Articles | Volume 14, issue 7
https://doi.org/10.5194/essd-14-3137-2022
© Author(s) 2022. 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-14-3137-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
STAR NDSI collection: a cloud-free MODIS NDSI dataset (2001–2020) for China
Yinghong Jing
School of Resource and Environmental Sciences, Wuhan University, Wuhan
430079, China
School of Remote Sensing and Information Engineering, Wuhan
University, Wuhan 430079, China
Huanfeng Shen
CORRESPONDING AUTHOR
School of Resource and Environmental Sciences, Wuhan University, Wuhan
430079, China
Collaborative Innovation Centre of Geospatial Technology, Wuhan
430079, China
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Die Hu, Yuan Wang, Han Jing, Linwei Yue, Qiang Zhang, Lei Fan, Qiangqiang Yuan, Huanfeng Shen, and Liangpei Zhang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-411, https://doi.org/10.5194/essd-2024-411, 2024
Preprint under review for ESSD
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The existing L-band Vegetation Optical Depth (VOD) products suffer from data gaps and coarse resolution of historical data. Our study begins with the reconstruction of missing data and then develops a spatiotemporal fusion model to generate global daily seamless 9-km L-VOD products from 2010 to 2021, which are crucial for understanding the global carbon cycle. The dataset is freely accessible for use in environmental monitoring.
Xiaobin Guan, Zhihao Sun, Dong Chu, Guanglei Xie, Yuchen Wang, and Huanfeng Shen
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-465, https://doi.org/10.5194/essd-2023-465, 2023
Manuscript not accepted for further review
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Although there are various XCO2 products, they are all limited by the spatial resolution or spatiotemporal coverage. In this study, the first global 0.05° XCO2 product (GCXCO2) for 21 years is generated by combining the OCO-2 satellite observations and models simulations. The dynamic normalization strategy is applied to enhance the temporal expansibility of stacking learning model, and the product is superior than the model simulations showing similar characteristic with OCO-2 observations.
Yonghong Zheng, Huanfeng Shen, Rory Abernethy, and Rob Wilson
Biogeosciences, 20, 3481–3490, https://doi.org/10.5194/bg-20-3481-2023, https://doi.org/10.5194/bg-20-3481-2023, 2023
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Investigations in central and western China show that tree ring inverted latewood intensity expresses a strong positive relationship with growing-season temperatures, indicating exciting potential for regions south of 30° N that are traditionally not targeted for temperature reconstructions. Earlywood BI also shows good potential to reconstruct hydroclimate parameters in some humid areas and will enhance ring-width-based hydroclimate reconstructions in the future.
Y. Tao, W. Huang, W. Gan, and H. Shen
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2022, 209–215, https://doi.org/10.5194/isprs-annals-V-3-2022-209-2022, https://doi.org/10.5194/isprs-annals-V-3-2022-209-2022, 2022
Xiaobin Guan, Huanfeng Shen, Yuchen Wang, Dong Chu, Xinghua Li, Linwei Yue, Xinxin Liu, and Liangpei Zhang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2021-156, https://doi.org/10.5194/essd-2021-156, 2021
Preprint withdrawn
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This study generated the first global 1-km continuous NDVI product (STFLNDVI) for 4-decades by fusing multi-source satellite products. Simulated and real-data assessments confirmed the satisfactory and stable accuracy of STFLNDVI regarding spatial details and temporal variations. STFLNDVI is an ideal solution to the trade-off between spatial resolution and time coverage in current NDVI products, which of great significance for long-term regional and global vegetation and climate change studies.
L. Xu, J. Yang, S. Li, and X. Li
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 695–700, https://doi.org/10.5194/isprs-annals-V-3-2020-695-2020, https://doi.org/10.5194/isprs-annals-V-3-2020-695-2020, 2020
C. Zhou, J. Li, H. Shen, and Q. Yuan
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-5-2020, 101–107, https://doi.org/10.5194/isprs-annals-V-5-2020-101-2020, https://doi.org/10.5194/isprs-annals-V-5-2020-101-2020, 2020
R. Feng, X. Li, and H. Shen
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W5, 479–484, https://doi.org/10.5194/isprs-annals-IV-2-W5-479-2019, https://doi.org/10.5194/isprs-annals-IV-2-W5-479-2019, 2019
Xinghua Li, Yinghong Jing, Huanfeng Shen, and Liangpei Zhang
Hydrol. Earth Syst. Sci., 23, 2401–2416, https://doi.org/10.5194/hess-23-2401-2019, https://doi.org/10.5194/hess-23-2401-2019, 2019
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This paper is a review article on the cloud removal methods of MODIS snow cover products.
Z. Kugler, G. Szabó, H. M. Abdulmuttalib, C. Batini, H. Shen, A. Barsi, and G. Huang
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4, 315–320, https://doi.org/10.5194/isprs-archives-XLII-4-315-2018, https://doi.org/10.5194/isprs-archives-XLII-4-315-2018, 2018
Tongwen Li, Chengyue Zhang, Huanfeng Shen, Qiangqiang Yuan, and Liangpei Zhang
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-3, 143–147, https://doi.org/10.5194/isprs-annals-IV-3-143-2018, https://doi.org/10.5194/isprs-annals-IV-3-143-2018, 2018
Zhiwei Li, Huanfeng Shen, Yancong Wei, Qing Cheng, and Qiangqiang Yuan
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-3, 149–152, https://doi.org/10.5194/isprs-annals-IV-3-149-2018, https://doi.org/10.5194/isprs-annals-IV-3-149-2018, 2018
X. Meng, H. Shen, Q. Yuan, H. Li, and L. Zhang
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W7, 831–835, https://doi.org/10.5194/isprs-archives-XLII-2-W7-831-2017, https://doi.org/10.5194/isprs-archives-XLII-2-W7-831-2017, 2017
Xinxin Liu, Huanfeng Shen, Qiangqiang Yuan, Liangpei Zhang, and Qing Cheng
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-6, 57–61, https://doi.org/10.5194/isprs-annals-III-6-57-2016, https://doi.org/10.5194/isprs-annals-III-6-57-2016, 2016
Related subject area
Domain: ESSD – Global | Subject: Meteorology
Global tropical cyclone size and intensity reconstruction dataset for 1959–2022 based on IBTrACS and ERA5 data
GloUTCI-M: a global monthly 1 km Universal Thermal Climate Index dataset from 2000 to 2022
Earth Virtualization Engines (EVE)
A global gridded dataset for cloud vertical structure from combined CloudSat and CALIPSO observations
Global datasets of hourly carbon and water fluxes simulated using a satellite-based process model with dynamic parameterizations
Global high-resolution drought indices for 1981–2022
ET-WB: water-balance-based estimations of terrestrial evaporation over global land and major global basins
GSDM-WBT: global station-based daily maximum wet-bulb temperature data for 1981–2020
The PANDA automatic weather station network between the coast and Dome A, East Antarctica
Zhiqi Xu, Jianping Guo, Guwei Zhang, Yuchen Ye, Haikun Zhao, and Haishan Chen
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-329, https://doi.org/10.5194/essd-2024-329, 2024
Revised manuscript accepted for ESSD
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Tropical cyclones (TCs) are powerful weather systems that can cause extreme disasters. Here we generate a global long-term TC size and intensity reconstruction dataset, covering a time period from 1959 to 2022, with a 3-hour temporal resolution, using machine learning model. These can be valuable for filling observational data gaps, advancing our understanding of TC climatology, thereby facilitating risk assessments and defenses against TC-related disasters.
Zhiwei Yang, Jian Peng, Yanxu Liu, Song Jiang, Xueyan Cheng, Xuebang Liu, Jianquan Dong, Tiantian Hua, and Xiaoyu Yu
Earth Syst. Sci. Data, 16, 2407–2424, https://doi.org/10.5194/essd-16-2407-2024, https://doi.org/10.5194/essd-16-2407-2024, 2024
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We produced a monthly Universal Thermal Climate Index dataset (GloUTCI-M) boasting global coverage and an extensive time series spanning March 2000 to October 2022 with a high spatial resolution of 1 km. This dataset is the product of a comprehensive approach leveraging multiple data sources and advanced machine learning models. GloUTCI-M can enhance our capacity to evaluate thermal stress experienced by the human, offering substantial prospects across a wide array of applications.
Bjorn Stevens, Stefan Adami, Tariq Ali, Hartwig Anzt, Zafer Aslan, Sabine Attinger, Jaana Bäck, Johanna Baehr, Peter Bauer, Natacha Bernier, Bob Bishop, Hendryk Bockelmann, Sandrine Bony, Guy Brasseur, David N. Bresch, Sean Breyer, Gilbert Brunet, Pier Luigi Buttigieg, Junji Cao, Christelle Castet, Yafang Cheng, Ayantika Dey Choudhury, Deborah Coen, Susanne Crewell, Atish Dabholkar, Qing Dai, Francisco Doblas-Reyes, Dale Durran, Ayoub El Gaidi, Charlie Ewen, Eleftheria Exarchou, Veronika Eyring, Florencia Falkinhoff, David Farrell, Piers M. Forster, Ariane Frassoni, Claudia Frauen, Oliver Fuhrer, Shahzad Gani, Edwin Gerber, Debra Goldfarb, Jens Grieger, Nicolas Gruber, Wilco Hazeleger, Rolf Herken, Chris Hewitt, Torsten Hoefler, Huang-Hsiung Hsu, Daniela Jacob, Alexandra Jahn, Christian Jakob, Thomas Jung, Christopher Kadow, In-Sik Kang, Sarah Kang, Karthik Kashinath, Katharina Kleinen-von Königslöw, Daniel Klocke, Uta Kloenne, Milan Klöwer, Chihiro Kodama, Stefan Kollet, Tobias Kölling, Jenni Kontkanen, Steve Kopp, Michal Koran, Markku Kulmala, Hanna Lappalainen, Fakhria Latifi, Bryan Lawrence, June Yi Lee, Quentin Lejeun, Christian Lessig, Chao Li, Thomas Lippert, Jürg Luterbacher, Pekka Manninen, Jochem Marotzke, Satoshi Matsouoka, Charlotte Merchant, Peter Messmer, Gero Michel, Kristel Michielsen, Tomoki Miyakawa, Jens Müller, Ramsha Munir, Sandeep Narayanasetti, Ousmane Ndiaye, Carlos Nobre, Achim Oberg, Riko Oki, Tuba Özkan-Haller, Tim Palmer, Stan Posey, Andreas Prein, Odessa Primus, Mike Pritchard, Julie Pullen, Dian Putrasahan, Johannes Quaas, Krishnan Raghavan, Venkatachalam Ramaswamy, Markus Rapp, Florian Rauser, Markus Reichstein, Aromar Revi, Sonakshi Saluja, Masaki Satoh, Vera Schemann, Sebastian Schemm, Christina Schnadt Poberaj, Thomas Schulthess, Cath Senior, Jagadish Shukla, Manmeet Singh, Julia Slingo, Adam Sobel, Silvina Solman, Jenna Spitzer, Philip Stier, Thomas Stocker, Sarah Strock, Hang Su, Petteri Taalas, John Taylor, Susann Tegtmeier, Georg Teutsch, Adrian Tompkins, Uwe Ulbrich, Pier-Luigi Vidale, Chien-Ming Wu, Hao Xu, Najibullah Zaki, Laure Zanna, Tianjun Zhou, and Florian Ziemen
Earth Syst. Sci. Data, 16, 2113–2122, https://doi.org/10.5194/essd-16-2113-2024, https://doi.org/10.5194/essd-16-2113-2024, 2024
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To manage Earth in the Anthropocene, new tools, new institutions, and new forms of international cooperation will be required. Earth Virtualization Engines is proposed as an international federation of centers of excellence to empower all people to respond to the immense and urgent challenges posed by climate change.
Leah Bertrand, Jennifer E. Kay, John Haynes, and Gijs de Boer
Earth Syst. Sci. Data, 16, 1301–1316, https://doi.org/10.5194/essd-16-1301-2024, https://doi.org/10.5194/essd-16-1301-2024, 2024
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The vertical structure of clouds has a major impact on global energy flows, air circulation, and the hydrologic cycle. Two satellite instruments, CloudSat radar and CALIPSO lidar, have taken complementary measurements of cloud vertical structure for over a decade. Here, we present the 3S-GEOPROF-COMB product, a globally gridded satellite data product combining CloudSat and CALIPSO observations of cloud vertical structure.
Jiye Leng, Jing M. Chen, Wenyu Li, Xiangzhong Luo, Mingzhu Xu, Jane Liu, Rong Wang, Cheryl Rogers, Bolun Li, and Yulin Yan
Earth Syst. Sci. Data, 16, 1283–1300, https://doi.org/10.5194/essd-16-1283-2024, https://doi.org/10.5194/essd-16-1283-2024, 2024
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We produced a long-term global two-leaf gross primary productivity (GPP) and evapotranspiration (ET) dataset at the hourly time step by integrating a diagnostic process-based model with dynamic parameterizations. The new dataset provides us with a unique opportunity to study carbon and water fluxes at sub-daily time scales and advance our understanding of ecosystem functions in response to transient environmental changes.
Solomon H. Gebrechorkos, Jian Peng, Ellen Dyer, Diego G. Miralles, Sergio M. Vicente-Serrano, Chris Funk, Hylke E. Beck, Dagmawi T. Asfaw, Michael B. Singer, and Simon J. Dadson
Earth Syst. Sci. Data, 15, 5449–5466, https://doi.org/10.5194/essd-15-5449-2023, https://doi.org/10.5194/essd-15-5449-2023, 2023
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Drought is undeniably one of the most intricate and significant natural hazards with far-reaching consequences for the environment, economy, water resources, agriculture, and societies across the globe. In response to this challenge, we have devised high-resolution drought indices. These indices serve as invaluable indicators for assessing shifts in drought patterns and their associated impacts on a global, regional, and local level facilitating the development of tailored adaptation strategies.
Jinghua Xiong, Abhishek, Li Xu, Hrishikesh A. Chandanpurkar, James S. Famiglietti, Chong Zhang, Gionata Ghiggi, Shenglian Guo, Yun Pan, and Bramha Dutt Vishwakarma
Earth Syst. Sci. Data, 15, 4571–4597, https://doi.org/10.5194/essd-15-4571-2023, https://doi.org/10.5194/essd-15-4571-2023, 2023
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To overcome the shortcomings associated with limited spatiotemporal coverage, input data quality, and model simplifications in prevailing evaporation (ET) estimates, we developed an ensemble of 4669 unique terrestrial ET subsets using an independent mass balance approach. Long-term mean annual ET is within 500–600 mm yr−1 with a unimodal seasonal cycle and several piecewise trends during 2002–2021. The uncertainty-constrained results underpin the notion of increasing ET in a warming climate.
Jianquan Dong, Stefan Brönnimann, Tao Hu, Yanxu Liu, and Jian Peng
Earth Syst. Sci. Data, 14, 5651–5664, https://doi.org/10.5194/essd-14-5651-2022, https://doi.org/10.5194/essd-14-5651-2022, 2022
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We produced a new dataset of global station-based daily maximum wet-bulb temperature (GSDM-WBT) through the calculation of wet-bulb temperature, data quality control, infilling missing values, and homogenization. The GSDM-WBT covers the complete daily series of 1834 stations from 1981 to 2020. The GSDM-WBT dataset handles stations with many missing values and possible inhomogeneities, which could better support the studies on global and regional humid heat events.
Minghu Ding, Xiaowei Zou, Qizhen Sun, Diyi Yang, Wenqian Zhang, Lingen Bian, Changgui Lu, Ian Allison, Petra Heil, and Cunde Xiao
Earth Syst. Sci. Data, 14, 5019–5035, https://doi.org/10.5194/essd-14-5019-2022, https://doi.org/10.5194/essd-14-5019-2022, 2022
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The PANDA automatic weather station (AWS) network consists of 11 stations deployed along a transect from the coast (Zhongshan Station) to the summit of the East Antarctic Ice Sheet (Dome A). It covers the different climatic and topographic units of East Antarctica. All stations record hourly air temperature, relative humidity, air pressure, wind speed and direction at two or three heights. The PANDA AWS dataset commences from 1989 and is planned to be publicly available into the future.
Cited articles
Aalstad, K., Westermann, S., and Bertino, L.: Evaluating satellite
retrieved fractional snow-covered area at a high-Arctic site using
terrestrial photography, Remote Sens. Environ., 239, 111618, https://doi.org/10.1016/j.rse.2019.111618, 2020.
Akyurek, Z., Hall, D. K., Riggs, G. A., and Sensoy, A.: Evaluating the
utility of the ANSA blended snow cover product in the mountains of eastern
Turkey, Int. J. Remote Sens., 31, 3727–3744, https://doi.org/10.1080/01431161.2010.483484, 2010.
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.
Bormann, K. J., Brown, R. D., Derksen, C., and Painter, T. H.: Estimating
snow-cover trends from space, Nat. Clim. Change, 8, 923–927, https://doi.org/10.1038/s41558-018-0318-3, 2018.
Brown, R., Derksen, C., and Wang, L. B.: A multi-data set analysis of
variability and change in Arctic spring snow cover extent, 1967–2008,
J. Geophys. Res.-Atmos., 115, D16111, https://doi.org/10.1029/2010jd013975, 2010.
Che, T., Dai, L. Y., Zheng, X. M., Li, X. F., and Zhao, K.: Estimation of
snow depth from passive microwave brightness temperature data in forest
regions of northeast China, Remote Sens. Environ., 183, 334–349, https://doi.org/10.1016/j.rse.2016.06.005, 2016.
Che, T., Li, X., Liu, S., Li, H., Xu, Z., Tan, J., Zhang, Y., Ren, Z., Xiao, L., Deng, J., Jin, R., Ma, M., Wang, J., and Yang, X.: Integrated hydrometeorological, snow and frozen-ground observations in the alpine region of the Heihe River Basin, China, Earth Syst. Sci. Data, 11, 1483–1499, https://doi.org/10.5194/essd-11-1483-2019, 2019.
Chen, S., Wang, X., Guo, H., Xie, P., and Sirelkhatim, A. M.: Spatial and
temporal adaptive gap-filling method producing daily cloud-free NDSI time
series, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 13, 2251–2263,
https://doi.org/10.1109/JSTARS.2020.2993037, 2020.
Chen, X. N., Long, D., Liang, S. L., He, L., Zeng, C., Hao, X. H., 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, 2018.
Çiftçi, B. B., Kuter, S., Akyürek, Z., and Weber, G. W.:
Fractional snow cover mapping by artificial neural networks and support
vector machines, ISPRS Annals of the Photogrammetry, Remote Sensing and
Spatial Information Sciences, 4, 179–187, https://doi.org/10.5194/isprs-annals-IV-4-W4-179-2017, 2017.
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.
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.
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.
Gafurov, A., Vorogushyn, S., Farinotti, D., Duethmann, D., Merkushkin, A., and Merz, B.: Snow-cover reconstruction methodology for mountainous regions based on historic in situ observations and recent remote sensing data, The Cryosphere, 9, 451–463, https://doi.org/10.5194/tc-9-451-2015, 2015.
Gao, J., Williams, M. W., Fu, X. D., Wang, G. Q., and Gong, T. L.:
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.
Gao, Y., Xie, H. J., and Yao, T. D.: Developing Snow Cover Parameters Maps
from MODIS, AMSR-E, and Blended Snow Products, Photogramm. Eng. Remote
Sens., 77, 351–361, https://doi.org/10.14358/pers.77.4.351,
2011.
Gladkova, I., Grossberg, M., Bonev, G., Romanov, P., and Shahriar, F.:
Increasing the accuracy of MODIS/Aqua snow product using quantitative image
restoration technique, IEEE Geosci. Remote Sens. Lett., 9, 740–743,
https://doi.org/10.1109/LGRS.2011.2180505, 2012.
Hall, D. K. and Riggs, G. A.: MODIS/Terra CGF Snow Cover Daily L3 Global 500m SIN Grid, Version 61, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/MODIS/MOD10A1F.061, 2020.
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.
Hall, D. K., Riggs, G. A., DiGirolamo, N. E., and Román, M. O.: Evaluation of MODIS and VIIRS cloud-gap-filled snow-cover products for production of an Earth science data record, Hydrol. Earth Syst. Sci., 23, 5227–5241, https://doi.org/10.5194/hess-23-5227-2019, 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.
He, G. J., Feng, X. Z., Xiao, P. F., Xia, Z. H., Wang, Z., Chen, H., Li, H.,
and Guo, J. J.: Dry and Wet Snow Cover Mapping in Mountain Areas Using SAR
and Optical Remote Sensing Data, IEEE J. Sel. Top. Appl. Earth Obs. Remote
Sens., 10, 2575–2588, https://doi.org/10.1109/jstars.2017.2673409, 2017.
Hou, J. and Huang, C.: Improving mountainous snow cover fraction mapping
via artificial neural networks combined with MODIS and ancillary topographic
data, IEEE T. Geosci. Remote, 52, 5601–5611, https://doi.org/10.1109/TGRS.2013.2290996, 2014.
Hou, J., Huang, C., Zhang, Y., and Guo, J.: On the Value of Available MODIS
and Landsat8 OLI Image Pairs for MODIS Fractional Snow Cover Mapping Based
on an Artificial Neural Network, IEEE T. Geosci. Remote, 58,
4319–4334, https://doi.org/10.1109/TGRS.2019.2963075, 2020.
Huang, X.: MODIS daily cloudless binary snow products in Northern Hemisphere from 2000 to 2015, National Cryosphere Desert Data Center [data set], https://doi.org/10.12072/ncdc.CCI.db0044.2020, 2020.
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. X., Yu, B. L., We, J. P., Kang, E. L., Xu, M., Wang, S.
J., Klein, A., and Chen, Y. N.: 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.
Jing, Y., Shen, H., Li, X., and Guan, X.: 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.
Jing, Y. H.: A spatio-temporal adaptive fusion method with error correction
for cloud-free MODIS NDSI estimation (Version 01), Zenodo [code],
https://doi.org/10.5281/zenodo.6396149, 2022.
Jing, Y. H., Li, X. H., and Shen, H. F.: STAR NDSI collection: A cloud-free
MODIS NDSI dataset (2001–2020) for China (Version 01), Zenodo [data set],
https://doi.org/10.5281/zenodo.5644386, 2021.
Klein, A. G. and Barnett, A. C.: 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.
Konzelmann, T. and Ohmura, A.: Radiative fluxes and their impact on the
energy-balance of the Greenland ice-sheet, J. Glaciol., 41, 490–502,
https://doi.org/10.3189/s0022143000034833, 1995.
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.
Kuter, S., Akyurek, Z., and Weber, G. W.: Retrieval of fractional snow
covered area from MODIS data by multivariate adaptive regression splines,
Remote Sens. Environ., 205, 236–252, https://doi.org/10.1016/j.rse.2017.11.021, 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, X. H., Fu, W. X., Shen, H. F., Huang, C. L., and Zhang, L. P.:
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.
Liang, H., Huang, X. D., Sun, Y. H., Wang, Y. L., and Liang, T. G.:
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.
Malmros, J. K., Mernild, S. H., Wilson, R., Tagesson, T., and Fensholt, R.:
Snow cover and snow-albedo changes in the central Andes of Chile and
Argentina from daily MODIS observations (2000–2016), Remote Sens. Environ.,
209, 240–252, https://doi.org/10.1016/j.rse.2018.02.072, 2018.
Moosavi, V., Malekinezhad, H., and Shirmohammadi, B.: Fractional snow cover
mapping from MODIS data using wavelet-artificial intelligence hybrid models,
J. Hydrol., 511, 160–170, https://doi.org/10.1016/j.jhydrol.2014.01.015, 2014.
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.
Muhammad, S. and Thapa, A.: Daily Terra–Aqua MODIS cloud-free snow and Randolph Glacier Inventory 6.0 combined product (M*D10A1GL06) for high-mountain Asia between 2002 and 2019, Earth Syst. Sci. Data, 13, 767–776, https://doi.org/10.5194/essd-13-767-2021, 2021.
National Meteorological Information Center and Tibet Meteorological Bureau, China: Observational snow depth dataset of the Tibetan Plateau (Version 1.0) (1961–2013), National Tibetan Plateau Data Center [data set], https://doi.org/10.11888/Snow.tpdc.270558, 2018.
Pachauri, R., Meyer, L., Plattner, G., and Stocker, T.: Synthesis report. Contribution of working groups I, II and III to the fifth assessment report of the intergovernmental panel on climate change, Intergovernmental Panel on Climate Change, Geneva, Switzerland, https://epic.awi.de/id/eprint/37530/1/IPCC_AR5_SYR_Final.pdf (last access: 6 July 2022), 2014.
Parajka, J. and Bloschl, 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., Pepe, M., Rampini, A., Rossi, S., and Bloschl, G.: A regional
snow-line method for estimating snow cover from MODIS during cloud cover, J.
Hydrol., 381, 203–212, https://doi.org/10.1016/j.jhydrol.2009.11.042, 2010.
Qiu, Y., Wang, X., Han, L., Chang, L., and Shi, L.: Daily Fractional Snow Cover (FSC) Data set over High Asia, Science Data Bank [data set], https://doi.org/10.11922/sciencedb.457, 2017.
Riggs, G. A., Hall, D. K., and Román, M. O.: MODIS snow products collection 6 user guide, National Snow and Ice Data Center: Boulder, CO, USA, 66, https://modis-snow-ice.gsfc.nasa.gov/uploads/snow_user_guide_C6.1_final_revised_april.pdf (last access: 6 July 2022), 2015.
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.
Sibson, R.: A brief description of natural neighbor interpolation, chap.
2, in: Interpolating Multivariate Data, edited by: Barnett, V., John Wiley,
Chichester, 21–36, 1981.
Tang, Z. G., Wang, J., Li, H. Y., and Yan, L. 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.
Tong, R., Parajka, J., Komma, J., and Blöschl, G.: Mapping snow cover
from daily Collection 6 MODIS products over Austria, J. Hydrol., 590,
125548, https://doi.org/10.1016/j.jhydrol.2020.125548, 2020.
Wang, J., Che, T., Li, Z., Li, H., Hao, X., Zheng, Z., Xiao, P., Li, X.,
Huang, X., and Zhong, X.: Investigation on Snow Characteristics and Their
Distribution in China, Adv. Earth Sci., 33, 12–16, https://doi.org/10.11867/j.issn.1001-8166.2018.01.0012, 2018.
Wang, X. W., Xie, H. J., and Liang, T. G.: Evaluation of MODIS snow cover
and cloud mask and its application in Northern Xinjiang, China, Remote Sens.
Environ., 112, 1497–1513, https://doi.org/10.1016/j.rse.2007.05.016, 2008.
Yu, J. Y., Zhang, G. Q., Yao, T. D., Xie, H. J., Zhang, H. B., Ke, C. Q.,
and Yao, R. Z.: 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.
Yuan, Q., Shen, H., Li, T., Li, Z., Li, S., Jiang, Y., Xu, H., Tan, W.,
Yang, Q., Wang, J., Gao, J., and Zhang, L.: Deep learning in environmental
remote sensing: Achievements and challenges, Remote Sens. Environ., 241,
111716, https://doi.org/10.1016/j.rse.2020.111716, 2020.
Zhang, G., Xie, H., Yao, T., Liang, T., and Kang, S.: Snow cover dynamics of
four lake basins over Tibetan Plateau using time series MODIS data
(2001–2010), Water Resour. Res., 48, W10529, https://doi.org/10.1029/2012WR011971, 2012.
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., 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.
Short summary
Snow variation is a vital factor in global climate change. Satellite-based approaches are effective for large-scale environmental monitoring. Nevertheless, the high cloud fraction seriously impedes the remote-sensed investigation. Therefore, a recent 20-year cloud-free snow cover collection in China is generated for the first time. This collection can serve as a basic dataset for hydrological and climatic modeling to explore various critical environmental issues.
Snow variation is a vital factor in global climate change. Satellite-based approaches are...
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