KEYWORDS: Equipment, Satellites, Spectroscopy, Calibration, Hyperspectral imaging, Signal to noise ratio, Data processing, Space operations, Reflectivity, Satellite communications
Imaging spectroscopy enables the observation and monitoring of surface properties thanks to the diagnostic capability of contiguous, spectral measurements from the Visible to the Shortwave Infra-Red portion of the electromagnetic spectrum. These observations of the Earth’s surface support the generation of a wide variety of new products and services, spanning across different domains relevant to various European Union (EU) policies that are currently not being met or can be substantially improved, not only for the public, but also for the private downstream sector. The Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) aims to provide routine hyperspectral observations over the land and coastal zones through the Copernicus Programme in support of EU- and related policies for the management of natural resources, assets, and benefits. This unique Visible-to-shortwave Infra-Red spectroscopy based observational capability will in particular support new and enhanced services for food security, agriculture and raw materials. For the development of the Space Segment Contract (Phase B2/C/D/E1) Thales Alenia Space (France) as Satellite Prime and OHB (Germany) as Instrument Prime were selected. The contract was signed in November 2020 and the corresponding Kick-Off released the start of Phase B2. The System Requirement Review (SRR) was conducted in July 2021 and the Preliminary Design Review (PDR) is being conducted in 2022. Currently there are two satellites foreseen and each of the satellites will embark a HyperSpectral Instrument (HSI), a pushbroom-type grating Imaging Spectrometer with high Signal-to-Noise Ratio (SNR), high radiometric accuracy and data uniformity. HSI consists of a single telescope for three single-channel spectrometers covering each one-third of the total swath of approximately 130 km. The spectral range of each spectrometer is covering the entire spectral range from 400 to 2500 nm. CHIME data will be processed and disseminated through the Copernicus core Ground Segment allowing the generation of CHIME core products: L2A (bottom-of-atmosphere surface reflectance in cartographic geometry), L1C (top-of-atmosphere reflectance in cartographic geometry) and L1B (top-of-atmosphere radiance in sensor geometry). Additional higher level prototype products related to key vegetation, soil and raw material properties are also being developed. In this contribution, besides the mission requirements and planning, the main outcomes of the activities in Phase A/B1 and B2, as well as the planned activities for Phase C/D/E will be presented, covering the scientific support studies, the technical developments, and the user community preparatory activities.
For over 40 years ESA’s Earthnet Programme has played a significant role as part of ESA’s mandatory activities, being a major contributor to the Global Earth Observation System of Systems (GEOSS). This role involved providing the framework for integrating non-ESA missions, i.e. Third Party Missions (TPM), into the overall ESA Earth Observation (EO) strategy. Complementary to ESA-owned EO missions, the programme allows European users access to a large portfolio of TPM and is particularly important for promoting the international use of EO data.
In line with the Earthnet Programme objectives first established in 1977, ESA aims to foster cooperation and collaboration with not only other national space agencies, but also commercial mission providers. In recent years the availability of low cost small satellites and the innovation of constellations resulted in an increased number of commercial companies who have established business models to provide information services fed by their own satellite systems. These New Space players are now portraying an important role in the EO international strategy. Some of these new missions are potential candidates for Earthnet TPMs, and ESA have therefore set up a project to assess the quality and the suitability of these missions and also to establish dialogues with the various mission providers in order to improve the overall coherence of the EO system. This project is known as the Earthnet Data Assessment Pilot (EDAP).
The EDAP consortium, headed by Telespazio VEGA UK is aimed at the provision of various clusters of expertise to perform an early data quality assessment of existing or future EO missions from national or commercial providers, which may potentially become TPMs within ESA’s Earthnet Programme. Complementary to this support is a focus on the generation of methodologies and guidelines for training and capacity building with each mission provider in regards to performing efficient data quality assessments in preparation for future missions.
This work presents how the EDAP activities are organized and executed, and will also provide details of the various missions included within each of the instrument-specific domains covering Optical Sensors, Synthetic Aperture Radar (SAR) and Atmospheric missions. Important multi-mission aspects will also be presented for studies that will require inputs from several missions, possibly spanning multiple instrument domains; such studies contribute to interoperability across existing and future missions and help foster synergies between these missions.
In the context of Long Term Data Preservation, ESA, with the support of the Instrument Data Evaluation and Quality Analysis Service (IDEAS+) team, reprocessed over 600’000 Landsat 1-5 Multi-Spectral Scanner (MSS) products acquired in the period 1975 - 2001 from the European Ground Stations complementing the existing 1 million Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) Level 1 products, with a geographical coverage from Greenland to Continental Europe and North Africa. The reprocessing software and quality control tool developed for the reprocessing present new features aimed at improving the radiometric and geometric accuracy, image data recovery and including additional quality assurance information. An accurate geometric and radiometric accuracy evaluation across the over 40-years long operative life of Landsat is vital for the exploitation of long time series analysis and for the future development of multi-mission Analysis Ready Data (ARD). This paper proposes to evaluate the geometric accuracy and the radiometric calibration accuracy of the Landsat Level 1 products delivered by ESA. It is obvious that across Landsat historical mission the accuracy is not the same; data are acquired with the MSS, the TM and the ETM+ sensors with improved characteristics for the most recent ones. All these differences between remote sensing systems, did not preclude to engineer processing algorithms with one main objective to harmonize physical measurement and ensure interoperability with the ongoing missions such as Landsat 8 - OLI / Sentinel 2 – MSI. This approach is a key aspect for the future development of multi mission ARD. The geometric quality assurance parameters are exposed in the Level 1 products. These scene based parameters allows to filter out and select, for a given region, if needed the most accurate products. Also, to demonstrate that these parameters are consistent is fundamental. Because of the ageing of missions, specifically for MSS missions, the processing cannot solely rely on information coming from telemetry. It is mandatory to apply ground model and to estimate both external and internal orientation parameters for what concerned the geometric model. Furthermore, for some parameters, the use of single scene for calibration of geo referencing model is not sufficient and the estimate of parameters becomes more robust when considering, instead of one scene, all data recorded in the acquisition period and downlinked to the receiving station. Also, the proposed methodology herein validates the stability of geometric accuracy for a long period of time within the orbit. The product quality assurance parameters are compared with the same ones but inferred from an image matching methods comparing disparity between two geometric grids; the input ESA image and geometric reference image (Global Land Survey data). A comparison with relative USGS products is also performed.
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