Irrigation according to reliable estimates of crop water requirements (CWR) is one of the key strategies to ensure long-term sustainability of irrigated agriculture. In southern Mediterranean regions, during the irrigation season, CWR is almost totally controlled by the potential evapotranspiration of the irrigated crop. An innovative system for forecasting crop potential evapotranspiration (ETp) has been implemented recently in the Campania region (southern Italy). The system produces ETp forecasts with a lead time of up to 5 days, by coupling the visible and near-infrared crop imagery with numerical weather prediction outputs of a limited area model. The forecasts are delivered to farmers with a simple and intuitive web app interface, which makes daily real-time ETp maps accessible from desktop computers, tablets and smartphones. Forecast performances were evaluated for maize fields of two farms in two irrigation seasons (2014\uffe2\uff80\uff932015). The mean absolute bias of the forecasted ETp was <0.3 mm/day and the RMSE was <0.6 mm/day, both for lead times up to 5 days.
", "keywords": ["2. Zero hunger", "Earth observation", "Crop water requirements", "0207 environmental engineering", "forecasting", "02 engineering and technology", "15. Life on land", "01 natural sciences", "numerical weather predictions", "13. Climate action", "potential evapotranspiration", "11. Sustainability", "Genetics", "Animal Science and Zoology", "Agronomy and Crop Science", "Crop water requirements; Earth observation; forecasting; numerical weather predictions; potential evapotranspiration; Animal Science and Zoology; Agronomy and Crop Science; Genetics", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.1017/s0021859618000084"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/The%20Journal%20of%20Agricultural%20Science", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1017/s0021859618000084", "name": "item", "description": "10.1017/s0021859618000084", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1017/s0021859618000084"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-02-28T00:00:00Z"}}, {"id": "10.1109/JURSE.2017.7924591", "type": "Feature", "geometry": null, "properties": {"license": "Restricted", "updated": "2026-07-26T16:18:22Z", "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. Clean energy", "Urban Climate", "Urban Energy Budget", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://xplorestaging.ieee.org/ielx7/7919506/7924526/07924591.pdf?arnumber=7924591"}, {"href": "https://doi.org/10.1109/JURSE.2017.7924591"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2017%20Joint%20Urban%20Remote%20Sensing%20Event%20%28JURSE%29", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1109/JURSE.2017.7924591", "name": "item", "description": "10.1109/JURSE.2017.7924591", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1109/JURSE.2017.7924591"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-03-01T00:00:00Z"}}, {"id": "10.1109/JURSE.2017.7924592", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:18:22Z", "type": "Journal Article", "created": "2017-05-12", "title": "EO-based products in support of urban heat fluxes estimation", "description": "Presently, there is a growing need for information suitable to effectively characterize the Urban Energy Budget (UEB) and, hence, to properly estimate the magnitude of the anthropogenic heat flux Q F . Indeed, a precise knowledge of Q F - whose implications for urban planners are still prone to large uncertainties - is fundamental for implementing effective strategies to improve thermal comfort and energy efficiency. To address this challenging issue, the Horizon 2020 URBANFLUXES project aims at developing a novel methodology for accurately estimating the different terms of the UEB based on the use of Earth Observation (EO) data and, hence, at reliably characterizing the Q F spatiotemporal patterns and its implications on urban climate. In this paper, we aim at giving an overview of the EO-based products which have been identified as the most useful in the framework of the considered study. In particular, the suite which has been implemented so far in the first phase of the project includes biophysical parameters, morphology parameters as well as land-cover maps.", "keywords": ["Anthropogenic Heat Flux", "H2020 URBANFLUXES", "13. Climate action", "11. Sustainability", "0211 other engineering and technologies", "Earth Observation", "Urban Remote Sensing", "02 engineering and technology", "01 natural sciences", "7. Clean energy", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://xplorestaging.ieee.org/ielx7/7919506/7924526/07924592.pdf?arnumber=7924592"}, {"href": "https://doi.org/10.1109/JURSE.2017.7924592"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2017%20Joint%20Urban%20Remote%20Sensing%20Event%20%28JURSE%29", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1109/JURSE.2017.7924592", "name": "item", "description": "10.1109/JURSE.2017.7924592", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1109/JURSE.2017.7924592"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-03-01T00:00:00Z"}}, {"id": "10.1117/12.2576171", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-26T16:19:00Z", "type": "Journal Article", "created": "2020-09-18", "title": "An integrated service-based solution addressing the modernised common agriculture policy regulations and environmental perspectives", "description": "The EU-funded DIONE project (grant agreement No. 870378) offers an innovative close-to-market (TRL7) solution\u00a0seeking to improve the traditional methods of agricultural monitoring. The project introduces a cloud-based Software as\u00a0a Service (SaaS) system architecture, building on a fusion of novel technologies that will support the forthcoming needs\u00a0of the modernized Common Agriculture Policy (CAP) and the \u201cGreening\u201d perspectives, with an automated area-based\u00a0monitoring system. In particular, an interoperable and harmonized system is designed, connecting large volumes of Earth\u00a0Observation data (Satellite, UAV, and in-situ) and user-generated highly precise geolocated data (geo-tagged photos, soil\u00a0measurements, etc.). DIONE\u2019s system architecture encompasses customized and third-party frameworks, where\u00a0heterogeneous and multi-source data are stored, processed and managed using Artificial Intelligence (AI) algorithms.\u00a0These harmonized, curated and open accessed data are then provided as Open Geospatial Consortium (OGC)-compliant,\u00a0web-service layers (WMS, WFS, and WCS). Furthermore, the proposed solution formulates a scalable, flexible,\u00a0interoperable, and semantically enriched environment, taking advantage of a Spatial Data Infrastructure (SDI)\u00a0framework capabilities, whilst allowing an interactive connection among different tools and components through\u00a0 RESTful APIs. Our approach establishes a novel, cloud-based, accurate and inexpensive agriculture monitoring solution,\u00a0enabling the real-time provision of multi-source data to relevant stakeholders such as Paying Agencies, Policy Officers\u00a0and Control &\u00a0 Certification Bodies, and other domain experts. The system architecture was formulated exploiting a co-design\u00a0methodology, aiming to ensure a long-term and sustainable solution. Two large-scale demonstrations will take\u00a0place in Lithuania and Cyprus, evaluating the system capabilities in real-life and operational conditions.", "keywords": ["Spatial Data Infrastructure", "2. Zero hunger", "OGC services", "RESTful API", "13. Climate action", "11. Sustainability", "Common Agriculture Policy", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "15. Life on land", "Earth Observation", " Software as a Service (SaaS) platform", "12. Responsible consumption"]}, "links": [{"href": "https://doi.org/10.1117/12.2576171"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing%20for%20Agriculture%2C%20Ecosystems%2C%20and%20Hydrology%20XXII", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1117/12.2576171", "name": "item", "description": "10.1117/12.2576171", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1117/12.2576171"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-09-20T00:00:00Z"}}, {"id": "10.21203/rs.3.rs-5128244/v2", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:19:50Z", "type": "Journal Article", "created": "2025-07-14", "title": "Spatiotemporal prediction of soil organic carbon density in Europe (2000\u20132022) using earth observation and machine learning", "description": "This article describes a comprehensive framework for soil organic carbon density (SOCD, kg/m3) modeling and mapping, based on spatiotemporal random forest (RF) and quantile regression forests (QRF). A total of 45,616 SOCD observations and various Earth observation (EO) feature layers were used to produce 30 m SOCD maps for the EU at four-year intervals (2000\uffe2\uff80\uff932022) and four soil depth intervals (0\uffe2\uff80\uff9320 cm, 20\uffe2\uff80\uff9350 cm, 50\uffe2\uff80\uff93100 cm, and 100\uffe2\uff80\uff93200 cm). Per-pixel 95% probability prediction intervals (PIs) and extrapolation risk probabilities are also provided. Model evaluation indicates good overall accuracy (R2 = 0.63 and CCC = 0.76 for hold-out independent tests). Prediction accuracy varies by land cover, depth interval and year of prediction with the worst accuracy for shrubland and deeper soils 100\uffe2\uff80\uff93200 cm. The PI validation confirmed effective uncertainty estimation, though with reduced accuracy for higher SOCD values. Shapley analysis identified soil depth as the most influential feature, followed by vegetation, long-term bioclimate, and topographic features. While pixel-level uncertainty is substantial, spatial aggregation reduces uncertainty by approximately 66%. Detecting SOCD changes remains challenging but offers a baseline for future improvements. Maps, based primarily on topsoil data from cropland, grassland, and woodland, are best suited for applications related to these land covers and depths. We recommend that users interpret the maps in conjunction with local knowledge and consider the accompanying uncertainty and extrapolation risk layers. All data and code are available under an open license at https://doi.org/10.5281/zenodo.13754343 and https://github.com/AI4SoilHealth/SoilHealthDataCube/.In this paper, we present the results of our study on the operational application of the reflectance-based crop coefficient for assessing table grape irrigation requirements. The methodology was applied to provide irrigation advice and to assess the irrigation performance. The net irrigation water requirements (NIWR) simulated using the reflectance-based basal crop coefficient were provided to the farmer during the growing season and compared with the actual irrigation volumes applied. Two treatments were implemented in the field, increasing and reducing the irrigation doses by 25%, respectively, compared to the regular management. The experiment was carried out in a commercial orchard during three consecutive growing seasons in Northern Chile. The NIWR based on the model was approximately 900 mm per season for the orchard at tree maturity. The experimental results demonstrate that the regular irrigation applied covered only 76% of the NIWR for the whole season, and the analysis of monthly and weekly accumulated values indicates several periods of water shortage. The regular management system tended to underestimate the water requirements from October to January and overestimate the water requirements after harvest from February to April. The level of the deficit of water was quantified using such plant physiological parameters as stem water potential, vegetative development (coverage), and fruit productivity. The estimated NIWR was roughly covered in the treatment where the irrigation dose was increased, and the analyses of the crop production and fruit quality point to the relative advantage of this treatment. Finally, we conclude that the proposed approach allows the analysis of irrigation performance on the scale of commercial fields. These analytic capabilities are based on the well-demonstrated relationship of the crop evapotranspiration with the information provided by satellite images, and provide valuable information for irrigation management by identifying periods of water shortage and over-irrigation.
", "keywords": ["0106 biological sciences", "2. Zero hunger", "NDVI", "Science", "Q", "evapotranspiration", "earth observation", "04 agricultural and veterinary sciences", "15. Life on land", "01 natural sciences", "6. Clean water", "0401 agriculture", " forestry", " and fisheries", "crop water requirements", "plant water status", "crop coefficient", "table grape"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/9/12/1276/pdf"}, {"href": "https://doi.org/10.3390/rs9121276"}, {"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/rs9121276", "name": "item", "description": "10.3390/rs9121276", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs9121276"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-12-08T00:00:00Z"}}, {"id": "10.3390/rs11091138", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:20:55Z", "type": "Journal Article", "created": "2019-05-13", "title": "Advances in the Remote Sensing of Terrestrial Evaporation", "description": "Characterizing the terrestrial carbon, water, and energy cycles depends strongly on a capacity to accurately reproduce the spatial and temporal dynamics of land surface evaporation. For this, and many other reasons, monitoring terrestrial evaporation across multiple space and time scales has been an area of focused research for a number of decades. Much of this activity has been supported by developments in satellite remote sensing, which have been leveraged to deliver new process insights, model development and methodological improvements. In this Special Issue, published contributions explored a range of research topics directed towards the enhanced estimation of terrestrial evaporation. Here we summarize these cutting-edge efforts and provide an overview of some of the state-of-the-art approaches for retrieving this key variable. Some perspectives on outstanding challenges, issues, and opportunities are also presented.
", "keywords": ["Atmospheric sciences", "CubeSats", "Life on Land", "Classical Physics", "Science", "0207 environmental engineering", "02 engineering and technology", "high-resolution", "01 natural sciences", "Physical Geography and Environmental Geoscience", "Article", "evaporation", "land surface modeling", "remote sensing", "Engineering", "novel sensing", "Physical geography and environmental geoscience", "0105 earth and related environmental sciences", "Earth observation", "Q", "Geomatic engineering", "15. Life on land", "Geomatic Engineering", "land surface flux", "13. Climate action", "cubesats"]}, "links": [{"href": "https://www.mdpi.com/2072-4292/11/9/1138/pdf"}, {"href": "https://escholarship.org/content/qt1sh5v7hp/qt1sh5v7hp.pdf"}, {"href": "https://doi.org/10.3390/rs11091138"}, {"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/rs11091138", "name": "item", "description": "10.3390/rs11091138", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs11091138"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-05-13T00:00:00Z"}}, {"id": "10.4995/cigeo2021.2021.12694", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:21:18Z", "type": "Journal Article", "created": "2021-10-11", "title": "A review of the use of remote sensing for monitoring and quantifying carbon sequestration in marginal lands", "description": "In recent years, Remote Sensing (RS) and its derived products have been used as a key tool for the detection, monitoring,management and future use of Marginal Lands (ML). Currently, there is no single, universally accepted definition of theterm and there is a wide variety of synonyms. In this paper, we conduct a compilation of synonyms and meanings thatencompass the term, as well as propose a definition. To reach this objective, an overview of the state of the art of ML isdone, visualising trends by science maps, based on bibliographic data of established research journals, found in GoogleScholar, Web of Science (WoS) and Scopus search engines. The bibliographic review carried out shows that the study ofML has traditionally been carried out with an ad hoc basis focused on the objective to be achieved, this aspect and otherknowledge gaps are discussed to analyse the global study of ML. Due to the broad spectrum of uses in which ML havebeen studied, the work has been focused on RS for monitoring and characterizing ML, focusing on two different aspects:(i) satellite monitoring of marginal lands; and (ii) determining carbon sequestration potential of marginal lands using remotesensing.
", "keywords": ["Cartography", "Carbon sequestration", "Earth observation", "Uso del suelo", "Cultural Heritage", "Marginal lands", "Remote sensing", "15. Life on land", "12. Responsible consumption", "3D Modelling", "Geophysics", "Captura de carbono", "13. Climate action", "Land use", "11. Sustainability", "Teledetecci\u00f3n", "Tierras marginales", "marginal lands", " remote sensing", " carbon sequestration", " land use", "Geocomputing", "Environmental applications", "Geodesy"]}, "links": [{"href": "https://doi.org/10.4995/cigeo2021.2021.12694"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Proceedings%20-%203rd%20Congress%20in%20Geomatics%20Engineering%20-%20CIGeo", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.4995/cigeo2021.2021.12694", "name": "item", "description": "10.4995/cigeo2021.2021.12694", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.4995/cigeo2021.2021.12694"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-07-07T00:00:00Z"}}, {"id": "10.4995/cigeo2021.2021.12729", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:21:18Z", "type": "Journal Article", "created": "2021-10-11", "title": "Methodological proposal for the identification of marginal lands with remote sensing-derived products and ancillary data", "description": "The concept of marginal land (ML) is dynamic and depends on various factors related to the environment, climate, scale,culture, and economic sector. The current methods for identifying ML are diverse, they employ multiple parameters andvariables derived from land use and land cover, and mostly reflect specific management purposes. A methodologicalapproach for the identification of marginal lands using remote sensing and ancillary data products and validated on samplesfrom four European countries (i.e., Germany, Spain, Greece, and Poland) is presented in this paper. The methodologyproposed combines land use and land cover data sets as excluding indicators (forest, croplands, protected areas,impervious areas, land-use change, water bodies, and permanent snow areas) and environmental constraints informationas marginality indicators: (i) physical soil properties, in terms of slope gradient, erosion, soil depth, soil texture, percentageof coarse soil texture fragments, etc.; (ii) climatic factors e.g. aridity index; (iii) chemical soil properties, including soil pH,cation exchange capacity, contaminants, and toxicity, among others. This provides a common vision of marginality thatintegrates a multidisciplinary approach. To determine the ML, we first analyzed the excluding indicators used to delimit theareas with defined land use. Then, thresholds were determined for each marginality indicator through which the landproductivity progressively decreases. Finally, the marginality indicator layers were combined in Google Earth Engine. Theresult was categorized into 3 levels of productivity of ML: high productivity, low productivity, and potentially unsuitable land.The results obtained indicate that the percentage of marginal land per country is 11.64% in Germany, 19.96% in Spain,18.76% in Greece, and 7.18% in Poland. The overall accuracies obtained per country were 60.61% for Germany, 88.87%for Spain, 71.52% for Greece, and 90.97% for Poland.
", "keywords": ["Cartography", "Land cover", "Cultural Heritage", "Cobertura de suelo", "3D Modelling", "11. Sustainability", "Teledetecci\u00f3n", "Environmental applications", "Uso de suelo", "2. Zero hunger", "Earth observation", "Tierra abandonada", "Remote sensing", "15. Life on land", "GIS", "SIG", "Geophysics", "Idle land", "13. Climate action", "Degradaci\u00f3n del suelo", "Land use", "Land degradation", "land use", " land cover", " idle land", " land degradation", " GIS", " remote sensing", " Google Earth Engine", "Geocomputing", "Google Earth Engine", "Geodesy"]}, "links": [{"href": "https://doi.org/10.4995/cigeo2021.2021.12729"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Proceedings%20-%203rd%20Congress%20in%20Geomatics%20Engineering%20-%20CIGeo", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.4995/cigeo2021.2021.12729", "name": "item", "description": "10.4995/cigeo2021.2021.12729", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.4995/cigeo2021.2021.12729"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-07-07T00:00:00Z"}}, {"id": "10.5281/zenodo.15166358", "type": "Feature", "geometry": null, "properties": {"license": "unspecified", "updated": "2026-07-26T16:22:53Z", "type": "Dataset", "title": "RapidCrops: A pan-European label dataset for large-scale crop classification", "description": "Under the AI4SoilHealth project we have created a dataset (\u201cRapidCrops\u201d) to support the automatic mapping of crop types across Europe. Crop type information is essential for monitoring soil health as it provides\u00a0 systematic insights into crop rotations over time and supports efforts to detect other cropping practices that affect soil health (e.g. tillage & cover crops). The RapidCrops dataset provides approximately 99M agricultural parcel boundaries with harmonised crop type information across a wide spatio-temporal extent; with coverage across seven EU countries for 5-7 years. Based on parcel boundaries and crop type information reported under the EU IACS programme, our methodology seeks to improve the usability of the parcel boundaries without diluting their integrity. Additional attributes are provided to support the use of the data in ML workflows; especially for those leveraging EO data. The dataset builds on top of the EuroCrops [1,2] crop type harmonisation initiative and the fiboa [3] open data standard for parcel boundaries. For enhanced access to the data, the dataset is also made freely available on Source Cooperative [4]. The dataset was also utilised under the Horizon Europe project Open-Earth-Monitor to perform pan-European crop identification across 51M parcels from the year 2022; classifying each parcel into one of 29 crop types [5]. Please see the license terms for underlying datasets below: Data source Data licensing terms Austria INSPIRE public access license & CC-BY-AT 4.0 Denmark INSPIRE public access license & INSPIRE no conditions & CC0 1.0 Universal France Custom open license Germany Custom open licenses: NRW, Brandenberg, LS Netherlands INSPIRE public access license & INSPIRE no conditions & Dutch creative commons license Portugal CC BY 4.0 Spain Custom open license", "keywords": ["crop classification", "earth observation", "reference data"], "contacts": [{"organization": "Holden, Piers, Davis, Timothy, Holmes, Christopher, Senaras, Caglar, Wania, Annett,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15166358"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15166358", "name": "item", "description": "10.5281/zenodo.15166358", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15166358"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-04-08T00:00:00Z"}}, {"id": "10.5281/zenodo.15166359", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-26T16:22:53Z", "type": "Dataset", "title": "RapidCrops: A pan-European label dataset for large-scale crop classification", "description": "Under the AI4SoilHealth project we have created a dataset (\u201cRapidCrops\u201d) to support the automatic mapping of crop types across Europe. Crop type information is essential for monitoring soil health as it provides\u00a0 systematic insights into crop rotations over time and supports efforts to detect other cropping practices that affect soil health (e.g. tillage & cover crops). The RapidCrops dataset provides approximately 99M agricultural parcel boundaries with harmonised crop type information across a wide spatio-temporal extent; with coverage across seven EU countries for 5-7 years. Based on parcel boundaries and crop type information reported under the EU IACS programme, our methodology seeks to improve the usability of the parcel boundaries without diluting their integrity. Additional attributes are provided to support the use of the data in ML workflows; especially for those leveraging EO data. The dataset builds on top of the EuroCrops [1,2] crop type harmonisation initiative and the fiboa [3] open data standard for parcel boundaries. For enhanced access to the data, the dataset is also made freely available on Source Cooperative [4]. The dataset was also utilised under the Horizon Europe project Open-Earth-Monitor to perform pan-European crop identification across 51M parcels from the year 2022; classifying each parcel into one of 29 crop types [5]. Please see the license terms for underlying datasets below: Data source Data licensing terms Austria INSPIRE public access license & CC-BY-AT 4.0 Denmark INSPIRE public access license & INSPIRE no conditions & CC0 1.0 Universal France Custom open license Germany Custom open licenses: NRW, Brandenberg, LS Netherlands INSPIRE public access license & INSPIRE no conditions & Dutch creative commons license Portugal CC BY 4.0 Spain Custom open license", "keywords": ["crop classification", "earth observation", "reference data"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15166359"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15166359", "name": "item", "description": "10.5281/zenodo.15166359", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15166359"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-04-08T00:00:00Z"}}, {"id": "10.5281/zenodo.15730426", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:23:05Z", "type": "Report", "title": "PREPSOIL workshop report - Earth observation for soil health monitoring; obstacles and proposals in overcoming them", "description": "Description of a workshop on 'Earth observation for soil health monitoring; obstacles and \u00a0proposals in overcoming them' held on 7 November 2024. The report is an addition to PREPSOIL 5.2, which contains a review of scientific knowledge (bibliography, expert opinions, current EU projects), an inventory of the technological resources mobilised (vectors, sensors, current and planned products, services), and the identification of obstacles to greater use of Earth observations for soil monitoring and measurement needs to reduce/minimise these difficulties.", "keywords": ["Earth observation", "Soil", "soil health", "soil sensing", "soil monitoring"], "contacts": [{"organization": "van Egmond, Fenny", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15730426"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15730426", "name": "item", "description": "10.5281/zenodo.15730426", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15730426"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-06-25T00:00:00Z"}}, {"id": "10.5281/zenodo.15763496", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:23:06Z", "type": "Report", "title": "Technical feasibility in using CLMS satellite-based EO to estimate soil health indicators", "description": "The slideshow contains a summary of the main results issued from PREPSOIL Task T5.2.", "keywords": ["2. Zero hunger", "Earth observation", "Technology", "Monitoring", "Scientific knowledge", "Communication", "Skills", "Sustainable soil management", "Success factors", "Healthy Soils", "15. Life on land", "PREPSOIL", "Remote Sensing", "Gaps"], "contacts": [{"organization": "Renault, Pierre, Xie, Guanyao, Weiss, Marie,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15763496"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15763496", "name": "item", "description": "10.5281/zenodo.15763496", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15763496"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-06-28T00:00:00Z"}}, {"id": "10.5281/zenodo.5615357", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:23:25Z", "type": "Dataset", "title": "Supplementary Table for Earth observation data-driven cropland soil monitoring: A review", "description": "Table including 46 manuscripts written in English referring to topsoil monitoring related to Earth observation data-driven cropland soil monitoring: A review paper.", "keywords": ["soil organic carbon", "hyperspectral", "spectral signatures", "carbon farming", "deep learning", "earth observation", "food security", "15. Life on land", "common agricultural policy"], "contacts": [{"organization": "Tziolas, Nikolaos, Tsakiridis, Nikolaos, Chabrillat, Sabine, Dematt\u00ea, Jos\u00e9 A.M., Ben-Dor, Eyal, Gholizadeh, Asa, Zalidis, George, Van Wesemael, Bas,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.5615357"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.5615357", "name": "item", "description": "10.5281/zenodo.5615357", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5615357"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-10-22T00:00:00Z"}}, {"id": "10.5281/zenodo.7152598", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-26T16:23:35Z", "type": "Dataset", "title": "Soil Organic Carbon Content estimations over the Lithuanian pilot area (2022)", "description": "In the context of the EU-funded project DIONE (No. 870378), Soil Organic Carbon Content (SOC) estimations have been released as outputs of novel machine learning algorithms which combined the point measurements (i.e. soil properties detected by the Soil Scanning Systems) with temporal EO multispectral imagery and other ancillary variables, enabling end-users, and for the DIONE case, the national paying agency of Lithuania (National Paying Agency - NPA) to mine meaningful information about overall soil health and the effects applied agricultural practices.