biostudies-literatureUnknown12(1)Li SAccurate land cover data was fundamental for formulating sound land planning and sustainable development strategies. This study focused on the Tibetan Plateau (TP), a globally sensitive ecological area, and developed a locally tailored annual 30 m resolution land cover dataset from 1990 to 2023 (TPLCD). Leveraging the Google Earth Engine (GEE) platform for Landsat data processing, LandTrendr was employed to generate robust, high-precision training samples. Subsequently, random forest classification and spatiotemporal smoothing strategies were applied to precisely map the land cover dynamics of the TP. Rigorous validation through visual interpretation, authoritative third-party datasets (Geo-Wiki and GLCVSS), and thematic dataset cross-comparisons, revealed an overall accuracy of 84.8%, and a Kappa coefficient of 0.78, fully affirming the dataset's high reliability. This dataset provided invaluable empirical evidence for understanding the vulnerability and adaptability of the TP's ecosystem.Scientific data510https://www.ebi.ac.uk/biostudies/studies/S-EPMC11950319biostudies-literatureAnnual 30 m land cover dataset on the Tibetan Plateau from 1990 to 2023.PMC11950319Tao ZJi QLi SXu DSun FLiu RGe QLiu WfalseAnnual 30 m land cover dataset on the Tibetan Plateau from 1990 to 2023.Accurate land cover data was fundamental for formulating sound land planning and sustainable development strategies. This study focused on the Tibetan Plateau (TP), a globally sensitive ecological area, and developed a locally tailored annual 30 m resolution land cover dataset from 1990 to 2023 (TPLCD). Leveraging the Google Earth Engine (GEE) platform for Landsat data processing, LandTrendr was employed to generate robust, high-precision training samples. Subsequently, random forest classification and spatiotemporal smoothing strategies were applied to precisely map the land cover dynamics of the TP. Rigorous validation through visual interpretation, authoritative third-party datasets (Geo-Wiki and GLCVSS), and thematic dataset cross-comparisons, revealed an overall accuracy of 84.8%, and a Kappa coefficient of 0.78, fully affirming the dataset's high reliability. This dataset provided invaluable empirical evidence for understanding the vulnerability and adaptability of the TP's ecosystem.2025-01-01T00:00:00Z2025 Mar2026-05-29T19:03:47.892Z2026-04-08T05:45:38.329ZS-EPMC119503194014834710.1038/s41597-025-04759-6