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Arctic PASSION - Polar Monthly Mean Ice Surface Temperature (AP-MMIST) for the time period 1982 to 2024

The Arctic PASSION Polar Monthly Mean IST data set (AP-MMIST) is a combined surface temperature product covering open ocean, marginal ice zone and closed sea ice areas, represented by Sea Surface Temperatures (SST), Marginal Ice Zone Temperatures (MIZT) and sea Ice Surface Temperatures (IST). Beside ocean and sea ice the data set also includes surface temperatures from the Greenland and Antarctic ice sheets. AP-MMIST has been jointly developed and produced by Arctic PASSION WP-1 and the Sea Ice Thematic Assembly Centre (Sea Ice TAC) under the Copernicus Climate Change Service (C3S - service contract: 2022/C3S2_312b_MOi_SC1). The AP-MMIST is a monthly averaged temperature product based on the C3S daily IST CDR and ICDR level 3 data. The daily mean C3S IST data set is a resampled and averaged daily mean IST product using Global Area Coverage - Advanced Very High-Resolution Radiometer (AVHRR) IST level 2 data as input. The level 2 and 3 CDR and ICDR data records are described in Algorithm Theoretical Baseline Document (Eastwood et al., 2023). The surface temperature retrieval algorithm used to produce the basic level 2 product is a traditional split window algorithm using two Thermal InfraRed (TIR) channels to compensate for atmosphere and angular emissivity dependency. This is described in the Algorithm Theoretical Baseline Document (Eastwood et al., 2023). The level 1 TIR input data set is the full data record from the AVHRR on-board NOAA satellite platforms since 1982, as well as AVHRR records on-board Metop satellites since 2006. The product output format is NetCDF with standard attributes, following CF convention to the degree possible. The monthly data are divided into 2 monthly files, one for each hemisphere, SH and NH.

Schwerpunktprogramm (SPP) 1158: Antarctic Research with Comparable Investigations in Arctic Sea Ice Areas; Bereich Infrastruktur - Antarktisforschung mit vergleichenden Untersuchungen in arktischen Eisgebieten, Einflusss von Umweltveränderungen auf antarktisches Phytoplankton untersucht mit Hilfe eines synergistischen multi- und hyper-spektralen Satellitendatenansatzes

Klimamodelle sagen voraus, dass sich in naher Zukunft im Antarktischen Ozean signifikant die Temperatur und der PH-Wert ändern werden, bedingt durch den Anstieg der Konzentrationen troposphärischer Treibhausgase und vor allem durch den erhöhten Kohlenstoffdioxidausstoß aus fossilen Brennstoffen. Solche Änderungen wirken sich auf die Zusammensetzung des Phytoplanktons aus und damit auch auf die Stoffkreisläufe wichtiger Elemente (Kohlenstoff, Stickstoff, usw.). Ziel dieses interdisziplinären Projektes ist die genauere Bestimmung der räumlichen und zeitlichen Variabilität der Biomasse von unterschiedlichen Phytoplanktontypen im Antarktischen Ozean. Einerseits wird hiermit das Verständnis der Rolle des antarktischen Phytoplanktons für das Ökosystem vertieft und andererseits deren Beitrag für den globalen Kohlenstoffzyklus genauer quantifiziert. Durch die einzigartige Kombination von Satellitendaten zweier unterschiedlicher Instrumententypen soll die Konzentration verschiedener Phytoplankton-Typen im Antarktischen Ozean zum ersten Mal mit umfassender zeitlicher und räumlicher Abdeckung bestimmt werden. Die Gesamtbiomasse wird durch eine an die Antarktis angepasste Prozessierung mit Hilfe multispektraler Satellitenmessdaten berechnet. Der Anteil wesentlicher Phytoplanktontypen an der Gesamtbiomasse wird anhand der Auswertung charakteristischer Absorptionsstrukturen von hyperspektralen Messdaten (PhytoDOAS-Methode) ermittelt. Somit soll ein synergetisches Produkt aus sich ergänzenden Informationen multi- und hyperspektraler Satelliteninstrumente entwickelt werden, das auf ähnliche Satelliteninstrumente, deren Messungen in naher Zukunft starten, übertragbar sein wird. Damit kann dann ein Datensatz über die Verteilung von Phytoplanktontypen über Dekaden erstellt werden. Mit dem im Projekt entstehenden Datensatz über die Verteilung der Phytoplanktontypen soll deren Variabilität und Korrelation mit sich ändernden Umweltfaktoren im Antarktischen Ozean in den vergangenen untersucht werden. Darüber hinaus soll unser Datensatz genutzt werden, zur Verbesserung und Evaluierung eines Ökosystem-Models, welches die Biogeographie verschiedener Phytoplanktontypen durch Parametrisierung physiologischer Eigenschaften an ein Ozeanzirkulatonsmodell errechnet. Mit Hilfe des Langzeitdatensatz und dem damit verbundenen Wissen über die Variabilität der Phytoplanktontypen, wird ein Fundament geschaffen, um den Einfluss der Klimaveränderungen im Antarktischen Ozean zu bemessen.

Satellite Color Images, Vegetation Indices, and Metabolism Indices from Zwickau, Germany from 1984 – 2023

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.

Satellite Color Images, Vegetation Indices, and Metabolism Indices from Emden, Germany from 1985 – 2023

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.

Satellite Color Images, Vegetation Indices, and Metabolism Indices from Schwerin, Germany from 1984 – 2023

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.

Updated global compilation of coccolithophore calcification measurements from unperturbed water samples including ancillary data

This database expands the Poulton et al., 2018 (doi:10.1594/PANGAEA.888182) database of pelagic calcium carbonate (CP) rate measurements from isotopic tracer uptake in incubated discrete water samples, as discussed in Daniels et al., 2018 (doi:10.5194/essd-10-1859-2018), and accompanies Marsh et al. (in prep.). The database now includes more CP (new data n = 400; complete database n = 3165), net primary production rate (PP) (new data n = 399; complete database n = 3150), total coccolithophore cell counts (new data n = 240; complete database n = 1512), and Emiliania huxleyi cell counts (new data n = 27; complete database n = 612). This expanded database maintains the record of data, including the principal investigator, expedition, OS region, doi reference (where available), collection date and year, sample ID, latitude, longitude, sampling and light depth, and method of measuring CP. We further expand the Poulton et al. (2018) data collection by including ancillary and environmental data, including: optical depth (OD, n = 3165), pHtotal (hereinafter referred to as pHT, n = 398), temperature (n = 1160), salinity (n = 1161), and the concentrations of chlorophyll a (n = 1363), NOx (NO3 or the sum of NO3 + NO2, n = 1161), silicic acid (Si(OH)4, n= 1156), phosphate (PO4, n = 1232), dissolved inorganic carbon (DIC, n = 318), total alkalinity (TA, n = 307), bicarbonate ion concentration (n = 349), and carbonate ion concentration (n = 352). All data was matched to CP, sample bottle identifiers (Niskin bottle numbers), and/or sampling depth values. This global database (81 °N - 64 °S, 132 °E - 174 °W) now covers expeditions and upper ocean measurements (0 - 193 m) from 1989 to 2024. Global in-situ geolocated data spanning time is valuable for modelling, satellite algorithms, and capturing calcium carbonate production in the global ocean. This expanded database, including the environmental, nutrient, chlorophyll a, and carbonate chemistry data, also allows for analysis of factors influencing calcium carbonate production on a global scale. This data amalgamation contributes to understanding the biogeochemistry of the oceans, global carbon cycle, and ocean acidification.

Sentinel-5P TROPOMI Surface Nitrogendioxide (NO2), Level 4 – Regional (Germany and neighboring countries)

The TROPOMI instrument onboard the Copernicus SENTINEL-5 Precursor satellite is a nadir-viewing, imaging spectrometer that provides global measurements of atmospheric properties and constituents on a daily basis. It is contributing to monitoring air quality and climate, providing critical information to services and decision makers. The instrument uses passive remote sensing techniques by measuring the top of atmosphere solar radiation reflected by and radiated from the earth and its atmosphere. The four spectrometers of TROPOMI cover the ultraviolet (UV), visible (VIS), Near Infra-Red (NIR) and Short Wavelength Infra-Red (SWIR) domains of the electromagnetic spectrum. The operational trace gas products generated at DLR on behave ESA are: Ozone (O3), Nitrogen Dioxide (NO2), Sulfur Dioxide (SO2), Formaldehyde (HCHO), Carbon Monoxide (CO) and Methane (CH4), together with clouds and aerosol properties. This product displays the Nitrogen Dioxide (NO2) near surface concentration for Germany and neighboring countries as derived from the POLYPHEMUS/DLR air quality model. Surface NO2 is mainly generated by anthropogenic sources, e.g. transport and industry. POLYPHEMUS/DLR is a state-of-the-art air quality model taking into consideration - meteorological conditions, - photochemistry, - anthropogenic and natural (biogenic) emissions, - TROPOMI NO2 observations for data assimilation. This Level 4 air quality product (surface NO2 at 15:00 UTC) is based on innovative algorithms, processors, data assimilation schemes and operational processing and dissemination chain developed in the framework of the INPULS project. The DLR project INPULS develops (a) innovative retrieval algorithms and processors for the generation of value-added products from the atmospheric Copernicus missions Sentinel-5 Precursor, Sentinel-4, and Sentinel-5, (b) cloud-based (re)processing systems, (c) improved data discovery and access technologies as well as server-side analytics for the users, and (d) data visualization services.

Sentinel-5P TROPOMI - Aerosol Optical Depth (AOD), Level 3 - Global

Aerosol optical depth (AOD) as derived from TROPOMI observations. AOD describes the attenuation of the transmitted radiant power by the absence of aerosols. Attenuation can be caused by absorption and/or scattering. AOD is the primary parameter to evaluate the impact of aerosols on weather and climate. Daily AOD observations are binned onto a regular latitude-longitude grid. The TROPOMI instrument onboard the Copernicus SENTINEL-5 Precursor satellite is a nadir-viewing, imaging spectrometer that provides global measurements of atmospheric properties and constituents on a daily basis. It is contributing to monitoring air quality and climate, providing critical information to services and decision makers. The instrument uses passive remote sensing techniques by measuring the top of atmosphere solar radiation reflected by and radiated from the earth and its atmosphere. The four spectrometers of TROPOMI cover the ultraviolet (UV), visible (VIS), Near Infra-Red (NIR) and Short Wavelength Infra-Red (SWIR) domains of the electromagnetic spectrum. The operational trace gas products generated at DLR on behave ESA are: Ozone (O3), Nitrogen Dioxide (NO2), Sulfur Dioxide (SO2), Formaldehyde (HCHO), Carbon Monoxide (CO) and Methane (CH4), together with clouds and aerosol properties. This product is created in the scope of the project INPULS. It develops (a) innovative retrieval algorithms and processors for the generation of value-added products from the atmospheric Copernicus missions Sentinel-5 Precursor, Sentinel-4, and Sentinel-5, (b) cloud-based (re)processing systems, (c) improved data discovery and access technologies as well as server-side analytics for the users, and (d) data visualization services.

Sentinel-5P TROPOMI - Aerosol Single-Scattering Albedo (ASSA), Level 3 - Global

Aerosol single-scattering albedo (ASSA) as derived from TROPOMI observations. ASSA is a measure of how much light is scattered by aerosols compared to how much is absorbed. It is important for understanding the impact of aerosols on climate and radiative forcing. ASSA is unitless; a value of unity implies that extinction is completely due to scattering; conversely, a single-scattering albedo of zero implies that extinction is completely due to absorption. Daily ASSA observations are binned onto a regular latitude-longitude grid. The TROPOMI instrument onboard the Copernicus SENTINEL-5 Precursor satellite is a nadir-viewing, imaging spectrometer that provides global measurements of atmospheric properties and constituents on a daily basis. It is contributing to monitoring air quality and climate, providing critical information to services and decision makers. The instrument uses passive remote sensing techniques by measuring the top of atmosphere solar radiation reflected by and radiated from the earth and its atmosphere. The four spectrometers of TROPOMI cover the ultraviolet (UV), visible (VIS), Near Infra-Red (NIR) and Short Wavelength Infra-Red (SWIR) domains of the electromagnetic spectrum. The operational trace gas products generated at DLR on behave ESA are: Ozone (O3), Nitrogen Dioxide (NO2), Sulfur Dioxide (SO2), Formaldehyde (HCHO), Carbon Monoxide (CO) and Methane (CH4), together with clouds and aerosol properties. This product is created in the scope of the project INPULS. It develops (a) innovative retrieval algorithms and processors for the generation of value-added products from the atmospheric Copernicus missions Sentinel-5 Precursor, Sentinel-4, and Sentinel-5, (b) cloud-based (re)processing systems, (c) improved data discovery and access technologies as well as server-side analytics for the users, and (d) data visualization services.

Sentinel-5P TROPOMI – Aerosol Index (AI), Level 3 – Global

Aerosol Index (AI) as derived from TROPOMI observations. AI is an indicator for episodic aerosol plumes from dust outbreaks, volcanic ash, and biomass burning. The TROPOMI instrument onboard the Copernicus SENTINEL-5 Precursor satellite is a nadir-viewing, imaging spectrometer that provides global measurements of atmospheric properties and constituents on a daily basis. It is contributing to monitoring air quality and climate, providing critical information to services and decision makers. The instrument uses passive remote sensing techniques by measuring the top of atmosphere solar radiation reflected by and radiated from the earth and its atmosphere. The four spectrometers of TROPOMI cover the ultraviolet (UV), visible (VIS), Near Infra-Red (NIR) and Short Wavelength Infra-Red (SWIR) domains of the electromagnetic spectrum. The operational trace gas products generated at DLR on behave ESA are: Ozone (O3), Nitrogen Dioxide (NO2), Sulfur Dioxide (SO2), Formaldehyde (HCHO), Carbon Monoxide (CO) and Methane (CH4), together with clouds and aerosol properties. This product is created in the scope of the project INPULS. It develops (a) innovative retrieval algorithms and processors for the generation of value-added products from the atmospheric Copernicus missions Sentinel-5 Precursor, Sentinel-4, and Sentinel-5, (b) cloud-based (re)processing systems, (c) improved data discovery and access technologies as well as server-side analytics for the users, and (d) data visualization services.

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