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The ESA Earth System Model 3.0

The ESA Earth System Model (ESA ESM) provides a synthetic data set of the time-variable global gravity field that includes realistic mass variations in atmosphere, oceans, terrestrial water storage, continental ice sheets, and the solid Earth on a wide set of spatial and temporal frequencies. For more than 10 years already, it is widely applied as a source model in end-to-end simulation studies for future gravity missions, but has been also utilized to study novel gravity observing concepts on the ground. For those purposes, the ESM needs to include a wide range of signals even at very small spatial scales which might not yet have been reliably observed by any active satellite mission. The updated ESA ESM 3.0 improves upon its predecessor by utilizing ECMWF’s ERA5 atmospheric reanalysis along with dedicated simulated ocean bottom pressure data from the MPIOM ocean model. In addition, it offers a small ensemble of co- and post-seismic earthquake signals, an updated GIA model, additional ice mass balance signals from previously not considered Arctic glaciers, sub-monthly surface-mass balance changes and a more realistic representation of ice sheet dynamics. Extreme hydrometeorological events as well as climate-driven and anthropogenic impacts on continental water storage are represented through an update of the hydrological component. Additionally, the ESM separately includes ocean bottom pressure variations along the western slope of the Atlantic, representing variations in the meridional overturning circulation as a critically important component of the interactively coupled global climate system as well as estimated trend signals from sediment erosion and subsequent marine deposition. The ESA ESM 3.0 is available with a 6-hourly resolution from January 2007 until December 2020 in the from of Stokes coefficients up to degree and order 180.

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

Erosionsprozesse in degradierten Arganbeständen in Südmarokko

Boden und Vegetation endemischer Arganbestände in Marokko werden durch Expansion und Intensivierung der Agrarwirtschaft sowie Überweidung zunehmend degradiert. Überschirmte Flächen nehmen ab, unbedeckte Flächenanteile zwischen den Arganien nehmen zu. Infolge verminderter Infiltration steigen Oberflächenabfluss- und Bodenabtragsraten stark an. Auf den degradierten Böden kann sich nur lückenhafter Unterwuchs (Krautige und Gras) und kein Jungwuchs mehr ausbilden. Durch Untersuchungen verschieden stark degradierter Arganbestände werden in diesem Vorhaben Grenzwerte herausgearbeitet, ab denen bodenerodierende Prozesse initiiert werden, sowie solche, ab denen von einer Dynamisierung der Prozesse, insbesondere Rinnen- und Gully-Erosion, auszugehen ist. Dazu werden in drei Testgebieten im Hohen und Anti-Atlas eingezäunte Aufforstungsflächen mit ungeschützten Flächen auf verschiedenen Hangneigungen verglichen. Die Entwicklung der Bestandsdichten wird mit hochauflösenden CORONA-Satellitenbildern aus dem Jahr 1968 und großmaßstäbigen Luftbildern von 2017/18 quantifiziert, welche mit unbemannten Fluggeräten (UAVs) aufgenommen werden. Die Wuchsform der Bäume wird mit Structure from Motion (SfM)-Verfahren (3D-Modelle aus Multikopter-Aufnahmen) dokumentiert und klassifiziert. Untersuchungen zur Korngrößenverteilung, Aggregatstabilität, organischen Bodensubstanz und Bodennährstoffen sollen hypothesengeleitet den - mit steigendem Abstand der Bäume - sinkenden Einfluss der baumüberschirmten Fläche auf die erweiterten Zwischenbaumflächen aufzeigen. Mit Beregnungsversuchen und Infiltrationsmessungen werden Erodibilität und Infiltrationsvermögen der Zwischenbaumflächen in verschiedenen Degradationsstadien untersucht. Der Sedimentaustrag aus linearen Erosionsformen wird durch ein SfM-Monitoring mittels 3D-Modellen quantifiziert. Steinbedeckung und Viehwege lassen sich aus den selbst erstellten Luftbildern ermitteln. Viehzählungen und Interviews mit Schlüsselinformanten ergänzen die Kenntnisse über den Beweidungsdruck durch Schafe und Ziegen auf die Arganbestände. Anhand der Untersuchungen zur Degradation von Bestandsdichten, Zwischenbaum- und baumüberschirmten Flächen können die Arganbestände in mit Werten unterfütterte Stabilitätsklassen unterteilt werden. Die durch das Multi-Methoden-Konzept erarbeiteten Grenzwerte zeigen die Dynamisierung der Bodenerosionsprozesse unter Arganbeständen und belegen, dass bestimmte Erosionsprozesse verschiedenen Degradationszuständen der Fläche sowie unterschiedlichen Bestandsdichten zugeordnet werden können. Dies ist eine notwendige Voraussetzung für die nachhaltige Bewirtschaftung der Arganbestandsflächen.

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).

METOP GOME-2 - Formaldehyde (HCHO) - Global

Gridded Level 3 formaldehyde (HCHO) column densities derived from the Metop/GOME-2-instruments. In the troposphere HCHO is a short-lived atmospheric constituent caused by incomplete combustion processes, e.g. fossil fuel consumption or biomass buring or it is build by atmospheric chemical species from other volatile organic component (VOCs). It plays an important role in the build up of tropospheric ozone. The total HCHO column is retrieved from GOME solar back-scattered measurements in the UV wavelength region 328.5nm to 346nm [using the DOAS method]. In addition, an air mass factor based on monthly climatological HCHO 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).

G20/2025/160 - Neugenehmigung eines BHKW in 24589 Nortorf

Die Stadtwerke Nortorf AöR, Poststraße 21, 24589 Nortorf plant die Neuanlage eines Satelliten Blockheizkraftwerkes in 24589 Nortorf, Heinkenborsteler Weg, Gemarkung Nortorf, Flur 543, Flurstücke 6, 7, 8, 9. Gegenstand des Genehmigungsantrags sind im Wesentlichen folgende Maßnahmen: • Errichtung eines Blockheizkraftwerks in einem Gebäude mit einer Feuerungswärmeleistung von 9,369 Megawatt, mit dazugehörenden Kühlaggregaten, • Errichtung eines Abgaskamins mit einer Höhe von 10,40 m, • Errichtung eines Warmwasserspeichers mit einer Höhe von 16,59 m und einem Außendurchmesser von 16,27 m, einem Volumen von 3.000 Kubikmetern, • Errichtung eines AdBlue Tanks mit einem Fassungsvermögen von 9.000 Litern.

G20/2026/027 - Neugenehmigung eines Satelliten-BHKW in 24363 Holtsee

Die Dujos Holtsee GmbH & Co. KG in 24363 Holtsee, Trömbek 2a, plant die Neuanlage eines Satelliten Blockheizkraftwerkes in 24363 Holtsee, Dorfstraße 8a, Gemarkung Holtsee, Flur 3, Flurstücke 35/30 und 72/16. Gegenstand des Genehmigungsantrags sind im Wesentlichen folgende Maßnahmen: • Errichtung eines Blockheizkraftwerkes in einem Gebäude mit einer Feuerungswärmeleistung von 5,913 Megawatt mit dazugehörenden Kühlaggregaten, • Errichtung eines Abgaskamins mit einer Höhe von 22,9 m; • Errichtung eines Warmwasserspeichers mit einer Höhe von 17,50 m und einem Außendurchmesser von 12,73 m mit einem Volumen von 2.000 Kubikmetern, • Errichtung eines AdBlue Tanks mit einem Fassungsvermögen von 4.500 Litern.

Simulated L2 and L3 products for impact studies of the NGGM and MAGIC gravity missions

Since 2002, time-lapse satellite gravimetry missions have successfully observed global time-variable mass transport. The GRACE (Gravity Recovery And Climate Experiment; Tapley et al., 2004, 2019) mission and its successor, GRACE-FO (GRACE-Follow On; Chen et al., 2022; Landerer et al., 2020), have almost continuously delivered monthly observations of the gravity field for more than two decades. As GRACE-FO approaches the end of its lifetime, new satellite gravity missions are planned for launch. For the continuation of the record, GRACE-C (GRACE-Continuity by NASA and the German Space Agency at DLR with support from GFZ, BMWK, BMFB, HGF and MPG ) is planned to be launched in 2028 in a near-polar orbit at an altitude of ~500 km. GRACE-C will be followed by the Next Generation Gravity Mission (NGGM) launched by the European Space Agency (ESA) in 2032 in an inclined orbit of 65–70 degrees at an altitude of ~400 km. In their overlapping period, these two satellite pairs form the Mass-change And Geoscience International Constellation (MAGIC). In the frame of the ESA SING (Studying the Impact of the NGGM and MAGIC Gravity missions) project (SING project website, 2026), extensive simulations have been performed to evaluate the added value of extended gravity field measurements in time with enhanced spatial and temporal resolution, and, reduced latency in data availability. Synthetic observations of the gravity field have been generated at Levels 2 and 3 for GRACE-C-like, NGGM and MAGIC satellite configurations using a closed-loop numerical simulator integrating instrument noise, background model errors, and realistic satellite orbits. The simulations utilize target Earth signals from the ESA Earth System Model ESM 2.0 (Dobslaw et al., 2015), including hydrology, ice, and solid Earth components. Two parameterisation strategies used in this study yield simulated gravity solutions of mean fields at 5-day and 30-day resolutions. The other two strategies result in direct estimation of the trend and annual signal (trendannual) and direct estimation of the long-term trend (trendonly). For each parameterisation strategy, the data products are separated into three levels (L2, L2P, and L3). L2 are Stokes coefficients of the simulated Earth’s potential provided separately for each mission scenario and expressed in the spherical harmonic basis in ICGEM format. L2p and L3 synthetic data represent simulated surface mass anomalies provided separately for each mission scenario and expressed in equivalent water heights over regular 1°*1° grids in NetCDF format. The L3 data were corrected for Glacial Isostatic Adjustment (GIA), while the L2p data were not. . The full description of the data and methods is provided in the data description publication (Schlaak et al. in prep.), and the file structure of this data set is fully described in the file inventory. The resolution of all data products (L2a, L2b, L2P, L3) for each mission scenario (GRACE-C-like, NGGM, MAGIC) depends on the simulation type described in Schlaak et al. (in prep.). For 5-daily solutions, the spatial resolution corresponds to ca. 285 km (d/o 70). For monthly solutions, the temporal resolution is 30 days, with a spatial resolution of ca. 166 km (d/o 120). Trend and annual signals (trend-and-annual solutions) have been estimated simultaneously over a period of 12 years (with 1-year increments) and a spatial resolution of ca. 150 km (d/o 130). For trend-only solutions, the spatial resolution is increased to 125 km, with trend estimates over 5 and 12 years. Additionally, empirical Variance-Covariance Matrices (VCMs) are provided for L2a and L3b data for the 5-daily and monthly simulation types, computed from Monte Carlo simulations (Schlaak et al., in prep.).

Satellite Color Images, Vegetation Indices, and Metabolism Indices from Kitzingen, 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 Berlin-West, 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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