## 2 Materials and Methods
### Methodology  
The objective of this study is to make the longest time series land cover dataset at HRB (1986-2015) with high accuracy, high spatial resolution, and excellent consistency to support the analysis of long-term climate, environmental, and ecological changes from the perspective of the regional scale or the river basin scale. The Landsat series of satellite imagery composed of eight successive satellites can be traced back to 1972 and it is, therefore, the only choice to work on it. The statistics of data availability in Table 2 and the surface reflectance composites of 1986 and 2014 in Figure 2 further prove that Landsat series satellite data are feasible for this work. However, several factors stopped us from making high-quality and excellent-consistency land cover datasets by only using Landsat series satellite data and they are concluded as follows:  
1. Many comprehensive classification methods, such as GlobeLand30 ([PERSON], 1972) using Landsat series satellite data have been developed but the ones with high accuracy usually require manual intervention; thus, more than 10 years' land cover maps with areas over 140,000 km\({}^{2}\) do not allow a lot of manual intervention. 2. Although methods based on time series analysis like LCMM have the capability for making high-quality land cover datasets ([PERSON] et al., 2015), a 16-day revisiting period of Landsat series satellite data does not support constructing time series data at HRB. 3. Although machine learning methods, such as FROM-GLC30 ([PERSON] et al., 2015) have a lot of advantages and have been employed to map land cover using remote sensing imagery, they are usually limited by sampling amount and representativity; the requirement for consistency on data of these methods cannot be met by Landsat series satellite data (see Figure 2), while they are applied to make a long time series land cover dataset. In order to solve the above problems, we proposed a new time series land cover mapping method based on machine learning by taking the advantages of multiple satellite data and the well explored HRB is taken as the experimental site for land cover mapping and thereafter validation. The procedure of the proposed method is illustrated in Figure 3 and the major idea is described as follows: \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Code** & **Type** & **Description** & **The Type at LCMM** \\ \hline \multirow{4}{*}{1} & & & 11 Maize \\  & & & 12 Spring wheat \\  & & Land types used in agriculture, horticulture, etc. & 13 Highland barley \\  & & including com, wheat, irrigation, dry land, and & 14 Rape \\  & & & other croplands & 15 cotton \\  & & & 16 Alfalfa \\  & & & 17 Orchard \\  & & & 18 Other crops \\ \hline \multirow{2}{*}{2} & & Land with trees and their coverage being more & 21 Evergreeniferous forest \\  & & than 30\%, including deciduous forests and & 22 Deciduous broadleaf forest \\  & & evergreen coniferous forests & \\ \hline
3 & Grasslands & Lands with herbaceous cover & 31 Grasslands \\ \hline
4 & Shrublands & Deciduous shrubs and evergreen shrubs with & 40 Shrublands \\  & & coverage greater than 30\% & \\ \hline
5 & Wetlands & Aquatic herbaceous plants are observable from the & 51 Wetland \\  & & image as a non-water cover & \\ \hline
6 & Waterbodies & Rivers, lakes, reservoirs/ponds. & 41 Waterbodies \\ \hline
7 & Urban and built-up & Cities, villages, roads, and other manmade objects & 61 Urban and build-up \\ \hline \multirow{4}{*}{8} & & Bare rocks, bare soils, desert, dry salt flats, dry & \\  & & river, and lack bottoms, and all other types of land & 71 Barren land \\  & & not covered by vegetation except implanted & \\  & & croplands and urban built-up areas & \\ \hline
9 & Snow and ice & Lands under perennial snow and ice & 81 Snow and ice \\  & & & 82 Glaciers \\ \hline \end{tabular}
\end{table} 
Table 3: Land cover datasets of 2011–2015 from LCMM method using HJ-1/CCD data, and high-resolution images. 1.