S1 InSAR Coherence

Developing, implementing and validating methods for mapping land-cover and vegetation (forests, agricultural land) using multitemporal S-1 data, with emphasis on the study and exploitation of the information content in the multitemporal InSAR coherence time-series

SINCOHMAP is an ESA SEOM project with the objective of developing, analysing and validating novel methodologies for land cover & vegetation mapping using Sentinel-1 Interferometric Coherence Evolution. One of the main objectives of the project is to quantify the impact in using S-1 InSAR (Interferometric Synthetic Aperture Radar) data relative to traditional land cover and vegetation mapping using optical data (especially Sentinel-2, hereafter named S-2) or SAR-based (Synthetic Aperture Radar) approaches. In consequence, the project is designed to clearly state this point.

Expected outcomes

• InSAR coherence fingerprint: novel methodology tailored to the exploitation of the coherence evolution
• Machine learning approach at pixel level using Sentinel-1 and Sentinel-2
• Machine learning approach at object level using Sentinel-1 and Sentinel-2
• Alternative strategies proposed and extracted from a Round Robin consultation