## 4 Conclusions and Discussions  
In this paper, a time series land cover mapping method is proposed to produce long time series of a land cover dataset with high accuracy and consistency, especially for earlier  
Figure 8: Two close-view examples of time series land covers from 1986–2015. Figure 9: The area variations for every five years since 1986 for the major land covers at HRB. years with fewer and lower quality data. The proposed method takes the advantages of time series Landsat images and the high-quality land cover datasets from the LCMM method; the high-quality land cover datasets from LCMM are used for quickly locating the accurate training samples and the RF classifier is employed to train the collected samples of each year to finally get the land cover maps. Based on the comprehensive validation, an average classification accuracy of 90.32% and an average kappa coefficient of 0.88 are achieved, which is suited for most of the applications and research at HRB. Compared to some other land cover datasets, such as LCMM, GlobeLand30 [11], and FROM-GLC30 [9], the proposed method is more applicable for long time series analysis while land process modeling because of the following advantages:  
1. It has the longest time series land cover dataset at HRB with 30 m spatial resolution, which starts from 1986. 2. It has an average classification accuracy of over 90% and has high temporal consistency, making it the best land cover map at HRB among the available ones. 3. The automatic strategy for collecting training samples from high-quality land cover maps and transferring samples to earlier years makes it efficient and accurate. Therefore, the proposed method provides a solution for making high-quality land cover maps of earlier years, even though new and high-quality data are not available. Although the new method is developed for HRB, the methodology can be extended to other regions, which is the next plan of our research. While the new method can be applied to other regions, the land cover map from LCMM needs to be made for the first time and it will take a lot of work; therefore, instead of making land cover maps from LCMM, the strategy for transferring the training samples at HRB to other regions needs to be explored in the perspective of practical use. Conceptualization, B.Z. ; Data curation, A.Y. and K.J. ; Funding acquisition, B.Z. ; Methodology, B.Z. and A.Y. ; Project administration, B.Z. ; Software, A.Y., and K.J. ; Supervision, B.Z. ; Validation, A.Y., K.J., and J.W. ; Writing--original draft, B.Z. ; Writing--review and editing, A.Y. All authors have read and agreed to the published version of the manuscript. This study was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (grant XDA20100101) and the project titled \"Advance Research Project of Civil Space Technology\" (grant D040402). Not applicable. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: Not applicable. The Landsat-TM/OLI data were downloaded from the USGS website ([[http://landsat.usgs.gov](http://landsat.usgs.gov)]([http://landsat.usgs.gov](http://landsat.usgs.gov)), accessed on 28 November 2020). The HJ-1/CCD data were downloaded from the CRESDA website (https://[[http://www.cresda.com](http://www.cresda.com)]([http://www.cresda.com](http://www.cresda.com)), accessed on 28 November 2020). The land cover dataset from 2011--2015 was downloaded from GSFC/NASA. The authors sincerely thank the reviewers who provided helpful comments for the manuscript. The authors declare no conflict of interest. References  
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