# Near-Surface and High-Resolution Satellite Time Series for Detecting Crop Phenology
## 2 Materials and Methods
### Methodology  
#### 2.2.1 Imagery Pre-Processing  
For the PhenoCam imagery of each study site, we first delineated a region of interest (ROI) of the crop canopy in the imagery for the subsequent phenological analysis. The ROI was delineated to cover the crop canopies located in the foreground of imagery to be representative of the broader landscape of each site while avoiding the non-target species (e.g., weed), soil, and sky. Delineation of an ROI in the foreground further diminished the effects of adverse weather conditions (e.g., cloud and fog), which could negatively affect the quality of characteristic phenology from PhenoCams [46]. The GCC time series of the ROI was then extracted from the imagery. GCC was calculated using Equation (1).  
\[\text{GCC}=\frac{G_{DN}}{R_{DN}+G_{DN}+B_{DN}} \tag{1}\]  
Here, \(G_{DN}\), \(R_{DN}\), and \(B_{DN}\) denote the average pixel digital numbers over the ROI for the green, red, and blue bands of the imagery, respectively.  
GCC was developed to characterize the canopy greenness and had been extensively used in the RGB (red, green, and blue) imagery for monitoring canopy seasonal dynamics [37; 63]. As GCC values tended to be reduced by snow or sub-optimal lighting conditions, we further generated the three-day composite using the 90 th percentile value of GCC calculated over a three-day moving window to reduce the impacts of the variation in weather conditions, atmospheric interference, and illumination geometry [44]. As regards the Plan  
Figure 2: The average number of cloud-free PlanetScope images per month across the study sites. The study sites are denoted using the site letters in Figure 1.  
etScope imagery of each study site, we also delineated an ROI that took into account the corresponding PhenoCam location, PhenoCam orientation, and landscape characteristics, so the ROI in the PlanetScope imagery could approximate the crop canopy characteristics of that of the corresponding PhenoCam data. As the most widely used vegetation index, the normalized difference vegetation index (NDVI) was calculated from the PlanetScope ROI to generate the seasonal time series curves.  
For both PhenoCam and PlanetScope, the extracted time series were subsequently filtered using the spline function to remove the outliers and spurious observations. The spline filter fitted a smoothing spline curve to the time series and detected the outliers based on the deviations from the curve. The degree of smoothing in the filter was determined using [PERSON]'s Information Criterion [38]. The resultant time series was further smoothed using the median of a three-point moving window to remove the remaining noise. The median smoothing was conducted iteratively until the time series became stable. During the dormant seasons, the time series might be affected by weeds, cover crops, etc., (Figure 3). The local fluctuations (e.g., orange box in Figure 3) caused by the off-season vegetation covers might confound the phenological patterns of the target crop species. We thus delimited the time series into segments, with each one representing a growth cycle, using the curve turning points (e.g., peaks and pits). The time series segments outside the growth cycle of the target crop species were replaced by the median dormant season values (e.g., the black curve in Figure 3). With the imagery pre-preprocessing, we attempted to remove the outliers and undesired off-season vegetation signals while retaining the seasonal phenological trajectory of the target crop species.