{"type": "FeatureCollection", "features": [{"id": "10.1016/j.soilbio.2018.01.030", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:18:31Z", "type": "Journal Article", "created": "2018-02-13", "title": "Soil quality \u2013 A critical review", "description": "Sampling and analysis or visual examination of soil to assess its status and use potential is widely practiced from plot to national scales. However, the choice of relevant soil attributes and interpretation of measurements are not straightforward, because of the complexity and site-specificity of soils, legacy effects of previous land use, and trade-offs between ecosystem services. Here we review soil quality and related concepts, in terms of definition, assessment approaches, and indicator selection and interpretation. We identify the most frequently used soil quality indicators under agricultural land use. We find that explicit evaluation of soil quality with respect to specific soil threats, soil functions and ecosystem services has rarely been implemented, and few approaches provide clear interpretation schemes of measured indicator values. This limits their adoption by land managers as well as policy. We also consider novel indicators that address currently neglected though important soil properties and processes, and we list the crucial steps in the development of a soil quality assessment procedure that is scientifically sound and supports management and policy decisions that account for the multi-functionality of soil. This requires the involvement of the pertinent actors, stakeholders and end-users to a much larger degree than practiced to date.", "keywords": ["Monitoring", "Ecosystem service", "Land quality", "Soil fertility", "stakeholders", "Soil quality", "tierras", "Soil health", "Stakeholder", "soil quality", "agentes interesados", "Soil capability", "2. Zero hunger", "Minimum data set", "soil health", "soil fertility", "indicadores", "04 agricultural and veterinary sciences", "15. Life on land", "indicators", "6. Clean water", "ecosystem service", "land", "monitoring", "Indicator", "Soil function", "0401 agriculture", " forestry", " and fisheries", "Soil threat"]}, "links": [{"href": "https://doi.org/10.1016/j.soilbio.2018.01.030"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Soil%20Biology%20and%20Biochemistry", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.soilbio.2018.01.030", "name": "item", "description": "10.1016/j.soilbio.2018.01.030", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.soilbio.2018.01.030"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-05-01T00:00:00Z"}}, {"id": "2117/345158", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-26T16:29:28Z", "type": "Journal Article", "created": "2020-06-22", "title": "ModIs Dust AeroSol (MIDAS): A global fine resolution dust optical depth dataset", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. Monitoring and describing the spatiotemporal variability of dust aerosols is crucial to understand their multiple effects, related feedbacks and impacts within the Earth system. This study describes the development of the MIDAS (ModIs Dust AeroSol) dataset. MIDAS provides columnar daily dust optical depth (DOD at 550\u2009nm) at global scale and fine spatial resolution (0.1\u00b0\u2009\u00d7\u20090.1\u00b0) over a decade (2007\u20132016). This new dataset combines quality filtered satellite aerosol optical depth (AOD) retrievals from MODIS-Aqua at swath level (Collection 6, Level 2), along with DOD-to-AOD ratios provided by MERRA-2 reanalysis to derive DOD on the MODIS native grid. The uncertainties of MODIS AOD and MERRA-2 dust fraction with respect to AERONET and CALIOP, respectively, are taken into account for the estimation of the total DOD uncertainty (including measurement and sampling uncertainties). MERRA-2 dust fractions are in very good agreement with CALIOP column-integrated dust fractions across the dust belt, in the Tropical Atlantic Ocean and the Arabian Sea; the agreement degrades in North America and the Southern Hemisphere where dust sources are smaller. MIDAS, MERRA-2 and CALIOP DODs strongly agree when it comes to annual and seasonal spatial patterns; however, deviations of dust loads' intensity are evident and regionally dependent. Overall, MIDAS is well correlated with ground-truth AERONET-derived DODs (R\u2009=\u20090.882), only showing a small negative bias (\u22120.009 or \u22125.307\u2009%). Among the major dust areas of the planet, the highest R values (up to 0.977) are found at sites of N. Africa, Middle East and Asia. MIDAS expands, complements and upgrades existing observational capabilities of dust aerosols and it is suitable for dust climatological studies, model evaluation and data assimilation.</p></article>", "keywords": ["Dust forecast", ":Enginyeria agroaliment\u00e0ria::Ci\u00e8ncies de la terra i de la vida::Climatologia i meteorologia [\u00c0rees tem\u00e0tiques de la UPC]", "Dust particles", "TA715-787", "Environmental engineering", "TA170-171", "Tropospheric aerosols", "Satellite aerosol optical depth", "16. Peace & justice", "ModIs Dust AeroSol (MIDAS)", "01 natural sciences", "\u00c0rees tem\u00e0tiques de la UPC::Enginyeria agroaliment\u00e0ria::Ci\u00e8ncies de la terra i de la vida::Climatologia i meteorologia", "Earthwork. Foundations", "Conjunts de dades", "13. Climate action", "Stratospheric aerosols", "Dust aerosols", "Data sets", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://amt.copernicus.org/articles/14/309/2021/amt-14-309-2021.pdf"}, {"href": "https://amt.copernicus.org/articles/14/309/2021/amt-14-309-2021-supplement.pdf"}, {"href": "https://doi.org/2117/345158"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Atmospheric%20Measurement%20Techniques", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "2117/345158", "name": "item", "description": "2117/345158", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2117/345158"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-06-22T00:00:00Z"}}, {"id": "10.5194/amt-2020-222", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-26T16:24:17Z", "type": "Journal Article", "created": "2020-06-22", "title": "ModIs Dust AeroSol (MIDAS): A global fine resolution dust optical depth dataset", "description": "<p>Abstract. Monitoring and describing the spatiotemporal variability of dust aerosols is crucial to understand their multiple effects, related feedbacks and impacts within the Earth system. This study describes the development of the MIDAS (ModIs Dust AeroSol) dataset. MIDAS provides columnar daily dust optical depth (DOD at 550\uffe2\uff80\uff89nm) at global scale and fine spatial resolution (0.1\uffc2\uffb0\uffe2\uff80\uff89\uffc3\uff97\uffe2\uff80\uff890.1\uffc2\uffb0) over a decade (2007\uffe2\uff80\uff932016). This new dataset combines quality filtered satellite aerosol optical depth (AOD) retrievals from MODIS-Aqua at swath level (Collection 6, Level 2), along with DOD-to-AOD ratios provided by MERRA-2 reanalysis to derive DOD on the MODIS native grid. The uncertainties of MODIS AOD and MERRA-2 dust fraction with respect to AERONET and CALIOP, respectively, are taken into account for the estimation of the total DOD uncertainty (including measurement and sampling uncertainties). MERRA-2 dust fractions are in very good agreement with CALIOP column-integrated dust fractions across the dust belt, in the Tropical Atlantic Ocean and the Arabian Sea; the agreement degrades in North America and the Southern Hemisphere where dust sources are smaller. MIDAS, MERRA-2 and CALIOP DODs strongly agree when it comes to annual and seasonal spatial patterns; however, deviations of dust loads' intensity are evident and regionally dependent. Overall, MIDAS is well correlated with ground-truth AERONET-derived DODs (R\uffe2\uff80\uff89=\uffe2\uff80\uff890.882), only showing a small negative bias (\uffe2\uff88\uff920.009 or \uffe2\uff88\uff925.307\uffe2\uff80\uff89%). Among the major dust areas of the planet, the highest R values (up to 0.977) are found at sites of N. Africa, Middle East and Asia. MIDAS expands, complements and upgrades existing observational capabilities of dust aerosols and it is suitable for dust climatological studies, model evaluation and data assimilation.                         </p>", "keywords": ["Dust forecast", ":Enginyeria agroaliment\u00e0ria::Ci\u00e8ncies de la terra i de la vida::Climatologia i meteorologia [\u00c0rees tem\u00e0tiques de la UPC]", "Dust particles", "CALIOP", "TA715-787", "Environmental engineering", "Dust", "TA170-171", "Tropospheric aerosols", "Satellite aerosol optical depth", "16. Peace & justice", "ModIs Dust AeroSol (MIDAS)", "01 natural sciences", "\u00c0rees tem\u00e0tiques de la UPC::Enginyeria agroaliment\u00e0ria::Ci\u00e8ncies de la terra i de la vida::Climatologia i meteorologia", "DUST-GLASS", "MODIS", "Earthwork. Foundations", "Conjunts de dades", "13. Climate action", "Stratospheric aerosols", "Dust aerosols", "Data sets", "MIDAS", "MERRA-2", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://amt.copernicus.org/articles/14/309/2021/amt-14-309-2021.pdf"}, {"href": "https://amt.copernicus.org/articles/14/309/2021/amt-14-309-2021-supplement.pdf"}, {"href": "https://doi.org/10.5194/amt-2020-222"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Atmospheric%20Measurement%20Techniques", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/amt-2020-222", "name": "item", "description": "10.5194/amt-2020-222", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/amt-2020-222"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-06-22T00:00:00Z"}}, {"id": "10.3390/rs10050761", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-26T16:23:33Z", "type": "Journal Article", "created": "2018-05-15", "title": "Unsupervised Classification Algorithm for Early Weed Detection in Row-Crops by Combining Spatial and Spectral Information", "description": "<p>In agriculture, reducing herbicide use is a challenge to reduce health and environmental risks while maintaining production yield and quality. Site-specific weed management is a promising way to reach this objective but requires efficient weed detection methods. In this paper, an automatic image processing has been developed to discriminate between crop and weed pixels combining spatial and spectral information extracted from four-band multispectral images. Image data was captured at 3 m above ground, with a camera (multiSPEC 4C, AIRINOV, Paris) mounted on a pole kept manually. For each image, the field of view was approximately 4 m \uffc3\uff97 3 m and the resolution was 6 mm/pix. The row crop arrangement was first used to discriminate between some crop and weed pixels depending on their location inside or outside of crop rows. Then, these pixels were used to automatically build the training dataset concerning the multispectral features of crop and weed pixel classes. For each image, a specific training dataset was used by a supervised classifier (Support Vector Machine) to classify pixels that cannot be correctly discriminated using only the initial spatial approach. Finally, inter-row pixels were classified as weed and in-row pixels were classified as crop or weed depending on their spectral characteristics. The method was assessed on 14 images captured on maize and sugar beet fields. The contribution of the spatial, spectral and combined information was studied with respect to the classification quality. Our results show the better ability of the spatial and spectral combination algorithm to detect weeds between and within crop rows. They demonstrate the improvement of the weed detection rate and the improvement of its robustness. On all images, the mean value of the weed detection rate was 89% for spatial and spectral combination method, 79% for spatial method, and 75% for spectral method. Moreover, our work shows that the plant in-line sowing can be used to design an automatic image processing and classification algorithm to detect weed without requiring any manual data selection and labelling. Since the method required crop row identification, the method is suitable for wide-row crops and high spatial resolution images (at least 6 mm/pix).</p>", "keywords": ["[SDV.SA]Life Sciences [q-bio]/Agricultural sciences", "2. Zero hunger", "[SDV.SA] Life Sciences [q-bio]/Agricultural sciences", "[SDV]Life Sciences [q-bio]", "weed detection", "SVM", "04 agricultural and veterinary sciences", "spatial information", "15. Life on land", "630", "6. Clean water", "image processing", "[SDV] Life Sciences [q-bio]", "multispectral information", "automatic training data set generation", "automatic training dataset generation", "0401 agriculture", " forestry", " and fisheries", "weed detection;image processing;spatial information;multispectral information;automatic training data set generation", "weed detection; image processing; spatial information; multispectral information; automatic training data set generation; SVM"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/10/5/761/pdf"}, {"href": "https://doi.org/10.3390/rs10050761"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/rs10050761", "name": "item", "description": "10.3390/rs10050761", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs10050761"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-05-15T00:00:00Z"}}, {"id": "10.5194/bg-19-3505-2022", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:24:19Z", "type": "Journal Article", "created": "2022-07-28", "title": "Reviews and syntheses: The promise of big diverse soil data, moving current practices towards future potential", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. In the age of big data, soil data are more available and richer than ever, but \u2013 outside of a few large soil survey resources \u2013 they remain largely unusable for informing soil management and understanding Earth system processes beyond the original study. Data science has promised a fully reusable research pipeline where data from past studies are used to contextualize new findings and reanalyzed for new insight. Yet synthesis projects encounter challenges at all steps of the data reuse pipeline, including unavailable data, labor-intensive transcription of datasets, incomplete metadata, and a lack of communication between collaborators. Here, using insights from a diversity of soil, data, and climate scientists, we summarize current practices in soil data synthesis across all stages of database creation: availability, input, harmonization, curation, and publication. We then suggest new soil-focused semantic tools to improve existing data pipelines, such as ontologies, vocabulary lists, and community practices. Our goal is to provide the soil data community with an overview of current practices in soil data and where we need to go to fully leverage big data to solve soil problems in the next century.                     </p></article>", "keywords": ["FOS: Computer and information sciences", "0301 basic medicine", "Data Sharing", "Information Systems and Management", "literature review", "1904 Earth-Surface Processes", "Social Sciences", "data set", "01 natural sciences", "Decision Sciences", "Data science", "Life", "QH501-531", "910 Geography & travel", "soil analysis", "database", "QH540-549.5", "2. Zero hunger", "QE1-996.5", "000", "Ecology", "communication", "Physics", "Earth", "Geology", "[SDU.ENVI] Sciences of the Universe [physics]/Continental interfaces", " environment", "World Wide Web", "10122 Institute of Geography", "soil survey", "Physical Sciences", "Data Reuse", "environment", "Information Systems", "Evolution", "future prospect", "Data management", "Data Sharing and Stewardship in Science", "Database", "Big data", "03 medical and health sciences", "Behavior and Systematics", "Data mining", "0105 earth and related environmental sciences", "[SDU.OCEAN]Sciences of the Universe [physics]/Ocean", "Management and Reproducibility of Scientific Workflows", "Metadata", "Data curation", "Atmosphere", "[SDU.OCEAN] Sciences of the Universe [physics]/Ocean", " Atmosphere", "Acoustics", "15. Life on land", "Computer science", "1105 Ecology", " Evolution", " Behavior and Systematics", "Surface Processes", "Harmonization", "FOS: Biological sciences", "Computer Science", "Environmental Science", "[SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces", "soil management", "Research Data", "Environmental DNA in Biodiversity Monitoring"]}, "links": [{"href": "https://doi.org/10.5194/bg-19-3505-2022"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Biogeosciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/bg-19-3505-2022", "name": "item", "description": "10.5194/bg-19-3505-2022", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/bg-19-3505-2022"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-07-28T00:00:00Z"}}, {"id": "10.5194/bg-16-4851-2019", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:24:18Z", "type": "Journal Article", "created": "2019-12-20", "title": "\"Global biosphere\u2013climate interaction: a causal appraisal of observations and models over multiple temporal scales\"", "description": "<p>Abstract. Improving the skill of Earth system models (ESMs) in representing climate\uffe2\uff80\uff93vegetation interactions is crucial to enhance our predictions of future climate and ecosystem functioning. Therefore, ESMs need to correctly simulate the impact of climate on vegetation, but likewise feedbacks of vegetation on climate must be adequately represented. However, model predictions at large spatial scales remain subjected to large uncertainties, mostly due to the lack of observational patterns to benchmark them. Here, the bidirectional nature of climate\uffe2\uff80\uff93vegetation interactions is explored across multiple temporal scales by adopting a spectral Granger causality framework that allows identification of potentially co-dependent variables. Results based on global and multi-decadal records of remotely sensed leaf area index (LAI) and observed atmospheric data show that the climate control on vegetation variability increases with longer temporal scales, being higher at inter-annual than multi-month scales. Globally, precipitation is the most dominant driver of vegetation at monthly scales, particularly in (semi-)arid regions. The seasonal LAI variability in energy-driven latitudes is mainly controlled by radiation, while air temperature controls vegetation growth and decay in high northern latitudes at inter-annual scales. These observational results are used as a benchmark to evaluate four ESM simulations from the Coupled Model Intercomparison Project Phase\uffc2\uffa05 (CMIP5). Findings indicate a tendency of ESMs to over-represent the climate control on LAI dynamics and a particular overestimation of the dominance of precipitation in arid and semi-arid regions at inter-annual scales. Analogously, CMIP5 models overestimate the control of air temperature on seasonal vegetation variability, especially in forested regions. Overall, climate impacts on LAI are found to be stronger than the feedbacks of LAI on climate in both observations and models; in other words, local climate variability leaves a larger imprint on temporal LAI dynamics than vice versa. Note however that while vegetation reacts directly to its local climate conditions, the spatially collocated character of the analysis does not allow for the identification of remote feedbacks, which might result in an underestimation of the biophysical effects of vegetation on climate. Nonetheless, the widespread effect of LAI variability on radiation, as observed over the northern latitudes due to albedo changes, is overestimated by the CMIP5 models. Overall, our experiments emphasise the potential of benchmarking the representation of particular interactions in online ESMs using causal statistics in combination with observational data, as opposed to the more conventional evaluation of the magnitude and dynamics of individual variables.                     </p>", "keywords": ["0301 basic medicine", "Evolution", "LAND-SURFACE MODELS", "01 natural sciences", "RECENT TRENDS", "03 medical and health sciences", "Behavior and Systematics", "Life", "QH501-531", "NET PRIMARY PRODUCTION", "QH540-549.5", "Earth-Surface Processes", "0105 earth and related environmental sciences", "QE1-996.5", "EARTH SYSTEM MODEL", "Ecology", "LEAF-AREA INDEX", "Biology and Life Sciences", "Geology", "15. Life on land", "DATA SETS", "13. Climate action", "Earth and Environmental Sciences", "FEEDBACKS", "CO2", "VEGETATION", "SENSITIVITY"]}, "links": [{"href": "https://bg.copernicus.org/articles/16/4851/2019/bg-16-4851-2019.pdf"}, {"href": "https://doi.org/10.5194/bg-16-4851-2019"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Biogeosciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/bg-16-4851-2019", "name": "item", "description": "10.5194/bg-16-4851-2019", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/bg-16-4851-2019"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-12-20T00:00:00Z"}}, {"id": "10.5194/essd-13-367-2021", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-26T16:24:30Z", "type": "Journal Article", "created": "2021-02-13", "title": "Copernicus Atmosphere Monitoring Service TEMPOral profiles (CAMS-TEMPO): global and European emission temporal profile maps for atmospheric chemistry modelling", "description": "<p>Abstract. We present the Copernicus Atmosphere Monitoring Service TEMPOral profiles (CAMS-TEMPO), a dataset of global and European emission temporal profiles that provides gridded monthly, daily, weekly and hourly weight factors for atmospheric chemistry modelling. CAMS-TEMPO includes temporal profiles for the priority air pollutants (NOx; SOx; NMVOC, non-methane volatile organic compound; NH3; CO; PM10; and PM2.5) and the greenhouse gases (CO2 and CH4) for each of the following anthropogenic source categories: energy industry (power plants), residential combustion, manufacturing industry, transport (road traffic and air traffic in airports) and agricultural activities (fertilizer use and livestock). The profiles are computed on a global 0.1\uffe2\uff80\uff89\uffc3\uff97\uffe2\uff80\uff890.1\uffe2\uff88\uff98 and regional European 0.1\uffe2\uff80\uff89\uffc3\uff97\uffe2\uff80\uff890.05\uffe2\uff88\uff98 grid following the domain and sector classification descriptions of the global and regional emission inventories developed under the CAMS programme. The profiles account for the variability of the main emission drivers of each sector. Statistical information linked to emission variability (e.g. electricity production and traffic counts) at national and local levels were collected and combined with existing meteorology-dependent parametrizations to account for the influences of sociodemographic factors and climatological conditions. Depending on the sector and the temporal resolution (i.e. monthly, weekly, daily and hourly) the resulting profiles are pollutant-dependent, year-dependent (i.e. time series from 2010 to 2017) and/or spatially dependent (i.e. the temporal weights vary per country or region). We provide a complete description of the data and methods used to build the CAMS-TEMPO profiles, and whenever possible, we evaluate the representativeness of the proxies used to compute the temporal weights against existing observational data. We find important discrepancies when comparing the obtained temporal weights with other currently used datasets. The CAMS-TEMPO data product including the global (CAMS-GLOB-TEMPOv2.1, https://doi.org/10.24380/ks45-9147, Guevara et al., 2020a) and regional European (CAMS-REG-TEMPOv2.1, https://doi.org/10.24380/1cx4-zy68, Guevara et al., 2020b) temporal profiles are distributed from the Emissions of atmospheric Compounds and Compilation of Ancillary Data (ECCAD) system (https://eccad.aeris-data.fr/, last access: February 2021).                     </p>", "keywords": ["China", "Atmospheric chemistry", "550", "Anthropogenic emissions", "Ammonia emissions", "Urbanisation", "Environment", "7. Clean energy", "[SDU] Sciences of the Universe [physics]", "11. Sustainability", "Air-pollution", "GE1-350", "Gridded emissions", "Fuel use", "QE1-996.5", "[SDU.OCEAN] Sciences of the Universe [physics]/Ocean", " Atmosphere", "Inventory", "Geology", "Environmental sciences", "Data product", "Qu\u00edmica atmosf\u00e8rica", "13. Climate action", "Air quality", "Transport model", "Data sets", "Bottom-up", "\u00c0rees tem\u00e0tiques de la UPC::Desenvolupament hum\u00e0 i sostenible::Degradaci\u00f3 ambiental::Contaminaci\u00f3 atmosf\u00e8rica", ":Desenvolupament hum\u00e0 i sostenible::Degradaci\u00f3 ambiental::Contaminaci\u00f3 atmosf\u00e8rica [\u00c0rees tem\u00e0tiques de la UPC]", "Air pollutants"]}, "links": [{"href": "https://essd.copernicus.org/articles/13/367/2021/essd-13-367-2021.pdf"}, {"href": "https://doi.org/10.5194/essd-13-367-2021"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Earth%20System%20Science%20Data", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/essd-13-367-2021", "name": "item", "description": "10.5194/essd-13-367-2021", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/essd-13-367-2021"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-02-12T00:00:00Z"}}, {"id": "10.5194/essd-13-3707-2021", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:24:30Z", "type": "Journal Article", "created": "2021-01-07", "title": "C-band radar data and in situ measurements for the monitoring of wheat crops in a semi-arid area (center of Morocco)", "description": "<p>Abstract. A better understanding of the hydrological functioning of irrigated crops using remote sensing observations is of prime importance in the semi-arid areas where the water resources are limited. Radar observations, available at high resolution and revisit time since the launch of Sentinel-1 in 2014, have shown great potential for the monitoring of the water content of the upper soil and of the canopy. In this paper, a complete set of data for radar signal analysis is shared to the scientific community for the first time to our knowledge. The data set is composed of Sentinel-1 products and in situ measurements of soil and vegetation variables collected during three agricultural seasons over drip-irrigated winter wheat in the Haouz plain in Morocco. The in situ data gathers soil measurements (time series of half-hourly surface soil moisture, surface roughness and agricultural practices) and vegetation measurements collected every week/two weeks including above-ground fresh and dry biomasses, vegetation water content based on destructive measurements, cover fraction, leaf area index and plant height. Radar data are the backscattering coefficient and the interferometric coherence derived from Sentinel-1 GRDH (Ground Range Detected High resolution) and SLC (Single Look Complex) products, respectively. The normalized difference vegetation index derived from Sentinel-2 data based on Level-2A (surface reflectance and cloud mask) atmospheric effects-corrected products is also provided. This database, which is the first of its kind made available in open access, is described here comprehensively in order to help the scientific community to evaluate and to develop new or existing remote sensing algorithms for monitoring wheat canopy under semi-arid conditions. The data set is particularly relevant for the development of radar applications including surface soil moisture and vegetation parameters retrieval using either physically based or empirical approaches such as machine and deep learning algorithms. The database is archived in the DataSuds repository and is freely-accessible via the DOI:  https://doi.org/10.23708/8D6WQC  (Ouaadi et al., 2020a).                         </p>", "keywords": ["550", "Arid", "Soil Moisture", "0211 other engineering and technologies", "FOS: Mechanical engineering", "02 engineering and technology", "Digital Soil Mapping Techniques", "Normalized Difference Vegetation Index", "630", "Agricultural and Biological Sciences", "Engineering", "Pathology", "GE1-350", "2. Zero hunger", "QE1-996.5", "Vegetation Monitoring", "Water content", "Ecology", "Geography", "Statistics", "Life Sciences", "Hydrology (agriculture)", "Geology", "Remote Sensing in Vegetation Monitoring and Phenology", "04 agricultural and veterinary sciences", "Remote sensing", "Soil Erosion and Agricultural Sustainability", "6. Clean water", "Satellite Observations", "Archaeology", "Physical Sciences", "Leaf area index", "Telecommunications", "Medicine", "Vegetation (pathology)", "Environmental Engineering", "Data set", "[SDU.STU]Sciences of the Universe [physics]/Earth Sciences", "Aerospace Engineering", "Soil Science", "Environmental science", "Digital Soil Mapping", "[SDU] Sciences of the Universe [physics]", "Global Soil Information", "FOS: Mathematics", "Biology", "Radar", "Synthetic Aperture Radar Interferometry", "Canopy", "FOS: Environmental engineering", "Soil Properties", "Paleontology", "FOS: Earth and related environmental sciences", "15. Life on land", "Remote Sensing of Soil Moisture", "Surface Deformation Monitoring", "Computer science", "Agronomy", "Environmental sciences", "Geotechnical engineering", "[SDU]Sciences of the Universe [physics]", "13. Climate action", "FOS: Biological sciences", "Environmental Science", "[SDU.STU] Sciences of the Universe [physics]/Earth Sciences", "0401 agriculture", " forestry", " and fisheries", "Mathematics"]}, "links": [{"href": "https://essd.copernicus.org/articles/13/3707/2021/essd-13-3707-2021.pdf"}, {"href": "https://doi.org/10.5194/essd-13-3707-2021"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Earth%20System%20Science%20Data", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/essd-13-3707-2021", "name": "item", "description": "10.5194/essd-13-3707-2021", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/essd-13-3707-2021"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-01-07T00:00:00Z"}}, {"id": "10.5194/essd-12-753-2020", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:24:29Z", "type": "Journal Article", "created": "2019-10-07", "title": "A pan-African high-resolution drought index dataset", "description": "<p>Abstract. Droughts in Africa cause severe problems such as crop failure, food shortages, famine, epidemics and even mass migration. To minimize the effects of drought on water and food security over Africa, a high-resolution drought dataset is essential to establish robust drought hazard probabilities and to assess drought vulnerability considering a multi- and cross-sectorial perspective that includes crops, hydrological systems, rangeland, and environmental systems. Such assessments are essential for policy makers, their advisors, and other stakeholders to respond to the pressing humanitarian issues caused by these environmental hazards. In this study, a high spatial resolution Standardized Precipitation-Evapotranspiration Index (SPEI) drought dataset is presented to support these assessments. We compute historical SPEI data based on Climate Hazards group InfraRed Precipitation with Station data (CHIRPS) precipitation estimates and Global Land Evaporation Amsterdam Model (GLEAM) potential evaporation estimates. The high resolution SPEI dataset (SPEI-HR) presented here spans from 1981 to 2016 (36 years) with 5\uffe2\uff80\uff89km spatial resolution over the whole Africa. To facilitate the diagnosis of droughts of different durations, accumulation periods from 1 to 48 months are provided. The quality of the resulting dataset was compared with coarse-resolution SPEI based on Climatic Research Unit (CRU) Time-Series (TS) datasets, and Normalized Difference Vegetation Index (NDVI) calculated from the Global Inventory Monitoring and Modeling System (GIMMS) project, as well as with root zone soil moisture modelled by GLEAM. Agreement found between coarse resolution SPEI from CRU TS (SPEI-CRU) and the developed SPEI-HR provides confidence in the estimation of temporal and spatial variability of droughts in Africa with SPEI-HR. In addition, agreement of SPEI-HR versus NDVI and root zone soil moisture \uffe2\uff80\uff93 with average correlation coefficient (R) of 0.54 and 0.77, respectively \uffe2\uff80\uff93 further implies that SPEI-HR can provide valuable information to study drought-related processes and societal impacts at sub-basin and district scales in Africa. The dataset is archived in Centre for Environmental Data Analysis (CEDA) with link: https://doi.org/10.5285/bbdfd09a04304158b366777eba0d2aeb (Peng et al., 2019a)                         </p>", "keywords": ["CALIFORNIA DROUGHT", "IMPACTS", "2. Zero hunger", "QE1-996.5", "EVAPOTRANSPIRATION", "GLOBAL ASSESSMENT", "WATER-RESOURCES", "DATA PRODUCTS", "0207 environmental engineering", "1. No poverty", "Geology", "02 engineering and technology", "15. Life on land", "01 natural sciences", "6. Clean water", "Environmental sciences", "PRECIPITATION CLIMATOLOGY CENTER", "DATA SETS", "13. Climate action", "Earth and Environmental Sciences", "GREATER HORN", "11. Sustainability", "GE1-350", "SATELLITE", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://essd.copernicus.org/articles/12/753/2020/essd-12-753-2020.pdf"}, {"href": "https://doi.org/10.5194/essd-12-753-2020"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Earth%20System%20Science%20Data", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/essd-12-753-2020", "name": "item", "description": "10.5194/essd-12-753-2020", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/essd-12-753-2020"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-10-07T00:00:00Z"}}, {"id": "10568/90970", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:28:32Z", "type": "Journal Article", "created": "2018-02-12", "title": "Soil quality \u2013 A critical review", "description": "Sampling and analysis or visual examination of soil to assess its status and use potential is widely practiced from plot to national scales. However, the choice of relevant soil attributes and interpretation of measurements are not straightforward, because of the complexity and site-specificity of soils, legacy effects of previous land use, and trade-offs between ecosystem services. Here we review soil quality and related concepts, in terms of definition, assessment approaches, and indicator selection and interpretation. We identify the most frequently used soil quality indicators under agricultural land use. We find that explicit evaluation of soil quality with respect to specific soil threats, soil functions and ecosystem services has rarely been implemented, and few approaches provide clear interpretation schemes of measured indicator values. This limits their adoption by land managers as well as policy. We also consider novel indicators that address currently neglected though important soil properties and processes, and we list the crucial steps in the development of a soil quality assessment procedure that is scientifically sound and supports management and policy decisions that account for the multi-functionality of soil. This requires the involvement of the pertinent actors, stakeholders and end-users to a much larger degree than practiced to date.", "keywords": ["Monitoring", "Ecosystem service", "Land quality", "Soil fertility", "stakeholders", "tierras", "Soil health", "Stakeholder", "soil quality", "agentes interesados", "Soil capability", "2. Zero hunger", "Minimum data set", "soil health", "soil fertility", "indicadores", "04 agricultural and veterinary sciences", "15. Life on land", "indicators", "6. Clean water", "ecosystem service", "land", "monitoring", "Indicator", "Soil function", "0401 agriculture", " forestry", " and fisheries", "Soil threat"]}, "links": [{"href": "https://doi.org/10568/90970"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Soil%20Biology%20and%20Biochemistry", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10568/90970", "name": "item", "description": "10568/90970", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10568/90970"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-05-01T00:00:00Z"}}, {"id": "144d478f-7e02-4b8d-859f-237fed0184c9", "type": "Feature", "geometry": {"type": "Polygon", "coordinates": [[[11.27, 51.36], [11.27, 53.56], [14.77, 53.56], [14.77, 51.36], [11.27, 51.36]]]}, "properties": {"updated": "2025-06-11", "type": "Service", "created": "2024-08-01T00:00:00.000+02:00", "language": "ger", "title": "INSPIRE-WMS Land Cover / InVeKoS Landscape Elements BB", "description": "Der interoperable INSPIRE-WMS ist ein Darstellungsdienst, der Daten im Annex-Schema Bodenbedeckungsvektor (abgeleitet aus dem origin\u00e4ren Datensatz: Digitales Feldblock Kataster) bereitstellt. Gem\u00e4\u00df der INSPIRE-Datenspezifikation Land Cover (D2.8.II.2_v3.1.0) liegen die Inhalte INSPIRE-konform vor. Der WMS beinhaltet den folgenden Layer: \n\t\u2022 LC.LandCoverSurfaces: Ein einzelnes, durch einen Punkt oder eine Fl\u00e4che dargestelltes Element des Bodenbedeckungsdatensatzes.\n\t---\n\tThe compliant INSPIRE-WFS is a view service that delivers data in the Annex-Schema Land Cover Vector (derived from the original data set: Land Parcel Information System LPIS). The content is compliant to the INSPIRE data specification for the annex theme Land Cover (D2.8.II.2_v3.1.0). The WMS includes the following layer: \n\t\u2022 LC.LandCoverSurfaces: An individual element of the LC dataset represented by  a point or polygon. Ma\u00dfstab: 1:2400; Bodenaufl\u00f6sung: nullm; Scanaufl\u00f6sung (DPI): null", "formats": [{"name": "OGC:WFS"}, {"name": "OGC Web Map Service"}], "keywords": ["Bodenbedeckung", "infoMapAccessService", "land cover data set", "land cover unit", "Landschaftselemente", "interoperable Daten", "interoperabel", "Interoperability", "landscape feature", "IACS", "agricultural land", "Geospatial", "farming", "Georaum", "Oberfl\u00e4chenbeschreibung", "LPIS", "InVeKoS", "Basiskarten", "opendata", "Regional", "AGRI", "inspireidentifiziert", "land cover", "INSPIRE-Daten", "Landwirtschaftliche Fl\u00e4che", "landscape element", "Brandenburg [Land]", "agriculture", "Common Agricultural Policy", "Landwirtschaft", "Bilddaten", "Gemeinsame Agrarpolitik", "Landschaftselement", "Landbedeckung"], "contacts": [{"name": "Falk, Norbert, Herr", "organization": "Ministerium f\u00fcr Land- und Ern\u00e4hrungswirtschaft, Umwelt und Verbraucherschutz (MLEUV)", "position": "EU-Zahlstelle", "roles": ["owner"], "phones": [{"value": "+49 331 866 0"}], "emails": [{"value": "zahlstelle@mleuv.brandenburg.de"}], "addresses": [{"deliveryPoint": ["Postbox 601150, 14411 Potsdam"], "city": "Potsdam", "administrativeArea": "Brandenburg", "postalCode": "14467", "country": "DEU"}], "links": [{"href": {"url": "https://mleuv.brandenburg.de", "protocol": null, "protocol_url": "", "name": null, "name_url": "", "description": null, "description_url": "", "applicationprofile": null, "applicationprofile_url": "", "function": null}}]}, {"name": "Lantzsch, Birgit, Frau", "organization": "Ministerium f\u00fcr Land- und Ern\u00e4hrungswirtschaft, Umwelt und Verbraucherschutz (MLEUV)", "position": "Abteilung 3 - L\u00e4ndliche Entwicklung, Landwirtschaft und Forsten, Referat 33 - Agrarumweltma\u00dfnahmen, \u00f6kologischer Landbau, Direktzahlungen", "roles": ["pointOfContact"], "phones": [{"value": "+49 331 866 7624"}], "emails": [{"value": "invekos.dz@mleuv.brandenburg.de"}], "addresses": [{"deliveryPoint": ["Postbox 601150, 14411 Potsdam"], "city": "Potsdam", "administrativeArea": "Brandenburg", "postalCode": "14467", "country": "DEU"}], "links": [{"href": {"url": "https://mleuv.brandenburg.de", "protocol": null, "protocol_url": "", "name": null, "name_url": "", "description": null, "description_url": "", "applicationprofile": null, "applicationprofile_url": "", "function": null}}]}], "themes": [{"concepts": [{"id": "Bodenbedeckung"}], "scheme": "GEMET - INSPIRE themes, version 1.0"}, {"concepts": [{"id": "infoMapAccessService"}], "scheme": "Service Classification, version 1.0"}, {"concepts": [{"id": "Regional"}], "scheme": "http://inspire.ec.europa.eu/metadata-codelist/SpatialScope"}, {"concepts": [{"id": "land cover"}, {"id": "INSPIRE-Daten"}, {"id": "Landwirtschaftliche Fl\u00e4che"}, {"id": "landscape element"}, {"id": "Brandenburg [Land]"}, {"id": "agriculture"}, {"id": "Common Agricultural Policy"}, {"id": "Landwirtschaft"}, {"id": "Bilddaten"}], "scheme": "UMTHES Thesaurus"}, {"concepts": [{"id": "Gemeinsame Agrarpolitik"}, {"id": "Landschaftselement"}, {"id": "Landbedeckung"}], "scheme": "GEMET - Concepts, version 3.1"}], "title_alternate": "lcv_dfbk_lf_wms"}, "links": [{"href": "https://inspire.brandenburg.de/services/lcv_dfbk_lf_wfs?service=WFS&request=GetCapabilities", "name": "Inspire-Daten", "description": "Nur \u00fcber den Downloaddienst https://inspire.brandenburg.de/services/lcv_dfbk_lf_wfs? verf\u00fcgbar. Beinhaltet nur den Stand der aktuellsten Referenz des aktuellen Pflegejahres.", "protocol": "OGC:WFS", "rel": "download"}, {"href": "https://inspire.brandenburg.de/services/lcv_dfbk_lf_wms?service=WMS&request=GetCapabilities", "name": "Dienst \"INSPIRE-WMS Land Cover / InVeKoS Landschaftselemente BB\" (GetCapabilities)", "protocol": "OGC Web Map Service", "rel": null}, {"href": "https://inspire.brandenburg.de/services/lcv_dfbk_lf_wms?service=WMS&request=GetCapabilities"}, {"href": "https://inspire.brandenburg.de/services/lcv_dfbk_lf_wms?"}, {"href": "https://inspire.brandenburg.de/services/lcv_dfbk_lf_wms?"}, {"href": "https://isk.geobasis-bb.de/md-thumbnail/wms-lcv-dfbk-lf.png", "name": "preview", "description": "Web image thumbnail (URL)", "protocol": "WWW:LINK-1.0-http--image-thumbnail", "rel": "preview"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/7faa23ac-a153-46e9-b4d1-893284d0a82b", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "144d478f-7e02-4b8d-859f-237fed0184c9", "name": "item", "description": "144d478f-7e02-4b8d-859f-237fed0184c9", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/144d478f-7e02-4b8d-859f-237fed0184c9"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date-time": "2023-01-01T00:00:00Z"}}, {"id": "20.500.11850/562259", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:29:14Z", "type": "Journal Article", "created": "2022-07-28", "title": "Reviews and syntheses: The promise of big diverse soil data, moving current practices towards future potential", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. In the age of big data, soil data are more available and richer than ever, but \u2013 outside of a few large soil survey resources \u2013 they remain largely unusable for informing soil management and understanding Earth system processes beyond the original study. Data science has promised a fully reusable research pipeline where data from past studies are used to contextualize new findings and reanalyzed for new insight. Yet synthesis projects encounter challenges at all steps of the data reuse pipeline, including unavailable data, labor-intensive transcription of datasets, incomplete metadata, and a lack of communication between collaborators. Here, using insights from a diversity of soil, data, and climate scientists, we summarize current practices in soil data synthesis across all stages of database creation: availability, input, harmonization, curation, and publication. We then suggest new soil-focused semantic tools to improve existing data pipelines, such as ontologies, vocabulary lists, and community practices. Our goal is to provide the soil data community with an overview of current practices in soil data and where we need to go to fully leverage big data to solve soil problems in the next century.</p></article>", "keywords": ["FOS: Computer and information sciences", "0301 basic medicine", "Data Sharing", "Information Systems and Management", "literature review", "1904 Earth-Surface Processes", "Social Sciences", "data set", "01 natural sciences", "Decision Sciences", "Data science", "Life", "QH501-531", "910 Geography & travel", "soil analysis", "database", "QH540-549.5", "2. Zero hunger", "QE1-996.5", "000", "Ecology", "communication", "Physics", "Earth", "Geology", "[SDU.ENVI] Sciences of the Universe [physics]/Continental interfaces", " environment", "World Wide Web", "10122 Institute of Geography", "soil survey", "Physical Sciences", "Data Reuse", "environment", "Information Systems", "Evolution", "future prospect", "Data management", "Data Sharing and Stewardship in Science", "Database", "Big data", "03 medical and health sciences", "Behavior and Systematics", "Data mining", "0105 earth and related environmental sciences", "[SDU.OCEAN]Sciences of the Universe [physics]/Ocean", "Management and Reproducibility of Scientific Workflows", "Metadata", "Data curation", "Atmosphere", "[SDU.OCEAN] Sciences of the Universe [physics]/Ocean", " Atmosphere", "Acoustics", "15. Life on land", "Computer science", "1105 Ecology", " Evolution", " Behavior and Systematics", "Surface Processes", "Harmonization", "FOS: Biological sciences", "Computer Science", "Environmental Science", "[SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces", "soil management", "Research Data", "Environmental DNA in Biodiversity Monitoring"]}, "links": [{"href": "https://doi.org/20.500.11850/562259"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Biogeosciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "20.500.11850/562259", "name": "item", "description": "20.500.11850/562259", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/20.500.11850/562259"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-07-28T00:00:00Z"}}, {"id": "2117/342462", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:29:28Z", "type": "Journal Article", "created": "2021-02-13", "title": "Copernicus Atmosphere Monitoring Service TEMPOral profiles (CAMS-TEMPO): global and European emission temporal profile maps for atmospheric chemistry modelling", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. We present the Copernicus Atmosphere Monitoring Service TEMPOral profiles (CAMS-TEMPO), a dataset of global and European emission temporal profiles that provides gridded monthly, daily, weekly and hourly weight factors for atmospheric chemistry modelling. CAMS-TEMPO includes temporal profiles for the priority air pollutants (NOx; SOx; NMVOC, non-methane volatile organic compound; NH3; CO; PM10; and PM2.5) and the greenhouse gases (CO2 and CH4) for each of the following anthropogenic source categories: energy industry (power plants), residential combustion, manufacturing industry, transport (road traffic and air traffic in airports) and agricultural activities (fertilizer use and livestock). The profiles are computed on a global 0.1\u2009\u00d7\u20090.1\u2218 and regional European 0.1\u2009\u00d7\u20090.05\u2218 grid following the domain and sector classification descriptions of the global and regional emission inventories developed under the CAMS programme. The profiles account for the variability of the main emission drivers of each sector. Statistical information linked to emission variability (e.g. electricity production and traffic counts) at national and local levels were collected and combined with existing meteorology-dependent parametrizations to account for the influences of sociodemographic factors and climatological conditions. Depending on the sector and the temporal resolution (i.e. monthly, weekly, daily and hourly) the resulting profiles are pollutant-dependent, year-dependent (i.e. time series from 2010 to 2017) and/or spatially dependent (i.e. the temporal weights vary per country or region). We provide a complete description of the data and methods used to build the CAMS-TEMPO profiles, and whenever possible, we evaluate the representativeness of the proxies used to compute the temporal weights against existing observational data. We find important discrepancies when comparing the obtained temporal weights with other currently used datasets. The CAMS-TEMPO data product including the global (CAMS-GLOB-TEMPOv2.1, https://doi.org/10.24380/ks45-9147, Guevara et al., 2020a) and regional European (CAMS-REG-TEMPOv2.1, https://doi.org/10.24380/1cx4-zy68, Guevara et al., 2020b) temporal profiles are distributed from the Emissions of atmospheric Compounds and Compilation of Ancillary Data (ECCAD) system (https://eccad.aeris-data.fr/, last access: February 2021).</p></article>", "keywords": ["China", "Atmospheric chemistry", "550", "Anthropogenic emissions", "Ammonia emissions", "Urbanisation", "Environment", "7. Clean energy", "[SDU] Sciences of the Universe [physics]", "11. Sustainability", "Air-pollution", "GE1-350", "Gridded emissions", "Fuel use", "QE1-996.5", "[SDU.OCEAN] Sciences of the Universe [physics]/Ocean", " Atmosphere", "Inventory", "Geology", "Environmental sciences", "Data product", "Qu\u00edmica atmosf\u00e8rica", "13. Climate action", "Air quality", "Transport model", "Data sets", "Bottom-up", "\u00c0rees tem\u00e0tiques de la UPC::Desenvolupament hum\u00e0 i sostenible::Degradaci\u00f3 ambiental::Contaminaci\u00f3 atmosf\u00e8rica", ":Desenvolupament hum\u00e0 i sostenible::Degradaci\u00f3 ambiental::Contaminaci\u00f3 atmosf\u00e8rica [\u00c0rees tem\u00e0tiques de la UPC]", "Air pollutants"]}, "links": [{"href": "https://essd.copernicus.org/articles/13/367/2021/essd-13-367-2021.pdf"}, {"href": "https://doi.org/2117/342462"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Earth%20System%20Science%20Data", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "2117/342462", "name": "item", "description": "2117/342462", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2117/342462"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-02-12T00:00:00Z"}}, {"id": "3045418312", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-26T16:30:08Z", "type": "Journal Article", "created": "2020-06-22", "title": "ModIs Dust AeroSol (MIDAS): A global fine resolution dust optical depth dataset", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. Monitoring and describing the spatiotemporal variability of dust aerosols is crucial to understand their multiple effects, related feedbacks and impacts within the Earth system. This study describes the development of the MIDAS (ModIs Dust AeroSol) dataset. MIDAS provides columnar daily dust optical depth (DOD at 550\u2009nm) at global scale and fine spatial resolution (0.1\u00b0\u2009\u00d7\u20090.1\u00b0) over a decade (2007\u20132016). This new dataset combines quality filtered satellite aerosol optical depth (AOD) retrievals from MODIS-Aqua at swath level (Collection 6, Level 2), along with DOD-to-AOD ratios provided by MERRA-2 reanalysis to derive DOD on the MODIS native grid. The uncertainties of MODIS AOD and MERRA-2 dust fraction with respect to AERONET and CALIOP, respectively, are taken into account for the estimation of the total DOD uncertainty (including measurement and sampling uncertainties). MERRA-2 dust fractions are in very good agreement with CALIOP column-integrated dust fractions across the dust belt, in the Tropical Atlantic Ocean and the Arabian Sea; the agreement degrades in North America and the Southern Hemisphere where dust sources are smaller. MIDAS, MERRA-2 and CALIOP DODs strongly agree when it comes to annual and seasonal spatial patterns; however, deviations of dust loads' intensity are evident and regionally dependent. Overall, MIDAS is well correlated with ground-truth AERONET-derived DODs (R\u2009=\u20090.882), only showing a small negative bias (\u22120.009 or \u22125.307\u2009%). Among the major dust areas of the planet, the highest R values (up to 0.977) are found at sites of N. Africa, Middle East and Asia. MIDAS expands, complements and upgrades existing observational capabilities of dust aerosols and it is suitable for dust climatological studies, model evaluation and data assimilation.                         </p></article>", "keywords": ["Dust forecast", ":Enginyeria agroaliment\u00e0ria::Ci\u00e8ncies de la terra i de la vida::Climatologia i meteorologia [\u00c0rees tem\u00e0tiques de la UPC]", "Dust particles", "CALIOP", "TA715-787", "Environmental engineering", "Dust", "TA170-171", "Tropospheric aerosols", "Satellite aerosol optical depth", "16. Peace & justice", "ModIs Dust AeroSol (MIDAS)", "01 natural sciences", "\u00c0rees tem\u00e0tiques de la UPC::Enginyeria agroaliment\u00e0ria::Ci\u00e8ncies de la terra i de la vida::Climatologia i meteorologia", "DUST-GLASS", "MODIS", "Earthwork. Foundations", "Conjunts de dades", "13. Climate action", "Stratospheric aerosols", "Dust aerosols", "Data sets", "MIDAS", "MERRA-2", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://amt.copernicus.org/articles/14/309/2021/amt-14-309-2021.pdf"}, {"href": "https://amt.copernicus.org/articles/14/309/2021/amt-14-309-2021-supplement.pdf"}, {"href": "https://doi.org/3045418312"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Atmospheric%20Measurement%20Techniques", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "3045418312", "name": "item", "description": "3045418312", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/3045418312"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-06-22T00:00:00Z"}}, {"id": "308833f8-459c-4ea2-b3a6-2772d1c24798", "type": "Feature", "geometry": {"type": "Polygon", "coordinates": [[[11.27, 51.36], [11.27, 53.56], [14.77, 53.56], [14.77, 51.36], [11.27, 51.36]]]}, "properties": {"updated": "2025-06-11", "type": "Service", "created": "2024-08-01T00:00:00.000+02:00", "language": "ger", "title": "INSPIRE-WMS Land Use / InVeKoS Landwirtschaftliche Fl\u00e4chen BB", "description": "Der interoperable INSPIRE-WMS ist ein Darstellungsdienst, der Daten im Annex-Schema Existierende Bodennutzung (abgeleitet aus dem origin\u00e4ren Datensatz: Digitales Feldblock Kataster Brandenburg) bereitstellt. Gem\u00e4\u00df der INSPIRE-Datenspezifikation Land Use (D2.8.III.4_v3.1.1) liegen die Inhalte INSPIRE-konform vor. Der WMS beinhaltet den folgenden Layer: \n\t\u2022 LU.ExistingLandUse: Ein Objekt zur existierenden Bodennutzung beschreibt die Bodennutzung in einem Gebiet miteinheitlicher Bodennutzungskategorie oder homogener Kombination verschiedener Bodennutzungen.\n\n\t---\n\tThe compliant INSPIRE-WFS is a view service that delivers data in the Annex-Schema Existing Land Use (derived from the original data set: Land Parcel Information System LPIS). The content is compliant to the INSPIRE data specification for the annex theme Land Use (D2.8.III.4_v3.1.1). The WMS includes the following layer: \n\t\u2022 LU.ExistingLandUse: An existing land use object describes the land use of an area having a homogeneous combination of land use types. Ma\u00dfstab: 1:2400; Bodenaufl\u00f6sung: nullm; Scanaufl\u00f6sung (DPI): null", "formats": [{"name": "OGC:WFS"}, {"name": "OGC Web Map Service"}], "keywords": ["Bodenbedeckung", "infoMapAccessService", "existing land use data set", "existing land use object", "Feldblock", "interoperable Daten", "interoperabel", "interoperability", "IACS", "agricultural land", "Geospatial", "farming", "Georaum", "Planungsunterlagen", "LPIS", "InVeKoS", "opendata", "Regional", "AGRI", "inspireidentifiziert", "land use", "INSPIRE-Daten", "Landwirtschaftliche Fl\u00e4che", "agricultural area", "Brandenburg [Land]", "Landwirtschaft", "agriculture", "Common Agricultural Policy", "Gemeinsame Agrarpolitik", "Kataster"], "contacts": [{"name": "Falk, Norbert, Herr", "organization": "Ministerium f\u00fcr Land- und Ern\u00e4hrungswirtschaft, Umwelt und Verbraucherschutz (MLEUV)", "position": "EU-Zahlstelle", "roles": ["owner"], "phones": [{"value": "+49 331 866 0"}], "emails": [{"value": "zahlstelle@mleuv.brandenburg.de"}], "addresses": [{"deliveryPoint": ["Postbox 601150, 14411 Potsdam"], "city": "Potsdam", "administrativeArea": "Brandenburg", "postalCode": "14467", "country": "DEU"}], "links": [{"href": {"url": "https://mleuv.brandenburg.de", "protocol": null, "protocol_url": "", "name": null, "name_url": "", "description": null, "description_url": "", "applicationprofile": null, "applicationprofile_url": "", "function": null}}]}, {"name": "Lantzsch, Birgit, Frau", "organization": "Ministerium f\u00fcr Land- und Ern\u00e4hrungswirtschaft, Umwelt und Verbraucherschutz (MLEUV)", "position": "Abteilung 3 - L\u00e4ndliche Entwicklung, Landwirtschaft und Forsten, Referat 33 - Agrarumweltma\u00dfnahmen, \u00f6kologischer Landbau, Direktzahlungen", "roles": ["pointOfContact"], "phones": [{"value": "+49 331 866 7624"}], "emails": [{"value": "invekos.dz@mleuv.brandenburg.de"}], "addresses": [{"deliveryPoint": ["Postbox 601150, 14411 Potsdam"], "city": "Potsdam", "administrativeArea": "Brandenburg", "postalCode": "14467", "country": "DEU"}], "links": [{"href": {"url": "https://mleuv.brandenburg.de", "protocol": null, "protocol_url": "", "name": null, "name_url": "", "description": null, "description_url": "", "applicationprofile": null, "applicationprofile_url": "", "function": null}}]}], "themes": [{"concepts": [{"id": "Bodenbedeckung"}], "scheme": "GEMET - INSPIRE themes, version 1.0"}, {"concepts": [{"id": "infoMapAccessService"}], "scheme": "Service Classification, version 1.0"}, {"concepts": [{"id": "Regional"}], "scheme": "http://inspire.ec.europa.eu/metadata-codelist/SpatialScope"}, {"concepts": [{"id": "land use"}, {"id": "INSPIRE-Daten"}, {"id": "Landwirtschaftliche Fl\u00e4che"}, {"id": "agricultural area"}, {"id": "Brandenburg [Land]"}, {"id": "Landwirtschaft"}, {"id": "agriculture"}, {"id": "Common Agricultural Policy"}], "scheme": "UMTHES Thesaurus"}, {"concepts": [{"id": "Gemeinsame Agrarpolitik"}, {"id": "Kataster"}], "scheme": "GEMET - Concepts, version 3.1"}], "title_alternate": "elu_dfbk_aa_wms"}, "links": [{"href": "https://inspire.brandenburg.de/services/elu_dfbk_aa_wfs?service=WFS&request=GetCapabilities", "name": "Inspire-Daten", "description": "Nur \u00fcber den Downloaddienst https://inspire.brandenburg.de/services/elu_dfbk_aa_wfs? verf\u00fcgbar. Beinhaltet nur den Stand der aktuellsten Referenz des aktuellen Pflegejahres.", "protocol": "OGC:WFS", "rel": "download"}, {"href": "https://inspire.brandenburg.de/services/elu_dfbk_aa_wms?service=WMS&request=GetCapabilities", "name": "Dienst \"INSPIRE-WMS Land Use / InVeKoS Landwirtschaftliche Fl\u00e4chen BB\" (GetCapabilities)", "protocol": "OGC Web Map Service", "rel": null}, {"href": "https://inspire.brandenburg.de/services/elu_dfbk_aa_wms?service=WMS&request=GetCapabilities"}, {"href": "https://inspire.brandenburg.de/services/elu_dfbk_aa_wms?"}, {"href": "https://inspire.brandenburg.de/services/elu_dfbk_aa_wms?"}, {"href": "https://isk.geobasis-bb.de/md-thumbnail/wms-elu-dfbk-aa.png", "name": "preview", "description": "Web image thumbnail (URL)", "protocol": "WWW:LINK-1.0-http--image-thumbnail", "rel": "preview"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/e1e6667e-f63c-476f-9e10-70833777f1a3", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "308833f8-459c-4ea2-b3a6-2772d1c24798", "name": "item", "description": "308833f8-459c-4ea2-b3a6-2772d1c24798", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/308833f8-459c-4ea2-b3a6-2772d1c24798"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date-time": "2023-01-01T00:00:00Z"}}, {"id": "3129610671", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-26T16:30:15Z", "type": "Journal Article", "created": "2021-02-13", "title": "Copernicus Atmosphere Monitoring Service TEMPOral profiles (CAMS-TEMPO): global and European emission temporal profile maps for atmospheric chemistry modelling", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. We present the Copernicus Atmosphere Monitoring Service TEMPOral profiles (CAMS-TEMPO), a dataset of global and European emission temporal profiles that provides gridded monthly, daily, weekly and hourly weight factors for atmospheric chemistry modelling. CAMS-TEMPO includes temporal profiles for the priority air pollutants (NOx; SOx; NMVOC, non-methane volatile organic compound; NH3; CO; PM10; and PM2.5) and the greenhouse gases (CO2 and CH4) for each of the following anthropogenic source categories: energy industry (power plants), residential combustion, manufacturing industry, transport (road traffic and air traffic in airports) and agricultural activities (fertilizer use and livestock). The profiles are computed on a global 0.1\u2009\u00d7\u20090.1\u2218 and regional European 0.1\u2009\u00d7\u20090.05\u2218 grid following the domain and sector classification descriptions of the global and regional emission inventories developed under the CAMS programme. The profiles account for the variability of the main emission drivers of each sector. Statistical information linked to emission variability (e.g. electricity production and traffic counts) at national and local levels were collected and combined with existing meteorology-dependent parametrizations to account for the influences of sociodemographic factors and climatological conditions. Depending on the sector and the temporal resolution (i.e. monthly, weekly, daily and hourly) the resulting profiles are pollutant-dependent, year-dependent (i.e. time series from 2010 to 2017) and/or spatially dependent (i.e. the temporal weights vary per country or region). We provide a complete description of the data and methods used to build the CAMS-TEMPO profiles, and whenever possible, we evaluate the representativeness of the proxies used to compute the temporal weights against existing observational data. We find important discrepancies when comparing the obtained temporal weights with other currently used datasets. The CAMS-TEMPO data product including the global (CAMS-GLOB-TEMPOv2.1, https://doi.org/10.24380/ks45-9147, Guevara et al., 2020a) and regional European (CAMS-REG-TEMPOv2.1, https://doi.org/10.24380/1cx4-zy68, Guevara et al., 2020b) temporal profiles are distributed from the Emissions of atmospheric Compounds and Compilation of Ancillary Data (ECCAD) system (https://eccad.aeris-data.fr/, last access: February 2021).                     </p></article>", "keywords": ["China", "Atmospheric chemistry", "550", "Anthropogenic emissions", "Ammonia emissions", "Urbanisation", "Environment", "7. Clean energy", "[SDU] Sciences of the Universe [physics]", "11. Sustainability", "Air-pollution", "GE1-350", "Gridded emissions", "Fuel use", "QE1-996.5", "[SDU.OCEAN] Sciences of the Universe [physics]/Ocean", " Atmosphere", "Inventory", "Geology", "Environmental sciences", "Data product", "Qu\u00edmica atmosf\u00e8rica", "13. Climate action", "Air quality", "Transport model", "Data sets", "Bottom-up", "\u00c0rees tem\u00e0tiques de la UPC::Desenvolupament hum\u00e0 i sostenible::Degradaci\u00f3 ambiental::Contaminaci\u00f3 atmosf\u00e8rica", ":Desenvolupament hum\u00e0 i sostenible::Degradaci\u00f3 ambiental::Contaminaci\u00f3 atmosf\u00e8rica [\u00c0rees tem\u00e0tiques de la UPC]", "Air pollutants"]}, "links": [{"href": "https://essd.copernicus.org/articles/13/367/2021/essd-13-367-2021.pdf"}, {"href": "https://doi.org/3129610671"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Earth%20System%20Science%20Data", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "3129610671", "name": "item", "description": "3129610671", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/3129610671"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-02-12T00:00:00Z"}}, {"id": "41ebefd4-0ab6-4120-9e06-51a0514fe318", "type": "Feature", "geometry": {"type": "Polygon", "coordinates": [[[11.27, 51.36], [11.27, 53.56], [14.77, 53.56], [14.77, 51.36], [11.27, 51.36]]]}, "properties": {"updated": "2025-06-11", "type": "Service", "created": "2024-08-01T00:00:00.000+02:00", "language": "ger", "title": "INSPIRE-WFS Land Cover / InVeKoS Nichtlandwirtschaftliche f\u00f6rderf\u00e4hige Fl\u00e4chen BB", "description": "Der interoperable INSPIRE-WFS ist ein Downloaddienst, der Daten im Annex-Schema Bodenbedeckungsvektor (abgeleitet aus dem origin\u00e4ren Datensatz: Digitales Feldblock Kataster) bereitstellt. Gem\u00e4\u00df der INSPIRE-Datenspezifikation Land Cover (D2.8.II.2_v3.1.0) liegen die Inhalte INSPIRE-konform vor. Der WFS beinhaltet die folgenden FeatureTypes:\n\t\u2022 Bodenbedeckungsdatensatz (lcv:LandCoverDataset): Eine Vektordarstellung f\u00fcr Bodenbedeckungsdaten.\n\t\u2022 Bodenbedeckungseinheit (lcv:LandCoverUnit): Ein einzelnes, durch einen Punkt oder eine Fl\u00e4che dargestelltes Element des Bodenbedeckungsdatensatzes.\n\t---\n\tThe compliant INSPIRE-WFS is a download service that delivers data in the Annex-Schema Land Cover Vector (derived from the original data set: Land Parcel Information System LPIS). The content is compliant to the INSPIRE data specification for the annex theme Land Cover (D2.8.II.2_v3.1.0). The WFS includes the following feature types: \n\t\u2022 Land cover dataset (lcv:LandCoverDataset): A vector representation for Land Cover data.\n\t\u2022 Land cover unit (lcv:LandCoverUnit): An individual element of the LC dataset represented by  a point or polygon. 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Gem\u00e4\u00df der INSPIRE-Datenspezifikation Land Use (D2.8.III.4_v3.0.0) liegen die Inhalte INSPIRE-konform vor. Der WFS beinhaltet die folgenden FeatureTypes:\n\t\u2022 Datensatz zur existierenden Bodennutzung (elu:ExistingLandUseDataSet): Ein Datensatz zur existierenden Bodennutzung ist eine Sammlung von Fl\u00e4chen, f\u00fcr die Informationen zur existierenden (gegenw\u00e4rtigen oder fr\u00fcheren) Bodennutzung angegeben sind.\n\t\u2022 Objekt zur existierenden Bodennutzung (elu:ExistingLandUseObject): Ein Objekt zur existierenden Bodennutzung beschreibt die Bodennutzung in einem Gebiet mit einheitlicher Bodennutzungskategorie oder homogener Kombination verschiedener Bodennutzungen.\n\t---\n\tThe compliant INSPIRE-WFS is a download service that delivers data in the Annex-Schema Existing Land Use (derived from the original data set: Land Parcel Information System LPIS). The content is compliant to the INSPIRE data specification for the annex theme Land Use (D2.8.III.4_v3.0.0). The WFS includes the following feature types: \n\t\u2022 Existing land use data set (elu:ExistingLandUseDataSet): An existing land use data set is a collection of areas for which information on existing (present or past) land uses is provided.\n\t\u2022 Existing land use object (elu:ExistingLandUseObject): An existing land use object describes the land use of an area having a homogeneous combination of land use types. Ma\u00dfstab: 1:2400; Bodenaufl\u00f6sung: nullm; Scanaufl\u00f6sung (DPI): null", "formats": [{"name": "OGC:WFS"}, {"name": "OGC Web Feature Service"}], "keywords": ["Bodenbedeckung", "infoFeatureAccessService", "existing land use data set", "existing land use object", "Feldblock", "interoperable Daten", "interoperabel", "interoperability", "IACS", "agricultural land", "geospatial", "farming", "Georaum", "Planungsunterlagen", "LPIS", "InVeKoS", "opendata", "AGRI", "inspireidentifiziert", "land use", "INSPIRE-Daten", "Landwirtschaftliche Fl\u00e4che", "agricultural area", "Brandenburg [Land]", "Landwirtschaft", "agriculture", "Common Agricultural Policy", "Gemeinsame Agrarpolitik", "Kataster"], "contacts": [{"name": "Falk, Norbert, Herr", "organization": "Ministerium f\u00fcr Land- und Ern\u00e4hrungswirtschaft, Umwelt und Verbraucherschutz (MLEUV)", "position": "EU-Zahlstelle", "roles": ["owner"], "phones": [{"value": "+49 331 866 0"}], "emails": [{"value": "zahlstelle@mleuv.brandenburg.de"}], "addresses": [{"deliveryPoint": ["Postbox 601150, 14411 Potsdam"], "city": "Potsdam", "administrativeArea": "Brandenburg", "postalCode": "14467", "country": "DEU"}], "links": [{"href": {"url": "https://mleuv.brandenburg.de", "protocol": null, "protocol_url": "", "name": null, "name_url": "", "description": null, "description_url": "", "applicationprofile": null, "applicationprofile_url": "", "function": null}}]}, {"name": "Lantzsch, Birgit, Frau", "organization": "Ministerium f\u00fcr Land- und Ern\u00e4hrungswirtschaft, Umwelt und Verbraucherschutz (MLEUV)", "position": "Abteilung 3 - L\u00e4ndliche Entwicklung, Landwirtschaft und Forsten, Referat 33 - Agrarumweltma\u00dfnahmen, \u00f6kologischer Landbau, Direktzahlungen", "roles": ["pointOfContact"], "phones": [{"value": "+49 331 866 7624"}], "emails": [{"value": "invekos.dz@mleuv.brandenburg.de"}], "addresses": [{"deliveryPoint": ["Postbox 601150, 14411 Potsdam"], "city": "Potsdam", "administrativeArea": "Brandenburg", "postalCode": "14467", "country": "DEU"}], "links": [{"href": {"url": "https://mleuv.brandenburg.de", "protocol": null, "protocol_url": "", "name": null, "name_url": "", "description": null, "description_url": "", "applicationprofile": null, "applicationprofile_url": "", "function": null}}]}], "themes": [{"concepts": [{"id": "Bodenbedeckung"}], "scheme": "GEMET - INSPIRE themes, version 1.0"}, {"concepts": [{"id": "infoFeatureAccessService"}], "scheme": "Service Classification, version 1.0"}, {"concepts": [{"id": "land use"}, {"id": "INSPIRE-Daten"}, {"id": "Landwirtschaftliche Fl\u00e4che"}, {"id": "agricultural area"}, {"id": "Brandenburg [Land]"}, {"id": "Landwirtschaft"}, {"id": "agriculture"}, {"id": "Common Agricultural Policy"}], "scheme": "UMTHES Thesaurus"}, {"concepts": [{"id": "Gemeinsame Agrarpolitik"}, {"id": "Kataster"}], "scheme": "GEMET - Concepts, version 3.1"}], "title_alternate": "elu_dfbk_aa_wfs"}, "links": [{"href": "https://inspire.brandenburg.de/services/elu_dfbk_aa_wfs?service=WFS&request=GetCapabilities", "name": "Inspire-Daten", "description": "Nur \u00fcber den Downloaddienst https://inspire.brandenburg.de/services/elu_dfbk_aa_wfs? verf\u00fcgbar. 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Gem\u00e4\u00df der INSPIRE-Datenspezifikation Land Cover (D2.8.II.2_v3.1.0) liegen die Inhalte INSPIRE-konform vor. Der WMS beinhaltet den folgenden Layer: \n\t\u2022 LC.LandCoverSurfaces: Ein einzelnes, durch einen Punkt oder eine Fl\u00e4che dargestelltes Element des Bodenbedeckungsdatensatzes.\n\t---\n\tThe compliant INSPIRE-WFS is a view service that delivers data in the Annex-Schema Land Cover Vector (derived from the original data set: Land Parcel Information System LPIS). The content is compliant to the INSPIRE data specification for the annex theme Land Cover (D2.8.II.2_v3.1.0). The WMS includes the following layer: \n\t\u2022 LC.LandCoverSurfaces: An individual element of the LC dataset represented by  a point or polygon. 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Gem\u00e4\u00df der INSPIRE-Datenspezifikation Land Cover (D2.8.II.2_v3.1.0) liegen die Inhalte INSPIRE-konform vor. Der WFS beinhaltet die folgenden FeatureTypes:\n\t\u2022 Bodenbedeckungsdatensatz (lcv:LandCoverDataset): Eine Vektordarstellung f\u00fcr Bodenbedeckungsdaten.\n\t\u2022 Bodenbedeckungseinheit (lcv:LandCoverUnit): Ein einzelnes, durch einen Punkt oder eine Fl\u00e4che dargestelltes Element des Bodenbedeckungsdatensatzes.\n\t---\n\tThe compliant INSPIRE-WFS is a download service that delivers data in the Annex-Schema Land Cover Vector (derived from the original data set: Land Parcel Information System LPIS). The content is compliant to the INSPIRE data specification for the annex theme Land Cover (D2.8.II.2_v3.1.0). The WFS includes the following feature types: \n\t\u2022 Land cover dataset (lcv:LandCoverDataset): A vector representation for Land Cover data.\n\t\u2022 Land cover unit (lcv:LandCoverUnit): An individual element of the LC dataset represented by  a point or polygon. 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