## 1 Introduction  
With population growth, economic development, and various factors, land cover information has been identified as one of the key data components in many aspects of global change research and environmental applications [1, 2]. Large-scale land cover classification and mapping provides a source of data for many of the research works on global change and is an important input variable to global change models (such as net productivity models, ecosystem metabolic models, and carbon cycle models). Most global change models need to be supported by large areas of land cover information [3, 4]. At the same time, the speed and magnitude of land changes are constantly changing with time and space. For two maps covering long time spans, there is a lack of corresponding process information, and the long time series of land cover datasets can capture the complexity of ground changes [5] while quantifying these changes. Therefore, long time series land cover classification data are of great significance for land change monitoring [6], identification, and planning assessment [7, 8]. With the development of remote sensing technology, the quantity and quality of remote sensing data are also constantly increasing, and the data can be used for geographic area monitoring around the clock [9; 10]. Therefore, this study of a wide range of long time series land cover classifications can be carried out well. Many countries and international organizations have used different image processing techniques and data, such as Landsat, SPOT, Advanced Very High Resolution Radiometer (AVHRR) and Moderate-resolution Imaging Spectroradiometer (MODIS) data, to conduct land cover research at the regional, intercontinental and global scales. Global and regional land cover classification products have been researched and produced by several countries and institutions [11; 12]. There are still some shortcomings in the currently existing classified datasets. First, the current global land cover datasets are mostly concentrated after 2000. Categorical data before 2000, especially time-continuous products, are scarce due to the lack of continuous time series data [13; 14]. In addition, the current database for this study of land cover classification generally uses surface reflectance data [15; 16] or the vegetation index (VI) obtained from reflectance data [13; 17]. The data categories are relatively singular and lack surface feature information. Different land types have different reflection features, which is the basis for the use of reflectivity for land cover classification [18]. The similarity of the vegetation spectrum will cause the confusion of categories when using the reflectance for vegetation classification. One solution is to use time series data for land cover classification, which has been accepted by many researchers and has yielded good results [19; 20]. Over the past three decades, remote sensing satellite observations have produced a large number of time series remote sensing products, which has made it possible to obtain long time series land cover classifications from up to 2000 years ago, such as Global LAnd Surface Satellite (GLASS) [21]. Global LAnd Surface Satellite (GLASS) generates a range of geophysical, physical and chemical parameter values using multi-source data and multi-algorithm integration. Many quantitative remote sensing products have been produced such as albedo (Albedo) [22], evapotranspiration index (ET) [23], the leaf area index (LAI) [24], gross primary productivity (GPP) [25], and fraction of absorbed photosynthetically active radiation (FAPAR) [26]. These products cover the years 1982-2017 at 1-5 km and 8-day resolutions. These products do not just contain a large amount of land feature information such as vegetation cover, photosynthetic absorption, surface reflection, radiation emission, latent heat flux and biomass, but also can extract interannual variation of surface features. Therefore, they are highly suitable for long time series land cover classification.