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METOP GOME-2 - Ozone (O3) - Global

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. Currently, there are three GOME-2 instruments operating on board EUMETSAT's Meteorological Operational satellites MetOp-A, -B, and -C, launched in October 2006, September 2012, and November 2018, respectively. GOME-2 can measure a range of atmospheric trace constituents, with the emphasis on global ozone distributions. 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 level 2 products in the framework of EUMETSAT's Satellite Application Facility on Atmospheric Chemistry Monitoring (AC-SAF). GOME-2 near-real-time products are available already two hours after sensing. The operational ozone total column products are generated using the algorithm GDP (GOME Data Processor) version 4.x integrated into the UPAS (Universal Processor for UV / VIS Atmospheric Spectrometers) processor for generating level 2 trace gas and cloud products. The new improved DOAS-style (Differential Optical Absorption Spectroscopy) algorithm called GDOAS, was selected as the basis for GDP version 4.0 in the framework of an ESA ITT. GDP 4.x performs a DOAS fit for ozone slant column and effective temperature followed by an iterative AMF / VCD computation using a single wavelength. For more details please refer to relevant peer-review papers listed on the GOME and GOME-2 documentation pages: https://atmos.eoc.dlr.de/app/docs/

METOP GOME-2 - Cloud Top Pressure (CTP) - Global

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. Currently, there are three GOME-2 instruments operating on board EUMETSAT's Meteorological Operational satellites MetOp-A, -B and -C, launched in October 2006, September 2012, and November 2018, respectively. GOME-2 can measure a range of atmospheric trace constituents, with the emphasis on global ozone distributions. 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 level 2 products in the framework of EUMETSAT's Satellite Application Facility on Atmospheric Chemistry Monitoring (AC-SAF). GOME-2 near-real-time products are available already two hours after sensing. OCRA (Optical Cloud Recognition Algorithm) and ROCINN (Retrieval of Cloud Information using Neural Networks) are used for retrieving the following geophysical cloud properties from GOME and GOME-2 data: cloud fraction (cloud cover), cloud-top pressure (cloud-top height), and cloud optical thickness (cloud-top albedo). OCRA is an optical sensor cloud detection algorithm that uses the PMD devices on GOME / GOME-2 to deliver cloud fractions for GOME / GOME-2 scenes. ROCINN takes the OCRA cloud fraction as input and uses a neural network training scheme to invert GOME / GOME-2 reflectivities in and around the O2-A band. VLIDORT [Spurr (2006)] templates of reflectances based on full polarization scattering of light are used to train the neural network. ROCINN retrieves cloud-top pressure and cloud-top albedo. The cloud-top pressure for GOME scenes is derived from the cloud-top height provided by ROCINN and an appropriate pressure profile. For more details please refer to relevant peer-review papers listed on the GOME and GOME-2 documentation pages: https://atmos.eoc.dlr.de/app/docs/

Großräumige Analyse von Verbuschungsflächen mit NOAA-AVHRR-Daten in Namibia

Seit 1979 erfassen Satelliten der NOAA-Serie die Erde und liefern damit eine der längsten kontinuierlichen Bild-Datenreihen von Satelliten überhaupt. Durch ihre großflächige Abdeckung, ihre hohe zeitliche Auflösung und ihren kostengünstigen Empfang eignen sich diese Daten hervorragend zum Monitoring. Bislang werden diese langen Zeitreihen noch kaum herangezogen, um langfristige Veränderungen von Oberflächenphänomenen zu beschreiben, denn der Großteil der Fernerkundungsarbeiten beschäftigt sich mit neueren Sensoren und deren Anwendungen. Gerade vor dem Hintergrund der Landdegradierung durch unangepaßte Landnutzung in den Trockenräumen der Erde sollten die vorhandenen archivierten Datenreihen zur Langzeitanalyse aber genutzt werden und die Ergebnisse in Konzepte des Landmanagements einfließen. In Namibia vollzieht sich in den Nationalparks und dem Weideland die Landdegradierung durch eine massive Verbuschung, v.a. mit Acacia mellifera. Die Verbuschungsdynamik der letzten 20 Jahre soll in Etosha mit NOA-AVHRR-Daten erfasst werden. Die Ergebnisse aus dem Etosha-Nationalpark können dann zum Monitoring der Verbuschung in Namibia von örtlichen Institutionen eingesetzt werden. So ist die Inwertsetzung der Daten gewährleistet und durch die Weiterentwicklung der NOAA-Serie durch das MODIS-System auch für die Zukunft gewährleistet.

Northern Eurasia Earth Science Partnership Initiative (NEESPI)

The Northern Eurasia Earth Science Partnership Initiative, or NEESPI, is a currently active, yet strategically evolving program of internationally-supported Earth systems science research, which has as its foci issues in northern Eurasia that are relevant to regional and Global scientific and decision-making communities (see NEESPI Mission Statement). This part of the globe is undergoing significant changes - particularly those changes associated with a rapidly warming climate in this region and with important changes in governmental structures since the early 1990s and their associated influences on land use and the environment across this broad expanse. How this carbon-rich, cold region component of the Earth system functions as a regional entity and interacts with and feeds back to the greater Global system is to a large extent unknown. Thus, the capability to predict future changes that may be expected to occur within this region and the consequences of those changes with any acceptable accuracy is currently uncertain. One of the reasons for this lack of regional Earth system understanding is the relative paucity of well-coordinated, multidisciplinary and integrating studies of the critical physical and biological systems. By establishing a large-scale, multidisciplinary program of funded research, NEESPI is aimed at developing an enhanced understanding of the interactions between the ecosystem, atmosphere, and human dynamics in northern Eurasia. Specifically, the NEESPI strives to understand how the land ecosystems and continental water dynamics in northern Eurasia interact with and alter the climatic system, biosphere, atmosphere, and hydrosphere of the Earth. The contemporaneous changes in climate and land use are impacting the biological, chemical, and physical functions of the northern Eurasia, but little data and fewer models are available that can be used to understand the current status of this expansive regional system, much less the influence of the northern Eurasia region on the Global climate. NEESPI seeks to secure the necessary financial and related institutional support from an international cadre of sponsors for developing a viable understanding of the functioning of northern Eurasia and the impacts of extant changes on the regional and Earth systems. Many types of ground and integrative (e.g., satellite; GIS) data will be needed and many models must be applied, adapted or developed for properly understanding the functioning of this cold and diverse regional system. Mechanisms for obtaining the requisite data sets and models and sharing them among the participating scientists are essential and require international and active governmental participation. (abridged text)

Satelliten-Thermalbilder. Ein neues Hilfsmittel fuer die Umweltforschung

Bericht ueber eine in den Jahren 1979 und 1980 durchgefuehrte Arbeit mit Daten des HCMM-Satelliten (Heat Capacity Mapping Mission). Inhalte: geometrische Entzerrung, digitale Ueberlagerung mit Karten und anderen Satellitendaten, Probleme der relativen und absoluten Eichung, Beziehung des Musters der Strahlungstemperaturen zu topographischen Strukturen und zum Gefuege der Landnutzung, Verknuepfung mit anderen thermischen Parametern.

Pollution Transport to Switzerland inferred from Satellite Observations (POLTRASAT)

POLTROSAT is a 3-year PhD project that started in May 2003 and gets support from both the Swiss Agency of Environment, Forests and Landscape (BUWAL) and the Swiss Federal Laboratories for Materials Science and Technology (Empa). It aims in evaluating the usability of space-borne data for air pollution surveillance purposes based on measurements from GOME and SCIAMACHY

Modelluntersuchungen zu Rotations-Schwingungs-Anregungen von Hydroxyl-Molekülen in der Mesosphäre und Rotationstemperaturen

Eine der Standardmethoden zur Temperaturbestimmung in der Mesopausen-Region basiert auf spektroskopischen Messungen der Rotationstemperaturen von Hydroxyl-Molekülen. Eine wichtige Frage bei der Interpretation der gemessenen Rotationstemperaturen ist die Frage nach der Thermalisierung der Rotationszustände. Bisher gibt es jedoch nur wenige Untersuchungen zu diesem Thema.Das Ziel dieses Projektes ist, Hydroxyl-Moleküle in verschiedenen Rotations-Schwingungs-Zuständen in der oberen Mesosphäre und unteren Thermosphäre zu untersuchen. Zu diesem Zweck soll ein kinetisches Modell der Schwingungs- und Rotations-Anregungen von OH entwickelt werden. Das Modell soll verwendet werden, um die Konzentrationen von angeregten Hydroxyl-Molekülen und Emissionsraten in verschiedenen Höhen und für verschiedene atmosphärische Bedingungen zu simulieren. Insbesondere sollen die Besetzungen der Rotationszustände analysiert werden, um Abweichung vom lokalen thermodynamischen Gleichgewicht bewerten zu können. Die Modellergebnisse sollen mit bodengestüzten Messungen und Satelliten-Messungen verglichen werden.

Trockenstress-Monitoring von Getreide durch Fernerkundung

Extreme Temperaturen und Wassermangel verursachen Trockenstress an landwirtschaftlichen Kulturen. Weltweit werden die Auswirkungen von Trockenstress auf wichtige Feldfrüchten untersucht und Methoden zur Überwachung und zur Früherkennung von Trockenstress und anderen Stressfaktoren untersucht. Damit soll der gezielte. Einsatz agrotechnischer Maßnahmen wie Fruchtwechsel, Düngung, Bodenbearbeitung und Bewässerungsplanung unterstützt werden, um Ernteeinbußen zu verhindern. Ein weiterer Aspekt sind mögliche Auswirkungen der globalen Erwärmung auf die landwirtschaftliche Produktion, die sich zu einem der Hauptthemen der Forschung auf dem Gebiet das Klimawandels entwickeln. Erdbeobachtung von Satelliten aus ermöglicht die rationelle Überwachung des Zustands landwirtschaftlicher Kulturen über große Flächen. lü jüngster Zeit wurden neue Sensorsysteme entwickelt und in Erdumlauf gebracht, die neue Möglichkeiten auch für das Monitoring von Trockenstress landwirtschaftlicher Kulturen eröffnen. Die wesentlichen Merkreale dieser neuen optischen Sensoren sind hohe spektrale Auflösung (kleine Bandbreiten bis 10 nm herunter, eine große Anzahl von Spektralkanälen - bis zu einigen hundert, was im Prinzip Spektroskopie vom Satelliten aus ermöglicht), hohe räumliche Auflösung (Bildelementgrößen am Boden bis 60 cm herunter), und hohe zeitliche Auflösung -(bis zu täglicher Aufnahmemöglichkeit jedes Punktes der Erdoberfläche). Das Ziel dieses Projekts ist es, unter Ausnützung der neuen Möglichkeiten optischer Fernerkundung und der synergistischen Effekte der unterschiedlich Sensortypen Fernerkundungsmethoden zur Erkennung und zur Überwachung von Trockenstress an landwirtschaftlichen Kulturen zu entwickeln. Dazu werden physikalische Vegetationsmodelle angepasst und verbessert, die den Zusammenhang zwischen der Trockenstressintensität und Reflexionseigenschaften von Pflanzenbeständen quantitativ beschreiben. Methoden zur Analyse von Fernerkundungsbilddaten unter Verwendung dieser Vegetationsmodelle werden entwickelt. Dabei werden sowohl reflektierte als auch emittierte (thermale) Infrarotstrahlung berücksichtigt. Da es keine Sensoren gibt, die gleichzeitig alle drei der oben angeführten Arten der hohen Auflösung (spektral, räumlich und zeitlich) erfüllen, kommt der Kombination von Daten unterschiedlicher Sensoren besondere Bedeutung zu (image information fusion). Die Methodenwerden für ausgewählte Fruchtarten (Weizen und Mais) unter Anbaubedingungen in Österreich und Deutschland entwickelt und getestet.

Satellite Color Images, Vegetation Indices, and Metabolism Indices from Würzburg, 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 Frankfurt-Oder, 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.

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