{"type": "FeatureCollection", "features": [{"id": "10.1016/j.geoderma.2019.114061", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:16:42Z", "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/10.1016/j.geoderma.2019.114061"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Geoderma", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.geoderma.2019.114061", "name": "item", "description": "10.1016/j.geoderma.2019.114061", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.geoderma.2019.114061"}, {"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": "10.5281/zenodo.8089699", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:24:56Z", "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/10.5281/zenodo.8089699"}, {"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.8089699", "name": "item", "description": "10.5281/zenodo.8089699", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8089699"}, {"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": "10.1016/j.agrformet.2006.01.007", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:15:42Z", "type": "Journal Article", "created": "2006-02-25", "title": "A Multi-Site Analysis Of Random Error In Tower-Based Measurements Of Carbon And Energy Fluxes", "description": "Measured surface-atmosphere fluxes of energy (sensible heat, H, and latent heat, LE) and CO2 (FCO2) represent the \u2018\u2018true\u2019\u2019 flux plus or minus potential random and systematic measurement errors. Here, we use data from seven sites in the AmeriFlux network, including five forested sites (two of which include \u2018\u2018tall tower\u2019\u2019 instrumentation), one grassland site, and one agricultural site, to conduct a cross-site analysis of random flux error. Quantification of this uncertainty is a prerequisite to model-data synthesis (data assimilation) and for defining confidence intervals on annual sums of net ecosystem exchange or making statistically valid comparisons between measurements and model predictions. We differenced paired observations (separated by exactly 24 h, under similar environmental conditions) to infer the characteristics of the random error in measured fluxes. Random flux error more closely follows a double-exponential (Laplace), rather than a normal (Gaussian), distribution, and increase as a linear function of the magnitude of the flux for all three scalar fluxes. Across sites, variation in the random error follows consistent and robust patterns in relation to environmental variables. For example, seasonal differences in the random error for H are small, in contrast to both LE and FCO2, for which the random errors are roughly three-fold larger at the peak of the growing season compared to the dormant season. Random errors also generally scale with Rn (H and LE) and PPFD (FCO2). For FCO2 (but not H or LE), the random error decreases with increasing wind speed. Data from two sites suggest that FCO2 random error may be slightly smaller when a closed-path, rather than open-path, gas analyzer is used.", "keywords": ["Random error", "Flux", "550", "carbon", "Uncertainty", "0207 environmental engineering", "AmeriFlux", "Eddy covariance", "02 engineering and technology", "15. Life on land", "01 natural sciences", "Carbon", "flux", "Measurement error", "13. Climate action", "Natural Resources and Conservation", "Data assimilation", "eddy covariance", "Ameriflux", "uncertainty", "random error", "data assimilation", "measurement error", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.1016/j.agrformet.2006.01.007"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agricultural%20and%20Forest%20Meteorology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.agrformet.2006.01.007", "name": "item", "description": "10.1016/j.agrformet.2006.01.007", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.agrformet.2006.01.007"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2006-01-01T00:00:00Z"}}, {"id": "10.1016/j.renene.2021.02.003", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:17:01Z", "type": "Journal Article", "created": "2020-11-05", "title": "Virtual fatigue diagnostics of wake-affected wind turbine via Gaussian Process Regression", "description": "

We propose a data-driven model to predict the short-term fatigue Damage Equivalent Loads (DEL) on a wake-affected wind turbine based on wind field inflow sensors and/or loads sensors deployed on an adjacent up-wind wind turbine. Gaussian Process Regression (GPR) with Bayesian hyperparameters calibration is proposed to obtain a surrogate from input random variables to output DELs in the blades and towers of the up-wind and wake-affected wind turbines. A sensitivity analysis based on the hyperparameters of the GPR and Kullback-Leibler divergence is conducted to assess the effect of different input on the obtained DELs. We provide qualitative recommendations for a minimal set of necessary and sufficient input random variables to minimize the error in the DEL predictions on the wake-affected wind turbine. Extensive simulations are performed comprising different random variables, including wind speed, turbulence intensity, shear exponent and inflow horizontal skewness. Furthermore, we include random variables related to the blades lift and drag coefficients with direct impact on the rotor aerodynamic induction, which governs the evolution and transport of the meandering wake. In addition, different spacing between the wind turbines and W\u00f6hler exponents for calculation of DELs are considered. The maximum prediction normalized mean squared error, obtained in the tower base DELs in the fore-aft direction of the wake affected wind turbine, is less than 4%. In the case of the blade root DELs, the overall prediction error is less than 1%. The proposed scheme promotes utilization of sparse structural monitoring (loads) measurements for improving diagnostics on wake-affected turbines.

", "keywords": ["bepress|Physical Sciences and Mathematics|Physics|Engineering Physics", "engrXiv|Engineering|Risk Analysis", "engrXiv|Engineering|Other Engineering", "bepress|Engineering", "engrXiv|Engineering|Mechanical Engineering|Fluid Mechanics", "bepress|Engineering|Mechanical Engineering", "engrXiv|Engineering|Mechanical Engineering", "bepress|Engineering|Mechanical Engineering|Applied Mechanics", "Gaussian Process Regression", "02 engineering and technology", "7. Clean energy", "Virtual sensing", "wind turbine", "bepress|Engineering|Computational Engineering", "engrXiv|Engineering|Civil and Environmental Engineering", "0202 electrical engineering", " electronic engineering", " information engineering", "uncertainty", "Fatigue", "wake", "engrXiv|Engineering|Civil and Environmental Engineering|Structural Engineering", "Uncertainty", "engrXiv|Engineering|Mechanical Engineering|Applied Mechanics", "Bayesian Calibration", "engrXiv|Engineering|Engineering Physics", "bepress|Engineering|Risk Analysis", "engrXiv|Engineering", "bepress|Engineering|Civil and Environmental Engineering", "engrXiv|Engineering|Computational Engineering", "Wake", "bepress|Engineering|Aerospace Engineering|Aerodynamics and Fluid Mechanics", "bepress|Engineering|Civil and Environmental Engineering|Structural Engineering", "fatigue", "bepress|Engineering|Other Engineering", "Sensitivity analysis", "Wind turbine", "Bayesian Gaussian process regression"]}, "links": [{"href": "https://doi.org/10.1016/j.renene.2021.02.003"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Renewable%20Energy", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.renene.2021.02.003", "name": "item", "description": "10.1016/j.renene.2021.02.003", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.renene.2021.02.003"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-11-05T00:00:00Z"}}, {"id": "PMC6668394", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:30:06Z", "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

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.Measurements of nitrous oxide (N2O) emissions from agriculture are essential for understanding the complex soil\uffe2\uff80\uff93crop\uffe2\uff80\uff93climate processes, but there are practical and economic limits to the spatial and temporal extent over which measurements can be made. Therefore, N2O models have an important role to play. As models are comparatively cheap to run, they can be used to extrapolate field measurements to regional or national scales, to simulate emissions over long time periods, or to run scenarios to compare mitigation practices. Process\uffe2\uff80\uff90based models can also be used as an aid to understanding the underlying processes, as they can simulate feedbacks and interactions that can be difficult to distinguish in the field. However, when applying models, it is important to understand the conceptual process differences in models, how conceptual understanding changed over time in various models, and the model requirements and limitations to ensure that the model is well suited to the purpose of the investigation and the type of system being simulated. The aim of this paper is to give the reader a high\uffe2\uff80\uff90level overview of some of the important issues that should be considered when modeling. This includes conceptual understanding of widely used models, common modeling techniques such as calibration and validation, assessing model fit, sensitivity analysis, and uncertainty assessment. We also review examples of N2O modeling for different purposes and describe three commonly used process\uffe2\uff80\uff90based N2O models (APSIM, DayCent, and DNDC).Wind turbine fatigue estimation is based on time\uffe2\uff80\uff90consuming Monte Carlo simulations for various wind conditions, followed by cycle\uffe2\uff80\uff90counting procedures and the application of engineering damage models. The outputs of the fatigue simulations are large in volume and of high dimensionality, as they typically consist of estimates on finite\uffe2\uff80\uff90element computational meshes. The strain and stress tensor time series, which are the primary quantities of interest when considering the problem of fatigue estimation, are dictated by complex vibration characteristics due to the coupled effect of aerodynamics, structural dynamics, geometrically non\uffe2\uff80\uff90linear mechanics, and control. A Variational Auto\uffe2\uff80\uff90Encoder (VAE) is trained in order to model the probability distribution of the accumulated fatigue on the root cross\uffe2\uff80\uff90section of a simulated wind turbine blade. The VAE is conditioned on historical data that correspond to coarse wind\uffe2\uff80\uff90field measurement statistics, such as mean hub\uffe2\uff80\uff90height wind speed, standard deviation of hub\uffe2\uff80\uff90height wind speed and shear exponent. In the absence of direct measurements of structural loads, the proposed technique finds applications in making long\uffe2\uff80\uff90term probabilistic deterioration predictions from historical Supervisory, Control, and Data Acquisition (SCADA) data, while capturing the inherent aleatoric uncertainty due to the incomplete information on strain time series of the wind turbine structure, when only SCADA data statistics are available.

", "keywords": ["CVAE", "deep generative models", "high dimensional simulation outputs", "uncertainty quantification", "TJ807-830", "blade root fatigue", "conditional variational autoencoder", "SCADA", "wind turbine blade", "7. Clean energy", "Renewable energy sources"]}, "links": [{"href": "https://doi.org/10.1002/we.2621"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Wind%20Energy", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1002/we.2621", "name": "item", "description": "10.1002/we.2621", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1002/we.2621"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-02-11T00:00:00Z"}}, {"id": "10.1016/j.apenergy.2012.07.023", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:15:48Z", "type": "Journal Article", "created": "2012-08-30", "title": "Ghg Emission Performance Of Various Liquid Transportation Biofuels In Finland In Accordance With The Eu Sustainability Criteria", "description": "The European Union (EU) has set a binding greenhouse gas (GHG) emission reduction target for transportation biofuels and other bioliquids. In this study, the GHG emissions of various biofuel chains considered as relevant in large-scale production in Finland were calculated in accordance with the EU sustainability criteria. Special attention was paid to uncertainties and the sensitivities of certain parameters. According to the results, it is impossible in many cases to unambiguously conclude whether or not a biofuel chain passes the emission-saving limit provided by the EU. This may reduce the willingness to invest in biofuel production. Major sources of uncertainties and sensitivities are nitrous oxide emissions from soil and nitrogen fertilisation, emissions of process heat production and soil carbon stock changes in biomass production. Several propositions are made in order to reduce the uncertainty of the results and to make the EU sustainability criteria for biofuels more harmonised and accurate", "keywords": ["330", "greenhouse gas emissions", "Ys", "0211 other engineering and technologies", "02 engineering and technology", "kest\u00e4vyyskriteerit", "ep\u00e4varmuus", "7. Clean energy", "biofuels", "12. Responsible consumption", "liikennebiopolttoaineet", "EU sustainability criteria", "kasvihuonekaasup\u00e4\u00e4st\u00f6t", "uncertainly", "13. Climate action", "11. Sustainability", "SDG 13 - Climate Action", "0202 electrical engineering", " electronic engineering", " information engineering", "sustainability criteria", "SDG 7 - Affordable and Clean Energy", "transportation biofuels", "biopolttoaineet", "uncertainty", "ta218"]}, "links": [{"href": "https://doi.org/10.1016/j.apenergy.2012.07.023"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Applied%20Energy", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.apenergy.2012.07.023", "name": "item", "description": "10.1016/j.apenergy.2012.07.023", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.apenergy.2012.07.023"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2013-02-01T00:00:00Z"}}, {"id": "10.1016/j.envc.2021.100204", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:16:17Z", "type": "Journal Article", "created": "2021-07-06", "title": "Studying temporal variations of indoor radon as a vital step towards rational and harmonized international regulation", "description": "Regulations and measurement protocols for indoor radon testing differ between Europe and the US, with Europe implementing a reference level as opposed to the American two-step approach based on an action level. Moreover, none of the afore-mentioned regulatory approaches considers the temporal uncertainty of radon, a factor that usually significantly exceeds instrumental uncertainty. Discussed hereafter is the innovative principle of indoor radon regulation that considers both temporal and instrumental uncertainties. A quantitative relation between Action and Reference levels is being established for the first time. A statistical method for assessing the coefficient of temporal radon variation K(t) depending on the mode and duration of measurements is discussed. New data on the values of K(t) in hot climates and unstable geology typical for Israel are obtained. It is also shown that the influence of meteorological factors, tidal forces and seismic activity on the behavior of indoor radon does not improve the measurement protocol. It is concluded that building a statistically representative array of calculated coefficients of temporal radon variation K(t) with a large number (200\u2013300) of continuous annual indoor radon monitoring in different countries is a vital step towards establishing rational and harmonized international regulation.", "keywords": ["Temporal uncertainty", "Environmental sciences", "Indoor radon", "Reference level", "13. Climate action", "Annual monitoring", "Action level", "Measurement protocol", "GE1-350", "16. Peace & justice", "01 natural sciences", "0104 chemical sciences", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.1016/j.envc.2021.100204"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Environmental%20Challenges", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.envc.2021.100204", "name": "item", "description": "10.1016/j.envc.2021.100204", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.envc.2021.100204"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-08-01T00:00:00Z"}}, {"id": "10.1016/j.geoderma.2019.114145", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:16:42Z", "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/10.1016/j.geoderma.2019.114145"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Geoderma", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.geoderma.2019.114145", "name": "item", "description": "10.1016/j.geoderma.2019.114145", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.geoderma.2019.114145"}, {"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": "10.1016/j.geoderma.2025.117216", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:16:43Z", "type": "Journal Article", "created": "2025-02-17", "title": "Digital mapping of peat thickness and extent in Finland using remote sensing and machine learning", "description": "Accurate data on peat extent and thickness is essential for managing drained peatlands and reducing greenhouse gas emissions. Machine learning-based digital soil mapping offers an effective approach for large-scale peat occurrence prediction. In this study, we present a workflow for producing peat occurrence maps for the whole of Finland. For this, we used random forest classification to map areas with peat thicknesses of\u00a0\u2265\u00a010\u00a0cm, \u226530\u00a0cm, \u226540\u00a0cm, and\u00a0>\u00a060\u00a0cm. The input data consisted of 3.5 million point observations and 188 feature rasters from various sources. We carefully split the reference data into training and test sets, allowing for independent and robust model validation. Feature selection included an initial screening for multicollinearity using correlation-based feature pruning, followed by final selection using a genetic algorithm. Feature importance was evaluated using permutation importance and SHAP values. The resulting models utilized 26\u201333 features, achieving overall accuracies and F1-scores between 86\u201395\u00a0% and 0.82\u20130.95, respectively. The most important features included soil wetness indices, terrain roughness indices, and natural gamma radiation. Additionally, we provided an approach for evaluating spatial prediction uncertainty based on the models\u2019 internal prediction agreement. Compared to existing superficial deposit maps, our peat predictions significantly improve the spatial detail of peatlands at the national level, offering new opportunities for land use planning and emission mitigation. Our exceptionally comprehensive approach is broadly applicable, offering new insights into optimizing machine learning-based digital peatland mapping, particularly through refining feature selection to account for local conditions and enhance prediction accuracy.", "keywords": ["550", "Peatland", "Science", "Peat thickness", "Q", "Remote sensing", "630", "remote sensing", "machine learning", "Digital soil mapping", "Machine learning", "Feature selection", "Nation-wide dataset", "Uncertainty quantification"]}, "links": [{"href": "https://doi.org/10.1016/j.geoderma.2025.117216"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Geoderma", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.geoderma.2025.117216", "name": "item", "description": "10.1016/j.geoderma.2025.117216", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.geoderma.2025.117216"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-03-01T00:00:00Z"}}, {"id": "10.1016/j.gexplo.2025.107868", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:16:44Z", "type": "Journal Article", "created": "2025-07-21", "title": "Improving spatial interpolation for anomaly analysis in presence of sparse, clustered or imprecise data sets", "description": "In this study, we present a new method of interpolation and anomaly detection especially designed for sparse, clustered or imprecise environmental data (SIC). Such data cannot be processed by current state of the art spatial methods and models, including the most widely used, such as kriging. Indeed, the statistics obtained on SIC data (on the order of 5\u201330) do not allow us to define a covariance or to calibrate the numerous hyper-parameters of sophisticated Bayesian or deep image prior models. We therefore adapted an information dissemination algorithm to handle SIC data. This probabilistic model has been enriched (anisotropy, de-clustering, auto-variography, multi-support, treatment of covariates, and censored data) in a way that fully meets the needs for environmental SIC data and can be used in conjunction with hybrid propagation of epistemic and aleatoric uncertainties and anomaly detection, whatever their mathematical form. The new interpolator for anomaly detection was applied on a very small set of 13 sparse data points characteristic of small-scale environmental studies, on digital-challenge datasets and on two real datasets, i.e., a large-scale geochemical dataset and a SIC urban soil dataset. Results highlight the added value of the proposed algorithm, that is able to pinpoint anomalies in SIC data, while avoiding in particular the smoothing effects of certain previous methods", "keywords": ["Sparse clustered", "Uncertainty", "[SDU.STU] Sciences of the Universe [physics]/Earth Sciences", "Spatial interpolation", "Anomaly detection", "European geochemistry"], "contacts": [{"organization": "Belb\u00e8ze, St\u00e9phane, Rohmer, J\u00e9r\u00e9my, Guyonnet, Dominique, N\u00e9grel, Philippe, Tarvainen, Timo,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.1016/j.gexplo.2025.107868"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Geochemical%20Exploration", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.gexplo.2025.107868", "name": "item", "description": "10.1016/j.gexplo.2025.107868", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.gexplo.2025.107868"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-12-01T00:00:00Z"}}, {"id": "10.1016/j.iswcr.2024.10.002", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:16:47Z", "type": "Journal Article", "created": "2024-10-09", "title": "Visible, near-infrared, and shortwave-infrared spectra as an input variable for digital mapping of soil organic carbon", "description": "This study proposes a novel methodology to employ discrete point spectra as input variable for digital mapping of soil organic carbon (SOC). Accordingly, two SOC modeling approaches were used in three agricultural sites in Czech Republic: i) machine learning (ML) including partial least squares regression (PLSR), cubist, random forest (RF), and support vector regression (SVR), and ii) regression kriging (RK) by the combination of ordinary kriging (OK) and PLSR (PLSR-K), cubist (cubist-K), RF (RF-K), and SVR (SVR-K). Models were developed on environmental predictor covariates (EPCs) and thirty genetic algorithms (GA)-selected visible, near-infrared, and shortwave-infrared (VNIR\u2013SWIR) wavelengths spectra, individually and combined. Thirty rasters were then created using interpolation of the selected spectra and served as the input variables \u2013 with and without EPCs \u2013 to test and compare the developed models and SOC predictive maps with each other and with those retrieved from the third approach: iii) kriging using OK of the measured and ML-predicted SOC. The impact of employing selected wavelengths\u2019 spectra and EPCs on models' performance was investigated using independent test samples and the uncertainty associated with the produced maps. Using interpolated spectra as the only input variable yielded a relatively acceptable accuracy (Nov\u00e1 Ves: RMSE\u00a0=\u00a00.19%, \u00dadrnice: RMSE\u00a0=\u00a00.12%, Klu\u010dov: RMSE\u00a0=\u00a00.13%). In comparison, the interpolated spectra coupled with EPCs enhanced the results. Regarding the uncertainty, however, the ML-based SOC maps were more reliable, than RK-based ones. Furthermore, maps produced using both spectra and EPCs showed less uncertainty than those constructed on the individual datasets.", "keywords": ["SOC modeling and mapping", "Regression kriging", "EJP SOIL", "ProbeField", "550", "Interpolated spectra", "EJPSOIL", "Machine learning", "Uncertainty", "TA1-2040", "Engineering (General). Civil engineering (General)"]}, "links": [{"href": "https://doi.org/10.1016/j.iswcr.2024.10.002"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/International%20Soil%20and%20Water%20Conservation%20Research", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.iswcr.2024.10.002", "name": "item", "description": "10.1016/j.iswcr.2024.10.002", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.iswcr.2024.10.002"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-03-01T00:00:00Z"}}, {"id": "10.1016/j.proeng.2017.09.509", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:17:00Z", "type": "Journal Article", "created": "2017-09-12", "title": "Fatigue assessment of a wind turbine blade when output from multiple aero-elastic simulators are available", "description": "Open AccessAero-elasticity is a term that refers to the interaction between the aerodynamic, inertial and elastic loads when a structure is exposed to fluid flow such as turbulent wind inflow. Various commercial and research-based simulators are available to compute the wind turbine aero-elastic loads. These aero-elastic simulators are of varying complexity and might bear different underlying assumptions, pertaining to physics, mathematical and computational formulations. However, currently established practice dictates that the adopted aero-elastic simulators are verified and validated on the basis of measurements from test wind turbines. As a result, it is generally hard to establish one simulator as superior to another in terms of their predicted output. The objective in this paper is to statistically aggregate the fatigue load on a wind turbine blade when simultaneous simulations are performed using multiple simulators. The simulators of the wind turbine blade are of varying fidelity, and uncertainty in the modelling and assumptions on the model inputs are implicitly included, and taken into account in the statistical analysis. The main concept followed here is that rather than treating the output of the simulators as individual information sources, we consider them as part of an ensemble, which can be clustered and then aggregated to predict the \u201cmost likely\u201d fatigue load, hence reducing the inherent model-form uncertainty.", "keywords": ["Finite elements", "Uncertainty", "Wind turbine; Aeroelasticity; Uncertainty; Fatigue; Ensemble Aggregation; Data fusion; Finite elements; Machine learning", "02 engineering and technology", "Data fusion", "7. Clean energy", "01 natural sciences", "0201 civil engineering", "Ensemble Aggregation", "Machine learning", "Aeroelasticity", "0101 mathematics", "Wind turbine", "Fatigue"]}, "links": [{"href": "https://doi.org/10.1016/j.proeng.2017.09.509"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Procedia%20Engineering", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.proeng.2017.09.509", "name": "item", "description": "10.1016/j.proeng.2017.09.509", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.proeng.2017.09.509"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-01-01T00:00:00Z"}}, {"id": "10.1038/s41597-019-0143-1", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:18:15Z", "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

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.Accurately estimating evapotranspiration (ET) at large spatial scales is essential to our understanding of land-atmosphere coupling and the surface balance of water and energy. Comparisons between remote sensing-based ET models are difficult due to diversity in model formulation, parametrization and data requirements. The constituent components of ET have been shown to deviate substantially among models as well as between models and field estimates. This study analyses the sensitivity of three global ET remote sensing models in an attempt to isolate the error associated with forcing uncertainty and reveal the underlying variables driving the model components. We examine the transpiration, soil evaporation, interception and total ET estimates of the Penman-Monteith model from the Moderate Resolution Imaging Spectroradiometer (PM-MOD), the Priestley-Taylor Jet Propulsion Laboratory model (PT-JPL) and the Global Land Evaporation Amsterdam Model (GLEAM) at 42 sites where ET components have been measured using field techniques. We analyse the sensitivity of the models based on the uncertainty of the input variables and as a function of the raw value of the variables themselves. We find that, at 10% added uncertainty levels, the total ET estimates from PT-JPL, PM-MOD and GLEAM are most sensitive to Normalized Difference Vegetation Index (NDVI) (%RMSD = 100.0), relative humidity (%RMSD = 122.3) and net radiation (%RMSD = 7.49), respectively. Consistently, systemic bias introduced by forcing uncertainty in the component estimates is mitigated when components are aggregated to a total ET estimate. These results suggest that slight changes to forcing may result in outsized variation in ET partitioning and relatively smaller changes to the total ET estimates. Our results help to explain why model estimates of total ET perform relatively well despite large inter-model divergence in the individual ET component estimates.

", "keywords": ["550", "Science", "TROPICAL RAIN-FOREST", "0208 environmental biotechnology", "evapotranspiration", "0207 environmental engineering", "02 engineering and technology", "interception", "SOIL-MOISTURE", "transpiration", "modelling", "partitioning", "soil evaporation", "uncertainty", "DROUGHT", "evapotranspiration; modelling; sensitivity; uncertainty; transpiration; soil evaporation; interception; partitioning", "CLIMATE-CHANGE", "Q", "Biology and Life Sciences", "PLANT TRANSPIRATION", "sensitivity", "6. Clean water", "CHIHUAHUAN DESERT", "GLOBAL TERRESTRIAL EVAPOTRANSPIRATION", "13. Climate action", "Earth and Environmental Sciences", "LAND EVAPORATION", "WATER-BALANCE", "FEEDBACKS", "[PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph]", "[PHYS.ASTR] Physics [physics]/Astrophysics [astro-ph]"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/10/10/1601/pdf"}, {"href": "https://doi.org/10.3390/rs10101601"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/rs10101601", "name": "item", "description": "10.3390/rs10101601", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs10101601"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-10-09T00:00:00Z"}}, {"id": "10.1111/gcb.15441", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:19:23Z", "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

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.

", "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", "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", "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/10.1111/gcb.15441"}, {"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": "10.1111/gcb.15441", "name": "item", "description": "10.1111/gcb.15441", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1111/gcb.15441"}, {"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": "10.3389/fbuil.2017.00069", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:21:38Z", "type": "Journal Article", "created": "2017-12-07", "title": "Gaussian Process Time-Series Models for Structures under Operational Variability", "description": "Open AccessISSN:2297-3362", "keywords": ["metamodels", "random coefficient", "02 engineering and technology", "Engineering (General). Civil engineering (General)", "0201 civil engineering", "time-series models", "HT165.5-169.9", "Structural Health Monitoring", "Structural Health Monitoring; Gaussian Process Time-Series Models", "gaussian process", "TA1-2040", "Gaussian Process Time-Series Models", "uncertainty", "City planning"]}, "links": [{"href": "https://doi.org/10.3389/fbuil.2017.00069"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Frontiers%20in%20Built%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3389/fbuil.2017.00069", "name": "item", "description": "10.3389/fbuil.2017.00069", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3389/fbuil.2017.00069"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-12-08T00:00:00Z"}}, {"id": "10.14279/depositonce-15380", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:20:18Z", "type": "Journal Article", "created": "2022-02-24", "title": "Decoupling between ecosystem photosynthesis and transpiration: a last resort against overheating", "description": "Abstract

Ecosystems are projected to face extreme high temperatures more frequently in the near future. Various biotic coping strategies exist to prevent heat stress. Controlled experiments have recently provided evidence for continued transpiration in woody plants during high air temperatures, even when photosynthesis is inhibited. Such a decoupling of photosynthesis and transpiration would represent an effective strategy (\uffe2\uff80\uff98known as leaf or canopy cooling\uffe2\uff80\uff99) to prevent lethal leaf temperatures. At the ecosystem scale, continued transpiration might dampen the development and propagation of heat extremes despite further desiccating soils. However, at the ecosystem scale, evidence for the occurrence of this decoupling is still limited. Here, we aim to investigate this mechanism using eddy-covariance data of thirteen woody ecosystems located in Australia and a causal graph discovery algorithm. Working at half-hourly time resolution, we find evidence for a decoupling of photosynthesis and transpiration in four ecosystems which can be classified as Mediterranean woodlands. The decoupling occurred at air temperatures above 35 \uffe2\uff88\uff98C. At the nine other investigated woody sites, we found that vegetation CO2 exchange remained coupled to transpiration at the observed high air temperatures. Ecosystem characteristics suggest that the canopy energy balance plays a crucial role in determining the occurrence of a decoupling. Our results highlight the value of causal-inference approaches for the analysis of complex physiological processes. With regard to projected increasing temperatures and especially extreme events in future climates, further vegetation types might be pushed to threatening canopy temperatures. Our findings suggest that the coupling of leaf-level photosynthesis and stomatal conductance, common in land surface schemes, may need be re-examined when applied to high-temperature events.

", "keywords": ["heat wave", "570", "AUSTRALIA", "Science", "QC1-999", "UNCERTAINTY", "Environmental technology. Sanitary engineering", "01 natural sciences", "transpiration", "FLUX TOWER", "ddc:570", "GE1-350", "TOLERANCE", "TEMPERATURE", "TD1-1066", "0105 earth and related environmental sciences", "photosynthesis", "CONDUCTANCE", "Physics", "Q", "04 agricultural and veterinary sciences", "15. Life on land", "WATER-USE", "MODEL", "Environmental sciences", "13. Climate action", "Earth and Environmental Sciences", "ecosystem functioning", "PINUS-TAEDA", "0401 agriculture", " forestry", " and fisheries", "ELEVATED CO2", "570 Biowissenschaften; Biologie"]}, "links": [{"href": "https://doi.org/10.14279/depositonce-15380"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Environmental%20Research%20Letters", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.14279/depositonce-15380", "name": "item", "description": "10.14279/depositonce-15380", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.14279/depositonce-15380"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-03-14T00:00:00Z"}}, {"id": "10.1371/journal.pone.0184198", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:20:17Z", "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/10.1371/journal.pone.0184198"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/PLOS%20ONE", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1371/journal.pone.0184198", "name": "item", "description": "10.1371/journal.pone.0184198", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1371/journal.pone.0184198"}, {"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": "10.17169/refubium-31202", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:20:38Z", "type": "Journal Article", "created": "2021-05-21", "title": "Global data on earthworm abundance, biomass, diversity and corresponding environmental properties", "description": "Abstract

Earthworms are an important soil taxon as ecosystem engineers, providing a variety of crucial ecosystem functions and services. Little is known about their diversity and distribution at large spatial scales, despite the availability of considerable amounts of local-scale data. Earthworm diversity data, obtained from the primary literature or provided directly by authors, were collated with information on site locations, including coordinates, habitat cover, and soil properties. Datasets were required, at a minimum, to include abundance or biomass of earthworms at a site. Where possible, site-level species lists were included, as well as the abundance and biomass of individual species and ecological groups. This global dataset contains 10,840 sites, with 184 species, from 60 countries and all continents except Antarctica. The data were obtained from 182 published articles, published between 1973 and 2017, and 17 unpublished datasets. Amalgamating data into a single global database will assist researchers in investigating and answering a wide variety of pressing questions, for example, jointly assessing aboveground and belowground biodiversity distributions and drivers of biodiversity change.

", "keywords": ["2401.17 Invertebrados", "0301 basic medicine", "592", "Data Descriptor", "Ecology and Evolutionary Biology", "earthworms", "Data Descriptor ; Biodiversity ; Biogeography ; Community ecology", "Plan_S-Compliant-OA", "https://purl.org/becyt/ford/1.6", "[SDV.EE.ECO] Life Sciences [q-bio]/Ecology", " environment/Ecosystems", "Diversity data", "Biomass", "S Agriculture (General)", "Ekologia ja evoluutiobiologia", "[SDV.SA.SDS] Life Sciences [q-bio]/Agricultural sciences/Soil study", "biodiversity", "2. Zero hunger", "maaper\u00e4", "abundance", "Data", "Diversity", "0303 health sciences", "Ecology", "Q", "eli\u00f6yhteis\u00f6t", "Biodiversity", "maaper\u00e4eli\u00f6st\u00f6", "ddc:", "Computer Science Applications", "Biogeography", "2401.06 Ecolog\u00eda animal", "international", "Statistics", " Probability and Uncertainty", "environment/Ecosystems", "Information Systems", "Statistics and Probability", "Ecolog\u00eda (Biolog\u00eda)", "570", "lierot", "Science", "Invertebrados", "577", "Global database", "[SDV.SA.SDS]Life Sciences [q-bio]/Agricultural sciences/Soil study", "Library and Information Sciences", "574", "333", "soil", "eli\u00f6maantiede", "Education", "diversity", "03 medical and health sciences", "[SDV.EE.ECO]Life Sciences [q-bio]/Ecology", " environment/Ecosystems", "BIODIVERSITY CHANGE", "Life Science", "Earthworms", "Datasets", "Animals", "Community ecology", "Oligochaeta", "https://purl.org/becyt/ford/1", "eartworm", "biogeography", "Ecosystem", "LAND-USE", "biomass", "500", "Biology and Life Sciences", "PLATFORM", "Global dataset", "Oligochaeta/classification", "500 Naturwissenschaften und Mathematik::570 Biowissenschaften; Biologie::570 Biowissenschaften; Biologie", "Ecolog\u00eda", "15. Life on land", "biodiversiteetti", "Environmental sciences", "[SDE.BE] Environmental Sciences/Biodiversity and Ecology", "maaper\u00e4el\u00e4imist\u00f6", "Ecology", " evolutionary biology", "13. Climate action", "Earthworm", "[SDV.EE.ECO]Life Sciences [q-bio]/Ecology", "570 Life sciences; biology", "[SDE.BE]Environmental Sciences/Biodiversity and Ecology", "eartworm ; abundance ; biomass ; diversity", "COMMUNITIES", "community ecology"]}, "links": [{"href": "https://www.nature.com/articles/s41597-021-00912-z.pdf"}, {"href": "https://pub.epsilon.slu.se/25868/1/phillips_h_r_p_et_al_211019.pdf"}, {"href": "https://boris.unibe.ch/165726/1/48.__Global_data_on_earthworm_abundance__biomass__diversity_and_corresponding_environmental_properties.pdf"}, {"href": "https://www.iris.unict.it/bitstream/20.500.11769/509583/1/SCIENTIFIC%20DATA%20%282021%29%20GLOBAL%20DATA%20ON%20EARTHWORMS.pdf"}, {"href": "https://rau.repository.guildhe.ac.uk/id/eprint/16454/1/Phillips_et_al-2021-Scientific_Data.pdf"}, {"href": "https://doi.org/10.17169/refubium-31202"}, {"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": "10.17169/refubium-31202", "name": "item", "description": "10.17169/refubium-31202", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.17169/refubium-31202"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-05-21T00:00:00Z"}}, {"id": "10.20944/preprints202407.0543.v1", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:20:50Z", "type": "Journal Article", "created": "2024-07-24", "title": "Willingness to Pay for Agricultural Soil Quality Protection and Improvement", "description": "

Understanding and estimating the economic value that society places on agricultural soil quality protection and improvement can guide the development of policies aimed at mitigating pollution, promoting conservation, or incentivizing sustainable land management practices. We estimate the general public\u2019s willingness to pay (WTP) for agricultural soil quality protection and improvement in Spain (n = 1000) and the UK (n = 984) using data from a cross-sectional survey via Qualtrics panels in March\u2013April 2021. We use a double-bound dichotomous choice contingent valuation approach to elicit the individuals\u2019 WTP. We investigate the effect of uncertainty on the success of policies aiming at achieving soil protection. In addition, to understand the heterogeneity in individuals\u2019 WTP for agricultural soil quality protection and improvement, we model individuals\u2019 WTP through individuals\u2019 awareness and attitudes toward agricultural soil quality protection and the environment; trust in institutions; risk and time preferences; pro-social behavior; and socio-demographics in Spain and the UK. We found that there is significant public support for agricultural soil quality protection and improvement in Spain and the UK. We also found that the support does not vary significantly under uncertainty of success of policies aiming at achieving soil protection. However, the individual\u2019s reasons for supporting agricultural soil quality protection and improvement are found to depend on the level of uncertainty and country. Hence, promoting public support for soil protection needs to be tailored according to the level of the general public\u2019s perceived uncertainty and geographic location.

", "keywords": ["2. Zero hunger", "S", "1. No poverty", "Agriculture", "04 agricultural and veterinary sciences", "15. Life on land", "01 natural sciences", "risk preferences", "11. Sustainability", "0401 agriculture", " forestry", " and fisheries", "soil quality", "uncertainty", "willingness to pay", "contingent valuation", "sustainable land management", "0105 earth and related environmental sciences"], "contacts": [{"organization": "Francisco Jos\u00e9 Areal", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.20944/preprints202407.0543.v1"}, {"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": "10.20944/preprints202407.0543.v1", "name": "item", "description": "10.20944/preprints202407.0543.v1", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.20944/preprints202407.0543.v1"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-07-08T00:00:00Z"}}, {"id": "10.21203/rs.3.rs-5128244/v2", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:20:52Z", "type": "Journal Article", "created": "2025-07-14", "title": "Spatiotemporal prediction of soil organic carbon density in Europe (2000\u20132022) using earth observation and machine learning", "description": "

This article describes a comprehensive framework for soil organic carbon density (SOCD, kg/m3) modeling and mapping, based on spatiotemporal random forest (RF) and quantile regression forests (QRF). A total of 45,616 SOCD observations and various Earth observation (EO) feature layers were used to produce 30 m SOCD maps for the EU at four-year intervals (2000\uffe2\uff80\uff932022) and four soil depth intervals (0\uffe2\uff80\uff9320 cm, 20\uffe2\uff80\uff9350 cm, 50\uffe2\uff80\uff93100 cm, and 100\uffe2\uff80\uff93200 cm). Per-pixel 95% probability prediction intervals (PIs) and extrapolation risk probabilities are also provided. Model evaluation indicates good overall accuracy (R2 = 0.63 and CCC = 0.76 for hold-out independent tests). Prediction accuracy varies by land cover, depth interval and year of prediction with the worst accuracy for shrubland and deeper soils 100\uffe2\uff80\uff93200 cm. The PI validation confirmed effective uncertainty estimation, though with reduced accuracy for higher SOCD values. Shapley analysis identified soil depth as the most influential feature, followed by vegetation, long-term bioclimate, and topographic features. While pixel-level uncertainty is substantial, spatial aggregation reduces uncertainty by approximately 66%. Detecting SOCD changes remains challenging but offers a baseline for future improvements. Maps, based primarily on topsoil data from cropland, grassland, and woodland, are best suited for applications related to these land covers and depths. We recommend that users interpret the maps in conjunction with local knowledge and consider the accompanying uncertainty and extrapolation risk layers. All data and code are available under an open license at https://doi.org/10.5281/zenodo.13754343 and https://github.com/AI4SoilHealth/SoilHealthDataCube/.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.Abstract. Satellite Earth observations (EO) are an accurate and reliable data source for atmospheric and environmental science. Their increasing spatial and temporal resolution, as well as the seamless availability over ungauged regions, make them appealing for hydrological modeling. This work shows recent advances in the use of high-resolution satellite-based Earth observation data in hydrological modelling. In a set of experiments, the distributed hydrological model Continuum is set up for the Po River Basin (Italy) and forced, in turn, by satellite precipitation and evaporation, while satellite-derived soil moisture and snow depths are ingested into the model structure through a data-assimilation scheme. Further, satellite-based estimates of precipitation, evaporation and river discharge are used for hydrological model calibration, and results are compared with those based on ground observations. Despite the high density of conventional ground measurements and the strong human influence in the focus region, all satellite products show strong potential for operational hydrological applications, with skillful estimates of river discharge throughout the model domain. Satellite-based evaporation and snow depths marginally improve (by 2 % and 4 %) the mean Kling-Gupta efficiency (KGE) at 27 river gauges, compared to a baseline simulation (KGEmean = 0.51) forced by high-quality conventional data. Precipitation has the largest impact on the model output, though the satellite dataset on average shows poorer skills compared to conventional data. Interestingly, a model calibration heavily relying on satellite data, as opposed to conventional data, provides a skillful reconstruction of river discharges, paving the way to fully satellite-driven hydrological applications.

", "keywords": ["Technology", "DATA", "ASSIMILATION", "Po River", "FLOOD RISK", "0211 other engineering and technologies", "0207 environmental engineering", "UNCERTAINTY", "02 engineering and technology", "high resolution satellite products", "Environmental technology. Sanitary engineering", "01 natural sciences", "G", "Geography. Anthropology. Recreation", "EARTH", "GE1-350", "continuum hydrological model", "RAINFALL", "TD1-1066", "0105 earth and related environmental sciences", "T", "RADAR ALTIMETRY DATA", "LAND-SURFACE", "6. Clean water", "Environmental sciences", "13. Climate action", "Earth and Environmental Sciences", "HYDRODYNAMIC MODEL", "OBSERVATION", "DISCHARGE ESTIMATION", "SOIL-MOISTURE PRODUCTS"]}, "links": [{"href": "https://hess.copernicus.org/articles/26/3921/2022/hess-26-3921-2022.pdf"}, {"href": "https://doi.org/10.5194/hess-26-3921-2022"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Hydrology%20and%20Earth%20System%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/hess-26-3921-2022", "name": "item", "description": "10.5194/hess-26-3921-2022", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/hess-26-3921-2022"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-12-23T00:00:00Z"}}, {"id": "10.25678/00044n", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:21:34Z", "type": "Software", "title": "Data for: Hydrogeological Uncertainty Estimation with the Analytic Element Method", "description": "This dataset contains the code and output files (upload.zip) for the corresponding publication, as well as a copy of the MODFLOW model (MODFLOW.zip) and its Python interface (FloPy.zip) required to reproduce all results reported.", "keywords": ["mcmc", "analytic element method", "uncertainty quantification"], "contacts": [{"organization": "Ramgraber, Max, Schirmer, Mario,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.25678/00044n"}, {"rel": "self", "type": "application/geo+json", "title": "10.25678/00044n", "name": "item", "description": "10.25678/00044n", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.25678/00044n"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-01-01T00:00:00Z"}}, {"id": "10.3390/app10176132", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:21:50Z", "type": "Journal Article", "created": "2020-09-03", "title": "Visualizations of Uncertainties in Precision Agriculture: Lessons Learned from Farm Machinery", "description": "

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.

", "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/10.3390/app10176132"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Applied%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/app10176132", "name": "item", "description": "10.3390/app10176132", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/app10176132"}, {"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": "10.3390/land13081118", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:21:57Z", "type": "Journal Article", "created": "2024-07-24", "title": "Willingness to Pay for Agricultural Soil Quality Protection and Improvement", "description": "

Understanding and estimating the economic value that society places on agricultural soil quality protection and improvement can guide the development of policies aimed at mitigating pollution, promoting conservation, or incentivizing sustainable land management practices. We estimate the general public\u2019s willingness to pay (WTP) for agricultural soil quality protection and improvement in Spain (n = 1000) and the UK (n = 984) using data from a cross-sectional survey via Qualtrics panels in March\u2013April 2021. We use a double-bound dichotomous choice contingent valuation approach to elicit the individuals\u2019 WTP. We investigate the effect of uncertainty on the success of policies aiming at achieving soil protection. In addition, to understand the heterogeneity in individuals\u2019 WTP for agricultural soil quality protection and improvement, we model individuals\u2019 WTP through individuals\u2019 awareness and attitudes toward agricultural soil quality protection and the environment; trust in institutions; risk and time preferences; pro-social behavior; and socio-demographics in Spain and the UK. We found that there is significant public support for agricultural soil quality protection and improvement in Spain and the UK. We also found that the support does not vary significantly under uncertainty of success of policies aiming at achieving soil protection. However, the individual\u2019s reasons for supporting agricultural soil quality protection and improvement are found to depend on the level of uncertainty and country. Hence, promoting public support for soil protection needs to be tailored according to the level of the general public\u2019s perceived uncertainty and geographic location.

", "keywords": ["2. Zero hunger", "S", "1. No poverty", "Agriculture", "04 agricultural and veterinary sciences", "15. Life on land", "01 natural sciences", "risk preferences", "11. Sustainability", "0401 agriculture", " forestry", " and fisheries", "soil quality", "uncertainty", "willingness to pay", "contingent valuation", "sustainable land management", "0105 earth and related environmental sciences"], "contacts": [{"organization": "Francisco Jos\u00e9 Areal", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.3390/land13081118"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Land", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/land13081118", "name": "item", "description": "10.3390/land13081118", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/land13081118"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-07-08T00:00:00Z"}}, {"id": "10.3390/s17040720", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:22:06Z", "type": "Journal Article", "created": "2017-03-30", "title": "A Data-Driven Diagnostic Framework for Wind Turbine Structures: A Holistic Approach", "description": "

The complex dynamics of operational wind turbine (WT) structures challenges the applicability of existing structural health monitoring (SHM) strategies for condition assessment. At the center of Europe\uffe2\uff80\uff99s renewable energy strategic planning, WT systems call for implementation of strategies that may describe the WT behavior in its complete operational spectrum. The framework proposed in this paper relies on the symbiotic treatment of acting environmental/operational variables and the monitored vibration response of the structure. The approach aims at accurate simulation of the temporal variability characterizing the WT dynamics, and subsequently at the tracking of the evolution of this variability in a longer-term horizon. The bi-component analysis tool is applied on long-term data, collected as part of continuous monitoring campaigns on two actual operating WT structures located in different sites in Germany. The obtained data-driven structural models verify the potential of the proposed strategy for development of an automated SHM diagnostic tool.

", "keywords": ["operational spectrum", "Chemical technology", "time varying autoregressive moving average (TV-ARMA) models", "Operational spectrum", "wind turbines; data-driven framework; uncertainty propagation; operational spectrum; time varying autoregressive moving average (TV-ARMA) models; polynomial chaos expansion (PCE)", "Data-driven framework", "uncertainty propagation", "TP1-1185", "02 engineering and technology", "7. Clean energy", "data-driven framework", "Article", "0201 civil engineering", "13. Climate action", "wind turbines", "polynomial chaos expansion (PCE)", "Uncertainty propagation", "Wind turbines", "Data-driven framework; Operational spectrum; Polynomial chaos expansion (PCE); Time varying autoregressive moving average (TV-ARMA) models; Uncertainty propagation; Wind turbines", "Polynomial chaos expansion (PCE)", "Time varying autoregressive moving average (TV-ARMA) models"]}, "links": [{"href": "http://www.mdpi.com/1424-8220/17/4/720/pdf"}, {"href": "https://doi.org/10.3390/s17040720"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Sensors", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/s17040720", "name": "item", "description": "10.3390/s17040720", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/s17040720"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-03-30T00:00:00Z"}}, {"id": "10.3929/ethz-b-000278733", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:22:16Z", "type": "Journal Article", "created": "2018-07-06", "title": "Cost\u2013benefit optimization of structural health monitoring sensor networks", "description": "

Structural health monitoring (SHM) allows the acquisition of information on the structural integrity of any mechanical system by processing data, measured through a set of sensors, in order to estimate relevant mechanical parameters and indicators of performance. Herein we present a method to perform the cost\uffe2\uff80\uff93benefit optimization of a sensor network by defining the density, type, and positioning of the sensors to be deployed. The effectiveness (benefit) of an SHM system may be quantified by means of information theory, namely through the expected Shannon information gain provided by the measured data, which allows the inherent uncertainties of the experimental process (i.e., those associated with the prediction error and the parameters to be estimated) to be accounted for. In order to evaluate the computationally expensive Monte Carlo estimator of the objective function, a framework comprising surrogate models (polynomial chaos expansion), model order reduction methods (principal component analysis), and stochastic optimization methods is introduced. Two optimization strategies are proposed: the maximization of the information provided by the measured data, given the technological, identifiability, and budgetary constraints; and the maximization of the information\uffe2\uff80\uff93cost ratio. The application of the framework to a large-scale structural problem, the Pirelli tower in Milan, is presented, and the two comprehensive optimization methods are compared.

", "keywords": ["Stochastic Processes", "structural health monitoring", "structural health monitoring; Bayesian inference; cost\u2013benefit analysis; stochastic optimization; information theory; Bayesian experimental design; surrogate modeling; model order reduction", "Chemical technology", "Cost-Benefit Analysis", "Bayesian inference", "Bayesian experimental design", "Uncertainty", "Bayes Theorem", "TP1-1185", "02 engineering and technology", "stochastic optimization", "Bayesian experimental design; Bayesian inference; Benefit analysis; Cost; Information theory; Model order reduction; Stochastic optimization; Structural health monitoring; Surrogate modeling; Algorithms; Monte Carlo Method; Nonlinear Dynamics; Stochastic Processes; Uncertainty; Bayes Theorem; Cost-Benefit Analysis; Analytical Chemistry; Atomic and Molecular Physics", " and Optics; Biochemistry; Instrumentation; Electrical and Electronic Engineering", "Article", "surrogate modeling", "0201 civil engineering", "Nonlinear Dynamics", "model order reduction", "cost\u2013benefit analysis", "Monte Carlo Method", "Algorithms", "information theory"]}, "links": [{"href": "http://www.mdpi.com/1424-8220/18/7/2174/pdf"}, {"href": "https://re.public.polimi.it/bitstream/11311/1085132/1/Sensors_2018b.pdf"}, {"href": "https://doi.org/10.3929/ethz-b-000278733"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Sensors", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3929/ethz-b-000278733", "name": "item", "description": "10.3929/ethz-b-000278733", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3929/ethz-b-000278733"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-07-06T00:00:00Z"}}, {"id": "10.5061/dryad.2f70818", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:22:27Z", "type": "Dataset", "title": "Data from: Differences in carbon stocks along an elevational gradient in tropical mountain forests of Colombia", "description": "unspecifiedTropical mountain forests provide an exceptional opportunity to evaluate the patterns of variation of carbon stocks along elevational gradients that correspond to well-defined temperature gradients. We predicted that carbon stored in live aboveground biomass, aboveground necromass, and soil components of forests on the eastern flank of the Colombian Andes would change with elevation along this gradient extending from 750 to 2800 m above sea level. The rationale was that the corresponding change in temperature (14\u00b0C to 26\u00b0C) would influence tree growth and decomposition of organic matter. To address this hypothesis, we examined the carbon stored in these three components using data from 20 0.25-ha plots located along this elevational gradient. The mean total carbon stock found in the study region was 241.3\u00b137.5 Mg C/ha. Aboveground carbon stocks decreased with elevation (p =0.001), as did necromass carbon stocks (p =0.016). Although soil organic carbon stocks did not differ significantly along the gradient (p =0.153), they contributed proportionately more at higher than at lower elevations, counterbalancing the opposite trends in aboveground carbon and necromass carbon stocks. As such, total carbon stocks did not vary significantly along the elevational gradient (p =0.576).", "keywords": ["carbon stocks", "soil organic carbon", "live aboveground biomass", "aboveground necromass", "15. Life on land", "Colombian Andes", "uncertainty analysis"], "contacts": [{"organization": "Phillips, Juan, Ramirez, Sebastian, Wayson, Craig, Duque, Alvaro,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.2f70818"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.2f70818", "name": "item", "description": "10.5061/dryad.2f70818", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.2f70818"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-06-19T00:00:00Z"}}, {"id": "10.5061/dryad.rn8pk0pm8", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:22:38Z", "type": "Dataset", "created": "2024-06-28", "title": "Uncertainties in greenhouse gas emission factors: A comprehensive analysis of switchgrass-based biofuel production", "description": "unspecifiedThis study investigates uncertainties in greenhouse gas (GHG) emission factors related to switchgrass-based biofuel production in Michigan. Using three life cycle assessment (LCA) databases\u2014 US lifecycle inventory database (USLCI), GREET, and Ecoinvent\u2014each with multiple versions, we recalculated the global warming intensity (GWI) and GHG mitigation potential in a static calculation. Employing Monte Carlo simulations along with local and global sensitivity analyses, we assess uncertainties and pinpoint key parameters influencing GWI. The convergence of results across our previous study, static calculations, and Monte Carlo simulations enhances the credibility of estimated GWI values. Static calculations, validated by Monte Carlo simulations, offer reasonable central tendencies, providing a robust foundation for policy considerations. However, the wider range observed in Monte Carlo simulations underscores the importance of potential variations and uncertainties in real-world applications. Sensitivity analyses identify biofuel yield, GHG emissions of electricity, and soil organic carbon (SOC) change as pivotal parameters influencing GWI. Decreasing uncertainties in GWI may be achieved by making greater efforts to acquire more precise data on these parameters. Our study emphasizes the significance of considering diverse GHG factors and databases in GWI assessments and stresses the need for accurate electricity fuel mixes, crucial information for refining GWI assessments and informing strategies for sustainable biofuel production.", "keywords": ["Sensitivity Analysis", "Switchgrass", "FOS: Environmental engineering", "Cellulosic biofuel", "Global warming intensity", "Greenhouse gas emission factor", "LCA database", "uncertainty analysis"], "contacts": [{"organization": "Kim, Seungdo, Dale, Bruce, Basso, Bruno,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.rn8pk0pm8"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.rn8pk0pm8", "name": "item", "description": "10.5061/dryad.rn8pk0pm8", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.rn8pk0pm8"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-07-16T00:00:00Z"}}, {"id": "10.5194/gmd-11-4139-2018", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:22:55Z", "type": "Journal Article", "created": "2018-04-25", "title": "Global hydro-climatic biomes identified via multitask learning", "description": "

Abstract. The most widely-used global land cover and climate classifications are based on vegetation characteristics and/or climatic conditions derived from observational data. However, these classification schemes do not directly stem from the interaction between the local climate and the biotic environment. In this work, we model the dynamic interplay between vegetation and local climate in order to delineate ecoregions that share a coherent response to hydro-climate variability. Our novel framework is based on a multi-task learning approach that discovers the spatial relationships among different locations by learning a low-dimensional representation of predictive structures. This low-dimensional representation is combined with a clustering algorithm that yields a classification of biomes with coherent behaviour. Experimental results using global observation-based data sets indicate that, without the need to prescribe any land cover information, our method is able to identify regions of coherent climate-vegetation interactions that agree well with the expectations derived from traditional global land cover maps. The resulting global hydro-climatic biomes can be used to analyse the anomalous behaviour of specific ecosystems in response to climate extremes and to benchmark climate-vegetation interactions in Earth system models.

", "keywords": ["0301 basic medicine", "QE1-996.5", "0303 health sciences", "INCREASES", "MODELS", "0207 environmental engineering", "Biology and Life Sciences", "INVESTIGATE", "UNCERTAINTY", "Geology", "WORLD MAP", "02 engineering and technology", "15. Life on land", "FRAMEWORK", "01 natural sciences", "CLASSIFICATION", "03 medical and health sciences", "CONTEXT", "13. Climate action", "Earth and Environmental Sciences", "VEGETATION", "GEOGRAPHICALLY WEIGHTED REGRESSION", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://gmd.copernicus.org/articles/11/4139/2018/gmd-11-4139-2018.pdf"}, {"href": "https://doi.org/10.5194/gmd-11-4139-2018"}, {"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": "10.5194/gmd-11-4139-2018", "name": "item", "description": "10.5194/gmd-11-4139-2018", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/gmd-11-4139-2018"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-04-25T00:00:00Z"}}, {"id": "10.5194/gmd-2021-98", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:22:56Z", "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": "

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.

", "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/10.5194/gmd-2021-98"}, {"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": "10.5194/gmd-2021-98", "name": "item", "description": "10.5194/gmd-2021-98", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/gmd-2021-98"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-05-17T00:00:00Z"}}, {"id": "10.5281/zenodo.14230218", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:23:45Z", "type": "Report", "title": "Methodological guidelines to collect data and assess the costs of Carbon Farming practices and associated MRV", "description": "This report, a deliverable of the MARVIC project, provides methodological guidelines for collecting data and assessing the costs of carbon farming (CF) practices and the associated Monitoring, Reporting, and Verification (MRV) methodologies. The data collected using these guidelines will serve as critical inputs for economic analyses conducted in Work Package 5 (WP5), supporting tasks such as i) assessing the economic impacts and cost-effectiveness of CF practices and schemes, accounting for MRV costs, ii) informing workshops on MRV systems in Test Cases (TCs), and iii) the evaluation of cost-effective regulatory schemes. They will also provide insight into the trade-offs between the cost and accuracy of the MRV methodologies that will be developed in MARVIC (WP2). This document is intended for scientists and experts in charge of the 26 MARVIC Test Cases across Europe, covering a wide range of Land-Uses and Soil Types (LUSTs), i.e., arable land on mineral soils, grassland on mineral soils, managed peatland, and agroforestry/woody crops. It provides practical guidance for standardized data collection, ensuring consistency and comparability of results. It addresses the definition and assessment of various cost (and revenue) categories induced by the adoption of a carbon farming practice and the implementation of an MRV methodology: operational costs (e.g., fertilizers, seeds, labor), opportunity costs (e.g., foregone revenues), and transaction costs (e.g., time spent to gather information on carbon farming schemes and MRV expenses). MRV costs, as a subset of transaction costs, depend on the type of policy, the precision required by the latter, and the methodology used. The report provides practical examples of CF practice costs calculation (e.g., cover /catch crops agroforestry, and peatland rewetting) and an example of MRV cost (for the French Label Bas Carbon certification framework). These are illustrative examples only, and serve to demonstrate data collection and calculation methodologies. Several options for MRV methodologies will be designed in the frame of the MARVIC project, for the different land use and soil types. Once they are available, WP1 will provide a list and a detailed description of the major MARVIC MRV methodologies, including a breakdown into main steps and potential cost items. This information will be used to draft a simple \u201cMRV cost\u201d spreadsheet to help Test Cases collect data and assess the costs of the relevant MRV methodologies for their specific context. WP2, WP3, and Test Cases will also provide information on the accuracy and uncertainty associated with the measurements, models, and operational processing chain tools used to assess carbon removals. This information will be used in Task 5.1 economic models to assess the present and future economic impacts and cost-effectiveness of CF policies, accounting for MRV costs.", "keywords": ["MRV cost", "Cost-effectiveness", "Accuracy and uncertainty", "Monitoring", " Reporting", " and Verification (MRV)", "Carbon farming"], "contacts": [{"organization": "BAMI\u00c8RE, Laure, LOUHICHI, Kamel, PHOTINODELLIS, Roxane, MAITAH, Mansoor, ELOFSSON, Katarina, HASLER, Berit,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14230218"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14230218", "name": "item", "description": "10.5281/zenodo.14230218", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14230218"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-12-06T00:00:00Z"}}, {"id": "2164/6134", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:27:03Z", "type": "Journal Article", "created": "2016-05-13", "title": "Modeling Soil Processes: Review, Key Challenges, and New Perspectives", "description": "Core Ideas

A community effort is needed to move soil modeling forward.

Establishing an international soil modeling consortium is key in this respect.

There is a need to better integrate existing knowledge in soil models.

Integration of data and models is a key challenge in soil modeling.

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.

", "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": "10.5281/zenodo.15404523", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:24:09Z", "type": "Report", "title": "Barriers and incentives for sharing input-data needed in carbon farming and MRV systems in Europe", "description": "An EU-coherent Monitoring, Reporting, and Verification (MRV) system needs the development of agreed rules for data sharing among public and private sectors, together with open access tools and procedures enabling for the standardisation and harmonisation of data. The following principles are recognised as essential to promote data sharing: collecting once and using for multiple purposes, respecting personal data privacy, defining and agreeing on the data re-use, giving a service back, and rewarding intellectual property rights.", "keywords": ["sharing", "data", "harmonisation", "carbon farming", "standardisation", "modeling", "fair", "uncertainty"], "contacts": [{"organization": "Fantappi\u00e8, Maria, Tziachris, Panagiotis, ASCHONITIS, VASSILIS, Monhonval, Arthur, Formaglio, Greta, Thakur, Gitanjali, Pierre-Philippe, Claude, Piccini, Chiara, Ilaria Falconi, FARINA, ROBERTA,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15404523"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15404523", "name": "item", "description": "10.5281/zenodo.15404523", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15404523"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-05-14T00:00:00Z"}}, {"id": "10.5281/zenodo.3477551", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:24:31Z", "type": "Report", "title": "Unsupervised local cluster-weighted Bagging the output from multiple stochastic simulators", "description": "Unsupervised local cluster-weighted Bagging the output from multiple stochastic simulators. The objective of this research
is to derive the local posterior predictive distribution when output from multiple (multi-fidelity) stochastic simulators are available.", "keywords": ["Model aggregation", "Model uncertainty", "Bagging", "Model combination", "multi-fidelity stochastic simulators", "Clustering", "Model Fusion"], "contacts": [{"organization": "Abdallah, Imad", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.3477551"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.3477551", "name": "item", "description": "10.5281/zenodo.3477551", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.3477551"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.8089771", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:24:56Z", "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/10.5281/zenodo.8089771"}, {"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.8089771", "name": "item", "description": "10.5281/zenodo.8089771", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8089771"}, {"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": "10.5281/zenodo.8090543", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:24:56Z", "type": "Journal Article", "created": "2020-09-03", "title": "Visualizations of Uncertainties in Precision Agriculture: Lessons Learned from Farm Machinery", "description": "

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.

", "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/10.5281/zenodo.8090543"}, {"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.8090543", "name": "item", "description": "10.5281/zenodo.8090543", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8090543"}, {"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": "20.500.14017/81a6df94-d40c-4db1-86dc-539a3cb8aaf8", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:26:52Z", "type": "Journal Article", "created": "2022-07-18", "title": "Net irrigation requirement under different climate scenarios using AquaCrop over Europe", "description": "

Abstract. Global soil water availability is challenged by the effects of climate change and a growing population. On average, 70\u2009% of freshwater extraction is attributed to agriculture, and the demand is increasing. In this study, the effects of climate change on the evolution of the irrigation water requirement to sustain current crop productivity are assessed by using the Food and Agriculture Organization (FAO) crop growth model AquaCrop version 6.1. The model is run at 0.5\u2218lat\u00d70.5\u2218long resolution over the European mainland, assuming a general C3-type of crop, and forced by climate input data from the Inter-Sectoral Impact Model Intercomparison Project phase three (ISIMIP3). First, the AquaCrop surface soil moisture (SSM) forced with two types of ISIMIP3 historical meteorological datasets is evaluated with satellite-based SSM estimates in two ways. When driven by ISIMIP3a reanalysis meteorology, daily simulated SSM values have an unbiased root mean square difference of 0.08 and 0.06\u2009m3\u2009m\u22123, with SSM retrievals from the Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions, respectively, for the years 2015\u20132016 (2016 is the end year of the reanalysis data). When forced with ISIMIP3b meteorology from five global climate models (GCMs) for the years 2015\u20132020, the historical simulated SSM climatology closely agrees with the satellite-based SSM climatologies. Second, the evaluated AquaCrop model is run to quantify the future irrigation requirement, for an ensemble of five GCMs and three different emission scenarios. The simulated net irrigation requirement (Inet) of the three summer months for a near and far future climate period (2031\u20132060 and 2071\u20132100) is compared to the baseline period of 1985\u20132014 to assess changes in the mean and interannual variability of the irrigation demand. Averaged over the continent and the model ensemble, the far future Inet is expected to increase by 22\u2009mm per month (+30\u2009%) under a high-emission scenario Shared Socioeconomic Pathway (SSP) 3\u20137.0. Central and southern Europe are the most impacted, with larger Inet increases. The interannual variability in Inet is likely to increase in northern and central Europe, whereas the variability is expected to decrease in southern regions. Under a high mitigation scenario (SSP1\u20132.6), the increase in Inet will stabilize at around 13\u2009mm per month towards the end of the century, and interannual variability will still increase but to a smaller extent. The results emphasize a large uncertainty in the Inet projected by various GCMs.

", "keywords": ["IMPACTS", "LAND", "Technology", "Environmental Engineering", "AGRICULTURE", "DEFICIT IRRIGATION", "SIMULATE YIELD RESPONSE", "0207 environmental engineering", "UNCERTAINTY", "02 engineering and technology", "CROP WATER PRODUCTIVITY", "Environmental technology. Sanitary engineering", "01 natural sciences", "0905 Civil Engineering", "G", "DATA ASSIMILATION", "Geography. Anthropology. Recreation", "GE1-350", "Geosciences", " Multidisciplinary", "TD1-1066", "0105 earth and related environmental sciences", "2. Zero hunger", "Science & Technology", "3707 Hydrology", "T", "Geology", "15. Life on land", "TRENDS", "6. Clean water", "MODEL", "Environmental sciences", "0907 Environmental Engineering", "13. Climate action", "Physical Sciences", "Water Resources", "4013 Geomatic engineering", "0406 Physical Geography and Environmental Geoscience", "3709 Physical geography and environmental geoscience"]}, "links": [{"href": "https://hess.copernicus.org/articles/26/3731/2022/hess-26-3731-2022.pdf"}, {"href": "https://doi.org/20.500.14017/81a6df94-d40c-4db1-86dc-539a3cb8aaf8"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Hydrology%20and%20Earth%20System%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "20.500.14017/81a6df94-d40c-4db1-86dc-539a3cb8aaf8", "name": "item", "description": "20.500.14017/81a6df94-d40c-4db1-86dc-539a3cb8aaf8", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/20.500.14017/81a6df94-d40c-4db1-86dc-539a3cb8aaf8"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-12T00:00:00Z"}}, {"id": "10261/277923", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:25:57Z", "type": "Journal Article", "created": "2022-07-18", "title": "Net irrigation requirement under different climate scenarios using AquaCrop over Europe", "description": "

Abstract. Global soil water availability is challenged by the effects of climate change and a growing population. On average, 70\u2009% of freshwater extraction is attributed to agriculture, and the demand is increasing. In this study, the effects of climate change on the evolution of the irrigation water requirement to sustain current crop productivity are assessed by using the Food and Agriculture Organization (FAO) crop growth model AquaCrop version 6.1. The model is run at 0.5\u2218lat\u00d70.5\u2218long resolution over the European mainland, assuming a general C3-type of crop, and forced by climate input data from the Inter-Sectoral Impact Model Intercomparison Project phase three (ISIMIP3). First, the AquaCrop surface soil moisture (SSM) forced with two types of ISIMIP3 historical meteorological datasets is evaluated with satellite-based SSM estimates in two ways. When driven by ISIMIP3a reanalysis meteorology, daily simulated SSM values have an unbiased root mean square difference of 0.08 and 0.06\u2009m3\u2009m\u22123, with SSM retrievals from the Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions, respectively, for the years 2015\u20132016 (2016 is the end year of the reanalysis data). When forced with ISIMIP3b meteorology from five global climate models (GCMs) for the years 2015\u20132020, the historical simulated SSM climatology closely agrees with the satellite-based SSM climatologies. Second, the evaluated AquaCrop model is run to quantify the future irrigation requirement, for an ensemble of five GCMs and three different emission scenarios. The simulated net irrigation requirement (Inet) of the three summer months for a near and far future climate period (2031\u20132060 and 2071\u20132100) is compared to the baseline period of 1985\u20132014 to assess changes in the mean and interannual variability of the irrigation demand. Averaged over the continent and the model ensemble, the far future Inet is expected to increase by 22\u2009mm per month (+30\u2009%) under a high-emission scenario Shared Socioeconomic Pathway (SSP) 3\u20137.0. Central and southern Europe are the most impacted, with larger Inet increases. The interannual variability in Inet is likely to increase in northern and central Europe, whereas the variability is expected to decrease in southern regions. Under a high mitigation scenario (SSP1\u20132.6), the increase in Inet will stabilize at around 13\u2009mm per month towards the end of the century, and interannual variability will still increase but to a smaller extent. The results emphasize a large uncertainty in the Inet projected by various GCMs.

", "keywords": ["IMPACTS", "LAND", "Technology", "Environmental Engineering", "AGRICULTURE", "DEFICIT IRRIGATION", "SIMULATE YIELD RESPONSE", "0207 environmental engineering", "UNCERTAINTY", "02 engineering and technology", "CROP WATER PRODUCTIVITY", "Environmental technology. Sanitary engineering", "01 natural sciences", "0905 Civil Engineering", "G", "DATA ASSIMILATION", "Geography. Anthropology. Recreation", "GE1-350", "Geosciences", " Multidisciplinary", "TD1-1066", "0105 earth and related environmental sciences", "2. Zero hunger", "Science & Technology", "3707 Hydrology", "T", "Geology", "15. Life on land", "TRENDS", "6. Clean water", "MODEL", "Environmental sciences", "0907 Environmental Engineering", "13. Climate action", "Physical Sciences", "Water Resources", "4013 Geomatic engineering", "0406 Physical Geography and Environmental Geoscience", "3709 Physical geography and environmental geoscience"]}, "links": [{"href": "https://biblio.vub.ac.be/vubirfiles/86261359/Busschaert_etal_2022_HESS.pdf"}, {"href": "https://hess.copernicus.org/articles/26/3731/2022/hess-26-3731-2022.pdf"}, {"href": "https://doi.org/10261/277923"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Hydrology%20and%20Earth%20System%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10261/277923", "name": "item", "description": "10261/277923", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10261/277923"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-12T00:00:00Z"}}, {"id": "38448699", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:28:02Z", "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": "21.11116/0000-0006-C73B-8", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:26:57Z", "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"}}], "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&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&f=html", "hreflang": "en-US"}, {"rel": "collection", "type": "application/json", "title": "Collection URL", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main", "hreflang": "en-US"}, {"type": "application/geo+json", "rel": "first", "title": "items (first)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=uncertainty&", "hreflang": "en-US"}, {"rel": "next", "type": "application/geo+json", "title": "items (next)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=uncertainty&offset=50", "hreflang": "en-US"}], "numberMatched": 66, "numberReturned": 50, "distributedFeatures": [], "timeStamp": "2026-04-17T02:22:09.794538Z"}