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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).

Hochaufgelöste Satellitenbilder von XCO2 und XCH4, Teilprojekt 2: Simulationen

WMS Nachtaufnahme Hamburg

Dieser WebMapService (WMS) zeigt eine Nachtaufnahme von Hamburg. Zur genaueren Beschreibung der Daten und Datenverantwortung nutzen Sie bitte den Verweis zur Datensatzbeschreibung.

Satellite Color Images, Vegetation Indices, and Metabolism Indices from Selb, 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 Köln, 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.

Schwerpunktprogramm (SPP) 1788: Study of Earth system dynamics with a constellation of potential field missions, Globale Beobachtung von sporadischen E Schichten in der Ionosphäre und ihre Verbindung zu atmosphärischen und ionosphärischen Phänomenen

Kopplungsprozesse zwischen der Ionosphäre und der neutralen Atmosphäre spielen eine wichtige Rolle für die dynamischen Prozesse in der oberen Atmosphäre. Neue Fortschritte im Verständnis dieser Prozesse wurden erreicht seitdem Satelliten im erdnahen Orbit kontinuierlich hochgenaue Daten der thermosphärischen und ionosphärischen Parameter (z.B. Massendichten, zonale Winde und Elektronendichteprofile) bereitstellen. Mit diesem Projekt planen wir die Beobachtung der Auftretenshäufigkeit und Eigenschaften sporadischer E Schichten auf globaler Skala. Die Untersuchungen basieren auf GPS Radiookkultationen der Satelliten CHAMP, GRACE, TerraSAR-X, TanDEM-X und FORMOSAT-3/COSMIC. Seit dem Start des Satelliten CHAMP im Jahre 2001 wurden mehr als 5 Millionen der Radiookkultationsprofile aufgezeichnet, was ermöglicht, dass das Auftreten und die Eigenschaften der sporadischen E Schichten in hoher räumlicher Auflösung analysiert werden können. Weiterhin ermöglicht die Zeitreihe erste statistische Trendanalysen der genannten Parameter. Während der Durchführung des Projektes soll der momentan genutzt numerischer Algorithmus zur Detektion von sporadischen E Schichten um ein Modul erweitert werden, der ermöglichen wird auch Rückschlüsse auf die Eigenschaften der Schichten zu ziehen. Globale Beobachtungen der Intensitäten sporadischer E Schichten existieren aktuell nicht und werden von uns zum erstmalig bereitgestellt werden. Diese Datenbasis kann genutzt werden, um statistische Änderungen im Verhalten der sporadischen E Schichten zu Untersuchen. Ebenfalls werden wir untersuchen, ob Abhängigkeit der sporadische Eigenschaften von anderen geophysikalischen Parametern, wie beispielsweise die Abnahme des Erdmagnetfeldes, der Solarzyklus, atmosphärische Gezeiten, Meteoreinfall oder Plamadichteabnahmen in der Ionosphäre zu finden sind.

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.

Natural Forest Management in Caracarai, Roraima, Brazil

Objectives: Sustainable management of tropical moist forests through private forest owners will become increasingly important. Media report that in Brazil, particularly in Amazonia, approx. 80 percent of the timber harvested is from illegal sources. Private management of forests according to internationally acknowledged standards offers an opportunity to significantly lower the portion of illegally cut timber. Moreover, it contributes significantly to the conservation of the Amazon forest. Private forest owners show a clear long-term commitment towards the implementation of management standards according that is ecologically compatible, socially acceptable and economically viable. The project area, a pristine forest in legal Amazonia in the transition zone between moist tropical forests and savannas (cerrado), is extremely diverse in floristic and faunistic terms. The institute cooperates with the private forest owner. Main tasks are to document the faunistic and floristic diversity, to calculate the Annual Allowable Cut and to elaborate concepts for site-specific silviculture. Results: To date (Oct. 2006) the following activities were started: - a comprehensive inventory system for planning at the FMU-level has been successfully introduced; - the inventory system for the annual coupe area has been designed and data for the first coupe are being processed; - the annual allowable cut is currently calculated based on the results of the above described inventories; - two fauna surveys are completed; one focusing on large mammals and one on the avi-fauna. A long-term monitoring concept to assess the influence of forest management on the faunistic diversity is currently under development; - forest zoning is completed applying terrestrial surveys and interpreting high-resolution satellite images; - a study on the use of Bethollethia excelsa-fruits (Brazil nuts) is currently implemented; - a study on timber properties of lesser known species is currently implemented.

Nachtaufnahme Hamburg

Bei dieser Nachtaufnahme handelt es sich um ein Mosaik, welches aus zwei unterschiedlichen Datensätzen besteht. Datensatz 1 [Zentrum und Randgebiete] - Satellitenaufnahmen: Jilin-1 - Spektrale Auflösung: RGB - Aufnahmezeitraum: 12/21 – 01/22 - Quelle: Chang Guang Satellite Technology CO., LTD (CGSTL)/Veritas Imagery Services Ltd Datensatz 2 [vereinzelte Randgebiete] - Astronautenphotographie: NASA - Spektrale Auflösung: RGB - Aufnahmezeitraum: 03/22 - Quelle: Earth Science and Remote Sensing Unit, NASA Johnson Space Center; https://eol.jsc.nasa.gov/ Hinweise: Die Datensätze unterscheiden sich hinsichtlich ihrer geometrischen Auflösung. Die Datensätze sind spektral nicht zueinander kalibriert. Verwendung nur zu Visualisierungszwecken.

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.

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