## 4 Conclusions and Discussion  
In this paper, a time series land cover mapping method is proposed to produce an annual land cover dataset at 30 m resolution with high temporal consistency for the years before 1990 for which it is very difficult to retrieve 30 m satellite data. The proposed method employs the SRGAN model to construct an SR network to improve the 5 km NDVI from AVHRR to 250 m NDVI in the first place; subsequently, the BFAST change detection algorithm was used to analyze the relationship between the variations from the annual 250 m NDVI based on SR and the CNLUCC land cover datasets to quickly update the annual land cover datasets. Compared with the CLUD-A datasets at the same period, the overall accuracy in 1986 is improved by 3.01%. Based on the comprehensive validation from model, spatial, temporal and change perspectives, the proposed method is applicable for long time series analysis and land process modeling because of the following advantages:  
1. It is able to provide an early annual land cover dataset dating back to the 1980s with 30 m spatial resolution even though only ARHRR data were available. Furthermore, the annual land cover dataset can effectively reflect the yearly variation of land cover, which is vital for land processing modeling and managing ecology and water resources at early years without higher resolution observations. 2. The super-resolution experiments not only enhance spatial resolution but also enrich the spatial details and texture perception features. The SRGAN model overcomes the issue of all change pixels within a coarse resolution pixel being considered as changes in the same year during BFAST breakpoint detection, which is typically encountered when directly resampling low-resolution images to high resolution. 3. The results of the BFAST change detection demonstrate that the algorithm is capable of accurately extracting abrupt changes in the NDVI time series. This capability can be extended to other regions with multiple land cover datasets at 5/10 year intervals, allowing for the generation of annual land cover products for further scientific research applications. Given our previous experimental foundation in the Qilian Mountains, coupled with the region's diverse vegetation, climate types, and comprehensive topographical features, this study employs a combination of SRGAN and BFAST methods for testing and validation in this area. The methodology relies solely on experimental data (AVHRR, MODIS, and discrete land cover datasets) and does not require prior knowledge, making it applicable to other regions. However, some of the following situations also need to be considered: First, regarding data availability, AVHRR data are globally accessible with daily coverage, but early historical data may suffer from cloud cover and data gaps. To address these quality issues, Section 2.2 of the paper describes a preprocessing method involving cloud masking and spatiotemporal data filtering to fill in the missing parts, effectively removing cloud cover and filling data gaps, which can be extended to larger areas [25]. More importantly, this prototype can be transferred and applied to other regions with multiple reliable discrete land cover datasets such as the CORINE land cover datasets for European Countries at 6-year intervals from 1985 to 2018 [50], National Land Cover Database Land Cover Collection for the U.S. at 5-year intervals from 2001 to 2016 [40], and other regional datasets. Additionally, in practical land cover mapping activities, the time consumption of applying the SRGAN-BFAST method over large spatial areas is a critical indicator. Specifically, the SRGAN model requires training on graphics processing unit accelerators, and the BFAST algorithm necessitates multi-CPU parallel processing, which demands high computational performance.