及时准确地掌握甘草空间分布信息，能够为甘草产业健康发展和精细化管理提供科学的数据支撑。依托GEE（Google Earth Engine）平台，以内蒙古甘草主产区磴口县为研究区，以哨兵1号（Sentinel-1）、哨兵2号（Sentinel-2）和SRTM数字高程数据为数据源，构建多源多维分类特征集合，设计6种不同方案探讨光谱特征与不同类型特征相结合以及特征优选对甘草识别的影响，最后使用随机森林分类方法对甘草进行识别。结果表明：指数特征在甘草识别中的贡献率最为显著，纹理特征的贡献率位居其次，光谱特征贡献率位列第三，极化特征的贡献率相对较低，而地形特征的贡献最小。特征优选能够减少冗余，并且提升识别精度，总体精度为91.15%，Kappa系数为0.877 1。

Timely and accurate acquisition of licorice spatial distribution information can provide scientific data support for the healthy development and refined management of the licorice industry. Based on the Google Earth Engine (GEE) platform, using the main licorice-producing area of Dengkou County in Inner Mongolia as the study area, and utilizing Sentinel-1, Sentinel-2, and SRTM digital elevation data as data sources, a multi-source and multi-dimensional classification feature set was constructed. Six different schemes were designed to explore the combination of spectral features with different types of features and the impact of feature selection on licorice identification. The random forest classification method was used for licorice identification. The results show that the index features have the most significant contribution rate in licorice identification, followed by textural features, spectral features rank third, polarization features have a relatively lower contribution rate, and topographic features contribute the least to licorice identification. Selecting features can eliminate unnecessary ones, thereby enhancing the precision of identifying licorice, achieving an overall accuracy of 91.15% and a Kappa statistic of 0.877 1.