# Coarse-Resolution Satellite Images Overestimate Urbanization Effects on Vegetation Spring Phenology
## 4 Results and Discussion
### Simulation Experiments  
To further confirm whether spatial resolutions of satellite images can cause misestimation of vegetation spring phenology, a simulation experiment was conducted using the Land Surface Phenology Simulation software [33] to examine the spring phenology detected from pixels with varying degrees of mixture between two vegetation endmembers. First, we simulated NDVI profiles of mixed pixels though the linear combination of two vegetation endmembers (i.e., vegetation pixel with spring phenological dates of the first quartile Q1 and third quartile Q3 in urban and rural areas respectively) and with varying degrees of mixture (from non-mixed to 90% mixed) as shown in Figure 12 in rural and urban areas. For instance, the 10% mixed means that the simulated NDVI profile contains 90% Q1 NDVI profile and 10% Q3 NDVI profile. After that, several widely used methods and thresholds, i.e., seasonal amplitude (as known as relative threshold method) with thresholds of 0.1 and 0.3, inflexion-based method and midpoint method, were used to extract vegetation spring phenology of these simulated mixed NDVI profiles.  
Figure 11: Average IQR at different spatial resolutions in rural and urban areas.  
Figure 10: Frequency histogram of vegetation spring phenology at spatial resolution (10m).  
Figure 12 shows spring phenological dates extracted from the simulated mixed pixels. It reveals that vegetation spring phenology detected from NDVI time series becomes earlier when the mixed pixels have more proportions of NDVI Q1 with earlier spring phenology. Moreover, compared with rural areas, the earlier shifting of spring phenology in urban areas is stronger with mixed proportions (Figure 12e,f,g,h). This simulation experiment confirmed that the amplified urbanization effects on spring phenology shown in Figure 9 is the result of vegetation mixture in coarse pixels. Additionally, the simulation experiment shows that change in vegetation spring phenology is marginal when mixing two rural NDVI profiles with similar spring phenology dates (the change is around 5 days), as the red line shows in Figure 12e. However, the change of vegetation spring phenology was sharp when two urban NDVI profiles were mixed. For example, the detected spring phenology date of the simulated mixed NDVI profile is around 20 days earlier when the vegetation proportion decreases from pure to 90% mixed, as the blue line shows in Figure 12e. In addition, other simulated results extracted by different approaches and parameters show a similar conclusion, that is, mixed spring phenology in urban areas has more significant changes than rural areas (Figure 12f,g,h), which reflects the strong robustness of the seasonal amplitude method used to export spring phenology in this study. Moreover, according to a previous study [59], spatial patterns of deciduous forest spring phenology extracted by five different methods are highly similar. Similarly, a recent study [33] also validated that the results of deciduous forest spring phenology extraction for mixed pixels have very high consistency using relative threshold and inflexion-based methods.