Abstract: Timely and accurate estimation of the area and distribution of crops is vital for food security. In previous research, the main challenges of early crop identification in southern China were: 1) cloudy days are frequent; 2) most parcels are small and crop types are variant; 3) high spatial and temporal resolution synthetic aperture radar (SAR) data is lack. The European Space Agency Sentinel-1A (S1A) satellite, which recently became operational, is a satellite system providing global coverage of SAR with 12-days revisit period at high spatial resolution at 10 m. This provides a new opportunity for early crop identification in southern China. Compared with classical machine learning methods, deep learning has many advantages, such as end-to-end training, mobility, it provides a new chance for using high spatio-temporal data efficiently. One-dimensional convolutional neural network (1D CNN) and recurrent neural network (RNN) have been shown to be effective deep learning methods for extracting temporal features for classification tasks. However, the parameters (but not hyper-parameters) of RNN are determined by the length of the time series. Compared with RNN, 1D CNN has the advantages of simple structure and few parameters, but very few studies have applied 1D CNN to time series data for early stage crop's identification at present. In this study, we proposed to combine 1D CNN hyper-parameters training with an incremental time series training method to attain a classification model for each time point in the growth season. Firstly, we trained the 1D CNN hyper-parameters using the full time series data during the growth season, and refer to the 1D CNN with hyper-parameters as a classifier. Then, starting at the first time point, the incremental training process was carried out, at the acquisition time point of each S1A image, input all data in the growing season before (including) the point to train other parameters of the classifier at that point, and then obtained a classification model with all parameter values (including the previous hyper-parameters) at each time point. Finally, test accuracies of each time point were assessed for each crop type to determine the optimal time series length. A case study was conducted in Zhanjiang City, China. To verify the effectiveness of this method, a comparative experiment with the classical random forest (RF) method was carried out. In order to evaluate different polarizations mode (VV, VH, VH+VV) of S1A data for crop classification in the study area, we performed the above training process for each polarization time series data. The results were as follows: 1) the classification accuracies of VH+VV, VH, and VV decrease in order, based on the VH+VV backscatter coefficient time series, the maximum Kappa coefficient values of 1D CNN and RF model were 0.924 and 0.916 respectively, illustrating that S1A time series data was valid for crop classification task in the study area; 2) in the early growing season of 2017 of the study area, F-measures of 1D CNN and RF model were above 0.85, which indicated that 1D CNN in this work was effective for early crop classification. All results indicated that the proposed training method of 1D CNN was valid for early stage crop's classification. At the same time, 33 optical images of sentinel-2 in the study area of 2017 growth season were downloaded, of which only 6 were not hindered by clouds. Therefore, S1A SAR with 12 days revisit period can not only obtain data under any weather conditions, but also track crop growth accurately. This method provides a new perspective for the application of deep learning in early stage crop's classification tasks. In addition, all parameters of 1D CNN can be trained by using time series data from different years. Although the performances of 1D CNN almost similar to those of the RF, deep learning models have advantages that other methods do not have. Therefore, we believe that deep learning methods will play an important role in early crop identification in the near future.