Integration of Sentinel-1 and Sentinel-2 data for Earth surface
  classification using Machine Learning algorithms implemented on Google Earth
  Engine
Francesca Razzano Mariapia Rita Iandolo Chiara Zarro G. S. Yogesh Silvia Liberata Ullo
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.