## 4 Results
### Different Remote Sensing Indices Temporal Contribution and Variation Characteristics
Table 11 shows the top10 optimal feature sets of the best combination types for corn identification in each period. On 8 May, a 67.1% corn recognition accuracy was achieved with index combination A. The maximum recognition accuracy for the period to 13 May (72%) was achieved using the two combinations of AB. The classification achieved the maximum recognition accuracy for the period to 28 May (76.8%) using the three combinations of ABF. On 12 June, the maximum recognition accuracy for the period was achieved using five combinations of CDHIJ (82%). On 7 July, the maximum recognition accuracy for the period (98.2%) was achieved using eight and nine combinations of CEFGHIKM and ACDEFGHMN. In addition, the best combination of 15 combinations for the period achieved satisfactory early identification accuracy for corn (\(\geq\)94%). Figure 11 shows the frequency of key change temporal in the top-10 feature sets of all the best combination types for corn in this period. E, M, and L appeared to have higher frequencies indicating that the differences between 7 July and 12 June and 13 May and 28 May were obvious enough to identify corn effectively. In addition, the growth difference information on 28 May and 28 April (H) was also obvious. (3) Early identifying characteristics and temporal changes characteristics of soybeans Figure 12 shows the proportion of different feature types in the top10 optimal soybeans feature sets of all best combination types at each period. Corn and soybean have similar phenology in their early growth stage, and their performance is similar. As of early June, the soybeans had gone through the seeding, emergence, and three-leaf stages and with low vegetation cover information. The NDTI, SR, LSWI, and B11 indexes accounted for a relatively large proportion of this period. In early July, soybean enters the flowering stage and exhibits laverander-colored petals. During this period, its vegetative and reproductive development is concurrent and vigorous. The B11, NDRE3, LSWI, Vigreen, RESI, EVI, B12, and NDRE2 indexes play a key role in the identification of soybeans in this period, which is slightly different from corn. Table 12 shows the top10 optimal feature sets of the best combination types for soybeans identification in each period. The best temporal combinations for soybean on 8 May,  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Date**} & \multicolumn{2}{c}{**Buel**} & \multicolumn{2}{c}{**Optimation Features**} & \multicolumn{4}{c}{**Optimation Features**} \\  & \multicolumn{2}{c}{**Type**} & & & & & & & & & \\ \hline
8 May & A & 67.1\% & A, NDTI & A, LSWI & A, B11 & A, SR & A, B2 & A, J,RNDTI & A, IS & A, J,NWB2 & A, O,SVM & \\
13 May & AB & 71.7\% & B, LSWI & A, NDTI & A, B11 & B, NDTI & B,  
12 June, and 7 July were the same as those for corn. The maximum identification accuracies achieved using A, CDHIJ, and BEFHIM were 63%, 81.8%, and 98.5%, respectively. On 13 May, the maximum soybean identification accuracy (70%) was achieved using three combinations of ABF, while on 28 May, the maximum identification accuracy (76.2%) was achieved using three combinations of AFG. On 7 July, the maximum identification accuracy for each combination was above 94% to achieve the soybean early identification accuracy. Figure 13 shows the frequency of key change temporal in the top10 feature sets of all the best combination types for soybeans in this period. The frequency of E is the highest, followed by M, N, and H. Adding these different time phases can effectively capture soybean growth information and enable them to be identified effectively.