## 2 Materials and Methods
### Data and Preprocessing  
In this study, the lack of Landsat data prior to 1986 proved insufficient to create annual land cover maps for the years 1980-1990 (Figure 2). Therefore, three categories of data were utilized: AVHRR from NOAA series of satellites, MODIS imagery, CNLUCC and annual land use/land cover datasets (CLUD-A), with high-resolution images from Google Earth for sample verification and validation. The details of these data are listed in Table 1. Due to the requirements of change detection algorithms for complete annual time series data and the absence of a complete annual AVHRR surface reflectance dataset for 1981 (24 June 1981-31 December 2013) and associated quality issues, this study used AVHRR time series data from 1982 to 1990. Further details are discussed in Section 2.3.4. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data** & **Date** & **Description** & **Resolution** & **Source** \\ \hline \multirow{6}{*}{AVHRR} & \multirow{6}{*}{1982–1990, 2001} & The 2001 time series data were used to & & \\  & & produce a low-resolution training dataset & & \\  & & for the SRGAN model; 1982–1990 time & 5.5 km & Google Earth engine data sets \\  & & series data were used to super resolution & & or USGS \\  & & and change detection & & \\  & & The 2001 time series data were used to & & \\ MODIS & 2001 & produce a high-resolution training dataset & 250 m, 500 m & Google Earth engine data sets \\  & & for the SRGAN model & & or USGS \\ CNLUCC & 1980, 1990 & The land cover datasets with high accuracy & & \\  & & and consistency were used as the base maps & 30 m & Resource and Environmental Science Data Platform ([[https://www.resdc.cn/DOI/](https://www.resdc.cn/DOI/)]([https://www.resdc.cn/DOI/](https://www.resdc.cn/DOI/)) \\  & & for updating annual land cover & & \\  & & Annual land use datasets were used to & & \\ CLUD-A & 1980–1990 & Annual dataset consistency evaluation & & \\  & & and comparison & & \\ Google & & The high-resolution images were used for & & \\ Earth & - & selecting the training samples and validation, & 2–10 m & Historical data available from Google Earth \\ images & & but data are deficient in some areas & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Detailed information and description of the study data. Figure 2: Multiple satellite images’ time range and availability. The NDVI derived from MODIS and AVHRR sensors is a critical index for vegetation conditions and has been widely used in monitoring forest disturbance and recovery, crop-land phenology, and other land use land cover (LULC) change studies [21; 22; 23]. Because the NDVI of most land cover types such as grassland, forest, and cropland vary cyclically throughout a year, the NDVI of unused land remains relatively constant, and the NDVI of buildings and water bodies is easily distinguishable from other types; NDVI is a useful basis for land cover change analysis. In order to make a usable time series NDVI composite, the following preprocessing procedures were carried out (Figure 3). 1. \(NDVI\) was calculated according to the red band (\(Red\)) and near-infrared band (\(NIR\)) of surface reflectance products, and the formula is as follows: \[NDVI=\frac{(NIR-Red)}{(NIR+Red)},\] (1)
2. The CFMASK algorithm [24] was used to generate the quality assessment (QA) band and cloud contamination was subsequently removed
3. The MODIS and AVHRR images were subjected to a semi-monthly median synthesis to mitigate the impact of extreme values and reduce noise. It was observed that AVHRR data for January-March and October-December had significant data gaps and poor quality. Consequently, a time series of NDVI composites was generated for the period from April to September, yielding 12 semi-monthly images. This period corresponds to the peak growth phase for most vegetation, facilitating subsequent NDVI-based change detection analyses
4. Aiming to minimize the missing data, a reconstruction algorithm [25] striving to ensure the authenticity and integrity of the data was employed to reconstruct the missing portion of the data.