# Uncertainty Analysis of Multisource Land Cover Products in China
## 4 Discussion  
In this research, the forest cover and nontree vegetation cover of three sets of land cover products in China from 2001 to 2012 were discussed to explore the uncertainty between the products. As shown in the conceptual illustration of the uncertainty of land cover products (Figure 9), the three selected products each had different characteristics: MODIS was a qualitative product with discrete classes in a pixel. This model was easy to express, but its shortcomings were quite obvious. When there were mixed pixels or the resolution was low, the accuracy would be very low. VCF was a quantitative product with land cover fractions. This qualitative feature had obvious advantages. It was quite sensitive to changes in land cover types but not sensitive to changes in spatial resolution. The disadvantage was that its data had only three layers: Tree cover, nontree vegetation, and bare ground. The ESA CCI land cover product was a semiquantitative product, which could be regarded as a transformation from qualitative to quantitative and had the properties of both qualitative and quantitative products (Figure 9b). The characteristics of these three representative land cover products are visually shown in the conceptual illustration (Figure 9). When different land cover types were studied, semiquantitative products were consistent with qualitative products or quantitative products. When the forest coverage in the study area was monitored, the semiquantitative ESA CCI product and the quantitative VCF product had good consistency (Figure 9c). When the nontree vegetation coverage in the study area was monitored, the semiquantitative ESA CCI product and the qualitative VCF product had good consistency (Figure 9a). The consistency increased when the resolution became  
Figure 8: Overall variability of the uncertainty with an increase in the spatial resolution for forests and nontree vegetation of the three land cover products (**(a,c)** represent the mean value of the difference; **(b,d)** represent the root mean square error of the difference). coarser, but the coarser resolution inevitably decreased the classification accuracy. The decrease in uncertainty inevitably sacrificed the accuracy, including the accuracy and consistency of land cover products, which were two irreconcilable contradictions (Figure 9d). The overall consistency in the study area was counted with the change in resolution. As the resolution decreased (and the accuracy decreased), the uncertainty of multisource land cover products also decreased (and the consistency increased). This change was not linear, and there was an inflection point, which was approximately 0.1\({}^{\circ}\) (Figure 9e). In exploring spatial consistency, we found that areas with more significant uncertainty were plateaus or hilly areas. The reason for this pattern was that there was a problem of mixed pixels in low-resolution remote sensing data, which led to considerable differences among the dataset. In the research, the pixel corresponded to a large area on the ground (>5 km). There were multiple vegetation types on the hills. The influence of mixed pixels was also difficult to eliminate using semiquantitative ESA CCI products and qualitative VCF products. The impact was further magnified in the complex mountainous areas. In this study, the areas with more significant spatial uncertainty included the Greater Khingan Mountains, the southeast hills, the Hengduan Mountains, and the Yunnan-Guizhou Plateau. The areas with less uncertainty included the North China Plain, the Northeast Plain, and the Xinjiang Basin. In addition, the pixel difference was quantified with the change in spatial resolution, which would help scholars select products with suitable resolution in combination with the uncertainty of different regions when performing regional remote sensing inversion and model simulation. The developing trend of land cover products was quantitative.