{"type": "FeatureCollection", "features": [{"id": "10.24193/subbphil.2021.2s.05", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:19:08Z", "type": "Journal Article", "created": "2021-11-11", "title": "EXP\u00c9RIMENTER LA PENS\u00c9E EN SCH\u00c9MAS-IMAGES. DES ADOLESCENTS S\u2019INTERROGENT \u00ab D\u2019O\u00d9 VIENNENT LES PENS\u00c9ES ? \u00bb", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Experimenting Thinking in Image Schemas. Teenagers are Wondering \u201cWhere Do Thoughts Come From?\u201d. An intellectual view of philosophy as an activity focusing on understanding abstract concepts and their relationships deprives philosophical exercise of the participation of the body and senses. If we reject the mind-body dualism, as Dewey, Johnson, etc. did, then we are constantly engaged in interactions with the world and others, and can thus consider the act of thinking from our own experiences. Inspired by an experimentalist conception of school and life, as well as the method of inquiry developed by Dewey, the Philosophy for Children program provides an inquiry process that invites participants to conceptualize and reason philosophically in a collaborative manner. Do these practices implement an embodied cognition? To find out, we selected a discussion as a case study and analyzed it based on the observation that the issue to be discussed by the participants - \u201cwhere do thoughts come from?\u201d contains two image schemas: path (come from) and source (where). We have noted a variety and a significant number of expressions (\u201cthey come from within\u201d, \u201cthey come from what happens outside\u201d, etc.) whose analysis enhances a better understanding of how an experience of understanding the origins of our thoughts fits into the discourse and contributes to a collective conceptualization of \u201cthinking\u201d.</p></article>", "keywords": ["4. Education", "B1-5802", "image schemas", " perceptual experience", " conceptualisation", " community of philosophical inquiry", " experimentalism.", "Philosophy (General)", "16. Peace & justice", "3. Good health"]}, "links": [{"href": "https://doi.org/10.24193/subbphil.2021.2s.05"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Studia%20Universitatis%20Babe%C8%99-Bolyai%20Philosophia", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.24193/subbphil.2021.2s.05", "name": "item", "description": "10.24193/subbphil.2021.2s.05", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.24193/subbphil.2021.2s.05"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-10-30T00:00:00Z"}}, {"id": "10.5281/zenodo.14044657", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:20:31Z", "type": "Dataset", "title": "SERENA EJPSOIL BE Flanders soil sealing cookbook", "description": "Open AccessThe internal EJP SOIL project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national and European scales.The data was prepared according to the Level 2 methodology of the SERENA soil sealing cookbook. For Belgium, the application was carried out at the regional scale for the Flanders region. \u00a0The automatically generated yearly soil sealing maps (1 m resolution GeoTIFF rasters)\u00a0combine \u201cknown\u201d sealing from administrative databases (buildings and transport infrastructure) with modelled sealing based on artificial intelligence. Administrative databases do not (adequately) cover parking lots, private driveways and garden terraces, which are a substantial part of the sealed area in Flanders. Hence, a machine learning model was built for deriving this remaining sealing from aerial imagery. For this purpose, an assessor manually labeled the sealed parts on a subset of the images. Based on this training set, a convolutional neural network model was used to produce a sealing probability map, which was converted to a binary modelled sealing map. Finally, a continuity correction was applied to ensure a temporally consistent result across the yearly maps. \u00a0The objective of the SERENA project was to develop methods to calculate and map soil-based ecosystem services and soil threats. The selected indicator was the degree of soil sealing. By evaluating this degree at two moments in time, the change in soil sealing can be determined. \u00a0\u00a0The following data were used:\u00a0         Large-scale Reference Database (Grootschalig Referentiebestand or Basiskaart), the digital topographic reference map for Flanders (vector)\u00a0           Medium-scale annual winter aerial images of Flanders (15 or 25 cm raster resolution)    This dataset is originally hosted at Geopunt (www.geopunt.be). For the most up-to-date version of the dataset, please access the data from the Geopunt repository.", "keywords": ["soil sealing", "remote sensing", "BELGIUM (FLANDERS)", "aerial images", "SERENA", "EJP-Soil", "photointerpretation"], "contacts": [{"organization": "Cockx, Kasper, Oorts, Katrien,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14044657"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14044657", "name": "item", "description": "10.5281/zenodo.14044657", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14044657"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-10-30T00:00:00Z"}}, {"id": "10.5281/zenodo.7948400", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:21:23Z", "type": "Report", "title": "Farm management information systems as tools for revealing management zones inside the fields", "description": "INTRODUCTION and OBJECTIVES: There is a huge need to increase the productivity in agriculture to feed the world\u2019s growing population. However, this increase needs to be achieved in a sustainable way, without jeopardising the ecosystem and environment. Innovations in AgTech are accelerating this process and providing adequate solutions for optimisation of on-field decision-making, but they are often isolated and inaccessible to the farmers. The objective of our work was to design a comprehensive farm management system that takes scientific achievements and enables farmers to use them in their daily operations. MATERIAL and METHOD: In order to digitally transform the Serbian agriculture, we designed AgroSense farm management information system. It was launched in 2017 and has since gathered more than 20,000 users, whose total area equals one fourth of all farmland in Serbia. The platform has a number of modules for weather forecast, historical weather records, digital field books, satellite image processing etc., while the newest addition is the drone image processing module. This module allows 3rd party drone services to scan the fields and upload the data to the platform, after which, the images are processed and analysed. The analysis is directed towards zone management delineation, which is the first step in application of precision agriculture technologies. Zones are detected within the field as areas with homogeneous soil and elevation properties. This is done by applying k-means, an unsupervised machine learning model for clusterisation of data, i.e. pixels in this case. This algorithm minimises the intra-class variance (variance of pixels within the zone) and maximises the inter-class variance (variance between pixels from different classes. This zone delineation can be done on a pixel-level if the objective of zone delineation is e.g. choosing the right locations for soil sampling, or on the level of the tractor swath if the goal is e.g. the variable-rate application of fertiliser. The number of zones and the swath width are variable parameters, left to the user to choose, according to the size of the field, type of the equipment and other factors. RESULTS and CONCLUSIONS: The resulting platform was deployed in 2021 and tested on a number of users. It yielded excellent results and served for optimising the route and sampling location of unmanned ground vehicles (UGVs), characterisation of fields and variable application of fertiliser. Future work includes development of other algorithms for more complex image recognition tasks, such as row detection, leaf area assessment and disease/weed mapping.", "keywords": ["2. Zero hunger", "13. Climate action", "15. Life on land", "drones; precision agriculture; image processing; machine learning"], "contacts": [{"organization": "Marko, Oskar, Brdar, Sanja, Pani\u0107, Marko, Mini\u0107, Vladan, Pejak, Branislav, Crnojevi\u0107, Vladimir,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7948400"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7948400", "name": "item", "description": "10.5281/zenodo.7948400", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7948400"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-06-16T00:00:00Z"}}, {"id": "10.1007/s11356-017-8823-x", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:14:58Z", "type": "Journal Article", "created": "2017-03-24", "title": "Quantitative characterization of pore structure of several biochars with 3D imaging", "description": "Open Access16 pages, 4 figures. The final publication is available at Springer via http://dx.doi.org/10.1007/s11356-017-8823-x", "keywords": ["x-ray tomography", "Condensed Matter - Materials Science", "soil amendment", "pore structure", "ta1171", "ta1182", "Water", "Materials Science (cond-mat.mtrl-sci)", "FOS: Physical sciences", "04 agricultural and veterinary sciences", "01 natural sciences", "6. Clean water", "Diffusion", "Imaging", " Three-Dimensional", "image analysis", "Charcoal", "Image Processing", " Computer-Assisted", "0401 agriculture", " forestry", " and fisheries", "biochar", "Porosity", "soil amendments", "ta218", "water retention", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://link.springer.com/content/pdf/10.1007/s11356-017-8823-x.pdf"}, {"href": "https://doi.org/10.1007/s11356-017-8823-x"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Environmental%20Science%20and%20Pollution%20Research", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1007/s11356-017-8823-x", "name": "item", "description": "10.1007/s11356-017-8823-x", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1007/s11356-017-8823-x"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-03-24T00:00:00Z"}}, {"id": "10.1016/j.rse.2023.113986", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:16:03Z", "type": "Journal Article", "created": "2024-01-21", "title": "On-orbit calibration and performance of the EMIT imaging spectrometer", "description": "Open AccessArticle signat per 56 autors: David R. Thompson, Robert O. Green, Christine Bradley, Philip G. Brodrick, Natalie Mahowald, Eyal Ben Dor, Matthew Bennett, Michael Bernas, Nimrod Carmon, K. Dana Chadwick, Roger N. Clark, Red Willow Coleman, Evan Cox, Ernesto Diaz, Michael L. Eastwood, Regina Eckert, Bethany L. Ehlmann, Paul Ginoux, Mar\u00eda Gon\u00e7alves Ageitos, Kathleen Grant, Luis Guanter, Daniela Heller Pearlshtien, Mark Helmlinger, Harrison Herzog, Todd Hoefen, Yue Huang, Abigail Keebler, Olga Kalashnikova, Didier Keymeulen, Raymond Kokaly, Martina Klose, Longlei Li, Sarah R. Lundeen, John Meyer, Elizabeth Middleton, Ron L. Miller, Pantazis Mouroulis, Bogdan Oaida, Vincenzo Obiso, Francisco Ochoa, Winston Olson-Duvall, Gregory S. Okin, Thomas H. Painter, Carlos P\u00e9rez Garc\u00eda-Pando, Randy Pollock, Vincent Realmuto, Lucas Shaw, Peter Sullivan, Gregg Swayze, Erik Thingvold, Andrew K. Thorpe, Suresh Vannan, Catalina Villarreal, Charlene Ung, Daniel W. Wilson, Sander Zandbergen.", "keywords": ["Mineral dusts", "Teledetecci\u00f3", "550", "Radiative forcing", "7. Clean energy", "Validation", "\u00c0rees tem\u00e0tiques de la UPC::F\u00edsica::Astronomia i astrof\u00edsica", "Spectrometer--Calibration", "Pols minerals", "Visible-shortwave infrared spectroscopy", "info:eu-repo/classification/ddc/550", "ddc:550", "International space station", "Remote sensing", "Mineralogy", "Espect\u00f2metres--Calibratge", "Imaging spectroscopy", "EMIT", "Earth sciences", "Atmospheric correction", "\u00c0rees tem\u00e0tiques de la UPC::Enginyeria de la telecomunicaci\u00f3::Radiocomunicaci\u00f3 i exploraci\u00f3 electromagn\u00e8tica::Teledetecci\u00f3", "13. Climate action", "Hyperspectral imagery", "Calibration", "Mineral dust cycle", "NASA"]}, "links": [{"href": "https://doi.org/10.1016/j.rse.2023.113986"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing%20of%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.rse.2023.113986", "name": "item", "description": "10.1016/j.rse.2023.113986", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.rse.2023.113986"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-03-01T00:00:00Z"}}, {"id": "0beee14d-aa34-46ba-9f43-de5cf2308b53", "type": "Feature", "geometry": {"type": "Polygon", "coordinates": [[[2.75, 49.45], [2.75, 50.85], [6.5, 50.85], [6.5, 49.45], [2.75, 49.45]]]}, "properties": {"rights": "Conditions d'utilisation sp\u00e9cifiques", "updated": "2024-02-28T11:07:43.81Z", "type": "Service", "created": "2015-01-01", "language": "fre", "title": "INSPIRE - INSPIRE Discovery Service for Wallonia", "description": "Ce service expose selon le standard CSW le contenu catalogue Metawal relatif \u00e0 INSPIRE. \n\nIl contient la description des de donn\u00e9es et services g\u00e9ographiques couvrant le territoire wallon ou produites et/ou g\u00e9r\u00e9es par des instances publiques wallonnes qui sont concern\u00e9es par la directive INSPIRE.\n\nDepuis le 19/11/2021 il est pass\u00e9 en https.", "formats": [{"name": "OGC:CSW"}], "keywords": ["Visualiseur de catalogue", "Service de catalogue", "D\u00e9nominations g\u00e9ographiques", "Altitude", "B\u00e2timents", "Ortho-imagerie", "Sources d'\u00e9nergie", "G\u00e9ologie", "Habitats et biotopes", "Adresses", "Lieux de production et sites industriels", "Sant\u00e9 et s\u00e9curit\u00e9 des personnes", "Zones \u00e0 risque naturel", "R\u00e9seaux de transport", "Services d'utilit\u00e9 publique et services publics", "Sols", "Zones de gestion", " de restriction ou de r\u00e9glementation et unit\u00e9s de d\u00e9claration", "Installations de suivi environnemental", "Sites prot\u00e9g\u00e9s", "Hydrographie", "R\u00e9gions biog\u00e9ographiques", "Occupation des terres", "Usage des sols", "Syst\u00e8mes de maillage g\u00e9ographique", "Reporting INSPIRE", "m\u00e9tadonn\u00e9es", "M\u00e9tawal", "ISO", "CSW", "19115", "19119", "description", "metadata", "discovery", "service", "R\u00e9gional", "G\u00e9ospatiales", "2023/138"], "contacts": [{"name": null, "organization": "Helpdesk carto du SPW (SPW - Secr\u00e9tariat g\u00e9n\u00e9ral - SPW Digital - D\u00e9partement Donn\u00e9es transversales - Gestion et valorisation de la donn\u00e9e)", "position": null, "roles": ["pointOfContact"], "phones": [{"value": null}], "emails": [{"value": "helpdesk.carto@spw.wallonie.be"}], "addresses": [{"deliveryPoint": [null], "city": null, "administrativeArea": null, "postalCode": null, "country": null}], "links": [{"href": null}]}, {"name": null, "organization": "Gestion et valorisation de la donn\u00e9e (SPW - Secr\u00e9tariat g\u00e9n\u00e9ral - SPW Digital - D\u00e9partement Donn\u00e9es transversales - Gestion et valorisation de la donn\u00e9e)", "position": null, "roles": ["custodian"], "phones": [{"value": null}], "emails": [{"value": "helpdesk.carto@spw.wallonie.be"}], "addresses": [{"deliveryPoint": [null], "city": null, "administrativeArea": null, "postalCode": null, "country": null}], "links": [{"href": null}]}, {"name": null, "organization": "Service public de Wallonie (SPW)", "position": null, "roles": ["owner"], "phones": [{"value": null}], "emails": [{"value": "helpdesk.carto@spw.wallonie.be"}], "addresses": [{"deliveryPoint": [null], "city": null, "administrativeArea": null, "postalCode": null, "country": null}], "links": [{"href": {"url": "https://geoportail.wallonie.be", "protocol": "WWW:LINK", "protocol_url": "", "name": "G\u00e9oportail de la Wallonie", "name_url": "", "description": "G\u00e9oportail de la Wallonie", "description_url": "", "applicationprofile": null, "applicationprofile_url": "", "function": "information"}}]}], "themes": [{"concepts": [{"id": "Visualiseur de catalogue"}, {"id": "Service de catalogue"}], "scheme": "http://inspire.ec.europa.eu/metadata-codelist/SpatialDataServiceCategory"}, {"concepts": [{"id": "D\u00e9nominations g\u00e9ographiques"}, {"id": "Altitude"}, {"id": "B\u00e2timents"}, {"id": "Ortho-imagerie"}, {"id": "Sources d'\u00e9nergie"}, {"id": "G\u00e9ologie"}, {"id": "Habitats et biotopes"}, {"id": "Adresses"}, {"id": "Lieux de production et sites industriels"}, {"id": "Sant\u00e9 et s\u00e9curit\u00e9 des personnes"}, {"id": "Zones \u00e0 risque naturel"}, {"id": "R\u00e9seaux de transport"}, {"id": "Services d'utilit\u00e9 publique et services publics"}, {"id": "Sols"}, {"id": "Zones de gestion, de restriction ou de r\u00e9glementation et unit\u00e9s de d\u00e9claration"}, {"id": "Installations de suivi environnemental"}, {"id": "Sites prot\u00e9g\u00e9s"}, {"id": "Hydrographie"}, {"id": "R\u00e9gions biog\u00e9ographiques"}, {"id": "Occupation des terres"}, {"id": "Usage des sols"}, {"id": "Syst\u00e8mes de maillage g\u00e9ographique"}], "scheme": "http://inspire.ec.europa.eu/theme"}, {"concepts": [{"id": "Reporting INSPIRE"}], "scheme": "https://metawal.wallonie.be/thesaurus/infrasig"}, {"concepts": [{"id": "R\u00e9gional"}], "scheme": "http://inspire.ec.europa.eu/metadata-codelist/SpatialScope"}, {"concepts": [{"id": "G\u00e9ospatiales"}], "scheme": "http://data.europa.eu/bna/asd487ae75"}, {"concepts": [{"id": "2023/138"}], "scheme": "http://data.europa.eu/r5r/applicableLegislation"}], "title_alternate": "csw-inspire"}, "links": [{"href": "https://metawal.wallonie.be/geonetwork/inspire/fre/csw?SERVICE=CSW&VERSION=2.0.2&REQUEST=GetCapabilities", "name": "Service de d\u00e9couverte INSPIRE pour la Wallonie - GetCapabilities", "description": "Service de d\u00e9couverte INSPIRE de la Wallonie. Ce service peut \u00eatre encapsul\u00e9 dans une application pour acc\u00e9der au contenu INSPIRE du catalogue Metawal.", "protocol": "OGC:CSW", "rel": "browsing"}, {"href": "https://metawal.wallonie.be/geonetwork/inspire/fre/csw", "name": "INSPIRE - Service de d\u00e9couverte pour la Wallonie", "description": "Service de d\u00e9couverte INSPIRE pour la Wallonie, \u00e9galement accessible depuis https://geoservices.wallonie.be/metawal/csw-inspire?request=GetCapabilities&service=CSW&acceptVersions=2.0.2", "protocol": "OGC:CSW"}, {"rel": "self", "type": "application/geo+json", "title": "0beee14d-aa34-46ba-9f43-de5cf2308b53", "name": "item", "description": "0beee14d-aa34-46ba-9f43-de5cf2308b53", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/0beee14d-aa34-46ba-9f43-de5cf2308b53"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date-time": "2024-02-28T11:07:43Z"}}, {"id": "10.1016/j.isprsjprs.2017.10.016", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:15:53Z", "type": "Journal Article", "created": "2017-11-06", "title": "Estimation And Mapping Of Above-Ground Biomass Of Mangrove Forests And Their Replacement Land Uses In The Philippines Using Sentinel Imagery", "description": "Abstract   The recent launch of the Sentinel-1 (SAR) and Sentinel-2 (multispectral) missions offers a new opportunity for land-based biomass mapping and monitoring especially in the tropics where deforestation is highest. Yet, unlike in agriculture and inland land uses, the use of Sentinel imagery has not been evaluated for biomass retrieval in mangrove forest and the non-forest land uses that replaced mangroves. In this study, we evaluated the ability of Sentinel imagery for the retrieval and predictive mapping of above-ground biomass of mangroves and their replacement land uses. We used Sentinel SAR and multispectral imagery to develop biomass prediction models through the conventional linear regression and novel Machine Learning algorithms. We developed models each from SAR raw polarisation backscatter data, multispectral bands, vegetation indices, and canopy biophysical variables. The results show that the model based on biophysical variable Leaf Area Index (LAI) derived from Sentinel-2 was more accurate in predicting the overall above-ground biomass. In contrast, the model which utilised optical bands had the lowest accuracy. However, the SAR-based model was more accurate in predicting the biomass in the usually deficient to low vegetation cover non-forest replacement land uses such as abandoned aquaculture pond, cleared mangrove and abandoned salt pond. These models had 0.82\u20130.83 correlation/agreement of observed and predicted value, and root mean square error of 27.8\u201328.5\u202fMg\u202fha \u22121 . Among the Sentinel-2 multispectral bands, the red and red edge bands (bands 4, 5 and 7), combined with elevation data, were the best variable set combination for biomass prediction. The red edge-based Inverted Red-Edge Chlorophyll Index had the highest prediction accuracy among the vegetation indices. Overall, Sentinel-1 SAR and Sentinel-2 multispectral imagery can provide satisfactory results in the retrieval and predictive mapping of the above-ground biomass of mangroves and the replacement non-forest land uses, especially with the inclusion of elevation data. The study demonstrates encouraging results in biomass mapping of mangroves and other coastal land uses in the tropics using the freely accessible and relatively high-resolution Sentinel imagery.", "keywords": ["land use change", "580", "sentinel imagery", "mangrove", "biomass", "550", "Philippines", "0211 other engineering and technologies", "04 agricultural and veterinary sciences", "02 engineering and technology", "15. Life on land", "biomass mapping", "13. Climate action", "0401 agriculture", " forestry", " and fisheries"]}, "links": [{"href": "https://doi.org/10.1016/j.isprsjprs.2017.10.016"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/ISPRS%20Journal%20of%20Photogrammetry%20and%20Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.isprsjprs.2017.10.016", "name": "item", "description": "10.1016/j.isprsjprs.2017.10.016", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.isprsjprs.2017.10.016"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-12-01T00:00:00Z"}}, {"id": "10.1016/j.jaap.2018.07.011", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:15:53Z", "type": "Journal Article", "created": "2018-07-20", "title": "Effects of pyrolysis temperature on the hydrologically relevant porosity of willow biochar", "description": "Open AccessBiochar pore space consists of porosity of multiple length scales. In direct water holding applications like water storage for plant water uptake, the main interest is in micrometre-range porosity since these pores are able to store water that is easily available for plants. Gas adsorption measurements which are commonly used to characterize the physical pore structure of biochars are not able to quantify this pore-size range. While pyrogenetic porosity (i.e. pores formed during pyrolysis process) tends to increase with elevated process temperature, it is uncertain whether this change affects the pore space capable to store plant available water. In this study, we characterized biochar porosity with x-ray tomography which provides quantitative information on the micrometer-range porosity. We imaged willow dried at 60 $^ circ$C and biochar samples pyrolysed in three different temperatures (peak temperatures 308, 384, 489 $^ circ$C, heating rate 2 $^ circ$C min$^{-1}$). Samples were carefully prepared and traced through the experiments, which allowed investigation of porosity development in micrometre size range. Pore space was quantified with image analysis of x-ray tomography images and, in addition, nanoscale porosity was examined with helium ion microscopy. The image analysis results show that initial pore structure of the raw material determines the properties of micrometre-range porosity in the studied temperature range. Thus, considering the pore-size regime relevant to the storage of plant available water, pyrolysis temperature in the studied range does not provide means to optimize the biochar structure. However, these findings do not rule out that process temperature may affect the water retention properties of biochars by modifying the chemical properties of the pore surfaces.", "keywords": ["Fysiikka", "porosity", "FOS: Physical sciences", "Applied Physics (physics.app-ph)", "kuivatislaus", "01 natural sciences", "huokoisuus", "image analysis", "biochar", "ta216", "ta218", "0105 earth and related environmental sciences", "x-ray tomography", "biohiili", "Condensed Matter - Materials Science", "ta114", "Physics", "ta1182", "Materials Science (cond-mat.mtrl-sci)", "Physics - Applied Physics", "04 agricultural and veterinary sciences", "slow pyrolysis", "6. Clean water", "kuvantaminen", "kuva-analyysi", "0401 agriculture", " forestry", " and fisheries"]}, "links": [{"href": "https://doi.org/10.1016/j.jaap.2018.07.011"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Analytical%20and%20Applied%20Pyrolysis", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.jaap.2018.07.011", "name": "item", "description": "10.1016/j.jaap.2018.07.011", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.jaap.2018.07.011"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-09-01T00:00:00Z"}}, {"id": "10.1007/s11104-021-05010-y", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:14:55Z", "type": "Journal Article", "created": "2021-07-07", "title": "Root anatomy and soil resource capture", "description": "Abstract                 Background                 <p>Suboptimal water and nutrient availability are primary constraints in global agriculture. Root anatomy plays key roles in soil resource acquisition. In this article we summarize evidence that root anatomical phenotypes present opportunities for crop breeding.</p>                                Scope                 <p>Root anatomical phenotypes influence soil resource acquisition by regulating the metabolic cost of soil exploration, exploitation of the rhizosphere, the penetration of hard soil domains, the axial and radial transport of water, and interactions with soil biota including mycorrhizal fungi, pathogens, insects, and the rhizosphere microbiome. For each of these topics we provide examples of anatomical phenotypes which merit attention as selection targets for crop improvement. Several cross-cutting issues are addressed including the importance of phenotypic plasticity, integrated phenotypes, C sequestration, in silico modeling, and novel methods to phenotype root anatomy including image analysis tools.</p>                                Conclusions                 <p>An array of anatomical phenes have substantial importance for the acquisition of water and nutrients. Substantial phenotypic variation exists in crop germplasm. New tools and methods are making it easier to phenotype root anatomy, determine its genetic control, and understand its utility for plant fitness. Root anatomical phenotypes are underutilized yet attractive breeding targets for the development of the efficient, resilient crops urgently needed in global agriculture.</p>", "keywords": ["Carbon sequestration", "0106 biological sciences", "0301 basic medicine", "2. Zero hunger", "Root; Anatomy; Water; Nutrients; Transport; Insects; Pathogens; Mycorrhiza; Carbon sequestration; Modeling; Image analysis; Plasticity", "Plasticity", "Modeling", "Water", "Transport", "Nutrients", "15. Life on land", "01 natural sciences", "Image analysis", "Insects", "03 medical and health sciences", "Root", "Anatomy", "Pathogens", "Mycorrhiza"]}, "links": [{"href": "https://doi.org/10.1007/s11104-021-05010-y"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Plant%20and%20Soil", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1007/s11104-021-05010-y", "name": "item", "description": "10.1007/s11104-021-05010-y", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1007/s11104-021-05010-y"}, {"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": "10.1007/s11242-015-0572-z", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:14:56Z", "type": "Journal Article", "created": "2015-09-14", "title": "Visualization and Characterization of Heterogeneous Water Flow in Double-Porosity Media by Means of X-ray Computed Tomography", "description": "Three-dimensional visualization of dynamic water transport process in soil by 1 computed tomography (CT) technique is still limited by its low temporal resolution. In order 2 to monitor dynamically water transport in soil, a compromise has to be found between water 3 flow velocity and CT acquisition time. Furthermore, an efficient image analysis method is 1 4 necessary. In this work, we followed the water transport in three dimensions by CT imaging 5 across a double-porosity media constituted of two distinct materials, i.e. sand and porous 6 clay spheres. The CT acquisition parameters were adjusted to the water pore velocity so that 7 we succeeded to register the water front displacement per time range of 25 min. We also used 8 the image subtraction method to extract water distribution evolution with time with a space 9 resolution of 6 \u00d7 10 \u22123 cm. Both time and space resolution are relatively high compared with 10 other dynamic studies. The water content profiles showed that the clay spheres remained 11 in their dry state during water infiltration, while the water transport only occurred in the 12 sand matrix. These results are consistent with macroscopic experiments. The water front 13 visualized by CT showed a non-symmetrical shape which was related to water transfer in 14 non-equilibrium as shown by column displacement experiments.", "keywords": ["2. Zero hunger", "550", "[SDE.IE]Environmental Sciences/Environmental Engineering", "0208 environmental biotechnology", "Porous media", "0207 environmental engineering", "02 engineering and technology", "[SDV.SA.SDS]Life Sciences [q-bio]/Agricultural sciences/Soil study", "6. Clean water", "620", "Image analysis", "3D visualization", "[SDU.STU.HY] Sciences of the Universe [physics]/Earth Sciences/Hydrology", "[SDE.IE] Environmental Sciences/Environmental Engineering", "[SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology", "[SDV.SA.SDS] Life Sciences [q-bio]/Agricultural sciences/Soil study", "Computed tomography", "Water transport"]}, "links": [{"href": "http://link.springer.com/content/pdf/10.1007/s11242-015-0572-z"}, {"href": "https://doi.org/10.1007/s11242-015-0572-z"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Transport%20in%20Porous%20Media", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1007/s11242-015-0572-z", "name": "item", "description": "10.1007/s11242-015-0572-z", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1007/s11242-015-0572-z"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2015-09-14T00:00:00Z"}}, {"id": "10.1016/j.jag.2024.103659", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:15:54Z", "type": "Journal Article", "created": "2024-01-21", "title": "Automatized Sentinel-2 mosaicking for large area forest mapping", "description": "Creating maps of forest inventory variables is commonly taking advantage of satellite images, which are mosaicked together for gaining larger coverage. Recently, mosaicking has increasingly shifted towards user friendly cloud-based online environments such as Google Earth Engine (GEE), which are equipped with huge image repositories and extensive processing capabilities. This enables the easy transferability of workflows into new image sets and diversifies the range of methodological options for mosaicking. The quality control of the output mosaic, ensuring that the reflectance values are representative to the targeted land cover, is however primarily based on certain assumptions or pre-set rules which may not always produce an optimal result. Our study focuses on assessing and comparing the performance of three different mosaicking algorithms for predicting forest inventory variables, based on an extensive set of field data on the main site type, fertility class, and volume and biomass of growing stock. One of the compared mosaics derives from manual image selection, thus enabling rigorous visual quality control, and two others are resting on GEE-assisted automatized methods which include applying a percentile-based statistic over all the input reflectance values and selecting the best pixels using predefined quality indicators. The results indicate that the manual and the percentile-based mosaics are generally providing the best and relatively equal performance levels. Compared to them, the quality-based mosaic has slightly lower accuracy particularly when predicting continuous variables (i.e., the volume and biomass of growing stock) and it suffers from minor image defects. For the total volume of growing stock, for example, the RMS errors are 56.22 % for the manual, 56.33 % for the percentile-based, and 59.47 % for the quality-based mosaics, respectively. These results indicate that from the perspective of large area forest mapping, automatically generated mosaics may provide approximately similar accuracy as compared to manually controlled workflow at a fraction of the workload.", "keywords": ["Image mosaicking", "Physical geography", "791", "forest research", "04 agricultural and veterinary sciences", "15. Life on land", "Feature prediction", "01 natural sciences", "GB3-5030", "Environmental sciences", "0401 agriculture", " forestry", " and fisheries", "GE1-350", "Sentinel-2", "Google Earth Engine", "satellite images", "Forest inventory", "0105 earth and related environmental sciences"], "contacts": [{"organization": "Balazs Andras, Tuominen Sakari, Pitk\u00e4nen Timo P.,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.1016/j.jag.2024.103659"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/International%20Journal%20of%20Applied%20Earth%20Observation%20and%20Geoinformation", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.jag.2024.103659", "name": "item", "description": "10.1016/j.jag.2024.103659", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.jag.2024.103659"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-03-01T00:00:00Z"}}, {"id": "10.1016/j.biombioe.2018.10.004", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:15:23Z", "type": "Journal Article", "created": "2018-10-11", "title": "How and why does willow biochar increase a clay soil water retention capacity?", "description": "Abstract   Addition of biochar into a soil changes its water retention properties by modifying soil textural and structural properties. In addition, internal micrometer-scale porosity that is able to directly store readily plant available water affects soil water retention properties. This study shows how precise knowledge of the internal micrometer-scale pore size distribution of biochar can deepen the understanding of the biochar-water interactions in soils. The micrometer-scale porosity of willow biochar was quantitatively and qualitatively characterized using X-ray tomography, 3D image analysis and Helium ion microscopy. The effect of biochar application on clay soil water retention was studied by conventional water retention curve approach. The results indicate that the internal pores of biochar, with sizes of at 50 and 10\u202f\u03bcm (equivalent pore diameter), increased soil porosity and the amount of readily plant available water. After biochar addition, changes in soil porosity were detected at pore size regimes 5\u201310 and 25\u202f\u03bcm, i.e. biochar pore sizes multiplied by factor 0.5. The detected pore size distribution of biochar does not predict directly (1:1 compatibility) the changes observed in the soil moisture characteristics. It is likely that biochar chemistry and pore morphology affect biochar-water interactions via e.g. surface roughness and contact angle. In addition, biochar induced changes in soil structure and texture affected soil moisture characteristics. However, the approach presented is an attractive pathway to more generalized understanding on how and why biochar internal porosity affects soil moisture characteristics.", "keywords": ["570", "Fysiikka", "ta1171", "mikroskopia", "savi", "01 natural sciences", "630", "huokoisuus", "soil water retention", "tomografia", "219", "3D image analysis", "biochar", "3D-mallinnus", "ta216", "ta218", "219 Environmental biotechnology", "0105 earth and related environmental sciences", "x-ray tomography", "biohiili", "maaper\u00e4", "ta114", "Physics", "ta1182", "04 agricultural and veterinary sciences", "15. Life on land", "6. Clean water", "mikrorakenteet", "plant available water", "helium ion microscopy", "0401 agriculture", " forestry", " and fisheries", "vesipitoisuus", "X-ray tomography"]}, "links": [{"href": "https://doi.org/10.1016/j.biombioe.2018.10.004"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Biomass%20and%20Bioenergy", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.biombioe.2018.10.004", "name": "item", "description": "10.1016/j.biombioe.2018.10.004", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.biombioe.2018.10.004"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-12-01T00:00:00Z"}}, {"id": "10.1101/2021.02.13.430456", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:17:22Z", "type": "Journal Article", "created": "2021-02-13", "title": "Plant-environment microscopy tracks interactions of Bacillus subtilis with plant roots across the entire rhizosphere", "description": "Abstract<p>Our understanding of plant-microbe interactions in soil is limited by the difficulty of observing processes at the microscopic scale throughout plants\uffe2\uff80\uff99 large volume of influence. Here, we present the development of 3D live microscopy for resolving plant-microbe interactions across the environment of an entire seedling growing in a transparent soil in tailor-made mesocosms, maintaining physical conditions for the culture of both plants and microorganisms. A tailor made dual-illumination light-sheet system acquired scattering signals from the plant whilst fluorescence signals were captured from transparent soil particles and labelled microorganisms, allowing the generation of quantitative data on samples approximately 3600 mm3in size with as good as 5 \uffce\uffbcm resolution at a rate of up to one scan every 30 minutes. The system tracked the movement ofBacillus subtilispopulations in the rhizosphere of lettuce plants in real time, revealing previously unseen patterns of activity. Motile bacteria favoured small pore spaces over the surface of soil particles, colonising the root in a pulsatile manner. Migrations appeared to be directed towards the root cap, the point \uffe2\uff80\uff9cfirst contact\uffe2\uff80\uff9d, before subsequent colonisation of mature epidermis cells. Our findings show that microscopes dedicated to live environmental studies present an invaluable tool to understand plant-microbe interactions.</p", "keywords": ["0301 basic medicine", "570", "Microscopy", "Silicon", "0303 health sciences", "Temperature", "root-microbe interactions", "Equipment Design", "Biological Sciences", "Environment", "15. Life on land", "Plant Roots", "630", "Fluorescence", "Soil", "03 medical and health sciences", "Seedlings", "Calibration", "Rhizosphere", "Image Processing", " Computer-Assisted", "environmental imaging", "rhizosphere", "Soil Microbiology", "Bacillus subtilis", "Lactuca"]}, "links": [{"href": "https://eprints.whiterose.ac.uk/178939/18/e2109176118.full.pdf"}, {"href": "https://pnas.org/doi/pdf/10.1073/pnas.2109176118"}, {"href": "https://doi.org/10.1101/2021.02.13.430456"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Proceedings%20of%20the%20National%20Academy%20of%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1101/2021.02.13.430456", "name": "item", "description": "10.1101/2021.02.13.430456", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1101/2021.02.13.430456"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-02-13T00:00:00Z"}}, {"id": "10.1186/s43591-025-00112-1", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:18:09Z", "type": "Journal Article", "created": "2025-03-02", "title": "Tracks of travel: unveiling tire particle concentrations in Swiss cantonal road soils", "description": "Abstract           <p>Tire wear particles (TWP) originating from tire abrasion on roads are a major source of microplastics to the environment. Together with associated pollutants like polycyclic aromatic hydrocarbons and trace metals, TWP are emitted to roadside soils in the immediate vicinity of road networks. Our study aimed at quantifying TWP number and mass concentrations and investigating particle features in low-traffic roadside soils using a novel particle-based analytical approach. On the example of fifteen Swiss cantonal roadside soils, with average daily traffic volumes of 2,290 vehicles per day\uffe2\uff88\uff92\uffe2\uff80\uff891, we sampled composite samples from distances of 1, 2, 5 and 10\uffc2\uffa0m to the roadside. TWP were extracted via density separation and wet-chemical sample purification. TWP analysis was performed using microscope images and trainable Weka segmentation image analysis. Furthermore, associated road pollutants like polycyclic aromatic hydrocarbons, benzop[a]pyrene and trace metals were analysed using TQ GC-MS/MS and ICP-MS. We found average concentrations of 111,000 TWP per kg soil dry weight (TWP kg\uffe2\uff88\uff92\uffe2\uff80\uff891) highest values reaching 615,000 TWP kg\uffe2\uff88\uff92\uffe2\uff80\uff891 and mean TWP masses of 52.7\uffe2\uff80\uff89\uffc2\uffb1\uffe2\uff80\uff8983.2\uffc2\uffa0mg TWP kg\uffe2\uff88\uff92\uffe2\uff80\uff891. TWP had a minimal Feret diameter of 62.8\uffe2\uff80\uff89\uffc2\uffb1\uffe2\uff80\uff8945.6\uffc2\uffa0\uffce\uffbcm on average and showed mean circularity values of 0.7\uffe2\uff80\uff89\uffc2\uffb1\uffe2\uff80\uff890.2, resulting in elliptic particle morphology. TWP concentrations and sizes decreased with increasing distance from the road. Positive relationships were found between TWP numbers and polycyclic aromatic hydrocarbons, benzop[a]pyrene and zinc concentrations in roadside soils. However, a moderate relationship to speed limits was identified. We were able to demonstrate that even in low-traffic areas, roadside soils act as an environmental sink for high concentrations of TWPs and associated pollutants and that spatial distribution and the spread of TWP to soils strongly dependent on the distance to the road.</p", "keywords": ["TP1080-1185", "Trace metals", "TD172-193.5", "Tire wear particles", "Organic compounds", "Traffic", "Polymers and polymer manufacture", "Environmental pollution", "Image analysis"], "contacts": [{"organization": "Dominika Kundel, Andrea Wiget, Andreas Fliessbach, Moritz Bigalke, Collin J. Weber,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.1186/s43591-025-00112-1"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Microplastics%20and%20Nanoplastics", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1186/s43591-025-00112-1", "name": "item", "description": "10.1186/s43591-025-00112-1", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1186/s43591-025-00112-1"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-03-03T00:00:00Z"}}, {"id": "10.1016/j.jcs.2019.102829", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:15:56Z", "type": "Journal Article", "created": "2019-09-06", "title": "Morpho-densitometric traits for quinoa (Chenopodium quinoa Willd.) seed phenotyping by two X-ray micro-CT scanning approaches", "description": "Recent studies are increasingly focusing on quinoa (Chenopodium quinoa) as a high-quality protein-rich food source and, in general, on seed quality. This latter is a complex trait difficult to characterize with standard measurements or analyses. X-ray micro-CT allows to visualise the internal structure of small objects and has been already used in seed research, mostly for maize kernel characterization. To date this technique has not yet been applied to study quinoa seeds, despite the increasing interest for their nutritional properties. The aim of this work was to explore the use of X-ray microtomography to provide new traits improving the seed phenotyping of quinoa. Two different scanning approaches have been compared: one based on the simultaneous scanning of multiple seeds (30) at lower resolution (20 ?m voxel size) and one based on the scanning of a single seed at higher resolution (2 ?m voxel size). Such approaches were tested on a study case consisting of four different quinoa genotypes. Among the measured morpho-densitometric parameters, the embryo volume and weight ratios (derived from bulk and single seed scanning, respectively) showed high positive correlation with the total protein content, while the thickest fraction of the pericarp was the best correlated with the presence of saponins.", "keywords": ["Fitomejoramiento", "2. Zero hunger", "0301 basic medicine", "0303 health sciences", "Evaluaci\u00f3n", "Rayos-X", "Seed quality", "Semillas", "Variaci\u00f3n gen\u00e9tica", "Quinua", "Per\u00fa", "03 medical and health sciences", "https://purl.org/pe-repo/ocde/ford#2.11.01", "Quinoa", "Radiaci\u00f3n", "3D image analysis", "X-ray micro-CT", "Chenopodium quinoa", "Fenotipos"]}, "links": [{"href": "https://iris.cnr.it/bitstream/20.500.14243/386031/1/prod_406462-doc_142170.pdf"}, {"href": "https://iris.cnr.it/bitstream/20.500.14243/386031/2/prod_406462-doc_142171.pdf"}, {"href": "https://iris.cnr.it/bitstream/20.500.14243/386031/4/prod_406462-doc_147996.pdf"}, {"href": "https://doi.org/10.1016/j.jcs.2019.102829"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Cereal%20Science", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.jcs.2019.102829", "name": "item", "description": "10.1016/j.jcs.2019.102829", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.jcs.2019.102829"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-11-01T00:00:00Z"}}, {"id": "10.1016/j.geoderma.2017.10.020", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:15:51Z", "type": "Journal Article", "created": "2017-11-06", "title": "Characterising and linking X-ray CT derived macroporosity parameters to infiltration in soils with contrasting structures", "description": "<p>Soils deliver the regulating ecosystem services of water infiltration and distribution, which can be controlled by macropores. Parameterizing macropore hydraulic properties is challenging due to the lack of direct measurement methods. With tension-disc infiltrometry hydraulic properties near saturation can be measured. Differentiating between hydrologically active and non-active pores, at a given water potential, indirectly assesses macropore continuity. Water flow through macropores is controlled by macropore size distribution, tortuosity, and connectivity, which can be directly derived by X-ray computed tomography (CT). Our objective was to parameterize macropore hydraulic properties based on the imaged macropore network of three horizons of an Andosol and a Gleysol. Hydraulic conductivity K <sub>unsat</sub> was derived from infiltration measurements. Soil cores from the infiltration areas were scanned with X-ray CT. K <sub>unsat</sub> was significantly higher in the Andosol than in the Gleysol at all water potentials, and decreased significantly with depth in both soils. The in situ measurements guided the definition of new macroporosity parameters from the X-ray CT reconstructions. For the Andosol, K <sub>unsat</sub> was best predicted using the imaged-limited macroporosity. A low total macroporosity, coupled with a high macropore density, indicated the abundance of smaller macropores, leading to homogeneous matrix flux. Imaged macropores were not well connected. In contrast, the Gleysol had a bi-modal macropore system with few very large, but well-connected macropores. K <sub>unsat</sub> was best predicted using the imaged macroporosity consisting only of macropores with diameters between 0.75 and 3 mm. Our research demonstrates that linking traditional soil physical measurements with soil-visualization techniques has a huge potential to improve parameterizing macropore hydraulic properties. The relevance of the relationships found in this study for larger scales and other soil types still needs to be tested, for example by a multi-scale investigation including a much wider range of different soils. </p>", "keywords": ["[SDE] Environmental Sciences", "Hydraulic parameters", "0207 environmental engineering", "04 agricultural and veterinary sciences", "02 engineering and technology", "15. Life on land", "6. Clean water", "Image analysis", "Tension disc in\ufb01ltrometr", "Tension disc infiltrometry", "Pore network", "13. Climate action", "Soil structure", "[SDE.ES] Environmental Sciences/Environment and Society", "0401 agriculture", " forestry", " and fisheries"]}, "links": [{"href": "https://doi.org/10.1016/j.geoderma.2017.10.020"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Geoderma", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.geoderma.2017.10.020", "name": "item", "description": "10.1016/j.geoderma.2017.10.020", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.geoderma.2017.10.020"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-03-01T00:00:00Z"}}, {"id": "10.1016/j.geoderma.2021.115656", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:15:51Z", "type": "Journal Article", "created": "2021-12-15", "title": "A framework for determining the total salt content of soil profiles using time-series Sentinel-2 images and a random forest-temporal convolution network", "description": "Soil salinization causes a deterioration in soil health and threatens crop growth. Rapid identification of salinization in farmlands is of great significance to improve soil functions and to maintain sustainable land management. As salt moves in soil profiles during plowing and irrigation, the commonly used protocol for measuring and monitoring salt content in topsoil does not provide a thorough assessment. In order to quantify and comprehensively evaluate the salt content in deep soil, this study developed a novel framework for monitoring total salt content in the soil profile to a depth of 1 m by combining information from time-series satellite images and machine learning. The field experiments were conducted in Alar, Southern Xinjiang, with a total of 120 soil samples and 582 measurements of EM38-MK2 apparent electrical conductivity in 2019 and 2020 to quantify the vertical variation in the salt content. A total of 42 covariates derived from time-series Sentinel-2 images, including 20 salinity indices, 10 soil indices, and 12 vegetation indices were used for modeling salinity in the soil profile. From the total covariates, 22 were selected using the Random Forest. Soil salinity which was modeled using a Temporal Convolution Network in 2019 and 2020 and forecast for 2021. The model effectively revealed the spatial and temporal variability of the salt content in the soil profile with R<sup>2</sup> of 0.71 and 0.65 for 2019 and 2020, respectively. The proposed new framework provides an effective method to estimate the salt content in the soil profile for precision agriculture in arid and semi-arid regions.", "keywords": ["2. Zero hunger", "Soil salinity", "Random Forest", "13. Climate action", "Time-series images", "Soil profile", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "15. Life on land", "6. Clean water", "Temporal Convolution Network"]}, "links": [{"href": "https://doi.org/10.1016/j.geoderma.2021.115656"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Geoderma", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.geoderma.2021.115656", "name": "item", "description": "10.1016/j.geoderma.2021.115656", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.geoderma.2021.115656"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-03-01T00:00:00Z"}}, {"id": "10.1016/j.geoderma.2025.117299", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:15:51Z", "type": "Journal Article", "created": "2025-04-19", "title": "Synchrotron-based 3D X-ray computed tomography reveals root system architecture: Plastic responses to phosphorus placement", "description": "We used synchrotron-based X-ray computed tomography (SRXCT) to visualize root distribution in soil cores. X-ray CT is emerging as a leading technique to study plant roots, but SRXCT offers potential advantages compared with conventional X-ray sources, including producing X-rays of higher intensity that are collimated, monochromatic and tuneable; delivering high-resolution data whilst avoiding issues such as beam-hardening and source divergence. We demonstrate the suitability of SRXCT for observing the root system of wheat plants growing in two soils (Calcisol and Ultisol) in response to placement of different phosphorus fertilisers. To optimize scanning quality, we tested the use of an inverse \u2018mask\u2019 in front of the soil cores to achieve a more uniform attenuation along the sample, thereby avoiding saturation of the detector along the thinnest parts of the soil cores. Secondly, we developed a deep learning approach for segmentation and quantification of root length and diameter. Our results demonstrate the use of SRXCT as a tool for studying root system architecture in soil at high spatial resolution. The SRXCT method marks a new stride towards advancing our understanding of root structures in unprecedented detail, opening further avenues for exploring plant-soil interactions.", "keywords": ["X-ray computed tomography", "Image segmentation", "Plant roots", "Root system architecture", "Soil phosphorus", "Science", "Q", "Root distribution", "Synchrotron"]}, "links": [{"href": "https://doi.org/10.1016/j.geoderma.2025.117299"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Geoderma", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.geoderma.2025.117299", "name": "item", "description": "10.1016/j.geoderma.2025.117299", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.geoderma.2025.117299"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-05-01T00:00:00Z"}}, {"id": "10.1016/j.jenvman.2024.123141", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:15:57Z", "type": "Journal Article", "created": "2024-11-07", "title": "Four approaches to setting soil health targets and thresholds in agricultural soils", "description": "Soil health is a key concept in worldwide efforts to reverse soil degradation, but to be used as a tool to improve soils, it must be definable at a policy level and quantifiable in some way. Soil indicators can be used to define soil health and quantify the degree to which soils fulfil expected functions. Indicators are assessed using target and/or threshold values, which define achievable levels of the indicators or functions. However, defining robust targets and thresholds is not a trivial task, as they should account for soil, climate, land-use, management, and history, among others. This paper introduces and discusses (through theory and stakeholder feedback) four approaches to setting targets and thresholds: fixed, reference, distribution and relative change. Three approaches (not including relative change) are then illustrated using a case study, located in Denmark, Italy, and France, which highlights key strengths and weaknesses of each approach. Finally, a framework is presented that facilitates both choosing the most appropriate target/threshold method for a given context, and using targets/thresholds to trigger follow-up actions to promote soil health.", "keywords": ["Conservation of Natural Resources", "Monitoring", "Supplementary Data", "QH301 Biology", "Denmark", "Framework", "610", "https://ars.els-cdn.com/content/image/1-s2.0-S030147972403127X-mmc1.docx", "[SDV.SA.SDS]Life Sciences [q-bio]/Agricultural sciences/Soil study", "01 natural sciences", "QH301", "Soil", "framework", "Soil health", "SDG 13 - Climate Action", "threshold", "Indicators", "[SDV.SA.SDS] Life Sciences [q-bio]/Agricultural sciences/Soil study", "SDG 15 - Life on Land", "0105 earth and related environmental sciences", "GE", "Targets", "soil health", "thresholds", "Agriculture", "04 agricultural and veterinary sciences", "indicators", "monitoring", "Italy", "targets", "0401 agriculture", " forestry", " and fisheries", "Thresholds", "France", "GE Environmental Sciences"], "contacts": [{"organization": "Matson, Amanda, Fantappi\u00e8, Maria, Campbell, Grant A., Miranda-V\u00e9lez, Jorge, Faber, Jack, Gomes, Lucas Carvalho, Hessel, Rudi, Lana, Marcos, Mocali, Stefano, Smith, Pete, Robinson, David, Bispo, Antonio, van Egmond, Fenny, Keesstra, Saskia, Saby, Nicolas P. A., Smreczak, Bozena, Froger, Claire, Suleymanov, Azamat, Chenu, Claire,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.1016/j.jenvman.2024.123141"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Environmental%20Management", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.jenvman.2024.123141", "name": "item", "description": "10.1016/j.jenvman.2024.123141", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.jenvman.2024.123141"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-12-01T00:00:00Z"}}, {"id": "10.1093/jxb/erab174", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:17:16Z", "type": "Journal Article", "created": "2020-12-03", "title": "Digging roots is easier with AI", "description": "Abstract<p>The scale of root quantification in research is often limited by the time required for sampling, measurement and processing samples. Recent developments in Convolutional Neural Networks (CNN) have made faster and more accurate plant image analysis possible which may significantly reduce the time required for root measurement, but challenges remain in making these methods accessible to researchers without an in-depth knowledge of Machine Learning. We analyzed root images acquired from three destructive root samplings using the RootPainter CNN-software that features an interface for corrective annotation for easier use. Root scans with and without non-root debris were used to test if training a model, i.e., learning from labeled examples, can effectively exclude the debris by comparing the end-results with measurements from clean images. Root images acquired from soil profile walls and the cross-section of soil cores were also used for training and the derived measurements were compared with manual measurements. After 200 minutes of training on each dataset, significant relationships between manual measurements and RootPainter-derived data were noted for monolith (R2=0.99), profile wall (R2=0.76) and core-break (R2=0.57). The rooting density derived from images with debris was not significantly different from that derived from clean images after processing with RootPainter. Rooting density was also successfully calculated from both profile wall and soil core images, and in each case the gradient of root density with depth was not significantly different from manual counts. Our results demonstrate that the proposed approach using CNN can lead to substantial reductions in root sample processing workloads, increasing the potential scale of future root investigations.</p>", "keywords": ["0301 basic medicine", "root phenotyping", "profile wall", "root washing", "segmentation", "deep learning", "Convolutional neural network", "04 agricultural and veterinary sciences", "15. Life on land", "Soil", "03 medical and health sciences", "core-break", "monolith", "soil coring", "Image Processing", " Computer-Assisted", "0401 agriculture", " forestry", " and fisheries", "Neural Networks", " Computer", "Software"]}, "links": [{"href": "http://academic.oup.com/jxb/article-pdf/72/13/4680/38807872/erab174.pdf"}, {"href": "https://doi.org/10.1093/jxb/erab174"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Experimental%20Botany", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1093/jxb/erab174", "name": "item", "description": "10.1093/jxb/erab174", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1093/jxb/erab174"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-12-02T00:00:00Z"}}, {"id": "10.1071/sr21268", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:17:01Z", "type": "Journal Article", "created": "2022-01-18", "title": "Lessons from a landmark 1991 article on soil structure: distinct precedence of non-destructive assessment and benefits of fresh perspectives in soil research", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>In 1991, at the launch of a national symposium devoted to soil structure, the Australian Society of Soil Science invited Professor John Letey to deliver a keynote address, which was later published in the society\u2019s journal. In his lecture, he shared the outcome of his reflexion about what the assessment of soil structure should amount to, in order to produce useful insight into the functioning of soils. His viewpoint was that the focus should be put on the openings present in the structure, rather than on the chunks of material resulting from its mechanical dismantlement. In the present article, we provide some historical background for Letey\u2019s analysis, and try to explain why it took a number of years for the paradigm shift that he advocated to begin to occur. Over the last decade, his perspective that soil structure needs to be characterised via non-destructive methods appears to have gained significant momentum, which is likely to increase further in the near future, as we take advantage of recent technological advances. Other valuable lessons that one can derive from Letey\u2019s pioneering article relate to the extreme value for everyone, even neophytes, to constantly ask questions about where research on given topics is heading, what its goals are, and whether the methods that are used at a certain time are optimal.</p></article>", "keywords": ["570", "soil image analysis", "soil microorganisms", "[SDE.MCG]Environmental Sciences/Global Changes", "Soil measuring", "earthworms", "micromorphology", "Aggregate stability", "Soil functions", "01 natural sciences", "630", "Soil fauna", "soil organic matter", "Earthworms", "Micromorphology", "Computed tomography", "aggregate stability", "0105 earth and related environmental sciences", "soil measuring", "2. Zero hunger", "Soil organic matter", "computed tomography", "04 agricultural and veterinary sciences", "soil functions", "15. Life on land", "[SDE.MCG] Environmental Sciences/Global Changes", "Soil image analysis", "0401 agriculture", " forestry", " and fisheries", "soil fauna", "earthworms; micromorphology", "Soil microorganisms"]}, "links": [{"href": "https://doi.org/10.1071/sr21268"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Soil%20Research", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1071/sr21268", "name": "item", "description": "10.1071/sr21268", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1071/sr21268"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-19T00:00:00Z"}}, {"id": "10.1073/pnas.2109176118", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:17:02Z", "type": "Journal Article", "created": "2021-02-13", "title": "Plant-environment microscopy tracks interactions of Bacillus subtilis with plant roots across the entire rhizosphere", "description": "Abstract<p>Our understanding of plant-microbe interactions in soil is limited by the difficulty of observing processes at the microscopic scale throughout plants\uffe2\uff80\uff99 large volume of influence. Here, we present the development of 3D live microscopy for resolving plant-microbe interactions across the environment of an entire seedling growing in a transparent soil in tailor-made mesocosms, maintaining physical conditions for the culture of both plants and microorganisms. A tailor made dual-illumination light-sheet system acquired scattering signals from the plant whilst fluorescence signals were captured from transparent soil particles and labelled microorganisms, allowing the generation of quantitative data on samples approximately 3600 mm3in size with as good as 5 \uffce\uffbcm resolution at a rate of up to one scan every 30 minutes. The system tracked the movement ofBacillus subtilispopulations in the rhizosphere of lettuce plants in real time, revealing previously unseen patterns of activity. Motile bacteria favoured small pore spaces over the surface of soil particles, colonising the root in a pulsatile manner. Migrations appeared to be directed towards the root cap, the point \uffe2\uff80\uff9cfirst contact\uffe2\uff80\uff9d, before subsequent colonisation of mature epidermis cells. Our findings show that microscopes dedicated to live environmental studies present an invaluable tool to understand plant-microbe interactions.</p>", "keywords": ["0301 basic medicine", "570", "Microscopy", "Silicon", "0303 health sciences", "Temperature", "root-microbe interactions", "Equipment Design", "Biological Sciences", "Environment", "15. Life on land", "Plant Roots", "630", "Fluorescence", "Soil", "03 medical and health sciences", "Seedlings", "Calibration", "Rhizosphere", "Image Processing", " Computer-Assisted", "environmental imaging", "rhizosphere", "Soil Microbiology", "Bacillus subtilis", "Lactuca"]}, "links": [{"href": "https://eprints.whiterose.ac.uk/178939/18/e2109176118.full.pdf"}, {"href": "https://pnas.org/doi/pdf/10.1073/pnas.2109176118"}, {"href": "https://doi.org/10.1073/pnas.2109176118"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Proceedings%20of%20the%20National%20Academy%20of%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1073/pnas.2109176118", "name": "item", "description": "10.1073/pnas.2109176118", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1073/pnas.2109176118"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-02-13T00:00:00Z"}}, {"id": "10.1098/rspa.2017.0178", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:17:21Z", "type": "Journal Article", "created": "2017-11-22", "title": "Fluid flow in porous media using image-based modelling to parametrize Richards' equation", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>The parameters in Richards' equation are usually calculated from experimentally measured values of the soil\u2013water characteristic curve and saturated hydraulic conductivity. The complex pore structures that often occur in porous media complicate such parametrization due to hysteresis between wetting and drying and the effects of tortuosity. Rather than estimate the parameters in Richards' equation from these indirect measurements, image-based modelling is used to investigate the relationship between the pore structure and the parameters. A three-dimensional, X-ray computed tomography image stack of a soil sample with voxel resolution of 6\u2009\u03bcm has been used to create a computational mesh. The Cahn\u2013Hilliard\u2013Stokes equations for two-fluid flow, in this case water and air, were applied to this mesh and solved using the finite-element method in COMSOL Multiphysics. The upscaled parameters in Richards' equation are then obtained via homogenization. The effect on the soil\u2013water retention curve due to three different contact angles, 0\u00b0, 20\u00b0 and 60\u00b0, was also investigated. The results show that the pore structure affects the properties of the flow on the large scale, and different contact angles can change the parameters for Richards' equation.</p></article>", "keywords": ["0301 basic medicine", "Richards\u2019 equation", "330", "EP/M020355/1", "QH301 Biology", "530", "QH301", "03 medical and health sciences", "porous media", "646809DIMR", "Journal Article", "BB/L025620/1", "BB/J00868/1", "NE/L00237/1", "Research Articles", "0303 health sciences", "Civil_env_eng", "Natural Environment Research Council (NERC)", "621", "6. Clean water", "004", "620", "3. Good health", "image-based modelling", "Richards' equation", "Engineering and Physical Sciences Research Council (EPSRC)", "Biotechnology and Biological Sciences Research Council (BBSRC)", "BB/P004180/1", "BB/L025825/1", "European Research Council"]}, "links": [{"href": "https://repository.uwl.ac.uk/id/eprint/4979/1/20170178.full.pdf"}, {"href": "https://eprints.soton.ac.uk/415076/1/ImageBasedRichardsPRST.pdf"}, {"href": "https://eprints.soton.ac.uk/415076/2/SupplementaryFigure.pdf"}, {"href": "https://royalsocietypublishing.org/doi/pdf/10.1098/rspa.2017.0178"}, {"href": "https://doi.org/10.1098/rspa.2017.0178"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Proceedings%20of%20the%20Royal%20Society%20A%3A%20Mathematical%2C%20Physical%20and%20Engineering%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1098/rspa.2017.0178", "name": "item", "description": "10.1098/rspa.2017.0178", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1098/rspa.2017.0178"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-11-01T00:00:00Z"}}, {"id": "10.1105/tpc.20.00318", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:17:24Z", "type": "Journal Article", "created": "2020-10-10", "title": "ARADEEPOPSIS, an Automated Workflow for Top-View Plant Phenomics using Semantic Segmentation of Leaf States", "description": "Linking plant phenotype to genotype is a common goal to both plant breeders and geneticists. However, collecting phenotypic data for large numbers of plants remain a bottleneck. Plant phenotyping is mostly image based and therefore requires rapid and robust extraction of phenotypic measurements from image data. However, because segmentation tools usually rely on color information, they are sensitive to background or plant color deviations. We have developed a versatile, fully open-source pipeline to extract phenotypic measurements from plant images in an unsupervised manner. ARADEEPOPSIS (https://github.com/Gregor-Mendel-Institute/aradeepopsis) uses semantic segmentation of top-view images to classify leaf tissue into three categories: healthy, anthocyanin rich, and senescent. This makes it particularly powerful at quantitative phenotyping of different developmental stages, mutants with aberrant leaf color and/or phenotype, and plants growing in stressful conditions. On a panel of 210 natural Arabidopsis (Arabidopsis thaliana) accessions, we were able to not only accurately segment images of phenotypically diverse genotypes but also to identify known loci related to anthocyanin production and early necrosis in genome-wide association analyses. Our pipeline accurately processed images of diverse origin, quality, and background composition, and of a distantly related Brassicaceae. ARADEEPOPSIS is deployable on most operating systems and high-performance computing environments and can be used independently of bioinformatics expertise and resources.", "keywords": ["0301 basic medicine", "0303 health sciences", "Genotype", "Large-Scale Biology Articles", "Arabidopsis", "Computational Biology", "Semantics", "Workflow", "Plant Leaves", "03 medical and health sciences", "Phenotype", "Image Processing", " Computer-Assisted", "Phenomics", "Software", "Genome-Wide Association Study"]}, "links": [{"href": "https://doi.org/10.1105/tpc.20.00318"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/The%20Plant%20Cell", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1105/tpc.20.00318", "name": "item", "description": "10.1105/tpc.20.00318", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1105/tpc.20.00318"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-10-09T00:00:00Z"}}, {"id": "10.3389/fpls.2020.00889", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:19:17Z", "type": "Journal Article", "created": "2020-06-23", "title": "An Optimized in situ Quantification Method of Leaf H2O2 Unveils Interaction Dynamics of Pathogenic and Beneficial Bacteria in Wheat", "description": "Hydrogen peroxide (H2O2) functions as an important signaling molecule in plants during biotic interactions. However, the extent to which H2O2 accumulates during these interactions and its implications in the development of disease symptoms is unclear. In this work, we provide a step-by-step optimized protocol for in situ quantification of relative H2O2 concentrations in wheat leaves infected with the pathogenic bacterium Pseudomonas syringae pv. atrofaciens (Psa), either alone or in the presence of the beneficial bacterium Herbaspirillum seropedicae (RAM10). This protocol involved the use of 3-3'diaminobenzidine (DAB) staining method combined with image processing to conduct deconvolution and downstream analysis of the digitalized leaf image. The application of a linear regression model allowed to relate the intensity of the pixels resulting from DAB staining with a given concentration of H2O2. Decreasing H2O2 accumulation patterns were detected at increasing distances from the site of pathogen infection, and H2O2 concentrations were different depending on the bacterial combinations tested. Notably, Psa-challenged plants in presence of RAM10 accumulated less H2O2 in the leaf and showed reduced necrotic symptoms, pointing to a potential role of RAM10 in reducing pathogen-triggered H2O2 levels in young wheat plants.", "keywords": ["biotic interactions", "0301 basic medicine", "0303 health sciences", "03 medical and health sciences", "color deconvolution", "hydrogen peroxide (H2O2)", "Plant culture", "Plant Science", "3-3\u2032diaminobenzidine (DAB)", "image processing", "SB1-1110"]}, "links": [{"href": "https://doi.org/10.3389/fpls.2020.00889"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Frontiers%20in%20Plant%20Science", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3389/fpls.2020.00889", "name": "item", "description": "10.3389/fpls.2020.00889", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3389/fpls.2020.00889"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-06-23T00:00:00Z"}}, {"id": "10.1109/TMI.2017.2743819", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:17:25Z", "type": "Journal Article", "created": "2017-08-24", "title": "Sparse Recovery in Magnetic Resonance Imaging With a Markov Random Field Prior", "description": "Recent research in compressed sensing of magnetic resonance imaging (CS-MRI) emphasizes the importance of modeling structured sparsity, either in the acquisition or in the reconstruction stages. Subband coefficients of typical images show certain structural patterns, which can be viewed in terms of fixed groups (like wavelet trees) or statistically (certain configurations are more likely than others). Wavelet tree models have already demonstrated excellent performance in MRI recovery from partial data. However, much less attention has been given in CS-MRI to modeling statistically spatial clustering of subband data, although the potentials of such models have been indicated. In this paper, we propose a practical CS-MRI reconstruction algorithm making use of a Markov random field prior model for spatial clustering of subband coefficients and an efficient optimization approach based on proximal splitting. The results demonstrate an improved reconstruction performance compared with both the standard CS-MRI methods and the recent related methods.", "keywords": ["Mice", "Image Processing", " Computer-Assisted", "0202 electrical engineering", " electronic engineering", " information engineering", "Animals", "Brain", "Humans", "02 engineering and technology", "Magnetic Resonance Imaging", "Algorithms", "Markov Chains"], "contacts": [{"organization": "Marko Pani\u0107, Jan Aelterman, Vladimir Crnojevi\u0107, Aleksandra Pi\u017eurica,", "roles": ["creator"]}]}, "links": [{"href": "http://xplorestaging.ieee.org/ielx7/42/8053927/08016375.pdf?arnumber=8016375"}, {"href": "https://doi.org/10.1109/TMI.2017.2743819"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/IEEE%20Transactions%20on%20Medical%20Imaging", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1109/TMI.2017.2743819", "name": "item", "description": "10.1109/TMI.2017.2743819", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1109/TMI.2017.2743819"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-10-01T00:00:00Z"}}, {"id": "10.1109/jstars.2019.2958847", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:17:26Z", "type": "Journal Article", "created": "2020-01-22", "title": "Sentinel-1 InSAR Coherence for Land Cover Mapping: A Comparison of Multiple Feature-Based Classifiers", "description": "Open AccessThis article investigates and demonstrates the suitability of the Sentinel-1 interferometric coherence for land cover and vegetation mapping. 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. Cosmic physics", "ta1171", "0211 other engineering and technologies", "02 engineering and technology", "01 natural sciences", "land cover mapping", "ta216", "TC1501-1800", "[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing", "SDG 15 - Life on Land", "0105 earth and related environmental sciences", "ta213", "QC801-809", "[SPI.ELEC] Engineering Sciences [physics]/Electromagnetism", "interferometric coherence", "Remote sensing", "synthetic aperture radar (SAR)", "15. Life on land", "[SPI.TRON] Engineering Sciences [physics]/Electronics", "SDG 11 - Sustainable Cities and Communities", "[SPI.TRON]Engineering Sciences [physics]/Electronics", "Ocean engineering", "Synthetic aperture radar (SAR)", "[SPI.ELEC]Engineering Sciences [physics]/Electromagnetism", "\u00c0rees tem\u00e0tiques de la UPC::Enginyeria de la telecomunicaci\u00f3::Radiocomunicaci\u00f3 i exploraci\u00f3 electromagn\u00e8tica::Teledetecci\u00f3", ":Enginyeria de la telecomunicaci\u00f3::Radiocomunicaci\u00f3 i exploraci\u00f3 electromagn\u00e8tica::Teledetecci\u00f3 [\u00c0rees tem\u00e0tiques de la UPC]", "13. 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": "10.1109/tmi.2017.2743819", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:17:27Z", "type": "Journal Article", "created": "2017-08-24", "title": "Sparse Recovery in Magnetic Resonance Imaging With a Markov Random Field Prior", "description": "Recent research in compressed sensing of magnetic resonance imaging (CS-MRI) emphasizes the importance of modeling structured sparsity, either in the acquisition or in the reconstruction stages. Subband coefficients of typical images show certain structural patterns, which can be viewed in terms of fixed groups (like wavelet trees) or statistically (certain configurations are more likely than others). Wavelet tree models have already demonstrated excellent performance in MRI recovery from partial data. However, much less attention has been given in CS-MRI to modeling statistically spatial clustering of subband data, although the potentials of such models have been indicated. In this paper, we propose a practical CS-MRI reconstruction algorithm making use of a Markov random field prior model for spatial clustering of subband coefficients and an efficient optimization approach based on proximal splitting. The results demonstrate an improved reconstruction performance compared with both the standard CS-MRI methods and the recent related methods.", "keywords": ["Mice", "Image Processing", " Computer-Assisted", "0202 electrical engineering", " electronic engineering", " information engineering", "Animals", "Brain", "Humans", "02 engineering and technology", "Magnetic Resonance Imaging", "Algorithms", "Markov Chains"], "contacts": [{"organization": "Marko Pani\u0107, Jan Aelterman, Vladimir Crnojevi\u0107, Aleksandra Pi\u017eurica,", "roles": ["creator"]}]}, "links": [{"href": "http://xplorestaging.ieee.org/ielx7/42/8053927/08016375.pdf?arnumber=8016375"}, {"href": "https://doi.org/10.1109/tmi.2017.2743819"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/IEEE%20Transactions%20on%20Medical%20Imaging", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1109/tmi.2017.2743819", "name": "item", "description": "10.1109/tmi.2017.2743819", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1109/tmi.2017.2743819"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-10-01T00:00:00Z"}}, {"id": "10.3390/rs10091495", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:19:29Z", "type": "Journal Article", "created": "2018-09-19", "title": "Irrigation Mapping Using Sentinel-1 Time Series at Field Scale", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>The recently launched Sentinel-1 satellite with a Synthetic Aperture Radar (SAR) sensor onboard offers a powerful tool for irrigation monitoring under various weather conditions, with high spatial and temporal resolution. This research discusses the potential of different metrics calculated from the Sentinel-1 time series for mapping irrigated fields. A methodology for irrigation mapping using SAR data is proposed. The study is performed using VV (vertical\u2013vertical) and VH (vertical\u2013horizontal) polarizations over an agricultural site in Urgell, Catalunya (Spain). With field segmentation information from SIGPAC (the Geographic Information System for Agricultural Parcels), the backscatter intensities are averaged within each field. From the Sentinel-1 time series for each field, the statistics and metrics, including the mean value, the variance of the signal, the correlation length, and the fractal dimension, are analyzed. With the Support Vector Machine (SVM), the classification of irrigated crops, irrigated trees, and non-irrigated fields is performed with the metrics vector. The results derived from the SVM are validated with ground truthing from SIGPAC over the whole study area, with a good overall accuracy of 81.08%. Random Forest (RF) machine classification is also tested in this study, which gives an accuracy of around 82.2% when setting the tree depth at three. The methodology is based only on SAR data, which makes it applicable to all areas, even with frequent cloud cover, but this method may be less robust when irrigation is less dominated to soil moisture change.</p></article>", "keywords": ["[SDE] Environmental Sciences", "550", "Science", "IMAGE SATELLITE", "irrigated farming", "0211 other engineering and technologies", "0207 environmental engineering", "02 engineering and technology", "630", "irrigation", "remote sensing", "cartography", "CULTURE IRRIGUEE", "TELEDETECTION", "CARTOGRAPHIE", "2. Zero hunger", "HUMIDITE DU SOL", "Q", "soil water content", "15. Life on land", "6. Clean water", "classification", "[SDE]Environmental Sciences", "Sentinel-1", "soil moisture", "soil moisture; SAR; Sentinel-1; irrigation; classification", "SAR"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/10/9/1495/pdf"}, {"href": "https://doi.org/10.3390/rs10091495"}, {"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/rs10091495", "name": "item", "description": "10.3390/rs10091495", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs10091495"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-09-18T00:00:00Z"}}, {"id": "10.1111/nph.14705", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:17:53Z", "type": "Journal Article", "created": "2017-07-31", "title": "High\u2010resolution synchrotron imaging shows that root hairs influence rhizosphere soil structure formation", "description": "Summary<p>   <p>In this paper, we provide direct evidence of the importance of root hairs on pore structure development at the root\uffe2\uff80\uff93soil interface during the early stage of crop establishment.</p>  <p>This was achieved by use of high\uffe2\uff80\uff90resolution (c. 5\uffc2\uffa0\uffce\uffbcm) synchrotron radiation computed tomography (SRCT) to visualise both the structure of root hairs and the soil pore structure in plant\uffe2\uff80\uff93soil microcosms. Two contrasting genotypes of barley (Hordeum vulgare), with and without root hairs, were grown for 8\uffc2\uffa0d in microcosms packed with sandy loam soil at 1.2\uffc2\uffa0g\uffc2\uffa0cm\uffe2\uff88\uff923 dry bulk density. Root hairs were visualised within air\uffe2\uff80\uff90filled pore spaces, but not in the fine\uffe2\uff80\uff90textured soil regions.</p>  <p>We found that the genotype with root hairs significantly altered the porosity and connectivity of the detectable pore space (&gt;\uffc2\uffa05\uffc2\uffa0\uffce\uffbcm) in the rhizosphere, as compared with the no\uffe2\uff80\uff90hair mutants. Both genotypes showed decreasing pore space between 0.8 and 0.1\uffc2\uffa0mm from the root surface. Interestingly the root\uffe2\uff80\uff90hair\uffe2\uff80\uff90bearing genotype had a significantly greater soil pore volume\uffe2\uff80\uff90fraction at the root\uffe2\uff80\uff93soil interface.</p>  <p>Effects of pore structure on diffusion and permeability were estimated to be functionally insignificant under saturated conditions when simulated using image\uffe2\uff80\uff90based modelling.</p>  </p", "keywords": ["construction", "EP/M020355/1", "QH301 Biology", "non-invasive imaging", "Plant Roots", "630", "root hairs", "noninvasive imaging", "QH301", "Soil", "Imaging", " Three-Dimensional", "646809DIMR", "synchrotron", "Computer Simulation", "BB/L025620/1", "BB/J00868/1", "NE/L00237/1", "Hordeum vulgare", "580", "2. Zero hunger", "Civil_env_eng", "Natural Environment Research Council (NERC)", "Research", "Hordeum", "04 agricultural and veterinary sciences", "15. Life on land", "image-based modelling", "Engineering and Physical Sciences Research Council (EPSRC)", "Rhizosphere", "Biotechnology and Biological Sciences Research Council (BBSRC)", "0401 agriculture", " forestry", " and fisheries", "rhizosphere", "soil structure", "synchroton", "Porosity", "BB/P004180/1", "Synchrotrons", "BB/L025825/1", "European Research Council"]}, "links": [{"href": "https://repository.uwl.ac.uk/id/eprint/4981/1/Koebernick_et_al-2017-New_Phytologist.pdf"}, {"href": "https://eprints.soton.ac.uk/412143/1/s1_ln2680534218582232741703867393Hwf_1771574461IdV_16951475526805342FIRST_LOOK_PDF0001.pdf"}, {"href": "https://nph.onlinelibrary.wiley.com/doi/pdf/10.1111/nph.14705"}, {"href": "https://doi.org/10.1111/nph.14705"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/New%20Phytologist", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1111/nph.14705", "name": "item", "description": "10.1111/nph.14705", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1111/nph.14705"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-07-31T00:00:00Z"}}, {"id": "10.1111/nph.18387", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:17:54Z", "type": "Journal Article", "created": "2020-04-18", "title": "RootPainter: deep learning segmentation of biological images with corrective annotation", "description": "<p>We present RootPainter, a GUI-based software tool for the rapid training of deep neural networks for use in biological image analysis. RootPainter facilitates both fully-automatic and semi-automatic image segmentation. We investigate the effectiveness of RootPainter using three plant image datasets, evaluating its potential for root length extraction from chicory roots in soil, biopore counting and root nodule counting from scanned roots. We also use RootPainter to compare dense annotations to corrective ones which are added during the training based on the weaknesses of the current model.</p>", "keywords": ["Buildings and machinery", "0301 basic medicine", "phenotyping", "root nodule", "biopore", "interactive machine learning", "Research", "segmentation", "deep learning", "rhizotron", "Breeding and genetics", "Machine Learning", "Soil", "03 medical and health sciences", "Deep Learning", "GUI", "Farm nutrient management", "Image Processing", " Computer-Assisted", "Neural Networks", " Computer"]}, "links": [{"href": "https://www.biorxiv.org/content/10.1101/2020.04.16.044461v1.full.pdf"}, {"href": "https://nph.onlinelibrary.wiley.com/doi/pdf/10.1111/nph.18387"}, {"href": "https://doi.org/10.1111/nph.18387"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/New%20Phytologist", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1111/nph.18387", "name": "item", "description": "10.1111/nph.18387", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1111/nph.18387"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-04-18T00:00:00Z"}}, {"id": "10.1111/sum.12049", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:17:56Z", "type": "Journal Article", "created": "2013-05-06", "title": "Response Of Soil Structure And Hydraulic Conductivity To Reduced Tillage And Animal Manure In A Temperate Loamy Soil", "description": "Abstract<p>We studied the combined effects of reduced tillage and animal manure on soil structure and hydraulic conductivity (K) in the 2\uffe2\uff80\uff9310 and 12\uffe2\uff80\uff9320\uffc2\uffa0cm layers in a loamy soil. The study was performed at the end of a 7\uffe2\uff80\uff90yr field trial and included three tillage treatments (mouldboard ploughing until 25\uffc2\uffa0cm depth: MP, shallow tillage until 12\uffc2\uffa0cm depth: ST, no\uffe2\uff80\uff90till: NT) and two fertilizer application treatments (mineral or poultry manure). Soil structure was assessed through bulk density (\uffcf\uff81b), micromorphological and macropore\uffe2\uff80\uff90space characteristics. K was measured in situ at \uffe2\uff88\uff920.6, \uffe2\uff88\uff920.2 and \uffe2\uff88\uff920.05\uffc2\uffa0kPa. Untilled layers had a vermicular microstructure resulting from earthworm activity, whereas tilled layers displayed a mixture of crumb and channel microstructures. Untilled layers had the highest \uffcf\uff81b and twice as much lower total macroporosity area (pores\uffc2\uffa0&gt;\uffc2\uffa0240\uffc2\uffa0\uffce\uffbcm in equivalent diameter) than tilled layers, reflected by the smallest area of macropores 310\uffe2\uff80\uff932000\uffc2\uffa0\uffce\uffbcm in diameter and the smallest area of large complex macropores. K under untilled layers was 12\uffe2\uff80\uff9362% lower than that under tilled layers, but differences were statistically significant only at \uffe2\uff88\uff920.05\uffc2\uffa0kPa in the 2\uffe2\uff80\uff9310\uffc2\uffa0cm. No significant interaction between tillage and nutrient application treatments was detected for all properties. Compared with mineral fertilizer, poultry manure resulted in a similar \uffcf\uff81b but 20% greater total macroporosity area and 30% higher K at \uffe2\uff88\uff920.2\uffc2\uffa0kPa. Overall, the sensitivity of soil structure and K to poultry manure were relatively small compared with tillage. We suggest that cultivation practices other than animal manure application are needed to improve physical properties under reduced tillage.</p>", "keywords": ["[SDE] Environmental Sciences", "2. Zero hunger", "macroporosity", "[SDV]Life Sciences [q-bio]", "organic fertilization", "No-tillage", "600", "04 agricultural and veterinary sciences", "micromorphology", "15. Life on land", "630", "6. Clean water", "[SDV] Life Sciences [q-bio]", "image analysis", "[SDE]Environmental Sciences", "0401 agriculture", " forestry", " and fisheries"]}, "links": [{"href": "https://doi.org/10.1111/sum.12049"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Soil%20Use%20and%20Management", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1111/sum.12049", "name": "item", "description": "10.1111/sum.12049", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1111/sum.12049"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2013-05-06T00:00:00Z"}}, {"id": "10.1126/science.aal1727", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:17:58Z", "type": "Journal Article", "created": "2017-05-26", "title": "Satellites reveal contrasting responses of regional climate to the widespread greening of Earth", "description": "<p>Increasing terrestrial biomass has important impacts on the climate that affects it.</p>", "keywords": ["Population Density", "Satellite Imagery", "Multidisciplinary", "Time Factors", "Climate", "Climate Change", "Temperature", "Biophysical Phenomena; Climate Change; Population Density; Sunlight; Temperature; Time Factors; Climate; Models", " Theoretical; Plant Physiological Phenomena; Satellite Imagery", "Models", " Theoretical", "15. Life on land", "01 natural sciences", "Biophysical Phenomena", "13. Climate action", "Sunlight", "European Commission", "Plant Physiological Phenomena", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.1126/science.aal1727"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Science", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1126/science.aal1727", "name": "item", "description": "10.1126/science.aal1727", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1126/science.aal1727"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-06-16T00:00:00Z"}}, {"id": "10.1126/science.aal4108", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:17:58Z", "type": "Journal Article", "created": "2017-07-12", "title": "A Human-Driven Decline In Global Burned Area", "description": "Burn less, baby, burn less           <p>             Humans have, and always have had, a major impact on wildfire activity, which is expected to increase in our warming world. Andela             et al.             use satellite data to show that, unexpectedly, global burned area declined by \uffe2\uff88\uffbc25% over the past 18 years, despite the influence of climate. The decrease has been largest in savannas and grasslands because of agricultural expansion and intensification. The decline of burned area has consequences for predictions of future changes to the atmosphere, vegetation, and the terrestrial carbon sink.           </p>           <p>             Science             , this issue p.             1356           </p>", "keywords": ["[SDE] Environmental Sciences", "Satellite Imagery", "Carbon Sequestration", "Conservation of Natural Resources", "550", "General Science & Technology", "Climate", "Veterinary and Food Sciences", "Fires", "Theoretical", "Models", "11. Sustainability", "Human Activities", "SDG 2 - Zero Hunger", "Ecosystem", "Agricultural", "info:eu-repo/classification/ddc/550", "ddc:550", "Forestry Sciences", "Agriculture", "Models", " Theoretical", "15. Life on land", "Earth sciences", "13. Climate action", "Ecological Applications", "[SDE]Environmental Sciences", "Environmental Sciences"]}, "links": [{"href": "https://escholarship.org/content/qt6v95t473/qt6v95t473.pdf"}, {"href": "https://escholarship.org/content/qt6b42q71s/qt6b42q71s.pdf"}, {"href": "https://doi.org/10.1126/science.aal4108"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Science", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1126/science.aal4108", "name": "item", "description": "10.1126/science.aal4108", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1126/science.aal4108"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-06-30T00:00:00Z"}}, {"id": "10.1371/journal.pone.0125404", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:18:17Z", "type": "Journal Article", "created": "2015-05-06", "title": "The Contribution Of Mangrove Expansion To Salt Marsh Loss On The Texas Gulf Coast", "description": "Landscape-level shifts in plant species distribution and abundance can fundamentally change the ecology of an ecosystem. Such shifts are occurring within mangrove-marsh ecotones, where over the last few decades, relatively mild winters have led to mangrove expansion into areas previously occupied by salt marsh plants. On the Texas (USA) coast of the western Gulf of Mexico, most cases of mangrove expansion have been documented within specific bays or watersheds. Based on this body of relatively small-scale work and broader global patterns of mangrove expansion, we hypothesized that there has been a recent regional-level displacement of salt marshes by mangroves. We classified Landsat-5 Thematic Mapper images using artificial neural networks to quantify black mangrove (Avicennia germinans) expansion and salt marsh (Spartina alterniflora and other grass and forb species) loss over 20 years across the entire Texas coast. Between 1990 and 2010, mangrove area grew by 16.1 km(2), a 74% increase. Concurrently, salt marsh area decreased by 77.8 km(2), a 24% net loss. Only 6% of that loss was attributable to mangrove expansion; most salt marsh was lost due to conversion to tidal flats or water, likely a result of relative sea level rise. Our research confirmed that mangroves are expanding and, in some instances, displacing salt marshes at certain locations. However, this shift is not widespread when analyzed at a larger, regional level. Rather, local, relative sea level rise was indirectly implicated as another important driver causing regional-level salt marsh loss. Climate change is expected to accelerate both sea level rise and mangrove expansion; these mechanisms are likely to interact synergistically and contribute to salt marsh loss.", "keywords": ["Satellite Imagery", "0106 biological sciences", "Science", "Climate Change", "Marshes", "Poaceae", "01 natural sciences", "333", "Image Interpretation", " Computer-Assisted", "11. Sustainability", "14. Life underwater", "Mangrove swamps", "Ecosystem", "0105 earth and related environmental sciences", "Gulf of Mexico", "Artificial neural networks", "Winter", "Q", "R", "15. Life on land", "Texas", "Habitats", "13. Climate action", "Wetlands", "Medicine", "Avicennia", "Seasons", "Research Article"], "contacts": [{"organization": "Armitage, Anna R., Highfield, Wesley E., Brody, Samuel D., Louchouarn, Patrick,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.1371/journal.pone.0125404"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/PLOS%20ONE", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1371/journal.pone.0125404", "name": "item", "description": "10.1371/journal.pone.0125404", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1371/journal.pone.0125404"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2015-05-06T00:00:00Z"}}, {"id": "10.15201/hungeobull.69.3.4", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:18:22Z", "type": "Journal Article", "created": "2020-10-02", "title": "Crop growth, carbon sequestration and soil erosion in an organic vineyard of the Vill\u00e1ny Wine District, Southwest Hungary", "description": "<p>A more resilient adaptation to changing climate calls for crop diversification in vineyards, too. As a contribution to the H2020 collaborative project of the European Union, called Diverfarming, and part of the agroecological experiments during 2018 and 2019, grapevine biomass growth was monitored in connection with carbon storage types in soil and in the deposits removed by soil erosion. Phenometry was carried out interpreting segmented images to follow changes in biomass. It was found that crop growth could be best described by the Richards growth function. The distinction between grapevine and intercrop growth, however, requires further refinement in image analysis. In the laboratory TOC and Ntotal were measured for both the soil and the plant organs as well as for the eroded sediments. Greenhouse gas emissions and photosynthesis were monitored. Looking at the change of Leaf Area Index (LAI) over the growing period, image analysis pointed out the role of cut shoots from pruning in the C and N cycles. Maximum leaf area (at ripening) for guyot cultivation technique was extimated at 7,840 m2 ha-1. Soil loss by erosion was established by sediment traps at the end of vinestock rows. The grain size distribution analysis led to the remarkable result that as erosion proceeded, the ratio of the sand fraction increased but remained within the range for the textural class of loam. Organic matter contents grew to 38 g kg-1. The rate of soil erosion is higher in ploughed than in grassed interrows by orders of magnitude.</p>", "keywords": ["2. Zero hunger", "Geography (General)", "soil erosion", "leaf area index", "biomass", "Leaf Area Index", "04 agricultural and veterinary sciences", "15. Life on land", "C/N ratio", "carbon sequestration", "crop diversification", "image analysis", "13. Climate action", "G1-922", "0401 agriculture", " forestry", " and fisheries", "phenometry", "c/n ratio", "organic vineyard"]}, "links": [{"href": "https://doi.org/10.15201/hungeobull.69.3.4"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Hungarian%20Geographical%20Bulletin", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.15201/hungeobull.69.3.4", "name": "item", "description": "10.15201/hungeobull.69.3.4", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.15201/hungeobull.69.3.4"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-10-02T00:00:00Z"}}, {"id": "10.3390/rs13163272", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:19:30Z", "type": "Journal Article", "created": "2021-08-19", "title": "UAV-Based Land Cover Classification for Hoverfly (Diptera: Syrphidae) Habitat Condition Assessment: A Case Study on Mt. Stara Planina (Serbia)", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Habitat degradation, mostly caused by human impact, is one of the key drivers of biodiversity loss. This is a global problem, causing a decline in the number of pollinators, such as hoverflies. In the process of digitalizing ecological studies in Serbia, remote-sensing-based land cover classification has become a key component for both current and future research. Object-based land cover classification, using machine learning algorithms of very high resolution (VHR) imagery acquired by an unmanned aerial vehicle (UAV) was carried out in three different study sites on Mt. Stara Planina, Eastern Serbia. UAV land cover classified maps with seven land cover classes (trees, shrubs, meadows, road, water, agricultural land, and forest patches) were studied. Moreover, three different classification algorithms\u2014support vector machine (SVM), random forest (RF), and k-NN (k-nearest neighbors)\u2014were compared. This study shows that the random forest classifier performs better with respect to the other classifiers in all three study sites, with overall accuracy values ranging from 0.87 to 0.96. The overall results are robust to changes in labeling ground truth subsets. The obtained UAV land cover classified maps were compared with the Map of the Natural Vegetation of Europe (EPNV) and used to quantify habitat degradation and assess hoverfly species richness. It was concluded that the percentage of habitat degradation is primarily caused by anthropogenic pressure, thus affecting the richness of hoverfly species in the study sites. In order to enable research reproducibility, the datasets used in this study are made available in a public repository.</p></article>", "keywords": ["<i>Map of the Natural Vegetation of Europe</i>", "Orfeo ToolBox", "unmanned aerial vehicle; object-based image analysis; Orfeo ToolBox; QGIS; random forest; hoverfly; Map of the Natural Vegetation of Europe", "Science", "Q", "0211 other engineering and technologies", "Unmanned aerial vehicle", "02 engineering and technology", "15. Life on land", "01 natural sciences", "Object-based image analysis", "Map of the Natural Vegetation of Europe", "13. Climate action", "unmanned aerial vehicle;\u00a0object-based image analysis;\u00a0Orfeo ToolBox;\u00a0QGIS;\u00a0random forest;\u00a0hoverfly;\u00a0Map of the Natural Vegetation of Europe", "unmanned aerial vehicle", "object-based image analysis", "Hoverfly", "QGIS", "random forest", "Random forest", "hoverfly", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/13/16/3272/pdf"}, {"href": "https://doi.org/10.3390/rs13163272"}, {"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/rs13163272", "name": "item", "description": "10.3390/rs13163272", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs13163272"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-08-18T00:00:00Z"}}, {"id": "10.20350/digitalcsic/13978", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:18:41Z", "type": "Report", "title": "Gu\u00eda c\u00e1lculo de erosi\u00f3n en ladera con QGIS", "description": "Open AccessDesde finales de 2019, la colaboraci\u00f3n de la DOP de Estepa, el Instituto de Agricultura Sostenible del CSIC (IAS-CSIC) y el Dpto. de Agronom\u00eda de la Universidad de C\u00f3rdoba , ha permitido crear una herramienta para identificar a nivel de parcela la degradaci\u00f3n de suelo por erosi\u00f3n h\u00eddrica . Esta herramienta se ha desarrollado bajo los principios de la utilizaci\u00f3n de software libre (en concreto el sistema de informaci\u00f3n geogr\u00e1fica QGIS) y de informaci\u00f3n de uso abierto. En la guia adjunta se explica c\u00f3mo se ha obtenido dicho mapa para el control de la erosi\u00f3n en el territorio olivarero de la Denominaci\u00f3n de Origen de Estepa.", "keywords": ["Erosi\u00f3n", "Imagenes de sat\u00e9lite", "15. Life on land", "QGIS"], "contacts": [{"organization": "S\u00e1nchez Montero, Ana, G\u00f3mez Calero, Jos\u00e9 Alfonso, Guzm\u00e1n, Gema,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.20350/digitalcsic/13978"}, {"rel": "self", "type": "application/geo+json", "title": "10.20350/digitalcsic/13978", "name": "item", "description": "10.20350/digitalcsic/13978", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.20350/digitalcsic/13978"}, {"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/rs8110938", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:19:31Z", "type": "Journal Article", "created": "2016-11-11", "title": "Data Service Platform for Sentinel-2 Surface Reflectance and Value-Added Products: System Use and Examples", "description": "<p>This technical note presents the first Sentinel-2 data service platform for obtaining atmospherically-corrected images and generating the corresponding value-added products for any land surface on Earth. Using the European Space Agency\uffe2\uff80\uff99s (ESA) Sen2Cor algorithm, the platform processes ESA\uffe2\uff80\uff99s Level-1C top-of-atmosphere reflectance to atmospherically-corrected bottom-of-atmosphere (BoA) reflectance (Level-2A). The processing runs on-demand, with a global coverage, on the Earth Observation Data Centre (EODC), which is a public-private collaborative IT infrastructure in Vienna (Austria) for archiving, processing, and distributing Earth observation (EO) data. Using the data service platform, users can submit processing requests and access the results via a user-friendly web page or using a dedicated application programming interface (API). Building on the processed Level-2A data, the platform also creates value-added products with a particular focus on agricultural vegetation monitoring, such as leaf area index (LAI) and broadband hemispherical-directional reflectance factor (HDRF). An analysis of the performance of the data service platform, along with processing capacity, is presented. Some preliminary consistency checks of the algorithm implementation are included to demonstrate the expected product quality. In particular, Sentinel-2 data were compared to atmospherically-corrected Landsat-8 data for six test sites achieving a R2 = 0.90 and Root Mean Square Error (RMSE) = 0.031. LAI was validated for one test site using ground estimations. Results show a very good agreement (R2 = 0.83) and a RMSE of 0.32 m2/m2 (12% of mean value).</p>", "keywords": ["550", "reflectance", "t\u00e9l\u00e9d\u00e9tection", "Science", "0211 other engineering and technologies", "02 engineering and technology", "7. Clean energy", "remote sensing", "Traitement du signal et de l'image", "atmospheric correction", "remote sensing;sentinel-2;atmospheric correction;Sen2Cor;LAI;broadband HDRF", "[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing", "9. Industry and infrastructure", "sentinel-2", "Q", "Signal and Image processing", "04 agricultural and veterinary sciences", "broadband HDRF", "620", "LAI", "atmosph\u00e8re", "Sen2Cor", "13. Climate action", "0401 agriculture", " forestry", " and fisheries", "Sentinel-2", "[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing", "donn\u00e9e satellitaire"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/8/11/938/pdf"}, {"href": "https://doi.org/10.3390/rs8110938"}, {"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/rs8110938", "name": "item", "description": "10.3390/rs8110938", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs8110938"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2016-11-11T00:00:00Z"}}, {"id": "10.3390/rs13122261", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:19:30Z", "type": "Journal Article", "created": "2021-06-09", "title": "DeepIndices: Remote Sensing Indices Based on Approximation of Functions through Deep-Learning, Application to Uncalibrated Vegetation Images", "description": "<p>The form of a remote sensing index is generally empirically defined, whether by choosing specific reflectance bands, equation forms or its coefficients. These spectral indices are used as preprocessing stage before object detection/classification. But no study seems to search for the best form through function approximation in order to optimize the classification and/or segmentation. The objective of this study is to develop a method to find the optimal index, using a statistical approach by gradient descent on different forms of generic equations. From six wavebands images, five equations have been tested, namely: linear, linear ratio, polynomial, universal function approximator and dense morphological. Few techniques in signal processing and image analysis are also deployed within a deep-learning framework. Performances of standard indices and DeepIndices were evaluated using two metrics, the dice (similar to f1-score) and the mean intersection over union (mIoU) scores. The study focuses on a specific multispectral camera used in near-field acquisition of soil and vegetation surfaces. These DeepIndices are built and compared to 89 common vegetation indices using the same vegetation dataset and metrics. As an illustration the most used index for vegetation, NDVI (Normalized Difference Vegetation Indices) offers a mIoU score of 63.98% whereas our best models gives an analytic solution to reconstruct an index with a mIoU of 82.19%. This difference is significant enough to improve the segmentation and robustness of the index from various external factors, as well as the shape of detected elements.</p>", "keywords": ["multi-spectral", "[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing", "multispectral", "Science", "0211 other engineering and technologies", "[SDV.SA.STA] Life Sciences [q-bio]/Agricultural sciences/Sciences and technics of agriculture", "02 engineering and technology", "Spectral indice", "Deep-learning", "image; precision agriculture; spectral indices; multi-spectral; deep-learning; vegetation segmentation", "deep-learning", "[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing", "[SDV.SA.STA]Life Sciences [q-bio]/Agricultural sciences/Sciences and technics of agriculture", "[SDV.BV]Life Sciences [q-bio]/Vegetal Biology", "[SDV.BV] Life Sciences [q-bio]/Vegetal Biology", "image", "precision agriculture", "Precision agriculture", "Vegetation segmentation", "Multi-spectral", "Q", "04 agricultural and veterinary sciences", "15. Life on land", "004", "Image", "vegetation segmentation", "spectral indices", "0401 agriculture", " forestry", " and fisheries"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/13/12/2261/pdf"}, {"href": "https://www.mdpi.com/2072-4292/13/12/2261/pdf"}, {"href": "https://doi.org/10.3390/rs13122261"}, {"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/rs13122261", "name": "item", "description": "10.3390/rs13122261", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs13122261"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-06-09T00:00:00Z"}}, {"id": "10.24057/2071-9388-2019-10", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:19:07Z", "type": "Journal Article", "created": "2019-11-26", "title": "Simultaneous assessment of the summer urban heat island in Moscow megacity based on in situ observations, thermal satellite images and mesoscale modeling", "description": "<p>This study compares three popular approaches to quantify the urban heat island (UHI) effect in Moscow megacity in a summer season (June-August 2015). The first approach uses the measurements of the near-surface air temperature obtained from weather stations, the second is based on remote sensing from thermal imagery of MODIS satellites, and the third is based on the numerical simulations with the mesoscale atmospheric model COSMO-CLM coupled with the urban canopy scheme TERRA_URB. The first approach allows studying the canopy-layer UHI (CLUHI, or anomaly of a near- surface air temperature), while the second allows studying the surface UHI (SUHI, or anomaly of a land surface temperature), and both types of the UHI could be simulated by the atmospheric model. These approaches were compared in the daytime, evening and nighttime conditions. The results of the study highlight a substantial difference between the SUHI and CLUHI in terms of the diurnal variation and spatial structure. The strongest differences are found at the daytime, at which the SUHI reaches the maximal intensity (up to 10\uffc2\uffb0\uffd0\uffa1) whereas the CLUHI reaches the minimum intensity (1.5\uffc2\uffb0\uffd0\uffa1). However, there is a stronger consistency between CLUHU and SUHI at night, when their intensities converge to 5\uffe2\uff80\uff936\uffc2\uffb0\uffd0\uffa1. In addition, the nighttime CLUHI and SUHI have similar monocentric spatial structure with a temperature maximum in the city center. The presented findings should be taken into account when interpreting and comparing the results of UHI studies, based on the different approaches. The mesoscale model reproduces the CLUHI-SUHI relationships and provides good agreement with in situ observations on the CLUHI spatiotemporal variations (with near-zero biases for daytime and nighttime CLUHI intensity and correlation coefficients more than 0.8 for CLUHI spatial patterns). However, the agreement of the simulated SUHI with the remote sensing data is lower than agreement of the simulated CLUHI with in situ measurements. Specifically, the model tends to overestimate the daytime SUHI intensity. These results indicate a need for further in-depth investigation of the model behavior and SUHI\uffe2\uff80\uff93CLUHI relationships in general.</p>", "keywords": ["modis", "Geography (General)", "COSMO", "suhi", "0207 environmental engineering", "uhi", "land surface temperature", "UHI", "urban heat island", "moscow", "02 engineering and technology", "Moscow", "01 natural sciences", "thermal satellite images", "remote sensing", "MODIS", "13. Climate action", "Earth and Environmental Sciences", "SUHI", "cosmo", "urban climate", "11. Sustainability", "G1-922", "mesoscale modelling", "0105 earth and related environmental sciences"], "contacts": [{"organization": "Varentsov, Mikhail I., Grishchenko, Mikhail Y., Wouters, Hendrik,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.24057/2071-9388-2019-10"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/GEOGRAPHY%2C%20ENVIRONMENT%2C%20SUSTAINABILITY", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.24057/2071-9388-2019-10", "name": "item", "description": "10.24057/2071-9388-2019-10", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.24057/2071-9388-2019-10"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-12-31T00:00:00Z"}}, {"id": "10.3389/fenvs.2018.00061", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:19:14Z", "type": "Journal Article", "created": "2018-07-10", "title": "Recognizing Patterns: Spatial Analysis of Observed Microbial Colonization on Root Surfaces", "description": "Root surfaces are major sites of interactions between plants and associated microorganisms. Here, plants and microbes communicate via signaling molecules, compete for nutrients, and release substrates that may have beneficial or harmful effects on each other. Whilst the body of knowledge on the abundance and diversity of microbial communities at root-soil interfaces is now substantial, information on their spatial distribution at the microscale is still scarce. In this study, a standardized method for recognizing and analyzing microbial cell distributions on root surfaces is presented. Fluorescence microscopy was combined with automated image analysis and spatial statistics to explore the distribution of bacterial colonization patterns on rhizoplanes of rice roots. To test and evaluate the presented approach, a gnotobiotic experiment was performed using a potential nitrogen-fixing bacterial strain in combination with roots of wetland rice. The automated analysis procedure resulted in reliable spatial data of bacterial cells colonizing the rhizoplane. Among all replicate roots, the analysis revealed an increasing density of bacterial cells from the root tip to the region of root cell maturation. Moreover, bacterial cells showed significant spatial clustering and tended to be located around plant root cell borders. The quantitative data suggest that the structure of the root surface plays a major role in bacterial colonization patterns. Possible adaptations of the presented approach for future studies are discussed along with potential pitfalls such as inaccurate imaging. Our results demonstrate that standardized recognition and statistical evaluation of microbial colonization on root surfaces holds the potential to increase our understanding of microbial associations with roots and of the underlying ecological interactions.", "keywords": ["[SDE] Environmental Sciences", "0301 basic medicine", "570", "bacterial colonization", "[SDV]Life Sciences [q-bio]", "CATALYZED REPORTER DEPOSITION", "microbial ecology;root surface;bacterial colonization;point process;spatial statistics;image analysis;pattern recognition;wetland rice", "ECOLOGY", "microbial ecology", "Image analysis", "spatial statistics", "Microbial ecology", "03 medical and health sciences", "image analysis", "Pattern recognition", "root surface", "GE1-350", "Point process", "Wetland rice", "point process", "2. Zero hunger", "106022 Mikrobiologie", "0303 health sciences", "Spatial statistics", "IDENTIFICATION", "pattern recognition", "IN-SITU HYBRIDIZATION", "15. Life on land", "Bacterial colonization", "[SDV] Life Sciences [q-bio]", "SOIL", "Environmental sciences", "wetland rice", "Root surface", "[SDE]Environmental Sciences", "BACTERIA", "106022 Microbiology", "POPULATIONS", "COMMUNITIES"]}, "links": [{"href": "https://doi.org/10.3389/fenvs.2018.00061"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Frontiers%20in%20Environmental%20Science", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3389/fenvs.2018.00061", "name": "item", "description": "10.3389/fenvs.2018.00061", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3389/fenvs.2018.00061"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-07-10T00:00:00Z"}}, {"id": "10.3390/rs14122917", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:19:30Z", "type": "Journal Article", "created": "2022-06-20", "title": "Satellite Imagery to Map Topsoil Organic Carbon Content over Cultivated Areas: An Overview", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>There is a need to update soil maps and monitor soil organic carbon (SOC) in the upper horizons or plough layer for enabling decision support and land management, while complying with several policies, especially those favoring soil carbon storage. This review paper is dedicated to the satellite-based spectral approaches for SOC assessment that have been achieved from several satellite sensors, study scales and geographical contexts in the past decade. Most approaches relying on pure spectral models have been carried out since 2019 and have dealt with temperate croplands in Europe, China and North America at the scale of small regions, of some hundreds of km2: dry combustion and wet oxidation were the analytical determination methods used for 50% and 35% of the satellite-derived SOC studies, for which measured topsoil SOC contents mainly referred to mineral soils, typically cambisols and luvisols and to a lesser extent, regosols, leptosols, stagnosols and chernozems, with annual cropping systems with a SOC value of ~15 g\u00b7kg\u22121 and a range of 30 g\u00b7kg\u22121 in median. Most satellite-derived SOC spectral prediction models used limited preprocessing and were based on bare soil pixel retrieval after Normalized Difference Vegetation Index (NDVI) thresholding. About one third of these models used partial least squares regression (PLSR), while another third used random forest (RF), and the remaining included machine learning methods such as support vector machine (SVM). We did not find any studies either on deep learning methods or on all-performance evaluations and uncertainty analysis of spatial model predictions. Nevertheless, the literature examined here identifies satellite-based spectral information, especially derived under bare soil conditions, as an interesting approach that deserves further investigations. Future research includes considering the simultaneous analysis of imagery acquired at several dates i.e., temporal mosaicking, testing the influence of possible disturbing factors and mitigating their effects fusing mixed models incorporating non-spectral ancillary information.</p></article>", "keywords": ["2. Zero hunger", "550", "Science", "Q", "[SDU.STU]Sciences of the Universe [physics]/Earth Sciences", "Environmental Sciences (social aspects to be 507)", "Geology", "04 agricultural and veterinary sciences", "[SDV.SA.SDS]Life Sciences [q-bio]/Agricultural sciences/Soil study", "910", "15. Life on land", "satellite imagery", "630", "Remote Sensing", "soil organic carbon", "13. Climate action", "0401 agriculture", " forestry", " and fisheries", "spectral models"]}, "links": [{"href": "https://iris.cnr.it/bitstream/20.500.14243/532033/1/remotesensing-steropes%20review.pdf"}, {"href": "https://www.mdpi.com/2072-4292/14/12/2917/pdf"}, {"href": "https://pub.epsilon.slu.se/28706/1/vaoudour-e-et-al-220809.pdf"}, {"href": "https://doi.org/10.3390/rs14122917"}, {"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/rs14122917", "name": "item", "description": "10.3390/rs14122917", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs14122917"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-06-18T00:00:00Z"}}, {"id": "2117/345717", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:22:43Z", "type": "Journal Article", "created": "2021-05-17", "title": "Estimating lockdown-induced European NO2 changes using satellite and surface observations and air quality models", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. This study provides a comprehensive assessment of NO2 changes across the main European urban areas induced by COVID-19 lockdowns using satellite retrievals from the Tropospheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5p satellite, surface site measurements, and simulations from the Copernicus Atmosphere Monitoring Service (CAMS) regional ensemble of air quality models. Some recent TROPOMI-based estimates of changes in atmospheric NO2 concentrations have neglected the influence of weather variability between the reference and lockdown periods. Here we provide weather-normalized estimates based on a machine learning method (gradient boosting) along with an assessment of the biases that can be expected from methods that omit the influence of weather. We also compare the weather-normalized satellite-estimated NO2 column changes with weather-normalized surface NO2 concentration changes and the CAMS regional ensemble, composed of 11 models, using recently published estimates of emission reductions induced by the lockdown. All estimates show similar NO2 reductions. Locations where the lockdown measures were stricter show stronger reductions, and, conversely, locations where softer measures were implemented show milder reductions in NO2 pollution levels. Average reduction estimates based on either satellite observations (\u221223\u2009%), surface stations (\u221243\u2009%), or models (\u221232\u2009%) are presented, showing the importance of vertical sampling but also the horizontal representativeness. Surface station estimates are significantly changed when sampled to the TROPOMI overpasses (\u221237\u2009%), pointing out the importance of the variability in time of such estimates. Observation-based machine learning estimates show a stronger temporal variability than model-based estimates.</p></article>", "keywords": ["[SDE] Environmental Sciences", "550", "QC1-999", "551", "COVID-19 (Malaltia)", "01 natural sciences", "COVID-19 (Malaltia) -- Aspectes ambientals", "COVID-19 (Disease)", "Lockdown", "11. Sustainability", "Satellite images", "QD1-999", "0105 earth and related environmental sciences", "info:eu-repo/classification/ddc/550", "Air quality models", "Physics", "Aire -- Qualitat", "COVID-19", "Surface observations", "Satellite observations", "Chemistry", "Meteorology and Atmospheric Sciences", "13. Climate action", "[SDE]Environmental Sciences", "Air quality", "Meteorologi och atmosf\u00e4rsvetenskap", "\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]"]}, "links": [{"href": "https://doi.org/2117/345717"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Atmospheric%20Chemistry%20and%20Physics", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "2117/345717", "name": "item", "description": "2117/345717", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2117/345717"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-05-17T00:00:00Z"}}, {"id": "10.3390/agriculture15020164", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:19:19Z", "type": "Journal Article", "created": "2025-01-13", "title": "Comparative Evaluation of AI-Based Multi-Spectral Imaging and PCR-Based Assays for Early Detection of Botrytis cinerea Infection on Pepper Plants", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Pepper production is a critical component of the global agricultural economy, with exports reaching a remarkable $6.9B in 2023. This underscores the crop\u2019s importance as a major economic driver of export revenue for producing nations. Botrytis cinerea, the causative agent of gray mold, significantly impacts crops like fruits and vegetables, including peppers. Early detection of this pathogen is crucial for a reduction in fungicide reliance and economic loss prevention. Traditionally, visual inspection has been a primary method for detection. However, symptoms often appear after the pathogen has begun to spread. This study employs the Deep Learning algorithm YOLO for single-class segmentation on plant images to extract spatial details of pepper leaves. The dataset included hyperspectral images at discrete wavelengths (460 nm, 540 nm, 640 nm, 775 nm, and 875 nm) from derived vegetation indices (CVI, GNDVI, NDVI, NPCI, and PSRI) and from RGB. At an Intersection over Union with a 0.5 threshold, the Mean Average Precision (mAP50) achieved by the leaf-segmentation solution YOLOv11-Small was 86.4%. The extracted leaf segments were processed by multiple Transformer models, each yielding a descriptor. These descriptors were combined in ensemble and classified into three distinct classes using a K-nearest neighbor, a Long Short-Term Memory (LSTM), and a ResNet solution. The Transformer models that comprised the best ensemble classifier were as follows: the Swin-L (P:4 \u00d7 4\u2013W:12 \u00d7 12), the ViT-L (P:16 \u00d7 16), the VOLO (D:5), and the XCIT-L (L:24\u2013P:16 \u00d7 16), with the LSTM-based classification solution on the RGB, CVI, GNDVI, NDVI, and PSRI image sets. The classifier achieved an overall accuracy of 87.42% with an F1-Score of 81.13%. The per-class F1-Scores for the three classes were 85.25%, 66.67%, and 78.26%, respectively. Moreover, for B. cinerea detection during the initial as well as quiescent stages of infection prior to symptom development, qPCR-based methods (RT-qPCR) were used for quantification of in planta fungal biomass and integrated with the findings from the AI approach to offer a comprehensive strategy. The study demonstrates early and accurate detection of B. cinerea on pepper plants by combining segmentation techniques with Transformer model descriptors, ensembled for classification. This approach marks a significant step forward in the detection and management of crop diseases, highlighting the potential to integrate such methods into in situ systems like mobile apps or robots.</p></article>", "keywords": ["vision transformers", "Agriculture (General)", "segmentation", "deep learning", "<i>Botrytis cinerea</i>", "descriptor classification", "image classification", "S1-972"]}, "links": [{"href": "https://www.mdpi.com/2077-0472/15/2/164/pdf"}, {"href": "https://doi.org/10.3390/agriculture15020164"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agriculture", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/agriculture15020164", "name": "item", "description": "10.3390/agriculture15020164", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/agriculture15020164"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-01-13T00:00:00Z"}}, {"id": "10.3390/app142210693", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:19:21Z", "type": "Journal Article", "created": "2024-11-19", "title": "Corn Plant In-Row Distance Analysis Based on Unmanned Aerial Vehicle Imagery and Row-Unit Dynamics", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Uniform spatial distribution of plants is crucial in arable crops. Seeding quality is affected by numerous parameters, including the working speed and vibrations of the seeder. Therefore, investigating effective and rapid methods to evaluate seeding quality and the parameters affecting the seeders\u2019 performance is of high importance. With the latest advancements in unmanned aerial vehicle (UAV) technology, the potential for acquiring accurate agricultural data has significantly increased, making UAVs an ideal tool for scouting applications in agricultural systems. This study investigates the effectiveness of utilizing different plant recognition algorithms applied to UAV-derived images for evaluating seeder performance based on detected plant spacings. Additionally, it examines the impact of seeding unit vibrations on seeding quality by analyzing accelerometer data installed on the seeder. For the image analysis, three plant recognition approaches were tested: an unsupervised segmentation method based on the Visible Atmospherically Resistant Index (VARI), template matching (TM), and a deep learning model called Mask R-CNN. The Mask R-CNN model demonstrated the highest recognition reliability at 96.7%, excelling in detecting seeding errors such as misses and doubles, as well as in evaluating the quality of feed index and precision when compared to ground-truth data. Although the VARI-based unsupervised method and TM outperformed Mask R-CNN in recognizing double spacings, overall, the Mask R-CNN was the most promising. Vibration analysis indicated that the seeder\u2019s working speed significantly affected seeding quality. These findings suggest areas for potential improvements in machine technology to improve sowing operations.</p></article>", "keywords": ["0106 biological sciences", "Technology", "QH301-705.5", "T", "Physics", "QC1-999", "UAV imagery", "deep learning", "04 agricultural and veterinary sciences", "Engineering (General). Civil engineering (General)", "01 natural sciences", "FFT", "Chemistry", "seeding quality", "0401 agriculture", " forestry", " and fisheries", "TA1-2040", "Biology (General)", "QD1-999", "vibration analysis"]}, "links": [{"href": "https://www.mdpi.com/2076-3417/14/22/10693/pdf"}, {"href": "https://doi.org/10.3390/app142210693"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Applied%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/app142210693", "name": "item", "description": "10.3390/app142210693", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/app142210693"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-11-19T00:00:00Z"}}, {"id": "10.3390/rs10050761", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:19:29Z", "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": "10.37040/geografie2016121030463", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:19:36Z", "type": "Journal Article", "created": "2020-02-19", "title": "Semantic map: the case of \u00dast\u00ed nad Orlic\u00ed", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>The paper presents a method of creating a semantic map which follows up the tradition of mental mapping. The first step in the construction of a semantic map is a spatial definition of the shared image of a place/town. The second step is selection of the words and phrases that fit the shared meaning of a place/town. The third step then is their localization in the \u201cunderlying map\u201d. The size of individual expressions indicates the frequency with which the place was named so or to what extent the designation was shared among the respondents. Then the final step is \u201cregionalization\u201d of the space based on semantic proximity of the words used to describe individual locations. The semantic map method is presented using the example of the town of \u00dast\u00ed nad Orlic\u00ed. The empirical material is based on 21 interviews conducted in the town of \u00dast\u00ed nad Orlic\u00ed between January and September 2013.</p></article>", "keywords": ["mental map", "05 social sciences", "image of the city", "0211 other engineering and technologies", "0507 social and economic geography", "semantic map", "02 engineering and technology"], "contacts": [{"organization": "Osman, R. (Robert)", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.37040/geografie2016121030463"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Geografie", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.37040/geografie2016121030463", "name": "item", "description": "10.37040/geografie2016121030463", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.37040/geografie2016121030463"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2016-01-01T00:00:00Z"}}, {"id": "10.3390/ijgi10020102", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:19:24Z", "type": "Journal Article", "created": "2021-02-23", "title": "Machine Learning-Based Processing Proof-of-Concept Pipeline for Semi-Automatic Sentinel-2 Imagery Download, Cloudiness Filtering, Classifications, and Updates of Open Land Use/Land Cover Datasets", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Land use and land cover are continuously changing in today\u2019s world. Both domains, therefore, have to rely on updates of external information sources from which the relevant land use/land cover (classification) is extracted. Satellite images are frequent candidates due to their temporal and spatial resolution. On the contrary, the extraction of relevant land use/land cover information is demanding in terms of knowledge base and time. The presented approach offers a proof-of-concept machine-learning pipeline that takes care of the entire complex process in the following manner. The relevant Sentinel-2 images are obtained through the pipeline. Later, cloud masking is performed, including the linear interpolation of merged-feature time frames. Subsequently, four-dimensional arrays are created with all potential training data to become a basis for estimators from the scikit-learn library; the LightGBM estimator is then used. Finally, the classified content is applied to the open land use and open land cover databases. The verification of the provided experiment was conducted against detailed cadastral data, to which Shannon\u2019s entropy was applied since the number of cadaster information classes was naturally consistent. The experiment showed a good overall accuracy (OA) of 85.9%. It yielded a classified land use/land cover map of the study area consisting of 7188 km2 in the southern part of the South Moravian Region in the Czech Republic. The developed proof-of-concept machine-learning pipeline is replicable to any other area of interest so far as the requirements for input data are met.</p></article>", "keywords": ["Geography (General)", "0211 other engineering and technologies", "land use", "cloud masking", "04 agricultural and veterinary sciences", "02 engineering and technology", "15. Life on land", "satellite imagery", "machine learning", "land cover", "Sentinel 2", "machine learning; land use; land cover; satellite imagery; Sentinel 2; image classification; cloud masking; LightGBM estimator", "G1-922", "0401 agriculture", " forestry", " and fisheries", "LightGBM estimator", "image classification"]}, "links": [{"href": "http://www.mdpi.com/2220-9964/10/2/102/pdf"}, {"href": "https://www.mdpi.com/2220-9964/10/2/102/pdf"}, {"href": "https://doi.org/10.3390/ijgi10020102"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/ISPRS%20International%20Journal%20of%20Geo-Information", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/ijgi10020102", "name": "item", "description": "10.3390/ijgi10020102", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/ijgi10020102"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-02-23T00:00:00Z"}}, {"id": "10.3390/land11060774", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:19:25Z", "type": "Journal Article", "created": "2022-05-25", "title": "Investigating Plant Response to Soil Characteristics and Slope Positions in a Small Catchment", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Methods enabling stakeholders to receive information on plant stress in agricultural settings in a timely manner can help mitigate a possible decrease in plant productivity. The present work aims to study the soil\u2013plant interaction using field measurements of plant reflectance, soil water content, and selected soil physical and chemical parameters. Particular emphasis was placed on sloping transects. We further compared ground- and Sentinel-2 satellite-based Normalized Vegetation Index (NDVI) time series data in different land use types. The Photochemical Reflectance Index (PRI) and NDVI were measured concurrently with calculating the fraction of absorbed photochemically active radiation (fAPAR) and leaf area index (LAI) values of three vegetation types (a grassland, three vineyard sites, and a cropland with maize). Each land use site had an upper and a lower study point of a given slope. The NDVI, fAPAR, and LAI averaged values were the lowest for the grassland (0.293, 0.197, and 0.51, respectively), which showed the highest signs of water stress. Maize had the highest NDVI values (0.653) among vegetation types. Slope position affected NDVI, PRI, and fAPAR values significantly for the grassland and cropland (p &lt; 0.05), while the soil water content (SWC) was different for all three vineyard sites (p &lt; 0.05). The strongest connections were observed between soil physical and chemical parameters and NDVI values for the vineyard samples and the selected soil parameters and PRI for the grassland. Measured and satellite-retrieved NDVI values of the different land use types were compared, and strong correlations (r = 0.761) between the methods were found. For the maize, the satellite-based NDVI values were higher, while for the grassland they were slightly lower compared to the field-based measurements. Our study indicated that incorporating Sentinel-derived NDVI can greatly improve the value of field monitoring and provides an opportunity to extend field research in more depth. The present study further highlights the close relations in the soil\u2013plant\u2013water system, and continuous monitoring can greatly help in developing site-specific climate change mitigating methods.</p></article>", "keywords": ["2. Zero hunger", "land use sites", "NDVI", "S", "Agriculture", "soil parameters", "04 agricultural and veterinary sciences", "15. Life on land", "spectral reflectance", "satellite imagery", "plant stress", "13. Climate action", "0401 agriculture", " forestry", " and fisheries", "land use sites; soil parameters; plant stress; spectral reflectance; NDVI; satellite imagery"]}, "links": [{"href": "http://www.mdpi.com/2073-445X/11/6/774/pdf"}, {"href": "https://www.mdpi.com/2073-445X/11/6/774/pdf"}, {"href": "https://doi.org/10.3390/land11060774"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Land", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/land11060774", "name": "item", "description": "10.3390/land11060774", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/land11060774"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-05-25T00:00:00Z"}}], "links": [{"rel": "self", "type": "application/geo+json", "title": "This document as GeoJSON", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=image&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=image&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=image&", "hreflang": "en-US"}, {"rel": "next", "type": "application/geo+json", "title": "items (next)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=image&offset=50", "hreflang": "en-US"}], "numberMatched": 150, "numberReturned": 50, "distributedFeatures": [], "timeStamp": "2026-09-22T06:56:20.391551Z"}