## 6 Conclusions  
This study constructed a dataset of early crop growth characteristics based on temporal phase difference feature information and explored its potential for use in early crop recognition in typical black soil areas of Northeast China. Firstly, a multi-temporal crop growth characteristics dataset was constructed using the different information on crops in different periods of the early stages. Then, the feature optimization method was used to select the best feature set for all possible combinations in each period, and the early key identification characteristics of different crops and their stage change characteristics were explored. Finally, the performance differences of four classifiers in early crop recognition and the recognition accuracy levels of crops in different periods were analyzed. The conclusions are as follows:  
(1) The early crop growth method proposed in this study is intuitive and easy to understand. It can effectively amplify the differences between early-stage crops and improve the accuracy of crop identification. Therefore, it has great potential in the early identification of crops. It can also quickly and accurately map the crop in its early stages, providing information reference for relevant agricultural departments and having practical solid application value. (2) The difference time phase feature can distinguish between crops and improve their identification accuracy in the early stage. Rice changed obviously between mid-May and late April (F) periods; corn changed more obviously between early July and mid-June, late May, mid-May, and late May and late April, which were periods E, M, L, H; soybean changed more obviously between early July and mid-June, mid-May, early May, and late-May and late-April, which were periods E, M, N, H.  
(3) Short-wave infrared bands and vegetation index feature sensitivity to water information, and low vegetation coverage contributed more to the early identification of rice, such as LSWI, SR, B11, and NDTI. For corn and soybean, short-wave infrared band, red-edge index, and vegetation canopy cover indicators were key in identifying both, such as B11, NDRE3, LSWI, Vlgreen, RESI, and NDRE2. (4) Corn can be identified as early as 7 July, with both PA and UA above 97%; soybean can be identified as early as 7 July, with both above 94%; and rice can be identified as early as 8 May, with both above 90%. (5) GBDT and RF performed comparably in crop recognition, followed by SVM, while the Cart classifier was poorer. Conceptualization, M.W. and Q.L. ; methodology, M.W., H.W. and Y.Z. ; software, M.W. ; validation, M.W. and H.W. ; formal analysis, M.W. ; investigation, M.W., H.W., G.S. and Y.R. ; writing--original draft preparation, M.W. and H.W. ; writing--review and editing, M.W. ; visualization, M.W. ; funding acquisition, Q.L. and X.D. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Key R&D Program of China (2021 YFD1500103), the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA28070504), the National Science Foundation of China (42071403, 41701486), and the Key Program of High-resolution Earth Observation System (00-Y30B01-9001-22/23-CY-10, 20-Y30F10-9001-20/22, 11-Y20A16-9001-17/18-4). The authors declare no conflict of interest. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Period to** & **Cart** & **GBDT** & **RF** & **SVM** \\ \hline
8 May & 54.8\% & 63.1\% & 61.7\% & 61.5\% \\
13 May & 60.3\% & 70\% & 67.3\% & 66.7\% \\
28 May & 69.8\% & 75.5\% & 76.3\% & 73.9\% \\
12 June & 75.2\% & 81.8\% & 81.7\% & 80.7\% \\
7 July & 96.7\% & 98.4\% & 98.2\% & 94.8\% \\ \hline \hline \end{tabular}
\end{table} 
Table 18: Accuracy of soybeans identification in 2020 different periods. [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Evaluation of Three Deep Learning Models for Early Crop Classification Using Sentinel-1A Imagery Time Series--A Case Study in Zhanjiang, China. _Remote Sens._**2019**, _11_, 2673.