作物留茬覆盖作为保护性耕作的重要方式之一，快速、准确地获取其不同方式的分布情况对保护性耕作的实施现状监测及效果评估具有重要意义。现有的留茬监测方法主要集中于留茬覆盖度估算，而对不同留茬方式识别的研究较少。本文以Sentinel-1 SAR数据为主数据源，尝试探究其对玉米留茬方式的识别能力。利用留茬后向散射模型分离土壤散射贡献和留茬散射贡献，以消除土壤散射贡献干扰。提出融合留茬指数（Fusion residue index，FRI），结合雷达指数与SAR纹理，分析不同特征组合对留茬方式的识别能力。采用最优特征集进行玉米留茬方式的识别，完成实验区的不同玉米留茬方式制图。结果表明：采用消除土壤影响后的VH极化后向散射系数、FRI和SAR纹理等8个特征的识别表现最好，OA和Kappa系数分别为89.28%、0.84。相比采用消除土壤散射影响前，识别精度和Kappa系数提高了5.44%和0.09。研究结果为Sentinel-1 SAR 影像在留茬研究的广泛应用提供一种新的思路。

Crop stubble cover is one of the important ways of conservation tillage. Obtaining the distribution of different corn stubble cover modes quickly and accurately is of great significance to the implementation status monitoring and effect evaluation of conservation tillage. Due to its characteristics of all-weather, all-weather, and strong penetration, microwave remote sensing can not only ensure the acquisition of data in a short period for stubble monitoring, but also be sensitive to the information of surface roughness and crop residue structure, which provides rich information for the identification of stubble modes. Though there are some research considering the stubble monitoring with microwave data, they mainly focus on the estimation of stubble coverage, and there are few studies on the identification of different stubble modes. In addition, the microwave backscattering coefficient is affected by many factors, such as soil moisture, soil roughness and so on. So, the accuracy using microwave data simply to monitor stubble is limited. In this paper, using Sentinel-1 SAR data as the main data source，an identification method for corn stubble modes by removing soil backscatter is proposed. 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 to 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, combining with traditional common-used SAR features such as radar index and SAR textures, is used to analyze the backscattering coefficient characteristic of field surface with different stubble modes. With the analysis of identification ability, the best feature combination for stubble recognition is selected. Using the optimal feature set selected, a convolution neural network model based on 1D CNN is constructed to identify the corn stubble modes. And the corn stubble modes are mapped for the study area. The results showed that: (1) The overall accuracy of stubble identification is above 83% based on VH polarized data, FRI, GLCM1~GLCM6 with backscattering values, which proves that the feature set gotten 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 removing the soil backscatter contribution improves significantly. The OA and Kappa coefficients are 89.28% and 0.84, respectively. Compared with that 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 modes recognition. The study demonstrates the great potential of Sentinel-1 SAR data and backscattering models to access the distribution map of corn stubble modes and provides a new idea for the wide application of Sentinel-1 SAR image in the research of corn stubble.