# Coarse-Resolution Satellite Images Overestimate Urbanization Effects on Vegetation Spring Phenology
## 3 Methods
### Quantify Urbanization Effects  
Existing studies quantify the urbanization effects on vegetation spring phenology as the difference of the average vegetation spring phenology between urbanized and rural areas as shown in Equation (2) [24; 25].  
\[\text{D}=\text{AVSP}_{\text{rural}}-\text{AVSP}_{\text{urban}}, \tag{2}\]  
where AVSP means average vegetation spring phenology, and D indicates the rural-urban average difference, that is, urbanization effects on vegetation spring phenology. Considering only pixels of deciduous forests have clear growing cycles and their phenology is not manipulated by human activity, the other vegetation types (e.g., crops and evergreen forests) have been excluded when computing the urbanization effects using Equation (2) [29; 33]. Accordingly, a deciduous forest mask and a rural-urban boundary mask need to be generated for calculating urbanization effects by Equation (2). First, according to the high-resolution images in Google Earth, we manually inspected and collected evenly-distributed training samples into five major land cover types, deciduous forests (2622 pixels), mainly focusing on parks, streets and rural deciduous pixels, evergreen forests (76 pixels), water (1106 pixels), cropland (2382 pixels), and artificial surfaces (1621 pixels). Afterwards, multi-temporal Sentinel-2 images (10 m) classified these five land types, as shown in Figure 6a, by the support vector machine (SVM) method with better performance of land cover classification [57]. The proportions of these five classes are deciduous forests (48.2%), evergreen forests (1.5%), water (1.2%), cropland (26.1%), and artificial surfaces (23.0%). To acquire a purer deciduous mask, the pixels satisfying the following three criteria were used: (1) the average NDVI during June to September is greater than 0.10 [58] to ensure that substantial vegetation exists in the pixel, (2) the average NDVI from July to September is higher than 1.2 times the average NDVI during November to March [59] to ensure that vegetation has significant seasonality, and (3) RMSE between the original NDVI profile and smoothed NDVI should be lower than 0.08, which is validated by ground observations [52] to ensure that the original NDVI profile is not too noisy.  
The rural-urban boundary mask was generated by the NPP VIIRS nighttime light image. An optimal threshold of nighttime light should well separate the built-up area of Beijing and the surrounding croplands. To search for the optimal thresholds, we calculated the proportions of croplands and built-up area included in the urban area defined by different thresholds in the NPP VIIRS nighttime light image. In this way, the nighttime image pixel value 15 was selected as the optimal threshold for generating the rural-urban boundary as shown in Figure 6b. Additionally, the isolated small urbanized patches that are distant from the major urban area were excluded. To match vegetation spring phenological results at each spatial scale, the deciduous forest mask and urban extent mask are resampled to 30 m to 8 km by the Pixel Aggregate method. Specifically, in the aggregated coarse pixel, if the fraction of deciduous forests is larger than 0, this coarse pixel is regarded as deciduous forests.  
Figure 6: Land cover classification map (**a**) and rural–urban boundary (**b**).  
In addition, to avoid the impact of cropland on the spring vegetation detection of coarse pixels [33], those coarse deciduous forest pixels will be excludes if their fraction of cropland is larger than 0.05.