## 1 Introduction  
Land cover changes (LCCs) are the result of human activities and natural evolution and LCC has great impacts on the climate system and ecology [1]; it is subsequently an important factor in studying environmental changes and climate change. Therefore, a better understanding of LCC is necessary to provide a reference for evaluating the vulnerability of carbon and water cycles [2, 3] and other ecosystem processes related to global or regional change [4]. Especially, long time series of land cover maps have been available from long-term land cover mapping based on the sequence of remote sensing detection, which has provided more information on land change in the analysis of long-term climate, environmental, and ecological changes. Due to its capability of global coverage within a short period, remote sensing has become widely used for global land cover mapping, as it can capture the changes of land cover quickly. Until now, several moderate to fine resolution global land cover datasets have been publicly released [5; 6; 7; 8; 9; 10; 11; 12] and the spatial resolutions of these products are between 1 km and 30 m. They show strong consistency at the global scale, but they have large deviations at the regional scale. The main problems include: (1) most of the land cover datasets have a relatively low resolution (500 m and lower) and their accuracies are usually lower than 75% [8; 13]; among the released datasets, GlobeLand30 [14] and FROM-GLC30 [9] have a spatial resolution of 30 m, and their overall accuracies are 80% and 72% respectively; (2) the classification accuracy of these released datasets at the regional scale is much lower than the claimed accuracy, especially at heterogeneous areas, which is difficult to meet the modeling requirements, and (3) all the datasets are after 2000 and do not have long time series and therefore cannot support the analysis of long-term climate, environmental, and ecological changes. Recently, satellite constellations and satellite-loaded sensors with large swath, such as Sentinel-2, FORMOSAT-2, Chinese Huanjing-1, and GF-1, have been launched and subsequently have the capability to scan the whole globe every few days at high resolution; hence time series of remote sensing images are increasingly available. Some regional land cover maps with an accuracy of over 90% have been made [3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18] by employing time series analysis on satellite images. Furthermore, the authors of [19] developed a Land Cover mapping method by using multi-classifiers and multisource remotely sensed imagery (LCMM) by using HJ-1/CCD time series of images to make a finer land cover map at Heihe River Basin (HRB) from 2011-2015 [19]. However, satellite image time series were not available before 2011. Therefore, long time series of land cover datasets are in great demand but still unavailable, even at the regional scale. In order to make long time series of land cover datasets of high spatial resolution to support the analysis of long-term climate, environmental, and ecological changes, the Landsat series of satellite imagery is the only choice. It is composed of eight satellites and can be traced back to 1972. It provides data support for continuous detection of the global surface and is of great significance; furthermore, it is freely available [20] and the emergence of new computing tools, such as Google Earth Engine (GEE), provides powerful cloud computing capabilities [21]. However, the 16-day revisiting period makes it very difficult to construct time series data, so the methods based on time series analysis, such as LCMM, cannot be employed to make a high-quality land cover map, especially at early stages. In recent years, machine learning (ML) algorithms, such as random forest tree (RFT), support vector machine (SVM), and neural network (NN), in remote sensing land cover classification have been greatly improved in both efficiency and accuracy with the increase of computation capability and the technological development of artificial intelligence.