## 5 Discussion  
### Use of Quantitative Remote Sensing Products  
This study used a variety of quantitative remote sensing products, rather than only vegetation indices. The main reason behind this decision was that quantitative remote sensing products could reflect multiple ecological and physical characteristics of the surface at more levels [21; 78]. The classification of the surface can be distinguished by more refined surface features. Among the features, LAI is a characteristic parameter describing the vegetation canopy geometry, which can reflect the growth and development of vegetation. FAPAR characterizes the intensity of photosynthesis in the vegetation. ET is an important process of water movement in a soil-plant-atmosphere continuous system. Its strength is closely related to the underlying surface conditions and plants. Albedo is an important parameter of the numerical climate model and the surface energy balance equation. GPP refers to the total amount of organic carbon fixed by the photosynthesis of green plants per unit time and unit area. Vegetation first showed an increase and then a decrease in the NDVI year time series curve due to the growth cycle characteristics of budding-flowering-deciduous [79]. This feature contributes to the distinction between vegetation and non-vegetation. Because of human activities, cultivated land mostly undergoes two or three workings per year. Crops exhibit more than two peaks or three peaks on the NDVI year time series curve [80], and this curve has good characteristics for the distinction between crops and other vegetation in North China, South China and Sichuan. The albedo reflects the ability of the surface to absorb solar radiation. In general, the albedo of ice and snow is higher than those of other categories [81], followed by bare soil covered by waste soil and gravel. The albedo of a city is generally higher than that of vegetation or water. The albedo has a very good effect in distinguishing between cities and water bodies [82]. Grassland, woodland and shrubs also have certain differences in leaf area. The LAI also has a very good advantage in distinguishing between different vegetation groups [83]. The GPP, ET and FAPAR have explained the differences in the inter-annual variations of different types of land types from biological and thermal aspects. As shown in Figure 9, the performance of different land types in quantitative remote sensing parameters is different. The different performances of quantitative remote sensing products of different land types in one year is the theoretical basis for the long time series land cover classification. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Level 0** & **Level 1** & **Level 2** & **Level 3** & **Level 4** & **Level 5** & **Overall** \\ \hline CR & 0.844 & 0.911 & 0.924 & 0.927 & 0.937 & 0.924 & 0.911 \\ FR & 0.844 & 0.911 & 0.924 & 0.927 & 0.937 & 0.924 & 0.911 \\ SR & 0.926 & 0.82 & 0.855 & 0.887 & 0.952 & 0.967 & 0.901 \\ GR & 0.777 & 0.902 & 0.881 & 0.869 & 0.854 & 0.967 & 0.875 \\ WT & 0.942 & 0.944 & 0.945 & 0.976 & 0.984 & 0.993 & 0.964 \\ GL & 0.995 & 0.945 & 0.972 & 0.971 & 0.980 & 0.976 & 0.972 \\ WL & 0.960 & 0.947 & 0.945 & 0.969 & 0.980 & 0.986 & 0.961 \\ CT & 0.945 & 0.914 & 0.948 & 0.968 & 0.961 & 0.976 & 0.951 \\ DS & 0.929 & 0.873 & 0.891 & 0.853 & 0.829 & 0.974 & 0.892 \\ TD & 0.993 & 0.955 & 0.955 & 0.965 & 0.969 & 0.930 & 0.963 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Land cover percentage grading accuracy for 2010. Through the evaluation of the feature importance of random forests, feature importance of each category was extracted in the classification process, as shown in Figure 10. The first is cultivated land. ET and NDVI are more important for the crop category, mainly due to the cyclical NDVI characteristics of crops and the strong correlation between crops and ET [84]. Forest is mainly affected by NDVI and LAI. Mainly because of the high vegetation coverage of forest land.