Abstract
The Sentinel-1 mission has finally reached its maturity with the launch of the second Sentinel radar. Among the products delivered by the agency, the ground range detected class is raising more and more interest among users due to its reduced computational demand for information extraction and availability on cloud exploitation platforms, like the Google Earth Engine. In this paper, we present a novel multitemporal processing chain, suitable to be applied to Sentinel-1 ground range detected products to obtain RGB images, using a series of single polarization detected images. These products aim at being the equivalent for the recently introduced Level-1 $____alpha$ , exploiting a texture measure instead of the interferometric coherence, to properly render and enhance the presence of built-up areas. The discussion is supported by experiments showing the reliability of this newly introduced class of products in classic synthetic aperture radar applications like image photointerpretation, flood mapping, and long-term urban area monitoring.