Generation of MTG FCI and IRS INR observations

This study focuses on the generation of proxy data for two MTG payloads, FCI and IRS.

Generation of proxy data sets for new Earth Observation missions is crucial for testing the ground segment before the launch of new satellites. For most new missions, the generation of representative test data for all instrument’s aspects is a challenging task.

In this study proxy data is generated from the data from current missions with similar characteristics, with the emphasis on the geometrical contents for the Image Navigation and Registration (INR). (1) The precise locations of the coastlines, (2) the projection geometry and spatial sampling of the new instrument, and (3) the spectral values of the data provided by the current operational instruments over the coastlines are taken into account.

Figure 1
 
Figure 1: Original (left) and Enhanced (right) proxy Landmark
 

Objectives
Overview
Schedule
Data Resources
Study Meetings
Contact

 

Objectives

The objectives of this study are to:

  • Implement a configurable tool with the selected proxy data generation technique.
  • Generate FCI and IRS proxy observations around known landmarks.
  • Assess the quality of the generated datasets for INR and Geometric Quality Assessment (GQA) purposes.

Overview

FCI and IRS proxy observations are generated around known landmarks, based on SEVIRI and IASI data, respectively. These two sensor are spectrally comparable to the proxy data to be built. However, the spatial sampling distance of SEVIRI and IASI data is coarser than the ones of FCI and IRS. Thus, as the generated datasets are dedicated to INR and GQA applications, SEVIRI and IASI data are spatially enhanced in order to locate the landmark coastlines with a high precision on the FCI and IRS observations.

First, SEVIRI or IASI data is interpolated onto the FCI or IRS high resolution grid. Then, a precise coastline database and an iterative median filtering approach are used to spatially enhance the low resolution SEVIRI and IASI data. Moreover, for the generation of IRS proxy observations, IASI spectra are spectrally resampled, cut according to several defined spectral windows, and averaged to build pseudo-channels before the enhancement step.

Figure 2
 
Figure 2: FCI observation over Qatar for the spectral channel VIS-0.6
 

Figure 2 illustrates a FCI observation over Qatar for the spectral channel VIS-0.6. In addition, the assessment of FCI and IRS proxy observations is performed, with the objective to retrieve introduced sub-pixel offsets. Then, the edges of the observations are enhanced with a Sobel filter and matched with the corresponding landmark coastline extracted from the coastline database.

Figure 3
 
Figure 3: Assessment per introduced sub pixel offset
 

The assessment is performed for different spectral channels, acquisition times, landmarks, and introduced sub-pixel offsets. For FCI, the mean difference computed between the estimated offsets and the introduced offsets is equal to 0.05 pixel with a standard deviation lower than 0.05 pixel (Figure 3).

Figure 4
 
Figure 4: Assessment per spectral channel
 

Figure 4 presents the results per spectral channel. For IRS, the mean difference computed between the estimated offsets and the introduced offsets is equal to 0.09 pixel with a standard deviation lower than 0.08 pixel for both normal mode and imager mode. The results show that two IRS spectral windows defined at the beginning of the study are suitable for INR and GQA purposes.

Schedule

Phase Details
Kick-Off 18/10/2018
Duration 8 months
Status Completed: 25/06/2019
WP1 Definition of an approach to generate proxy INR observables
WP2 Preparation of FCI proxy data, generation of the FCI proxy landmarks, and tool implementation
WP3 Preparation of IRS proxy data, generation of the proxy IRS landmarks, and tool implementation
WP4 Assessment of suitability of the created INR proxy data
WP5 Creation of the interpolated INR proxy data for FCI proxy images

Data Resources

Wessel, P., and W. H. F. Smith, The Global Self-consistent, Hierarchical, High-Resolution Geography Database

SEVIRI 1.5 Image Data products
SEVIRI Cloud Mask products
IASI L1C GDS products
IASI L2 Cloud Parameter products

Study Meetings

Phase Date and location
KO 18/10/2018, EUMETSAT
PM1 30/11/2018, Telecon
PM2 28/01/2019, NOVELTIS
PM3 18/03/2019, Telecon
FM 02/05/2019, EUMETSAT

Contact

EUMETSAT Project Manager
Dr.-Ing. Janja Avbelj
Remote Sensing Scientist - INR
Remote Sensing and Products Division
EUMETSAT
Email
Florian Poustomis
NOVELTIS
Email
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