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METOP GOME-2 - Sulfur Dioxide (SO2) - Global

Gridded Level 3 SO2 total column densities derived from the Metop/GOME-2-instruments. Volcanoes are the largest soures of SO2 in the atmosphere, depending on the erruption the Sulfurous compounds can be injected into stratosphere but in most cases it stays within the troposphere. Another important source is the coal combustion. Desulfurisation facilities within the power stations have reduced the sulfur emissions around the globe. In the stratosphere sulfur is a key component for building up aerosols, which reflect parts of the solar irradiation. The total SO2 column is retrieved from GOME solar back-scattered measurements in the ultraviolet wavelength region [using the DOAS method]. Depending on the plume SO2 can be a very strong absorber, because of that the ODAS retrieval might have some smaller issues, they can be reduced by choosing different wavelenght ranges depending on the signal. We apply three different fitting windows between 310 and 360nm. For the AMF, we assume a plumeheight of 6 km altitude. The Global Ozone Monitoring Experiment-2 (GOME-2) instrument continues the long-term monitoring of atmospheric trace gas constituents started with GOME / ERS-2 and SCIAMACHY / Envisat. Three instruments operate on board EUMETSAT's Meteorological Operational satellites MetOp-A, -B, and -C, launched in 2006, 2012, and 2018, respectively. GOME-2 measures a range of atmospheric trace constituents, with the emphasis on global ozone distribution. Furthermore, cloud properties and intensities of ultraviolet radiation are retrieved. These data are crucial for monitoring the atmospheric composition and the detection of pollutants. DLR generates operational GOME-2 / MetOp products in the framework of EUMETSAT's Satellite Application Facility on Atmospheric Composition Monitoring (AC-SAF).

Schwerpunktprogramm (SPP) 1158: Antarctic Research with Comparable Investigations in Arctic Sea Ice Areas; Bereich Infrastruktur - Antarktisforschung mit vergleichenden Untersuchungen in arktischen Eisgebieten, Variation der antarktischen Wolkenkondensationskern- (CCN) und Eiskeim- (INP) Konzentrationen und Eigenschaften an NEumayer III im Vergleich zu deren Werten in der Arktis an der Forschungsstation Villum (VACCINE+)

Das aktuelle Klima der Erde verändert sich schneller, als von den meisten wissenschaftlichen Prognosen vorhergesagt wurde. Dabei erwärmen sich die Polargebiete schnellsten von allen Regionen der Erde. Die Polargebiete haben auch starke globale Auswirkungen auf das Erdklima und beeinflussen daher das Leben und die Lebensgrundlagen auf der ganzen Welt. Trotz der großen Fortschritte der Polarforschung der letzten Jahre gibt es nach wie vor schlecht verstandene Prozesse; einer davon ist die Aerosol-Wolke-Klima-Wechselwirkung, die daher auch nicht zufriedenstellend modelliert werden können. Wolken und deren Wechselwirkungen im Klimasystem sind eine der schwierigsten Komponenten bei der Modellierung, insbesondere in den Polarregionen, da es dort besonders schwierig ist, qualitativ hochwertige Messungen zu erhalten. Die Verfügbarkeit hochwertiger Messungen ist daher von entscheidender Bedeutung, um die zugrunde liegenden Prozesse zu verstehen und in Modelle integrieren zu können. Im ersten Teil des hier vorgeschlagenen Projekts schlagen wir, d.h. TROPOS, vor, die bestehenden Aerosolmessungen an der Neumayer III-Station um in-situ Wolkenkondensationskern- (CCN) und Eiskeim- (INP) Messungen zu erweitern für einen Zeitraum von fast zwei Jahren. Die erfassten Daten wie Anzahl der Konzentrationen, Hygroskopizität, INP-Gefrierspektren usw. werden mit meteorologischen Informationen (z.B. Rückwärtstrajektorien) und Informationen über die chemische Zusammensetzung der vorherrschenden Aerosolpartikel verknüpft, um Quellen für INP und CCN über den gesamten Jahreszyklus zu identifizieren. In einem optionalen dritten Jahr wollen wir die Ergebnisse der südlichen Hemisphäre mit den TROPOS-Langzeitmessungen des CCN und INP aus der Arktis (Villum Research Station) vergleichen, welche uns im Rahmen dieses Projekts von DFG-finanzierten TR 172, AC3, Projekt B04 zur Verfügung stehen werden. Ein Ergebnis des beantragten Projekts wird ein tieferes Verständnis dafür sein, welche Prozesse die CCN- und INP-Population in hohen Breiten dominieren. Die im Rahmen des vorliegenden Projekts gesammelten quantitativen Informationen über CCN und INP in hohen Breiten werden öffentlich zugänglich veröffentlicht, z.B. für die Evaluierung globaler Modelle und Satellitenretrievals.

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.

Dynamic World training dataset for global land use and land cover categorization of satellite imagery

The Dynamic World Training Data is a dataset of over 5 billion pixels of human-labeled ESA Sentinel-2 satellite image, distributed over 24000 tiles collected from all over the world. The dataset is designed to train and validate automated land use and land cover mapping algorithms. The 10m resolution 5.1km-by-5.1km tiles are densely labeled using a ten category classification schema indicating general land use land cover categories. The dataset was created between 2019-08-01 and 2020-02-28, using satellite imagery observations from 2019, with approximately 10% of observations extending back to 2017 in very cloudy regions of the world. This dataset is a component of the National Geographic Society - Google - World Resources Institute Dynamic World project. The dataset consists of two file types: GeoTIFF files of 510x510 pixel 10m resolution satellite image tiles markup provided by human labelers, and Excel (.xlsx) tables of metadata and class statistics for the above GeoTIFF files. The data is organized into three main folders. One folder contains training data labeled by a team of 25 expert human labelers recruited by National Geographic Society specifically for this project. A second folder contains training data labeled by a larger group of commissioned labelers provided by a commercial crowd-labeler service. The data in these folders is organized by hemisphere and biome number from the RESOLVE Ecoregions2017 biomes categories (https://ecoregions2017.appspot.com/). A third folder contains a validation dataset. This is a holdout set of training data for assessing model accuracy. None of this data is intended to be used in the formulation of the model. Each validation tile was independently labeled by three experts. The validation set contains two versions: the individual markup from each expert labeler, and the image composites of the individual markups. Each GeoTIFF file encodes information on the location of landscape feature classes as determined by a given labeler. Classes were labeled by visual examination of true color (RGB) composites of Sentinel-2 MultiSpectral Level-2A scenes. The Tier 1 class values used in this phase of the project are as follows: 0 No data (left unmarked), 1 Water, 2 Trees, 3 Grass, 4 Flooded Vegetation, 5 Crops, 6 Scrub, 7 Built Area, 8 Bare Ground, 9 Snow/Ice, 10 Cloud. This dataset does not include the original Sentinel-2 imagery tiles, but metadata on the exact image ID and date is provided The original Sentinel-2 imagery was obtained via Google Earth Engine. This data is available under a Creative Commons BY-4.0 license and requires the following attribution: This dataset is produced for the Dynamic World Project by National Geographic Society in partnership with Google and the World Resources Institute. Development of the Dynamic World training data was funded in part by the Gordon and Betty Moore Foundation.

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.

METOP GOME-2 - Tropospheric Nitrogen Dioxide (NO2) - Global

Gridded Level 3 tropospheric NO2 column densities derived from the Metop/GOME-2-instruments. In the troposphere NO2 is a short-lived atmospheric constituent caused by combustion processes, e.g. fossil fuel consumption or biomass buring or by lightning. NO2 plays an important role in the formation of ozone. The total NO2 column is retrieved from GOME solar back-scattered measurements in the visible wavelength region around 440nm [using the DOAS method]. To derive tropospheric NO2 columns, the estimated stratospheric component is substracted from the total column. In addition, an air mass factor based on monthly climatological NO2 profiles is considered. The Global Ozone Monitoring Experiment-2 (GOME-2) instrument continues the long-term monitoring of atmospheric trace gas constituents started with GOME / ERS-2 and SCIAMACHY / Envisat. Three instruments operate on board EUMETSAT's Meteorological Operational satellites MetOp-A, -B, and -C, launched in 2006, 2012, and 2018, respectively. GOME-2 measures a range of atmospheric trace constituents, with the emphasis on global ozone distribution. Furthermore, cloud properties and intensities of ultraviolet radiation are retrieved. These data are crucial for monitoring the atmospheric composition and the detection of pollutants. DLR generates operational GOME-2 / MetOp products in the framework of EUMETSAT's Satellite Application Facility on Atmospheric Composition Monitoring (AC-SAF).

Satellite Color Images, Vegetation Indices, and Metabolism Indices from Konstanz, 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.

Shipboard Automated Meteorological and Oceanographic System (SAMOS) air-sea fluxes, version 2, 2005-2015

Along ship track bulk turbulent heat and momentum fluxes derived using one-minute interval data collected by the Shipboard Automated Meteorological and Oceanographic System (SAMOS) initiative. The fluxes are provided as three products derived using three widely accepted air-sea flux algorithms (Smith 1988, COARE 3.5, and a 2013 update of Bourassa 2006). Datasets are organized by ship and flux algorithm. The dataset uses SAMOS data from 19 research vessels with observations spanning 2005-2015. Data include heat and momentum fluxes, state variables adjusted to 10 meters, and multiple inputs and outputs from the flux algorithms. These data have wide use for satellite product and model evaluation; air-sea interaction, marine biology, chemistry and geoscience research activities; climate science, and marine geoinformatics. The data are concentrated in the oceans around North America, but select data are available from most ocean basins. Fluxes are derived from along cruise track navigational, meteorological, and oceanographic observations from 19 research vessels. Fluxes are derived at one-minute intervals (the same as the source observations) and take advantage of extensive quality control flags on the source SAMOS data. Additional QC flags are applied to the derived fluxes, height adjusted state variables, and other outputs of the flux algorithms. All data are provided in CF and ACDD compliant netCDF files, with extensive metadata. The uncompressed data volume is ca. 5 GB.

Einsatzmoeglichkeiten von ERS-1-SAR-Daten fuer geowissenschaftliche Anwendungen im alpinen Gelaende

Von dem am 16. Juli 1991 erfolgreich gestarteten ersten europaeischen Erderkundungssatelliten ERS-1 werden wesentliche Beitraege zum besseren Verstaendnis der komplexen Vorgaenge und dynamischen Veraenderungen im Gesamtsystem Erde und somit Entscheidungsgrundlagen fuer Massnahmen zum Schutze unserer Umwelt erwartet. Hinsichtlich der Einsatz- und Auswertmoeglichkeiten der SAR-Daten ueber Landoberflaechen mit ausgepraegtem Relief (wie in der Schweiz) bestehen erst geringe Kenntnisse. Es bedarf hier umfangreicher Grundlagenforschung, um diese Daten bei der Loesung aktueller, unser Land betreffender Aufgaben sinnvoll einsetzen zu koennen.

Die Sanasilva-Inventur

*Der Gesundheitszustand der Bäume im Schweizer Wald wird seit 1985 mit der Sanasilva-Inventur repräsentativ erfasst. Die wichtigsten Merkmale sind die Kronenverlichtung und die Sterberate. Das systematische Probeflächen-Netz der Inventur ist im Laufe der Zeit ausgedünnt worden. In der Periode von 1985 bis 1992 wurden rund 8000 Bäume auf 700 Flächen im 4x4 km-Netz aufgenommen, 1993, 1994 und 1997 rund 4000 Bäume im 8x8 km-Netz und in den Jahren 1995, 1996 und 1998 bis 2002 rund 1100 Bäume im 16x16 km-Netz . Aufnahmemethode Alle drei Jahre (1997, 2000) wird die Sanasilva-Inventur auf dem 8x8-km Netz (ca. 170 Probeflächen ) durchgeführt. In den Jahren dazwischen findet die Inventur auf einem reduzierten 16x16-km Netz (49 Probeflächen) statt. Jede Fläche besteht aus zwei konzentrischen Kreisen. Der äussere Kreis hat ein Radius von 12.62 m (500 m2) und der innere ein Radius von 7.98 m (200 m2). Auf dem inneren Kreis werden alle Bäume mit einem Mindestdurchmesser in Brusthöhe von 12 cm und auf dem äusseren Kreis mit einem Mindestdurchmesser in Brusthöhe von 36 cm aufgenommen. In Nordrichtung wird zusätzlich in 30 m Entfernung eine identische Satellitenprobenfläche eingerichtet. Die Aufnahme findet in Juli und August statt. Eine Aufnahmegruppe besteht aus zwei Personen, von denen eine die Daten erhebt, und die andere die Daten eintippt. Die Daten werden mit dem Feldkomputer Paravant und der Software Tally erfasst. Die Aufgabenteilung wechselt zwischen Probeflächen. Auf dem 8x8-km Netz werden zusätzlich 10 Prozent der Flächen von einer unabhängigen zweiten Aufnahmegruppe zu Kontrollzwecken aufgenommen. Hauptmerkmale der Sanasilva-Inventur: Die Sanasilva-Inventur erfasst vor allem folgende Indikatoren des Baumzustandes: Die Kronenverlichtung wird beschrieben durch den Prozentanteil der Verlichtung einer Krone im Vergleich zu einem Baum gleichen Alters mit maximaler Belaubung/Benadelung an diesem Standort, den Anteil dieser Verlichtung, der nicht durch bekannte Ursachen erklärt werden kann, den Ort der Verlichtung, den Anteil und den Ort von unbelaubten/unbenadelten Ästen und Zweigen. Die Kronenverfärbung wird durch die Abweichung der mittleren Farbe (aufgenommen als Farbton, Reinheit und Helligkeit nach den Munsell Colour Charts) eines Baumes zu der für diese Baumart typischen Normalfarbe (Referenzfarbe) und durch das Vorhandensein, das Ausmass und den Ort der von der Referenzfarbe abweichenden Farben beschrieben. Der Zuwachs eines Baumes wird durch die zeitliche Veränderung der aufgenommen Baumgrössen beschrieben (Brusthöhendurchmesser, Höhe des Baumes, Kronenlänge und Kronenbreite). Weitere Merkmale sind die erkannten Ursachen der Kronenverlichtung, die Kronenkonkurrenz und das Vorkommen von Epiphyten, Mistel und Ranken in der Baumkrone.

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