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.
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.
Yanghai Yu, Yang Lei, Paul Siqueira, Xiaotong Liu, Denuo Gu, Anmin Fu, Yong Pang, Wenli Huang, and Jiancheng Shi
Earth Syst. Sci. Data, 17, 4397–4429, https://doi.org/10.5194/essd-17-4397-2025, https://doi.org/10.5194/essd-17-4397-2025, 2025
Short summary
Short summary
This study proposes a global-to-local approach for estimating forest height by fusing repeat-pass synthetic aperture radar interferometry and Global Ecosystem Dynamics Investigation (GEDI) data. Using Advanced Land Observing Satellite (ALOS-1) data and a twofold strategy to address temporal gaps, the method produced 30 m gridded forest height mosaics for the northeastern United States and China, demonstrating promising accuracies and offering potential for fusing data from future missions.
Qixiang Sun, Dabin Ji, Husi Letu, Yongqian Wang, Peng Zhang, Hong Liang, Chong Shi, Shuai Yin, and Jiancheng Shi
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-365, https://doi.org/10.5194/essd-2025-365, 2025
Preprint under review for ESSD
Short summary
Short summary
The Tibetan Plateau plays a vital role in Asia’s water cycle, but tracking water vapor in this mountainous region is difficult, especially under cloudy conditions. We developed a new satellite-based method to generate hourly water vapor data at 0.02-degree resolution from 2016 to 2022, now available at https://data.tpdc.ac.cn/en/data/4bb3c256-3cdb-4373-9924-f7ac16ddc717, which improves accuracy and reveals fine-scale moisture transport critical for understanding rainfall and extreme weather.
Jingtian Zhou, Yang Lei, Jinmei Pan, Cunren Liang, Yunjun Zhang, Weiliang Li, Chuan Xiong, and Jiancheng Shi
EGUsphere, https://doi.org/10.5194/egusphere-2025-2329, https://doi.org/10.5194/egusphere-2025-2329, 2025
Short summary
Short summary
Understanding how much water is stored in snow is important for tracking climate change and managing water supply. This study used satellite radar data from 2019 to 2021 to measure snow water changes in a mountain region of China. The results matched ground data well, especially in cold, dry conditions without heavy snowfall. A new phase calibration method helped improve accuracy, offering a useful reference for global snow monitoring using widely available satellite data.
Defeng Feng, Tianjie Zhao, Jingyao Zheng, Yu Bai, Youhua Ran, Xiaokang Kou, Lingmei Jiang, Ziqian Zhang, Pei Yu, Jinbiao Zhu, Jie Pan, Jiancheng Shi, and Yuei-An Liou
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-62, https://doi.org/10.5194/essd-2025-62, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
This study introduces a downscaling approach that integrates passive microwave and optical satellite data to generate a long-term (2002–2023), high-resolution (0.05°) global near-surface FT state dataset, ensuring daily seamless continuity. The dataset achieves an overall accuracy of 83.78%, consistent with the microwave-based dataset while offering enhanced spatial detail. This record providing detailed FT information, enhancing the understanding of hydrological and ecological impacts globally.
Jiajie Ying, Lingmei Jiang, Jinmei Pan, Chuan Xiong, and Jianwei Yang
EGUsphere, https://doi.org/10.5194/egusphere-2025-276, https://doi.org/10.5194/egusphere-2025-276, 2025
Short summary
Short summary
The Sentinel-1-based C-snow product has been widely used as reference data across various scales, but its reliability remains unknown. This study systematically evaluates its performance at 1, 10, and 25 km scales using ground-based measurements and airborne LiDAR data. The results show that performance is influenced by factors such as forest fraction, DEM, permanent ice, and wet snow. We also identify scale patterns differences compared to station and airborne datasets and explore the reasons.
Yixiao Fu, Cheng-Zhi Zou, Peng Zhang, Banghai Wu, Shengli Wu, Shi Liu, and Yu Wang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-608, https://doi.org/10.5194/essd-2024-608, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
This study presents a climate data record (CDR) of atmospheric column water vapor and sea surface temperature using over two decades of stable-orbit satellite-based passive microwave imagery observations. The evaluation results show that the CDR has long-term consistency and continuity, and is more accurate than other similar products in climate covariability, suggesting that the CDR is suitable for climate change research and for constraining climate model simulations.
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
Revised manuscript not accepted
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.
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