The Al Zaatari refugee camp in Jordan is situated approx. 12 km from the Syrian border and in close proximity to the city of Al Mafraq (10 km). Due to heavy rainfall in the region parts of the Zaatari camp are affected by flooding. The map shows the flood situation derived by semi-automatic image analysis of TerraSAR-X data acquired on January 10, 2013 at 03:38:49 UTC. Furthermore basic reference information, digitized on the basis of WorldView-2 satellite data acquired on January 03, 2013, at 08:52:52 UTC, is depicted. The contour lines were derived from ASTER GDEM 2 data (vertical accuracy +/- 6m). For a more detailed view on the flood situation, parts of the camp area are also shown in the zoom boxes. The results of the image interpretation and analysis have not been validated in the field. WorldView-2 satellite data acquired on January 03, 2013, is used as backdrop. Please note that flood waters in settlement areas might not be fully captured and the water extent might be underestimated due to sensor characteristics. Thus especially shallow water bodies might not be fully captured. The products elaborated for this Rapid Mapping Activity are realised to the best of our ability, within a very short time frame, optimising the material available. All geographic information has limitations due to the scale, resolution, date and interpretation of the original source materials. No liability concerning the content or the use thereof is assumed by the producer. The ZKI crisis maps are constantly updated. Please make sure to visit http://www.zki.dlr.de for the latest version of this product.

The EnMAP HSI L2A dataset collection comprises a standardized, consistent, systematically processed, and cloud-native level-2A dataset series for the entire mission. It is especially useful for big data or time series analyses. The dataset is processed with the atmospheric correction over land processor and is provided in cloud-optimized GeoTIFF format for direct access and download. The metadata follows the CEOS Analysis Ready Data (CEOS-ARD) framework. The database is constantly updated with newly acquired data. The Environmental Mapping and Analysis Program (EnMAP) is a German hyperspectral satellite mission that monitors and characterizes Earth’s environment on a global scale. EnMAP delivers accurate data that provides information on the status and evolution of terrestrial and aquatic ecosystems, supporting environmental monitoring, management, and decision-making. For more information, please see the mission website: https://www.enmap.org/mission/

The map shows the elevation of the surroundings of the Al Zaatari refugee camp in Jordan. The elevation information is derived from ASTER GDEM 2 data (vertical accuracy +/- 6m). Furthermore basic reference information, digitized on the basis of WorldView-2 satellite data acquired on January 03, 2013, at 08:52:52 UTC and LANDSAT-7 data acquired on December 16, 2012 at 08:07:11 UTC, is depicted. Not all settlements are captured. The results have not been validated in the field. ASTER GDEM 2 data as well as a hillshade derived from this data is used as backdrop. Please note, that information on elevation derived from ASTER data does not apply for the refugee camp area. ASTER data was acquired before 2011 and the elevation might have changed due to construction works. The products elaborated for this Rapid Mapping Activity are realised to the best of our ability, within a very short time frame, optimising the material available. All geographic information has limitations due to the scale, resolution, date and interpretation of the original source materials. No liability concerning the content or the use thereof is assumed by the producer. The ZKI crisis maps are constantly updated. Please make sure to visit http://www.zki.dlr.de for the latest version of this product.

The map shows the Al Zaatari refugee camp in Jordan. It is situated approx. 12 km from the Syrian border and in close proximity to the city of Al Mafraq (10 km). The camp was set up on July 28, 2012 to shelter refugees fleeing the conflict in Syria. The vector data have been digitized on the basis of WorldView-2 satellite data (0.5m spatial resolution) acquired on January 03, 2013. The results have not been validated in the field. WorldView-2 satellite data acquired on January 03, 2013 is used as backdrop. The products elaborated for this Rapid Mapping Activity are realised to the best of our ability, within a very short time frame, optimising the material available. All geographic information has limitations due to the scale, resolution, date and interpretation of the original source materials. No liability concerning the content or the use thereof is assumed by the producer. The ZKI crisis maps are constantly updated. Please make sure to visit http://www.zki.dlr.de for the latest version of this product.

The map shows the Al Zaatari refugee camp in Jordan. It is situated approx. 12 km from the Syrian border and in close proximity to the city of Al Mafraq (10 km). The camp was set up on July 28, 2012, to shelter refugees fleeing the conflict in Syria. The map shows general characteristics of the camp infrastructure, including camp extent, location of shelters, containers and facility buildings, road infrastructure and the runway area. For a more detailed view parts of the camp area are also shown in the zoom boxes. The vector data have been digitized on the basis of WorldView-2 satellite data (0.5 m spatial resolution) acquired on January 03, 2013. The results have not been validated in the field. WorldView-2 satellite data acquired on January 03, 2013, is used as backdrop. The products elaborated for this Rapid Mapping Activity are realised to the best of our ability, within a very short time frame, optimising the material available. All geographic information has limitations due to the scale, resolution, date and interpretation of the original source materials. No liability concerning the content or the use thereof is assumed by the producer. The ZKI crisis maps are constantly updated.

This product is a vector file of the districts of the Paraguayan Chaco. It contains information on the forest cover within each district for the years 1986 until 2020. Hence, this product aggregates the information of 34 annual forest maps of the Paraguayan Chaco to a district level and provides the basis for further analysis as conducted in the following publication: https://doi.org/10.3390/f13010025

Global sulphur dioxide (SO2) layer height as derived from Sentinel-5P/TROPOMI observations. Sulphur dioxide enters the atmosphere through volcanic eruptions and human-related activities. Daily observations are binned onto a regular latitude-longitude grid. The SO2 layer height is provided in kilometres. 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.

The dataset is based on the analysis of forest cover dynamics in the Paraguayan Chaco (northeastern part of Paraguay) between 1987 and 2020. The underlying forest masks were derived through annual forst classifications with a Random-Forest-Classifier trained on Landsat data from 1987 until 2020. The map shows the year in which the forest area was lost.

Grassland mowing dynamics (i.e. the timing and frequency of mowing events) have a strong impact on grassland functions and yields. As grasslands in Germany are managed on small-scale units and grass grows back quickly, satellite information with high spatial and temporal resolution is necessary to capture grassland mowing dynamics. Based on Sentinel-2 data time series, mowing events are detected throughout Germany and annual maps of the grassland mowing frequency generated. The grassland mowing detection approach operates per pixel, including preprocessing of the Enhanced Vegetation Index (EVI) time series and a calibrated rule-based grassland mowing detection which is specified in more detail in Reinermann et al. 2022, 2023.

The crevasse dataset provides information on crevasse locations at 0.2 m spatial resolution for selected regions in the Alps. Information on crevasse locations is important for mountaineers and field researchers to plan a safe traverse over a glacier. This dataset is generated based on a multitask deep neural network for automated crevasse mapping from high-resolution airborne remote sensing imagery. The model was trained and evaluated over seven training and six test areas located in the Öetztal and Stubai Alps. By simultaneously preforming edge detection and segmentation tasks, the multitask model is able to robustly detect glacier crevasses of different shapes within different illumination conditions with a balanced accuracy of 86%. To prove large-scale applicability, this dataset includes high-resolution crevasse maps for the entire Öetztal and Stubai Alps based on imagery acquired within the years 2019 and 2020. Furthermore, high-quality crevasse maps for all glaciers surrounding Großglockner, Piz Palü, and Ortler were generated for available imagery in the years 2022 and 2023, repsectively. The here presented datasets can be integrated into hiking maps and digital cartography tools to provide mountaineers and field researcher with up-to-date crevasse information but also inform modelers on the distribution of stress within a glacier. Accompanying Publication: Baumhoer, C., Leibrock, S., Zapf, C., Beer, W., Kuenzer, C., in review. Automated crevasse mapping for Alpine glaciers: A multitask deep neural network approach. International Journal of Applied Earth Observation and Geoinformation.