Articles | Volume 16, issue 7
https://doi.org/10.5194/essd-16-3307-2024
https://doi.org/10.5194/essd-16-3307-2024
Data description paper
 | 
19 Jul 2024
Data description paper |  | 19 Jul 2024

A 10 m resolution land cover map of the Tibetan Plateau with detailed vegetation types

Xingyi Huang, Yuwei Yin, Luwei Feng, Xiaoye Tong, Xiaoxin Zhang, Jiangrong Li, and Feng Tian

Related authors

Generation of global 1 km daily land surface – air temperature difference and sensible heat flux products from 2000 to 2020
Hui Liang, Shunlin Liang, Bo Jiang, Tao He, Feng Tian, Jianglei Xu, Wenyuan Li, Fengjiao Zhang, and Husheng Fang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-136,https://doi.org/10.5194/essd-2025-136, 2025
Preprint under review for ESSD
Short summary
A seamless global daily 5 km soil moisture product from 1982 to 2021 using AVHRR satellite data and an attention-based deep learning model
Yufang Zhang, Shunlin Liang, Han Ma, Tao He, Feng Tian, Guodong Zhang, and Jianglei Xu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-553,https://doi.org/10.5194/essd-2024-553, 2025
Preprint under review for ESSD
Short summary
UNSUPERVISED SEGMENTATION OF SMALLHOLDER FIELDS IN MOZAMBIQUE USING PLANETSCOPE IMAGERY
M. C. A. Picoli, J. Radoux, X. Tong, A. Bey, P. Rufin, M. Brandt, R. Fensholt, and P. Meyfroidt
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 975–981, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-975-2022,https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-975-2022, 2022
Impacts of the seasonal distribution of rainfall on vegetation productivity across the Sahel
Wenmin Zhang, Martin Brandt, Xiaoye Tong, Qingjiu Tian, and Rasmus Fensholt
Biogeosciences, 15, 319–330, https://doi.org/10.5194/bg-15-319-2018,https://doi.org/10.5194/bg-15-319-2018, 2018

Related subject area

Domain: ESSD – Land | Subject: Land Cover and Land Use
CCD-Rice: a long-term paddy rice distribution dataset in China at 30 m resolution
Ruoque Shen, Qiongyan Peng, Xiangqian Li, Xiuzhi Chen, and Wenping Yuan
Earth Syst. Sci. Data, 17, 2193–2216, https://doi.org/10.5194/essd-17-2193-2025,https://doi.org/10.5194/essd-17-2193-2025, 2025
Short summary
U-Surf: a global 1 km spatially continuous urban surface property dataset for kilometer-scale urban-resolving Earth system modeling
Yifan Cheng, Lei Zhao, TC Chakraborty, Keith Oleson, Matthias Demuzere, Xiaoping Liu, Yangzi Che, Weilin Liao, Yuyu Zhou, and Xinchang “Cathy” Li
Earth Syst. Sci. Data, 17, 2147–2174, https://doi.org/10.5194/essd-17-2147-2025,https://doi.org/10.5194/essd-17-2147-2025, 2025
Short summary
The Earth Topography 2022 (ETOPO 2022) global DEM dataset
Michael MacFerrin, Christopher Amante, Kelly Carignan, Matthew Love, and Elliot Lim
Earth Syst. Sci. Data, 17, 1835–1849, https://doi.org/10.5194/essd-17-1835-2025,https://doi.org/10.5194/essd-17-1835-2025, 2025
Short summary
The 20 m Africa rice distribution map of 2023
Jingling Jiang, Hong Zhang, Ji Ge, Lijun Zuo, Lu Xu, Mingyang Song, Yinhaibin Ding, Yazhe Xie, and Wenjiang Huang
Earth Syst. Sci. Data, 17, 1781–1805, https://doi.org/10.5194/essd-17-1781-2025,https://doi.org/10.5194/essd-17-1781-2025, 2025
Short summary
A 30m resolution annual cropland extent dataset of Africa in recent decades of the 21st century
Zihang Lou, Dailiang Peng, Zhou Shi, Hongyan Wang, Yaqiong Zhang, Xue Yan, Zhongxing Chen, Su Ye, Le Yu, Jinkang Hu, Yulong Lv, Hao Peng, Yizhou Zhang, and Bing Zhang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-133,https://doi.org/10.5194/essd-2025-133, 2025
Revised manuscript accepted for ESSD
Short summary

Cited articles

Abdi, A. M.: Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data, GISci. Remote Sens., 57, 1–20, https://doi.org/10.1080/15481603.2019.1650447, 2020. a, b
Agency, E. S.: Land Cover CCI Product user guide version 2, https://www.esa-landcover-cci.org/?q=webfm_send/84 (last access: 9 August  2023), 2014. a
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, https://doi.org/10.1007/978-3-030-56485-8_3, 2001. a
Brown, C. F., Brumby, S. P., Guzder-Williams, B., Birch, T., Hyde, S. B., Mazzariello, J., Czerwinski, W., Pasquarella, V. J., Haertel, R., Ilyushchenko, S., Schwehr, K., Weisse, M., Stolle, F., Hanson, C., Guinan, O., Moore, R., and Tait, A. M.: Dynamic World, Near real-time global 10 m land use land cover mapping, Sci. Data, 9, 251, https://doi.org/10.1038/s41597-022-01307-4, 2022. a
Cai, L., Wang, S., Jia, L., Wang, Y., Wang, H., Fan, D., and Zhao, L.: Consistency Assessments of the land cover products on the Tibetan Plateau, IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens., 15, 5652–5661, https://doi.org/10.1109/JSTARS.2022.3188650, 2022. a
Download
Short summary
The Tibetan Plateau, with its diverse vegetation ranging from forests to alpine grasslands, plays a key role in understanding climate change impacts. Existing maps lack detail or miss unique ecosystems. Our research, using advanced satellite technology and machine learning, produced the map TP_LC10-2022. Comparisons with other maps revealed TP_LC10-2022's excellence in capturing local variations. Our map is significant for in-depth ecological studies.
Share
Altmetrics
Final-revised paper
Preprint