In this study, Synthetic Aperture Radar (SAR) and optical data are both considered for Earth surface classification. Specifically, the integration of Sentinel-1 (S-1) and Sentinel-2 (S-2) data is carried out through supervised Machine Learning (ML) algorithms implemented on the Google Earth Engine (GEE) platform for the classification of a particular region of interest. Achieved results demonstrate how in this case radar and optical remote detection provide complementary information, benefiting surface cover classification and generally leading to increased mapping accuracy. In addition, this paper works in the direction of proving the emerging role of GEE as an effective cloud-based tool for handling large amounts of satellite data.

翻译：本研究同时利用合成孔径雷达（SAR）与光学数据进行地表分类。具体而言，通过谷歌地球引擎（GEE）平台实现的监督式机器学习（ML）算法，对哨兵一号（S-1）和哨兵二号（S-2）数据进行融合处理，以完成特定感兴趣区域的分类。实验结果表明，雷达与光学遥感检测在此场景下提供互补性信息，有利于地表覆盖分类，总体可提升制图精度。此外，本文旨在验证GEE作为处理海量卫星数据的云端工具所展现的渐增效能。