Mit dem 'Data Cube', der gegenwärtig im UBA entwickelt wird, stehen die Daten zur Umwelt zukünftig auch maschinenlesbar über Schnittstellen zur Verfügung. Dadurch entstehen neue Möglichkeiten für die datenbasierte Kommunikation und eine (teil-)automatisierte Umweltberichterstattung. Mit dem Projekt sollen neue interaktive Formate - wie Dashboards - für verschiedene Zielgruppen und für aktuelle Themen (z.B. für die künftige Berichterstattung zur Lebensqualität) entwickelt und für die Umweltberichterstattung fruchtbar gemacht werden. Darüber hinaus soll mit dem Projekt untersucht werden, inwieweit KI-Ansätze bzw. Methoden des maschinellen Lernens zu einer effizienteren und aktuelleren Umweltberichterstattung beitragen können (z.B. AI Powered Content Generation, Trend-Detector).
In einer Machbarkeitsstudie wurden wesentliche Anwendungsfälle und maßgebliche, repräsentative Nutzergruppen skizziert, quantitativer & qualitativer Mehrwert eines solchen Angebots für Nutzergruppen dargestellt, Inhalte & Funktionalitäten aufgezeigt, rechtliche, organisatorische & technische Maßgaben und erforderliche Fortentwicklungen dargelegt, Prognosen über den Zeitrahmen sowie erforderliche personelle & finanzielle Ressourcen für den Aufbau & Betrieb abgegeben, die Realisierbarkeiten unter den dargestellten Bedingungen eingeschätzt sowie erste Funktionalitäten zur erleichterten Recherche eingerichtet und ein Fahrplan für den schrittweisen Aufbau einzelner Werkzeuge erarbeitet. Mit dem Umsetzungskonzept sollen die Ergebnisse aufgegriffen und die stufenweise Umsetzung vorbereitet / vertieft werden. Das Konzept soll, priorisiert nach den erarbeiteten Anwendungsfällen und Nutzergruppen sowie den aus der Machbarkeitsstudie abgeleiteten Zielen, Beteiligungen am Aufbau & Betrieb qualitativ (wer, warum) und quantitativ (wann, wie oft) darstellen, Rechte & Pflichten der Beteiligten niederlegen, erforderliche technische Infrastrukturkomponenten sowie weitere Werkzeuge hinsichtlich ihrer Anforderungen an die Leistungsfähigkeit beschreiben, in Beziehung zu bestehenden Komponenten setzen und ggf. auf Letztgenannte zurückgreifen, Schritte zur haushälterischen Absicherung des Aufbaus und Betriebs aufzeigen, Schritte zur rechtlichen Absicherung des Betriebs aufzeigen, Verantwortungen für Aufbau & Betrieb zuordnen, entstehende Mehrwerte sichtbar machen und für die Umsetzung durch eine Geschäftsbereichsbehörde geeignet sein. Ergebnisse der Forschungsprojekte zum Thematischen Umweltatlas, Daten-Standardwerk der deutschen Umwelt-, Bau- und Stadtentwicklungspolitik, Datennutzungskonzept des UBA, zu Nutzungsmöglichkeiten von Fernerkundungsdaten und Erkenntnisse der Evaluierung des (früheren) PortalU sollen einfließen.
<p>Mit den Daten zur Umwelt bietet das Umweltbundesamt (UBA) bereits eine große Bandbreite an aktuellen Daten zum Zustand der Umwelt. Der UBA Data Cube macht diese noch besser nutzbar: Das neue System bietet maschinenlesbare Datensätze, offene Dateiformate, Schnittstellen (APIs) und Optionen zur Individualisierung. Das Portal ist verfügbar, wird aber noch intensiv optimiert – Feedback willkommen!</p><p>Fit für die Zukunft:</p><p>Das UBA veröffentlichte 1984 den ersten bundesweiten Bericht zum Zustand der Umwelt – die „Daten zur Umwelt“. Bis heute gehört dieses Angebot zu den beliebtesten Publikationen des <a href="https://www.umweltbundesamt.de/service/glossar/u?tag=UBA#alphabar">UBA</a>. Ursprünglich eine reine Berichtsreihe, sind die Daten mittlerweile längst dauerhaft online verfügbar. In Zeiten rasant wachsender Datenmengen und steigender Anforderungen an die Verfügbarkeit dieser Daten braucht es jedoch neue Lösungen, um mit diesem Service zeitgemäß zu bleiben. Der in diesem Kontext entstandene <a href="https://www.umweltbundesamt.de/service/glossar/d?tag=Data_Cube#alphabar">Data Cube</a> ist ein entscheidender Schritt hin zu einer Kultur der offenen Daten (engl. <a href="https://www.umweltbundesamt.de/service/glossar/o?tag=Open_Data#alphabar">Open Data</a>). Er verbessert den Zugang zu umweltrelevanten Daten, aber auch die Transparenz des Regierungs- und Verwaltungshandels.</p><p>Das leistungsfähige System eröffnet vielfältige neue Möglichkeiten zur Suche, Erkundung, Analyse und Visualisierung von Daten. Der Data Cube richtet sich mit diesen Nutzungsmöglichkeiten an interessierte Bürger*innen und Medienvertreter*innen, aber auch an die Wissenschaft, Politiker*innen und Politikberatung sowie jegliche Arbeitsfelder mit Berührungspunkten zu Umweltfragen.</p><p>Was bedeutet „Data Cube“?</p><p>In einem <a href="https://www.umweltbundesamt.de/service/glossar/d?tag=Data_Cube#alphabar">Data Cube</a> werden die Daten als Elemente eines mehrdimensionalen Datenwürfels angeordnet. Die Dimensionen des Würfels beschreiben die Daten, und ermöglichen eine Filterung der Daten nach den vorhandenen Kategorien.</p><p>Zum Beispiel werden die <a href="https://www.umweltbundesamt.de/service/glossar/t?tag=Treibhausgas#alphabar">Treibhausgas</a>-Emissionen im Data Cube detailliert abgebildet:</p><p>Durch die einfach Filtermöglichkeiten können so mit einem Datensatz sowohl sehr allgemeine Fragestellungen (<a href="https://datacube.uba.de/vis?fs%5B0%5D=Themen%252C0%7CKlima%2523CLIMATE%2523&pg=0&fc=Themen&bp=true&snb=14&vw=tl&df%5Bds%5D=ds-dc-release&df%5Bid%5D=DF_CLIMATE_EMISSIONS_GHG_TRENDS&df%5Bag%5D=UBA&df%5Bvs%5D=1.0&dq=.A.TOTAL.GHG.KT_CO2_EQ&pd=1990%252C2024&to%5BTIME_PERIOD%5D=false">sind die Treibhausgas-Emissionen in Deutschland gesunken?</a>) als auch sehr spezifische Fragestellungen (<a href="https://datacube.uba.de/vis?fs[0]=Themen%2C0%7CKlima%23CLIMATE%23&pg=0&fc=Themen&bp=true&snb=14&vw=tl&df[ds]=ds-dc-release&df[id]=DF_CLIMATE_EMISSIONS_GHG_TRENDS&df[ag]=UBA&df[vs]=1.0&dq=.A.3B4gi%2B3B4giii.GHG.KT_CO2_EQ&pd=2000%2C2024&to[TIME_PERIOD]=false&ly[cl]=D_SOURCE_CATEGORIES&ly[rw]=TIME_PERIOD">wie haben sich die Methan-Emissionen aus dem Wirtschaftsdüngermanagement für Legehennen im Vergleich zu Puten zwischen 2000 und 2024 entwickelt?</a>) beantwortet werden. Die Ergebnisdaten können mensch- und maschinenlesbar heruntergeladen, visualisiert und geteilt werden.</p><p>Technische Umsetzung: Die .Stat Suite als Open-Source Lösung für den Data Cube</p><p>Die .Stat Suite ist eine modulare Lösung für die flexible und skalierbare Bereitstellung von Daten. Die Software wurde von der Statistical Information System Collaboration Community (<a href="https://siscc.org/">SIS-CC</a>) als Open-Source-Lösung produziert. Sie bietet die Möglichkeit, Daten über verschiedene Komponenten zu speichern, zu teilen, darzustellen und zu orchestrieren. Die .Stat Suite wird von verschiedenen nationalen oder internationalen Organisationen und Data-Providern genutzt – darunter die FAO, UNICEF oder die <a href="https://www.umweltbundesamt.de/service/glossar/o?tag=OECD#alphabar">OECD</a>. Die Daten werden über das SDMX-Format bereitgestellt, einem technischen Standard für den Austausch von Daten und <a href="https://www.umweltbundesamt.de/service/glossar/m?tag=Metadaten#alphabar">Metadaten</a>.</p><p>Die Infrastruktur des <a href="https://www.umweltbundesamt.de/service/glossar/d?tag=Data_Cube#alphabar">Data Cube</a> ist in einer Cloud-Umgebung realisiert und durch das „Infrastructure as code“-Prinzip umgesetzt. So können Prozesse einfach und zentral skaliert werden. Die agilen Anpassungen der „Continuous Integration“-Methode gewährleistet die stetige Weiterentwicklung des Data Cube, zum Beispiel durch Einbindung von Nutzer*innen-Feedback.</p><p>Die Daten werden maschinenlesbar bereitgestellt: Eine RestAPI erlaubt den Abruf von Daten, Metadaten und Datenstrukturen in maschinenlesbarer Form. Als Basis wird die SDMX RESTful <a href="https://www.umweltbundesamt.de/service/glossar/a?tag=API#alphabar">API</a> verwendet. Eine Dokumentation ist in <a href="https://www.umweltbundesamt.de/daten.uba.de/swagger/#/">Swagger</a> hinterlegt. </p><p>Ausprobieren und mitgestalten – wir freuen uns über Feedback!</p><p>Das aktuell verfügbare Angebot ist ein Startpunkt. Es kommen ständig neue Datensätze dazu und die Inhalte werden kontinuierlich optimiert. Auch die Oberfläche und Nutzer*innenführung werden noch umfangreich weiterentwickelt, immer unter dem Gesichtspunkt der Nutzungsfreundlichkeit. Erfahrungen zu sammeln, die Anwendungsperspektive einzunehmen und mit Nutzer*innen in Austausch zu treten, hat für uns hohe Priorität! Wir freuen uns daher jederzeit über Rückmeldungen an datacube [at] uba [dot] de.</p><p>Die Portalentwicklung findet als Open-Source-Projekt statt, der Code wird zukünftig auf der Plattform <a href="https://gitlab.opencode.de/uba-data-cube/">openCode</a> öffentlich einsehbar sein.</p><p>Wie es weitergeht:</p><p>Der <a href="https://www.umweltbundesamt.de/service/glossar/d?tag=Data_Cube#alphabar">Data Cube</a> startete 2024 als Pilotsystem und ging im April 2025 in den Produktivbetrieb über. Die Entwicklung ist auf dauerhaften Betrieb angelegt und zielt daher auf flexible Lösungen ab, die kontinuierlich an sich wandelnde Anforderungen und Rahmenbedingungen angepasst werden können. Alle im Projekt entwickelten Softwarekomponenten sollen Open Source zur Verfügung gestellt werden.</p><p>Hintergrund</p><p>Die Bundesregierung hat sich mit dem 4. Nationalen Aktionsplan Open Government (NAP) zur Einführung des <a href="https://www.umweltbundesamt.de/service/glossar/d?tag=Data_Cube#alphabar">Data Cube</a> verpflichtet: <a href="https://www.open-government-deutschland.de/opengov-de/ogp/aktionsplaene-und-berichte/4-nap/einfuehrung-eines-data-cubes-daten-zur-umwelt-2225458?view=">Steckbrief der Verpflichtung zum Data Cube im 4. NAP</a></p>
The "Germany Mosaic" is a time series of Landsat satellite images and vectorized segments covering the entirety of Germany from 1984 to 2023. The image data are divided into TK100 sheet sections (see further details: Blattschnitt der Topographischen Karte 1:100 000). The dataset provides optimized 6-band imagery for each year, representing summer (May to July) and autumn (August to October) seasons, along with vegetation indices such as NDVI (Normalized Difference Vegetation Index) and NirV (Near-Infrared Reflectance of Vegetation) for the same periods. Additionally, vectorized "zones" of approximately homogeneous pixels are available for each year. The spectral properties of the image data and the morphological characteristics of these zones are included as vector attributes (see Documentation: "Mosaic (1984–2023) - Data Description"). An overview of the coverage and quality of all sheet sections is provided as a vector layer titled D-Mosaik_Sheet-Sections within this document. The Germany Mosaic can also be considered a spatial-temporal Data Cube, enabling advanced analysis and integration into workflows requiring multi-dimensional data. This structure allows users to perform operations such as querying data across specific time periods, analyzing trends over decades, or aggregating spatial information to generate tailored insights for a wide range of research applications. In mid-latitudes, seasonal variations in vegetation—and consequently in the image data—are typically more pronounced than changes occurring over several years. The temporal segmentation of the dataset has been designed to encompass the entire vegetation period (May to October), with the division into summer and autumn periods capturing seasonal metabolic shifts in natural biotopes. This segmentation also records most agricultural changes, including sowing and harvesting activities. Depending on weather conditions, the individual image data represent either the median, mean value, or the best available image for the specified time period (see Documentation: "Mosaic (1984–2023) - Data Description). Remote sensing has become an indispensable tool for environmental research, particularly in landscape analysis. Beyond conventional applications, the Germany Mosaic supports the development of digital twins in environmental system research. By providing detailed spatial and temporal data, this dataset enables the modeling of virtual ecosystems, facilitating simulations, scenario testing, and predictive analyses for sustainable management. Moreover, the spatial and temporal trends captured by remotely sensed parameters complement traditional approaches in biological, ecological, geographical, and epidemiological research.
The "Germany Mosaic" is a time series of Landsat satellite images and vectorized segments covering the entirety of Germany from 1984 to 2023. The image data are divided into TK100 sheet sections (see further details: Blattschnitt der Topographischen Karte 1:100 000). The dataset provides optimized 6-band imagery for each year, representing summer (May to July) and autumn (August to October) seasons, along with vegetation indices such as NDVI (Normalized Difference Vegetation Index) and NirV (Near-Infrared Reflectance of Vegetation) for the same periods. Additionally, vectorized "zones" of approximately homogeneous pixels are available for each year. The spectral properties of the image data and the morphological characteristics of these zones are included as vector attributes (see Documentation: "Mosaic (1984–2023) - Data Description"). An overview of the coverage and quality of all sheet sections is provided as a vector layer titled D-Mosaik_Sheet-Sections within this document. The Germany Mosaic can also be considered a spatial-temporal Data Cube, enabling advanced analysis and integration into workflows requiring multi-dimensional data. This structure allows users to perform operations such as querying data across specific time periods, analyzing trends over decades, or aggregating spatial information to generate tailored insights for a wide range of research applications. In mid-latitudes, seasonal variations in vegetation—and consequently in the image data—are typically more pronounced than changes occurring over several years. The temporal segmentation of the dataset has been designed to encompass the entire vegetation period (May to October), with the division into summer and autumn periods capturing seasonal metabolic shifts in natural biotopes. This segmentation also records most agricultural changes, including sowing and harvesting activities. Depending on weather conditions, the individual image data represent either the median, mean value, or the best available image for the specified time period (see Documentation: "Mosaic (1984–2023) - Data Description). Remote sensing has become an indispensable tool for environmental research, particularly in landscape analysis. Beyond conventional applications, the Germany Mosaic supports the development of digital twins in environmental system research. By providing detailed spatial and temporal data, this dataset enables the modeling of virtual ecosystems, facilitating simulations, scenario testing, and predictive analyses for sustainable management. Moreover, the spatial and temporal trends captured by remotely sensed parameters complement traditional approaches in biological, ecological, geographical, and epidemiological research.
The "Germany Mosaic" is a time series of Landsat satellite images and vectorized segments covering the entirety of Germany from 1984 to 2023. The image data are divided into TK100 sheet sections (see further details: Blattschnitt der Topographischen Karte 1:100 000). The dataset provides optimized 6-band imagery for each year, representing summer (May to July) and autumn (August to October) seasons, along with vegetation indices such as NDVI (Normalized Difference Vegetation Index) and NirV (Near-Infrared Reflectance of Vegetation) for the same periods. Additionally, vectorized "zones" of approximately homogeneous pixels are available for each year. The spectral properties of the image data and the morphological characteristics of these zones are included as vector attributes (see Documentation: "Mosaic (1984–2023) - Data Description"). An overview of the coverage and quality of all sheet sections is provided as a vector layer titled D-Mosaik_Sheet-Sections within this document. The Germany Mosaic can also be considered a spatial-temporal Data Cube, enabling advanced analysis and integration into workflows requiring multi-dimensional data. This structure allows users to perform operations such as querying data across specific time periods, analyzing trends over decades, or aggregating spatial information to generate tailored insights for a wide range of research applications. In mid-latitudes, seasonal variations in vegetation—and consequently in the image data—are typically more pronounced than changes occurring over several years. The temporal segmentation of the dataset has been designed to encompass the entire vegetation period (May to October), with the division into summer and autumn periods capturing seasonal metabolic shifts in natural biotopes. This segmentation also records most agricultural changes, including sowing and harvesting activities. Depending on weather conditions, the individual image data represent either the median, mean value, or the best available image for the specified time period (see Documentation: "Mosaic (1984–2023) - Data Description). Remote sensing has become an indispensable tool for environmental research, particularly in landscape analysis. Beyond conventional applications, the Germany Mosaic supports the development of digital twins in environmental system research. By providing detailed spatial and temporal data, this dataset enables the modeling of virtual ecosystems, facilitating simulations, scenario testing, and predictive analyses for sustainable management. Moreover, the spatial and temporal trends captured by remotely sensed parameters complement traditional approaches in biological, ecological, geographical, and epidemiological research.
The "Germany Mosaic" is a time series of Landsat satellite images and vectorized segments covering the entirety of Germany from 1984 to 2023. The image data are divided into TK100 sheet sections (see further details: Blattschnitt der Topographischen Karte 1:100 000). The dataset provides optimized 6-band imagery for each year, representing summer (May to July) and autumn (August to October) seasons, along with vegetation indices such as NDVI (Normalized Difference Vegetation Index) and NirV (Near-Infrared Reflectance of Vegetation) for the same periods. Additionally, vectorized "zones" of approximately homogeneous pixels are available for each year. The spectral properties of the image data and the morphological characteristics of these zones are included as vector attributes (see Documentation: "Mosaic (1984–2023) - Data Description"). An overview of the coverage and quality of all sheet sections is provided as a vector layer titled D-Mosaik_Sheet-Sections within this document. The Germany Mosaic can also be considered a spatial-temporal Data Cube, enabling advanced analysis and integration into workflows requiring multi-dimensional data. This structure allows users to perform operations such as querying data across specific time periods, analyzing trends over decades, or aggregating spatial information to generate tailored insights for a wide range of research applications. In mid-latitudes, seasonal variations in vegetation—and consequently in the image data—are typically more pronounced than changes occurring over several years. The temporal segmentation of the dataset has been designed to encompass the entire vegetation period (May to October), with the division into summer and autumn periods capturing seasonal metabolic shifts in natural biotopes. This segmentation also records most agricultural changes, including sowing and harvesting activities. Depending on weather conditions, the individual image data represent either the median, mean value, or the best available image for the specified time period (see Documentation: "Mosaic (1984–2023) - Data Description). Remote sensing has become an indispensable tool for environmental research, particularly in landscape analysis. Beyond conventional applications, the Germany Mosaic supports the development of digital twins in environmental system research. By providing detailed spatial and temporal data, this dataset enables the modeling of virtual ecosystems, facilitating simulations, scenario testing, and predictive analyses for sustainable management. Moreover, the spatial and temporal trends captured by remotely sensed parameters complement traditional approaches in biological, ecological, geographical, and epidemiological research.
The "Germany Mosaic" is a time series of Landsat satellite images and vectorized segments covering the entirety of Germany from 1984 to 2023. The image data are divided into TK100 sheet sections (see further details: Blattschnitt der Topographischen Karte 1:100 000). The dataset provides optimized 6-band imagery for each year, representing summer (May to July) and autumn (August to October) seasons, along with vegetation indices such as NDVI (Normalized Difference Vegetation Index) and NirV (Near-Infrared Reflectance of Vegetation) for the same periods. Additionally, vectorized "zones" of approximately homogeneous pixels are available for each year. The spectral properties of the image data and the morphological characteristics of these zones are included as vector attributes (see Documentation: "Mosaic (1984–2023) - Data Description"). An overview of the coverage and quality of all sheet sections is provided as a vector layer titled D-Mosaik_Sheet-Sections within this document. The Germany Mosaic can also be considered a spatial-temporal Data Cube, enabling advanced analysis and integration into workflows requiring multi-dimensional data. This structure allows users to perform operations such as querying data across specific time periods, analyzing trends over decades, or aggregating spatial information to generate tailored insights for a wide range of research applications. In mid-latitudes, seasonal variations in vegetation—and consequently in the image data—are typically more pronounced than changes occurring over several years. The temporal segmentation of the dataset has been designed to encompass the entire vegetation period (May to October), with the division into summer and autumn periods capturing seasonal metabolic shifts in natural biotopes. This segmentation also records most agricultural changes, including sowing and harvesting activities. Depending on weather conditions, the individual image data represent either the median, mean value, or the best available image for the specified time period (see Documentation: "Mosaic (1984–2023) - Data Description). Remote sensing has become an indispensable tool for environmental research, particularly in landscape analysis. Beyond conventional applications, the Germany Mosaic supports the development of digital twins in environmental system research. By providing detailed spatial and temporal data, this dataset enables the modeling of virtual ecosystems, facilitating simulations, scenario testing, and predictive analyses for sustainable management. Moreover, the spatial and temporal trends captured by remotely sensed parameters complement traditional approaches in biological, ecological, geographical, and epidemiological research.
The "Germany Mosaic" is a time series of Landsat satellite images and vectorized segments covering the entirety of Germany from 1984 to 2023. The image data are divided into TK100 sheet sections (see further details: Blattschnitt der Topographischen Karte 1:100 000). The dataset provides optimized 6-band imagery for each year, representing summer (May to July) and autumn (August to October) seasons, along with vegetation indices such as NDVI (Normalized Difference Vegetation Index) and NirV (Near-Infrared Reflectance of Vegetation) for the same periods. Additionally, vectorized "zones" of approximately homogeneous pixels are available for each year. The spectral properties of the image data and the morphological characteristics of these zones are included as vector attributes (see Documentation: "Mosaic (1984–2023) - Data Description"). An overview of the coverage and quality of all sheet sections is provided as a vector layer titled D-Mosaik_Sheet-Sections within this document. The Germany Mosaic can also be considered a spatial-temporal Data Cube, enabling advanced analysis and integration into workflows requiring multi-dimensional data. This structure allows users to perform operations such as querying data across specific time periods, analyzing trends over decades, or aggregating spatial information to generate tailored insights for a wide range of research applications. In mid-latitudes, seasonal variations in vegetation—and consequently in the image data—are typically more pronounced than changes occurring over several years. The temporal segmentation of the dataset has been designed to encompass the entire vegetation period (May to October), with the division into summer and autumn periods capturing seasonal metabolic shifts in natural biotopes. This segmentation also records most agricultural changes, including sowing and harvesting activities. Depending on weather conditions, the individual image data represent either the median, mean value, or the best available image for the specified time period (see Documentation: "Mosaic (1984–2023) - Data Description). Remote sensing has become an indispensable tool for environmental research, particularly in landscape analysis. Beyond conventional applications, the Germany Mosaic supports the development of digital twins in environmental system research. By providing detailed spatial and temporal data, this dataset enables the modeling of virtual ecosystems, facilitating simulations, scenario testing, and predictive analyses for sustainable management. Moreover, the spatial and temporal trends captured by remotely sensed parameters complement traditional approaches in biological, ecological, geographical, and epidemiological research.
The "Germany Mosaic" is a time series of Landsat satellite images and vectorized segments covering the entirety of Germany from 1984 to 2023. The image data are divided into TK100 sheet sections (see further details: Blattschnitt der Topographischen Karte 1:100 000). The dataset provides optimized 6-band imagery for each year, representing summer (May to July) and autumn (August to October) seasons, along with vegetation indices such as NDVI (Normalized Difference Vegetation Index) and NirV (Near-Infrared Reflectance of Vegetation) for the same periods. Additionally, vectorized "zones" of approximately homogeneous pixels are available for each year. The spectral properties of the image data and the morphological characteristics of these zones are included as vector attributes (see Documentation: "Mosaic (1984–2023) - Data Description"). An overview of the coverage and quality of all sheet sections is provided as a vector layer titled D-Mosaik_Sheet-Sections within this document. The Germany Mosaic can also be considered a spatial-temporal Data Cube, enabling advanced analysis and integration into workflows requiring multi-dimensional data. This structure allows users to perform operations such as querying data across specific time periods, analyzing trends over decades, or aggregating spatial information to generate tailored insights for a wide range of research applications. In mid-latitudes, seasonal variations in vegetation—and consequently in the image data—are typically more pronounced than changes occurring over several years. The temporal segmentation of the dataset has been designed to encompass the entire vegetation period (May to October), with the division into summer and autumn periods capturing seasonal metabolic shifts in natural biotopes. This segmentation also records most agricultural changes, including sowing and harvesting activities. Depending on weather conditions, the individual image data represent either the median, mean value, or the best available image for the specified time period (see Documentation: "Mosaic (1984–2023) - Data Description). Remote sensing has become an indispensable tool for environmental research, particularly in landscape analysis. Beyond conventional applications, the Germany Mosaic supports the development of digital twins in environmental system research. By providing detailed spatial and temporal data, this dataset enables the modeling of virtual ecosystems, facilitating simulations, scenario testing, and predictive analyses for sustainable management. Moreover, the spatial and temporal trends captured by remotely sensed parameters complement traditional approaches in biological, ecological, geographical, and epidemiological research.
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