The first Canadian wetland inventory (CWI) map, which was based on Landsat data, was produced in 2019 using the Google Earth Engine (GEE) big data processing platform. The proposed GEE-based method to create the preliminary CWI map proved to be a cost, time, and computationally efficient approach. Although the initial effort to produce the CWI map was valuable with a 71% overall accuracy (OA), there were several inevitable limitations (e.g., low-quality samples for the training and validation of the map). Therefore, it was important to comprehensively investigate those limitations and develop effective solutions to improve the accuracy of the Landsat-based CWI (L-CWI) map. Over the past year, the L-CWI map was shared with several governmental, academic, environmental nonprofit, and industrial organizations. Subsequently, valuable feedback was received on the accuracy of this product by comparing it with various in situ data, photo-interpreted reference samples, land cover/land use maps, and high-resolution aerial images. It was generally observed that the accuracy of the L-CWI map was lower relative to the other available products. For example, the average OA in four Canadian provinces using in situ data was 60%. Moreover, including reliable in situ data, using an object-based classification method, and adding more optical and synthetic aperture radar datasets were identified as the main practical solutions to improve the CWI map in the future. Finally, limitations and solutions discussed in this study are applicable to any large-scale wetland mapping using remote sensing methods, especially to CWI generation using optical satellite data in GEE.

中文翻译：

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使用多源评估基于 Landsat 的加拿大湿地清单地图：使用遥感进行大规模湿地分类的挑战

第一张加拿大湿地清单 (CWI) 地图基于 Landsat 数据，于 2019 年使用谷歌地球引擎 (GEE) 大数据处理平台制作。所提出的用于创建初步 CWI 地图的基于 GEE 的方法被证明是一种成本、时间和计算效率高的方法。尽管最初制作 CWI 地图的努力具有 71% 的整体准确度 (OA)，但仍存在一些不可避免的限制（例如，用于地图训练和验证的低质量样本）。因此，全面调查这些局限性并制定有效的解决方案以提高基于 Landsat 的 CWI (L-CWI) 地图的准确性非常重要。在过去的一年中，L-CWI 地图与多个政府、学术、环境非营利组织和工业组织共享。随后，通过将其与各种现场数据、照片解译参考样本、土地覆盖/土地利用地图和高分辨率航空图像进行比较，收到了关于该产品准确性的宝贵反馈。一般观察到，L-CWI 地图的准确性相对于其他可用产品较低。例如，使用原位数据的加拿大四个省的平均 OA 为 60%。此外，包括可靠的原位数据、使用基于对象的分类方法以及添加更多光学和合成孔径雷达数据集被确定为未来改进 CWI 地图的主要实用解决方案。最后，本研究中讨论的局限性和解决方案适用于使用遥感方法的任何大规模湿地制图，尤其是在 GEE 中使用光学卫星数据生成 CWI。