潮间带区域因其独特的海陆交界位置而具有积泥沉淤、 净化水质、 预防土壤侵蚀、承载丰富物种、 提供生物栖息地和碳汇等多种生态服务功能， 但受围海造陆、过度开垦等日 益频繁的人类活动以及极端气候变化、 土壤侵蚀、 海平面上升等自然因素的双重影响， 潮间带湿地生态系统正面临着较大威胁。 潮间带植被种群为湿地健康状况提供先锋指标， 能为评估湿地生态系统提供关键的生物量信息。 传统湿地植被种群分类研究需通过耗费大量人力物力的野外勘察实现， 且其研究范围及分类精度均难以满足呈快速动态变化的潮间带区域的相关要求。 而具有覆盖面广、 信息量大、 更新数据快、 获取性强等优势的遥感技术为研究潮间带类高度动态变化区域提供了有效技术手段， 光学传感器因能及时捕获植被冠层特点及特征光谱而在土地覆盖土地利用研究方面具有相对优势， 可从中提取植被指数用以对地表植被进行研究。 地处沿海的潮间带区域以高云层覆盖率天气为主， 光学影像因不能穿透云层而难以达到高质量标准， 且光学传感器难以透过植被冠层捕获其下的信息， 导致光学影像对研究潮间带区域植被种群存在一定局限。 具有厘米级辐射波长的合成孔径雷达（SAR） 能够穿透云层和植被冠层而不受恶劣天气和昼夜影响， 能获取植被冠层下方的特征数据， 故 SAR 数据也成为植被覆被的研究热点。本文选取我国黄河河口区潮间带为研究区域， 采集 2016 年 7 月至 2017 年 7 月近似每月一景连续 12 景的高时间序列 Sentinel-2 遥感影像和双极化 Sentinel-1 雷达影像， 并从中提取 NDVI 植被指数， 分别利用四季单景、 多季节和高时间序列光学遥感影像和 SAR 数据对潮间带区域植被种群进行分类制图研究，探讨不同特征参数在分类研究中的重要性体现，探索多源遥感数据在黄河河口区潮间带湿地植被种群监测中的应用前景。研究结果表明：1） 对比单源与多源遥感数据组合的分类模型对黄河口潮间带植被种群的分类结果，得到模型分类效果由高到低遵循以下规律： 高时间序列 Sentinel-2、 Sentinel-1 影像结合 NDVI植被指数>高时间序列 Sentinel-2 影像结合 NDVI 植被指数>高时间序列 Sentinel-2 影像>多季节 Sentinel-2 影像>秋季单景 Sentinel-2 影像>冬季单景 Sentinel-2 影像>高时间序列Sentinel-1影像>夏季单景 Sentinel-2影像>春季单景 Sentinel-2影像>NDVI 植被指数统计量。 2） 多季节 Sentinel-2 遥感影像较任一单景 Sentinel-2 影像均能显著性地提高黄河口潮间带植被种群分类精度； 而高时间序列 Sentinel-2 遥感影像对比多季节 Sentinel-2 影像则不能显著性地提高黄河口潮间带植被种群分类精度。 NDVI 植被指数在黄河口潮间带植被种群分类研究中贡献较低， 不足以对分类结果产生显著性提升效果。 结合多源数据的分类模型较于任一单源数据模型均能显著性地提高对黄河口潮间带植被种群的分类精度。 3） 高时间序列 Sentinel-1 雷达影像取得的分类精度受地面潮间带涨潮影响呈较低分类水平。 由于选择的 Sentinel-1 影像过境拍摄时间为涨潮期， 潮水淹没在退潮期露出水面的植被种群和光滩， 受地表物质、 水含量和复介电常数变化影响， 获取的影像特征与地面样本具有一定差异， 导致分类精度降低。 4） 单一植被种群和非植被类型的分类精度明显较混生植被种群高，含有相同植被类型的种群间易发生混淆而降低生产者精度和用户精度。 5） 众多输入特征参数中， 秋季 Senitnel-2 光学影像中红边波段（波段 5， 6， 7） 对黄河口潮间带植被种群分类重要性最高， 其次为 NDVI 植被指数统计量； Sentinel-1 特征参数重要性普遍低于 Sentinel-2， 说明 Sentinel-2 数据更适用于黄河河口区潮间带植被种群分类研究。

The intertidal zones are commonly regarded as one of the most valuable and active wetland ecosystems, due to the interaction of land-sea interface and ecological functions in sludge accumulation, water purification, habitat providing and carbon sequestration. However, such valuable wetland ecosystems are facing a huge threat. Increasing anthropogenic pressure, climate change and sea level rise affect intertidal zones. The intertidal zones are degrading not only caused directly by land reclamation, land transformation, overexploitation and pollution, but also indirectly by trophic downgrading, and climate change. Plant communities indicate the health of the ecosystem, providing early signs of ecological degradation. Traditional methods for classifying and mapping plant communities in the wetland are commonly hindered by limited data accessibility and time-consuming field work, which aiming at inventorying vegetation is usually expensive, time-consuming and sometimes inaccurate. Satellite remote sensing technique has been widely used due to global scale, informative images, updated data and frequent acquisition, providing efficient and economical approaches to classify plant communities and estimates related biophysical and ecological parameters. With the development of remote sensing techniques, optical satellite images become more popular and efficient compared with field survey. However, the quality of optical images would be influenced by clouds and weather, and the optical satellite images were limited by the plant canopies which cannot penetrate canopies for underneath information. Thus weather-independent SAR data is considered to provide complementary information for mapping intertidal plant communities. In this study, the intertidal plant communities of the Yellow River Estuary were classified using random forest algorithm based on Sentinel-1 and Sentinel-2 time series images as well as the NDVI statistic parameters derived from Sentinel-2 time series. The variable importance of different input data from various classification scenarios was also evaluated. The main results are as follows, 1) The sort of the scenarios which got the highest overall accuracy to the lowest one for classifying and mapping the intertidal plant communities in the Yellow River Estuary is that the combined Sentinel-1 and Sentinel-2 time series data with NDVI parameters > Sentinel-2 time series data with NDVI > Sentinel-2 times series data > multi-temporal Sentinel-2 image > single-date Sentinel-2 images acquired in Autumn > single-date Sentinel-2 images acquired in Winter > Sentinel-1 time series data > single-date Sentinel-2 images acquired in Summer > single-date Sentinel-2 images acquired in Spring > NDVI parameters. 2) It was found that a higher mapping accuracy for the intertidal plant communities was achieved by multi-temporal Sentinel-2 images compared with any single-date images. The Sentinel-2 time series data could not significantly improve the mapping accuracy based on multi-temporal Sentinel-2 images due to extra images cannot provide information that is more significant for classification. Also using individual NDVI parameters could not get a satisfied mapping accuracy. However, multi-sensor data significantly improved the mapping accuracy based on single-sensor data for classifying the intertidal plant communities in the Yellow River Estuary. 3) The mapping accuracy for the intertidal plant communities classification derived from Sentinel-1 time series data was limited due to the time of satellites passing the study area was during the flood phase while other images were captured during ebb phase. 4) The mapping accuracy for mixed plant communities were much lower than the one with individual plant species, especially the mixed plant communities which included the same plant species. 5) Three red edge bands (band 5,6,7) from the image captured on a certain date of October 14, 2016 appeared as the first-ranked important metrics under condition for mapping intertidal plant communities, followed by the mean statistical NDVI data. And, the Sentinel-2 images were proven to be more suitable for mapping and classifying the intertidal plant communities than Sentinel-1 images.