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Sentinel Analysis Ready Data (Sentinel ARD)

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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 (30 June 2022) | Viewed by 28515

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Special Issue Editors

Dr. Gregory Giuliani

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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, Collections and Topics 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, Collections and Topics 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, Collections and Topics in MDPI journals

Dr. Jose Gomez-Dans

E-Mail Website
Guest Editor

Department of Geography, University College London, Gower Street, London WC1E 6BT, 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, Collections and Topics in MDPI journals

Dr. Cristian Rossi

E-Mail Website
Guest Editor

1. Satellite Applications Catapult, Electron Building, Fermi Avenue, Harwell Campus, Didcot, Oxfordshire OX11 0QR, UK
2. School of Geography and the Environment, University of Oxford, South Parks Road, Oxford OX1 3QY, UK
Interests: earth observation; optical and radar remote sensing; InSAR data processing; spatial finance; climate change adaptation; early warning
Special Issues, Collections and Topics in MDPI journals

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.

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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 (6 papers)

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19 pages, 9905 KiB

Open AccessArticle

A Flexible Multi-Temporal and Multi-Modal Framework for Sentinel-1 and Sentinel-2 Analysis Ready Data

by Priti Upadhyay, Mikolaj Czerkawski, Christopher Davison, Javier Cardona, Malcolm Macdonald, Ivan Andonovic, Craig Michie, Robert Atkinson, Nikela Papadopoulou, Konstantinos Nikas and Christos Tachtatzis

Remote Sens. 2022, 14(5), 1120; https://doi.org/10.3390/rs14051120 - 24 Feb 2022

Cited by 5 | Viewed by 4025

Abstract

The rich, complementary data provided by Sentinel-1 and Sentinel-2 satellite constellations host considerable potential to transform Earth observation (EO) applications. However, a substantial amount of effort and infrastructure is still required for the generation of analysis-ready data (ARD) from the low-level products provided by the European Space Agency (ESA). Here, a flexible Python framework able to generate a range of consistent ARD aligned with the ESA-recommended processing pipeline is detailed. Sentinel-1 Synthetic Aperture Radar (SAR) data are radiometrically calibrated, speckle-filtered and terrain-corrected, and Sentinel-2 multi-spectral data resampled in order to harmonise the spatial resolution between the two streams and to allow stacking with multiple scene classification masks. The global coverage and flexibility of the framework allows users to define a specific region of interest (ROI) and time window to create geo-referenced Sentinel-1 and Sentinel-2 images, or a combination of both with closest temporal alignment. The framework can be applied to any location and is user-centric and versatile in generating multi-modal and multi-temporal ARD. Finally, the framework handles automatically the inherent challenges in processing Sentinel data, such as boundary regions with missing values within Sentinel-1 and the filtering of Sentinel-2 scenes based on ROI cloud coverage. Full article

(This article belongs to the Special Issue Sentinel Analysis Ready Data (Sentinel ARD))

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19 pages, 2197 KiB

Open AccessArticle

Analysis-Ready Data from Hyperspectral Sensors—The Design of the EnMAP CARD4L-SR Data Product

by Martin Bachmann, Kevin Alonso, Emiliano Carmona, Birgit Gerasch, Martin Habermeyer, Stefanie Holzwarth, Harald Krawczyk, Maximilian Langheinrich, David Marshall, Miguel Pato, Nicole Pinnel, Raquel de losReyes, Mathias Schneider, Peter Schwind and Tobias Storch

Remote Sens. 2021, 13(22), 4536; https://doi.org/10.3390/rs13224536 - 11 Nov 2021

Cited by 6 | Viewed by 3005

Abstract

Today, the ground segments of the Landsat and Sentinel missions provide a wealth of well-calibrated, characterized datasets which are already orthorectified and corrected for atmospheric effects. Initiatives such as the CEOS Analysis Ready Data (ARD) propose and ensure guidelines and requirements so that such datasets can readily be used, and interoperability within and between missions is a given. With the increasing availability of data from operational and research-oriented spaceborne hyperspectral sensors such as EnMAP, DESIS and PRISMA, and in preparation for the upcoming global mapping missions CHIME and SBG, the provision of analysis ready hyperspectral data will also be of increasing interest. Within this article, the design of the EnMAP Level 2A Land product is illustrated, highlighting the necessary processing steps for CEOS Analysis Ready Data for Land (CARD4L) compliant data products. This includes an overview of the design of the metadata, quality layers and archiving workflows, the necessary processing chain (system correction, orthorectification and atmospheric correction), as well as the resulting challenges of this procedure. Thanks to this operational approach, the end user will be provided with ARD products including rich metadata and quality information, which can readily be integrated in analysis workflows, and combined with data from other sensors. Full article

(This article belongs to the Special Issue Sentinel Analysis Ready Data (Sentinel ARD))

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20 pages, 6192 KiB

Open AccessArticle

Optimal Solar Zenith Angle Definition for Combined Landsat-8 and Sentinel-2A/2B Data Angular Normalization Using Machine Learning Methods

by Jian Li and Baozhang Chen

Remote Sens. 2021, 13(13), 2598; https://doi.org/10.3390/rs13132598 - 02 Jul 2021

Cited by 2 | Viewed by 2488

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.

θ_{s z}

) 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

θ_{s z}

that is suitable for the normalization of bidirectional reflectance from the combined data in 2018. The observed

θ_{s z}

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

θ_{s z}

. 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 (

R^{2}

) of 0.994. SVR shows an RMSE of 1.396°, an MAE of 0.638°, and an

R^{2}

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

θ_{s z}

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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22 pages, 10491 KiB

Open AccessArticle

Design of a Local Nested Grid for the Optimal Combined Use of Landsat 8 and Sentinel 2 Data

by David Hernández-López, Laura Piedelobo, Miguel A. Moreno, Amal Chakhar, Damián Ortega-Terol and Diego González-Aguilera

Remote Sens. 2021, 13(8), 1546; https://doi.org/10.3390/rs13081546 - 16 Apr 2021

Cited by 7 | Viewed by 2636

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 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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20 pages, 6255 KiB

Open AccessArticle

Monitoring Vegetation Change in the Presence of High Cloud Cover with Sentinel-2 in a Lowland Tropical Forest Region in Brazil

by Tatiana Nazarova, Pascal Martin and Gregory Giuliani

Remote Sens. 2020, 12(11), 1829; https://doi.org/10.3390/rs12111829 - 05 Jun 2020

Cited by 16 | Viewed by 6769

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 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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14 pages, 17070 KiB

Open AccessTechnical Note

An Operational Analysis Ready Radar Backscatter Dataset for the African Continent

by Fang Yuan, Marko Repse, Alex Leith, Ake Rosenqvist, Grega Milcinski, Negin F. Moghaddam, Tishampati Dhar, Chad Burton, Lisa Hall, Cedric Jorand and Adam Lewis

Remote Sens. 2022, 14(2), 351; https://doi.org/10.3390/rs14020351 - 13 Jan 2022

Cited by 10 | Viewed by 6050

Abstract

Digital Earth Africa is now providing an operational Sentinel-1 normalized radar backscatter dataset for Africa. This is the first free and open continental scale analysis ready data of this kind that has been developed to be compliant with the CEOS Analysis Ready Data for Land (CARD4L) specification for normalized radar backscatter (NRB) products. Partnership with Sinergise, a European geospatial company and Earth observation data provider, has ensured this dataset is produced efficiently in the cloud infrastructure and can be sustained in the long term. The workflow applies radiometric terrain correction (RTC) to the Sentinel-1 ground range detected (GRD) product, using the Copernicus 30 m digital elevation model (DEM). The method is used to generate data for a range of sites around the world and has been validated as producing good results. This dataset over Africa is made available publicly as a AWS public dataset and can be accessed through the Digital Earth Africa platform and its Open Data Cube API. We expect this dataset to support a wide range of applications, including natural resource monitoring, agriculture, and land cover mapping across Africa. Full article

(This article belongs to the Special Issue Sentinel Analysis Ready Data (Sentinel ARD))

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