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 the environment and climate studies. Considering

that the LC in China has been altered dramatically with the economic

development in the past few decades, sequential and fine-scale LC monitoring

is in urgent need. However, currently, fine-resolution annual LC dataset

produced by the observational images is generally unavailable for China due

to the lack of sufficient training samples and computational capabilities.

To deal with this issue, we produced the first Landsat-derived annual China

land cover dataset (CLCD) on the Google Earth Engine (GEE) platform, which

contains 30 m annual LC and its dynamics in China from 1990 to 2019. We

first collected the training samples by combining stable samples extracted

from China's land-use/cover datasets (CLUDs) and visually interpreted

samples from satellite time-series data, Google Earth and Google Maps. Using

335 709 Landsat images on the GEE, several temporal metrics were constructed

and fed to the random forest classifier to obtain classification results. We

then proposed a post-processing method incorporating spatial–temporal

filtering and logical reasoning to further improve the spatial–temporal

consistency of CLCD. Finally, the overall accuracy of CLCD reached 79.31 %

based on 5463 visually interpreted samples. A further assessment based on

5131 third-party test samples showed that the overall accuracy of CLCD

outperforms that of MCD12Q1, ESACCI_LC, FROM_GLC and GlobeLand30. Besides, we intercompared the CLCD with several

Landsat-derived thematic products, which exhibited good consistencies with

the Global Forest Change, the Global Surface Water, and three impervious

surface products. Based on the CLCD, the trends and patterns of China's LC

changes during 1985 and 2019 were revealed, such as expansion of impervious

surface (+148.71 %) and water (+18.39 %), decrease in cropland

(−4.85 %) and grassland (−3.29 %), and increase in forest (+4.34 %). In

general, CLCD reflected the rapid urbanization and a series of ecological

projects (e.g. Gain for Green) in China and revealed the anthropogenic

implications on LC under the condition of climate change, signifying its

potential application in the global change research. The CLCD dataset

introduced in this article is freely available at