Abstract: Abstract: The planting distribution of paddy rice has been widely extracted to identify the flooding characteristics of paddy rice during the transplanting period. Optical remote sensing images can be mostly used as the data source at present. However, the transplanting period cannot characterize full spectral characteristics of the paddy rice lifespanthe, spectral characteristics of paddy rice should be considered in multiple phenological periods. The pixel-based extraction of paddy rice planting distribution can also be susceptible to the data source noise. Fortunately, an object-oriented extraction can be selected to effectively reduce the impact of data source noise in the field. Since the remote sensing images are limited by the acquisition and processing costs, only a few studies focused on the application of multiple paddy rice phenological stages in the extraction of paddy rice planting distribution in large areas. Alternatively, the emergence of the Google Earth Engine (GEE) platform in earth science data and analysis can be accessible to large amounts of remote sensing data for free and with high efficiency. Taking Panjin City, Liaoning Province of China as the research area, this study aims to realize the object-oriented extraction of rice planting areas using phenological features from the GEE platform. Four phenological periods of paddy rice were selected, namely the sowing, transplanting, heading, and maturity period. Specifically, the sowing period with the Bare Soil Index (BSI) was selected from March 15 to April 30. The transplanting period was selected as the Green Chlorophyll Vegetation Index (GCVI) and the Modified normalized difference water index (MNDWI) from May 10 to June 20. The heading period was the Normalized Difference Red Edge Index (NDRE) and Normalized difference vegetation index (NDVI) from June 30 to September 10. The maturity period with the Plant Senescence Reflectance Index (PSRI) was selected from September 20 to October 20. The 2020 Sentinel-2 time series images were filtered to construct the datasets in the four paddy rice phenological periods. Then, the spectral indices corresponding to each phenological period were calculated and synthesized by the median. Finally, the six images were synthesized into one multi-band image as the original image. The images were segmented with the Simple Non-Iterative Clustering (SNIC) available in the GEE platform. Texture features were also calculated with the Gray Level Co-occurrence Matrix (GLCM). Six models were also established for the paddy rice distribution using the random forest (RF) and Support Vector Machine (SVM), including the pixel-based RF model (PB_RF), object-oriented RF model (SNIC_RF), object-oriented RF model with texture features (SNIC_GLCM_RF), pixel-based support SVM model (PB_SVM), object-oriented SVM model (SNIC_SVM), and object-oriented SVM model with texture features (SNIC_GLCM_SVM). A full extraction of paddy rice planting distribution was implemented using the six models in the study area in 2020. An optimal model was achieved to verify the model accuracy in the field survey of paddy rice. The results show that the RF performed better than the SVM in the extraction of paddy rice planting distribution. The object-oriented method can be widely expected to improve the extraction accuracy of paddy rice planting distribution. The highest extraction accuracy was also achieved in the SNIC_RF model. Correspondingly, the overall accuracy and Kappa coefficient were 96.83% and 0.934, respectively. The field survey data was used to verify the model, where the field survey accuracy of paddy rice was 95.43%, fully meeting the requirements of regional paddy rice planting distribution and area monitoring.