The 30 m annual land cover dataset and its dynamics in China from 1990 to 2019

Abstract. Land cover (LC) determines the energy exchange, water and carbon cycle between Earth's spheres. Accurate LC information is a fundamental parameter for environment climate studies. Considering that in China has been altered dramatically with economic development past few decades, sequential fine-scale monitoring urgent need. However, currently, fine-resolution annual dataset produced by observational images generally unavailable due to lack of sufficient training samples computational capabilities. To deal this issue, we first Landsat-derived land (CLCD) on Google Earth Engine (GEE) platform, which contains 30 m its dynamics from 1990 2019. We collected combining stable extracted China's land-use/cover datasets (CLUDs) visually interpreted satellite time-series data, Maps. Using 335 709 Landsat GEE, several temporal metrics were constructed fed random forest classifier obtain classification results. then proposed post-processing method incorporating spatial–temporal filtering logical reasoning further improve consistency CLCD. Finally, overall accuracy CLCD reached 79.31 % based 5463 samples. A assessment 5131 third-party test showed outperforms MCD12Q1, ESACCI_LC, FROM_GLC GlobeLand30. Besides, intercompared thematic products, exhibited good consistencies Global Forest Change, Surface Water, three impervious surface products. Based CLCD, trends patterns changes during 1985 2019 revealed, such as expansion (+148.71 %) (+18.39 %), decrease cropland (?4.85 grassland (?3.29 increase (+4.34 %). In general, reflected rapid urbanization series ecological projects (e.g. Gain Green) revealed anthropogenic implications under condition change, signifying potential application global change research. The introduced article freely available at https://doi.org/10.5281/zenodo.4417810 (Yang Huang, 2021).