# Coarse-Resolution Satellite Images Overestimate Urbanization Effects on Vegetation Spring Phenology
## 3 Methods
### Data Preprocessing
#### 3.1.1 Processing Sentinel-2 and Landsat-8 Images  
First, the Sentinel-2 LIC images were TOA radiance, so they were further calibrated to surface reflectance by an official software Sen2 Cor ([[http://step.esa.int/main/third-party-plugins-2/sen2](http://step.esa.int/main/third-party-plugins-2/sen2) cor/]([http://step.esa.int/main/third-party-plugins-2/sen2](http://step.esa.int/main/third-party-plugins-2/sen2) cor/)) to remove atmospheric effects. After that, three steps needed to be implemented to generate the high-quality time series at 10 m. In step 1, the Automatic Time-Series Analysis (ATSA) algorithm [40] was used to mask pixels of cloud and cloud shadow in Landsat-8 time series and Sentinel-2 time series respectively (see an example in Figure 3b). Based on the ATSA masks, manual inspection was executed to correct both omission and commission errors (Figure 3c). In step 2, those pixels  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Satellite**} & \multirow{2}{*}{**Date**} & \multirow{2}{*}{**DOY**} & \multicolumn{2}{c}{**Contaminated**} & \multicolumn{2}{c}{**Interpolated**} & \multirow{2}{*}{**Data Fusion 2**} \\  & & & & **Pixels (\%)** & & & \\ \hline
1 & Landsat-8 & 20160113 & 13 & 57.84 & 0 1 & Yes \\
2 & Landsat-8 & 20160214 & 45 & 21.75 & 21.75 & Yes \\
3 & Landsat-8 & 20160301 & 61 & 0 & 0 & Yes \\
4 & Sentinel-2 & 20160314 & 74 & 0 & 0 & No \\
5 & Sentinel-2 & 20160324 & 84 & 0 & 0 & No \\
6 & Sentinel-2 & 20160403 & 94 & 0 & 0 & No \\
7 & Landsat-8 & 20160418 & 109 & 16.39 & 16.39 & Yes \\
8 & Landsat-8 & 20160504 & 125 & 15.78 & 15.78 & Yes \\
9 & Sentinel-2 & 20160602 & 154 & 6.72 & 6.72 & No \\
10 & Landsat-8 & 20160808 & 221 & 14.39 & 14.39 & Yes \\
11 & Sentinel-2 & 20160821 & 234 & 5.50 & 5.50 & No \\
12 & Sentinel-2 & 20160831 & 244 & 6.14 & 6.14 & No \\
13 & Landsat-8 & 20160909 & 253 & 45.03 & 0 1 & Yes \\
14 & Sentinel-2 & 20160920 & 264 & 55.68 & 0 1 & No \\
15 & Sentinel-2 & 20160930 & 274 & 0.12 & 0.12 & No \\
16 & Sentinel-2 & 20161010 & 284 & 0.02 & 0.02 & No \\
17 & Sentinel-2 & 20161119 & 324 & 2.70 & 2.70 & No \\
18 & Landsat-8 & 20161128 & 333 & 0 & 0 & Yes \\
19 & Sentinel-2 & 20161209 & 344 & 26.80 & 26.80 & No \\
20 & Landsat-8 & 20161214 & 349 & 0 & 0 & Yes \\
21 & Sentinel-2 & 20161229 & 364 & 1.37 & 1.37 & No \\ \hline \hline \end{tabular}  
* Because clouds are larger (larger than 45% of the entire scene), the cloudy pixels in No.1, No.13 and No.14 images were not filled by the NSPI interpolation method. Instead, only their clear parts were used to compose the time series. 2 Landsat images were fused with Sentinel-2 images to downscale the resolution of 30 m to 10 m.  
\end{table}
Table 2: Summary of Landsat-8 and Sentinel-2 images used in this study.  
contaminated by cloud and cloud shadows were filled by the neighborhood similar pixel interpolator (NSPI) method (Figure 3d), a promising interpolation method using spatial and temporal information of adjacent pixels with similar characteristics [41; 42]. In step 3, Landsat-8 OLI and Sentinel-2 L2A images, after implementation of step 1 and step 2, were fused by the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) (Figure 3e), a spatiotemporal fusion algorithm which improved the classic data fusion method STARFM [43; 44] for heterogeneous landscapes [38]. From the zoom-in sub-image in Figure 3f,g, we can see that the spatial resolution of the Landsat image was improved after data fusion. In the end, a high-quality Sentinel-like time series at 10 m is generated.