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Special Issue "Sentinel Analysis Ready Data (Sentinel ARD)"
A special issue of Remote Sensing (ISSN 2072-4292).
Deadline for manuscript submissions: 30 April 2020.
Dr. Ioannis Manakos Website E-Mail
Centre for Research and Technology Hellas, Information Technologies Institute, Hellas 6th km Harilaou-Thermi, 57001 Thessaloniki, Greece
Interests: earth observation; geoinformation technologies; big data; time series analysis; uncertainty handling; biodiversity monitoring; food security
Dr. Olivier Hagolle Website E-Mail
Centre d’Etudes Spatiales de la BIOsphère (CESBIO), 18 avenue E.Belin, 31401 Toulous, France
Interests: optical remote sensing; earth observation; analysis ready data; absolute calibration; cloud detection; atmospheric correction; land surface monitoring
Dr. Jose Gomez-Dans Website E-Mail
Department of Geography, University College London, Gower Street , WC1E 6BT London, UK
Interests: remote sensing; data assimilation; global change; radiative transfer; inverse problems; gaussian processes; microwave remote sensing, optical remote sensing, thermal remote sensing, fire, vegetation, image processing, signal processing, vegetation modeling, fire modeling, data assimilation; emulation
Analysis-ready data (ARD) is a growing trend in the satellite Earth observations community, driven by the development and implementation of the Earth observations data cube (EODC) technology. The Committee on Earth Observations Satellites (CEOS) defines ARD as satellite data that have been processed to a minimum set of requirements and organized into a form that allows immediate analysis with a minimum of additional user effort and interoperability both through time and with other datasets. Systematic and regular provision of analysis-ready data (ARD) can significantly reduce the burden on EO data users by minimizing the time and scientific knowledge required to access and prepare remotely-sensed data having consistent and spatially aligned calibrated observations.
While Landsat ARD products are commonly used and generated either through USGS Landsat Collection 1 repository or automated custom preprocessing workflows, Sentinel ARD product generation is still an issue that has not been addressed yet as this is not commonly provided by the Copernicus Open Access Hub (http://scihub.copernicus.eu). This clearly limits the usage of Sentinel data, and methodologies are required to fully benefit from this European EO program. Additionally, applications that could exploit different observational streams are hampered by the difficulty of combining different products often designed without considering e.g. mixed sensor use-cases.
This Special Issue is consequently aiming to cover the most recent advances in ARD developments and implementations for Sentinel data, to support community consensus on Sentinel ARD. We therefore welcome contributions with respect to (but without being restricted to):
- Methods for generating Analysis Ready Data for both optical (Sentinel-2 and 3) and SAR imagery (Sentinel-1);
- Defining ARD level for thermal imagery (Sentinel-3);
- Defining guidelines for ARD product interoperability;
- Defining ARD level for Sentinel-5P (Air pollution);
- Significance of ARD for Data Producers; Data Distributors; Data Users;
- Data quality, reliability, flagging, etc.;
- Cost/Benefits analysis for ARD data;
- Thematic application of Sentinel ARD;
- Software tools that support generating analysis-ready data for both optical and SAR imagery;
- Support to policy framework such as the Sustainable Development Goals, the Paris Agreement, or Aichi targets;
- Links with initiatives like Copernicus or the Global Earth Observation System of Systems (GEOSS);
- Data cube interoperability;
- Error propagation and uncertainty handling;
- ARD standards;
- User driven requirements for ARD;
- The significance of sensor Cal/Val in ARD including issues related to cross-sensor interoperability.
Dr. Gregory Giuliani
Mr. Daniel Wicks
Dr. Ioannis Manakos
Dr. Olivier Hagolle
Dr. Jose Gómez-Dans
Dr. Cristian Rossi
- Giuliani G., Chatenoux B., Honeck E., RichardJ.-P. (2018) Towards Sentinel 2 Analysis Ready Data: A Swiss Data Cube Perspective. In: IGARSS 2018 - IEEE International Geoscience and Remote Sensing Symposium. Valencia (Spain). p. 8668-8671 DOI: 10.1109/IGARSS.2018.8517954
- Truckenbrodt J., Freemantle T., Williams C., Jones T., Small D., Dubois C., Thiel C., Rossi C., Syriou A., Giuliani G. (2019) Towards Sentinel-1 SAR Analysis Ready Data: A best practices assessment on preparing backscatter data for the cube. Data4(3):93 DOI: 10.3390/data4030093
- Ticehurst, C.; Zhou, Z.-S.; Lehmann, E.; Yuan, F.; Thankappan, M.; Rosenqvist, A.; Lewis, B.; Paget, M. Building a SAR-Enabled Data Cube Capability in Australia Using SAR Analysis Ready Data. Data2019, 4, 100. DOI: 10.3390/data4030100
- Holmes C. (2018) Analysis Ready Data Defined: https://medium.com/planet-stories/analysis-ready-data-defined-5694f6f48815
- Frantz, D., 2019. FORCE—Landsat+ Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11(9), p.1124.
- Yin, F., Lewis, P.E., Gomez-Dans, J. and Wu, Q., 2019. A sensor-invariant atmospheric correction method: application to Sentinel-2/MSI and Landsat 8/OLI.
- Hagolle, O., G Dedieu, B Mougenot, V Debaecker, B Duchemin, A Meygret, 2010, Correction of aerosol effects on multi-temporal images acquired with constant viewing angles: Application to Formosat-2 images,, Remote Sensing of Environment, 2010, 112 (4), 1689-1701
- Hagolle, O., M Huc, D Villa Pascual, G Dedieu, 2015, A Multi-Temporal and Multi-Spectral Method to Estimate Aerosol Optical Thickness over Land, for the Atmospheric Correction of FormoSat-2, LandSat, VENμS and Sentinel-2 Images, Remote Sensing 7 (3), 2015,,2668-2691
- Helder, D., Markham, B., Morfitt, R., Storey, J., Barsi, J., Gascon, F., Clerc, S., LaFrance, B., Masek, J., Roy, D. and Lewis, A., 2018. Observations and Recommendations for the Calibration of Landsat 8 OLI and Sentinel 2 MSI for improved data interoperability. Remote Sensing, 10(9), p.1340.
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- Earth observations
- Analysis-ready data
- Data cube
- Time-series analysis
- User-driven applications
- Cloud screening
- Atmospheric correction
- Error and uncertainty