Copernicus Earth observation in PoliRural+

Copernicus in JackDaw: from satellite pixels to usable layers

main tellingdepth: standard2 min markdown

The platform design (D4.1) names Copernicus as the authoritative source of satellite imagery and Earth observation (EO) data. A JackDaw user, however, rarely touches a Sentinel image. None of the eighteen Beta tool areas in D4.2 queries imagery directly; land cover, for example, comes as classification statistics from OLU_EU, part of the project’s Open Land Use database.

Models that turn imagery into indicators

D4.1 describes a cloud-removal and index model for crop monitoring. Clouds block optical imagery, so the model combines Sentinel-2 optical data with Sentinel-1 radar, which sees through clouds, and reconstructs cloud-free images with the UnCRtainTS neural network. It then computes vegetation indices such as NDVI. It was trained and validated on cloud-free frames from 2022 and 2023 agricultural data, measured by MAE and MSE; combining both satellites was more accurate than optical data alone.

D4.1 also plans an EO classification suite (crop detection, land-cover detection and change-detection mapping) to feed JackDaw. D4.1 also lists a change dataset, built with OLU, that tracks growth and loss of Corine and HILUCS land classes for 1990, 2000, 2005 and 2010.

What is being prepared

D4.2 is preparing the platform for STAC and OGC-based discovery of EO assets, with machine-actionable metadata that GeoRAG can use, and for Data Cubes that serve satellite time series, climate variables and derived indicators. These are the formats for questions about change over time, drought or vegetation condition. Cooperation with the FOCAL project adds cloud-gap mitigation and super-resolution methods.

D4.2’s warning applies here most of all: retrieval is only as useful as the metadata behind it. An image without a clear acquisition date, resolution and processing history cannot support a traceable answer.