cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5832727
title: mDRONES4rivers-project: Gyrocopter-imagery of the project area Nonnenwerth at the Rhine River, Germany

description: Spatially and temporally high-resolution data was acquired with the aid of multispectral sensors mounted on UAV and a gyrocopter platform for the purpose of classification. The work was part of the research and development project „Modern sensors and airborne remote sensing for the mapping of vegetation and hydromorphology along Federal waterways in Germany“ (mDRONES4rivers) in cooperation of the German Federal Institute of Hydrology (BfG), Geocoptix GmbH, Hochschule Koblenz und JB Hyperspectral Devices. <br> Within the project period (2019-2022) data was collected at different sites situated in Germany along the Rivers Rhine and Oder. All published data produced within the project can be found by searching for the keyword ‘mDRONES4rivers‘. <br> In this dataset, the following UAS data and metadata of the project site ‘Nonnenwerth’ (center coordinates [WGS84]: 50.637541°N, 7.208834°E; area: 45 ha) at the Rhine River in Germany is available for download:<br> • Multispectral orthophotos (GeoTiff; 5 bands: B, G, R, NIR, Flag; camera system: PanX 2.0 and PanX 3.0; resolution: ca. 30 cm/ca. 16 cm; abbreviation: PanX2_ORTHO/PanX3_ORTHO)<br> • Digital Surface Models (GeoTiff; 1 band; camera system: PanX 2.0 and PanX 3.0; resolution: ca. 30 cm; abbreviation: PanX_DEM)<br> • associated Technical Reports (PDF; technical metadata concerning data acquisition, and processing using Agisoft Metashape, 1x for multispectral orthophotos + digital surface model)<br> The above-mentioned files are provided for download as dataset stored in one directory per season depending on the date of data acquisition (e.g. mDRONES4rivers_NW_GYRO_2019_01_Winter.zip = projectname_projectsite_platform_year_no.season_name.season). To provide an overview of all files and general background information plus data preview the following files are stored in the info.zip folder: <br> • Overview table and metadata of the above-mentioned data (xlsx)<br> • Summary (PDF, Detailed description of sensors and data acquisition procedure, 1x for multispectral orthophotos + digital surface models) <br> Note: the data was processed with focus on spectral information and not for geodetic purposes. Georeferencing accuracy has not been checked in detail.
description: This dataset results from the joint project "mDRONES4rivers" funded by the research initiative mFUND of the German Federal Ministry for Digital and Transport – BMDV (19F2054A-D). Person in Charge: Jens Bongartz
DOI: 10.5281/zenodo.5832728
title: mDRONES4rivers-project: Gyrocopter-imagery of the project area Nonnenwerth at the Rhine River, Germany

description: Spatially and temporally high-resolution data was acquired with the aid of multispectral sensors mounted on UAV and a gyrocopter platform for the purpose of classification. The work was part of the research and development project „Modern sensors and airborne remote sensing for the mapping of vegetation and hydromorphology along Federal waterways in Germany“ (mDRONES4rivers) in cooperation of the German Federal Institute of Hydrology (BfG), Geocoptix GmbH, Hochschule Koblenz und JB Hyperspectral Devices. <br> Within the project period (2019-2022) data was collected at different sites situated in Germany along the Rivers Rhine and Oder. All published data produced within the project can be found by searching for the keyword ‘mDRONES4rivers‘. <br> In this dataset, the following UAS data and metadata of the project site ‘Nonnenwerth’ (center coordinates [WGS84]: 50.637541°N, 7.208834°E; area: 45 ha) at the Rhine River in Germany is available for download:<br> • Multispectral orthophotos (GeoTiff; 5 bands: B, G, R, NIR, Flag; camera system: PanX 2.0 and PanX 3.0; resolution: ca. 30 cm/ca. 16 cm; abbreviation: PanX2_ORTHO/PanX3_ORTHO)<br> • Digital Surface Models (GeoTiff; 1 band; camera system: PanX 2.0 and PanX 3.0; resolution: ca. 30 cm; abbreviation: PanX_DEM)<br> • associated Technical Reports (PDF; technical metadata concerning data acquisition, and processing using Agisoft Metashape, 1x for multispectral orthophotos + digital surface model)<br> The above-mentioned files are provided for download as dataset stored in one directory per season depending on the date of data acquisition (e.g. mDRONES4rivers_NW_GYRO_2019_01_Winter.zip = projectname_projectsite_platform_year_no.season_name.season). To provide an overview of all files and general background information plus data preview the following files are stored in the info.zip folder: <br> • Overview table and metadata of the above-mentioned data (xlsx)<br> • Summary (PDF, Detailed description of sensors and data acquisition procedure, 1x for multispectral orthophotos + digital surface models) <br> Note: the data was processed with focus on spectral information and not for geodetic purposes. Georeferencing accuracy has not been checked in detail.
description: This dataset results from the joint project "mDRONES4rivers" funded by the research initiative mFUND of the German Federal Ministry for Digital and Transport – BMDV (19F2054A-D). Person in Charge: Jens Bongartz
DOI: 10.5281/zenodo.5832653
title: mDRONES4rivers-project: Gyrocopter-imagery of the project area Niederwerth at the Rhine River, Germany

description: Spatially and temporally high-resolution data was acquired with the aid of multispectral sensors mounted on UAV and a gyrocopter platform for the purpose of classification. The work was part of the research and development project „Modern sensors and airborne remote sensing for the mapping of vegetation and hydromorphology along Federal waterways in Germany“ (mDRONES4rivers) in cooperation of the German Federal Institute of Hydrology (BfG), Geocoptix GmbH, Hochschule Koblenz und JB Hyperspectral Devices. <br> Within the project period (2019-2022) data was collected at different sites situated in Germany along the Rivers Rhine and Oder. All published data produced within the project can be found by searching for the keyword ‘mDRONES4rivers‘. <br> In this dataset, the following UAS data and metadata of the project site ‘Niederwerth’ (center coordinates [WGS84]: 50.386326°N, 7.613847°E; area: 25 ha) at the Rhine River in Germany is available for download:<br> • Multispectral orthophotos (GeoTiff; 5 bands: B, G, R, NIR, Flag; camera system: PanX 2.0 and PanX 3.0; resolution: ca. 30 cm/ca. 16 cm; abbreviation: PanX2_ORTHO/PanX3_ORTHO)<br> • Digital Surface Models (GeoTiff; 1 band; camera system: PanX 2.0 and PanX 3.0; resolution: ca. 30 cm; abbreviation: PanX_DEM)<br> • associated Technical Reports (PDF; technical metadata concerning data acquisition, and processing using Agisoft Metashape, 1x for multispectral orthophotos + digital surface model)<br> The above-mentioned files are provided for download as dataset stored in one directory per season depending on the date of data acquisition (e.g. mDRONES4rivers_NoW_GYRO_2019_01_Winter.zip = projectname_projectsite_platform_year_no.season_name.season). To provide an overview of all files and general background information plus data preview the following files are stored in the info.zip folder: <br> • Overview table and metadata of the above-mentioned data (xlsx)<br> • Summary (PDF, Detailed description of sensors and data acquisition procedure, 1x for multispectral orthophotos + digital surface models) <br> Note: the data was processed with focus on spectral information and not for geodetic purposes. Georeferencing accuracy has not been checked in detail.
description: This dataset results from the joint project "mDRONES4rivers" funded by the research initiative mFUND of the German Federal Ministry for Digital and Transport – BMDV (19F2054A-D). Person in Charge: Jens Bongartz
DOI: 10.5281/zenodo.5832654
title: mDRONES4rivers-project: Gyrocopter-imagery of the project area Niederwerth at the Rhine River, Germany

description: Spatially and temporally high-resolution data was acquired with the aid of multispectral sensors mounted on UAV and a gyrocopter platform for the purpose of classification. The work was part of the research and development project „Modern sensors and airborne remote sensing for the mapping of vegetation and hydromorphology along Federal waterways in Germany“ (mDRONES4rivers) in cooperation of the German Federal Institute of Hydrology (BfG), Geocoptix GmbH, Hochschule Koblenz und JB Hyperspectral Devices. <br> Within the project period (2019-2022) data was collected at different sites situated in Germany along the Rivers Rhine and Oder. All published data produced within the project can be found by searching for the keyword ‘mDRONES4rivers‘. <br> In this dataset, the following UAS data and metadata of the project site ‘Niederwerth’ (center coordinates [WGS84]: 50.386326°N, 7.613847°E; area: 25 ha) at the Rhine River in Germany is available for download:<br> • Multispectral orthophotos (GeoTiff; 5 bands: B, G, R, NIR, Flag; camera system: PanX 2.0 and PanX 3.0; resolution: ca. 30 cm/ca. 16 cm; abbreviation: PanX2_ORTHO/PanX3_ORTHO)<br> • Digital Surface Models (GeoTiff; 1 band; camera system: PanX 2.0 and PanX 3.0; resolution: ca. 30 cm; abbreviation: PanX_DEM)<br> • associated Technical Reports (PDF; technical metadata concerning data acquisition, and processing using Agisoft Metashape, 1x for multispectral orthophotos + digital surface model)<br> The above-mentioned files are provided for download as dataset stored in one directory per season depending on the date of data acquisition (e.g. mDRONES4rivers_NoW_GYRO_2019_01_Winter.zip = projectname_projectsite_platform_year_no.season_name.season). To provide an overview of all files and general background information plus data preview the following files are stored in the info.zip folder: <br> • Overview table and metadata of the above-mentioned data (xlsx)<br> • Summary (PDF, Detailed description of sensors and data acquisition procedure, 1x for multispectral orthophotos + digital surface models) <br> Note: the data was processed with focus on spectral information and not for geodetic purposes. Georeferencing accuracy has not been checked in detail.
description: This dataset results from the joint project "mDRONES4rivers" funded by the research initiative mFUND of the German Federal Ministry for Digital and Transport – BMDV (19F2054A-D). Person in Charge: Jens Bongartz
DOI: 10.5281/zenodo.6907605
title: A map of active cropland and short-term fallows across Northern Mozambique derived from PlanetScope data

description: <strong>Overview</strong> A map of smallholder-dominated landscapes covering the provinces Niassa, Zambezia, Cabo Delgado, and Nampula in Northern Mozambique. The map includes active cropland and short-term fallows as separate classes, as well as five land cover classes (herbaceous vegetation, open woodlands, closed woodlands, non-vegetated land, water). The map is based on PlanetScope mosaics and consequently comes at 4.77m spatial resolution. The download contains the following files: ps_lc_nmoz.tif / .qml: land cover map and associated QGIS style file ps_lc_nmoz_probmargins.tif / .qml: probability margins and associated QGIS style file training.gpkg: training samples with class labels LICENSE.pdf: NICFI data program user license <strong>Map accuracy</strong> We conducted an area-adjusted accuracy assessment based on a stratified random sample, which yielded important insights regarding accuracies and error types. The area-adjusted overall accuracy of the map is 88.9%, but users should be aware of the most important error types: Active cropland were overestimated, whereas local topographical depressions with moist soils, and regions with exposed soils/rocks and sparse vegetation cover were found to be falsely classified. Short-term fallows were underestimated, particularly in regions with high growth rates and extensive land management, such as parts of the northern and north-eastern study region. <strong>Further resources</strong> The production of this map was made possible through the NICFI data program, providing the PlanetScope mosaics and the Google Earth Engine cloud computing platform for preprocessing of the satellite data and classification. As such, the use of the map falls under the NICFI data program license agreement included in the download. The code for preprocessing the PlanetScope mosaics is based on the Google Earth Engine Python API and made available at https://github.com/philipperufin/eepypr/. We advise map users to read the preprint or the open access paper for detailed insights. In case of questions please consult these resources or contact the lead author of the work.
description: This work was supported by the FRS-FNRS, grant no. T.0154.21 and the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (Grant agreement No 677140 MIDLAND). This research contributes to the Global Land Program. The authors would like to thank Yara Ubisse, Sá Nogueira Lisboa, and Dr. Almeida Sitoe for their invaluable help in preparing and conducting the fieldwork in Northern Mozambique in 2021.
DOI: 10.5281/zenodo.6907606
title: A map of active cropland and short-term fallows across Northern Mozambique derived from PlanetScope data

description: <strong>Overview</strong> A map of smallholder-dominated landscapes covering the provinces Niassa, Zambezia, Cabo Delgado, and Nampula in Northern Mozambique. The map includes active cropland and short-term fallows as separate classes, as well as five land cover classes (herbaceous vegetation, open woodlands, closed woodlands, non-vegetated land, water). The map is based on PlanetScope mosaics and consequently comes at 4.77m spatial resolution. The download contains the following files: ps_lc_nmoz.tif / .qml: land cover map and associated QGIS style file ps_lc_nmoz_probmargins.tif / .qml: probability margins and associated QGIS style file training.gpkg: training samples with class labels LICENSE.pdf: NICFI data program user license <strong>Map accuracy</strong> We conducted an area-adjusted accuracy assessment based on a stratified random sample, which yielded important insights regarding accuracies and error types. The area-adjusted overall accuracy of the map is 88.9%, but users should be aware of the most important error types: Active cropland were overestimated, whereas local topographical depressions with moist soils, and regions with exposed soils/rocks and sparse vegetation cover were found to be falsely classified. Short-term fallows were underestimated, particularly in regions with high growth rates and extensive land management, such as parts of the northern and north-eastern study region. <strong>Further resources</strong> The production of this map was made possible through the NICFI data program, providing the PlanetScope mosaics and the Google Earth Engine cloud computing platform for preprocessing of the satellite data and classification. As such, the use of the map falls under the NICFI data program license agreement included in the download. The code for preprocessing the PlanetScope mosaics is based on the Google Earth Engine Python API and made available at https://github.com/philipperufin/eepypr/. We advise map users to read the preprint or the open access paper for detailed insights. In case of questions please consult these resources or contact the lead author of the work.
description: This work was supported by the FRS-FNRS, grant no. T.0154.21 and the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (Grant agreement No 677140 MIDLAND). This research contributes to the Global Land Program. The authors would like to thank Yara Ubisse, Sá Nogueira Lisboa, and Dr. Almeida Sitoe for their invaluable help in preparing and conducting the fieldwork in Northern Mozambique in 2021.
DOI: 10.5281/zenodo.6810792
title: The WorldStrat Dataset: Open High-Resolution Satellite Imagery With Paired Multi-Temporal Low-Resolution

description: <strong>What is this dataset?</strong> <strong>Nearly 10,000 km² of free high-resolution and matched low-resolution satellite imagery</strong> of unique locations which ensure stratified representation of all types of land-use across the world: from agriculture to ice caps, from forests to multiple urbanization densities. Those locations are also enriched with typically under-represented locations in ML datasets: sites of humanitarian interest, illegal mining sites, and settlements of persons at risk. Each high-resolution image (1.5 m/pixel) comes with multiple temporally-matched low-resolution images from the freely accessible lower-resolution Sentinel-2 satellites (10 m/pixel). We accompany this dataset with a paper, datasheet for datasets and an open-source Python package to: rebuild or extend the WorldStrat dataset, train and infer baseline algorithms, and learn with abundant tutorials, all compatible with the popular EO-learn toolbox. <strong>Why make this?</strong> We hope to foster broad-spectrum applications of ML to satellite imagery, and possibly develop the same power of analysis allowed by costly private high-resolution imagery from free public low-resolution Sentinel2 imagery. We illustrate this specific point by training and releasing several highly compute-efficient baselines on the task of Multi-Frame Super-Resolution. <strong>Licences</strong> The high-resolution Airbus imagery is distributed, with authorization from Airbus, under Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). The labels, Sentinel2 imagery, and trained weights are released under Creative Commons with Attribution 4.0 International (CC BY 4.0). The source code (will be shortly released on GitHub) under 3-Clause BSD license.
DOI: 10.5281/zenodo.6632082
title: Location, biophysical and agronomic parameters for croplands in Northern Ghana

description: We present a dataset describing (i) crop locations, (ii) biophysical parameters and (iii) crop yield and biomass was collected in 2020 and 2021 in Ghana, mostly focusing on maize in northern Ghana. The dataset contains repeated multiple measurements of leaf area index (LAI), leaf chlorophyll concentration over a large number of maize fields, as well as associated grain yield, biomass and polygons that delineate the fields.
DOI: 10.5281/zenodo.6563943
title: Supplemental Material for 'Global Soil Moisture Estimation based on GPM IMERG Data using a Site Specific Adjusted Antecedent Precipitation Index'

description: This repository hosts all supplemental material for the publication: <em>TBD</em>
DOI: 10.5281/zenodo.6563944
title: Supplemental Material for 'Global Soil Moisture Estimation based on GPM IMERG Data using a Site Specific Adjusted Antecedent Precipitation Index'

description: This repository hosts all supplemental material for the publication: <em>TBD</em>
DOI: 10.5281/zenodo.6577774
title: The ASTER dataset for EUREC4A

description: The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is mounted aboard Terra, a polar-orbiting satellite in a descending Sun-synchronous orbit with an Equator crossing time of 10:30 local solar time. ASTER recorded 412 images of trade cumulus cloud fields east of Barbados in support of the EUREC4A field campaign between 11 January and 15 February (Stevens et al., 2021). While the original L1B data is publicly available from NASA EOSDIS, the dataset here includes processed physical variables and geolocation information.<br> Only bands from the visual near-infrared (VNIR) and thermal infrared (TIR) radiometers are available since 2007. The files therefore include L1B radiances from VNIR and TIR bands, together with geolocation information, a cloud mask, and cloud top height estimates. The cloud mask is derived following a method described by Werner et al., 2016. The cloud top height estimates are derived through an infrared-window approach following Baum et al., 2012. Code for processing the original ASTER L1B data is available in the Python package <em>typhon</em> version 0.9.0, subpackage <em>cloudmask</em>.
description: Special thanks to the ASTER Science Team for scheduling the ASTER data acquisition in support of the EUREC4A field campaign!
description: {"references": ["Stevens et al. : EUREC4A, Earth Syst. Sci. Data, 13, 4067\u20134119, https://doi.org/10.5194/essd-13-4067-2021, 2021", "NASA/METI/AIST/Japan Spacesystems and U.S./Japan ASTER Science Team: ASTER Level 1B Data Set Registered Radiance at the Sensor, distributed by NASA EOSDIS Land Processes DAAC, https://doi.org/10.5067/ASTER/AST_L1B.003, 110 2001.", "Werner, F., Wind, G., Zhang, Z., Platnick, S., Di Girolamo, L., Zhao, 45 G., Amarasinghe, N., and Meyer, K.: Marine boundary layer cloud property retrievals from high-resolution ASTER observa- tions: case studies and comparison with Terra MODIS, Atmos. Meas. Tech., 9, 5869\u20135894, https://doi.org/10.5194/amt-9-5869- 2016, 2016.", "Baum, B. A., Menzel, W. P., Frey, R. A., Tobin, D. C., Holz, R. E., Ackerman, S. A., & Yang, P. (2012). MODIS cloud\u2010top property refine-ments for Collection 6.Journal of Applied Meteorology and Climatology,51(6), 1145\u20131163. https://doi.org/10.1175/JAMC\u2010D\u201011\u20100203.1", "Oliver Lemke, Lukas Kluft, John Mrziglod, Simon Pfreundschuh, Gerrit Holl, Richard Larsson, Takayoshi Yamada, Theresa Mieslinger, & Jakob Doerr. (2021). atmtools/typhon: Typhon Release 0.9.0 (v0.9.0). Zenodo [code]. https://doi.org/10.5281/zenodo.5786028"]}
DOI: 10.5281/zenodo.6577775
title: The ASTER dataset for EUREC4A

description: The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is mounted aboard Terra, a polar-orbiting satellite in a descending Sun-synchronous orbit with an Equator crossing time of 10:30 local solar time. ASTER recorded 412 images of trade cumulus cloud fields east of Barbados in support of the EUREC4A field campaign between 11 January and 15 February (Stevens et al., 2021). While the original L1B data is publicly available from NASA EOSDIS, the dataset here includes processed physical variables and geolocation information.<br> Only bands from the visual near-infrared (VNIR) and thermal infrared (TIR) radiometers are available since 2007. The files therefore include L1B radiances from VNIR and TIR bands, together with geolocation information, a cloud mask, and cloud top height estimates. The cloud mask is derived following a method described by Werner et al., 2016. The cloud top height estimates are derived through an infrared-window approach following Baum et al., 2012. Code for processing the original ASTER L1B data is available in the Python package <em>typhon</em> version 0.9.0, subpackage <em>cloudmask</em>.
description: Special thanks to the ASTER Science Team for scheduling the ASTER data acquisition in support of the EUREC4A field campaign!
description: {"references": ["Stevens et al. : EUREC4A, Earth Syst. Sci. Data, 13, 4067\u20134119, https://doi.org/10.5194/essd-13-4067-2021, 2021", "NASA/METI/AIST/Japan Spacesystems and U.S./Japan ASTER Science Team: ASTER Level 1B Data Set Registered Radiance at the Sensor, distributed by NASA EOSDIS Land Processes DAAC, https://doi.org/10.5067/ASTER/AST_L1B.003, 110 2001.", "Werner, F., Wind, G., Zhang, Z., Platnick, S., Di Girolamo, L., Zhao, 45 G., Amarasinghe, N., and Meyer, K.: Marine boundary layer cloud property retrievals from high-resolution ASTER observa- tions: case studies and comparison with Terra MODIS, Atmos. Meas. Tech., 9, 5869\u20135894, https://doi.org/10.5194/amt-9-5869- 2016, 2016.", "Baum, B. A., Menzel, W. P., Frey, R. A., Tobin, D. C., Holz, R. E., Ackerman, S. A., & Yang, P. (2012). MODIS cloud\u2010top property refine-ments for Collection 6.Journal of Applied Meteorology and Climatology,51(6), 1145\u20131163. https://doi.org/10.1175/JAMC\u2010D\u201011\u20100203.1", "Oliver Lemke, Lukas Kluft, John Mrziglod, Simon Pfreundschuh, Gerrit Holl, Richard Larsson, Takayoshi Yamada, Theresa Mieslinger, & Jakob Doerr. (2021). atmtools/typhon: Typhon Release 0.9.0 (v0.9.0). Zenodo [code]. https://doi.org/10.5281/zenodo.5786028"]}