Big Geodata and Spatial Data Infrastructures: a Perspective of a German Authority

ORIGINAL ARTICLE https://doi.org/10.1007/s41064-025-00351-0 PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science Big Geodata and Spatial Data Infrastructures: a Perspective of aGermanAuthority Florian Beyer1·PatricBrandt 2· Michael Schmidt3·SimonKönig 4·UlrikeStahl 5· Peter Baumann6· Burkhard Golla7·HeikeGerighausen 7·MarkusMöller 5 Received: 18 December 2024 / Accepted: 9 July 2025 © The Author(s) 2025 Abstract Digital transformation is the key to turning public authorities into organisations that make decisions based on data-driven insights. Big geodata analysis can enable public authorities to tackle complex sustainability issues in order to achieve long-term goals. However, the efficient management of large amounts of geodata through the implementation of viable and state-of-the-art data infrastructures represents a major challenge for public authorities. In this article, we propose a cloud-integrated decentralised Spatial Data Infrastructure (SDI) to meet the needs of public authorities mandated to provide data products and services based on Earth Observation (EO) imagery. We describe the SDI setup, the implementation process, big geodata components, and the integration of the Copernicus Data and Exploitation Platform—DE (CODE-DE), drawing on the specific SDI implementation in a federal agricultural authority in Germany. Two practical applications are illustrated, underpinning the added value of a cloud-integrated SDI. We elaborate on lessons learnt from the SDI-implementation by summarising key findings that may facilitate the effective establishment and use of the SDI, namely i) the need for an organisational strategy, ii) identifying stakeholders, including their participatory roles, and iii) planning of long-term financial and human resources. The SDI proposed serves as a blueprint for public authorities helping them on their way to become data service providers, leveraging the potential of big geodata, including EO imagery. Keywords CODE-DE · Digital transformation · Earth observation · Public authority · Cloud architecture Note on Previous Versions This article is based on an earlier preprint version (Beyer et al. 2023). However, the manuscript has been fundamentally revised in terms of structure, focus, and content. The revised version shifts the emphasis to large-scale geodata analysis and the associated geospatial data infrastructures, with particular attention to their implementation within a German public authority. To reflect this new focus, the introduction, use cases, and discussion sections have been comprehensively rewritten. Moreover, we added more parts to the descriptive section about our SDI and updated the CODE-DE section. Florian Beyer [email protected] Markus Möller [email protected] 1Institute for Crop and Soil Science, Julius Kühn Institute—Federal Research Centre for Cultivated Plants, Bundesallee 58, 38116 Braunschweig, Germany 2German Environment Agency, Competence Center for Climate Change Impacts & Adaptation, Wörlitzer Platz 1, 06844 Dessau-Roßlau, Germany 3Center for Remote Sensing of Land Surfaces, University of Bonn, Genscherallee 3, 53113 Bonn, Germany 4Department of Earth Observation, German Space Agency at German Aerospace Center, Königswinterer Str. 552–554, 53227 Bonn, Germany 5Department of Digitalisation and Artificial Intelligence, Julius Kühn Institute—Federal Research Centre for Cultivated Plants, Erwin-Baur-Str. 27, 06484 Quedlinburg, Germany 6School of Computer Science & Engineering, Constructor University, Campus Ring 12, 28759 Bremen, Germany 7Institute for Strategies and Technology Assessment, Julius Kühn Institute—Federal Research Centre for Cultivated Plants, Stahnsdorfer Damm 81, 14532 Kleinmachnow, Germany K PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science 1Introduction Digitisation and the use of big data in public agricultural authorities is essential for evidence-based decision-making on food production, biodiversity protection, and climate-related challenges. These decisions rely on large volumes of heterogeneous data, highlighting the need for robust structures and analytical capabilities in the public sector (BMEL 2019; Delgado et al. 2019; Overton et al. 2022). Geospatial data, including observations and measurements on any spatial scale, play an important role in characterising historical and current growing conditions on agricultural land (e.g., characteristics of soil and plants, weather, topography, land cover, and biophysical conditions). These data, from a wide variety of sensors, such as earth observation instruments, data volumes of several petabytes, and increasing transfer rates, are leading to completely new challenges in big data analysis (Reichstein et al. 2019). Above all, enormous volumes of data require critical changes in the approach to manage and analyse data in order to extract relevant information as quickly and efficiently as possible (Sudmanns et al. 2020; Hengl et al. 2022). In addition, the speed with which data is collected and created often exceeds the ability to integrate them into productive models (Reichstein et al. 2019). In particular, the (free) availability of satellite data leads to an exponential increase in data volume (Justice et al. 1998; Drusch et al. 2012; Wulder et al. 2016;Claverieetal. 2018; Shang and Zhu 2019; Defourny et al. 2019; Hengl et al. 2022). Similarly to the American Landsat programme (Wulder et al. 2019), the Copernicus programme is a longterm EO initiative of the European Commission (Schiavon et al. 2021), which operates a growing number of satellites, called “Sentinels”. According to the annual report 2023 of the Copernicus Data Space Ecosystem (2024) the Copernicus programme alone produces up to 30 TB of data daily and the entire archive reached more than 70 PB of data in 2023. There has been a significant increase in the number of providers offering web-based data storage and catalogues, along with cloud processing capabilities, on the same infrastructure (Schramm et al. 2021; stack overflow 2024). Among these providers are globally operating companies such as Amazon (Web Services; (Mete and Yomralioglu 2021)), Google (Google Earth Engine; (Gorelick et al. 2017)), and Microsoft (Microsoft Planetary Computer; (Luers 2021)). In addition, thematic cloud providers that address specific user groups have recently emerged. In the field of EO data analytics, these providers include publicly funded international services such as ESA’s Copernicus Data Space Ecosystem (CDSE; (Milcinski et al. 2024)), which supports browsing, interactive exploration, downloading and in-depth processing of Copernicus data. Numerous initiatives at national and subnational levels add to the landscape of EO cloud providers (Di Leo et al. 2023), making it increasingly difficult to maintain an overview (Hengl et al. 2022). Griffiths (2022) states that, according to Open Geospatial Consortium (OGC), there were at least 78 geo-related cloud platforms in Europe alone by 2022. Large geodata require suitable SDIs for storage and management, as well as powerful multi-user integration, processing and analysis, search, visualisation, and publication of multidimensional geodata (Bernard et al. 2014;Lokers et al. 2016; Kamilaris et al. 2017; Wolfert et al. 2017). With the onset of the big data era, a paradigm shift has been set in motion, moving data analysis away from centralised inhouse solutions towards distributed SDI and cloud services (Sudmanns et al. 2020; Wagemann et al. 2021). Consequently, geodata are no longer downloaded to the local processing computer infrastructure and especially not to single workstations. Instead, algorithms and processing power are transferred to the data (Azzari and Lobell 2017), stored in clouds. Bernard et al. (2014) stress that the successful implementation of SDIs also requires organisational structures that allow institutions to act flexibly and provide all the necessary instruments and measures, such as appropriate technical equipment, qualified personnel, and a financial and strategic framework. For public authorities in particular, solutions must be found that are tailored to the authority and its specific mandates, and in addition (1) enable the provision of geodata in accordance with the standards of Infrastructure for Spatial Information in the European Community (INSPIRE) (INSPIRE 2007a) while at the same time (2) meeting IT security requirements: 1. German authorities are obliged to provide data that fall within the scope of the Data Use Act as openly as possible, conceptually and by default (DUA 2021). Based on the European Directive INSPIRE and the EU Implementing Regulation 2023/138 considering high-value datasets (HVD 2023), the Geodata Access Act (GeoGZ 2009) for Germany regulates access to geodata, geodata services, and metadata from geodata-holding bodies. Consequently, public geodata and geodata services, as well as metadata need to be interoperably provided. 2. The German authorities are subject to an increasing number of restrictions and regulations regarding IT security aspects. These regulations lead to severe constraints when working on web-based digital infrastructures and cloud providers. Special criteria must be met for German public authorities in order to use cloud platforms (ITRat—CIO Bund 2015): – Platforms should be certified by Federal Office for Information Security (BSI) and meet cloud platform seK PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science curity standards (BSI for basic protection, ISO 27001 and BSI-C5 standard Cloud Computing Compliance Criteria Catalogue; (BSI 2021)). – In addition, in-house IT staff responsible for security must check and confirm that the cloud environment to be used poses no risk to internal infrastructure and sensitive data storage. – Further conditions apply to the geographic location of stored data, especially for data managed by public authorities, meaning that related data must be stored within the jurisdictional boundaries of a given country. This article addresses the SDI’s big data components of the Julius Kühn Institute (JKI), a higher federal authority within the scientific portfolio of the Federal Ministry of Food and Agriculture in Germany. First, the basic structure and functionalities of the SDI (Sects. 2.1 and 2.2) and the implementation process of the big data components at JKI are described, taking into account IT security and open data requirements, as well as big geodata capabilities (Sect. 2.3). Second, the functionalities enable scale-specific applications, illustrated by two use cases such as a parcel-specific geodata integration exercise and a Germany-wide derivation of a dynamic biodiversity indicator (Sect. 3). Finally, insights gained and lessons learnt from the implementation process and operation of the SDI are discussed from the perspective of a public authority (Sect. 4). Fig. 1 JKI’s Spatial Data Infrastructure (DMZ= demilitarised zone, DB= data base, VPN= virtual private network) 2 JKI’s Spatial Data Infrastructure (JKI SDI) The JKI SDI consists of three main components, which are interoperably connected through standardised interfaces that meet different security requirements (Fig. 1): 1. The JKI intranet is highly restricted to internal staff and contains critical JKI infrastructure as well as protected data. 2. Copernicus Data and Exploitation Platform—DE (CODEDE) is a cloud platform for German authorities that provides direct access to satellite imagery and processing capabilities (Sect. 2.1). 3. The so-called demilitarised zone (DMZ) comprises of (geo)databases for the storage and management of vector data and raster data, as well as their metadata. In addition to CODE-DE, the JKI Data Cube is another big geodata component in which multidimensional raster data are stored and made available through web services (Sect. 2.2). Vector data arestoredinPosgreSQLand Oracle databases, as well as in the geodata management system called GeoNode (Ogryzek et al. 2020). The software repository JKI Gitea (JKI 2023a) and the INSPIREcompliant metadata, also stored in GeoNode, are used for internal and external documentation, storage, and provision of geodata and software code. With the WebGIS Framework JKI Map Viewer (JKI 2023b) geodata (products) can be visualised interactively. K PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science 2.1 Copernicus Data and Exploitation Platform—DE (CODE-DE) Copernicus Data and Exploitation Platform—DE (CODEDE) is designed to meet the user needs of German public authorities and is part of the National Geoinformation Strategy (NGIS) (GDI-DE 2015;BMI2021). The platform is an initiative of the German Federal Ministry for Digital and Transport (BMDV, funder), the German Aerospace Center (DLR, project management), and CloudFerro (technical implementation). The project started in March 2016 (Storch et al. 2019) and has been in the second phase since April 01, 2020 (Gonzalez and Hoffmann 2020). The project will enter a third financing phase from October 2025, the duration of which has not yet been determined at the time of writing. The main objectives of CODE-DE are improving the accessibility and use of Copernicus data and enabling the implementation of downstream services. As public authorities in particular often lack their own computing power and storage capacity for big data, processing EO data in the cloud offers advantages to a large number of users in terms of available computing power and access to EO data repositories. The CODE-DE platform complies with the IT security standards of ISO 27001 (basic IT protection) and C5 (Cloud Computing Compliance Criteria Catalogue), certified by BSI (2020,2022). CODE-DE is a web-based EO cloud platform that renders multimission satellite data highly accessible and usable in a user-friendly way. This is achieved by several platform components such as a web-based data viewer, on-demand data preprocessing chains, and a scalable high-performance cloud computing backend (Fig. 2). In addition to raw satellite imagery, EO-based analysis-ready data (ARD) products are available, such as monthly Sentinel-1 and -2 image composites, as well as German-wide harmonised Sentinel-2 and Landsat data preprocessed using the Framework for Operational Radiometric Correction for Environmental Monitoring (FORCE) (Frantz 2019). A complete overview of the available data portfolio can be found in CODE-DE (2023b). All Copernicus data relevant to Germany are hosted in a data centre located in Frankfurt (Main), Germany. On the one hand, a data and resource mirror fulfils additional security requirements from German authorities that mandate data storage and usage within national boundaries. On the other hand, geographically separated, but interconnected, cloud mirrors also increase the resilience and failsafety of cloud infrastructure. Optionally, Copernicus data and Copernicus Services of global coverage are accessible through the interlinked Creotech Instruments S.A. Data and Information Access Services (CREODIAS) data catalogue (CREODIAS 2024). The CODE-DE infrastructure (Fig. 2) aims to meet the needs of users with varying degrees of remote sensing expertise, ranging from users with little or no experience to EO experts and programmers. CODE-DE is a hybrid service built on the open-source cloud computing architecture OpenStack. This architecture enables the provision of web access to EO data for viewing and downloading, while also offering sandbox capabilities to develop processing and analytical tools based on virtual machine (VM)s. All available services are accessible from the CODE-DE landing page https://code-de.org/en/. The EO Data Explorer (CODE-DE 2025) has been set up as a simple and user-friendly tool. It is used to search, view, and download satellite imagery. Predefined EO processing chains (for example, Sentinel-1 radar backscatter data and Sentinel-2 biophysical plant parameters) can be executed through EO Data Explorer and processed data products can be downloaded on-demand. This service and also preset OGC services have been specifically designed to enable novice users (e.g., users without programming skills) to work with EO data. CODE-DE also provides dedicated development environments for experts and developers, who often require extensive processing capabilities to perform big EO data analytics. A web browser-based Jupyter Lab environment (using Python, R, or Julia language) with GPU support is available enablings rapid prototyping and code-based, automated data processing. German public authorities, as the main target group, can apply for a data processing and storage quota to use the full processing capacity (Table 1) of CODE-DE using VMs with different configuration flavours (sizes of virtual machines). Resources are free of charge for all German authorities, with the option to book additional paid resources that go beyond the pre-paid CODE-DE infrastructure. Users receive a certain monthly amount of credits (Table 1), which are used to book certain resources in the quota. These include, for example, virtual machines, block storage, object storage, and floating IPs. It should be emphasised that only components, but not data traffic, cost credits. CODE-DE users may configure their working environments individually, within a given quota. Users have full flexibility to run and maintain their VMs, e.g. by changing volume and object storage or by running multiple instances simultaneously managed through a dashboard (OpenStack). Pre-installed images for Linux (Ubuntu and CentOS) or Microsoft Windows operating systems are available on each VM. For example, a most recent OSGeoLive image is a ready-touse virtual machine with a Linux operating system as well as pre-installed geo-software tools such as QGIS, European Space Agency (ESA)s Sentinel Application Platform (SNAP, (ESA 2025)) and many more. Users also have access to graphical processing units (GPUs), e.g. for applicaK PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science tions in deep learning or computer vision. The VMs come in different sizes and flavours. An overview of the operating systems and flavours available can be found in CODE-DE (2023a). Through CODE-DE, each VM has mounted the entire national Copernicus archive using the simple storage service protocol (S3) (an object storage that provides a web service interface; (AWS 2022)) to access Sentinel and CODE-DE Frontend Backend | Certified BSI C5 and BSI basline security, ISO 27001 EO Explorer (search API for EO) code-de.org (Website, User Forum, E-Learning) OGC-Services (WMS, WCS, ...) openstack (virtual machine management) Frankfurt (Main) in Germany Storage Unit CODE-DE managed self managed Processing Unit Quota devided in virtual machines of different flavors x CPU x RAM x GPU ... HDD / SSD Object Store (s3 buckets) German Copernicus Archive Third party data provider x CPU x RAM x GPU x CPU x RAM x GPU External data Provider External service provider Data Cubes ESA DLR Eumetsat ECWMF CREODIAS ... USER Community contributions Jupyter Lab (online process developing) Fig. 2 Schematic structure of the German CODE-DE cloud (OGC Open Geospatial Consortium, WMS Web Map Service, WCS Web Coverage Service, ECMWF European Centre for Medium-Range Weather Forecasts, HDD hard disk drive, SSD solid state drive) other EO data. According to CODE-DE support, the volume of Germany-wide data (Copernicus data, free geodata and community contributions) amounted to around 2.085 petabytes on 2025-04-14 (2,627,731 products). All VMs are interconnected, enabling internal data exchange and the fusion of computational power for distributed processing. In addition to previously mentioned data cube functionalK PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science Table 1 Updated quota models of CODE-DE resources for a virtual processing environment (Status of November 2024) Contingent CPU RAM [GB] Vol [TB] Obj [TB] Max. IP T GPU Credits Basic 4 8 0.5 0.5 4 6 – 110 Standard 8 32 1 2 8 6 – 330 Premium 16 128 5 10 16 6 – 1020 GPU A100 Premium 24 118 5 10 1 6 1 4950 GPU A6000 Standard 6 56 2 4 4 6 0.5 1570 GPU A6000 Premium 12 112 5 10 8 6 1 3200 CPU number of virtual main processor cores, RAM main memory, Vol volume block storage, Obj object storage, IP number of public floating IPs, Tmax. booking period in months, GPU graphical processing unit for AI tasks, credits monthly credit, currently 1 credit equals 1 euro ities, user-created value-added products can be integrated into CODE-DE Data Cubes to access these data with OGC services (Sect. 2.2). The JKI is a member of the CODEDE user advisory board. This advisory board is made up of several federal and state authorities in Germany mandated to use and provide geodata, especially EO data and related services. User experiences are exchanged during biannual board meetings that contribute to the development ofCODE-DEbytakingstockand making adjustments according to users needs, if necessary. 2.2 JKI Data Cube and Webservices Data Cubes form an accepted cornerstone for a more human-centric modelling of spatio-temporal data and ultimately ARD, in general: making these big data ready for any type of consumption. Consequently, a key big data component of JKI DMZ is JKI Data Cube. It uses the array database system rasdaman enterprise (Baumann 2021;rasdaman 2023), which is optimised for the distributed processing of large amounts of multidimensional raster data, such as 3D satellite image time series and 4D atmospheric data. This database technology can perform complex mathematical and statistical operations with raster data significantly more effectively than relational databases and Baumann et al. (2021) showed that it outperforms comparable technology in terms of storage access and highly parallelisable operations. All common data formats are supported, including GeoTIFF, NetCDF, and JSON, plus domain-specific formats such as GRIB2. APIs include the adopted OGC standards Web Map Service (WMS), Web Map Tile Service (WMTS), Web Coverage Service (WCS), and Web Coverage Processing Service (WCPS) as well as draft specifications like OGC API-Coverages and openEO. Access is possible via Python, with direct integration of array packages like xarray and numpy, and also through other languages plus web sockets. WCPS query writing is supported by the ChatCUBE AI assistant accessible at https://ai-cu. be/chatcube. The JKI Data Cube is one of the digital infrastructures included in the FAIRagro consortium. FAIRagro aims to establish an interoperable and scalable research data infrastructure by connecting different repositories in agrosystem research as part of the German National Research Data Infrastructure (NFDI) initiative (Ewert et al. 2021; Specka et al. 2023). The JKI Data Cube and the underlying rasdaman software also provide web service functionalities that enable direct access to ARD time series (Wagemann et al. 2018). Multidimensional data sets are managed locally and made available via the OGC WCS standard (OGC 2023a), which is based on the concept of coverages. These are digital representations of “fields” (as in physics) in general, and specifically serve to model multi-dimensional raster data in an encoding-independent manner; both regular and irregular spatio-temporal grids are supported. Coverages are specified at the abstract level in ISO19123-1 (2023)and as concrete, interoperable structures in ISO19123-2 (2018) standard, which are identical to the OGC Abstract Topic 6 and Coverage Implementation Schema (CIS). In addition, the EU INSPIRE legal framework for a common SDI relies on OGC coverages and WCS. The coverage data structure consists of four main components: the domain (Where are these data located in space and time?), the range (the data values), the range type (What is the structure of the range values and their meaning?) and metadata (any additional information the coverage should carry along). At the processing level, there is an abstract specification by ISO (ISO19123-3 2023) complemented by concrete OGC WCPS that defines a geo datacube query language. WCPS is the main service used in rasdaman. WCPS is part of the mature, established WCS which is a data access standard in contrast to the likewise supported WMS (OGC 2023b) and WMTS interfaces for map visualisation. Additionally, rasdaman supports the experimental, not yet standardised interfaces OGC API-Coverages and OGC GeoDataCubes. K PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science 2.3 Challenges and Obstacles in the Implementation of Big Data Components Figure 3illustrates on a timeline when the big geodata components JKI Data Cube and CODE-DE were integrated into JKI SDI. The technical implementation of the JKI Data Cube took 18 months, from its acquisition (1 January 2020) to full functionality (1 July 2021). The situation is similar with the integration of the CODE-DE cloud platform, which was partially implemented after 11 months and fully implemented after 20 months. The main reason for the long integration period, which mainly involved necessary port openings in the firewall, was the understanding of the stakeholders involved at this time. This was associated with the lack of communication frameworks for determining requirements and potential solutions, in particular, for the implementation of new and complex IT technology. Here, the basic principle of DevOps (development and operation) of agile software development can be used as an analogy to describe the problem (Harrison and Lively 2019). At JKI, as in most research authorities, there is a strict separation between developers (here scientists) and IT operations staff (here security officers, data protection officers, hardware and software administrators, research data management officers, and others). In JKI, IT operations are hidden behind a ticket system, which creates a certain distance to development/research departments. This slows down the implementation of far-reaching changes, such as the integration of cloud resources into the IT structure. Only the establishment of new communication structures and mixed teams, as described in the DevOps approach, allowed for a better mutual understanding of the requirements of modern methods and geodata processing in IT operations, on the one hand, and of IT security issues among developers/scientists, on Fig. 3 Implementation timeline of big geodata components JKI Data Cube and CODE-DE in the JKI SDI. The events above the timeline are related to CODE-DE, below to the JKI Data Cube. The events highlighted in red represent important milestones, the events in blue are related to the establishment of communication structures. Other events can be interpreted as minor as well as intermediate steps the other. The following work groups were created to ensure regular exchange: 1. A SDI project group, established on 19 May 2021, consisting of research and IT management, is clarifying overarching structural, financing, strategic, network, and security issues. Regular meetings at the management level serve as a form of agile communication, where expert groups address specific issues. 2. A geodata network group emerged from the JKI Data Cube group, was launched on 12 April 2023 and aims at exchanging technical and thematic aspects on issues related to the management, access and analysis of geodata. In contrast to the SDI project group, the access is not restricted and is open to interested technical and scientific staff. In addition, the group provides a forum to help identify and solve SDI related issues. As a first result, shortly after the establishment of the DevOps-like SDI project group, JKI Data Cube could be incorporated into DMZ and put online. A particular challenge was the integration of CODEDE into JKI SDI, as strict security standards mean that a German authority is not allowed to connect to servers on the Internet from the intranet via certain standard protocols and associated ports (e.g. ssh and port 22, see Sect. 1). The issue was solved, after several rounds of meetings, by setting up a Virtual Private Network (VPN) tunnel using open-source VPN software openVPN. CODE-DE can now be seen as part of JKI DMZ. This technical solution makes it possible to avoid assigning external (floating), i.e. publicly visible, IPs to the virtual machines on the platform. This means that the numerous machines are only visible in the JKI network and can be used and networked with the rest of the JKI SDI without restrictions (Fig. 1). K PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science The CODE-DE cloud infrastructure is now partly integrated in the institute’s intranet and is also partly located in the DMZ (Fig. 1). The latter is administrated by the authority’s IT service, providing a buffer zone between JKI’s protected intranet and the Internet. SDI components located within the DMZ are connected to and used from the intranet, but cannot access other internal sub-networks and components. To access all parts of JKI SDI, users are connected to virtual machines on CODE-DE, either directly via remote desktop or through tunnelled development environments (e.g. Visual Studio Code, Jupyter, RStudio Server), which enables the use of modern ML orientated programming languages such as Python and R. The process pipelines are developed and executed directly on the virtual machines. Both, individual CODE-DE components as well as various JKI-internal software solutions and data stores can be executed together and in parallel. 3 Use Cases This section will demonstrate, through two use cases, how the individual components of JKI SDI collaborate to integrate various data from different locations. The first example illustrates a small-scale, parcel-specific demonstrator (parcel= one agricultural field), where various geodata are merged from data cubes, with the data stored in both JKI DMZ and CODE-DE. The second use case shows that the data integration approach can be scaled to Germanywide applications. It is part of an actual research initiative focused on monitoring biodiversity in agricultural areas. An indicator is derived from multiple datasets in different storage locations to produce a nationwide map of winter wheat productivity in Germany. Data: PHASE (phenology) data Name: jki phaseX202Xwinterwheat annually GSD: 1 km Data: Sentinel-2 (ARD) Name: codede_reflectanceXboaXs2gg_irregular GSD: 10 m Data: Precipitation (DWD) Name: dwd_precipitation_daily GSD: 1 km data cubes WCS WCS WCPS (SAVI) [104., 143.,... , 297.] [2.1, 0.3, 12.1, ...] ... ... query results phenological entry dates in DOY daily precipitation in mm Sentinel-2 based SAVI integrated vizualisation see Figure 5 ROI [x y] [x1 y1,x2 y2,...,x6 y6,x1 y1] [x y] Fig. 4 Flow chart for integrating parameters through OGC web services (WC[P]S Web Coverage [Processing] Service), using a Region of Interest (ROI) to characterize the land use intensity of arable land (SAVI Soil-adjusted Vegetation Index, GSD Ground Sampling Distance, DOY Day of Year) 3.1 Parcel-specific Data Integration Exercise The JKI Data Cube is made up of individual thematic cubes that can differ in terms of spatial and temporal resolution and geographical extent (Fig. 4). Three of them are raster data time series, covering Germany: The PHASE Data Cubes (e.g. jki_phaseX202Xwinterwheat_annually for winter wheat) contain phaseand year-specific phenological entry dates for the period 1993 to 2022 derived from interpolated phenological observations (Gerstmann et al. 2016; Möller et al. 2020). These data cubes are openly available (LINK: https://sf. julius-kuehn.de/openapi/phase/). Data cubes dwd_precipitation_daily and dwd_temperatureXaverage_daily are the result of interpolated measurements from German Meteorological Service (DWD) stations. They contain daily maximum temperatures and daily precipitation totals, respectively, for the period 1961 to today. These data cubes are also openly available (LINK: https://sf.julius-kuehn.de/openapi/weather/). The data cube codede_reflectanceXboaXs2gg_irregular provides analysis-ready images consisting of 10 cloudmasked and bottom-of-atmosphere reflectance bands of the Sentinel-2 sensor (using ESAs L2A product, (ESA 2021)) for the entire area of Germany and the period from 2015 to the present. Due to the size of the data set of >50 terabytes (growing daily), the data set cannot be imported into the local JKI infrastructure due to a lack of storage resources. Indexing is used to link the data set to JKI Data Cube without having to copy the data. By linking the data from CODE-DE (stored in s3 buckets) to JKI Data Cube, the data can be made available as a WCS. Access to this data cube is restricted K PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science Fig. 5 Coupling of data of different geometric and temporal resolution based on a WC[P]S-based queries using the example of phenological winter wheat phases and corresponding daily precipitation sums [mm] and SAVI time series within a parcel for the vegetation period 2019/2020. The x-axis represents the DOY of the vegetation period. but can be granted indvidually (https://sf.julius-kuehn. de/openapi/S2_GermanyGrid_JKI/). A GitHub repository (https://github.com/florianbeyer/ JKIDataCubeDemo) was established as a demonstrator (Beyer et al. 2024), showing how data cubes with different thematic data, extent, and spatial resolution can be integrated on-the-fly and on-demand using WCS or WCPS and Python (Fig. 4and 5). The primary file in this repository is a Jupyter Notebook (https://github.com/florianbeyer/ JKIDataCubeDemo/blob/main/DemoPhaseWCS.ipynb), which serves as the main interface for data analysis and visualisation. In addition to the Jupyter Notebook, several Python files (functions/*.py) have been included in the repository. These files contain various functions that are integral to the operations performed in the notebook. The notebook shows an example for a single winter wheat field (see Fig. 4and 5) in Lower Saxony, Germany, for the vegetation period 2019/2020 (Beyer et al. 2024): 1. Initially, the notebook shows how to query the list of available data cubes at JKI and how to plot their metadata. 2. Afterward, three functions are applied to query the three data cubes mentioned above. (a) First, phenological entry dates are queried from the PHASE Data Cube to derive phenological stages for the exact year and location. (b) Second, Soil-Adjusted Vegetation Index (SAVI) (Huete 1988) is calculated from Sentinel-2 data directly on JKI Data Cube server using WCPS accordingtoEq.1to provide information on canopy development and vitality throughout the growing season. SAV I =NIR −RED NIR +RED +0:5 0:5 (1) where NIR is the near infrared reflectance, RED the red reflectance of the Sentinel-2 bands and the 0.5 values are standard parameters coming from the theory behind the SAVI adjusting the soil influence in the mixed pixels (Huete 1988; Rondeaux et al. 1996). (c) Third, precipitation is received from DWD Data Cube. 3. Finally, Fig. 5illustrates the result of the geodata integration exercise as a comprehensive graph with all the collected data for the vegetation period 2019/2020: The Days of Year (DOY) of the growing season are plotted on the x-axis, with the DOYs for 2019 shown as minus values with DOY-365 (due to the fact that winter cereals are already sown the year before harvest in Germany). The left y-axis represents the SAVI values, and the right y-axis represents the precipitation values in mm. The phenological phases or periods between two consecutive beginning phases (Möller et al. 2017) are represented by area colors, where blue is indicated for sowing and tillage operations, greenish colours are associated with vital phases, and yellowish and orange colours refer to ripening stages and harvest. The red line shows the progression of SAVI, with high and increasing SAVI values associated with vital phases and low and decreasing SAVI values associated with ripening phases. Finally, the daily precipitation values are represented by a blue bar plot. K PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science BMWK (2023) EO lab. https://eo-lab.org/de/. 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