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Sentinel-5P TROPOMI – Cloud-Top Height (CTH), Level 3 – Global

Global Cloud-Top Height (CTH) as derived from the Sentinel-5P/TROPOMI instrument. Clouds play a crucial role in the Earth's climate system and have significant effects on trace gas retrievals. The cloud-top height is retrieved from the O2-A band using the ROCINN algorithm. Daily observations are binned onto a regular latitude-longitude grid. The TROPOMI instrument aboard the SENTINEL-5P space craft is a nadir-viewing, imaging spectrometer covering wavelength bands between the ultraviolet and the shortwave infra-red. TROPOMI's purpose is to measure atmospheric properties and constituents. It is contributing to monitoring air quality and providing critical information to services and decision makers. The instrument uses passive remote sensing techniques by measuring the Top Of Atmosphere (TOA) 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, allowing operational retrieval of the following trace gas constituents: Ozone (O3), Nitrogen Dioxide (NO2), Sulfur Dioxide (SO2), Formaldehyde (HCHO), Carbon Monoxide (CO) and Methane (CH4). Within the INPULS project, innovative algorithms and processors for the generation of Level 3 and Level 4 products, improved data discovery and access technologies as well as server-side analytics for the users are developed.

Tree Species - Sentinel-1/2 - Germany, 2022

The Tree Species Germany product provides a map of dominant tree species across Germany for the year 2022 at a spatial resolution of 10 meters. The map depicts the distribution of ten tree species groups derived from multi-temporal optical Sentinel-2 data, radar data from Sentinel-1, and a digital elevation model. The input features explicitly incorporate phenological information to capture seasonal vegetation dynamics relevant for species discrimination. A total of over 80,000 training and test samples were compiled from publicly accessible sources, including urban tree inventories, Google Earth Pro, Google Street View, and field observations. The final classification was generated using an XGBoost machine learning algorithm. The Tree Species Germany product achieves an overall F1-score of 0.89. For the dominant species pine, spruce, beech, and oak, class-wise F1-scores range from 0.76 to 0.98, while F1-scores for other widespread species such as birch, alder, larch, Douglas fir, and fir range from 0.88 to 0.96. The product provides a consistent, high-resolution, and up-to-date representation of tree species distribution across Germany. Its transferable, cost-efficient, and repeatable methodology enables reliable large-scale forest monitoring and offers a valuable basis for assessing spatial patterns and temporal changes in forest composition in the context of ongoing climatic and environmental dynamics.

Forest Canopy Cover Loss (FCCL) - Germany - Monthly, Administrative Level

This vector dataset is based on a 10 m resolution raster dataset that shows forest canopy cover loss (FCCL) in Germany at a monthly resolution from September 2017 to September 2024. Results at pixel level were aggregated at municipality, district, and federal state level. For the results at administrative level we differentiate between deciduous and coniferous forests. We use the stocked area map 2018 (Langner et al. 2022, https://doi.org/10.3220/DATA20221205151218 ) as a reference forest mask. We differentiate between deciduous and coniferous forests by intersecting the stocked area map with a tree species map (Blickensdoerfer et al. 2024). Pixels of the classes birch, beech, oak, alder, deciduous trees with long lifespan and deciduous trees with short lifespan were classified as deciduous forest and pixels of the classes Douglas fir, spruce, pine, larch and fir as coniferous forest. The coverage of the two datasets is not identical, which is why a few areas of the forest reference map remained unclassified. These were filled with the dominant leaf type map of the Copernicus Land Monitoring Service (CLMS 2025). Therefore, the vector data at administrative level contains information about unclassified forest areas and the total forest area as the sum of deciduous, coniferous, and unclassified forests. The FCCL confidence at pixel level is lowest at the end of the time series because the number of repeated threshold exceedance is used as a criterion to record forest canopy cover losses. Therefore, we excluded July 2024 through September 2024 from the annual and overall statistics and summarized the respective FCCL as additional attribute. The dataset is a fully reprocessed continuation of the assessment in Thonfeld et al. (2022).

Radverkehr_Internet - Radverkehrsplan - TOP108 Ziele

Der Kartendienst stellt die freigegebenen Geodaten des Radverkehrsplans Saarland dar.:Darstellung der TOP108 Ziele des Saarlandes im Rahmen des Radverkehrsplanes, zur Verfügung gestellt von der Tourismuszentrale des Saarlandes.

Fließgewässermessstelle Luhe, Steg oh. Mdg. Naab, Luhe

Die Messstelle Luhe, Steg oh. Mdg. Naab (Messstellen-Nr: 6106) befindet sich im Gewässer Luhe in Bayern. Die Messstelle dient der Überwachung des biologischen Zustands, des chemischen Zustands, des Grundwasserstands im oberen Grundwasserstockwerk.

Fließgewässermessstelle uh Br Prittriching, Verlorener Bach

Die Messstelle uh Br Prittriching (Messstellen-Nr: 3129) befindet sich im Gewässer Verlorener Bach in Bayern. Die Messstelle dient der Überwachung des biologischen Zustands, des chemischen Zustands, des Grundwasserstands im oberen Grundwasserstockwerk.

Reduktion von (V)VOC-Emissionen aus NawaRo-Dämmstoffen unter besonderer Berücksichtigung neu aufkommender Schadstoffe

Das Gesamtziel des Vorhabens besteht in der Reduktion der Emission insbesondere neu aufkommender Schadstoffe aus NawaRo-Dämmstoffen. Für einige dieser Stoffe sind noch keine eindeutigen Wege der Generierung identifiziert worden. Dies gilt insbesondere für neu aufgekommene Stoffe wie VVOC. Die Entwicklung von Minderungsmaßnahmen steht folglich zumindest zum Teil noch aus. Ziel des Projektes ist es daher, VOC- und VVOC-Emissionen weiter zu senken, um Dämmstoffen aus nachwachsenden Rohstoffen mehr Verwendungsmöglichkeiten zu eröffnen. Als Hauptkomponenten wurden in Vorarbeiten organische Säuren, Aldehyde, Alkohole und andere, meist polare Verbindungen sowie SVOC identifiziert. Um die zur Reduzierung dieser Emissionen sinnvollen Entwicklungsschritte definieren zu können, fehlen zum Teil vertiefte Kenntnisse zu deren Entstehung aus Dämmstoffen aus nachwachsenden Rohstoffen. Dies gilt insbesondere für Substanzen wie Alkohole, VVOC und SVOC (Semi Volatile Organic Compounds). Außerdem sind viele dieser Dämmstoffe mit Flammschutzmitteln ausgerüstet. Inwieweit das das Emissionsverhalten möglicherweise indirekt beeinflusst, z.B. durch deren Einfluss auf den Feuchtehaushalt, ist nicht bekannt. Aus den bestehenden und zusätzlich gewonnenen Erkenntnissen zur Generierung der Emissionen sollen mindernde Veränderungen im Herstellprozess abgeleitet werden. Dabei kann auf Erkenntnisse aus dem Bereich der Herstellung von Holzwerkstoffen aufgebaut werden. Eine weitere Möglichkeit der Emissionsminderung ergibt sich aus dem Zusammenwirken verschiedener Materialien, z.B. Dämmstoffen und Folien. Vorkenntnisse über das Diffusionsverhalten von Wasserdampf und einiger weniger (V)VOC sind vorhanden, bedürfen aber einer Vervollständigung. Konkret soll daher die Möglichkeit dampfbremsender Folien, den Übergang solcher Stoffe in die Innenraumluft zu behindern, über die o.g. Stoffe hinaus geprüft werden.

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.

Grundwassermessstelle Neunaigen,

Die Messstelle Neunaigen (Messstellen-Nr: 2147, Bayern) dient der Überwachung des Grundwasserstands im oberen Grundwasserstockwerk, des biologischen Zustands, des chemischen Zustands.

SWIM Water Extent - Sentinel-1/2 - Daily

SWIM Water Extent is a global surface water product at 10 m pixel spacing based on Sentinel-1/2 data. The collection contains binary layers indicating open surface water for each Sentinel-1/2 scene. Clouds and cloud shadows are removed using ukis-csmask (see: https://github.com/dlr-eoc/ukis-csmask ) and are represented as NoData. The water extent extraction is based on convolutional neural networks (CNN). For further information, please see the following publications: https://doi.org/10.1016/j.rse.2019.05.022 and https://doi.org/10.3390/rs11192330

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