  The necessity of sustainable development for landscapes has emerged as an
important theme in recent decades. Current methods take a holistic approach to
landscape heritage and promote an interdisciplinary dialogue to facilitate
complementary landscape management strategies. With the socio-economic values
of the natural and cultural landscape heritage increasingly recognised
worldwide, remote sensing tools are being used more and more to facilitate the
recording and management of landscape heritage. Satellite remote sensing
technologies have enabled significant improvements in landscape research. The
advent of the cloud-based platform of Google Earth Engine has allowed the rapid
exploration and processing of satellite imagery such as the Landsat and
Copernicus Sentinel datasets. In this paper, the use of Sentinel-2 satellite
data in the identification of palaeo-riverscape features has been assessed in
the Po Plain, selected because it is characterized by human exploitation since
the Mid-Holocene. A multi-temporal approach has been adopted to investigate the
potential of satellite imagery to detect buried hydrological and anthropogenic
features along with Spectral Index and Spectral Decomposition analysis. This
research represents one of the first applications of the GEE Python API in
landscape studies. The complete FOSS-cloud protocol proposed here consists of a
Python code script developed in Google Colab which could be simply adapted and
replicated in different areas of the world
