Rapid and accurate monitoring of paddy rice planting areas distribution plays an important role in formulating regional agricultural production policies and protecting regional food security. With the successful launch of FY series satellites, domestic satellite data have been increasingly used in crop information monitoring, but there are few studies on the extraction of paddy rice planting distribution information based on FY data. In order to quickly and accurately obtain paddy rice planting distribution information and explore the application potential of FY remote sensing data in monitoring paddy rice planting distribution, the study was conducted to extract paddy rice planting distribution based on FY-3 MERSI data in Panjin county, Liaoning Province. Five images of FY MERSI data during the growth period of paddy rice in 2019 were used to calculate the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Ratio Vegetation Index (RVI) and NDWI-NDVI. The temporal sequence analysis of these vegetation indices was carried out on the interest areas of six land cover types in Panjin county, including paddy rice, building land, water body, natural vegetation, natural wetland and dry land. The optimal recognition mode and threshold were determined using NDVI, NDWI, RVI and NDWI-NDVI time series curves, and the remote sensing extraction model of paddy rice planting distribution was established. First, the paddy rice planting distribution was roughly extracted according to NDWI-NDVI>-0.14 at the transplanting stage and NDWI-NDVI<-0.4 at the heading stage. Then, other land cover types were masked based on the difference of NDVI, NDWI and RVI curve characteristics between paddy rice and other land cover types, and the spatial distribution of paddy rice planting in the study area in 2019 was obtained. Based on field survey data, the accuracy of paddy rice planting distribution in the study area was verified, and the overall accuracy was 75%. Accuracy verification was also conducted based on remote sensing visual interpretation data, the overall accuracy, Kappa coefficient, paddy rice mapping accuracy and user accuracy were 80.80%, 0.61, 80.00% and 86.96%, respectively. The paddy rice planting area in the study area in 2019 was 116 618.75 hm2, consistent with the data published in the 2019 Panjin Statistical Yearbook. The study shows that extracting paddy rice planting distribution based on FY-3 MERSI remote sensing image can satisfy the requirements of remote sensing monitoring of regional crop planting distribution. FY-3 MERSI has great application potential in extracting crop planting distribution information. The study enriches the remote sensing data sources for crop planting distribution monitoring and provides a theoretical basis for promoting the practical application of FY data.