{"type": "FeatureCollection", "features": [{"id": "10.1109/JURSE.2017.7924591", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:18:38Z", "type": "Journal Article", "created": "2017-05-12", "title": "ANthropogenic heat FLUX estimation from Space", "description": "The H2020-Space project URBANFLUXES (URBan ANthrpogenic heat FLUX from Earth observation Satellites) investigates the potential of Copernicus Sentinels to retrieve anthropogenic heat flux, as a key component of the Urban Energy Budget (UEB). URBANFLUXES advances the current knowledge of the impacts of UEB fluxes on urban heat island and consequently on energy consumption in cities. This will lead to the development of tools and strategies to mitigate these effects, improving thermal comfort and energy efficiency. In URBANFLUXES, the anthropogenic heat flux is estimated as a residual of UEB. Therefore, the rest UEB components, namely, the net all-wave radiation (Q*), the net change in heat storage (\u0394Qs) and the turbulent sensible (Q H ) and latent (Q E ) heat fluxes are independently estimated from Earth Observation (EO), whereas the advection term is included in the error of the anthropogenic heat flux estimation from the UEB closure. The project exploits Sentinels observations, which provide improved data quality, coverage and revisit times and increase the value of EO data for scientific work and future emerging applications. These observations can reveal novel scientific insights for the detection and monitoring of the spatial distribution of the urban energy budget fluxes in cities, thereby generating new EO opportunities. URBANFLUXES thus exploits the European capacity for space-borne observations to enable the development of operational services in the field of urban environmental monitoring and energy efficiency in cities.", "keywords": ["[SDU] Sciences of the Universe [physics]", "13. Climate action", "Copernicus Sentinels", "11. Sustainability", "0211 other engineering and technologies", "Earth Observation", "02 engineering and technology", "01 natural sciences", "7. 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In addition, this study analyzes the performance of this feature along with polarization and intensity products according to different classification strategies and algorithms. Seven different classification workflows were evaluated, covering pixel- and object-based analyses, unsupervised and supervised classification, different machine-learning classifiers, and the various effects of distinct input features in the SAR domain\u2014interferometric coherence, backscattered intensities, and polarization. All classifications followed the Corine land cover nomenclature. Three different study areas in Europe were selected during 2015 and 2016 campaigns to maximize diversity of land cover. Overall accuracies (OA), ranging from 70% to 90%, were achieved depending on the study area and methodology, considering between 9 and 15 classes. The best results were achieved in the rather flat area of Do\u00f1ana wetlands National Park in Spain (OA 90%), but even the challenging alpine terrain around the city of Merano in northern Italy (OA 77%) obtained promising results. The overall potential of Sentinel-1 interferometric coherence for land cover mapping was evaluated as very good. In all cases, coherence-based results provided higher accuracies than intensity-based strategies, considering 12 days of temporal sampling of the Sentinel-1 A stack. Both coherence and intensity prove to be complementary observables, increasing the overall accuracies in a combined strategy. The accuracy is expected to increase when Sentinel-1 A/B stacks, i.e., six-day sampling, are considered.", "keywords": ["Teledetecci\u00f3", "550", "Interferometric coherence", "Geophysics. 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Climate action", "Teor\u00eda de la Se\u00f1al y Comunicaciones", "Sentinel-1", "[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing", "Land cover mapping", "Copernicus"]}, "links": [{"href": "https://doi.org/10.1109/jstars.2019.2958847"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/IEEE%20Journal%20of%20Selected%20Topics%20in%20Applied%20Earth%20Observations%20and%20Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1109/jstars.2019.2958847", "name": "item", "description": "10.1109/jstars.2019.2958847", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1109/jstars.2019.2958847"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-01-01T00:00:00Z"}}, {"id": "10.3390/su14052732", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:21:21Z", "type": "Journal Article", "created": "2022-02-28", "title": "Progress in Developing Scale-Able Approaches to Field-Scale Water Accounting Based on Remote Sensing", "description": "<p>To increase water productivity and assess water footprints in irrigated systems, there is a need to develop cheap and readily available estimates of components of water balance at fine spatial scales. Recent developments in satellite remote sensing platforms and modelling capacities have opened opportunities to address this need, such as those being developed in the WaterSENSE project. This paper showed how evapotranspiration, soil moisture, and farm-dam water volumes can be quantified based on the Copernicus data from the Sentinel satellite constellation. This highlights distinct differences between energy balance and crop factor approaches and estimates that can be derived from the point scale to the landscape scale. Differences in the results are related to assumptions in deriving evapotranspiration from remote sensing data. Advances in different parts of the water cycle and opportunities for crop detection and yield forecasting mean that crop water productivity can be quantified at field to landscape scales, but uncertainties are highly dependent on input data availability and reference validation data.</p>", "keywords": ["13. Climate action", "water use efficiency; Copernicus satellite data; irrigated agriculture", "15. Life on land", "01 natural sciences", "6. 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The vast data archives made available through Copernicus and related infrastructures, combined with a low entry threshold into the domain of AI-technologies has made it possible, if not outright easy, to make meaningful predictions that divides individual agricultural fields into zones where variable rates of fertilizer, irrigation and/or pesticide are required for optimal soil productivity and minimized environmental impact. However, present solutions that control variable rate application hardware such as irrigation, fertilizer application etc. are \u2018black box technologies\u2019 to farmers, making predictions that may well be good but that necessarily are not trusted. This limits the uptake of precision agriculture technology and thus also the realization of its promised benefits. The Map Whiteboard concept at the centre of this submission is intended to plug into the \u201ctraditional\u201d workflow of variable rate applications and enables agricultural advisors/extension services and farmers to interact, adjust and share an understanding of the estimations made by the \u2018black box\u2019, thus increasing the trust in and improving the quality of the prediction models. The vision of the Map Whiteboard innovation was conceived out of a sequence of large-scale collaborative writing efforts using Google Docs. As opposed to traditional offline word processing tools, Google Docs allows multiple people to edit the same document]\u2014at the same time\u2014allowing all connected clients to see changes made to the document in real-time by synchronising all changes between all connected clients via the server. The ability to work on a shared body of text, avoiding the necessity to integrate fragments from multiple source documents and with multiple styles removed many obstacles associated with traditional document editing. The Map Whiteboard technology seeks to do the same for the traditional use of GIS tools. The overall vision for the technology is that a Map Whiteboard will be to GIS what Google Docs is to word processing. We are now introducing this technology as a tool for collaborative work farmers and advisory services offering them analysis of EO data.", "keywords": ["AI", "Collaborative Platform", "cloud", "Precision Agriculture", "Copernicus"], "contacts": [{"organization": "Charv\u00e1t, Karel, Berzins, Raitis, Bergheim, Runar, Zadra\u017eil, Franti\u0161ek, Macura, Jan, Langovskis, Dailis, \u0160nevajs, He\u0159man, Kub\u00ed\u010dkov\u00e1, Hana, Hor\u00e1kov\u00e1, \u0160\u00e1rka, Charv\u00e1t, Karel,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.6864305"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.6864305", "name": "item", "description": "10.5281/zenodo.6864305", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.6864305"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-06-29T00:00:00Z"}}, {"id": "10.5281/zenodo.6864304", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:23:42Z", "type": "Journal Article", "title": "Map Whiteboard As Collaboration Tool for Smart Farming Advisory Services", "description": "Precision agriculture, a branch of smart farming, holds great promise for modernization of European agriculture both in terms of environmental sustainability and economic outlook. The vast data archives made available through Copernicus and related infrastructures, combined with a low entry threshold into the domain of AI-technologies has made it possible, if not outright easy, to make meaningful predictions that divides individual agricultural fields into zones where variable rates of fertilizer, irrigation and/or pesticide are required for optimal soil productivity and minimized environmental impact. However, present solutions that control variable rate application hardware such as irrigation, fertilizer application etc. are \u2018black box technologies\u2019 to farmers, making predictions that may well be good but that necessarily are not trusted. This limits the uptake of precision agriculture technology and thus also the realization of its promised benefits. The Map Whiteboard concept at the centre of this submission is intended to plug into the \u201ctraditional\u201d workflow of variable rate applications and enables agricultural advisors/extension services and farmers to interact, adjust and share an understanding of the estimations made by the \u2018black box\u2019, thus increasing the trust in and improving the quality of the prediction models. The vision of the Map Whiteboard innovation was conceived out of a sequence of large-scale collaborative writing efforts using Google Docs. As opposed to traditional offline word processing tools, Google Docs allows multiple people to edit the same document]\u2014at the same time\u2014allowing all connected clients to see changes made to the document in real-time by synchronising all changes between all connected clients via the server. The ability to work on a shared body of text, avoiding the necessity to integrate fragments from multiple source documents and with multiple styles removed many obstacles associated with traditional document editing. The Map Whiteboard technology seeks to do the same for the traditional use of GIS tools. The overall vision for the technology is that a Map Whiteboard will be to GIS what Google Docs is to word processing. We are now introducing this technology as a tool for collaborative work farmers and advisory services offering them analysis of EO data.", "keywords": ["2. Zero hunger", "AI", "Collaborative Platform", "cloud", "Precision Agriculture", "15. Life on land", "Copernicus"], "contacts": [{"organization": "Charv\u00e1t, Karel, Berzins, Raitis, Bergheim, Runar, Zadra\u017eil, Franti\u0161ek, Macura, Jan, Langovskis, Dailis, \u0160nevajs, He\u0159man, Kub\u00ed\u010dkov\u00e1, Hana, Hor\u00e1kov\u00e1, \u0160\u00e1rka, Charv\u00e1t, Karel,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.6864304"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/EGU%20General%20Assembly%202022%2C%20Vienna%2C%20Austria%2C%2023%E2%80%9327%20May%202022%2C", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.6864304", "name": "item", "description": "10.5281/zenodo.6864304", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.6864304"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-01T00:00:00Z"}}, {"id": "arlpa_to:07.09.02-D_2020-03-25-12:00", "type": "Feature", "geometry": {"type": "Polygon", "coordinates": [[[6.63, 44.06], [6.63, 46.46], [9.21, 46.46], [9.21, 44.06], [6.63, 44.06]]]}, "properties": {"themes": [{"concepts": [{"id": "geoscientificInformation"}], "scheme": "https://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MD_TopicCategoryCode"}, {"concepts": [{"id": "Geologia"}, {"id": "Suolo"}], "scheme": "http://www.eionet.europa.eu/gemet/inspire_themes"}, {"concepts": [{"id": "Radar"}, {"id": "monitoraggio ambientale"}], "scheme": "https://www.eionet.europa.eu/gemet"}, {"concepts": [{"id": "Regionale"}], "scheme": "http://inspire.ec.europa.eu/metadata-codelist/SpatialScope"}], "rights": "Ogni iniziativa di divulgazione delle informazioni contenute nel dataset o da esso derivate (cartogrammi, relazioni, servizi informativi), dovr\u00e0 sempre citare la fonte del dato originale (autori, proprietario) secondo quando indicato dalla licenza cc-BY 4.0 (https://creativeco mmons.org/licenses/by/4.0/). Per eventuali aggregazioni o rielaborazioni dei dati forniti finalizzate alla realizzazione di prodotti diversi dall'originale, pur permanendo l'obbligo di citazione della fonte, si declina ogni responsabilit\u00e0. Vincoli per il mapservice: http://webgis.arpa.piemonte.it/w-metadoc/_Licenze/Licenza_map_service.pdf Contains modified Copernicus Sentinel data (2014-2018)", "updated": "2020-03-25", "type": "Dataset", "created": "2019-01-01", "language": "ita", "title": "Arpa Piemonte - SqueeSAR Sentinel-1  (2014-2018)", "description": "Il dataset riporta le risultanze dell'analisi con tecnologia radar-satellitare SqueeSAR(TM) svoltenell'Ambito del Programma europeo di cooperazione transfrontaliera tra Francia e Italia INTERREG ALCOTRA - Progetto ADVITAM (http://www.interreg-alcotra.eu/it/decouvrir-alcotra/les-projets-finances/ad-vitam). Le analisi sono state effettuate dalla ditta TRE-Altmira per conto del Dipartimento Tematico Rischi Naturali e Ambientali di Arpa Piemonte, utilizzando i dati delle piattaforme satellitari SENTINEL-1B e SENTINEL-1B del programma europeo Copernicus (https://www.copernicus.eu/en/about-copernicus) per il periodo compreso tra ottobre 2014 e ottobre 2018.", "formats": [{"name": "x-shapefile"}, {"name": "WWW:LINK-1.0-http--link"}], "keywords": ["Geologia", "Suolo", "Radar", "monitoraggio ambientale", "Regionale", "EU", "RNDT", "SqueeSAR", "Sentinel", "Radar", "Piemonte", "Copernicus", "Earth Observation", "telerilevamento", "remote sensing", "osservazione della Terra"], "contacts": [{"name": null, "organization": "Arpa Piemonte", "position": null, "roles": ["pointOfContact"], "phones": [{"value": null}], "emails": [{"value": "webgis@arpa.piemonte.it"}], "addresses": [{"deliveryPoint": [null], "city": null, "administrativeArea": null, "postalCode": null, "country": null}], "links": [{"href": {"url": "http://www.arpa.piemonte.it", "protocol": null, "protocol_url": "", "name": null, "name_url": "", "description": null, "description_url": "", "applicationprofile": null, "applicationprofile_url": "", "function": null}}]}], "denominator": "10000"}, "links": [{"href": "https://webgis.arpa.piemonte.it/agportal/apps/webappviewer/index.html?id=27fb8f8dad884da7b01cfecd6d3ae61a", "protocol": "WWW:LINK-1.0-http--link", "rel": null}, {"href": "http://webgis.arpa.piemonte.it/w-metadoc/thumbnail/DS_SqueeSAR_Sentinel.jpg", "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/arlpa_to%3A07.09.02-D_2020-03-25-12%3A00", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "arlpa_to:07.09.02-D_2020-03-25-12:00", "name": "item", "description": "arlpa_to:07.09.02-D_2020-03-25-12:00", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/arlpa_to:07.09.02-D_2020-03-25-12:00"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"interval": ["2014-10-01T00:00:00Z", "2018-10-01T00:00:00Z"]}}, {"id": "f85907ae-d123-471f-a44a-8cca993485a2", "type": "Feature", "geometry": null, "properties": {"license": "https://spdx.org/licenses/CC-BY-4.0.html", "updated": "2026-05-29T16:31:19Z", "type": "Dataset", "language": "en", "title": "Harmonised LUCAS in-situ land cover and use database for field surveys from 2006 to 2018 in the European Union", "description": "Accurately characterizing land surface changes with Earth Observation requires geo-localized ground truth. In the European Union (EU), a tri-annual surveyed sample of land cover and land use has been collected since 2006 under the Land Use/Cover Area frame Survey (LUCAS). A total of 1,351,293 observations at 651,780 unique locations for 117 variables along with 5.4 million photos were collected during five LUCAS surveys. Until now, these data have never been harmonised into one database, limiting full exploitation of the information. This paper describes the LUCAS point sampling/surveying methodology, including collection of standard variables such as land cover, environmental parameters, and full resolution landscape and point photos, and then describes the harmonisation process. The resulting harmonised database is the most comprehensive in-situ dataset on land cover and use in the EU. The database is valuable for geo-spatial and statistical analysis of land use and land cover change. Furthermore, its potential to provide multi-temporal in-situ data will be enhanced by recent computational advances such as deep learning.", "formats": [{"name": "CSV"}], "keywords": ["agriculture", "big-data", "computer-vision", "copernicus", "cropland", "deep-learning", "eu", "eurostat", "geo-spatial", "gps", "ground-truth", "in-situ", "land-cover", "remote-sensing", "soil", "statistics", "survey", "water-management"], "contacts": [{"organization": "http://publications.europa.eu/resource/authority/corporate-body/JRC", "roles": ["publisher"]}]}, "links": [{"href": "http://data.europa.eu/88u/dataset/f85907ae-d123-471f-a44a-8cca993485a2"}, {"href": "http://data.europa.eu/89h/f85907ae-d123-471f-a44a-8cca993485a2"}, {"rel": "self", "type": "application/geo+json", "title": "f85907ae-d123-471f-a44a-8cca993485a2", "name": "item", "description": "f85907ae-d123-471f-a44a-8cca993485a2", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/f85907ae-d123-471f-a44a-8cca993485a2"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"null": "date"}}], "links": [{"rel": "self", "type": "application/geo+json", "title": "This document as GeoJSON", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Copernicus&f=json", "hreflang": "en-US"}, {"rel": "alternate", "type": "text/html", "title": "This document as HTML", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Copernicus&f=html", "hreflang": "en-US"}, {"rel": "collection", "type": "application/json", "title": "Collection URL", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main", "hreflang": "en-US"}, {"type": "application/geo+json", "rel": "first", "title": "items (first)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Copernicus&", "hreflang": "en-US"}, {"rel": "last", "type": "application/geo+json", "title": "items (last)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Copernicus&offset=7", "hreflang": "en-US"}], "numberMatched": 7, "numberReturned": 7, "distributedFeatures": [], "timeStamp": "2026-05-30T11:07:57.619902Z"}