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The Theia Land Data Centre


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Highlights: This paper describes the Theia Land Data Centre, its service and data infrastructure, network of scientific expertise centres, product portfolio and main activities. The paper emphasizes in its conclusion the connection between Theia and the Copernicus programme at European level, and the forthcoming Remote Sensing Data Infrastructure network at international level.
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The Theia Land Data Centre
Nicolas Baghdadi (1), Marc Leroy (2), Pierre Maurel (1), Selma Cherchali (2), Magali Stoll (3),
Jean-François Faure (4), Jean-Christophe Desconnets (4), Olivier Hagolle (5), Jérôme Gasperi (2),
Philippe Pacholczyk (2)
Irstea, Maison de la Télédétection, Montpellier
Cnes, Toulouse
IRD, Maison de la Télédétection, Montpellier
IGN-Espace, Toulouse
Cesbio, Toulouse
Highlights: This paper describes the Theia Land Data Centre, its service and data infrastructure,
network of scientific expertise centres, product portfolio and main activities. The paper emphasizes in
its conclusion the connection between Theia and the Copernicus programme at European level, and the
forthcoming Remote Sensing Data Infrastructure network at international level.
Key words: remote sensing, data infrastructure
Background and Objectives
Ten French public institutions involved in Earth Observation, environmental studies and scientific research
(Cea, Cerema, Cnes, Cirad, Cnrs, Ign, Inra, Ird, Irstea and Météo-France), have launched in 2012 the Theia Land
Data Centre ( , pooling their expertise and resources to make satellite data available to the
environmental research community and actors in charge of public policies. The Theia primary mission is (i) to
build a national service and data infrastructure able to produce value-added space data over land and provide
services fitted to users’ needs, (ii) to support the sharing of experience and scientific knowledge on
methodologies relevant to process and use space data for land thematic issues.
The overall objective of the Theia Land Data Centre is to enhance scientific knowledge and support the
development of management capacities in domains related with anthropogenic action and climate impact on
ecosystems and territories, the observation, quantification and modelling of water and carbon cycles, the
monitoring and modelling of changes in societies and their activities (water management, urban planning,
agriculture, forest management, …), the understanding of biodiversity, its dynamics and related preservation
strategies. To this end, Theia is working to produce data, products, methods and services linked to space
observations of continental areas, from local (ecosystem and territory) to global scale, and make them available
to the user community.
Theia’s construction is based historically on the merge of activities linked to the Geosud project on the one
hand, and to Cnes research and development on the other hand.
Equipex Geosud project was selected as part of the call for proposals “Equipment of Excellence” in the
national Programme d’Investissements d’Avenir (large national bond issued in 2011). The project gathers a large
number of institutional partners in research, high education and public management of the environment and
territories. Equipex Geosud’s mission is to develop a national satellite imagery infrastructure to serve the
research on environment and territories and its applications in the management of public policies. The objective
is to contribute to the full development of the potential of satellite imagery. Geosud is described in depth in a
companion paper [1].
Cnes on its side has developed for more than a decade activities aiming at producing added value products
from satellite imagery and distributing them to the scientific user community. Activities such as Kalideos, with
long time series of Spot data on selected sites, Postel [2], a precursor of Theia, specialized in the production of
low resolution time series of biophysical variables at global scale, Hydroweb, time series of large lakes and
rivers level derived from altimetry data, Spot World Heritage, aiming at distributing freely to the international
community a large fraction of the global archive of Spot data at orthorectified level, fall in this general category.
Theia is based on a distributed services and data infrastructure, and a network of thematic and regional
expertise centres in various regions in France and in overseas French territories. Figure 1 shows Theia’s general
organisation and governance.
Figure 1 – Theia’s structure and governance scheme
Service and Data Infrastructure
Theia’s infrastructure is based on a concept of database federation system (Figure 2). Currently, Theia’s
infrastructure relies on two data centres- the Cnes data centre located in Toulouse, and the Geosud data centre
located in Montpellier, with an extension in Paris connected to the Geoportail data centre. The data centres are
interconnected across a very high speed (10 Gbits/s) telecommunication network Renater, a High school and
research national network. Each data centre is independent and responsible of pre-processing, storage, archiving
and distribution of its own data to the users.
Figure 2 – Theia’s Service & Data Infrastructure
Data are organized by project usually by sensor. Depending on the project, the level of processing can go
up to level 1 (e.g., orthorectification), 2 (e.g., atmospheric corrections), 3 (time composites) or more (e.g., land
cover maps). Once pre-processed, data are archived and prepared for the distribution layer. At Theia’s level,
interoperability between partners is achieved by the use of a standardized metadata model and a common
vocabulary. The communication between data centres is ensured by sharing mutalized components used by all
members of the federation : the Web portal, the Identity Provider, the Metacatalog and the Search Tool. Figure 2
shows the links between mutualized components (‘core components’) and the various data centres.
The association of new data centres to Theia’s data infrastructure is possible. It is subject to the publication
of data and metadata using REST web interface to ensure their inter-connection to shared services.
Specifications of web services, metadata model and common vocabulary provide the technical framework for
binding a new data centre to the Theia infrastructure, which can be easily integrated in a larger federation.
Network of Scientific Expertise Centres
The Scientific Expertise Centres (SECs) are laboratories or groups of national laboratories developing
innovative processes to use space data for land surfaces issues. They potentially have a dual purpose "thematic"
and / or "regional".
The “thematic” SECs are focused on added-value products, possibly with services associated with these
products. These are mono or multi-team, spread over one or more regions. Their objectives are to take part in the
validation of the products provided by the Service & Data Infrastructure, develop processes to use the data and
demonstrate new applications; contribute to network and federate the scientific players at regional, national and
even international levels, around thematic fields: farming, forestry, urban, coastal, mountain, global
“Thematic” SECs have been identified at this stage around the following added-value products : Surface
Reflectance, Albedo, Land cover, Vegetation biophysic variables, Evapotranspiration, Irrigated surfaces, Digital
soil mapping, Soil humidity, Forest biomass and changes in forest cover, Water levels of lakes and rivers,
Continental waters colours, Snow-covered surface, Urbanisation / Artificialisation, Risks associated with
infectious diseases, High frequency change detection.
Figure 3 – Theia Product Portfolio
“Regional” SEC objectives are to unify and coordinate users (scientists and public stakeholders) at regional
level, participate in community training efforts, particularly concerning added-value products developed by the
“thematic” SECs. Regional SECs have been set up so far in Alsace, Aquitaine, Languedoc- Roussillon and Midi-
Product portfolio
Products and services provided by Theia are intended to be quality-controlled, to cover broad territories and
long periods: annual satellite coverage of the national territory, high or very high resolution surface reflectance
time series, biogeophysical variables (biomass, water levels, surface humidity, etc) time series and products at
global scale, visualization and data processing tools, processing methods and algorithms, validation procedures,
methodological guidelines and frameworks for thematic applications. The products portfolio for the period 2013
– 2016 is shown in Figure 3. It includes images mainly in the optical domain at very high, high and low spatial
resolution. Specific features of the portfolio are outlined below.
Satellite data acquisition over the national territory and abroad
A programme of satellite data acquisition over the national territory has started, mainly driven by the Geosud
project. The program includes so far a yearly cover of the national territory at 5 m resolution with Spot, Rapid
Eye and IRS data for the years 1997, 2005, 2009 to 2012, the same at 1.5 m resolution with Spot 6 in 2014. It
will continue for the years to come through a specific partnership between 6 public institutions already members
of Theia, and the acquisision of a direct reception antenna and a terminal receiving Spot 6/7 data at Maison de la
Télédétection at Montpellier. The data acquisition also includes the very high resolution coverage of a number of
cities and sensitive areas (rivers, coastal line, habitats…) with Pléiades data. A specificity of the project is that
the data can be made available free of charge to the whole national public sector. User workshops are organised
to exchange information and feedback on user needs. Images are also acquired over areas in southern countries
in support to scientific partnerships abroad.
The Muscate production centre
A processing centre called Muscate has been developed by Cnes to transform data in surface reflectances
(level 2), using an innovative atmosphere correction scheme based on physical principles able to produce from
time series of raw data smooth temporal profiles of reflectances for each pixel. The processing line can process
data from Landsat, the Spot 1-5 series in the framework of the Spot World Heritage programme, and Sentinel-2
data. The target of the Theia Land Data Centre is to process Sentinel-2 at level 2 (atmospheric corrections) and
level 3 (monthly nearly cloud free composites) in a systematic way over Western Europe and other interest areas.
Muscate has already processed Landsat data over France, and from the Spot 4 / Take 5 experiment designed to
simulate the repetitiveness and coverage of Sentinel-2 data. It is currently processing data from a new similar
Take 5 experiment made this time with the Spot 5 satellite at the end of its commercial life.
Future activities
The lines of the product portfolio of Figure 3 are not commented further for the sake of conciseness. In the
future we intend to continue along the main directions outlined above, to extend the portfolio with new value-
added products developed by the Science Expertise Centres, strengthen the services and data infrastructure, and
develop an access to in-situ data useful for researchers.
European and international policy
Theia is a national effort but one of its goals is to become an element of a European network of actors
carrying similar activities and to be well articulated with the Copernicus European programme. Theia’s portfolio
is therefore conceived to be complementary to that of Copernicus. At the same time, we gear the product
development activities so that the developed products have if possible a European future. This has been the case
for global biophysical vegetation variables developed first in the Postel context and which are now part of the
Global Land Copernicus service. We will see whether Theia’s atmospheric correction products for Landsat and
Sentinel-2, and lakes and rivers water level, can become part of the Copernicus services in the near or mid-term
future. At the same time we attempt, through the reply to call for tenders issued by ESA or EU to insert Theia in
a network of European data centres having similar goals. It is felt that such a goal is essential to reach a full
interoperability between data infrastructures developed at country level.
Beyond the European level is the international one. At this stage, it is felt that the best answer that can be
given to the need of relations between actors at international level aiming at developing Earth Observation
applications at institutional level is to start the building of the Remote Sensing Data Infrastructure international
network which is the subject of the present workshop.
[1] Maurel, P., et al. Geosud. This issue.
[2] Leroy, M., P. Bicheron, R. Lacaze, F. Niño, and G. Bégni (2005). Postel, an initiative to develop
biogeophysical geocoded products. Journal of Computational Technologies, Volume 10, Partie 2, pp. 3-11.
... Our data spans three years of acquisition: 2018, 2019, and 2020, with respectively 36, 27 and 29 valid entries, see Figure 3. The length of sequences varies due to the automatic discarding of cloudy tiles by the data provider THEIA [3]. We do not apply further preprocessing such as cloud removal or radiometric calibration than what is already performed by the data provider THEIA. ...
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While annual crop rotations play a crucial role for agricultural optimization, they have been largely ignored for automated crop type mapping. In this paper, we take advantage of the increasing quantity of annotated satellite data to propose to model simultaneously the inter- and intra-annual agricultural dynamics of yearly parcel classification with a deep learning approach. Along with simple training adjustments, our model provides an improvement of over 6.3% mIoU over the current state-of-the-art of crop classification, and a reduction of over 21% of the error rate. Furthermore, we release the first large-scale multi-year agricultural dataset with over 300,000 annotated parcels.
... S2 is primarily aimed at freely offering high spatiotemporal resolution optical imagery for operational land cover and change detection mapping [43]. These data can be downloaded on the Theia Land Service website [44], which produces and distributes level 2A products, corrected atmospherically using the MAJA/MACCS software developed thanks to the coordination between Cnes/Cesbio and DLR [45][46][47][48]. ...
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Tree species identification and their geospatial distribution mapping are crucial for forest monitoring and management. The satellite-based remote sensing time series of Sentinel missions (Sentinel-1 and Sentinel-2) are a perfect tool to map the type, location, and extent of forest cover over large areas at local or global scale. This study is focused on the geospatial mapping of the endemic argan tree (Argania spinosa (L.) Skeels) and the identification of two other tree species (sandarac gum and olive trees) using optical and synthetic aperture radar (SAR) time series. The objective of the present work is to detect the actual state of forest species trees, more specifically the argan tree, in order to be able to study and analyze forest changes (degradation) and make new strategies to protect this endemic tree. The study was conducted over an area located in Essaouira province, Morocco. The support vector machine (SVM) algorithm was used for the classification of the two types of data. We first classified the optical data for tree species identification and mapping. Second, the SAR time series were used to identify the argan tree and distinguish it from other species. Finally, the two types of satellite images were combined to improve and compare the results of classification with those obtained from single-source data. The overall accuracy (OA) of optical classification reached 86.9% with a kappa coefficient of 0.84 and declined strongly to 37.22% (kappa of 0.29) for SAR classification. The fusion of multisensor data (optical and SAR images) reached an OA of 86.51%. A postclassification was performed to improve the results. The classified images were smoothed, and therefore, the quantitative and qualitative results showed an improvement, in particular for optical classification with a highest OA of 89.78% (kappa coefficient of 0.88). The study confirmed the potential of the multitemporal optical data for accurate forest cover mapping and endemic species identification. 1. Introduction The monitoring of forest cover plays a crucial role in biodiversity, feedstock, and water cycle, etc. Therefore, tree species discrimination is necessary and fundamental for this process. Satellite imagery together with machine learning techniques have become an irreplaceable tool for tree species mapping. Passive and/or active sensors provide valuable geospatial information to identify tree types. Recently, machine learning algorithms are techniques that have been successfully used based on remote sensing data for tree type classification [1]. Current satellite sensors, such as Sentinel missions, facilitated the tree species mapping at local as well as national scale. In literature, the most remote sensing data used for forest type mapping is optical imagery. For such data, the forest type classification is based on reflected spectral features that are acquired by optical sensors. However, the different forest type classes (tree crops) can be characterized by similar spectral signatures. Thus, tree species identification becomes difficult and complicated using only spectral features [2]. Weather is one of the factors that caused such a confusion due to the dependence of optical data on sunlight. SAR imagery is a supplement of optical data due to its capacity to acquire images in all-day and in all-weather conditions through penetrating rain and clouds. In 2004, Touzi et al. [3] used the C band of SAR data to discriminate forest tree species and figured out that the information of VV, HH, and VH polarizations identified better the forest trees without leaves. Another study [4] showed that information extracted from SAR imagery can discriminate easily between forest and nonforest types. In addition, the texture features [5, 6] extracted from gray-level cooccurrence matrix (GLCM) have been widely investigated in forest type classification using SAR imagery [6–9]. However, the results of land cover/land use (LC/LU) classification using SAR data are not consistent [9–11]. Therefore, many studies indicated that combining SAR features with optical data can improve the forest type classification results [12, 13]. Machine learning algorithms are powerful methods for forest and crop classification. SVM classification algorithm has proven its effectiveness for classifying forest tree species using satellite imagery [14–16]. The endemic Moroccan tree called argan (Argania spinosa) covered 950,000 ha in 2010 [17]. It is part of the semiarid Mediterranean domain in southwest Morocco in transition towards the Sahara [18]. The zone was declared a UNESCO MAB (Man and the Biosphere Reserve) in 1998. Argan trees have provided multiple ecosystem products and services including the provision of fruits from which argan oil is produced. Argan is a slow-growing tree with shrubby architecture which has a lifespan of around 200 years [19]. Argan trees have traditionally provided multiple ecosystem products and services including the provision of fruits from which argan oil is produced; leaves and young shoots eaten by sheep, goats, and camels; and wood for fiber and fuel [20]. During the last century, more than half of the argan forest of Morocco disappeared, mainly in the plains, and its mean density fell from 100 to less than 30 stumps/ha to meet the growing fuel needs of major Moroccan cities [21]. Recent work distinguishes between lowland and mountain argan plantations, where ecological and economic conditions differ significantly [22]. The former seems very threatened, as it has great agricultural potential and absorbs most of the emigration from mountain areas. Socioeconomic changes and the farms’ modernisation since the 1980s have led to a cumulative 2.6% reduction in forest cover over the last 17 years [23]. In mountain argan groves, where the user population retains a traditional pattern of use, several trends can be discerned. Therefore, these several factors caused degradation, desertification, and problems in the natural regeneration of the argan population [24, 25]. To overcome these problems, specific management strategies are needed, in such a way that the state of forest resources should be assessed with detailed and accurate geospatial distribution of different forest tree species and, more specifically, argan trees. The estimate of forest cover and its mapping relies mainly on fieldwork and ground surveys, which require cost and time. However, high-resolution satellite data such as Landsat and Sentinel images are available and provide huge amounts of data regularly on large areas in short time and remotely. Satellite image-based land cover land use mapping, generally, still have some challenges despite the improvement of spatial and temporal resolution [26–29]. Particularly, forest tree species discrimination can face the presence of similar classes in the study area and/or the presence of several features within the same pixel, contributing to spectral confusion between different forest over types [30, 31]. In this contest, we attempted to identify the argan tree from other forest species based on the classification of time series of SAR data and its derived products (GLCM texture features: correlation variance and mean), the classification NDVI time series derived from optical images, and finally, the classification of combined multisensor time series data using an SVM classifier. The achievement of the objective of the present work is illustrated in four steps, namely, (i) to model and discriminate the phenological evolution of the Moroccan argan tree from other tree species using S2-derived NDVI time series; (ii) to map the spatial extent of forest cover and identify its tree species; (iii) to evaluate the tree species mapping using optical data, SAR data, and the fusion of the two types of satellite imagery; and (iv) to evaluate and assess the potential of SAR data to improve the tree species classification performance and overcome the confusion constraints. 2. Background Morocco is one of the few countries in North Africa to have a diverse range of endemism and biodiversity [32]. The only species in its genus, Argania spinosa (L.) Skeels, belongs to the Sapotaceae, a tropical and subtropical tree family. It is endemic only to Morocco, where it grows in arid and semiarid climates with 150 to 400 mm of rainfall per year. It is a slow-growing spiny tree that can reach a maximum height of around 10 m (Figure 1). The leaves are small (20 mm), with a spatulated shape and an entire margin. Despite having evergreen foliage, leaves may be shed entirely or partially in response to summer stress.
... sentinel/home) produces and distributes ortho-rectified Sentinel-2 data expressed in reflectance at the top of the atmosphere, the 1C level. eia [19] produces and distributes level 2A data, corrected for atmospheric effects using the MAJA software developed thanks to the coordination between CNES and CESBIO [20][21][22]. is processing chain uses multitemporal information to detect clouds and their shadows, estimate the optical thickness of aerosols and the amount of water vapour, and correct for atmospheric effects. ...
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The monitoring of cultivated crops and the types of different land covers is a relevant environmental and economic issue for agricultural lands management and crop yield prediction. In this context, this paper aims to use and evaluate the contribution of multisensors classification based on machine learning classifiers to crop-type identification in a semiarid area of Morocco. It is a very heterogeneous zone characterized by mixed crops (tree crops with annual crops, same crop with different phenological states during the same agricultural season, crop rotation, etc.). Therefore, such heterogeneity made the crop-type discrimination more complicated. To overcome these challenges, the present work is the first study in this area which used the fusion of high spatiotemporal resolution Sentinel-1 and Sentinel-2 satellite images for land use and land cover mapping. Three machine learning classifier algorithms, artificial neural network (ANN), support vector machine (SVM), and maximum likelihood (ML), were applied to identify and map crop types in irrigated perimeter. In situ observations of the year 2018, for the R3 perimeter of Haouz plain in central Morocco, were used with satellite data of the same year to perform this work. The results showed that combined images acquired in C-band and the optical range improved clearly the crop-type classification performance (overall accuracy = 89%; Kappa = 0.85) compared to the classification results of optical or SAR data alone.
... disposition et de la facilitation de la d?couverte des images, plusieurs ini- tiatives gouvernementales et industrielles ont vu le jour, parmi lesquelles on retrouve des initiatives ? l'?chelle mondiale (GEOSS 8 , Earth Explorer USGS 9 ), europ?enne (INSPIRE [DIRECTIVE, 2007] [KAZMIERSKI et al., 2014], THEIA [BAGHDADI et al., 2015]). ...
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A l'heure actuelle, les images satellites constituent une source d'information incontournable face à de nombreux enjeux environnementaux (déforestation, caractérisation des paysages, aménagement du territoire, etc).En raison de leur complexité, de leur volume important et des besoins propres à chaque communauté, l'analyse et l'interprétation des images satellites imposent de nouveaux défis aux méthodes de fouille de données.Le parti-pris de cette thèse est d'explorer de nouvelles approches, que nous situons à mi-chemin entre représentation des connaissances et apprentissage statistique, dans le but de faciliter et d'automatiser l'extraction d'informations pertinentes du contenu de ces images. Nous avons, pour cela, proposé deux nouvelles méthodes qui considèrent les images comme des données quantitatives massives dépourvues de labels sémantiques et qui les traitent en se basant sur les connaissances disponibles.Notre première contribution est une approche hybride, qui exploite conjointement le raisonnement à base d'ontologie et le clustering semi-supervisé. Le raisonnement permet l'étiquetage sémantique des pixels à partir de connaissances issues du domaine concerné. Les labels générés guident ensuite la tâche de clustering, qui permet de découvrir de nouvelles classes tout en enrichissant l'étiquetage initial.Notre deuxième contribution procède de manière inverse. Dans un premier temps, l'approche s'appuie sur un clustering topographique pour résumer les données en entrée et réduire de ce fait le nombre de futures instances à traiter par le raisonnement. Celui ci n'est alors appliqué que sur les prototypes résultant du clustering, l'étiquetage est ensuite propagé automatiquement à l'ensemble des données de départ. Dans ce cas, l'importance est portée sur l'optimisation du temps de raisonnement et à son passage à l'échelle.Nos deux approches ont été testées et évaluées dans le cadre de la classification et de l'interprétation d'images satellites. Les résultats obtenus sont prometteurs et montrent d'une part, que la qualité de la classification peut être améliorée par une prise en compte automatique des connaissances et que l'implication des experts peut être allégée, et d'autre part, que le recours au clustering topographique en amont permet d'éviter le calcul des inférences sur la totalité des pixels de l'image.
... Image discovery services are based on the OGC CS-W standard (Catalog Service for the Web) and take into account the recommendations of the INSPIRE Directive. Moreover, this service is based on a semantic referential that enriches the description of the images in order to facilitate their selection by non-expert users [2]. The viewing and download services are implemented from OGC standard services WMS (Web Mapping Service) and WMTS (Web Mapping and Tiling Service). ...
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