{"type": "FeatureCollection", "features": [{"id": "20.500.14017/cddeded8-2ef5-4f98-a327-9b94e00ba846", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-24T16:22:39Z", "type": "Journal Article", "created": "2021-11-30", "title": "Performance analysis of regional AquaCrop (v6.1) biomass  and surface soil moisture simulations using satellite  and in situ observations", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. The current intensive use of agricultural land is affecting the land quality and contributes to climate change. Feeding the world's growing population under changing climatic conditions demands a global transition to more sustainable agricultural systems. This requires efficient models and data to monitor land cultivation practices at the field to global scale. This study outlines a spatially distributed version of the field-scale crop model AquaCrop version 6.1 to simulate agricultural biomass production and soil moisture variability over Europe at a relatively fine resolution of 30\u2009arcsec (\u223c1\u2009km). A highly efficient parallel processing system is implemented to run the model regionally with global meteorological input data from the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2), soil textural information from the Harmonized World Soil Database version 1.2 (HWSDv1.2), and generic crop information. The setup with a generic crop is chosen as a baseline for a future satellite-based data assimilation system. The relative temporal variability in daily crop biomass production is evaluated with the Copernicus Global Land Service dry matter productivity (CGLS-DMP) data. Surface soil moisture is compared against NASA Soil Moisture Active\u2013Passive surface soil moisture (SMAP-SSM) retrievals, the Copernicus Global Land Service surface soil moisture (CGLS-SSM) product derived from Sentinel-1, and in situ data from the International Soil Moisture Network (ISMN). Over central Europe, the regional AquaCrop model is able to capture the temporal variability in both biomass production and soil moisture, with a spatial mean temporal correlation of 0.8 (CGLS-DMP), 0.74 (SMAP-SSM), and 0.52 (CGLS-SSM). The higher performance when evaluating with SMAP-SSM compared to Sentinel-1 CGLS-SSM is largely due to the lower quality of CGLS-SSM satellite retrievals under growing vegetation. The regional model further captures the short-term and inter-annual variability, with a mean anomaly correlation of 0.46 for daily biomass and mean anomaly correlations of 0.65 (SMAP-SSM) and 0.50 (CGLS-SSM) for soil moisture. It is shown that soil textural characteristics and irrigated areas influence the model performance. Overall, the regional AquaCrop model adequately simulates crop production and soil moisture and provides a suitable setup for subsequent satellite-based data assimilation.                     </p></article>", "keywords": ["YIELD RESPONSE", "2. Zero hunger", "LAND", "QE1-996.5", "Science & Technology", "PRODUCTIVITY", "04 Earth Sciences", "0207 environmental engineering", "UNCERTAINTY", "Geology", "02 engineering and technology", "15. Life on land", "7. Clean energy", "01 natural sciences", "WHEAT YIELD", "37 Earth sciences", "DATA ASSIMILATION", "13. Climate action", "ASSESSMENTS", "Physical Sciences", "IMPLEMENTATION", "FAO CROP MODEL", "Geosciences", " Multidisciplinary", "HIGH-RESOLUTION", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://gmd.copernicus.org/articles/14/7309/2021/gmd-14-7309-2021.pdf"}, {"href": "https://doi.org/20.500.14017/cddeded8-2ef5-4f98-a327-9b94e00ba846"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Geoscientific%20Model%20Development", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "20.500.14017/cddeded8-2ef5-4f98-a327-9b94e00ba846", "name": "item", "description": "20.500.14017/cddeded8-2ef5-4f98-a327-9b94e00ba846", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/20.500.14017/cddeded8-2ef5-4f98-a327-9b94e00ba846"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-05-17T00:00:00Z"}}, {"id": "21.11116/0000-0006-C73B-8", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-08-24T16:22:43Z", "type": "Journal Article", "created": "2020-07-27", "title": "Persistence of soil organic carbon caused by functional complexity", "description": "Soil organic carbon management has the potential to aid climate change mitigation through drawdown of atmospheric carbon dioxide. To be effective, such management must account for processes influencing carbon storage and re-emission at different space and time scales. Achieving this requires a conceptual advance in our understanding to link carbon dynamics from the scales at which processes occur to the scales at which decisions are made. Here, we propose that soil carbon persistence can be understood through the lens of decomposers as a result of functional complexity derived from the interplay between spatial and temporal variation of molecular diversity and composition. For example, co-location alone can determine whether a molecule is decomposed, with rapid changes in moisture leading to transport of organic matter and constraining the fitness of the microbial community, while greater molecular diversity may increase the metabolic demand of, and thus potentially limit, decomposition. This conceptual shift accounts for emergent behaviour of the microbial community and would enable soil carbon changes to be predicted without invoking recalcitrant carbon forms that have not been observed experimentally. Functional complexity as a driver of soil carbon persistence suggests soil management should be based on constant care rather than one-time action to lock away carbon in soils.", "keywords": ["[SDE] Environmental Sciences", "DECOMPOSITION", "2. Zero hunger", "106022 Mikrobiologie", "[SDE.MCG]Environmental Sciences/Global Changes", "UNCERTAINTY", "04 agricultural and veterinary sciences", "INPUTS", "15. Life on land", "TRANSPORT", "MODEL", "[SDE.MCG] Environmental Sciences/Global Changes", "106026 \u00d6kosystemforschung", "13. Climate action", "SDG 13 \u2013 Ma\u00dfnahmen zum Klimaschutz", "[SDE]Environmental Sciences", "SDG 13 - Climate Action", "Meteorology & Atmospheric Sciences", "106022 Microbiology", "GROWTH", "0401 agriculture", " forestry", " and fisheries", "TURNOVER", "PLANT", "106026 Ecosystem research", "MATTER"]}, "links": [{"href": "http://www.nature.com/articles/s41561-020-0612-3.pdf"}, {"href": "https://escholarship.org/content/qt84n3398c/qt84n3398c.pdf"}, {"href": "https://doi.org/21.11116/0000-0006-C73B-8"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Nature%20Geoscience", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "21.11116/0000-0006-C73B-8", "name": "item", "description": "21.11116/0000-0006-C73B-8", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/21.11116/0000-0006-C73B-8"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-07-27T00:00:00Z"}}, {"id": "21.11116/0000-000E-A4F4-5", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-08-24T16:22:44Z", "type": "Journal Article", "created": "2024-03-06", "title": "Reply to: Model uncertainty obscures major driver of soil carbon", "description": "International audience", "keywords": ["0301 basic medicine", "[SDU.OCEAN]Sciences of the Universe [physics]/Ocean", "1000 Multidisciplinary", "0303 health sciences", "Multidisciplinary", "Atmosphere", "[SDU.OCEAN] Sciences of the Universe [physics]/Ocean", " Atmosphere", "Uncertainty", "[SDU.ENVI] Sciences of the Universe [physics]/Continental interfaces", " environment", "03 medical and health sciences", "10122 Institute of Geography", "[SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces", "910 Geography & travel", "environment"]}, "links": [{"href": "https://www.nature.com/articles/s41586-023-07000-9.pdf"}, {"href": "https://doi.org/21.11116/0000-000E-A4F4-5"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Nature", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "21.11116/0000-000E-A4F4-5", "name": "item", "description": "21.11116/0000-000E-A4F4-5", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/21.11116/0000-000E-A4F4-5"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-03-06T00:00:00Z"}}, {"id": "2164/17550", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-08-24T16:22:48Z", "type": "Journal Article", "created": "2020-11-07", "title": "Ensemble modelling, uncertainty and robust predictions of organic carbon in long\u2010term bare\u2010fallow soils", "description": "Abstract<p>Simulation models represent soil organic carbon (SOC) dynamics in global carbon (C) cycle scenarios to support climate\uffe2\uff80\uff90change studies. It is imperative to increase confidence in long\uffe2\uff80\uff90term predictions of SOC dynamics by reducing the uncertainty in model estimates. We evaluated SOC simulated from an ensemble of 26 process\uffe2\uff80\uff90based C models by comparing simulations to experimental data from seven long\uffe2\uff80\uff90term bare\uffe2\uff80\uff90fallow (vegetation\uffe2\uff80\uff90free) plots at six sites: Denmark (two sites), France, Russia, Sweden and the United Kingdom. The decay of SOC in these plots has been monitored for decades since the last inputs of plant material, providing the opportunity to test decomposition without the continuous input of new organic material. The models were run independently over multi\uffe2\uff80\uff90year simulation periods (from 28 to 80\uffc2\uffa0years) in a blind test with no calibration (Bln) and with the following three calibration scenarios, each providing different levels of information and/or allowing different levels of model fitting: (a) calibrating decomposition parameters separately at each experimental site (Spe); (b) using a generic, knowledge\uffe2\uff80\uff90based, parameterization applicable in the Central European region (Gen); and (c) using a combination of both (a) and (b) strategies (Mix). We addressed uncertainties from different modelling approaches with or without spin\uffe2\uff80\uff90up initialization of SOC. Changes in the multi\uffe2\uff80\uff90model median (MMM) of SOC were used as descriptors of the ensemble performance. On average across sites, Gen proved adequate in describing changes in SOC, with MMM equal to average SOC (and standard deviation) of 39.2 (\uffc2\uffb115.5)\uffc2\uffa0Mg\uffc2\uffa0C/ha compared to the observed mean of 36.0 (\uffc2\uffb119.7)\uffc2\uffa0Mg\uffc2\uffa0C/ha (last observed year), indicating sufficiently reliable SOC estimates. Moving to Mix (37.5\uffc2\uffa0\uffc2\uffb1\uffc2\uffa016.7\uffc2\uffa0Mg\uffc2\uffa0C/ha) and Spe (36.8\uffc2\uffa0\uffc2\uffb1\uffc2\uffa019.8\uffc2\uffa0Mg\uffc2\uffa0C/ha) provided only marginal gains in accuracy, but modellers would need to apply more knowledge and a greater calibration effort than in Gen, thereby limiting the wider applicability of models.</p", "keywords": ["[SDE] Environmental Sciences", "330", "550", "Supplementary Data", "soil organic carbon dynamics", "QH301 Biology", "[SDE.MCG]Environmental Sciences/Global Changes", "Soil organic carbon dynamics", "bare\u2010fallow soils", "[SDV.SA.SDS]Life Sciences [q-bio]/Agricultural sciences/Soil study", "630", "protocol for model comparison", "Russia", "QH301", "Soil", "NE/M021327/1", "SDG 13 - Climate Action", "Environmental Chemistry", "774378", "process based models", "European Commission", "[SDV.SA.SDS] Life Sciences [q-bio]/Agricultural sciences/Soil study", "Institut f\u00fcr Biochemie und Biologie", "General Environmental Science", "Sweden", "Global and Planetary Change", "Ecology", "Natural Environment Research Council (NERC)", "NE/P019455/1", "bare-fallow soils", "Uncertainty", "Agriculture", "04 agricultural and veterinary sciences", "15. Life on land", "Carbon", "United Kingdom", "process-based models", "[SDE.MCG] Environmental Sciences/Global Changes", "13. Climate action", "[SDE]Environmental Sciences", "bare-fallow soils; model parametrization; process-based models; protocol for model comparison; soil organic carbon dynamics", "0401 agriculture", " forestry", " and fisheries", "774124", "ddc:570", "France", "bare fallow soils", "model parametrization"]}, "links": [{"href": "https://air.unimi.it/bitstream/2434/809186/2/GCB-20-1834_Proof_fl.pdf"}, {"href": "https://onlinelibrary.wiley.com/doi/pdf/10.1111/gcb.15441"}, {"href": "https://doi.org/2164/17550"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Global%20Change%20Biology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "2164/17550", "name": "item", "description": "2164/17550", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2164/17550"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-11-24T00:00:00Z"}}, {"id": "2164/6134", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-08-24T16:22:49Z", "type": "Journal Article", "created": "2016-05-13", "title": "Modeling Soil Processes: Review, Key Challenges, and New Perspectives", "description": "Core Ideas                     <p>                                                                           <p>A community effort is needed to move soil modeling forward.</p>                                                                             <p>Establishing an international soil modeling consortium is key in this respect.</p>                                                                             <p>There is a need to better integrate existing knowledge in soil models.</p>                                                                             <p>Integration of data and models is a key challenge in soil modeling.</p>                                                                     </p>                     <p>The remarkable complexity of soil and its importance to a wide range of ecosystem services presents major challenges to the modeling of soil processes. Although major progress in soil models has occurred in the last decades, models of soil processes remain disjointed between disciplines or ecosystem services, with considerable uncertainty remaining in the quality of predictions and several challenges that remain yet to be addressed. First, there is a need to improve exchange of knowledge and experience among the different disciplines in soil science and to reach out to other Earth science communities. Second, the community needs to develop a new generation of soil models based on a systemic approach comprising relevant physical, chemical, and biological processes to address critical knowledge gaps in our understanding of soil processes and their interactions. Overcoming these challenges will facilitate exchanges between soil modeling and climate, plant, and social science modeling communities. It will allow us to contribute to preserve and improve our assessment of ecosystem services and advance our understanding of climate\uffe2\uff80\uff90change feedback mechanisms, among others, thereby facilitating and strengthening communication among scientific disciplines and society. We review the role of modeling soil processes in quantifying key soil processes that shape ecosystem services, with a focus on provisioning and regulating services. We then identify key challenges in modeling soil processes, including the systematic incorporation of heterogeneity and uncertainty, the integration of data and models, and strategies for effective integration of knowledge on physical, chemical, and biological soil processes. We discuss how the soil modeling community could best interface with modern modeling activities in other disciplines, such as climate, ecology, and plant research, and how to weave novel observation and measurement techniques into soil models. We propose the establishment of an international soil modeling consortium to coherently advance soil modeling activities and foster communication with other Earth science disciplines. Such a consortium should promote soil modeling platforms and data repository for model development, calibration and intercomparison essential for addressing contemporary challenges.</p>", "keywords": ["organic-matter dynamics", "550", "Sciences de l\u2019environnement & \u00e9cologie", "QH301 Biology", "Knowledge management", "0208 environmental biotechnology", "ECOSYSTEM SERVICES", "02 engineering and technology", "soil processes", "01 natural sciences", "Physical Geography and Environmental Geoscience", "Sciences de la Terre", "Biological process", "ANZSRC::3707 Hydrology", "DROUGHT SEVERITY INDEX", "SYNTHETIC-APERTURE RADAR", "ANZSRC::4106 Soil sciences", "SDG 13 - Climate Action", "Climate change", "0503 Soil Sciences", "GROUND-PENETRATING RADAR", "Integration of knowledge", "Life sciences", "ANZSRC::050399 Soil Sciences not elsewhere classified", "synthetic-aperture radar", "Physical Sciences", "Water Resources", "Knowledge and experience", "MULTIPLE ECOSYSTEM SERVICES", "knowledge integration", "570", "DIFFUSE-REFLECTANCE SPECTROSCOPY", "Environmental Engineering", "Physique", " chimie", " math\u00e9matiques & sciences de la terre", "Scientific discipline", "0703 Crop and Pasture Production", "0207 environmental engineering", "Soil Science", "soil science", "ORGANIC-MATTER DYNAMICS", "DATA ASSIMILATION", "Physical", " chemical", " mathematical & earth Sciences", "ANZSRC::0503 Soil Sciences", "Science disciplines", "PEDOTRANSFER FUNCTIONS", "Feedback mechanisms", "mod\u00e9lisation", "ground-penetrating radar", "Science & Technology", "ANZSRC::080110 Simulation and Modelling", "15. Life on land", "Sciences de la terre & g\u00e9ographie physique", "multiple ecosystem services", "root water-uptake", "Observation and measurement", "DIGITAL ELEVATION MODEL", "Quality of predictions", "SATURATED-UNSATURATED FLOW", "ARBUSCULAR MYCORRHIZAL FUNGI", "sciences du sol", "HYDRAULIC-PROPERTIES", "2. Zero hunger", "Agriculture", "diffuse-reflectance spectroscopy", "4106 Soil sciences", "ORGANIC-MATTER", "digital elevation model", "SDG 13 \u2013 Ma\u00dfnahmen zum Klimaschutz", "Sciences du vivant", "Uncertainty analysis", "0406 Physical Geography and Environmental Geoscience", "Life Sciences & Biomedicine", "Crop and Pasture Production", "101028 Mathematical modelling", "international soil modeling consortium", "[SDU.STU]Sciences of the Universe [physics]/Earth Sciences", "Environmental Sciences & Ecology", "arbuscular mycorrhizal fungi", "Ecosystems", "Climate models", "QH301", "Environmental sciences & ecology", "Life Science", "SEDIMENT TRANSPORT MODELS", "data integration", "sediment transport models", "approche ecosyst\u00e9mique", "0105 earth and related environmental sciences", "info:eu-repo/classification/ddc/550", "3707 Hydrology", "soil modeling", "ROOT WATER-UPTAKE", "SOLUTE TRANSPORT", "13. Climate action", "Earth and Environmental Sciences", "Soil Sciences", "[SDU.STU] Sciences of the Universe [physics]/Earth Sciences", "Earth Sciences", "Earth sciences & physical geography", "Soils", "101028 Mathematische Modellierung", "saturated-unsaturated flow", "Environmental Sciences", "root water-uptake", " sediment transport models", " diffuse-reflectance spectroscopy", " arbuscular mycorrhizal fungi", " multiple ecosystem services", " saturated-unsaturated flow", " ground-penetrating radar", " synthetic-aperture radar", " digital elevation model", " organic-matter dynamics."]}, "links": [{"href": "https://orbi.uliege.be/bitstream/2268/263634/1/Vereecken%20VZJ%202016.pdf"}, {"href": "http://onlinelibrary.wiley.com/wol1/doi/10.2136/vzj2015.09.0131/fullpdf"}, {"href": "https://escholarship.org/content/qt6976n34c/qt6976n34c.pdf"}, {"href": "https://doi.org/2164/6134"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Vadose%20Zone%20Journal", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "2164/6134", "name": "item", "description": "2164/6134", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2164/6134"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2016-05-01T00:00:00Z"}}, {"id": "2753196607", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-08-24T16:22:58Z", "type": "Journal Article", "created": "2017-09-01", "title": "Portfolio optimization for seed selection in diverse weather scenarios", "description": "The aim of this work was to develop a method for selection of optimal soybean varieties for the American Midwest using data analytics. We extracted the knowledge about 174 varieties from the dataset, which contained information about weather, soil, yield and regional statistical parameters. Next, we predicted the yield of each variety in each of 6,490 observed subregions of the Midwest. Furthermore, yield was predicted for all the possible weather scenarios approximated by 15 historical weather instances contained in the dataset. Using predicted yields and covariance between varieties through different weather scenarios, we performed portfolio optimisation. In this way, for each subregion, we obtained a selection of varieties, that proved superior to others in terms of the amount and stability of yield. According to the rules of Syngenta Crop Challenge, for which this research was conducted, we aggregated the results across all subregions and selected up to five soybean varieties that should be distributed across the network of seed retailers. The work presented in this paper was the winning solution for Syngenta Crop Challenge 2017.", "keywords": ["Crops", " Agricultural", "2. Zero hunger", "Models", " Statistical", "Glycine max", "Science", "Climate Change", "Q", "R", "Uncertainty", "Agriculture", "04 agricultural and veterinary sciences", "15. Life on land", "Portfolio optimisation", "Yield prediction", "Midwestern United States", "03 medical and health sciences", "0302 clinical medicine", "Seeds", "Medicine", "Regression Analysis", "0401 agriculture", " forestry", " and fisheries", "data analytics", "Weather", "Research Article"]}, "links": [{"href": "https://doi.org/2753196607"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/PLOS%20ONE", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "2753196607", "name": "item", "description": "2753196607", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2753196607"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-09-01T00:00:00Z"}}, {"id": "2960758411", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-08-24T16:23:07Z", "type": "Journal Article", "created": "2019-07-31", "title": "A new global gridded anthropogenic heat flux dataset with high spatial resolution and long-term time series", "description": "Abstract<p>Exploring global anthropogenic heat and its effects on climate change is necessary and meaningful to gain a better understanding of human\uffe2\uff80\uff93environment interactions caused by growing energy consumption. However, the variation in regional energy consumption and limited data availability make estimating long-term global anthropogenic heat flux (AHF) challenging. Thus, using high-resolution population density data (30 arc-second) and a top-down inventory-based approach, this study developed a new global gridded AHF dataset covering 1970\uffe2\uff80\uff932050 based historically on energy consumption data from the British Petroleum (BP); future projections were built on estimated future energy demands. The globally averaged terrestrial AHFs were estimated at 0.05, 0.13, and 0.16\uffe2\uff80\uff89W/m2 in 1970, 2015, and 2050, respectively, but varied greatly among countries and regions. Multiple validation results indicate that the past and future global gridded AHF (PF-AHF) dataset has reasonable accuracy in reflecting AHF at various scales. The PF-AHF dataset has longer time series and finer spatial resolution than previous data and provides powerful support for studying long-term climate change at various scales.</p", "keywords": ["Statistics and Probability", "Data Descriptor", "13. Climate action", "Library and Information Sciences", "Statistics", " Probability and Uncertainty", "7. Clean energy", "01 natural sciences", "Computer Science Applications", "Education", "Information Systems", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://www.nature.com/articles/s41597-019-0143-1.pdf"}, {"href": "https://doi.org/2960758411"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Scientific%20Data", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "2960758411", "name": "item", "description": "2960758411", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2960758411"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-07-31T00:00:00Z"}}, {"id": "2991645510", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-08-24T16:23:09Z", "type": "Journal Article", "created": "2019-11-28", "title": "High-resolution and three-dimensional mapping of soil texture of China", "description": "The lack of detailed three-dimensional soil texture information largely restricts many applications in agriculture, hydrology, climate, ecology and environment. This study predicted 90 m resolution spatial variations of sand, silt and clay contents at a national extent across China and at multiple depths 0\u20135, 5\u201315, 15\u201330, 30\u201360, 60\u2013100 and 100\u2013200 cm. We used 4579 soil profiles collected from a national soil series inventory conducted recently and currently available environmental covariates. The covariates characterized environmental factors including climate, parent materials, terrain, vegetation and soil conditions. We constructed random forest models and employed a parallel computing strategy for the predictions of soil texture fractions based on its relationship with the environmental factors. Quantile regression forest was used to estimate the uncertainty of the predictions. Results showed that the predicted maps were much more accurate and detailed than the conventional linkage maps and the SoilGrids250m product, and could well represent spatial variation of soil texture across China. The relative accuracy improvement was around 245\u2013370% relative to the linkage maps and 83\u2013112% relative to the SoilGrids250m product with regard to the R2, and it was around 24\u201326% and 14\u201319% respectively with regard to the RMSE. The wide range between 5% lower and 95% upper prediction limits may suggest that there was a substantial room to improve current predictions. Besides, we found that climate and terrain factors are major controllers for spatial patterns of soil texture in China. The heat and water-driven physical and chemical weathering and wind-driven erosion processes primarily shape the pattern of clay content. The terrain, wind and water-driven deposition, erosion and transportation sorting processes of soil particles primarily shape the pattern of silt. The findings provide clues for modeling future soil evolution and for national soil security management under the background of global and regional environmental changes.", "keywords": ["2. Zero hunger", "Digital soil mapping", "13. Climate action", "Large extent", "Machine learning", "Environmental factors", "Uncertainty", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "15. Life on land", "01 natural sciences", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/2991645510"}, {"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": "2991645510", "name": "item", "description": "2991645510", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2991645510"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-03-01T00:00:00Z"}}, {"id": "2997494414", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-08-24T16:23:09Z", "type": "Journal Article", "created": "2019-12-30", "title": "Pedoclimatic zone-based three-dimensional soil organic carbon mapping in China", "description": "Up-to-date maps of soil organic carbon (SOC) concentrations can provide vital information for monitoring global or regional soil C changes and soil quality. In this study, a national soil dataset collected in the 2010 s was applied to produce SOC maps of mainland China at soil depths of 0\u20135 cm, 5\u201315 cm, 15\u201330 cm, 30\u201360 cm, 60\u2013100 cm and 100\u2013200 cm. A stacking ensemble learning framework was utilized to take advantage of the optimal predictions from individual models. A voting-based ensemble learning model (VELM) was proposed with consideration of pedoclimatic zones. In this model, three machine learning models were separately trained for every pedoclimatic zone, and their predictions were selectively merged together. A weighted ensemble learning model (WELM), in which the parameterization considered all zones (i.e., the whole study area) simultaneously, was also trained for comparison. The overall R2 values of these two methods ranged from 0.16 to 0.57 and decreased with depth. Based on the independent validation, the R2 values ranged from 0.41 to 0.57 in the topsoil (0\u20135 cm, 5\u201315 cm and 15\u201330 cm). Overall accuracy metrics implied that the VELM and WELM yielded nearly the same prediction performances. However, model validation in the pedoclimatic zones showed that the VELM obviously outperformed the WELM, with the VELM generally improving the accuracy by 12.6%. Based on the independent validation, we also compared our predictions with other soil map products. Although the spatial patterns were similar, the predicted SOC maps outperformed two other products. The comparison of the two ensemble models should serve as a reminder that if new national or regional soil maps are generated, validation based on pedoclimatic zones or other soil-landscape units may be necessary before applying these maps.", "keywords": ["Digital soil mapping", "13. Climate action", "Ensemble learning", "Machine learning", "Uncertainty", "0401 agriculture", " forestry", " and fisheries", "Model comparison", "04 agricultural and veterinary sciences", "15. Life on land"], "contacts": [{"organization": "Song, Xiao-Dong, Wu, Hua-Yong, Ju, Bing, Liu, Feng, Yang, Fei, Li, De-Cheng, Zhao, Yu-Guo, Yang, Jin-Ling, Zhang, Gan-Lin,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/2997494414"}, {"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": "2997494414", "name": "item", "description": "2997494414", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2997494414"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-04-01T00:00:00Z"}}, {"id": "3083269615", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-08-24T16:23:19Z", "type": "Journal Article", "created": "2020-09-03", "title": "Visualizations of Uncertainties in Precision Agriculture: Lessons Learned from Farm Machinery", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Detailed measurements of yield values are becoming a common practice in precision agriculture. Field harvesters generate point Big Data as they provide yield measurements together with dozens of complex attributes in a frequency of up to one second. Such a flood of data brings uncertainties caused by several factors: accuracy of the positioning system used, trajectory overlaps, raising the cutting bar due to obstacles or unevenness, and so on. This paper deals with 2D and 3D cartographic visualizations of terrain, measured yield, and its uncertainties. Four graphic variables were identified as credible for visualizations of uncertainties in point Big Data. Data from two plots at a fully operational farm were used for this purpose. ISO 19157 was examined for its applicability and a proof-of-concept for selected uncertainty expression was defined. Special attention was paid to spatial pattern interpretations.</p></article>", "keywords": ["point Big Data", "Technology", "QH301-705.5", "T", "Physics", "QC1-999", "0211 other engineering and technologies", "interactive 3D visualization", "ISO 19157", "02 engineering and technology", "Engineering (General). Civil engineering (General)", "uncertainty expression", "Chemistry", "yield measurements", "0202 electrical engineering", " electronic engineering", " information engineering", "TA1-2040", "Biology (General)", "QD1-999"]}, "links": [{"href": "http://www.mdpi.com/2076-3417/10/17/6132/pdf"}, {"href": "https://www.mdpi.com/2076-3417/10/17/6132/pdf"}, {"href": "https://doi.org/3083269615"}, {"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": "3083269615", "name": "item", "description": "3083269615", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/3083269615"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-09-03T00:00:00Z"}}, {"id": "3045287773", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-08-24T16:23:17Z", "type": "Journal Article", "created": "2020-07-27", "title": "Persistence of soil organic carbon caused by functional complexity", "description": "Soil organic carbon management has the potential to aid climate change mitigation through drawdown of atmospheric carbon dioxide. To be effective, such management must account for processes influencing carbon storage and re-emission at different space and time scales. Achieving this requires a conceptual advance in our understanding to link carbon dynamics from the scales at which processes occur to the scales at which decisions are made. Here, we propose that soil carbon persistence can be understood through the lens of decomposers as a result of functional complexity derived from the interplay between spatial and temporal variation of molecular diversity and composition. For example, co-location alone can determine whether a molecule is decomposed, with rapid changes in moisture leading to transport of organic matter and constraining the fitness of the microbial community, while greater molecular diversity may increase the metabolic demand of, and thus potentially limit, decomposition. This conceptual shift accounts for emergent behaviour of the microbial community and would enable soil carbon changes to be predicted without invoking recalcitrant carbon forms that have not been observed experimentally. Functional complexity as a driver of soil carbon persistence suggests soil management should be based on constant care rather than one-time action to lock away carbon in soils.", "keywords": ["[SDE] Environmental Sciences", "DECOMPOSITION", "2. Zero hunger", "106022 Mikrobiologie", "[SDE.MCG]Environmental Sciences/Global Changes", "UNCERTAINTY", "04 agricultural and veterinary sciences", "INPUTS", "15. Life on land", "TRANSPORT", "MODEL", "[SDE.MCG] Environmental Sciences/Global Changes", "106026 \u00d6kosystemforschung", "13. Climate action", "SDG 13 \u2013 Ma\u00dfnahmen zum Klimaschutz", "[SDE]Environmental Sciences", "SDG 13 - Climate Action", "Meteorology & Atmospheric Sciences", "106022 Microbiology", "GROWTH", "0401 agriculture", " forestry", " and fisheries", "TURNOVER", "PLANT", "106026 Ecosystem research", "MATTER"]}, "links": [{"href": "http://www.nature.com/articles/s41561-020-0612-3.pdf"}, {"href": "https://escholarship.org/content/qt84n3398c/qt84n3398c.pdf"}, {"href": "https://doi.org/3045287773"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Nature%20Geoscience", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "3045287773", "name": "item", "description": "3045287773", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/3045287773"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-07-27T00:00:00Z"}}, {"id": "3164960866", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-24T16:23:26Z", "type": "Journal Article", "created": "2021-05-27", "title": "A spatiotemporal ensemble machine learning framework for generating land use / land cover time-series maps for Europe (2000 \u2013 2019) based on LUCAS, CORINE and GLAD Landsat", "description": "Abstract         <p>A seamless spatiotemporal machine learning framework for automated prediction, uncertainty assessment, and analysis of land use / land cover (LULC) dynamics is presented. The framework includes: (1) harmonization and preprocessing of high-resolution spatial and spatiotemporal covariate datasets (GLAD Landsat, NPP/VIIRS) including 5 million harmonized LUCAS and CORINE Land Cover-derived training samples, (2) model building based on spatial k-fold cross-validation and hyper-parameter optimization, (3) prediction of the most probable class, class probabilities and uncertainty per pixel, (4) LULC change analysis on time-series of produced maps. The spatiotemporal ensemble model was fitted by combining random forest, gradient boosted trees, and artificial neural network, with logistic regressor as meta-learner. The results show that the most important covariates for mapping LULC in Europe are: seasonal aggregates of Landsat green and near-infrared bands, multiple Landsat-derived spectral indices, and elevation. Spatial cross-validation of the model indicates consistent performance across multiple years with 62%, 70%, and 87% accuracy when predicting 33 (level-3), 14 (level-2), and 5 classes (level-1); with artificial surface classes such as 'airports' and 'railroads' showing the lowest match with validation points. The spatiotemporal model outperforms spatial models on known-year classification by 2.7% and unknown-year classification by 3.5%. Results of the accuracy assessment using 48,365 independent test samples shows 87% match with the validation points. Results of time-series analysis (time-series of LULC probabilities and NDVI images) suggest gradual deforestation trends in large parts of Sweden, the Alps, and Scotland. An advantage of using spatiotemporal ML is that the fitted model can be used to predict LULC in years that were not included in its training dataset, allowing generalization to past and future periods, e.g. to predict land cover for years prior to 2000 and beyond 2020. The generated land cover time-series data stack (ODSE-LULC), including the training points, is publicly available via the Open Data Science (ODS)-Europe Viewer.</p", "keywords": ["Time Factors", "Spatiotemporal", "QH301-705.5", "Data Mining and Machine Learning", "Urbanization", "Uncertainty", "Spatial analysis", "R", "Environmental monitoring", "04 agricultural and veterinary sciences", "15. Life on land", "01 natural sciences", "Europe", "Big data", "Machine learning", "Medicine", "0401 agriculture", " forestry", " and fisheries", "Biology (General)", "Landsat", "Ensemble", "Land use/land cover", "Environmental Monitoring", "Probability", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/3164960866"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/PeerJ", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "3164960866", "name": "item", "description": "3164960866", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/3164960866"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-05-27T00:00:00Z"}}, {"id": "38448699", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-08-24T16:23:38Z", "type": "Journal Article", "created": "2024-03-06", "title": "Reply to: Model uncertainty obscures major driver of soil carbon", "description": "International audience", "keywords": ["0301 basic medicine", "[SDU.OCEAN]Sciences of the Universe [physics]/Ocean", "1000 Multidisciplinary", "0303 health sciences", "Multidisciplinary", "Atmosphere", "[SDU.OCEAN] Sciences of the Universe [physics]/Ocean", " Atmosphere", "Uncertainty", "[SDU.ENVI] Sciences of the Universe [physics]/Continental interfaces", " environment", "03 medical and health sciences", "10122 Institute of Geography", "[SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces", "910 Geography & travel", "environment"]}, "links": [{"href": "https://www.nature.com/articles/s41586-023-07000-9.pdf"}, {"href": "https://doi.org/38448699"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Nature", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "38448699", "name": "item", "description": "38448699", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/38448699"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-03-06T00:00:00Z"}}, {"id": "38448702", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-08-24T16:23:38Z", "type": "Journal Article", "created": "2024-03-06", "title": "Model uncertainty obscures major driver of soil carbon", "description": "International audience", "keywords": ["0301 basic medicine", "[SDU.OCEAN]Sciences of the Universe [physics]/Ocean", "Atmosphere", "[SDU.OCEAN] Sciences of the Universe [physics]/Ocean", " Atmosphere", "carbon use efficiency", "Uncertainty", "01 natural sciences", "[SDU.ENVI] Sciences of the Universe [physics]/Continental interfaces", " environment", "03 medical and health sciences", "[SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces", "microbes", "environment", "Global soil carbon", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://www.nature.com/articles/s41586-023-06999-1.pdf"}, {"href": "https://escholarship.org/content/qt7vw1d7sf/qt7vw1d7sf.pdf"}, {"href": "https://doi.org/38448702"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Nature", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "38448702", "name": "item", "description": "38448702", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/38448702"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-03-06T00:00:00Z"}}, {"id": "50|od_____10978::c7e48a7f8537ad664c614ff3a1ea9599", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-08-24T16:23:50Z", "type": "Report", "title": "IMPROVING BASELINE MAPPING OF SOIL ORGANIC CARBON USING LOCAL GEOSTATISTICS: AN APPLICATION AT REGIONAL SCALE IN TUSCANY (ITALY)", "description": "The status and change of soil organic carbon (SOC) concentration are fundamental for assessing soil health, its functionality and contribution to climate regulation. Organic matter decline is one of eight soil threats identified in the EU Thematic Strategy for Soil Protection (EU 2006). The indicator for assessing SOC loss threat is based on SOC change but measuring any change in SOC content requires establishing a SOC baseline. Geostatistical methods are commonly used both for predicting SOC concentrations at unsampled locations and their mapping. However, most geostatistical algorithms rely on variograms, which are usually assumed to remain unchanged through the area of interest. Therefore, it is assumed the stationarity of data and its spatial structure (variogram) over this area. That is a strong assumption, which may not hold. Local geostatistics (LGS) approach allows the local optimization of geostatistical parameters involved in variogram-based models ensuring a better adequacy between the geostatistical model and the data. Particularly, LGS considers locally varying parameters to address non stationarity and local anisotropies and allows to focus on local particularities. The study was aimed at improving baseline mapping of soil organic carbon (0.30 m) using local geostatistics at regional scale in Tuscany (Italy). It was developed within the SERENA project (European Joint Programme on Agricultural Soil Management, EJP SOIL, European Union\u2019s Horizon 2020 R&amp;I programme, grant agreement N\u00b0 862695) using the SOC dataset of Tuscany Region presented in Gardin et al (2021). To improve the estimation of SOC concentrations by kriging, the elevation, being exhaustively available over the whole area of the Tuscany region, was used as an external drift. To assess the improvement of SOC prediction, the dataset was split into calculation and validation sets. The performance of the LGS approach was assessed by error statistics and compared to the results obtained using a global variogram model. Finally, the baseline map of SOC was complemented by the assessment of the spatial uncertainty using sequential Gaussian simulation.", "keywords": ["Soil organic carbon", "Baseline", "Local geostatistics", "Spatial uncertainty", "Tuscany"]}, "links": [{"href": "https://iris.cnr.it/bitstream/20.500.14243/472586/1/Buttafuoco%20et%20al%202024%20IUSS2024_ABSTRACT%20BOOK.pdf"}, {"href": "https://doi.org/50|od_____10978::c7e48a7f8537ad664c614ff3a1ea9599"}, {"rel": "self", "type": "application/geo+json", "title": "50|od_____10978::c7e48a7f8537ad664c614ff3a1ea9599", "name": "item", "description": "50|od_____10978::c7e48a7f8537ad664c614ff3a1ea9599", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/50|od_____10978::c7e48a7f8537ad664c614ff3a1ea9599"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-01-01T00:00:00Z"}}, {"id": "PMC5580993", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-08-24T16:24:57Z", "type": "Journal Article", "created": "2017-09-01", "title": "Portfolio optimization for seed selection in diverse weather scenarios", "description": "The aim of this work was to develop a method for selection of optimal soybean varieties for the American Midwest using data analytics. We extracted the knowledge about 174 varieties from the dataset, which contained information about weather, soil, yield and regional statistical parameters. Next, we predicted the yield of each variety in each of 6,490 observed subregions of the Midwest. Furthermore, yield was predicted for all the possible weather scenarios approximated by 15 historical weather instances contained in the dataset. Using predicted yields and covariance between varieties through different weather scenarios, we performed portfolio optimisation. In this way, for each subregion, we obtained a selection of varieties, that proved superior to others in terms of the amount and stability of yield. According to the rules of Syngenta Crop Challenge, for which this research was conducted, we aggregated the results across all subregions and selected up to five soybean varieties that should be distributed across the network of seed retailers. The work presented in this paper was the winning solution for Syngenta Crop Challenge 2017.", "keywords": ["Crops", " Agricultural", "2. Zero hunger", "Models", " Statistical", "Glycine max", "Science", "Climate Change", "Q", "R", "Uncertainty", "Agriculture", "04 agricultural and veterinary sciences", "15. Life on land", "Midwestern United States", "03 medical and health sciences", "0302 clinical medicine", "Seeds", "Medicine", "Regression Analysis", "0401 agriculture", " forestry", " and fisheries", "Weather", "Research Article"]}, "links": [{"href": "https://doi.org/PMC5580993"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/PLOS%20ONE", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "PMC5580993", "name": "item", "description": "PMC5580993", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/PMC5580993"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-09-01T00:00:00Z"}}], "links": [{"rel": "self", "type": "application/geo+json", "title": "This document as GeoJSON", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Uncertainty&offset=50&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=Uncertainty&offset=50&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=Uncertainty&offset=0", "hreflang": "en-US"}, {"rel": "last", "type": "application/geo+json", "title": "items (last)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Uncertainty&offset=66", "hreflang": "en-US"}], "numberMatched": 66, "numberReturned": 16, "distributedFeatures": [], "timeStamp": "2026-08-24T18:11:38.821797Z"}