## 5 Discussion  
(1) Potential of using crop growth difference information for early identification  
Currently, remote sensing recognition of crops is mainly based on all available images acquired throughout the growth period [68; 69; 70]. However, the limited imagery available in the early growth stages poses a great challenge to early crop identification [71]. The paper investigated the impact of using differential temporal information for early crop identification. Specifically, a dataset of early-stage growth characteristics was constructed based on differences in spectral features among all available temporal phases of the crop at each period, and all possible combinations of each period were identified. The results show that the different time phase features, such as mid-May and late April (F), are more critical for the early identification of rice; for the early identification of corn, the difference time phase features, such as early July and mid-June, late May, mid-May, and late May and late April (E, M, L, H) play a key role in its early identification; for the early identification of soybeans, the difference time phases features such as early July and mid-June, mid-May, early May, and late-May and late-April (E, M, N, H) contribute more to its early identification. In addition, we compared the F1 accuracy of each crop obtained by this method with that in the study [24]. They used Sentinel-2 remote sensing images to construct spectral and vegetation index feature sets of various crops in each growth period in an incremental manner. It investigated the early recognition of corn, rice, and soybean in Northeast China based on common classifiers. As can be seen from Tables 13-15, compared with [24], the recognition accuracy of crops at each stage obtained by our method is higher than that in this paper, mainly because the index we designed that can effectively highlight the crop growth characteristics, which comprehensively considers crop growth difference information and the time phase information, and can effectively amplify the difference between crops and distinguish them effectively. Therefore, the combination of spectral vegetation index features of crops with differential temporal features can effectively improve crop recognition accuracy at early growth stages. (2) Effective identification features of crops at different periods  
The use of suitable classification features can effectively improve the accuracy of remote sensing crop recognition [72; 73; 74; 75]. Many studies have shown that spectral and vegetation indices derived from Sentinel-2 multispectral remote sensing images play a more important role in crop identification than spatial texture information in Northeast China [34; 76]. Accordingly, this study selected ten spectral and 22 vegetation indices as image features and explored their differences for use in crop identification in different growth periods. We found that rice moisture information was more prominent in May and June, and some of the relevant vegetation indices constructed with short-wave infrared as input were more sensitive to the vegetation canopy moisture information [77], which could effectively capture the moisture information of rice and thus identify rice as early as possible. Figure 13: Frequency of the key change temporal on 7 July 2020. Corn and soybean are dryland crops with the same phenological period [78], and some indicators of low vegetation cover play a role in this period, but their recognition ability is limited. In July, when various crops enter their peak growth period, some indicators related to vegetation canopy cover and the red edge index can be fully utilized [72; 79] to effectively identify corn and soybeans. (3) Comparison of the performance of different classifiers in early crop identification  
In this study, four common classifiers were selected to evaluate their recognition effectiveness in the early stage of crops. These classifiers were selected based on their wide application in land cover [80; 81].