Abstract: Abstract: Rapid and accurate extraction of rice planting information is of great significance for regional rice planting monitoring, yield evaluation and production management. Thailand is located in the central part of Indo-china Peninsula, with a humid tropical monsoon climate, with an annual average temperature of 27 ℃ and an annual average precipitation of more than 1 000 mm. Many areas are suitable for double rice cultivation. However, because of the long rainy season and large amount of cloud, it is difficult to obtain high-quality optical remote sensing images for crop classification. In addition, the diversity of rice planting structure also hinders the accurate recognition of complex rice planting modes based on traditional optical images. In this paper, a multi-feature classification method for rice planting information extraction based on time series Sentinel-1 SAR data was proposed. First, all sentinel-1 SAR data available in a whole year were used to construct the time series profiles of backscatter coefficient at the pixel level and object level, respectively. The backscatter coefficient profiles were de-noised based on Savitzky-Golay filtering algorithm using the TIMESAT software, then the Dynamic Time Warping (DTW) distance-based algorithm at the pixel level (Pixel-Based DTW, PBDTW) and object level (Object-Based DTW, OBDTW) were applied to measuring the similarity of backscatter coefficient profiles between the target land classes and reference land classed. Furthermore, the max value, min value, mean and standard deviation of the backscatter coefficient were calculated. The time series statistical feature parameters were then integrated with membership features for Random Forest classification, and the performance of different combinations were assessed based on classification confusion matrix. The results showed that backscatter coefficient profile was an effective way to represent the phenological information contained in time-series Sentinel-1 SAR data. By matching the similarity of time series profiles, single rice and double rice could be well identified from other crops. After adding the time series statistical feature parameters, the user's accuracy and the producer's accuracy of PBDTW algorithm increased by 6.62 and 6.76 percentage points for single rice, and by 5.34 and 3.66 percentage points for the double rice. Compared with the OBDTW algorithm only, the user's accuracy and the producer's accuracy of OBDTW combined with time series statistical feature parameters algorithm increased by 5.3 and 4.82 percentage poins for single rice, and 3.34 and 5.46 percentage points for double rice. The results also indicated that OBDTW algorithm could reduce the influence of noise by calculating the average value of backscatter coefficients of all pixels belonging to the object, so the classification accuracy of OBDTW algroithm was higher than that of PBDTW algorithm. The combination of OBDTW together with time series statistical feature parameters had the highest classification accuracy, with the user's accuracy 81.46% and producer's accuracy 82.00% for single rice, and 86.87% and 84.08% for double rice, respectively. The results can provide a new way to extract rice planting information in the cloudy and rainy tropics.