A 10-m resolution impervious surface area map for the greater Mekong subregion from multisource remote sensing images

Here, the 2016-2022 impervious surface area map exclusively for the greater Mekong subregion is prepared. We present a novel machine-learning framework implemented on the Google Earth Engine platform that merges Sentinel-1 Synthetic Aperture Radar images and Sentinel-2 Multispectral images to extract impervious surface area. Furthermore, we also introduce a training sample migration strategy that eliminates the need for collecting additional training samples and automates multi-temporal impervious surface area mapping. Finally, we perform a quantitative assessment with validation samples interpreted from Google Earth. Results show that the overall accuracy and kappa coefficient of the final impervious surface area maps range from 93.67% to 93.75% and 0.872 to 0.873, respectively. This dataset provides comprehensive measurements of impervious surface coverage and configuration that will help to inform urban ecological studies.