## 5 Discussion  
### Reliability of Corn Mapping Using Optical and SAR Images in GEE  
Corn was well identified in Hebei Province using optical and SAR image metric composites in GEE and integrating the results with object-based segmentation. Compared with previous crop-type identification studies at large scales for practical crop monitoring, which used 250-m to 30-m spatial resolutions, this study represents an important attempt to map corn at a 10-m resolution at the province scale. The success of this research was attributed to the aggregation of data sources, efficient platforms, and advanced machine learning algorithms. The aggregation of Sentinel-2 and Sentinel-1 data provides important information for corn mapping in Hebei Province, which can be concluded from the comparison of F1-Scores between different feature combinations as input (Figure 13). Compared with using all of the features as input, one group contains only Sentinel-2 image metric composites, while the other group includes Sentinel-1 metric composites of the VV and VH bands. The F1-Scores of all the features as input, including optical and SAR image metric composites, was the highest in the three groups. The F1-Score of the group that contained only optical image metric composites was slightly lower. Figure 12: Importance of the features generated in the RF classifier indicating an error increase for ‘out-of-bag’ (OOB) samples when a feature was removed. Here, the RF classifier was applied in the Scikit-learn package of Python [70]. The importance value was averaged for 10 cross-validations. The normalized differenced vegetation index (NDVI) interval mean value between the 75\({}^{\text{th}}\) and 90\({}^{\text{th}}\) percentiles was the most important feature, and the NIR was the foremost band, with an overall importance of 16.1%. After the Sentinel-2B satellite delivered the first images on 15 June [73], the Sentinel-2 constellation was commissioned, providing a higher spatial resolution of 10 m and temporal resolution of five days for mapping corn with previous data sources. Compared with Landsat 8, which has an average of 36.4 observations in northeastern Asia for all of 2014 [10], the Sentinel-2 constellation observed Hebei Province 40.6 times in five months from June to October. The number of good observations from the Sentinel-2 constellation was 21.9 on average in less than half a year (Figure 14), while the frequency of Landsat 8 was only 20.3 for the entire year [10]. As shown in Figure 14, the number of good observations was spatially inhomogeneous and ranged from three times to 192 times. Although the Sentinel-2 constellation had 10-15 observations in 27.0% of the total area and 15-20 observations in 17.8% of the entire study area, Sentinel-2 revisited 3.0% of Hebei Province on clear days less than five times. The Sentinel-1 images, which were shown to be capable of crop classification [15,18], can provide additional information for corn mapping in these regions, despite the F1-Score of the group containing only SAR image metric composites that were lower than F1-Score of the group using all of the features as input (Figure 13). The combination of image metric composites and RFs performed well in mapping corn. Compared with MODIS, which produced eight-day/16-day/one-month composites, medium to high-resolution images, such as Landsat 7 and 8 and Sentinel-2, were not composited during the fixed period due to an indeterminacy of good observation times. These metric composites, including the percentile and interval mean composites, can capture important characteristics [8] and interpret images acquired on clear days by a number of metrics, which achieved a high level of accuracy in forest mapping [46,62] and crop-type mapping [1,8]. Based on cloud computing, the GEE platform efficiently processed and composited thousands of imagery scenes. After importing the Sentinel-1/2 images archived in GEE, the metric compositing, training, and application of the RF classifier to output the corn map takes only ~10 h at the province scale.