Studying the spatial distribution of soil types is an important academic and practical issue in agriculture. With the rapid development of remote sensing technology, the role of image texture as an auxiliary variable in remote sensing identification of objects has increased. It is of great importance to ascertain the optimal window size for extracting texture features and the multiscale fusion of texture feature parameters under the optimal window for different soil types. To reach this goal, soil types in a typical area of the Jiaodong Peninsula were selected as the subject investigated, homogeneity and entropy were selected as the two texture feature parameters, and the ability to identify the different soil types based on the textural features was systematically analyzed by using Landsat 8 remote sensing images. Moreover, the optimal window sizes for extracting texture features were determined, and the role of multiscale textural features in the classification of the soil types was also evaluated. The results show that the accuracy of classification significantly increased with the addition of textural features. The optimal single-scale window sizes for the homogeneity and entropy feature parameters were 19 × 19 and 21 × 21, respectively. The fusion of multiscale textural features further improved the classification accuracy. The optimal multiscale window sizes for the homogeneity were 7 × 7, 13 × 13, 19 × 19 and 21 × 21 and those for entropy were 5 × 5, 15 × 15, 21 × 21 and 23 × 23. Therefore, the method of using texture information in remote sensing images as auxiliary variables in digital soil mapping was feasible. The method of multiscale fusion of texture features, which resulted in greater classification accuracy, was better than that of single-scale window. These conclusions could play an important guiding role in soil digital mapping with remote sensing.

研究土壤类型的空间分布是农业中一个重要的学术和实践问题。随着遥感技术的飞速发展，图像纹理作为辅助变量在物体遥感识别中的作用越来越大。对于不同土壤类型，确定提取纹理特征的最佳窗口大小和纹理特征参数在最佳窗口下的多尺度融合具有重要意义。为实现这一目标，选取胶东半岛典型地区土壤类型为研究对象，选取均质性和熵作为两个纹理特征参数，系统地基于纹理特征识别不同土壤类型的能力。使用 Landsat 8 遥感影像进行分析。而且，确定了提取纹理特征的最佳窗口大小，并评估了多尺度纹理特征在土壤类型分类中的作用。结果表明，随着纹理特征的加入，分类精度显着提高。均匀性和熵特征参数的最佳单尺度窗口大小分别为 19 × 19 和 21 × 21。多尺度纹理特征的融合进一步提高了分类精度。均匀性的最佳多尺度窗口大小为 7 × 7、13 × 13、19 × 19 和 21 × 21，熵的最佳多尺度窗口大小为 5 × 5、15 × 15、21 × 21 和 23 × 23。因此，使用遥感图像中的纹理信息作为数字土壤制图的辅助变量是可行的。纹理特征的多尺度融合方法比单尺度窗口的分类精度更高，分类精度更高。这些结论对遥感土壤数字制图具有重要的指导作用。