Crop stubble cover is an important method of conservation tillage. Obtaining the distribution of different corn stubble cover modes quickly and accurately is vital to the implementation status monitoring and effect evaluation of conservation tillage. Microwave remote sensing has characteristics of all-weather and strong penetration. Thus, it not only can ensure the acquisition of data in a short period for stubble monitoring but also can be sensitive to the information of surface roughness and crop residue structure, which provides rich information for the identification of stubble modes. Some studies consider the stubble monitoring with microwave data, but they mainly focus on the estimation of stubble coverage, and the identification of different stubble modes is rarely explored. In addition, the microwave backscattering coefficient is affected by many factors, such as soil moisture and roughness. Thus, the accuracy using microwave data simply to monitor stubble is limited.In this study, an identification method for corn stubble modes by removing soil backscatter is proposed using Sentinel-1 SAR data as the main data source. Based on the autumn field sample data in 2019 in Lishu County, Jilin Province, the backscattering model of the corn stubble is designed to separate the corn stubble scattering contribution from the soil scattering contribution and reduce the interference of soil scattering contribution on the identification of the corn stubble modes. A new Fusion Radar Index (FRI), which is produced with Sentinel-1 SAR data and Sentinel-2 optical image, is combined with traditional commonly used SAR features such as radar index and SAR textures. It is used to analyze the backscattering coefficient characteristic of field surface with different stubble modes. The best feature combination for stubble recognition is selected through the analysis of identification ability. A convolution neural network model based on 1D CNN is constructed using the optimal feature set selected to identify the corn stubble modes. The corn stubble modes are also mapped for the study area. Results show that (1) the overall accuracy of stubble identification is above 83% based on VH polarized data, FRI, and GLCM1–GLCM6 with backscattering values, which proves that the feature set obtained from Sentinel-1 radar scattering characteristics is feasible and effective for identification of the corn stubble modes. (2) The identification performance of the corn stubble modes based on data without the soil backscatter contribution improves significantly. The OA and kappa coefficients are 89.28% and 0.84, respectively. Compared with those before removing the influence of soil scattering, the recognition accuracy and kappa coefficient are improved by 5.44% and 0.09. Therefore, separating the soil scattering contribution from the total scattering contribution based on the stubble radar backscattering model can effectively reduce the influence of soil factors on the monitoring of corn stubble and improve the accuracy of the corn stubble mode recognition.This study demonstrates the great potential of Sentinel-1 SAR data and backscattering models to access the distribution map of corn stubble modes. It also provides a new idea for the wide application of Sentinel-1 SAR image in the research of corn stubble.

remote sensing;Sentinel-1 SAR data;corn stubble;recognition of stubble modes;backscatter model;optimal feature set