ESSDD Earth System Science Data Discussions ESSDD Earth Syst. Sci. Data Discuss. 1866-3591 Göttingen, Germany 10.5194/essd-2026-628 Three decades of grassland canopy cover change in China (1990–2023) revealed by high-resolution mapping using extensive drone and Landsat data Jiang Xuanhao 1 2 3 Hu Tianyu 1 2 3 Huang Zhenying 1 2 Tang Zhiyao 4 Chen Leiyi https://orcid.org/0000-0002-3241-4676 1 2 Chen Wenli 2 5 Cheng Xiaoli 6 Cheng Yunxiang 7 Fan Yuejun 8 Gao Ruiru 9 Gao Tiangang 2 5 Gong Yanming 10 Guo Yanpei 4 11 Hu Xiaokang 12 Ji Chengjun 4 Li Liping 13 Li Shijin 14 Liu Guofang 15 Luo Wanyin 16 Ma Wenhong 17 Ma Hongyuan 7 Qiao Xiujuan 18 Song Chuangye 1 2 Wang Guoyan 19 Wang Lei 10 Wang Qinggang 20 Wang Yonghui 7 Wang Zhong https://orcid.org/0000-0003-1728-2932 21 Yang Xuejun https://orcid.org/0000-0002-8595-545X 1 2 Yang Yan https://orcid.org/0000-0003-0858-7603 22 Yang Yongchuan 23 Ye Xuehua 1 2 Yin Chunying 24 Yu Feihai 25 Zhang Pujin 26 Zhang Shuren 2 5 Zheng Chengyang 4 Zhou Luhong 27 Su Yanjun https://orcid.org/0000-0001-7931-339X 1 2 3 State Key Laboratory of Forage Breeding-by-Design and Utilization, Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China China National Botanical Garden, Beijing, 100093, China University of Chinese Academy of Sciences, Beijing 100049, China Institute of Ecology, College of Urban and Environmental Sciences, and Key Laboratory for Earth Surface Processes of the Ministry of Education, Peking University, Beijing 100871, China Key Laboratory of Systematic and Evolutionary Botany, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China State Key Laboratory of Vegetation Structure, Function and Construction and Ministry of Education Key Laboratory for Transboundary Ecosecurity of Southwest China and Key Laboratory of Soil Ecology and Health in Universities of Yunnan Province, School of Ecology and Environmental Sciences, Yunnan University, Kunming 650091, China Ministry of Education Key Laboratory of Ecology and Resource Use of the Mongolian Plateau & Inner Mongolia Key Laboratory of Grassland Ecology, School of Ecology and Environment, Inner Mongolia University, Hohhot 010021, China Qinghai Agri-animal Husbandry Vocational College, Xining 812100, China School of Life Science, Shanxi Normal University, Shanxi 030031, China Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China Yanchi Research Station, School of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, China College of Agriculture and Biological Science, Dali University, Co-Innovation Center for Cangshan Mountain and Erhai Lake Integrated Protection and Green Development of Yunnan Province, Dali 671003, China Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Beijing 100094, China Key Laboratory of Plant Resources Conservation and Sustainable Utilization, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, China College of Life and Environmental Sciences, Shaoxing University, Shaoxing 312000, China College of Aviation Meteorology, Civil Aviation Flight University of China, Sichuan 618307, China Northeast Institute of Geography and Agroecology, Chinese Academy of Science, Changchun 130102, China School of Life Science, Central China Normal University, Hubei 430079, China State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China College of Resources and Environmental Sciences, China Agricultural University, Beijing 100083, China Department of Ecology, College of Life Sciences, Wuhan University, Wuhan 430072, China Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China College of Environment & Ecology, Chongqing University, Chongqing 400045, China CAS Key Laboratory of Mountain Ecological Restoration and Bioresource Utilization & Ecological Restoration and Biodiversity Conservation Key Laboratory of Sichuan Province, Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu 610041, China Institute of Wetland Ecology & Clone Ecology/Zhejiang Provincial Key Laboratory of Evolutionary Ecology and Conservation, Taizhou University, Taizhou 310000, China Institute of Grassland Research, Inner Mongolia Academy of Agricultural and Animal Husbandry Sciences, Hohhot 010031, China Key Laboratory for Humid Subtropical Eco-geographical Processes of the Ministry of Education, School of Geographical Sciences, Fujian Normal University, Fuzhou 350007, China 10 08 2026 2026 1 36 Copyright: © 2026 Xuanhao Jiang et al. 2026 This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/ This article is available from https://essd.copernicus.org/preprints/essd-2026-628/ The full text article is available as a PDF file from https://essd.copernicus.org/preprints/essd-2026-628/essd-2026-628.pdf

Long-term, high-resolution canopy cover data are essential for understanding grassland ecosystem dynamics and informing sustainable management. However, existing products are largely limited to coarse spatial resolutions, constraining their utility for high-precision, large-scale analyses. In this study, we collected over 16,000 drone image tiles (30 m &times; 30 m) from 2,144 sites across China and developed a machine learning model to produce a spatially seamless, 30 m annual dataset of national grassland canopy cover from 1990 to 2023 by integrating drone and Landsat-series imagery. The model achieves high predictive accuracy (<em>R<sup>2</sup></em> = 0.73, RMSE = 18.4 %) and robust temporal transferability (<em>R<sup>2</sup></em> = 0.68, RMSE = 20.6 %). Comparisons with existing large-scale products demonstrated significantly improved accuracy and reduced residual artifacts, underscoring the robustness of our approach across diverse grassland types and time periods. Spatiotemporal analysis indicated a multi-decadal mean canopy cover of 43.80 &plusmn; 18.69 % across China&rsquo;s grasslands. Over the 34-year period, 41.76 % of grasslands exhibited significant increases, 57.16 % showed nonsignificant change, and 1.08 % experienced significant declines. Climatic factors&mdash;including drought, precipitation, and temperature&mdash;emerged as the dominant drivers of canopy cover dynamics at the national scale, although their effects exhibited pronounced spatial heterogeneity. In contrast, anthropogenic pressures played a secondary role overall but could override climatic influences at local scales. Collectively, these findings, together with the long-term, high-resolution canopy cover dataset developed in this study, provide an essential basis for advancing the understanding of grassland ecosystem dynamics and for supporting evidence-based conservation and sustainable management strategies, particularly under intensifying climate change and increasing frequency of extreme events. The national grassland canopy cover dataset generated in this study is archived on Zenodo and can be freely downloaded from <a href="https://doi.org/10.5281/zenodo.20301123" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.20301123</a> (Jiang et al., 2026).

 Chinese Academy of Sciences Key Project No. XDA26010101