去除土壤后向散射影响的SAR数据玉米留茬方式识别
Recognition of corn stubble modes from SAR data without the influence of soil backscatter
- 2023年27卷第11期 页码：2628-2639
纸质出版日期： 2023-11-07
DOI： 10.11834/jrs.20211034
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摘要
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FRI（Fusion Residue Index），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。
Abstract
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.
关键词
遥感Sentinel-1 SAR数据玉米留茬留茬方式识别后向散射模型最优特征集
Keywords
remote sensingSentinel-1 SAR datacorn stubblerecognition of stubble modesbackscatter modeloptimal feature set
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