资源一号02D卫星（ZY1-02D）于2019年成功发射， 2020年10月正式投入使用，是我国自主建造并成功运行的首颗民用高光谱业务卫星，具有广泛的应用前景。本研究以黄河三角洲湿地为研究区，以ZY1-02D 高光谱（AHSI）影像为数据源，结合无人机和地面调查数据，开展湿地景观分类研究。首先通过ZY1-02D AHSI获取地物反射率波谱曲线，分析不同地物波谱曲线的差异，作为地物识别和分类的依据；充分考虑研究区植被覆盖度的差异，结合无人机影像制定研究区7类基本地物和9类精细地物两种湿地景观分类体系；利用随机森林算法进行分类，并引入Tree SHAP方法进行波段重要性排序和选择；探究影响ZY1-02D AHSI分类的重要波段，选取与Landsat 8 OLI多光谱波段相重叠的波段进行分类，并与Landsat 8 OLI分类结果进行比较。结果表明：1）ZY1-02D AHSI数据能够较好地反映不同地物类型光谱曲线的差异；2）对于两种分类体系，仅用前40个重要波段的总体分类精度达到最高，7类基本地物分类和9类精细地物分类的分类精度分别为92.18%和90.76%，这40个波段大多位于可见光、近红外波段；3）对于两种分类体系，分别选取与Landsat 8 OLI多光谱波段重叠的24个和29个波段进行分类，分类精度达到90.01%和89.76%，均明显高于Landsat 8 OLI的分类精度；4）蓝、绿波段对于识别高、低密度互花米草和芦苇较为重要，短波红外波段对于芦苇的识别较为重要，红波段对于识别高、低密度碱蓬较为重要。ZY1-02D AHSI数据的波谱范围较窄，波谱连续，能够较好地体现地物光谱曲线的细微变化，在区分不同地物以及植被覆盖度差异上具有明显优势。本研究有利于及时有效地监测黄河三角洲湿地资源现状，为ZY1-02D高光谱数据在湿地生态监测应用提供科学参考依据。

The ZY1-02D satellite was successfully launched in 2019 and officially put into use in October 2020. It is the first civil hyperspectral satellite independently developed and operated by China and has a wide application prospect. Taking the Yellow River Delta wetland as study area, this paper investigated the performance of the ZY-02D Advanced HyperSpectral Instrument (AHSI) image in wetland landscape classification. First, the ZY1-02D AHSI spectral curves of typical land cover types in the study area were evaluated. Second, two levels of wetland landscape classification systems including 7 basic classes and 9 refined classes considering the difference of vegetation coverage were developed with the assistance of field surveys and Unmanned Aerial Vehicle (UAV) images. Then, Random Forest algorithm was used for classification, and Tree SHAP method was introduced to sort and select important bands. The most important ZY1-02D AHSI bands overlapping with Landsat 8 OLI bands of were selected for classification, and the classification results were compared. Our results showed that: 1) ZY1-02D AHSI data can represent the differences of spectral features of different landscape types; 2) For the two classification systems, the first 40 important bands achieved the highest overall classification accuracy. The classification accuracy of 7 basic classes and 9 refined classes were 92.18% and 90.76%, respectively. Most of the important bands were visible and near-infrared bands. 3) For the two classification systems, 24 and 29 bands overlapping with Landsat 8 OLI multi-spectral bands were selected for classification, respectively. The classification accuracy reached 90.01% and 89.76%, which were significantly higher than that of Landsat 8 OLI. 4) Blue and green bands are important for the identification of high and low density Spartina alterniflora and Phragmites australis; Blue, green and short-wave infrared bands are important for Phragmites australis identification, and red band is important for the identification of high and low density Suaeda salsa. Our results show that ZY1-02D AHSI data has obvious advantages in distinguishing different landscape features and differences in vegetation coverage. Our study is expected to provide scientific basis for the application of ZY1-02D hyperspectral data in wetland ecological resources.