberg, A.-B., Schwaizer, G., Weber, H., Wiesmann, A., Wunderle, S., and Nagler, T.: ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction – snow on ground (SCFG) from AVHRR (1982–2018), version 2.0, NERC EDS Centre for Environmental Data Analysis [data set], https://doi.org/10.5285/3f034f4a08854eb59d58e1fa92d207b6, 2022.
Nagler, T., Schwaizer, G., Molg, N., Keuris, L., Hetzenecker, M., and Metsämäki, S.: ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction – snow on ground (SCFG) from MODIS (2000–2020), version 2.0, NERC EDS Centre for Environmental Data Analysis [data set], https://doi.org/10.5285/8847a05eeda646a29da58b42bdf2a87c, 2022.
Niittynen, P., Heikkinen, R. K., and Luoto, M.: Decreasing snow cover alters functional composition and diversity of Arctic tundra, P. Natl. Acad. Sci. USA, 117, 21480–21487, https://doi.org/10.1073/pnas.2001254117, 2020.
Notarnicola, C.: Hotspots of snow cover changes in global mountain regions over 2000–2018, Remote Sens. Environ., 243, 111781, https://doi.org/10.1016/j.rse.2020.111781, 2020.
Painter, T. H., Dozier, J., Roberts, D. A., Davis, R. E., and Green, R. O.: Retrieval of subpixel snow-covered area and grain size from imaging spectrometer data, Remote Sens. Environ., 85, 64–77, https://doi.org/10.1016/S0034-4257(02)00187-6, 2003.
Painter, T. H., Rittger, K., McKenzie, C., Slaughter, P., Davis, R. E., and Dozier, J.: Retrieval of subpixel snow covered area, grain size, and albedo from MODIS, Remote Sens. Environ., 113, 868–879, https://doi.org/10.1016/j.rse.2009.01.001, 2009.
Pan, F.: AWT_Site_SD (v1.0.0), Zenodo [data set], https://doi.org/10.5281/zenodo.11367913, 2024a.
Pan, F.: AWT_MODIS_Daily FSC_Product_code (v1.0.0), Zenodo [code], https://doi.org/10.5281/zenodo.11367978, 2024b.
Pan, F., Jiang, L., Zheng, Z., Wang, G., Cui, H., Zhou, X., and Huang, J.: Retrieval of Fractional Snow Cover over High Mountain Asia Using 1 km and 5 km AVHRR/2 with Simulated Mid-Infrared Reflective Band, Remote Sens., 14, 3303, https://doi.org/10.3390/rs14143303, 2022.
Pan, J., Yang, J., Jiang, L., Xiong, C., Pan, F., Gao, X., Shi, J., and Chang, S.: Combination of Snow Process Model Priors and Site Representativeness Evaluation to Improve the Global Snow Depth Retrieval Based on Passive Microwaves, IEEE T. Geosci. Remote, 61, 1–20, https://doi.org/10.1109/TGRS.2023.3276651, 2023.
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.
Paudel, K. P. and Andersen, P.: Monitoring snow cover variability in an agropastoral area in the Trans Himalayan region of Nepal using MODIS data with improved cloud removal methodology, Remote Sens. Environ., 115, 1234–1246, https://doi.org/10.1016/j.rse.2011.01.006, 2011.
Raleigh, M. S., Rittger, K., Moore, C. E., Henn, B., Lutz, J. A., and Lundquist, J. D.: Ground-based testing of MODIS fractional snow cover in subalpine meadows and forests of the Sierra Nevada, Remote Sens. Environ., 128, 44–57, https://doi.org/10.1016/j.rse.2012.09.016, 2013.
Reuter, H. I., Nelson, A., and Jarvis, A.: An evaluation of void-filling interpolation methods for SRTM data, Int. J. Geogr. Inf. Sci., 21, 983–1008, https://doi.org/10.1080/13658810601169899, 2007.
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.
Rittger, K., Painter, T. H., and Dozier, J.: Assessment of methods for mapping snow cover from MODIS, Adv. Water Resour., 51, 367–380, https://doi.org/10.1016/j.advwatres.2012.03.002, 2013.
Rittger, K., Bair, E. H., Kahl, A., and Dozier, J.: Spatial estimates of snow water equivalent from reconstruction, Adv. Water Resour., 94, 345–363, https://doi.org/10.1016/j.advwatres.2016.05.015, 2016.
Rittger, K., Raleigh, M. S., Dozier, J., Hill, A. F., Lutz, J. A., and Painter, T. H.: Canopy Adjustment and Improved Cloud Detection for Remotely Sensed Snow Cover Mapping, Water Resour. Res., 56, e2019WR024914, https://doi.org/10.1029/2019WR024914, 2020.
Rittger, K., Bormann, K. J., Bair, E. H., Dozier, J., and Painter, T. H.: Evaluation of VIIRS and MODIS Snow Cover Fraction in High-Mountain Asia Using Landsat 8 OLI, Front. Remote Sens., 2, 1–15, https://doi.org/10.3389/frsen.2021.647154, 2021.
Roberts, D. A., Gardner, M., Church, R., Ustin, S., Scheer, G., and Green, R. O.: Mapping Chaparral in the Santa Monica Mountains Using Multiple Endmember Spectral Mixture Models, Remote Sens. Environ., 65, 267–279, https://doi.org/10.1016/S0034-4257(98)00037-6, 1998.
Rouault, E., Warmerdam, F., Schwehr, K., Kiselev, A., Butler, H., Łoskot, M., Szekeres, T., Tourigny, E., Landa, M., Miara, I., Elliston, B., Chaitanya, K., Plesea, L., Morissette, D., Jolma, A., Dawson, N., Baston, D., de Stigter, C., and Miura, H.: GDAL (v3.9.0), Zenodo [code], https://doi.org/10.5281/zenodo.11175199, 2024.
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.
Salomonson, V. V. and Appel, I.: Development of the Aqua MODIS NDSI fractional snow cover algorithm and validation results, IEEE T. Geosci. Remote Sens., 44, 1747–1756, https://doi.org/10.1109/Tgrs.2006.876029, 2006.
Selkowitz, D. J., Painter, T. H., Rittger, K. E., Schmidt, G., and Forster, R.: The USGS landsat snow covered area products: methods and preliminary validation, in: Automated Approaches for Snow and Ice Cover Monitoring Using Optical Remote Sensing, edited by: Selkowitz, D. J., The University of Utah, Salt Lake City, Utah, 76–119, https://doi.org/10.13140/RG.2.2.10347.59683, 2017.
Senan, R., Orsolini, Y. J., Weisheimer, A., Vitart, F., Balsamo, G., Stockdale, T. N., Dutra, E., Doblas-Reyes, F. J., and Basang, D.: Impact of springtime Himalayan–Tibetan Plateau snowpack on the onset of the Indian summer monsoon in coupled seasonal forecasts, Clim. Dynan., 47, 2709–2725, https://doi.org/10.1007/s00382-016-2993-y, 2016.
Shea, J. M., Menounos, B., Moore, R. D., and Tennant, C.: An approach to derive regional snow lines and glacier mass change from MODIS imagery, western North America, The Cryosphere, 7, 667–680, https://doi.org/10.5194/tc-7-667-2013, 2013.
Shi, J.: An Automatic Algorithm on Estimating Sub-Pixel Snow Cover from MODIS, Quatern. Sci., 32, 6–15, 2012.
Stillinger, T., Rittger, K., Raleigh, M. S., Michell, A., Davis, R. E., and Bair, E. H.: Landsat, MODIS, and VIIRS snow cover mapping algorithm performance as validated by airborne lidar datasets, The Cryosphere, 17, 567–590, https://doi.org/10.5194/tc-17-567-2023, 2023.
Sulla-Menashe, D., Gray, J. M., Abercrombie, S. P., and Friedl, M. A.: Hierarchical mapping of annual global land cover 2001 to present: The MODIS Collection 6 Land Cover product, Remote Sens. Environ., 222, 183–194, https://doi.org/10.1016/j.rse.2018.12.013, 2019.
Tang, B.-H., Shrestha, B., Li, Z.-L., Liu, G., Ouyang, H., Gurung, D. R., Giriraj, A., and Aung, K. S.: Determination of snow cover from MODIS data for the Tibetan Plateau region, Int. J. Appl. Earth Obs., 21, 356–365, https://doi.org/10.1016/j.jag.2012.07.014, 2013.
Tang, Z., Wang, J., Li, H., and Yan, L.: Spatiotemporal changes of snow cover over the Tibetan plateau based on cloud-removed moderate resolution imaging spectroradiometer fractional snow cover product from 2001 to 2011, J. Appl. Remote Sens., 7, 073582, https://doi.org/10.1117/1.JRS.7.073582, 2013.
Tang, Z., Wang, X., Wang, J., Wang, X., Li, H., and Jiang, Z.: Spatiotemporal Variation of Snow Cover in Tianshan Mountains, Central Asia, Based on Cloud-Free MODIS Fractional Snow Cover Product, 2001–2015, Remote Sens., 9, 1045, https://doi.org/10.3390/rs9101045, 2017.
Tang, Z., Deng, G., Hu, G., Zhang, H., Pan, H., and Sang, G.: Satellite observed spatiotemporal variability of snow cover and snow phenology over high mountain Asia from 2002 to 2021, J. Hydrol., 613, 128438, https://doi.org/10.1016/j.jhydrol.2022.128438, 2022.
Tran, H., Nguyen, P., Ombadi, M., Hsu, K., Sorooshian, S., and Qing, X.: A cloud-free MODIS snow cover dataset for the contiguous United States from 2000 to 2017, Sci. Data, 6, 180300, https://doi.org/10.1038/sdata.2018.300, 2019.
Wang, G., Jiang, L., Wu, S., Shi, J., Hao, S., and Liu, X.: Fractional Snow Cover Mapping from FY-2 VISSR Imagery of China, Remote Sens., 9, 983, https://doi.org/10.3390/rs9100983, 2017.
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 Sens., 11, 2391, https://doi.org/10.3390/rs11202391, 2019.
Wang, G., Jiang, L., Shi, J., and Su, X.: A Universal Ratio Snow Index for Fractional Snow Cover Estimation, IEEE Geosci. Remote Sens. Lett., 18, 721–725, https://doi.org/10.1109/LGRS.2020.2982053, 2021.
Wang, G., Jiang, L., Xiong, C., and Zhang, Y.: Characterization of NDSI Variation: Implications for Snow Cover Mapping, IEEE T. Geosci. Remote, 60, 1–18, https://doi.org/10.1109/TGRS.2022.3165986, 2022.
Wang, G., Jiang, L., Pan, F., Weng, H., and Zhang, Y.: Sensitivity of Snow NDSI to Simulated Snow Grain Shape Characteristics, IEEE Geosci. Remote Sens. Lett., 20, 1–5, https://doi.org/10.1109/LGRS.2022.3233379, 2023.
Wang, T., Peng, S., Lin, X., and Chang, J.: Declining snow cover may affect spring phenological trend on the Tibetan Plateau, P. Natl. Acad. Sci. USA, 110, E2854–E2855, https://doi.org/10.1073/pnas.1306157110, 2013.
Wang, X., Chen, S., and Wang, J.: An Adaptive Snow Identification Algorithm in the Forests of Northeast China, IEEE J. Sel. Top. Appl., 13, 5211–5222, https://doi.org/10.1109/JSTARS.2020.3020168, 2020.
Wu, X., Naegeli, K., Premier, V., Marin, C., Ma, D., Wang, J., and Wunderle, S.: Evaluation of snow extent time series derived from Advanced Very High Resolution Radiometer global area coverage data (1982–2018) in the Hindu Kush Himalayas, The Cryosphere, 15, 4261–4279, https://doi.org/10.5194/tc-15-4261-2021, 2021.
Xiao, X., He, T., Liang, S., Liu, X., Ma, Y., Liang, S., and Chen, X.: Estimating fractional snow cover in vegetated environments using MODIS surface reflectance data, Int. J. Appl. Earth Obs., 114, 103030, https://doi.org/10.1016/j.jag.2022.103030, 2022.
Xing, D., Hou, J., Huang, C., and Zhang, W.: Spatiotemporal Reconstruction of MODIS Normalized Difference Snow Index Products Using U-Net with Partial Convolutions, Remote Sens., 14, 1795, https://doi.org/10.3390/rs14081795, 2022.
Xu, Y., Shi, J., and Du, J.: An Improved Endmember Selection Method Based on Vector Length for MODIS Reflectance Channels, Remote Sens., 7, 6280–6295, https://doi.org/10.3390/rs70506280, 2015.
Yang, J., Jiang, L., Shi, J., Wu, S., Sun, R., and Yang, H.: Monitoring snow cover using Chinese meteorological satellite data over China, Remote Sens. Environ., 143, 192–203, https://doi.org/10.1016/j.rse.2013.12.022, 2014.
Yang, J., Jiang, L., Menard, C., 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, Y., Chen, R., Liu, G., Liu, Z., and Wang, X.: Trends and variability in snowmelt in China under climate change, Hydrol. Earth Syst. Sci., 26, 305–329, https://doi.org/10.5194/hess-26-305-2022, 2022.
Yao, T., Bolch, T., Chen, D., Gao, J., Immerzeel, W., Piao, S., Su, F., Thompson, L., Wada, Y., Wang, L., Wang, T., Wu, G., Xu, B., Yang, W., Zhang, G., and Zhao, P.: The imbalance of the Asian water tower, Nat. Rev. Earth Environ., 3, 618–632, https://doi.org/10.1038/s43017-022-00299-4, 2022.
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., Che, T., Yan, W., Ye, M., and Ma, N.: Ground-based evaluation of MODIS snow cover product V6 across China: Implications for the selection of NDSI threshold, Sci. Total Environ., 651, 2712–2726, https://doi.org/10.1016/j.scitotenv.2018.10.128, 2019.
Zhang, H., Zhang, F., Che, T., and Wang, S.: Comparative evaluation of VIIRS daily snow cover product with MODIS for snow detection in China based on ground observations, Sci. Total Environ., 724, 138156, https://doi.org/10.1016/j.scitotenv.2020.138156, 2020.
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.
Zhao, K., Peng, D., Gu, Y., Luo, X., Pang, B., and Zhu, Z.: Temperature lapse rate estimation and snowmelt runoff simulation in a high-altitude basin, Sci. Rep., 12, 13638, https://doi.org/10.1038/s41598-022-18047-5, 2022.
Zhu, J. and Shi, J.: An Algorithm for Subpixel Snow Mapping: Extraction of a Fractional Snow-Covered Area Based on Ten-Day Composited AVHRR/2 Data of the Qinghai-Tibet Plateau, IEEE Geosci. Remote Sens. Mag., 6, 86–98, https://doi.org/10.1109/mgrs.2018.2850963, 2018.
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.
It is important to strengthen the continuous monitoring of snow cover as a key indicator of...
Altmetrics
Final-revised paper
Preprint