Special Issue "Sentinel Analysis Ready Data (Sentinel ARD)"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Satellite Missions for Earth and Planetary Exploration".

Deadline for manuscript submissions: closed (31 August 2021).

Special Issue Editors

Dr. Gregory Giuliani
E-Mail Website
Guest Editor
Institute for Environmental Sciences & UNEP/GRID-Geneva, University of Geneva, 66 Boulevard Carl-Vogt, CH 1205 Geneva, Switzerland
Interests: earth observations; data cube; sustainable development; GEO/GEOSS; environmental sciences
Special Issues and Collections in MDPI journals
Mr. Daniel Wicks
E-Mail Website
Guest Editor
Satellite Applications Catapult, Electron Building, Fermi Avenue, Harwell Campus, Didcot, Oxfordshire, OX110QR, UK
Interests: remote sensing; earth observation; analysis ready data; data cube; environmental science; open data; UNSDG’s applications
Dr. Ioannis Manakos
E-Mail Website
Guest Editor
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
Special Issues and Collections in MDPI journals
Dr. Olivier Hagolle
E-Mail Website
Guest Editor
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
Special Issues and Collections in MDPI journals
Dr. Jose Gomez-Dans
E-Mail Website
Guest Editor
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
Special Issues and Collections in MDPI journals
Dr. Cristian Rossi
E-Mail Website
Guest Editor
Satellite Applications Catapult, Electron Building, Fermi Avenue, Harwell Campus, Didcot, Oxfordshire OX110QR, UK
Interests: remote sensing of the environment; data processing

Special Issue Information

Dear Colleagues,

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
Guest Editors

Related References

  • 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.

Manuscript Submission Information

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Keywords

  • Sentinel 
  • Earth observations 
  • Analysis-ready data 
  • Interoperability
  • Data cube 
  • Time-series analysis
  • User-driven applications
  • Cloud screening 
  • Atmospheric correction 
  • Error and uncertainty

Published Papers (3 papers)

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Research

Article
Optimal Solar Zenith Angle Definition for Combined Landsat-8 and Sentinel-2A/2B Data Angular Normalization Using Machine Learning Methods
Remote Sens. 2021, 13(13), 2598; https://doi.org/10.3390/rs13132598 - 02 Jul 2021
Viewed by 551
Abstract
Data from Landsat-8 and Sentinel-2A/2B are often combined for terrestrial monitoring because of their similar spectral bands. The bidirectional reflectance distribution function (BRDF) effect has been observed in both Landsat-8 and Sentinel-2A/2B reflectance data. However, there is currently no definition of solar zenith [...] Read more.
Data from Landsat-8 and Sentinel-2A/2B are often combined for terrestrial monitoring because of their similar spectral bands. The bidirectional reflectance distribution function (BRDF) effect has been observed in both Landsat-8 and Sentinel-2A/2B reflectance data. However, there is currently no definition of solar zenith angle (θsz) that is suitable for the normalization of the BRDF-adjusted reflectance from the three sensors’ combined data. This paper describes the use of four machine learning (ML) models to predict a global θsz that is suitable for the normalization of bidirectional reflectance from the combined data in 2018. The observed θsz collected globally, and the three locations in the Democratic Republic of Congo (26.622°E, 0.356°N), Texas in the USA (99.406°W 30.751°N), and Finland (25.194°E, 61.653°N), are chosen to compare the performance of the ML models. At a global scale, the ML models of Support Vector Regression (SVR), Multi-Layer Perception (MLP), and Gaussian Process Regression (GPR) exhibit comparably good performance to that of polynomial regression, considering center latitude as the input to predict the global θsz. GPR achieves the best overall performance considering the center latitude and acquisition time as inputs, with a root mean square error (RMSE) of 1.390°, a mean absolute error (MAE) of 0.689°, and a coefficient of determination (R2) of 0.994. SVR shows an RMSE of 1.396°, an MAE of 0.638°, and an R2 of 0.994, following GPR. For a specific location, the SVR and GPR models have higher accuracy than the polynomial regression, with GPR exhibiting the best performance, when center latitude and acquisition time are considered as inputs. GPR is recommended for predicting the global θsz using the three sensors’ combined data. Full article
(This article belongs to the Special Issue Sentinel Analysis Ready Data (Sentinel ARD))
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Article
Design of a Local Nested Grid for the Optimal Combined Use of Landsat 8 and Sentinel 2 Data
Remote Sens. 2021, 13(8), 1546; https://doi.org/10.3390/rs13081546 - 16 Apr 2021
Viewed by 409
Abstract
Earth Observation (EO) imagery is difficult to find and access for the intermediate user, requiring advanced skills and tools to transform it into useful information. Currently, remote sensing data is increasingly freely and openly available from different satellite platforms. However, the variety of [...] Read more.
Earth Observation (EO) imagery is difficult to find and access for the intermediate user, requiring advanced skills and tools to transform it into useful information. Currently, remote sensing data is increasingly freely and openly available from different satellite platforms. However, the variety of images in terms of different types of sensors, spatial and spectral resolutions generates limitations due to the heterogeneity and complexity of the data, making it difficult to exploit the full potential of satellite imagery. Addressing this issue requires new approaches to organize, manage, and analyse remote-sensing imagery. This paper focuses on the growing trend based on satellite EO and the analysis-ready data (ARD) to integrate two public optical satellite missions: Landsat 8 (L8) and Sentinel 2 (S2). This paper proposes a new way to combine S2 and L8 imagery based on a Local Nested Grid (LNG). The LNG designed plays a key role in the development of new products within the European EO downstream sector, which must incorporate assimilation techniques and interoperability best practices, automatization, systemization, and integrated web-based services that will potentially lead to pre-operational downstream services. The approach was tested in the Duero river basin (78,859 km2) and in the groundwater Mancha Oriental (7279 km2) in the Jucar river basin, Spain. In addition, a viewer based on Geoserver was prepared for visualizing the LNG of S2 and L8, and the Normalized Difference Vegetation Index (NDVI) values in points. Thanks to the LNG presented in this paper, the processing, storage, and publication tasks are optimal for the combined use of images from two different satellite sensors when the relationship between spatial resolutions is an integer (3 in the case of L8 and S2). Full article
(This article belongs to the Special Issue Sentinel Analysis Ready Data (Sentinel ARD))
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Article
Monitoring Vegetation Change in the Presence of High Cloud Cover with Sentinel-2 in a Lowland Tropical Forest Region in Brazil
Remote Sens. 2020, 12(11), 1829; https://doi.org/10.3390/rs12111829 - 05 Jun 2020
Cited by 7 | Viewed by 2347
Abstract
Forests play major roles in climate regulation, ecosystem services, carbon storage, biodiversity, terrain stabilization, and water retention, as well as in the economy of numerous countries. Nevertheless, deforestation and forest degradation are rampant in many parts of the world. In particular, the Amazonian [...] Read more.
Forests play major roles in climate regulation, ecosystem services, carbon storage, biodiversity, terrain stabilization, and water retention, as well as in the economy of numerous countries. Nevertheless, deforestation and forest degradation are rampant in many parts of the world. In particular, the Amazonian rainforest faces the constant threats posed by logging, mining, and burning for agricultural expansion. In Brazil, the “Sete de Setembro Indigenous Land”, a protected area located in a lowland tropical forest region at the border between the Mato Grosso and Rondônia states, is subject to illegal deforestation and therefore necessitates effective vegetation monitoring tools. Optical satellite imagery, while extensively used for landcover assessment and monitoring, is vulnerable to high cloud cover percentages, as these can preclude analysis and strongly limit the temporal resolution. We propose a cloud computing-based coupled detection strategy using (i) cloud and cloud shadow/vegetation detection systems with Sentinel-2 data analyzed on the Google Earth Engine with deep neural network classification models, with (ii) a classification error correction and vegetation loss and gain analysis tool that dynamically compares and updates the classification in a time series. The initial results demonstrate that such a detection system can constitute a powerful monitoring tool to assist in the prevention, early warning, and assessment of deforestation and forest degradation in cloudy tropical regions. Owing to the integrated cloud detection system, the temporal resolution is significantly improved. The limitations of the model in its present state include classification issues during the forest fire period, and a lack of distinction between natural vegetation loss and anthropogenic deforestation. Two possible solutions to the latter problem are proposed, namely, the mapping of known agricultural and bare areas and its subsequent removal from the analyzed data, or the inclusion of radar data, which would allow a large amount of finetuning of the detection processes. Full article
(This article belongs to the Special Issue Sentinel Analysis Ready Data (Sentinel ARD))
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