Reply on RC2

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).