Abstract: Crop lodging assessment is essential for evaluating yield damage and informing crop management decisions for sustainable agricultural production. Traditional evaluation methods and manual on-site measurements are time-consuming and labor- and capital-intensive. In this study, a remote sensing model to distinguish lodging rice was constructed based on spectral and textural features. To accurately extract the area of lodging rice from high-resolution remote sensing images, this study used Sentinel-2 multispectral images taken on September 27, 2019, to study the spectral and textural characteristics of lodging rice, in Tongjiang City, Heilongjiang Province. Analysis of the surface reflectance of normal rice and lodged rice, showed that reflectance of eight bands, including visible light, near-infrared, and shortwave infrared, increased after rice lodging; the reflectance of shortwave infrared, red light, and red edge 1 increased by more than 0.06. Except for the difference vegetation index (DVI), the typical vegetation indices of lodged rice, such as normalized difference vegetation index (NDVI), ratio vegetation index (RVI), enhanced vegetation index (EVI), and red edge position index (REP), decreased. There were significant differences between lodging rice and normal rice in the mean texture feature values of the red band, red edge 1, and shortwave infrared; the largest difference was for the mean texture value of the red band. Therefore, in this study, normalized difference vegetation index, land surface water index (LSWI), ratio vegetation index, difference vegetation index, and texture mean of the red band were used to construct the decision tree classification model. The results of remote sensing monitoring showed that rice lodging on the farm was decentralized. The area of rice disaster was larger in the west and south and smaller in the north. There was no lodging rice in the middle of the north and the east. Compared with the measured area, the area recognition errors of normal and lodged rice were 3.33% and 2.23%, respectively. When using random verification samples and model verification results for the confusion matrix analysis, the user accuracy and mapping accuracy of lodging rice were 92.0%, and the Kappa coefficient was 0.93. These results show that this method can be applied to remote sensing data from lodged rice in large areas and can provide a relevant basis for the investigation of rice lodging areas using high-resolution and multi-spectral remote sensing data.