This paper aims to present a novel EO-based soil monitoring approach leveraging open-access Copernicus Sentinel data and Google Earth Engine platform. Building on key results from existing data mining approaches to extract bare soil reflectance values the current study delivers valuable insights on the synergistic use of open access optical and radar images.

e-shape demonstrates a paradigm-shift from standard static soil mapping approaches toward generating more dynamic, on-demand, soil maps through advanced cloud computing resources that simplify access to and processing of a large volume of satellite image. Building on a novel multi-input convolutional neural network and existing data mining approaches, the current study promotes the exploitation of LUCAS soil database along with the synergistic use of Sentinel radar and optical data to eliminate the need for new field campaigns and calibration datasets. The developed methodology can be transferred and scaled-up to generate spatial explicit soil indicators over agricultural regions in the European territory.

Tziolas Ν., Tsakiridis Ν.,Ben-Dor Ε., Theocharis J., Zalidis G.Employing a Multi-Input Deep Convolutional Neural Network to Derive Soil Clay Content from a Synergy of Multi-Temporal Optical and Radar Imagery Data. Remote Sensing, 2020, 12, 1389.

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