{"type": "FeatureCollection", "features": [{"id": "10.5281/zenodo.7777673", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-04-03T16:25:39Z", "type": "Software", "title": "HiLSS Project", "description": "The R script code was developed by dr. F. Brandolini (Newcastle University, UK) to accompany the paper: 'Brandolini, F., Kinnaird, T.C., Srivastava, A., Turner S. - Modelling the impact of historic landscape change on soil erosion and degradation. Sci Rep 13, 4949 (2023)'. List of files included in HLC_RUSLE.zip: R_script_code named 'HLC_RUSLE' in .rmd format Output folder: Figures folder: .png products of the R script code Rasters folder: .png products of the R script code Tables folder: .pdf products of the R script code GeoTiff folder (.TIFF file format): Regional RUSLE Data GPKG: HLC dataset and Region Of Interest file in .gpkg format.", "keywords": ["13. Climate action", "Landscape Archaeology", "11. Sustainability", "RUSLE", "15. Life on land", "Historic Landscape Characterisation", "Soil Sustainability", "Soil Erosion Modelling", "12. Responsible consumption"], "contacts": [{"organization": "Filippo, Brandolini", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7777673"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7777673", "name": "item", "description": "10.5281/zenodo.7777673", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7777673"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-10-10T00:00:00Z"}}, {"id": "2746124018", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:28:09Z", "type": "Journal Article", "created": "2017-08-22", "title": "A theory of participation: what makes stakeholder and public engagement in environmental management work?", "description": "Abstract

This article differentiates between descriptive and explanatory factors to develop a typology and a theory of stakeholder and public engagement. The typology describes different types of public and stakeholder engagement, and the theory comprises four factors that explain much of the variation in outcomes (for the natural environment and/or for participants) between different types of engagement. First, we use a narrative literature search to develop a new typology of stakeholder and public engagement based on agency (who initiates and leads engagement) and mode of engagement (from communication to coproduction). We then propose a theory to explain the variation in outcomes from different types of engagement: (1) a number of socioeconomic, cultural, and institutional contextual factors influence the outcomes of engagement; (2) there are a number of process design factors that can increase the likelihood that engagement leads to desired outcomes, across a wide range of sociocultural, political, economic, and biophysical contexts; (3) the effectiveness of engagement is significantly influenced by power dynamics, the values of participants, and their epistemologies, that is, the way they construct knowledge and which types of knowledge they consider valid; and (4) engagement processes work differently and can lead to different outcomes when they operate over different spatial and temporal scales. We use the theoretical framework to provide practical guidance for those designing engagement processes, arguing that a theoretically informed approach to stakeholder and public engagement has the potential to markedly improve the outcomes of environmental decision\uffe2\uff80\uff90making processes.Event Webpage", "keywords": ["2. Zero hunger", "9. Industry and infrastructure", "11. Sustainability", "15. Life on land", "16. Peace & justice", "12. Responsible consumption"], "contacts": [{"organization": "Klem, Karel, Mistr, Martin, Kon\u00ed\u010dkov\u00e1, Na\u010fa, \u010cejkov\u00e1, Jana,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7957887"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7957887", "name": "item", "description": "10.5281/zenodo.7957887", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7957887"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-05-22T00:00:00Z"}}, {"id": "10.5281/zenodo.8019215", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:41Z", "type": "Dataset", "title": "Seasonal controls override forest harvesting effects on the composition of dissolved organic matter mobilized from boreal forest soil organic horizons", "description": "Dataset comprised of nutrient fluxes (DOC, TDN, NH4, TDN and SRP), optical parameters related to DOM composition (SUVA, spectral slopes and slope ratio), pH, and other nutrient and elemental ratios for passive pan lysimeters installed across terrestrial sites in Pynn's Brook, Newfoundland.", "keywords": ["optical properties", "carbon to nitrogen", "13. Climate action", "nutrient flux", "15. Life on land", "Organic matter flux"], "contacts": [{"organization": "Bowering, Keri L., Edwards, Kate A., Ziegler, Susan E.,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8019215"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8019215", "name": "item", "description": "10.5281/zenodo.8019215", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8019215"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-06-09T00:00:00Z"}}, {"id": "10.5281/zenodo.8057232", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:41Z", "type": "Dataset", "title": "Upscaling soil organic carbon measurements at the continental scale using multivariate clustering analysis and machine learning", "description": "Data Description: To improve SOC estimation in the United States, we upscaled site-based SOC measurements to the continental scale using multivariate geographic clustering (MGC) approach coupled with machine learning models. First, we used the MGC approach to segment the United States at 30 arc second resolution based on principal component information from environmental covariates (gNATSGO soil properties, WorldClim bioclimatic variables, MODIS biological variables, and physiographic variables) to 20 SOC regions. We then trained separate random forest model ensembles for each of the SOC regions identified using environmental covariates and soil profile measurements from the International Soil Carbon Network (ISCN) and an Alaska soil profile data. We estimated United States SOC for 0-30 cm and 0-100 cm depths were 52.6 + 3.2 and 108.3 + 8.2 Pg C, respectively. Files in collection (32): Collection contains 22 soil properties geospatial rasters, 4 soil SOC geospatial rasters, 2 ISCN site SOC observations csv files, and 4 R scripts gNATSGO TIF files: \u251c\u2500\u2500 available_water_storage_30arc_30cm_us.tif [30 cm depth soil available water storage]
\u251c\u2500\u2500 available_water_storage_30arc_100cm_us.tif [100 cm depth soil available water storage]
\u251c\u2500\u2500 caco3_30arc_30cm_us.tif [30 cm depth soil CaCO3 content]
\u251c\u2500\u2500 caco3_30arc_100cm_us.tif [100 cm depth soil CaCO3 content]
\u251c\u2500\u2500 cec_30arc_30cm_us.tif [30 cm depth soil cation exchange capacity]
\u251c\u2500\u2500 cec_30arc_100cm_us.tif [100 cm depth soil cation exchange capacity]
\u251c\u2500\u2500 clay_30arc_30cm_us.tif [30 cm depth soil clay content]
\u251c\u2500\u2500 clay_30arc_100cm_us.tif [100 cm depth soil clay content]
\u251c\u2500\u2500 depthWT_30arc_us.tif [depth to water table]
\u251c\u2500\u2500 kfactor_30arc_30cm_us.tif [30 cm depth soil erosion factor]
\u251c\u2500\u2500 kfactor_30arc_100cm_us.tif [100 cm depth soil erosion factor]
\u251c\u2500\u2500 ph_30arc_100cm_us.tif [100 cm depth soil pH]
\u251c\u2500\u2500 ph_30arc_100cm_us.tif [30 cm depth soil pH]
\u251c\u2500\u2500 pondingFre_30arc_us.tif [ponding frequency]
\u251c\u2500\u2500 sand_30arc_30cm_us.tif [30 cm depth soil sand content]
\u251c\u2500\u2500 sand_30arc_100cm_us.tif [100 cm depth soil sand content]
\u251c\u2500\u2500 silt_30arc_30cm_us.tif [30 cm depth soil silt content]
\u251c\u2500\u2500 silt_30arc_100cm_us.tif [100 cm depth soil silt content]
\u251c\u2500\u2500 water_content_30arc_30cm_us.tif [30 cm depth soil water content]
\u2514\u2500\u2500 water_content_30arc_100cm_us.tif [100 cm depth soil water content] SOC TIF files: \u251c\u2500\u250030cm SOC mean.tif [30 cm depth soil SOC]
\u251c\u2500\u2500100cm SOC mean.tif [100 cm depth soil SOC]
\u251c\u2500\u250030cm SOC CV.tif [30 cm depth soil SOC coefficient of variation]
\u2514\u2500\u2500100cm SOC CV.tif [100 cm depth soil SOC coefficient of variation] site observations csv files: ISCN_rmNRCS_addNCSS_30cm.csv 30cm ISCN sites SOC replaced NRCS sites with NCSS centroid removed data ISCN_rmNRCS_addNCSS_100cm.csv 100cm ISCN sites SOC replaced NRCS sites with NCSS centroid removed data
Data format: Geospatial files are provided in Geotiff format in Lat/Lon WGS84 EPSG: 4326 projection at 30 arc second resolution. Geospatial projection: 

GEOGCS['GCS_WGS_1984', DATUM['D_WGS_1984', SPHEROID['WGS_1984',6378137,298.257223563]], PRIMEM['Greenwich',0], UNIT['Degree',0.017453292519943295]] (base) [jbk@theseus ltar_regionalization]$ g.proj -w GEOGCS['wgs84', DATUM['WGS_1984', SPHEROID['WGS_1984',6378137,298.257223563]], PRIMEM['Greenwich',0], UNIT['degree',0.0174532925199433]]
", "keywords": ["gNATSGO", "the United States SOC", "US soil properties", "15. Life on land", "Gridded National Soil Survey Geographic Database", "International Soil Carbon Network (ISCN)"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8057232"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8057232", "name": "item", "description": "10.5281/zenodo.8057232", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8057232"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-01-25T00:00:00Z"}}, {"id": "10.5281/zenodo.8089856", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:42Z", "type": "Journal Article", "created": "2020-08-03", "title": "Source localization in resource-constrained sensor networks based on deep learning", "description": "Source localization with a network of low-cost motes with limited processing, memory, and energy resources is considered in this paper. The state-of-the-art methods are mostly based on complicated signal processing approaches in which motes send their (processed) data to a fusion center (FC) wherein the source is localized. These methods are resource-demanding and mostly do not meet the limitations of motes and network. In this paper, we consider distributed detection where each mote performs a binary hypothesis test to detect locally the existence of a desired source and sends its (potentially erroneous) decision to FC during just one bit (1 indicates source existence and 0 otherwise). Hence, both processing and bandwidth constraints are met. We propose to use an artificial neural network (ANN) to correct erroneous local decisions. After error correction, the region affected by the source is specified by nodes with decision 1. Moreover, we propose to localize the source by deep learning in FC which converts the network of decisions 1 and 0 to a black and white image with white pixels in the locations of motes with decision 1. The proposed schemes of error correction by ANN (ECANN) and source localization with deep learning (SoLDeL) were evaluated in a fire detection application. We showed that SoLDeL performs appropriately and scales well into large networks. Moreover, the applicability of ECANN in delineation of farm management zones was illustrated.", "keywords": ["Artificial neural network (ANN)", "Internet of things (IoT)", "0202 electrical engineering", " electronic engineering", " information engineering", "Deep learning", "Target tracking", "Error type II", "02 engineering and technology", "Decentralized detection", "15. Life on land", "Wireless sensor networks (WSN)", "Error type I", "Source localization"]}, "links": [{"href": "https://link.springer.com/content/pdf/10.1007/s00521-020-05253-3.pdf"}, {"href": "https://doi.org/10.5281/zenodo.8089856"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Neural%20Computing%20and%20Applications", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8089856", "name": "item", "description": "10.5281/zenodo.8089856", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8089856"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-08-03T00:00:00Z"}}, {"id": "10.5281/zenodo.8091863", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:43Z", "type": "Journal Article", "created": "2022-01-23", "title": "Designing a European-Wide Crop Type Mapping Approach Based on Machine Learning Algorithms Using LUCAS Field Survey and Sentinel-2 Data", "description": "

One of the most challenging aspects of obtaining detailed and accurate land-use and land-cover (LULC) maps is the availability of representative field data for training and validation. In this manuscript, we evaluate the use of the Eurostat Land Use and Coverage Area frame Survey (LUCAS) 2018 data to generate a detailed LULC map with 19 crop type classes and two broad categories for woodland and shrubland, and grassland. The field data were used in combination with Copernicus Sentinel-2 (S2) satellite data covering Europe. First, spatially and temporally consistent S2 image composites of (1) spectral reflectances, (2) a selection of spectral indices, and (3) several bio-geophysical indicators were created for the year 2018. From the large number of features, the most important were selected for classification using two machine-learning algorithms (support vector machine and random forest). Results indicated that the 19 crop type classes and the two broad categories could be classified with an overall accuracy (OA) of 77.6%, using independent data for validation. Our analysis of three methods to select optimum training data showed that by selecting the most spectrally different pixels for training data, the best OA could be achieved, and this already using only 11% of the total training data. Comparing our results to a similar study using Sentinel-1 (S1) data indicated that S2 can achieve slightly better results, although the spatial coverage was slightly reduced due to gaps in S2 data. Further analysis is ongoing to leverage synergies between optical and microwave data.

", "keywords": ["LUCAS 2018", "crop type classification", "crop type classification; random forest; support vector machine; LUCAS 2018", "Science", "Q", "0211 other engineering and technologies", "0401 agriculture", " forestry", " and fisheries", "support vector machine", "04 agricultural and veterinary sciences", "02 engineering and technology", "15. Life on land", "random forest"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/14/3/541/pdf"}, {"href": "https://www.mdpi.com/2072-4292/14/3/541/pdf"}, {"href": "https://doi.org/10.5281/zenodo.8091863"}, {"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": "10.5281/zenodo.8091863", "name": "item", "description": "10.5281/zenodo.8091863", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8091863"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-23T00:00:00Z"}}, {"id": "10.5281/zenodo.8091840", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:43Z", "type": "Journal Article", "created": "2022-08-26", "title": "Evaluation of Accuracy Enhancement in European-Wide Crop Type Mapping by Combining Optical and Microwave Time Series", "description": "

This investigation evaluates the potential of combining Copernicus Sentinel-1 (S1) and Sentinel-2 (S2) satellite data in producing a detailed Land Use and Land Cover (LULC) map with 19 crop type classes and 2 broader categories containing Woodland/Shrubland and Grassland over 28 Member States of Europe (EU-28). The Eurostat Land Use and Coverage Area Frame Survey (LUCAS) 2018 dataset is employed as ground truth for model training and validation. Monthly and yearly optical features from S2 spectral reflectance and spectral indices, alongside decadal (10-days) composites from an S1 microwave sensor, are extracted for the EU-28 territory for 2018 using Google Earth Engine (GEE). Five different feature sets using a mixture of indicators were created as input training data. A Random Forest (RF) machine learning algorithm was applied to classify these feature sets, and the generated classification models were compared using an identical validation dataset. Results show that S1 and S2 yearly features together are able to provide a full coverage map less dependent on cloud effects and having appropriate overall accuracy (OA). Based on this feature set, the 21 classes could be classified with an OA of 78.3% using the independent validation data set. The OA increases to 82.7% by grouping 21 classes into 8 broader categories. The comparison with similar studies using individual S1 and S2 data indicates that combining S1 and S2 time series can attain slightly better results while enhancing spatial coverage.

", "keywords": ["LUCAS 2018", "S", "0211 other engineering and technologies", "Agriculture", "04 agricultural and veterinary sciences", "02 engineering and technology", "15. Life on land", "crop type classification", "machine learning", "13. Climate action", "Sentinel-1", "0401 agriculture", " forestry", " and fisheries", "Sentinel-2", "time series", "Google Earth Engine"]}, "links": [{"href": "https://www.mdpi.com/2073-445X/11/9/1397/pdf"}, {"href": "https://doi.org/10.5281/zenodo.8091840"}, {"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": "10.5281/zenodo.8091840", "name": "item", "description": "10.5281/zenodo.8091840", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8091840"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-08-25T00:00:00Z"}}, {"id": "10.5281/zenodo.8091915", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:43Z", "type": "Journal Article", "created": "2022-08-02", "title": "Improving the documentation and findability of data services and repositories: A review of (meta)data management approaches", "description": "This scientific review paper aims at challenging a common point of view on metadata as a necessary evil and
something mandatory to the data creating and dataset publishing process. Metadata are instead presented as a crucial element to ensure the findability of data services and repositories. This paper describes a way through four levels of metadata management and publication, from default unstructured data, through schema-based metadata with literal values and/or URIs, towards linked open (meta)data providing explicit linkage between reliable data resources. Such research was conducted within the European Union\u2019s project PoliVisu. Special attention is given to the following: (1) guidance on publication aimed at the broad audience of search engine users and (2) the publication of geo (meta)data not only via standard technologies, such as the OGC Catalogue Service for Web and open data portals, but also through leading search engines (that are Schema.org-based).", "keywords": ["Geochemistry & Geophysics", "Technology", "Open linked data", "04 Earth Sciences", "02 engineering and technology", "46 Information and computing sciences", "09 Engineering", "Metadata review", "0202 electrical engineering", " electronic engineering", " information engineering", "Geosciences", " Multidisciplinary", "INSPIRE", "40 Engineering", "TOOLS", "Science & Technology", "Geodata", "LINKED-DATA", "Findability", "05 social sciences", "Geology", "37 Earth sciences", "MODEL", "ONTOLOGY", "Open linked metadata", "CATALOG SERVICES", "DISCOVERY", "Computer Science", "Physical Sciences", "Search engines", "Computer Science", " Interdisciplinary Applications", "08 Information and Computing Sciences", "0509 other social sciences", "METADATA", "SPATIAL INFORMATION"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8091915"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Computers%20%26amp%3B%20Geosciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8091915", "name": "item", "description": "10.5281/zenodo.8091915", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8091915"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-12-01T00:00:00Z"}}, {"id": "10.5281/zenodo.8092629", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:43Z", "type": "Journal Article", "created": "2022-06-15", "title": "Comparison of Methods for Reconstructing MODIS Land Surface Temperature under Cloudy Conditions", "description": "

Land surface temperature (LST) is a vital parameter associated with the land\u2013atmosphere interface. The Moderate Resolution Imaging Spectroradiometer (MODIS) LST product can provide precise LST with high time resolution, and is widely applied in various remote sensing temperature research. However, due to its inability to penetrate the cloud and fog, its quality is not able to meet the requirements of actual research. Hence, obtaining continuous and cloudless MODIS LST datasets remains challenging for researchers. The critical point is to reconstruct missing pixels. To compare the performance of different methods, first, three kinds of methods were used to reconstruct the missing pixels, namely, temporal, spatial, and spatiotemporal methods. The predicted values using these methods were validated by the automatic weather system data (AWS) in the Heihe river basin of China. The results demonstrated that, compared with other methods, linear temporal interpolation using Aqua data had the best performance in MODIS LST reconstruction in the Heihe river basin, with an RMSE of 7.13 K and an R2 of 0.82, and the NSE and PBias were 0.78 and \u22120.76%, respectively. Furthermore, the interpolation method was improved using adaptive windows and robust regression. First, the international Geosphere\u2013Biosphere Program (IGBP) classification was employed to distinguish the different land surface types. Then, the invalid LST values were reconstructed using adjacent days\u2019 effective LST values combined with a robust regression. Finally, a mean filter was applied to eliminate outliers. The overall results combined with ERA5 data were validated by AWS, with an RMSE of 6.96 K and an R2 of 0.79 and the NSE and PBias were 0.77 and \u22120.20%, respectively. The validation demonstrated that the scheme proposed in this paper is able to accurately reconstruct the missing values and improve the accuracy of the interpolation method to a certain extent when reconstructing MODIS LST.

", "keywords": ["Technology", "land surface temperature (LST)", "reconstruction", "land surface temperature (LST); remote sensing; interpolation; reconstruction; MODIS", "QH301-705.5", "T", "Physics", "QC1-999", "Engineering (General). Civil engineering (General)", "01 natural sciences", "interpolation", "6. Clean water", "Chemistry", "remote sensing", "MODIS", "13. Climate action", "TA1-2040", "Biology (General)", "QD1-999", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://www.mdpi.com/2076-3417/12/12/6068/pdf"}, {"href": "https://doi.org/10.5281/zenodo.8092629"}, {"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": "10.5281/zenodo.8092629", "name": "item", "description": "10.5281/zenodo.8092629", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8092629"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-06-15T00:00:00Z"}}, {"id": "10.5281/zenodo.8091934", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:43Z", "type": "Journal Article", "created": "2022-08-18", "title": "Data mining of urban soil spectral library for estimating organic carbon", "description": "Accurate quantification of urban soil organic carbon (SOC) is essential for understanding anthropogenic changes and further guiding effective city managements. Visible and near infrared (vis\u2013NIR) spectroscopy can monitor the SOC content in a time- and cost-effective manner. However, processes and mechanisms dominating the relationships between SOC and spectral data in urban soils remain unknown. The main objective of this paper was to evaluate whether multiple stratification strategies (i.e., based on land-use/land-cover [LULC], pH, and spectral clustering) resulted in better predicted performance for SOC compared to the non-stratified (global) models. Results showed that regarding the non-stratified models, the convolutional neural network (CNN) model exhibited the best performance (validation R2 = 0.73), followed by Cubist (validation R2 = 0.66) and memorybased learning (validation R2 = 0.65). After LULC stratification, Cubist model achieved the best prediction (validation R2 = 0.76), improving the value of ratio of performance to interquartile distance by 0.11 compared to the global CNN model. Areas with high SOC values were mainly located in the city center. Stratification by LULC class is a promising strategy for addressing the impact of the soil-landscape diversity and complexity on vis\u2013NIR spectral estimation of SOC in urban soil spectral library.", "keywords": ["Urban soil", "Stratified modeling", "13. Climate action", "Soil organic carbon", "11. Sustainability", "0401 agriculture", " forestry", " and fisheries", "Deep learning", "04 agricultural and veterinary sciences", "15. Life on land", "Soil spectral library"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8091934"}, {"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": "10.5281/zenodo.8091934", "name": "item", "description": "10.5281/zenodo.8091934", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8091934"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-11-01T00:00:00Z"}}, {"id": "10.5281/zenodo.8092676", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:43Z", "type": "Journal Article", "created": "2022-05-20", "title": "Improving remote sensing of salinity on topsoil with crop residues using novel indices of optical and microwave bands", "description": "Remote sensing indices have been proposed to characterize soil salinity. However, the sensitivity of these indicators is unstable owing to differences in geographic environment and vegetation type. This study investigated the performance of several existing indices to estimate the salinity of topsoil with residues in southern Xinjiang, China. The results showed that these indices were not satisfactory. In order to construct an index that can be used to directly indicate soil salinity in a specific area, novel salinity indices were calculated using optical bands (blue, green, red, vegetation red edge, and shortwave infrared bands) derived from Sentinel-2 multispectral data and Sentinel-1 radar data (backscattering coefficient VV, VH). To enhance the sensitivity of the optical bands, five transformation methods (logarithmic, reciprocal, first-, second-, and third-derivative) were applied to the original spectra. Based on previous studies, statistical methods were used to construct two-, three-, and four-bands indices. One constructed three-bands index with the second-derivative transformation, called the Enhanced Residues Soil Salinity Index (ERSSI), showed the highest correlation with topsoil salinity (r = 0.65 and 0.68 in training and testing). ERSSI establishes a linear relationship in soil salinity estimation with an R2 of 0.53 and a LCCC of 0.65 in training dataset, with an R2 of 0.51 and a LCCC of 0.73 in testing dataset. And it shows contribution in random forest regression with an R2 of 0.80 and a LCCC of 0.86 in training dataset, with an R2 of 0.77 and a LCCC of 0.81 in testing dataset. The ERSSI consisted of the B, G, and SWIR1 bands, and was sensitive to salinity variations in the residues remaining in farmland soils. This study provides a novel index and method for the accurate and robust assessment and mapping of salinity in farmland covered by crop residues.", "keywords": ["2. Zero hunger", "Soil salinity", "Residues remained soil", "Indices constructions", "Spectral response", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "15. Life on land", "Sentinel-2", "01 natural sciences", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8092676"}, {"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": "10.5281/zenodo.8092676", "name": "item", "description": "10.5281/zenodo.8092676", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8092676"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-09-01T00:00:00Z"}}, {"id": "10.5281/zenodo.8092708", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:43Z", "type": "Journal Article", "created": "2022-02-27", "title": "Adaptive Management of Cultivated Land Use Zoning Based on Land Types Classification: A Case Study of Henan Province", "description": "

Cultivated land serves as an important resource to ensure national food security, and how to allocate cultivated land reasonably and sustainably is an urgent problem that needs to be solved at present. Therefore, identifying land cultivability from the basic attributes of land and carrying out adaptive management measures in different zones is an effective way. Taking Henan province as a case study area, we proposed a research scheme for the adaptive management of cultivated land use zoning based on land types. First, a three-level land types classification system at the provincial level was established from five aspects\u2014climate, topography, geology, soil properties, and hydrological conditions\u2014and then Henan was divided into 39 first-level units, 4358 second-level units, and 6446 third-level units. On this basis, the changes in the status of land use in Henan province from 2009 to 2018 were analyzed from the four aspects of cultivated land utilization, population, grain yield, and GDP. The amount of cultivated land decreased, while the economy grew, the population increased, and grain yield increased, indicating that it is urgent to pay attention to the problem of cultivated land, and it is necessary to identify the potential space of cultivated land and manage and protect it reasonably. Based on the land types, evaluation of cultivability was carried out, the results showed that the degree of cultivability from high to low presented a transitional spatial distribution state from the east and the south to the middle, the north, and the west. Then superimposing the status of land use, six types of protection and management zones were proposed, and management suggestions were adaptively analyzed. The ideas and methods proposed in this study can be adapted to manage and utilize cultivated land from the perspective of sustainable utilization, which is of great significance for ensuring food security.

", "keywords": ["2. Zero hunger", "land type; cultivability evaluation; land use zoning; adaptive management; sustainable management", "adaptive management", "sustainable management", "S", "Agriculture", "land type", "04 agricultural and veterinary sciences", "15. Life on land", "12. Responsible consumption", "cultivability evaluation", "land use zoning", "13. Climate action", "11. Sustainability", "0401 agriculture", " forestry", " and fisheries"]}, "links": [{"href": "http://www.mdpi.com/2073-445X/11/3/346/pdf"}, {"href": "https://doi.org/10.5281/zenodo.8092708"}, {"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": "10.5281/zenodo.8092708", "name": "item", "description": "10.5281/zenodo.8092708", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8092708"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-02-25T00:00:00Z"}}, {"id": "10.5281/zenodo.8092713", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:43Z", "type": "Journal Article", "created": "2022-03-13", "title": "Development of Prediction Models for Estimating Key Rice Growth Variables Using Visible and NIR Images from Unmanned Aerial Systems", "description": "

The rapid and accurate acquisition of rice growth variables using unmanned aerial system (UAS) is useful for assessing rice growth and variable fertilization in precision agriculture. In this study, rice plant height (PH), leaf area index (LAI), aboveground biomass (AGB), and nitrogen nutrient index (NNI) were obtained for different growth periods in field experiments with different nitrogen (N) treatments from 2019\u20132020. Known spectral indices derived from the visible and NIR images and key rice growth variables measured in the field at different growth periods were used to build a prediction model using the random forest (RF) algorithm. The results showed that the different N fertilizer applications resulted in significant differences in rice growth variables; the correlation coefficients of PH and LAI with visible-near infrared (V-NIR) images at different growth periods were larger than those with visible (V) images while the reverse was true for AGB and NNI. RF models for estimating key rice growth variables were established using V-NIR images and V images, and the results were validated with an R2 value greater than 0.8 for all growth stages. The accuracy of the RF model established from V images was slightly higher than that established from V-NIR images. The RF models were further tested using V images from 2019: R2 values of 0.75, 0.75, 0.72, and 0.68 and RMSE values of 11.68, 1.58, 3.74, and 0.13 were achieved for PH, LAI, AGB, and NNI, respectively, demonstrating that RGB UAS achieved the same performance as multispectral UAS for monitoring rice growth.

", "keywords": ["2. Zero hunger", "digital imagery", "rice growth variables; unmanned aerial system; multispectral imagery; digital imagery; random forest model", "Science", "random forest model", "Q", "0401 agriculture", " forestry", " and fisheries", "rice growth variables", "04 agricultural and veterinary sciences", "15. Life on land", "multispectral imagery", "unmanned aerial system"], "contacts": [{"organization": "Zhengchao Qiu, Fei Ma, Zhenwang Li, Xuebin Xu, Changwen Du,", "roles": ["creator"]}]}, "links": [{"href": "http://www.mdpi.com/2072-4292/14/6/1384/pdf"}, {"href": "https://doi.org/10.5281/zenodo.8092713"}, {"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": "10.5281/zenodo.8092713", "name": "item", "description": "10.5281/zenodo.8092713", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8092713"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-03-13T00:00:00Z"}}, {"id": "10.5281/zenodo.8111474", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:44Z", "type": "Report", "title": "Data assimilation of boreal forest - mire ecotone soil C dynamics into Yasso07 model coupled with updated moisture modifier", "description": "Data of forest soil respiration, soil temperature and moisture, air temperature and precipitation, forest inventory, litter input and soil C stocks from the nine site types forming boreal forest-mire ecotone in Finland (61\u00ba 47', 24\u00ba 19') . The R codes reconstructing the analysis for running the Yasso07 model in the original configuration and updated with soil moisture function, its calibration, validation, etc. used for the publication 'Tupek et al. : Modeling boreal forest\u2019s mineral soil and peat C stock dynamics with Yasso07 model coupled with updated moisture modifier. Geoscientific model development, 2023'.", "keywords": ["soil organic matter", "mineral soil", "peat", "boreal forest", "15. Life on land", "soil moisture and temperature", "Yasso07 soil C model"], "contacts": [{"organization": "Tupek, Boris, Yurova, Alla, Lehtonen, Aleksi,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8111474"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8111474", "name": "item", "description": "10.5281/zenodo.8111474", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8111474"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-07-04T00:00:00Z"}}, {"id": "2944731604", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:28:17Z", "type": "Journal Article", "created": "2019-05-09", "title": "Integrated Use of Satellite Remote Sensing, Artificial Neural Networks, Field Spectroscopy, and GIS in Estimating Crucial Soil Parameters in Terms of Soil Erosion", "description": "

Soil erosion is one of the main causes of soil degradation among others (salinization, compaction, reduction of organic matter, and non-point source pollution) and is a serious threat in the Mediterranean region. A number of soil properties, such as soil organic matter (SOM), soil structure, particle size, permeability, and Calcium Carbonate equivalent (CaCO3), can be the key properties for the evaluation of soil erosion. In this work, several innovative methods (satellite remote sensing, field spectroscopy, soil chemical analysis, and GIS) were investigated for their potential in monitoring SOM, CaCO3, and soil erodibility (K-factor) of the Akrotiri cape in Crete, Greece. Laboratory analysis and soil spectral reflectance in the VIS-NIR (using either Landsat 8, Sentinel-2, or field spectroscopy data) range combined with machine learning and geostatistics permitted the spatial mapping of SOM, CaCO3, and K-factor. Synergistic use of geospatial modeling based on the aforementioned soil properties and the Revised Universal Soil Loss Equation (RUSLE) erosion assessment model enabled the estimation of soil loss risk. Finally, ordinary least square regression (OLSR) and geographical weighted regression (GWR) methodologies were employed in order to assess the potential contribution of different approaches in estimating soil erosion rates. The derived maps captured successfully the SOM, the CaCO3, and the K-factor spatial distribution in the GIS environment. The results may contribute to the design of erosion best management measures and wise land use planning in the study region.

", "keywords": ["Landsat 8", "2. Zero hunger", "soil erosion", "550", "Science", "Q", "04 agricultural and veterinary sciences", "Remote sensing", "15. Life on land", "01 natural sciences", "630", "field spectroscopy", "6. Clean water", "soil erosion; remote sensing; Sentinel-2; Landsat 8; ANN; RUSLE; field spectroscopy; OLSR; GWR", "remote sensing", "Field spectroscopy", "OLSR", "13. Climate action", "Soil erosion", "0401 agriculture", " forestry", " and fisheries", "RUSLE", "Sentinel-2", "ANN", "GWR", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/11/9/1106/pdf"}, {"href": "https://doi.org/2944731604"}, {"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": "2944731604", "name": "item", "description": "2944731604", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2944731604"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-05-09T00:00:00Z"}}, {"id": "10.5281/zenodo.8111475", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:44Z", "type": "Report", "title": "Data assimilation of boreal forest - mire ecotone soil C dynamics into Yasso07 model coupled with updated moisture modifier", "description": "Data of forest soil respiration, soil temperature and moisture, air temperature and precipitation, forest inventory, litter input and soil C stocks from the nine site types forming boreal forest-mire ecotone in Finland (61\u00ba 47', 24\u00ba 19') . The R codes reconstructing the analysis for running the Yasso07 model in the original configuration and updated with soil moisture function, its calibration, validation, etc. used for the publication 'Tupek et al. : Modeling boreal forest\u2019s mineral soil and peat C stock dynamics with Yasso07 model coupled with updated moisture modifier. Geoscientific model development, 2023'.", "keywords": ["soil organic matter", "mineral soil", "peat", "boreal forest", "15. Life on land", "soil moisture and temperature", "Yasso07 soil C model"], "contacts": [{"organization": "Tupek, Boris, Yurova, Alla, Lehtonen, Aleksi,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8111475"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8111475", "name": "item", "description": "10.5281/zenodo.8111475", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8111475"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-07-04T00:00:00Z"}}, {"id": "10.5281/zenodo.8146228", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:44Z", "type": "Dataset", "title": "Dataset of the manuscript \"Assessing the influence of Eisenia andrei on the decomposition of Casuarina equisetifolia litter in vermicompost.\"", "description": "Data generated during an experiment of decomposition of Casuarina equisetifolia litter by the application of vermicompost (VC) or the combination vermicompost + the earthworm Eisenia andrei (E). Six files are included: 'readme.csv' is a file where we explain the meaning of each column (and in which units is expressed) in each of the other five files. 'earthworm_N_biomass.csv' is a table with the number of Eisenia andrei individuals and the total earthworm fresh weight in each of the experimental units we sampled 'FTIR_spectra.csv' is a file with the raw spectral data we obtained from the litter by Fourier Transform Infrared spectroscopy combined with Attenuated Total Reflectance (FTIR-ATR). First column indicate the wavenumber (cm-1) and the other columns indicate the absorbance values of each litter sample for each wavenumber. 'litter_chemical_composition.csv' is a file with the raw data of the concentrations of different chemical elements measured in C. equisetifolia litter collected at different decomposition times. 'litter_mass_loss.csv' contains the dry weight data of the litter at time 0 and after each collection time, as well as the percentage of litter mass loss with time. . 'mesofaunal_com.csv' are the numbers of individuals of several groups of mesofaunal organisms (collembolans, mites, and others) we recovered in each of our experimental units.", "keywords": ["Fourier Transform Infrared spectroscopy", "decomposition", "Eisenia andrei", "litter", "litterbag experiment", "Casuarina equisetifolia", "microcosm"], "contacts": [{"organization": "Quintela-Sabar\u00eds, Celestino, Mendes, Luis Andr\u00e9, Dom\u00ednguez, Jorge,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8146228"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8146228", "name": "item", "description": "10.5281/zenodo.8146228", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8146228"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-07-14T00:00:00Z"}}, {"id": "10.5281/zenodo.8245951", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:46Z", "type": "Dataset", "title": "Influence of small-scale spatial variability of soil properties on yield formation of winter wheat", "description": "This is a data set of soil properties and plant properties of winter wheat. The data derived from a long-term field trial for the year 2016 at the Asendorf field station 70 km north of Hanover, Germany (49 m above sea level, 52\u00b045\u203248.4\u2032\u2032N 9\u00b001\u203224.3\u2032\u2032E) and a field site in Triesdorf, located in Northern Bavaria (450 m a.s.l., 49\u00b012'36.5'N 10\u00b038'33.9'E). Data includes soil (OC, bulk density, texture, pH-value) and plant data (grain yield, thousand grain weight, tillers per m\u00b2, spikes per m\u00b2). All methods and data will be described in an upcoming journal article in the Journal Plant and Soil (DOI:10.1007/s111104-023-06212-2).", "keywords": ["2. Zero hunger", "soil organic carbon", "tillers per m\u00b2", "spikes per m\u00b2", "soil depths", "grain yield", "thousand grain weight", "soil texture", "15. Life on land", "winter wheat"], "contacts": [{"organization": "Gro\u00df, Jonas, Gentsch, Norman,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8245951"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8245951", "name": "item", "description": "10.5281/zenodo.8245951", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8245951"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.8146229", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:44Z", "type": "Dataset", "title": "Dataset of the manuscript \"Assessing the influence of Eisenia andrei on the decomposition of Casuarina equisetifolia litter in vermicompost.\"", "description": "Data generated during an experiment of decomposition of Casuarina equisetifolia litter by the application of vermicompost (VC) or the combination vermicompost + the earthworm Eisenia andrei (E). Six files are included: 'readme.csv' is a file where we explain the meaning of each column (and in which units is expressed) in each of the other five files. 'earthworm_N_biomass.csv' is a table with the number of Eisenia andrei individuals and the total earthworm fresh weight in each of the experimental units we sampled 'FTIR_spectra.csv' is a file with the raw spectral data we obtained from the litter by Fourier Transform Infrared spectroscopy combined with Attenuated Total Reflectance (FTIR-ATR). First column indicate the wavenumber (cm-1) and the other columns indicate the absorbance values of each litter sample for each wavenumber. 'litter_chemical_composition.csv' is a file with the raw data of the concentrations of different chemical elements measured in C. equisetifolia litter collected at different decomposition times. 'litter_mass_loss.csv' contains the dry weight data of the litter at time 0 and after each collection time, as well as the percentage of litter mass loss with time. . 'mesofaunal_com.csv' are the numbers of individuals of several groups of mesofaunal organisms (collembolans, mites, and others) we recovered in each of our experimental units.", "keywords": ["Fourier Transform Infrared spectroscopy", "decomposition", "Eisenia andrei", "litter", "litterbag experiment", "Casuarina equisetifolia", "microcosm"], "contacts": [{"organization": "Quintela-Sabar\u00eds, Celestino, Mendes, Luis Andr\u00e9, Dom\u00ednguez, Jorge,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8146229"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8146229", "name": "item", "description": "10.5281/zenodo.8146229", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8146229"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-07-14T00:00:00Z"}}, {"id": "2117/364526", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:27:54Z", "type": "Journal Article", "created": "2022-03-17", "title": "Quantification of the dust optical depth across spatiotemporal scales with the MIDAS global dataset (2003\u20132017)", "description": "

Abstract. Quantifying the dust optical depth (DOD) and its uncertainty across spatiotemporal scales is key to understanding and constraining the dust cycle and its interactions with the Earth System. This study quantifies the DOD along with its monthly and year-to-year variability between 2003 and 2017 at global and regional levels based on the MIDAS (ModIs Dust AeroSol) dataset, which combines Moderate Resolution Imaging Spectroradiometer (MODIS)-Aqua retrievals and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), reanalysis products. We also describe the annual and seasonal geographical distributions of DOD across the main dust source regions and transport pathways. MIDAS provides columnar mid-visible (550\u2009nm) DOD at fine spatial resolution (0.1\u2218\u00d70.1\u2218), expanding the current observational capabilities for monitoring the highly variable spatiotemporal features of the dust burden. We obtain a global DOD of 0.032\u00b10.003 \u2013 approximately a quarter (23.4\u2009%\u00b12.4\u2009%) of the global aerosol optical depth (AOD) \u2013 with about 1\u00a0order of magnitude more DOD in the Northern Hemisphere (0.056\u00b10.004; 31.8\u2009%\u00b12.7\u2009%) than in the Southern Hemisphere (0.008\u00b10.001; 8.2\u2009%\u00b11.1\u2009%) and about 3.5 times more DOD over land (0.070\u00b10.005) than over ocean (0.019\u00b10.002). The Northern Hemisphere monthly DOD is highly correlated with the corresponding monthly AOD (R2=0.94) and contributes 20\u2009% to 48\u2009% of it, both indicating a dominant dust contribution. In contrast, the contribution of dust to the monthly AOD does not exceed 17\u2009% in the Southern Hemisphere, although the uncertainty in this region is larger. Among the major dust sources of the planet, the maximum DODs (\u223c1.2) are recorded in the Bod\u00e9l\u00e9 Depression of the northern Lake Chad Basin, whereas moderate-to-high intensities are encountered in the Western Sahara (boreal summer), along the eastern parts of the Middle East (boreal summer) and in the Taklamakan Desert (spring). Over oceans, major long-range dust transport is observed primarily along the tropical Atlantic (intensified during boreal summer) and secondarily in the North Pacific (intensified during boreal spring). Our calculated global and regional averages and associated uncertainties are consistent with some but not all recent observation-based studies. Our work provides a simple yet flexible method to estimate consistent uncertainties across spatiotemporal scales, which will enhance the use of the MIDAS dataset in a variety of future studies.

", "keywords": ["Mineral dusts", "3702 Climate change science (for-2020)", "QC1-999", "0201 Astronomical and Space Sciences (for)", "0401 Atmospheric Sciences (for)", "3701 Atmospheric Sciences (for-2020)", "01 natural sciences", "Meteorology & Atmospheric Sciences (science-metrix)", "Atmospheric Sciences", "\u00c0rees tem\u00e0tiques de la UPC::Enginyeria agroaliment\u00e0ria::Ci\u00e8ncies de la terra i de la vida::Climatologia i meteorologia", "Simulaci\u00f3 per ordinador", "Pols", "Meteorology & Atmospheric Sciences", "Datasets", "Dust optical depth (DOD)", "Earth System", "QD1-999", "0105 earth and related environmental sciences", ":Enginyeria agroaliment\u00e0ria::Ci\u00e8ncies de la terra i de la vida::Climatologia i meteorologia [\u00c0rees tem\u00e0tiques de la UPC]", "3701 Atmospheric sciences (for-2020)", "Physics", "MIDAS global dataset", "16. Peace & justice", "Climate Action", "Chemistry", "37 Earth Sciences (for-2020)", "13. Climate action", "Mineral dust particles", "13 Climate Action (sdg)", "Astronomical and Space Sciences"]}, "links": [{"href": "https://acp.copernicus.org/articles/22/3553/2022/acp-22-3553-2022.pdf"}, {"href": "https://escholarship.org/content/qt9v38c6qs/qt9v38c6qs.pdf"}, {"href": "https://doi.org/2117/364526"}, {"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": "2117/364526", "name": "item", "description": "2117/364526", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2117/364526"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-07-19T00:00:00Z"}}, {"id": "10.5281/zenodo.8147623", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:45Z", "type": "Dataset", "title": "EJPSOIL CarboSeq agrometeorological datasets", "description": "Open AccessAbstract The gridded dataset includes the monthly time series of\u00a0the precipitation, temperature and reference evapotranspiration variables derived from AgERA5 daily and AgERA5_ET0 monthly data, with a spatial resolution of 10 kilometers, covering the area interested by the project, for the period\u00a01979-2022. Data is provided as .tif files with their corresponding .rts files (SpatRasterTS object in R). Attached content The following ZIP archives containing the spatial raster time series are provided: ag5_2m_temperature_rts_monthly_19792022_EPSG3035.zip ag5_precipitation_flux_rts_monthly_19792022_EPSG3035.zip ag5_et0_rts_monthly_19792022_EPSG3035.zip In addition a document with a short description of data processing is provided.", "keywords": ["http://vocab.nerc.ac.uk/standard_name/precipitation_amount/", "http://vocab.nerc.ac.uk/standard_name/air_temperature/", "evapotranspiration", "15. Life on land", "https://www.eea.europa.eu/data-and-maps/indicators/water-retention-3/allen-et-al-1998", "climate", "AgERA5", "agriculture"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8147623"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8147623", "name": "item", "description": "10.5281/zenodo.8147623", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8147623"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-07-15T00:00:00Z"}}, {"id": "10.5281/zenodo.8354397", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:46Z", "type": "Dataset", "title": "Data and R-scripts for estimating carbon dioxide emissions from drained peatland forest soils for the greenhouse gas inventory of Finland", "description": "Open Access Introduction A new method for estimating carbon dioxide emissions from rained peatland forest soils was developed for the Greenhouse Gas Inventory of Finland (GHG inventory). The method is based on a set of models (Ojanen et al. 2014, Tuomi et al., 2009) that dynamically compile all relevant carbon inputs and outputs into a time series of soil CO2 emission. A complete description of the method is described in Alm et al. (2023). Here we present the input data and R-scripts (R Core Team, 2020) for computing the time series from year 1990 to 2022 of CO2 emission from soil in forest land on drained organic soil, like it was reported by the Finnish GHG inventory (Statistics Finland, 2023). Time series data The source of forest and area data is the Finnish National Forest Inventory (NFI) as a part of Luke Statutory Services. The NFI standing forest data in the data files includes annual country-wide estimates of mean basal area and standing biomass of Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies (L.) H. Karst) and all the broadleaved forest trees combined. The data concerns forest land on drained organic soil only (class FRA 1 according to the FAO forest land definition). The NFI data for each year has been averaged by different drained peatland forest site types (FTYPE) and by inventory regions of southern and northern Finland. The areas and proportions of FTYPEs of all drained peatland \u201cforests remaining forests\u201d (i.e., forests that have not undergone another change in land use in the past 20 years) in southern and northern Finland (Alm et al., 2023), derived from NFI12 (2014\u20132018). Annual litter input from harvest residues was estimated using statistics of harvested stem volumes by species, collected and published by Luke (Luke statistics). The stem volumes were converted to whole trees and further to litter fractions and further to The share of residues remaining in forest is estimated by subtracting the amount of the logging residues collected for energy use, the data obtained from Luke statistics/energy. The biomass of live trees, annual litterfall from live trees aboveground and root litter belowground are derived from the National Forest Inventory of Finland (inventory rounds NFI8 to NFI13). The R-code also includes calculation of annual litter production from the harvesting residues. The regression-based transfer models, implemented in the R-code, also need meteorological time series inputs: The soil organic matter decomposition model (Ojanen et al. 2014) uses May-October mean temperature. Decomposition model yasso07 (Tuomi et al., 2009), applied for estimating the CO2 release by decomposition of harvesting residues and above ground litter from natural mortality, is constrained by annual temperature, annual temperature amplitude and annual precipitation. Starting from the original country-wide grid produced by the Finnish Meteorological Institute (FMI) the weather time series were spatially averaged so that the FMI weather grid values were collected from those locations where peatlands representing each FTYPE in southern and northern Finland were observed by the NFI, respectively. The pre-prepared input data are given in files, see Table 1 for descriptions. Table 1. Description of input data files. File Description of data basal.areas.csv Time series of years 1990-2022 for annual average basal area (m2 ha-1) by year, by peatland forest site type (peat_type) and by tree species or group (tree_type). Values of peat_type correspond to FTYPE: 1 Herb-rich type 2 Vaccinium myrtillus type 4 Vaccinium vitis-idaea type 6 Dwarf shrub type 7 Cladina type Values of tree species or group correspond to: 1 Scots pine 2 Norway spruce 3 Broadleaved species biomass.csv Time series of years 1990-2022 for annual biomass (biomass, t ha-1 of dry mass) by year, by biomass component, by tree species and by peatland forest site type (tkg). Values of peat_type correspond to FTYPE: 1 Herb-rich type 2 Vaccinium myrtillus type 4 Vaccinium vitis-idaea type 6 Dwarf shrub type 7 Cladina type dead_litter.csv Time series of years 1990-2022 of annual aboveground litter from dead wood: Harvesting residues and natural mortality combined (C, t ha-1 of dry mass; lognat_litter). Values of region correspond to GHG inventory region: south South Finland north North Finland ghgi_litter.csv Time series of years 1990-2022 for litter AWEN-fractions (A=acid soluble, W=water soluble, E=ethanol soluble, N=non-soluble; C, t ha-1) by different litter types: Above-ground coarse woody litter (coarse_woody_litter), fine woody litter (fine_woody_litter), non-woody litter (non_woody_litter) by litter source and deposition type by region. \u201corg\u201d denotes organic soil. Values of region correspond to GHG inventory region: south South Finland north North Finland Values of ground correspond to litter deposition environment: above Above-ground litter below Below-ground litter lognat_decomp.csv Time series of years 1990-2022 for C, t ha-1 of dry mass, decomposed from logging residues and natural mortality by region. Values of variable \u201cregion\u201d correspond to GHG inventory region: south South Finland north North Finland logyasso_weather_data.csv Time series of years 1990-2022 for regional (region) precipitation sum (mm, sum_P), average annual temperature (\u00b0C, mean_T) and amplitude of the annual temperature (\u00b0C , ampli_T). Values of region correspond to GHG inventory region: south South Finland north North Finland total_area.csv Areas (ha) of drained peatland forests remaining forest land by region and peat_type. Values of variable \u201cregion\u201d correspond to GHG inventory region: south South Finland north North Finland Values of peat_type correspond to FTYPE: 1 Herb-rich type 2 Vaccinium myrtillus type 4 Vaccinium vitis-idaea type 6 Dwarf shrub type 7 Cladina type weather_data.csv Time series of years 1990-2022 for 30-year rolling mean temperature for the May-October period (roll_T) used by the soil decomposition models. The values are calculated for each FTYPE (peat_type) using their spatial distributions (see details in Alm et al., 2023). Values of variable \u201cregion\u201d correspond to GHG inventory region: south South Finland north North Finland Values of peat_type correspond to FTYPE: 1 Herb-rich type 2 Vaccinium myrtillus type 4 Vaccinium vitis-idaea type 6 Dwarf shrub type 7 Cladina type The R-scripts The scripts are an excerpt from the Finnish greenhouse gas inventory code set, applying the necessary pre-processed input data and producing the soil CO2 emissions for each FTYPE separately. The necessary R-packages (R Core Team, 2020) are managed in the script LIBRARIES.R. Guidance for running the R-scripts is given in the README.txt. References Alm, J., Wall, A., Myllykangas, J-P., Ojanen, P., Heikkinen, J., Henttonen, H. M., Laiho, R., Minkkinen, K., Tuomainen, T. and Mikola, J. A new method for estimating carbon dioxide emissions from drained peatland forest soils for the greenhouse gas inventory of Finland. Biogeosciences https://doi.org/10.5194/bg-20-1-2023, 2023. LUKE Statistics https://www.luke.fi/en/statistics/total-roundwood-removals-and-drain, last access 8.12.2022. https://www.luke.fi/en/statistics/commercial-fellings/commercial-fellings-72023. last access 8.12.2022. Statistics Finland 2023. URL: https://unfccc.int/documents/627718 (last access 13.9.2023). Ojanen, P., Lehtonen, A., Heikkinen, J., Penttil\u00e4, T., and Minkkinen, K.: Soil CO2 balance and its uncertainty in forestry drained peatlands in Finland, Forest Ecol. Manage., 325, 60\u201373, 2014. R Core Team: R: A language and environment for statistical computing. R Foundation forStatistical Computing, Vienna, Austria, URL https://www.R-project.org, 2020. Tuomi, M., Thum, T., J\u00e4rvinen, H., Fronzek, S., Berg, B., Harmon, M., Trofymow, J.A., Sevanto, S. and Liski, J.: Leaf litter decomposition - Estimates of global variability based on Yasso07 model, Ecol. Modell. 220 (23):3362-3371, 2009.", "keywords": ["13. Climate action", "greenhouse gas inventory", "11. Sustainability", "method", "peatland", "15. Life on land", "time series", "soil carbon dioxide balance", "Finland", "12. Responsible consumption"], "contacts": [{"organization": "Alm, Jukka, Wall, Antti, Myllykangas, Jukka-Pekka, Ojanen, Paavo, Heikkinen, Juha, Henttonen, Helena M., Laiho, Raija, Minkkinen, Kari, Tuomainen, Tarja, Mikola, Juha,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8354397"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8354397", "name": "item", "description": "10.5281/zenodo.8354397", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8354397"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-09-18T00:00:00Z"}}, {"id": "10.5281/zenodo.8149617", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:45Z", "type": "Dataset", "title": "Effects of fragmentation at fine scale in Mediterranean mountain grasslands", "description": "European mountain grasslands suffer a process of abandonment and are colonized by shrubs and forest. This makes a fragmentation at fine scale where a matrix forest surrounds the grassland fragments. Multiple studies have been realized about grassland fragmentation but in anthropic matrix like crops. There is no knowledge about the effect of fragmentation on the abandoned grasslands where the isolation can be minor but not the environmental change due to the surrounding forest in the smallest fragments. In this work, we studied abandoned Mediterranean mountain grasslands in an oak forest matrix. We surveyed the grassland communities and their soil properties in multiple fragments of different sizes and isolation. We classified the communities into different functional groups and calculated landscape variables of fragmentation. Then, we analysed the effect of the fragmentation on the richness of the functional groups and the grassland community. The results show that the fragmentation does not have any effect on the grasslands except on the communities in the extreme gradient of the vegetation succession, the annuals and fringe forest communities. The landscape configuration does not have effects on the grasslands. The smallest grasslands favoured the herbaceous fringe forest and the decrease of the annuals, due to higher amounts of soil organic carbon and less light availability and wetter conditions. Annual grasslands are more abundant in bigger fragments with drier conditions. The connectivity among fragments is not a problem for the grassland communities at fine scale. The typical grassland species show that they remain even in the smallest fragments although in theses the community is more similar to the forest fringe.", "keywords": ["Fragmentation", " grasslands", " communities", " annuals", " perennials", " Mediterranean", " forest", " connectivity", "15. Life on land"], "contacts": [{"organization": "S\u00e1nchez-D\u00e1vila, J.", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8149617"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8149617", "name": "item", "description": "10.5281/zenodo.8149617", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8149617"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-05-30T00:00:00Z"}}, {"id": "10.5281/zenodo.8407642", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:48Z", "type": "Report", "title": "Shedding light on the genetic diversity and evolutionary dynamics of geographic populations of Wisteria vein mosaic virus: a case study for the spread of emerging potyviruses in Europe?", "description": "Wisteria vein mosaic virus (WVMV) is a member of the genus Potyvirus associated with Wisteria mosaic disease (WMD), the most serious disease affecting Wisteria spp. In 2022, severe symptoms of WMD were observed on the leaves of a Chinese wisteria (W. sinensis) tree growing in an urban area in Apulia (Italy). The presence of WVMV was ascertained by RT-PCR analysis. Although the occurrence of WVMV in Italy had been posited in the late 1960s, no molecular information had been reported for any Italian isolate prior to this study. Subsequent phylogenetic analyses based on NIb and CP genes placed the WVMV Italian isolate within a large clade identified in the genus Potyvirus as the BCMV supergroup. Based on the increasing number of reports of the virus worldwide, we attempted an exploratory analysis of its genetic diversity and possible mechanisms that may have shaped its geographic population structure. Relying on the N-terminus of the CP, available for twenty WVMV isolates from Europe, Asia, and Oceania, sixteen different haplotypes were identified. A high haplotype diversity was found, particularly relevant in the European population. The measured dN/dS ratio led to the assumption that the target region is under purifying selection. Tests evaluating the neutrality of nucleotide variability showed different results for the European and Asian groups. The estimation of inter-population genetic differentiation showed a high level of gene flow between the two populations. Overall, our results provide a possible approach to understanding the mechanisms of WVMV emergence in Europe and draw attention to its further spread and the increasing threat of this and other neglected potyvirus species to the ornamental nursery sector.", "keywords": ["WVMV; selection pressure; population genetics; genetic diversity; gene flow; haplotype diversity; neutrality tests; FastME phylogeny", "3. Good health"], "contacts": [{"organization": "G. D'Attoma, A. Minafra", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8407642"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8407642", "name": "item", "description": "10.5281/zenodo.8407642", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8407642"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-09-18T00:00:00Z"}}, {"id": "10.5281/zenodo.8164437", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:45Z", "type": "Dataset", "title": "Dinoflagellate cyst and pollen counts in combination with environmental parameters from the northern Gulf of Mexico", "description": "unspecifiedCounts from dinoflagellate cysts and pollen from 21 surface sediments collected from the northern Gulf of Mexico. The dataset also includes the environmental parameters used for the redundancy analysis in the article. Supplement to: Yedema et al., (2023); Dinoflagellate cyst and pollen assemblages as tracers for marine productivity and river input in the northern Gulf of Mexico (https://doi.org/10.5194/jm-42-257-2023)", "keywords": ["nutrient concentration", "Gulf of Mexico", "NPP", "net primary production", "15. Life on land", "dinoflagellate cyst", "SSS", "SST", "sea surface temperature", "dinocyst", "pollen", "Mississippi river", "14. Life underwater", "sea surface salinity", "palynology", "Atchafalaya river"], "contacts": [{"organization": "Yedema, Yord W., Donders, Timme, Peterse, Francien, Sangiorgi, Francesca,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8164437"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8164437", "name": "item", "description": "10.5281/zenodo.8164437", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8164437"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-12-04T00:00:00Z"}}, {"id": "10.5281/zenodo.8171861", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:45Z", "type": "Dataset", "title": "Pan-EU Landmask: 10m Resolution Geospatial Land Coverage with Administrative Boundary details on country and regional level", "description": "Pan-EU Land Mask Summary Considering the land mask for pan-EU, we will closely match the data coverage of https://land.copernicus.eu/pan-european i.e. the official selection of countries listed here: https://lanEEA39d.copernicus.eu/portal_vocabularies/geotags/eea39. There are a total of three landmask files available, each of which is aligned with the standard spatial/temporal resolution and sizes of AI4SoilHealth Data Cube specifications, which is: Xmin = 900,000, Ymin = 899,000, Xmax = 7,401,000, Ymax = 5,501,000, with Coordinate reference system of epsg:3035. Additionally, these files include a corresponding look-up table that provides explanations for the values present in the raster data. The scripts used to generate these masks can be found here. The masks are: Landmask ISO-code country mask NUTS3 mask Name convention To ensure consistency and ease of use across and within the projects, the files here are named according to the standard OpenLandMap file-naming convention. The OpenLandMap file-naming convention works with 10 fields that basically define the most important properties of the data, this way users can search files, prepare data analysis etc, without even needing to access or open files. The 10 fields include: Generic variable name: country.code Variable procedure combination i.e. method standard (standard abbreviation): iso.3166 Position in the probability distribution / variable type: c Spatial support (usually horizontal block) in m or km: 30m Depth reference or depth interval e.g. below ('b'), above ('a') ground or at surface ('s'): s Time reference begin time (YYYYMMDD): 20210101 Time reference end time: 20211231 Bounding box (2 letters max): eu EPSG code: epsg.3035 Version code i.e. creation date: v20230722 An example of a file-name based on the description above: country.code_iso.3166_c_100m_s_20210101_20211231_eu_epsg.3035_v20230722 Landmask The basic principle to create the land mask is to include as much as land as possible, to avoid missing any land pixels and ensure precise differentiation between land, ocean and inland water bodies. Two reference datasets are used, WorldCover, 10 m resolution. EuroGlobalMap, with shapefiles of administrative boundaries, inland water bodies, ocean and landmask. When generating the land mask, the two reference datasets in a way that: If either of the two reference datasets identifies a pixel as land, it is considered a land pixel in our mask. Regarding ocean and inland water bodies, a pixel is classified as a water pixel only when both reference datasets confirm its identification as water. The landmask consists of 4 values: 10: not in the pan-EU area, i.e. out of mapping scope 1: land 2: inland water 3: ocean This landmask is available in 10m, 30m, 100m, 250m, and 1km resolution formats respectively. The coarse resolution landmasks (>10 m) are generated by resampling from the 10m resolution base map using resampling method \u201cmin\u201d in GDAL. This \u201cmin\u201d method allows taking the minimum values from the contributing pixels, to keep as much land as possible. ISO-3166 country code mask This ISO-3166 country code mask is created from EuroGlobalMap country shapefile. This mask is available in 10m, 30m and 100m resolution. In this raster file, each country is assigned a unique value, which allows for the interpretation and analysis of data associated with a specific country. The values are assigned to each country according to iso-3166 country code, which can be found in the corresponding look-up table. The coarse resolution masks (>10 m) are generated by resampling from the 10m resolution base map using resampling method \u201cmode\u201d in GDAL. NUTS-3 mask The nuts-3 code mask is created from the European NUTS3 shapefile. In this raster file, each unique NUT3 level area is assigned a unique value, which allows for the interpretation and analysis of data associated with specific NUTS3 regions. The values of pixels and its associated meanings can be found in the corresponding look-up table. This nut-3 code mask is available in 10m, 30m and 100m resolution formats. The coarse resolution masks (>10 m) are generated by resampling from the 10m resolution base map using resampling method \u201cmode\u201d in GDAL. It should be noted that the ISO-code country mask covers a more extensive area compared to the NUTS3 mask. This broader coverage includes countries like Ukraine and others beyond the NUTS3 mask, while NUTS mask shows more details about regional administrative boundaries.", "keywords": ["remote sensing", "EuroGlobalMap", "soil health", "WorldCover", "land mask", "pan Europe", "nuts3", "iso-3166", "15. Life on land", "earth obeservation"], "contacts": [{"organization": "Tian, Xuemeng, Ho, Yu-Feng, Witjes, Martijn, Parente, Leandro, Hengl, Tom, Minarik, Robert,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8171861"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8171861", "name": "item", "description": "10.5281/zenodo.8171861", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8171861"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-07-27T00:00:00Z"}}, {"id": "10.5281/zenodo.8171860", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:45Z", "type": "Dataset", "title": "Pan-EU Landmask: 10m Resolution Geospatial Land Coverage with Administrative Boundary details on country and regional level", "description": "Pan-EU Land Mask Summary Considering the land mask for pan-EU, we will closely match the data coverage of https://land.copernicus.eu/pan-european i.e. the official selection of countries listed here: https://lanEEA39d.copernicus.eu/portal_vocabularies/geotags/eea39. There are a total of three landmask files available, each of which is aligned with the standard spatial/temporal resolution and sizes of AI4SoilHealth Data Cube specifications, which is: Xmin = 900,000, Ymin = 899,000, Xmax = 7,401,000, Ymax = 5,501,000, with Coordinate reference system of epsg:3035. Additionally, these files include a corresponding look-up table that provides explanations for the values present in the raster data. The scripts used to generate these masks can be found here. The masks are: Landmask ISO-code country mask NUTS3 mask Name convention To ensure consistency and ease of use across and within the projects, the files here are named according to the standard OpenLandMap file-naming convention. The OpenLandMap file-naming convention works with 10 fields that basically define the most important properties of the data, this way users can search files, prepare data analysis etc, without even needing to access or open files. The 10 fields include: Generic variable name: country.code Variable procedure combination i.e. method standard (standard abbreviation): iso.3166 Position in the probability distribution / variable type: c Spatial support (usually horizontal block) in m or km: 30m Depth reference or depth interval e.g. below ('b'), above ('a') ground or at surface ('s'): s Time reference begin time (YYYYMMDD): 20210101 Time reference end time: 20211231 Bounding box (2 letters max): eu EPSG code: epsg.3035 Version code i.e. creation date: v20230722 An example of a file-name based on the description above: country.code_iso.3166_c_100m_s_20210101_20211231_eu_epsg.3035_v20230722 Landmask The basic principle to create the land mask is to include as much as land as possible, to avoid missing any land pixels and ensure precise differentiation between land, ocean and inland water bodies. Two reference datasets are used, WorldCover, 10 m resolution. EuroGlobalMap, with shapefiles of administrative boundaries, inland water bodies, ocean and landmask. When generating the land mask, the two reference datasets in a way that: If either of the two reference datasets identifies a pixel as land, it is considered a land pixel in our mask. Regarding ocean and inland water bodies, a pixel is classified as a water pixel only when both reference datasets confirm its identification as water. The landmask consists of 4 values: 10: not in the pan-EU area, i.e. out of mapping scope 1: land 2: inland water 3: ocean This landmask is available in 10m, 30m, 100m, 250m, and 1km resolution formats respectively. The coarse resolution landmasks (>10 m) are generated by resampling from the 10m resolution base map using resampling method \u201cmin\u201d in GDAL. This \u201cmin\u201d method allows taking the minimum values from the contributing pixels, to keep as much land as possible. ISO-3166 country code mask This ISO-3166 country code mask is created from EuroGlobalMap country shapefile. This mask is available in 10m, 30m and 100m resolution. In this raster file, each country is assigned a unique value, which allows for the interpretation and analysis of data associated with a specific country. The values are assigned to each country according to iso-3166 country code, which can be found in the corresponding look-up table. The coarse resolution masks (>10 m) are generated by resampling from the 10m resolution base map using resampling method \u201cmode\u201d in GDAL. NUTS-3 mask The nuts-3 code mask is created from the European NUTS3 shapefile. In this raster file, each unique NUT3 level area is assigned a unique value, which allows for the interpretation and analysis of data associated with specific NUTS3 regions. The values of pixels and its associated meanings can be found in the corresponding look-up table. This nut-3 code mask is available in 10m, 30m and 100m resolution formats. The coarse resolution masks (>10 m) are generated by resampling from the 10m resolution base map using resampling method \u201cmode\u201d in GDAL. It should be noted that the ISO-code country mask covers a more extensive area compared to the NUTS3 mask. This broader coverage includes countries like Ukraine and others beyond the NUTS3 mask, while NUTS mask shows more details about regional administrative boundaries.", "keywords": ["remote sensing", "EuroGlobalMap", "soil health", "WorldCover", "land mask", "pan Europe", "nuts3", "iso-3166", "15. Life on land", "earth obeservation"], "contacts": [{"organization": "Tian, Xuemeng, Ho, Yu-Feng, Witjes, Martijn, Parente, Leandro, Hengl, Tom, Minarik, Robert,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8171860"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8171860", "name": "item", "description": "10.5281/zenodo.8171860", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8171860"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-07-27T00:00:00Z"}}, {"id": "10.5281/zenodo.8194045", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:45Z", "type": "Dataset", "title": "Supplementary material/Organic carbon dynamics in clay soils: impact of management practices on microorganism structure and abundance under semi-arid conditions", "description": "Proper management of soil organic matter in arid and semi-arid regions improves organic carbon storage in the soil, helps in compact soil degradation, and mitigates climate change impacts, and preserves ecosystem functionality and sustainability food security. This study aims to provide a better insight into the biogeochemical processes that drive the organic carbon dynamics of saline clay soil in a semi-arid climate. The study is not intended to be exhaustive but contributes to analyzing the relationship between bacterial microflora, physicochemical properties, and organic carbon dynamics as a function of different soil management modes. The monitoring was carried out on three different plots located at the National Institute of Agronomic Research of Algeria. A physicochemical characterization of the soils was performed. A metagenomic study was also conducted to identify bacterial biodiversity using PCR-amplified DNA sequencing. The study results show that the control plot has the highest average organic carbon stock value at 47 Mg ha-1. This was followed by the amended plot and the conventional plot, respectively, with 43 Mg ha-1 and 38 Mg ha-1. In the context of this study, organic carbon dynamics would appear to depend on the interaction of several biotic and abiotic factors. Soil management methods would impact the density and diversity of bacterial microflora. This, in turn, affects the soil's physicochemical properties and, more specifically, organic carbon dynamics and storage.", "keywords": ["2. Zero hunger", "13. Climate action", "Biogeochemical processes", " organic carbon dynamics", " clay soil", " semi-arid area", " bacterial microflora", " physicochemical properties", " soil management methods.", "15. Life on land", "6. Clean water"], "contacts": [{"organization": "Fatiha, Faraoun", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8194045"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8194045", "name": "item", "description": "10.5281/zenodo.8194045", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8194045"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-07-28T00:00:00Z"}}, {"id": "10.5281/zenodo.8194083", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:45Z", "type": "Dataset", "title": "Organic carbon dynamics in clay soils: impact of management practices on microorganism structure and abundance under semi-arid conditions", "description": "Proper management of soil organic matter in arid and semi-arid regions improves organic carbon storage in the soil, helps in compact soil degradation, mitigates climate change impacts, and preserves ecosystem functionality and sustainability food security. This study aims to provide a better insight into the biogeochemical processes that drive the organic carbon dynamics of saline clay soil in a semi-arid area. The study is not intended to be exhaustive but contributes to analyzing the relationship between bacterial microflora, physicochemical properties, and organic carbon dynamics as a function of different soil management modes. The monitoring was carried out on three different plots located at the National Institute of Agronomic Research of Algeria. A physicochemical characterization of the soils was performed. A metagenomic study was also conducted to identify bacterial biodiversity using PCR-amplified DNA sequencing. The study results show that the control plot has the highest average organic carbon stock value at 47 Mg ha-1. This was followed by the amended plot and the conventional plot, respectively, with 43 Mg ha-1 and 38 Mg ha-1. In the context of this study, organic carbon dynamics would appear to depend on the interaction of several biotic and abiotic factors. Soil management methods would impact the density and diversity of bacterial microflora. This, in turn, affects the soil's physicochemical properties and, more specifically, organic carbon dynamics and storage.", "keywords": ["2. Zero hunger", "13. Climate action", "Biogeochemical processes", " organic carbon dynamics", " clay soil", " semi-arid area", " bacterial microflora", " physicochemical properties", "soil management methods.", "15. Life on land", "6. Clean water"], "contacts": [{"organization": "Bekhit, Nadia, Faraoun, Fatiha, Bennabi, Faiza, Abbassia Ayache, Toumi, Fawzia, Mlih, Rawan,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8194083"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8194083", "name": "item", "description": "10.5281/zenodo.8194083", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8194083"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-07-28T00:00:00Z"}}, {"id": "10.5281/zenodo.8246155", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:46Z", "type": "Report", "title": "ALL-Ready Training Module 3 - Management of Living Labs", "description": "The session titled 'Living Lab Model, Real-life Experimentation & Facilitation Techniques' and delivered by trainers Dimitri Schuurman & Gilles Wuyts from IMEC, is part of the broader Capacity Building Programme for Agroecology Living Labs developed as part of ALL-Ready. The workshop introduced participants to commonly used tools and methods in each phase of innovation management and shared some facilitation techniques for capturing user insights and needs in Agroecology projects.", "keywords": ["2. Zero hunger", "Real-life Experimentation", "9. Industry and infrastructure", "Facilitation Techniques", "Living Labs", "Agroecology"], "contacts": [{"organization": "Dimitri Schuurman, Gilles Wuyts,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8246155"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8246155", "name": "item", "description": "10.5281/zenodo.8246155", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8246155"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-08-14T00:00:00Z"}}, {"id": "10.5281/zenodo.8246156", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:46Z", "type": "Report", "title": "ALL-Ready Training Module 3 - Management of Living Labs", "description": "The session titled 'Living Lab Model, Real-life Experimentation & Facilitation Techniques' and delivered by trainers Dimitri Schuurman & Gilles Wuyts from IMEC, is part of the broader Capacity Building Programme for Agroecology Living Labs developed as part of ALL-Ready. The workshop introduced participants to commonly used tools and methods in each phase of innovation management and shared some facilitation techniques for capturing user insights and needs in Agroecology projects.", "keywords": ["2. Zero hunger", "Real-life Experimentation", "9. Industry and infrastructure", "Facilitation Techniques", "Living Labs", "Agroecology"], "contacts": [{"organization": "Schuurman, Dimitri, Wuyts, Gilles,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8246156"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8246156", "name": "item", "description": "10.5281/zenodo.8246156", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8246156"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-08-14T00:00:00Z"}}, {"id": "10.5281/zenodo.8328828", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:46Z", "type": "Report", "title": "D3.1 Overarching Event Plan with Guidelines for Event Organisers", "description": "The main objective of NATI00NS is to facilitate the deployment of the EU Soil Mission across EU Member States and Associated Countries regions, acting as a messenger of the Mission by raising awareness among national and regional stakeholders, providing access to quality-checked capacity-building materials and information, spurring the discussions on the best LL setups to address regional soil needs, and fostering early matchmaking for cross-regional LL clusters. This is done through two rounds of National Engagement Events, envisioned to take place in the first half of 2023 and the first half of 2024, accompanying the EC calls for proposals dedicated to the EU Soil Mission. The present deliverable provides guidelines and tips on the organisation of these events for the benefit of the NATI00NS consortium, and of the organisers of the different national engagement events.", "keywords": ["Soil Deal for Europe", "Soil health", "11. Sustainability", "Living Labs"], "contacts": [{"organization": "Osimanti, Francesco, Drago, Federico, De Majo, Claudio, Fantozzi, Eleonora, Khomsi, Mahdi, Mahmoud, Isra, Morello, Eugenio, Berggreen, Line C.,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8328828"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8328828", "name": "item", "description": "10.5281/zenodo.8328828", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8328828"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-07-28T00:00:00Z"}}, {"id": "10.5281/zenodo.8333110", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-04-03T16:25:46Z", "type": "Dataset", "title": "Growth Chamber mesocosm experiment to assess the effects of the OSS decoupled from the presence of G. senegalensis (PRJNA930014)", "description": "The Sahel region of West Africa is a vulnerable eco-region where a growing population and climate-change induced drought threaten food security. The subsistence farmers here grow pearl millet (Pennisetum glaucum) without fertilizers or irrigation. Local and biologically-based means of maintaining yields are needed, and an agroforestry system in Senegal - the Optimized Shrub-intercropping System (OSS) - provides a solution. In the OSS, the shrub Gueira senegalensis performs hydraulic lift, distributing deep subsurface water to neighboring millet plants. The shrub also supports a distinct microbial community and significantly improves carbon storage and nutrient dynamics. Here, we aimed to test whether shrub-impacted soils differed in microbiome and millet outcomes under simulated early-season drought in a growth chamber. Shrub impact was separated into residual impacts on microbiome and soil, versus ongoing shrub-derived organic matter (OM) input. decoupled from the effects of the growing shrub. Methods: We characterized the microbiota through dry-down and rewetting periods, with particular attention to lineages with known plant growth promoting (PGP) properties, via amplicon sequencing of the 16S rRNA gene V3-V4 region and the ITS2, . Results: Both bacterial and fungal communities were significantly altered by imposed drought, OM amendment, and original soil type (+/-OSS). The largest significant bacterial community impact under dry down occurred for +shrub/-OM treatments, and under rewetting for -OM treatment regardless of +/- OSS. Known bacterial PGP lineages were only enriched under drought in +OSS/-OM treatments. The fungal community behaved differently with a significant dry-down impact only in +OSS/+OM treatments, while rewetting enriched for fungal pathogens but only in -OSS/+OM soils. Decoupled from ongoing shrub growth, both residual shrub impacts and shrub OM inputs altered microbiota and increased millet biomass under drought. These results are part of a growing body of work aimed at understanding microbiome roles in increasing ecological resilience and combating food insecurity. Metagenomic and amplicon sequencing data are publicly available via NCBI PRJNA930014. Here we present all associated soil chemical, enzyme, and plant physical and chemical data", "keywords": ["2. Zero hunger", "sustainable agriculture", "Soil microbiome", "13. Climate action", "sahel", "15. Life on land", "pearl millet", "6. Clean water", "growth chamber"], "contacts": [{"organization": "Mason, Laura, Charles, Christine, Rich, Virginia, Dick, Richard,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8333110"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8333110", "name": "item", "description": "10.5281/zenodo.8333110", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8333110"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-09-11T00:00:00Z"}}, {"id": "10.5281/zenodo.8328829", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:46Z", "type": "Report", "title": "D3.1 Overarching Event Plan with Guidelines for Event Organisers", "description": "The main objective of NATI00NS is to facilitate the deployment of the EU Soil Mission across EU Member States and Associated Countries regions, acting as a messenger of the Mission by raising awareness among national and regional stakeholders, providing access to quality-checked capacity-building materials and information, spurring the discussions on the best LL setups to address regional soil needs, and fostering early matchmaking for cross-regional LL clusters. This is done through two rounds of National Engagement Events, envisioned to take place in the first half of 2023 and the first half of 2024, accompanying the EC calls for proposals dedicated to the EU Soil Mission. The present deliverable provides guidelines and tips on the organisation of these events for the benefit of the NATI00NS consortium, and of the organisers of the different national engagement events.", "keywords": ["Soil Deal for Europe", "Soil health", "11. Sustainability", "Living Labs"], "contacts": [{"organization": "Osimanti, Francesco, Drago, Federico, De Majo, Claudio, Fantozzi, Eleonora, Khomsi, Mahdi, Mahmoud, Isra, Morello, Eugenio, Berggreen, Line C.,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8328829"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8328829", "name": "item", "description": "10.5281/zenodo.8328829", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8328829"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-07-28T00:00:00Z"}}, {"id": "2164/11291", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:27:57Z", "type": "Journal Article", "created": "2018-09-05", "title": "The effect of root exudates on rhizosphere water dynamics", "description": "

Most water and nutrients essential for plant growth travel across a thin zone of soil at the interface between roots and soil, termed the rhizosphere. Chemicals exuded by plant roots can alter the fluid properties, such as viscosity, of the water phase, potentially with impacts on plant productivity and stress tolerance. In this paper, we study the effects of plant exudates on the macroscale properties of water movement in soil. Our starting point is a microscale description of two fluid flow and exudate diffusion in a periodic geometry composed from a regular repetition of a unit cell. Using multiscale homogenization theory, we derive a coupled set of equations that describe the movement of air and water, and the diffusion of plant exudates on the macroscale. These equations are parametrized by a set of cell problems that capture the flow behaviour. The mathematical steps are validated by comparing the resulting homogenized equations to the original pore scale equations, and we show that the difference between the two models is \u22727% for eight cells. The resulting equations provide a computationally efficient method to study plant\u2013soil interactions. This will increase our ability to predict how contrasting root exudation patterns may influence crop uptake of water and nutrients.

", "keywords": ["Richards\u2019 equation", "Hydrology", " hydrography", " oceanography", "General Mathematics", "Porous media", "homogenization", "General Physics and Astronomy", "630", "porous media", "646809DIMR", "QD", "BB/L025620/1", "/dk/atira/pure/subjectarea/asjc/2600/2600", "name=General Engineering", "BB/J00868/1", "NE/L00237/1", "/dk/atira/pure/subjectarea/asjc/2200/2200", "Research Articles", "Homogenization", "Natural Environment Research Council (NERC)", "Flows in porous media; filtration; seepage", "General Engineering", "04 agricultural and veterinary sciences", "15. Life on land", "QD Chemistry", "name=General Mathematics", "EP/P020887/1", "Richards' equation", "Engineering and Physical Sciences Research Council (EPSRC)", "name=General Physics and Astronomy", "13. Climate action", "Biotechnology and Biological Sciences Research Council (BBSRC)", "0401 agriculture", " forestry", " and fisheries", "/dk/atira/pure/subjectarea/asjc/3100/3100", "BB/P004180/1", "European Research Council"]}, "links": [{"href": "https://eprints.soton.ac.uk/423010/1/Paper_Final.pdf"}, {"href": "https://royalsocietypublishing.org/doi/pdf/10.1098/rspa.2018.0149"}, {"href": "https://doi.org/2164/11291"}, {"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": "2164/11291", "name": "item", "description": "2164/11291", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2164/11291"}, {"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": "10.5281/zenodo.8382919", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:47Z", "type": "Dataset", "title": "Europe's forest harvesting regimes dataset", "description": "The data contains quantification of forest harvesting regimes in 11 European countries based on permanent plots of forest inventories.\u00a0The results are aggregated on a resolution of 1 degree latitude/longitude. The results and the methods for obtaining them are reported in detail in the publication below: Suvanto, S., Esquivel-Muelbert, A., Schelhaas, M.J., Astigarraga, J., Astrup, R., Cienciala, E., Fridman, J., Henttonen, H.M., Kunstler, G., K\u00e4ndler, G., K\u00f6nig, L.A., Ruiz-Benito, P., Senf, C., Stadelmann, G., Starcevic, A., Talarczyk, A., Zavala, M.A., Pugh, T.A.M. (2025). Understanding Europe\u2019s Forest Harvesting Regimes. Earth's Future 13 (2), e2024EF005225, https://doi.org/10.1029/2024EF005225. The file variables_harvest_regimes_zenodo1.csv contains the results for each 1-degree grid cell. The columns in the file are described below (please see more details in the paper and it's supplementary material): Colum name Description Unit In Suvanto et al. lat Latitude of the grid cell center point degree \u00a0 lon Longitude of the grid cell center point degree \u00a0 country Country (in case of grid cells covering area from multiple countries, each country has a separate record in the data) \u00a0 \u00a0 n_plots Number of forest inventory plots in the grid cell count Supplement, Fig. S3 n_harvest Number of forest inventory plots with harvest in the grid cell count Supplement, Fig. S3 harvest_rate Total harvest rate, calculated as the percentage of tree basal area harvested in the grid cell per year. percentage (%) Fig. 2, Fig. 3 harvest_freq Frequency of harvest events, calculated as the percentage of plots harvested per year. percentage (%) Fig. 2, Fig. 3 harvest_intensity_mean Mean intensity of harvest events, calculated as the average percentage of tree basal area removed in a harvest event. percentage (%) Fig. 2, Fig. 3 harvest_intensity_sd Standard deviation of the intensity of harvest events, calculated as the standard deviation of the percentage of tree basal area removed in a harvest event. percentage (%) Supplement, Fig. S3 p_harvests_in_0_25 Fraction of harvest events with intensity of 0-25% fraction (between 0-1) Fig. 4 p_harvests_in_25_50 Fraction of harvest events with intensity of 25-50% fraction (0-1) Fig. 4 p_harvests_in_50_75 Fraction of harvest events with intensity of 50-75% fraction (0-1) Fig. 4 p_harvests_in_75_100 Fraction of harvest events with intensity of 75-100% fraction (0-1) Fig. 4", "keywords": ["Europe", "GEMET", "Forest management", "Forest production", "Land use", "Wood resource", "Forestry practice", "Forestry", "Cutting (forestry)", "EuroSciVoc", "Land management and planning"], "contacts": [{"organization": "Suvanto, Susanne, Esquivel-Muelbert, Adriane, Schelhaas, Mart-Jan, Astigarraga, Julen, Astrup, Rasmus, Cienciala, Emil, Fridman, Jonas, Henttonen, Helena M., Kunstler, Georges, K\u00e4ndler, Gerald, K\u00f6nig, Louis A., Ruiz-Benito, Paloma, Senf, Cornelius, Stadelmann, Golo, Starcevic, Ajdin, Talarczyk, Andrzej, Zavala, Miguel A., Pugh, Thomas,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8382919"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8382919", "name": "item", "description": "10.5281/zenodo.8382919", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8382919"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.8437160", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-04-03T16:25:48Z", "type": "Dataset", "title": "Concentrations of methane, sulfate and lipid biomarkers and carbon isotope values oof lipids in the sediments from the outer Laptev Sea", "description": "Open AccessThis work was supported by several funding agencies. The field campaign to obtain samples was supported by the Knut and Alice Wallenberg Foundation (KAW contract 2011.0027 to \u00d6G) as part of the SWERUS-C3 program. This work was also supported by the Swedish Research Council (VR Distinguished Professor Grant 2017-01601 to \u00d6G), and the Swedish Research Council for Sustainable Development Formas (2021-01750 to B.W.) and the National Natural Science Foundation of China (No. 42106046 to W.W.).", "keywords": ["Subsea permafrost", "13. Climate action", "14. Life underwater", "lipid biomarkers", "compound specific carbon isotope", "methane seep"], "contacts": [{"organization": "WU, Weichao, Holmstrand, Henry, Tarbier, Brittany, Wild, Birgit, Shakhova, Natalia, Kosmach, Denis, Semiletov, Igor, Bruchert, Volker, Xu, Yunping, Gustafsson, \u00d6rjan,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8437160"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8437160", "name": "item", "description": "10.5281/zenodo.8437160", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8437160"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-10-13T00:00:00Z"}}, {"id": "10.5281/zenodo.8399180", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:47Z", "type": "Dataset", "title": "EJPSOIL CarboSeq agrometeorological datasets", "description": "Open AccessAbstract The gridded dataset includes the monthly time series of\u00a0the precipitation, temperature and reference evapotranspiration variables derived from AgERA5 daily and AgERA5_ET0 monthly data, with a spatial resolution of 10 kilometers, covering the area interested by the project, for the period\u00a01979-2022. Data is provided as .tif files with their corresponding .rts files (SpatRasterTS object in R). Attached content The following ZIP archives containing the spatial raster time series are provided: ag5_2m_temperature_rts_monthly_19792022_EPSG3035.zip ag5_precipitation_flux_rts_monthly_19792022_EPSG3035.zip ag5_et0_rts_monthly_19792022_EPSG3035.zip In addition a document with a short description of data processing is provided.", "keywords": ["http://vocab.nerc.ac.uk/standard_name/precipitation_amount/", "http://vocab.nerc.ac.uk/standard_name/air_temperature/", "evapotranspiration", "15. Life on land", "https://www.eea.europa.eu/data-and-maps/indicators/water-retention-3/allen-et-al-1998", "climate", "AgERA5", "agriculture"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8399180"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8399180", "name": "item", "description": "10.5281/zenodo.8399180", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8399180"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-07-15T00:00:00Z"}}, {"id": "10.53479/23707", "type": "Feature", "geometry": null, "properties": {"license": "unspecified", "updated": "2026-04-03T16:25:48Z", "type": "Journal Article", "created": "2022-11-24", "title": "The economic impact of conflict-related and policy uncertainty shocks: the case of Russia", "description": "

We show how policy uncertainty and conflict-related shocks impact the dynamics of economic activity (GDP) in Russia. We use alternative indicators of \u201cconflict\u201d, relating to specific aspects of this general concept: geopolitical risk, social unrest, outbreaks of political violence and escalations into internal armed conflict. For policy uncertainty we employ the workhorse economic policy uncertainty (EPU) indicator. We use two distinct but complementary empirical approaches. The first is based on a time series mixed-frequency forecasting model. We show that the indicators provide useful information for forecasting GDP in the short run, even when controlling for a comprehensive set of standard high-frequency macro-financial variables. The second approach, is a SVAR model. We show that negative shocks to the selected indicators lead to economic slowdown, with a persistent drop in GDP growth and a short-lived but large increase in country risk.

", "keywords": ["8. Economic growth", "16. Peace & justice"]}, "links": [{"href": "https://doi.org/10.53479/23707"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Documentos%20de%20Trabajo", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.53479/23707", "name": "item", "description": "10.53479/23707", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.53479/23707"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-11-25T00:00:00Z"}}, {"id": "10.5281/zenodo.8407643", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:48Z", "type": "Report", "title": "Shedding light on the genetic diversity and evolutionary dynamics of geographic populations of Wisteria vein mosaic virus: a case study for the spread of emerging potyviruses in Europe?", "description": "Wisteria vein mosaic virus (WVMV) is a member of the genus Potyvirus associated with Wisteria mosaic disease (WMD), the most serious disease affecting Wisteria spp. In 2022, severe symptoms of WMD were observed on the leaves of a Chinese wisteria (W. sinensis) tree growing in an urban area in Apulia (Italy). The presence of WVMV was ascertained by RT-PCR analysis. Although the occurrence of WVMV in Italy had been posited in the late 1960s, no molecular information had been reported for any Italian isolate prior to this study. Subsequent phylogenetic analyses based on NIb and CP genes placed the WVMV Italian isolate within a large clade identified in the genus Potyvirus as the BCMV supergroup. Based on the increasing number of reports of the virus worldwide, we attempted an exploratory analysis of its genetic diversity and possible mechanisms that may have shaped its geographic population structure. Relying on the N-terminus of the CP, available for twenty WVMV isolates from Europe, Asia, and Oceania, sixteen different haplotypes were identified. A high haplotype diversity was found, particularly relevant in the European population. The measured dN/dS ratio led to the assumption that the target region is under purifying selection. Tests evaluating the neutrality of nucleotide variability showed different results for the European and Asian groups. The estimation of inter-population genetic differentiation showed a high level of gene flow between the two populations. Overall, our results provide a possible approach to understanding the mechanisms of WVMV emergence in Europe and draw attention to its further spread and the increasing threat of this and other neglected potyvirus species to the ornamental nursery sector.", "keywords": ["WVMV; selection pressure; population genetics; genetic diversity; gene flow; haplotype diversity; neutrality tests; FastME phylogeny", "3. Good health"], "contacts": [{"organization": "G. D'Attoma, A. Minafra", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8407643"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8407643", "name": "item", "description": "10.5281/zenodo.8407643", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8407643"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-09-18T00:00:00Z"}}, {"id": "10.5517/cc1j4r4k", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:50Z", "type": "Dataset", "title": "CCDC 1404738: Experimental Crystal Structure Determination", "description": "unspecifiedAn entry from the Cambridge Structural Database, the world\u2019s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.", "keywords": ["Space Group", "Crystallography", "Crystal System", "Crystal Structure", "(mu-cyclobutane-1", "2", "3", "4-tetrayltetrakis(diphenylphosphine))-tris(2", "2'-bipyridine)-platinum(ii)-ruthenium(ii) tetrakis(tetrafluoroborate) hydrate", "Cell Parameters", "Experimental 3D Coordinates"], "contacts": [{"organization": "Prock, J., Strabler, C., Viertl, W., Kopacka, H., Obendorf, D., M\u00fcller, T., Tordin, E., Salzl, S., Kn\u00f6r, G., Mauro, M., De Cola, L., Br\u00fcggeller, P.,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5517/cc1j4r4k"}, {"rel": "self", "type": "application/geo+json", "title": "10.5517/cc1j4r4k", "name": "item", "description": "10.5517/cc1j4r4k", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5517/cc1j4r4k"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2015-01-01T00:00:00Z"}}, {"id": "10.5517/cc3csbk", "type": "Feature", "geometry": null, "properties": {"license": "unspecified", "updated": "2026-04-03T16:25:50Z", "type": "Dataset", "title": "CCDC 100698: Experimental Crystal Structure Determination", "description": "Related Article: U.Dahlmann, C.Krieger, R.Neidlein|1998|Eur.J.Org.Chem.|1998|525|doi:10.1002/(SICI)1099-0690(199803)1998:3<525::AID-EJOC525>3.3.CO;2-U", "keywords": ["Space Group", "Crystallography", "3", "6", "13", "16-Tetrabromo-1", "4:5", "8:11", "14:15", "18-tetrasulfido(20)annulene", "Crystal System", "Crystal Structure", "Cell Parameters", "Experimental 3D Coordinates"], "contacts": [{"organization": "Dahlmann, U., Krieger, C., Neidlein, R.,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5517/cc3csbk"}, {"rel": "self", "type": "application/geo+json", "title": "10.5517/cc3csbk", "name": "item", "description": "10.5517/cc3csbk", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5517/cc3csbk"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "1998-01-01T00:00:00Z"}}], "links": [{"rel": "self", "type": "application/geo+json", "title": "This document as GeoJSON", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=pe&offset=2750&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=pe&offset=2750&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": "prev", "title": "items (prev)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=pe&offset=2700", "hreflang": "en-US"}, {"rel": "next", "type": "application/geo+json", "title": "items (next)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=pe&offset=2800", "hreflang": "en-US"}], "numberMatched": 8620, "numberReturned": 50, "distributedFeatures": [], "timeStamp": "2026-04-04T13:38:04.024298Z"}