Abstract. High-resolution and multitemporal impervious surface maps on large scales are crucial for environmental and socioeconomic studies. However, recently available multitemporal impervious surface maps of the Lancang-Mekong basin were limited at 30-m resolution with considerably low accuracy. Hence, the development of up-to-date, accurate, and multitemporal impervious surface maps with the 10-m resolution is urgently needed. In this article, a machine learning framework is demonstrated by fusing Sentinel-1 Synthetic Aperture Radar images and Sentinel-2 Multispectral Sensor images to map and study the annual dynamics of impervious surfaces in the Lancang-Mekong basin from 2016 to 2021 facilitated by Google Earth Engine. Moreover, a train sample migration strategy is proposed to automate impervious surface mapping for various time periods eliminating the need to collect additional train samples from this vast study area. Finally, qualitative and quantitative assessments are conducted using test samples from Google Earth and four existing state-of-the-art datasets. The result shows that the overall accuracy and Kappa of the final impervious surface maps range from 91.45 % to 92.44 % and 0.829 to 0.848, respectively, which demonstrates the feasibility and reliability of the proposed method and results. The LMISD is freely available from https://doi.org/10.5281/zenodo.6968739 (Sun et al., 2022).

中文翻译：

---

使用谷歌地球引擎在澜沧江-湄公河盆地进行高分辨率和多时相不透水地表测绘

摘要。大尺度的高分辨率和多时相不透水地表图对于环境和社会经济研究至关重要。然而，最近可获得的澜沧江-湄公河盆地多时相不透水地表图的分辨率仅限于 30 米，精度相当低。因此，迫切需要开发具有 10 米分辨率的最新、准确和多时相的不透水地表图。在本文中，通过融合 Sentinel-1 合成孔径雷达图像和 Sentinel-2 多光谱传感器图像来绘制和研究 2016 年至 2021 年澜沧江-湄公河盆地不透水表面的年度动态，从而展示了一个机器学习框架，该动态由谷歌地球推动。引擎。而且，提出了一种火车样本迁移策略，以自动执行不同时间段的不透水表面映射，从而无需从这个广阔的研究区域收集额外的火车样本。最后，使用来自 Google 地球的测试样本和四个现有的最先进数据集进行定性和定量评估。结果表明，最终不透水面图的整体精度和 Kappa 分别在 91.45 % 到 92.44 % 和 0.829 到 0.848 之间，证明了所提方法和结果的可行性和可靠性。LMISD 可从 https://doi.org/10.5281/zenodo.6968739 (Sun et al., 2022) 免费获得。使用来自 Google 地球的测试样本和四个现有的最先进数据集进行定性和定量评估。结果表明，最终不透水面图的整体精度和 Kappa 分别在 91.45 % 到 92.44 % 和 0.829 到 0.848 之间，证明了所提方法和结果的可行性和可靠性。LMISD 可从 https://doi.org/10.5281/zenodo.6968739 (Sun et al., 2022) 免费获得。使用来自 Google 地球的测试样本和四个现有的最先进数据集进行定性和定量评估。结果表明，最终不透水面图的整体精度和 Kappa 分别在 91.45 % 到 92.44 % 和 0.829 到 0.848 之间，证明了所提方法和结果的可行性和可靠性。LMISD 可从 https://doi.org/10.5281/zenodo.6968739 (Sun et al., 2022) 免费获得。