## 3 Methodology
### Image Compositing  
A monthly and metric composite of multitemporal images was adopted for the time series analysis. To take advantage of the time series of images and avoid the problem that the number of available values was uncertain due to cloudiness during the rainy month and uneven revisit times, statistical  
Figure 4: Workflow for cloud computing-based methods using optical and synthetic aperture radar (SAR) images. The pixel-based classification of corn using random forests (RFs) with composite metrics and the integration between pixel-based results and multiresolution object-based segmentation were the two major modules. analysis of the series was employed to distinguish different crops. The percentile and mean value of the series are commonly used statistical measures, and have been applied successfully to identify tree cover [46; 62] and paddy rice [1]. The percentile and mean value can be calculated regardless of the length of the series or the lack of complete data. Therefore, we choose the percentile and interval mean value as representative features of the series. For Sentinel-2, two groups of metrics were calculated for six reflectance and three VI bands [46; 50; 62]: (1) Percentile composites: 10%, 25%, 50%, 75%, and 95% percentiles were produced in the GEE platform. First, all of the preprocessed Sentinel-2 data from June to October were stored in 'ImageCollection', which is a stack or time series of images and in which the images are easily filtered by a date range and footprint. Then, the collection of images was converted to a two-dimensional array by leveraging the method of 'ImageCollection.toArray()' in GEE. The two-dimensional array stored the entire observations along the temporal and band dimension for each pixel, which is flexible enough to handle the time series images. Then, a percentile reducer was applied to extract the percentiles along the temporal dimension in the two-dimensional array. (2) The mean values of the 10-25%, 10-90%, 10-100%, 25-50%, 25-75%, 50-75%, 75-90%, 90-100%, and 0-100% intervals were calculated for six spectral and three vegetation indices to determine the value between the two selected percentiles for each pixel across all of the images, which was conducted in GEE with the 'intervalMean(0' reduction method. Considering two sample points of corn and another crop (cotton) as an example (Figure 5), the red line represents the NDVI time series of a rotation of winter wheat and summer corn, which is dominant in Hebei Province, while the green line shows the NDVI time series of cotton. The NDVI time series is exhibited in Figure 5a, while the series of quantiles sorted by value are shown in Figure 5b. We typically prefer to interpret the time series according to time, but the number of available values was uncertain due to cloudiness during the rainy month and uneven revisit times. The lack of some data did not affect the percentile composites as long as the inflection point was recorded. Additionally, the slight deviation of the crop calendar or curve shift did not change these two groups of metrics, which is useful for large-scale applications with similar but not identical crop calendars [1]. Figure 5: Example of monthly and metric composites for corn and other crops (cotton). In (**a**), the original NDVI time series of corn and other crops are shown as red and green dashed lines, respectively. The solid line is the smoothed time series with a Savitzky–Golay filter. The percentile and interval mean composites are shown in (**b**). For Sentinel-1, the number of available values was also uncertain not due to cloudiness, but rather due to the different revisit times. Two groups of metrics for the VV and VH bands were created to represent the series of images: (1) For the monthly composites, the median value of the June, July, August, September, and October data was measured to obtain the phenology information. The monthly composites are applicable for only SAR data due to the continuity of the data, regardless of cloudiness.