Abstract: Timely and effective estimation has been critical to capturing the distribution information of field-level crops in precision agriculture. However, the spatiotemporal coverage of optical images can be confined to the fragmentation and heterogeneity of crops in cloudy and rainy south of China. In this study, a rapid and accurate extraction was implemented for the field-scale crop distribution in the Lingnan region of China using spatiotemporal filtering of Sentinel-1 time-series data and the crop phenological information with the field data. The European Space Agency Sentinel-1A (S1A) satellite data was selected to effectively alleviate the insufficient optical image available in the study area, particularly with the spatial resolution of 10 m and the 12-day revisit period. A field-scale classification was also proposed using the Sentinel-1 dual-polarization time-series and crop phenological information. Specifically, the typical phenological characteristics were established in the spatiotemporal dimensions. A field experiment was performed on the XGBoost machine learning for the diverse plant types in Nansha District, Guangzhou City in the study area. As such, the high-precision mapping of crop type was achieved using the time-series of the Sentinel-1 data. A filtering approach was proposed to suppress the spot noise from two levels in the spatiotemporal dimensions. The mixed cell noise at the edge was effectively suppressed to smooth the abnormal fluctuations in the time-series, compared with the traditional filtering in the classification of synthetic aperture radar data. Firstly, the cropland area in each field was vectorized using GF-2 images. The field sizes were then replaced with the fixed filter window for the spatiotemporal denoising of SAR data before classification, which was used to obtain higher classification accuracy. Secondly, the Sentinel-1 dual-polarization (VV + VH) images along with the ground sample data were utilized to train and evaluate the performance of the field- and pixel-scale extraction in the crop type mapping. Lastly, the phenological characteristic variables were constructed by the time-series features, where the field-scale extraction was combined to improve the accuracy of field-scale mapping. The results showed as follows. 1) The classification using the time-series features of the field effectively suppressed the speckle noises in the SAR images, where the overall accuracy and the Kappa coefficient were improved by 12.5 percentage points and 0.19, respectively, compared with the time-series features of the pixel. 2) Compared with the classification using the only time-series features of Sentinel-1 (VV+VH) after spatiotemporal filtering, the phenological feature variables in the classification presented the better accuracy, where the Kappa coefficient was 0.91, while the sown area accuracy of sugarcane and banana reached 82.04% and 71.01%, respectively. Consequently, the time-series of Sentinel-1 image combined with the XGBoost and the radar data spatiotemporal filtering can be widely expected to achieve highly accurate crop identification and planting area extraction. At the same time, the finding can provide a strong reference for agricultural remote sensing from the data source and classification, disaster management, and post-disaster relief in the Lingnan region.