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HyperSpectral pre-operational data injection into Copernicus

cosine Remote Sensing BV (NL)


The aim of this project is to develop and validate novel EO-based hyperspectral data generated by an unprecedented miniaturized hyperspectral sensor (HyperScout-2) employed in the ESA EO directorate small sat mission PhiSat-1. The processed data output of this project will advance existing EO capabilities. The processing and the distribution of the data to the community will enable original advancements in open science practices. It will boost up leading-edge short term studies advancing key areas of Earth system science and maximizing the scientific impact of European EO assets. The data processed in this project will serve as primary applications the ones foreseen in FSSCAT and PhiSat-1 missions. Following the distribution, many other applications will be possible by users, e.g. flooding, fire prediction, detection and monitoring, Urban Heat Islands, vegetation and crop status, water quality, change detection, soil moisture.
The overall goal of this project is to pre-process HyperScout 2 data from level 0 to level 1C, thus making them available for different non-space applications. The main objectives derived from this goal are listed below:
  • OBJ-01: Define the L-1C product scheme. The L-1C product will be a georeferenced hyperspectral cube of TOA reflectances along with associated meta-data such as processing related information, mapping specifications, viewing angles, etc. Here, which specific metadata to be included will be decided along with the file format (e.g. HDF5) and organization of the final product.
  • OBJ-02: Select and fine tune the most suitable processing chain. A processing chain will be selected among the ones currently employed at cosine for different projects, and will be fine tuned to the specific needs of the project. The data processing chain begins with a L-0 product and outputs a L-1C product which match the specifications defined from OBJ-01.
  • OBJ-03: Develop methods to validate the quality of the product. A validation environment will be developed which quantifies the quality of the hyperspectral cubes geometrically, radiometrically and spectrally. The accuracy of the meta-data will also be assessed in the environment.
  • OBJ-04: Process the selected L0 VNIR dataset and assess the quality of the final product. Using the environment developed for OBJ-04, the quality of the L-1C product will be assessed.
  • OBJ-05: collect lessons learned and identify improvements for the data processing algorithms.


Prime contractor
cosine Remote Sensing BV (NL)