## 2 Materials and Methods
### Data and Preprocessing  
In this study, four categories of data were used and they include TM and OLI from Landsat series of satellites, SRTMGL1_003 from ASTER, land cover datasets of 2011-2015 from the LCMM method [19] using HJ-1/CCD data, and high-resolution images from Google Earth for sample verification and validation. The details of these data are listed in Table 1. Since the Landsat/TM images are not enough for land cover mapping before 1986, the earliest year in this study is 1986. In total, 3257 scenes of TM/OLI images including 2084 Landsat5/TM and 1173 Landsat8/OLI surface reflectance images were used from GEE in this study. The number of scenes for each year is listed in Table 2. Among them, the number in 1986 is the least, with only 109 scenes, and the year 2014 has the most scenes, 439. In addition, the number of Landsat8/OLI is more than that of Landsat5/TM. Due to the failure of the Landsat7 satellite, ETM+ was not used [32]. Figure 2 shows the seasonal reflectance image composites for 1986 and 2014 and the composites of 2014 look better than those of 1986 because of more available data (see statistics on Table 2). Especially, the autumn composite of 1986 has a lot of noise induced by clouds, so the final land cover may degrade. The details will be discussed in the discussion section.  
In order to make a usable surface reflectance composite, the following preprocessing procedures were carried out.  
1. The CFMASK algorithm [33] was used to generate the quality assessment (QA) band and cloud contamination was subsequently removed.
2. For Landsat 5 images, a negative buffering method [34] was employed to remove bad pixels at edges.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data** & **Date** & **Description** & **Source** \\ \hline TM and OLI from Landsat & 1986–2015 & Major data in this study for land cover mapping & Google earth engine data sets or USGS \\ \hline SRTMGL1\_003 & 2000 & The SRTMGL1 version 3 data obtained terrain information such as elevation and slope and assist in classification & Google earth engine data sets or USGS \\ \hline Land cover dataset from LCMM method and HJ-1/CCD & 2011–2015 & The land cover datasets with high accuracy and consistency were used for quickly and accurately retrieving examples for the machine learning method They made using HJ-1/CCD data & [[http://westdc.westgis.ac.cn/data/6](http://westdc.westgis.ac.cn/data/6) bbf9a3f-e/?d8-4255-9 ecb-131e1543316d]([http://westdc.westgis.ac.cn/data/6](http://westdc.westgis.ac.cn/data/6) bbf9a3f-e/?d8-4255-9 ecb-131e1543316d), accessed on machine learning method They made using HJ-1/CCD data & 19 April 2021 \\ \hline Google Earth high-resolution images & – & The high-resolution data are used for verifying the training samples and validation & Historical data available from the Google earth \\ \hline \hline \end{tabular}
\end{table}
Table 1: Land cover datasets of 2011–2015 from the LCMM method using HJ-1/CCD data and high-resolution images.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Data** & **Date** & **Description** \\ \hline
2015 & Landsat 8 OLI SR & 433 \\
2014 & Landsat 8 OLI SR & 439 \\
2013 & Landsat 8 OLI SR & 301 \\
2011 & Landsat 5 TM SR & 302 \\
2010 & Landsat 5 TM SR & 332 \\
2005 & Landsat 5 TM SR & 364 \\
2000 & Landsat 5 TM SR & 375 \\
1995 & Landsat 5 TM SR & 291 \\
1990 & Landsat 5 TM SR & 311 \\
1986 & Landsat 5 TM SR & 109 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Data and descriptions in this study.  
3. The percentile reducer in GEE was used to mosaic the Landsat images within one season and 25% was used as a threshold for better noise removing.
4. Aiming at minimizing the missing data, a reconstruction algorithm [35] striving to ensure the authenticity and integrity of the data was employed to reconstruct the missing portion of the data.