Accurate wetland extraction using remote sensing technology poses significant challenges due to the complex hydrological dynamics, diverse landscapes, and varied wetland types. Constructing a reliable sample set is a critical first step in overcoming these challenges for large-scale wetland mapping. To meet the demand for global wetland mapping, this study (1) proposes a multi-level wetland classification system suitable for remote sensing, incorporating the soil moisture, vegetation cover and temporal dynamic characteristics of wetlands; (2) introduces a theoretically plausible wetland sample identification method based on the ecological, geographical and temporal dynamic characteristics of wetland ecosystems; (3) develops an approach that combines the Inundation-Frequency and Ecological Remote Sensing Indicators for global wetland sampling based on global climatic zones. The global wetland sample set was finally produced with 64,486 samples. The dataset revealed that seasonal marsh, swamp, mangrove, floodplain, salt marsh, tidal flat and permanent marsh accounted for 22.99%, 20.05%, 18.06%, 14.58%, 12.38%, 10.62% and 1.29% of the total sample set, respectively. Furthermore, the water body sample set comprised 13,402 samples, distributed among permanent (45.50%), seasonal (31.35%) and temporary (23.15%) water bodies. The proposed knowledge-based method, which makes use of big earth-observing data and the Google Earth Engine platform, has been demonstrated to have the capability to generate reliable wetland samples with a high degree of accuracy. This represents the first effort to create a global wetland sample set, which has the potential to offer critical support for comprehensive wetland mapping initiatives

中文翻译：

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将生态知识与 Google Earth Engine 集成，在全球测绘中实现多样化的湿地采样


由于复杂的水文动力学、多样的景观和不同的湿地类型，使用遥感技术进行精确湿地提取带来了重大挑战。构建可靠的样本集是克服大规模湿地测绘挑战的关键第一步。为满足全球湿地制图需求，本研究 （1） 提出了一种适用于遥感的多级湿地分类系统，结合了湿地的土壤水分、植被覆盖和时间动态特征;（2） 基于湿地生态系统的生态、地理和时间动态特征，引入了一种理论上合理的湿地样本识别方法;（3） 开发了一种将洪水泛滥频率和生态遥感指标相结合的方法，用于基于全球气候带的全球湿地采样。全球湿地样本集最终由 64,486 个样本组成。数据集显示，季节性沼泽、沼泽、红树林、洪泛区、盐沼、滩涂和永久性沼泽分别占总样本集的 22.99%、20.05%、18.06%、14.58%、12.38%、10.62% 和 1.29%。此外，水体样本集包括 13,402 个样本，分布在永久 （45.50%）、季节性 （31.35%） 和临时 （23.15%） 水体中。所提出的基于知识的方法利用大型地球观测数据和 Google Earth Engine 平台，已被证明能够生成具有高度准确性的可靠湿地样本。这是创建全球湿地样本集的首次努力，有可能为全面的湿地测绘计划提供关键支持