{"type": "FeatureCollection", "features": [{"id": "10.1016/j.jag.2024.103718", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:16:23Z", "type": "Journal Article", "created": "2024-02-20", "title": "Interseasonal transfer learning for crop mapping using Sentinel-1 data", "description": "Crop maps are highly desired information in modern agriculture as they enable possessors to manage their business in the most optimal way. Usually in remote sensing, crop mapping is performed using satellite images and within-season ground truth samples that are collected in extensive survey campaigns every year, neglecting information and knowledge that past seasons\u2019 classification models provided. This paper assessed different temporal transferring approaches, including transfer learning, together with traditional crop mapping approach to provide an exhaustive comparison. Transferring approaches differed in portion of knowledge utilized from a historical model and that coming from a target season dataset. Three distinct algorithms, Random Forest, Convolutional Neural Network and Transformer, were employed and evaluated using highly dense time series of Sentinel-1 data. Source and target domain were respectively represented by two sets, 2017\u20132020 and 2021 season data, and 9 different crop types were classified. Results showcased that transferring a model has a great potential in crop mapping when little to no ground truth data is available for the target season. However, traditional approach catches up rather quickly and even surpasses transfer learning approach in terms of performance after a certain portion of target domain data is collected. Without target season ground truth data, model transferring can yield modest crop mapping performance of 78% for F1 score, between 84% and 86% F1 score with transfer learning employed in conjunction with limited target season ground truth (i.e. between 120 and 720 parcels), and 88% F1 score at best with traditional approach (ca. 720 parcels). Even though a good discriminatory is found between different crop types, there is still a room for improvement regarding the least represented classes in the dataset. The study significantly contributes to the area of agricultural monitoring and management by demonstrating the effectiveness of transfer learning while lessening the necessity for extensive and labor-intensive data collection, thereby fostering cost and time efficiency. Utilizing Sentinel-1 data, it provides a practical and efficient solution for agricultural analysis worldwide regardless of cloudiness.", "keywords": ["2. Zero hunger", "Physical geography", "Crop mapping", "0211 other engineering and technologies", "04 agricultural and veterinary sciences", "02 engineering and technology", "15. Life on land", "Transfer learning", "GB3-5030", "Environmental sciences", "Sentinel-1", "Pre-trained model", "0401 agriculture", " forestry", " and fisheries", "GE1-350", "Domain"]}, "links": [{"href": "https://doi.org/10.1016/j.jag.2024.103718"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/International%20Journal%20of%20Applied%20Earth%20Observation%20and%20Geoinformation", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.jag.2024.103718", "name": "item", "description": "10.1016/j.jag.2024.103718", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.jag.2024.103718"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-04-01T00:00:00Z"}}, {"id": "10.1016/j.jag.2022.103101", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:16:23Z", "type": "Journal Article", "created": "2022-11-10", "title": "Forest foliage fuel load estimation from multi-sensor spatiotemporal features", "description": "Foliage fuel is the most flammable component in crown fires. Spatiotemporal dynamics of foliage fuel load (FFL) are important for fire managers to assess fire risk. Here, we integrated optical data from the Landsat 8 Operational Land Imager (OLI) with synthetic aperture radar (SAR) data from Sentinel-1 to estimate FFL. We first reconstructed seamless time series from the Landsat 8 and Sentinel-1 imagery by accounting for unequal time intervals between image observations and outliers. We then extracted temporal features that are proxies of the intra- and inter-annual dynamics from these time series. In addition, we derived spatial features from the imagery that quantify spatial context and therefore used varying window sizes. The random forest regression was implemented to assess the importance of the spatiotemporal features, reduce errors, and derive robust FFL estimates. The satellite estimates were validated against 96 field measurements from Pinus yunnanensis forests in the Liangshan Yi Autonomous Prefecture, Sichuan Province, China. Both the spatiotemporal features of SAR and optical data importantly contributed to FFL estimation. When only optical data was used, the model achieved a R2 of 0.75 (relative Root Mean Squared Error (rRMSE)\u00a0=\u00a025.3\u00a0%), while when only SAR data was used the R2 was 0.76 (rRMSE\u00a0=\u00a025.6\u00a0%). However, when optical and SAR data were combined, the R2 increased to 0.81 (rRMSE\u00a0=\u00a023.2\u00a0%). We also found that temporal features were more important predictors of FFL than features that captured spatial context. We demonstrated our FFL mapping method by a case study in the Chinese Sichuan Province, in relation to the occurrence of a fire. Our method needs additional validation over different tree species and forest types, yet has potential for mapping forest fuel loads and fire risk.", "keywords": ["Landsat 8", "Physical geography", "04 agricultural and veterinary sciences", "15. Life on land", "Fire risk", "01 natural sciences", "GB3-5030", "Spatiotemporal features", "Environmental sciences", "Forest foliage fuel load", "Sentinel-1", "0401 agriculture", " forestry", " and fisheries", "GE1-350", "SDG 14 - Life Below Water", "Random forest", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.1016/j.jag.2022.103101"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/International%20Journal%20of%20Applied%20Earth%20Observation%20and%20Geoinformation", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.jag.2022.103101", "name": "item", "description": "10.1016/j.jag.2022.103101", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.jag.2022.103101"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-12-01T00:00:00Z"}}, {"id": "10.1016/j.jag.2024.103659", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:16:23Z", "type": "Journal Article", "created": "2024-01-21", "title": "Automatized Sentinel-2 mosaicking for large area forest mapping", "description": "Creating maps of forest inventory variables is commonly taking advantage of satellite images, which are mosaicked together for gaining larger coverage. Recently, mosaicking has increasingly shifted towards user friendly cloud-based online environments such as Google Earth Engine (GEE), which are equipped with huge image repositories and extensive processing capabilities. This enables the easy transferability of workflows into new image sets and diversifies the range of methodological options for mosaicking. The quality control of the output mosaic, ensuring that the reflectance values are representative to the targeted land cover, is however primarily based on certain assumptions or pre-set rules which may not always produce an optimal result. Our study focuses on assessing and comparing the performance of three different mosaicking algorithms for predicting forest inventory variables, based on an extensive set of field data on the main site type, fertility class, and volume and biomass of growing stock. One of the compared mosaics derives from manual image selection, thus enabling rigorous visual quality control, and two others are resting on GEE-assisted automatized methods which include applying a percentile-based statistic over all the input reflectance values and selecting the best pixels using predefined quality indicators. The results indicate that the manual and the percentile-based mosaics are generally providing the best and relatively equal performance levels. Compared to them, the quality-based mosaic has slightly lower accuracy particularly when predicting continuous variables (i.e., the volume and biomass of growing stock) and it suffers from minor image defects. For the total volume of growing stock, for example, the RMS errors are 56.22 % for the manual, 56.33 % for the percentile-based, and 59.47 % for the quality-based mosaics, respectively. These results indicate that from the perspective of large area forest mapping, automatically generated mosaics may provide approximately similar accuracy as compared to manually controlled workflow at a fraction of the workload.", "keywords": ["Image mosaicking", "Physical geography", "791", "forest research", "04 agricultural and veterinary sciences", "15. Life on land", "Feature prediction", "01 natural sciences", "GB3-5030", "Environmental sciences", "0401 agriculture", " forestry", " and fisheries", "GE1-350", "Sentinel-2", "Google Earth Engine", "satellite images", "Forest inventory", "0105 earth and related environmental sciences"], "contacts": [{"organization": "Balazs Andras, Tuominen Sakari, Pitk\u00e4nen Timo P.,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.1016/j.jag.2024.103659"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/International%20Journal%20of%20Applied%20Earth%20Observation%20and%20Geoinformation", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.jag.2024.103659", "name": "item", "description": "10.1016/j.jag.2024.103659", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.jag.2024.103659"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-03-01T00:00:00Z"}}, {"id": "10.1016/j.ejrh.2021.100882", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:15:54Z", "type": "Journal Article", "created": "2021-07-30", "title": "Surface water and groundwater interaction at long-term exploited riverbank filtration site based on groundwater flow modelling (Mosina-Krajkowo, Poland)", "description": "Study region: Poland, Warta River catchment. Study focus: The study aimed to explain the reasons for spatial variability in chloride concentrations at the Mosina-Krajkowo riverbank filtration (RBF) site located along the river. This variability is attributed to RBF\u2019s different intensity along the river sections, related, among others, to clogging development. The RBF effectiveness was studied using groundwater flow modelling by: examining the water balance in zones established on hydrogeological setting and chloride concentrations; travel time of the bankfiltrate investigation; RBF parametrisation (i.e. infiltration per unit area and specific infiltration per unit of riverbank). New Hydrological Insights for the Region: The study identifies zones of the most favourable RBF conditions and establishes the variability causes. The overall share bankfiltrate was found at 75.8 %. Its spatial variation ranged widely from 41.1\u201389.3%, confirming the usefulness of the RBF performance sectional analysis in managing this type of site. The highest proportion of surface water (>80 %) occurred along the straight river section, where the riverbed was built by fine and medium sands (preventing penetration of organic suspension into the aquifer). In contrast, the lowest values (<42 %) occurred in the meander zone (with the most favourable RBF conditions at the beginning of site operation), where deep erosion reached coarse-grained sediments in the river bottom, followed by the development of clogging processes and a decrease in the RBF efficiency with time.", "keywords": ["Physical geography", "QE1-996.5", "Riverbed clogging", "Numerical modelling", "0208 environmental biotechnology", "0207 environmental engineering", "Geology", "Modflow", "02 engineering and technology", "Riverbank filtration", "6. Clean water", "Modpath", "GB3-5030"]}, "links": [{"href": "https://doi.org/10.1016/j.ejrh.2021.100882"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Hydrology%3A%20Regional%20Studies", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.ejrh.2021.100882", "name": "item", "description": "10.1016/j.ejrh.2021.100882", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.ejrh.2021.100882"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-10-01T00:00:00Z"}}, {"id": "10.1016/j.ejrh.2021.100903", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:15:54Z", "type": "Journal Article", "created": "2021-09-03", "title": "Evaluation of pedotransfer functions for predicting soil hydraulic properties: A voyage from regional to field scales across Europe", "description": "Study region: Europe. A total of 660, 522, and 4940 soil samples belonging to GRIZZLY, HYPRES, and EU-HYDI databases, respectively, were used for parametric evaluation. Study focus: The soil water retention and hydraulic conductivity functions are crucial input information for land surface models. Determining these functions by using direct methods is hampered by excessive time and unaffordable costs required for field activities and laboratory analyses. Pedotransfer functions (PTFs) are widely-used indirect techniques enabling soil hydraulic properties to be predicted by using easily-retrievable soil information. In a parametric evaluation, the predictive capability of PTFs is examined by comparing measured and estimated soil water retention parameters and saturated hydraulic conductivity. Yet information about the performance of PTFs for specific modeling applications is mandatory to evaluate PTF effectiveness in greater depth. This approach is commonly defined as functional evaluation. New hydrological insights for the region: The best performing four PTFs selected in the parametric evaluations are tested under two functional evaluations. The first encompasses a spatial interpolation with a geostatistical technique, whereas the second employs Hydrus-1D to simulate the water balance components along an experimental transect. Our results reinforce and integrate the insights of previous studies about the use of a PTF, and highlight the ability, or inability, of this technique to adequately reproduce the observed spatial variability of soil hydraulic properties and simulated water fluxes.", "keywords": ["S1 Agriculture (General) / mez\u0151gazdas\u00e1g \u00e1ltal\u00e1ban", "Physical geography", "QE1-996.5", "Water retention function", "Hydrus-1D", "saturated hydraulic conductivity", "0208 environmental biotechnology", "0207 environmental engineering", "Geology", "02 engineering and technology", "15. Life on land", "Semi-variogram", "S590 Soill / Talajtan", "Saturated hydraulic conductivity", "6. Clean water", "GB3-5030", "Kriging", "semi-variogram", "functional evaluation", "water retention function", "Functional evaluation", "kriging", "water retention function", " saturated hydraulic conductivity", " semi-variogram", " kriging", " functional evaluation", " Hydrus-1D"]}, "links": [{"href": "https://doi.org/10.1016/j.ejrh.2021.100903"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Hydrology%3A%20Regional%20Studies", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.ejrh.2021.100903", "name": "item", "description": "10.1016/j.ejrh.2021.100903", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.ejrh.2021.100903"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-10-01T00:00:00Z"}}, {"id": "10.1016/j.srs.2024.100118", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:16:56Z", "type": "Journal Article", "created": "2024-01-28", "title": "Satellite-based soil organic carbon mapping on European soils using available datasets and support sampling", "description": "Soil organic carbon (SOC) plays a major role in the global carbon cycle and is an important factor for soil health and fertility. Accurate mapping of SOC and other influencing parameters are crucial to guide the optimization of agricultural land management to maintain and restore soil health, to increase soil fertility, and thus to quantify its potential for sequestering CO2. Remote sensing and machine learning techniques offer promising approaches for predicting SOC distribution. In this study, we used remote sensing data and machine learning algorithms to map SOC at regional to large scale, which we then combined with temporospatial and spectral signature-based soil sampling to integrate local ground measurements. A rigorous validation approach was performed where several independent unseen datasets with a high number of samples were used, which additionally involved densely sampled fields. We found that our approach could predict SOC with an average percentage error of less than 10\u00a0% with an R2 of 0.91 using support sampling on croplands located on mineral soils, demonstrating the potential of remote sensing, machine learning, and specific ground measurements for mapping SOC. Our results suggest that this approach could make small carbon differences measurable and inform carbon sequestration efforts and improve our understanding of the impacts of land use and field management practices on soil carbon cycling.", "keywords": ["2. Zero hunger", "Physical geography", "Precision agriculture", "Science", "Q", "04 agricultural and veterinary sciences", "Remote sensing", "15. Life on land", "01 natural sciences", "GB3-5030", "13. Climate action", "Soil health", "Machine learning", "Soil carbon mapping", "0401 agriculture", " forestry", " and fisheries", "Soil carbon sequestration", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.1016/j.srs.2024.100118"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Science%20of%20Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.srs.2024.100118", "name": "item", "description": "10.1016/j.srs.2024.100118", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.srs.2024.100118"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-06-01T00:00:00Z"}}, {"id": "10.1029/2021ms002730", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:17:28Z", "type": "Journal Article", "created": "2022-02-17", "title": "Characterising the response of vegetation cover to water limitation in Africa using geostationary satellites", "description": "Abstract<p>Hydrological interactions between vegetation, soil, and topography are complex, and heterogeneous in semi\uffe2\uff80\uff90arid landscapes. This along with data scarcity poses challenges for large\uffe2\uff80\uff90scale modeling of vegetation\uffe2\uff80\uff90water interactions. Here, we exploit metrics derived from daily Meteosat data over Africa at ca. 5\uffc2\uffa0km spatial resolution for ecohydrological analysis. Their spatial patterns are based on Fractional Vegetation Cover (FVC) time series and emphasize limiting conditions of the seasonal wet to dry transition: the minimum and maximum FVC of temporal record, the FVC decay rate and the FVC integral over the decay period. We investigate the relevance of these metrics for large scale ecohydrological studies by assessing their co\uffe2\uff80\uff90variation with soil moisture, and with topographic, soil, and vegetation factors. Consistent with our initial hypothesis, FVC minimum and maximum increase with soil moisture, while the FVC integral and decay rate peak at intermediate soil moisture. We find evidence for the relevance of topographic moisture variations in arid regions, which, counter\uffe2\uff80\uff90intuitively, is detectable in the maximum but not in the minimum FVC. We find no clear evidence for wide\uffe2\uff80\uff90spread occurrence of the \uffe2\uff80\uff9cinverse texture effect\uffe2\uff80\uff9d on FVC. The FVC integral over the decay period correlates with independent data sets of plant water storage capacity or rooting depth while correlations increase with aridity. In arid regions, the FVC decay rate decreases with canopy height and tree cover fraction as expected for ecosystems with a more conservative water\uffe2\uff80\uff90use strategy. Thus, our observation\uffe2\uff80\uff90based products have large potential for better understanding complex vegetation\uffe2\uff80\uff90water interactions from regional to continental scales.</p>", "keywords": ["Physical geography", "GROUNDWATER-DEPENDENT ECOSYSTEMS", "water limitation", "GC1-1581", "geostationary", "SOIL-MOISTURE", "Oceanography", "01 natural sciences", "ecohydrology", "ROOTING DEPTH", "ACTIVE-ROLE", "WOODY COVER", "0105 earth and related environmental sciences", "fractional vegetation cover", "HYDROLOGIC PROCESSES", "15. Life on land", "6. Clean water", "GB3-5030", "MODEL", "CLIMATE", "13. Climate action", "Earth and Environmental Sciences", "PRECIPITATION", "Africa", "PATTERNS", "Research Article"]}, "links": [{"href": "https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2021MS002730"}, {"href": "https://doi.org/10.1029/2021ms002730"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Advances%20in%20Modeling%20Earth%20Systems", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1029/2021ms002730", "name": "item", "description": "10.1029/2021ms002730", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1029/2021ms002730"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-11-25T00:00:00Z"}}, {"id": "10.1080/10106049.2025.2493741", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:18:04Z", "type": "Journal Article", "created": "2025-04-28", "title": "The model for grain wheat yield prediction at high spatial resolution based on physical-geographical properties and satellite vegetation indices", "description": "Precision agriculture is promising approach for improving agricultural production, especially nowadays when the population is rapidly increasing. For that, crop yield estimation provides valuable information. The main research focus was to predict within-field grain yield and detect its drivers. The Random Forest regression model on data from diverse sources at the 10-meter spatial resolution was developed. The study was conducted in the Vojvodina region (Serbia) for eight wheat-planted fields, having precise grain yield data. Open-source data including 15 vegetation indices (VIs) was calculated from Sentinel-2 satellite bands, physical-geographical features obtained from the digital elevation model and soil properties. The model succeeded in predicting the wheat grain yield with the RMSE of 0.66 t/ha (average yield of 0.09 t/ha) and the best predictors were VIs considering chlorophyll and moisture content in plants, while physical-geographical properties managed to explain within-field variability. This methodology can be applied to other crops (maize, soybean).", "keywords": ["Topography", "remote sensing", "Physical geography", "machine learning", "remotesensing", "wheat yield", "GB3-5030"], "contacts": [{"organization": "Blagojevi\u0107, Dragana, Pajevi\u0107, Nina, Mimi\u0107, Gordan, \u0106ukovi\u0107, Stefanija, Markovi\u0107, Slobodan B., Maestrini, Bernardo, Brdar, Sanja,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.1080/10106049.2025.2493741"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Geocarto%20International", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1080/10106049.2025.2493741", "name": "item", "description": "10.1080/10106049.2025.2493741", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1080/10106049.2025.2493741"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-04-28T00:00:00Z"}}, {"id": "10.3390/soilsystems3010021", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:20:51Z", "type": "Journal Article", "created": "2019-03-25", "title": "Abiotic and Biotic Factors Influencing the Effect of Microplastic on Soil Aggregation", "description": "<p>Plastic is an anthropogenic, ubiquitous and persistent contaminant accumulating in our environment. The consequences of the presence of plastics for soils, including soil biota and the processes they drive, are largely unknown. This is particularly true for microplastic. There is only little data available on the effect of microplastics on key soil processes, including soil aggregation. Here, we investigated the consequences of polyester microfiber contamination on soil aggregation of a sandy soil under laboratory conditions. We aimed to test if the microfiber effects on soil aggregation were predominantly physical or biological. We found that soil biota addition (compared to sterile soil) had a significant positive effect on both the formation and stabilization of soil aggregates, as expected, while wet-dry cycles solely affected aggregate formation. Polyester microfiber contamination did not affect the formation and stability of aggregates. But in the presence of soil biota, microfibers reduced soil aggregate stability. Our results show that polyester microfibers have the potential to alter soil structure, and that these effects are at least partially mediated by soil biota.</p>", "keywords": ["570", "wet-dry cycle", "Physical geography", "04 agricultural and veterinary sciences", "15. Life on land", "01 natural sciences", "GB3-5030", "soil aggregation", "Chemistry", "soil microbes", "13. Climate action", "0401 agriculture", " forestry", " and fisheries", "polyester", "microplastic", "QD1-999", "fiber", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://www.mdpi.com/2571-8789/3/1/21/pdf"}, {"href": "https://www.mdpi.com/2571-8789/3/1/21/pdf"}, {"href": "https://doi.org/10.3390/soilsystems3010021"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Soil%20Systems", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/soilsystems3010021", "name": "item", "description": "10.3390/soilsystems3010021", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/soilsystems3010021"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-03-22T00:00:00Z"}}, {"id": "10.3390/soilsystems3030045", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:20:51Z", "type": "Journal Article", "created": "2019-07-15", "title": "Sounds of Soil: A New World of Interactions under Our Feet?", "description": "<p>Soils are biodiversity-dense and constantly carry chemical flows of information, with our mental image of soil being dark and quiet. But what if soil biota tap sound, or more generally, vibrations as a source of information? Vibrations are produced by soil biota, and there is accumulating evidence that such vibrations, including sound, may also be perceived. We here argue for potential advantages of sound/vibration detection, which likely revolve around detection of potential danger, e.g., predators. Substantial methodological retooling will be necessary to capture this form of information, since sound-related equipment is not standard in soils labs, and in fact this topic is very much at the fringes of the classical soil research at present. Sound, if firmly established as a mode of information exchange in soil, could be useful in an \uffe2\uff80\uff98acoustics-based\uffe2\uff80\uff99 precision agriculture as a means of assessing aspects of soil biodiversity, and the topic of sound pollution could move into focus for soil biota and processes.</p>", "keywords": ["580", "disturbance", "0301 basic medicine", "2. Zero hunger", "570", "Physical geography", "0303 health sciences", "15. Life on land", "soil", "GB3-5030", "sound", "Chemistry", "03 medical and health sciences", "13. Climate action", "vibration", "QD1-999", "biodiversity"]}, "links": [{"href": "https://www.mdpi.com/2571-8789/3/3/45/pdf"}, {"href": "https://doi.org/10.3390/soilsystems3030045"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Soil%20Systems", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/soilsystems3030045", "name": "item", "description": "10.3390/soilsystems3030045", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/soilsystems3030045"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-07-14T00:00:00Z"}}, {"id": "10.3390/soilsystems7030078", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:20:51Z", "type": "Journal Article", "created": "2023-09-11", "title": "Research Evolution on the Impact of Agronomic Practices on Soil Health from 1996 to 2021: A Bibliometric Analysis", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>In the last two decades, there has been a significant shift in focus towards soil health by international institutions, organizations, and scholars. Recognizing the vital role of soil in sustaining agriculture, ecosystems, and mitigating climate change, there has been a concerted effort to study and understand soil health more comprehensively. In this study, a bibliometric analysis was performed in order to determine the research trend of the articles published in the Scopus database in the last 26 years on soil health experimental studies and agronomic practices conducted in field conditions on agricultural soils. It has been observed that, after 2013, there has been a significant increase in research articles on soil health, with the USA and India research institutions ranking as the most productive on this topic. There is an asymmetry in international cooperation among research institutions, as well as for scholars. In addition, the research topic is gradually shifting from the effects of soil management strategies, especially nutrient management, on soil organic carbon and yield to the study of the impact of soil management on biochemistry and microbiological soil activities and greenhouse gas emissions. Future research should focus into more integrated approaches to achieve soil indicators enabling to evaluate the impact of sustainable management practices (e.g., cropping practices) on soil health.</p></article>", "keywords": ["2. Zero hunger", "Physical geography", "agronomic practices", "soil health", "04 agricultural and veterinary sciences", "15. Life on land", "01 natural sciences", "12. Responsible consumption", "GB3-5030", "Chemistry", "bibliometric analysis", "13. Climate action", "0401 agriculture", " forestry", " and fisheries", "QD1-999", "0105 earth and related environmental sciences"], "contacts": [{"organization": "Mohamed Houssemeddine Sellami, Fabio Terribile,", "roles": ["creator"]}]}, "links": [{"href": "https://www.mdpi.com/2571-8789/7/3/78/pdf"}, {"href": "https://doi.org/10.3390/soilsystems7030078"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Soil%20Systems", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/soilsystems7030078", "name": "item", "description": "10.3390/soilsystems7030078", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/soilsystems7030078"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-09-11T00:00:00Z"}}, {"id": "10.3390/soilsystems9010010", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:20:52Z", "type": "Journal Article", "created": "2025-01-27", "title": "Application of Self-Organizing Maps to Explore the Interactions of Microorganisms with Soil Properties in Fruit Crops Under Different Management and Pedo-Climatic Conditions", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Background: Self-organizing maps (SOMs) are a class of neural network algorithms able to visually describe a high-dimensional dataset onto a two-dimensional grid. SOMs were explored to classify soils based on an array of physical, chemical, and biological parameters. Methods: The SOM analysis was performed considering soil physical, chemical, and microbial data gathered from an array of apple orchards and strawberry plantations managed by organic or conventional methods and located in different European climatic zones. Results: The SOM analysis considering the \u201cclimatic zone\u201d categorical variables was able to discriminate the samples from the three zones for both crops. The zones were associated with different soil textures and chemical characteristics, and for both crops, the Continental zone was associated with microbial parameters\u2014including biodiversity indices derived from the NGS data analysis. However, the SOM analysis based on the \u201cmanagement method\u201d categorical variables was not able to discriminate the soils between organic and integrated management. Conclusions: This study allowed for the discrimination of soils of medium- and long-term fruit crops based on their pedo-climatic characteristics and associating these characteristics to some indicators of the soil biome, pointing to the possibility of better understanding the interactions among diverse variables, which could support unraveling the intricate web of relationships that define soil quality.</p></article>", "keywords": ["Physical geography", "Chemistry", "soil microbiome diversity", "apple", "strawberry", "neural networks", "QD1-999", "GB3-5030"]}, "links": [{"href": "https://www.mdpi.com/2571-8789/9/1/10/pdf"}, {"href": "https://doi.org/10.3390/soilsystems9010010"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Soil%20Systems", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/soilsystems9010010", "name": "item", "description": "10.3390/soilsystems9010010", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/soilsystems9010010"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-01-26T00:00:00Z"}}, {"id": "10.3390/soilsystems3020039", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:20:51Z", "type": "Journal Article", "created": "2019-06-12", "title": "Mapping Soil Biodiversity in Europe and the Netherlands", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Soil is fundamental for the functioning of terrestrial ecosystems, but our knowledge about soil organisms and the habitat they provide (shortly: Soil biodiversity) is poorly developed. For instance, the European Atlas of Soil Biodiversity and the Global Soil Biodiversity Atlas contain maps with rather coarse information on soil biodiversity. This paper presents a methodology to map soil biodiversity with limited data and models. Two issues were addressed. First, the lack of consensus to quantify the soil biodiversity function and second, the limited data to represent large areas. For the later issue, we applied a digital soil mapping (DSM) approach at the scale of the Netherlands and Europe. Data of five groups of soil organisms (earthworms, enchytraeids, micro-arthropods, nematodes, and micro-organisms) in the Netherlands were linked to soil habitat predictors (chemical soil attributes) in a regression analysis. High-resolution maps with soil characteristics were then used together with a model for the soil biodiversity function with equal weights for each group of organisms. To predict soil biodiversity at the scale of Europe, data for soil biological (earthworms and bacteria) and chemical (pH, soil organic matter, and nutrient content) attributes were used in a soil biodiversity model. Differential weights were assigned to the soil attributes after consulting a group of scientists. The issue of reducing uncertainty in soil biodiversity modelling and mapping by the use of data from biological soil attributes is discussed. Considering the importance of soil biodiversity to support the delivery of ecosystem services, the ability to create maps illustrating an aggregate measure of soil biodiversity is a key to future environmental policymaking, optimizing land use, and land management decision support taking into account the loss and gains on soil biodiversity.</p></article>", "keywords": ["2. Zero hunger", "Physical geography", "Soil multi-functionality", "soil biodiversity", "04 agricultural and veterinary sciences", "soil functions", "15. Life on land", "Soil functions", "Soil biodiversity", "GB3-5030", "Chemistry", "Digital soil mapping", "13. Climate action", "soil multi-functionality", "digital soil mapping", "Ecosystem services", "0401 agriculture", " forestry", " and fisheries", "ecosystem services", "Biology", "QD1-999"]}, "links": [{"href": "http://www.mdpi.com/2571-8789/3/2/39/pdf"}, {"href": "https://www.mdpi.com/2571-8789/3/2/39/pdf"}, {"href": "https://doi.org/10.3390/soilsystems3020039"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Soil%20Systems", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/soilsystems3020039", "name": "item", "description": "10.3390/soilsystems3020039", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/soilsystems3020039"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-06-12T00:00:00Z"}}, {"id": "11588/856948", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:24:42Z", "type": "Journal Article", "created": "2021-09-02", "title": "Evaluation of pedotransfer functions for predicting soil hydraulic properties: A voyage from regional to field scales across Europe", "description": "Study region: Europe. A total of 660, 522, and 4940 soil samples belonging to GRIZZLY, HYPRES, and EU-HYDI databases, respectively, were used for parametric evaluation. Study focus: The soil water retention and hydraulic conductivity functions are crucial input information for land surface models. Determining these functions by using direct methods is hampered by excessive time and unaffordable costs required for field activities and laboratory analyses. Pedotransfer functions (PTFs) are widely-used indirect techniques enabling soil hydraulic properties to be predicted by using easily-retrievable soil information. In a parametric evaluation, the predictive capability of PTFs is examined by comparing measured and estimated soil water retention parameters and saturated hydraulic conductivity. Yet information about the performance of PTFs for specific modeling applications is mandatory to evaluate PTF effectiveness in greater depth. This approach is commonly defined as functional evaluation. New hydrological insights for the region: The best performing four PTFs selected in the parametric evaluations are tested under two functional evaluations. The first encompasses a spatial interpolation with a geostatistical technique, whereas the second employs Hydrus-1D to simulate the water balance components along an experimental transect. Our results reinforce and integrate the insights of previous studies about the use of a PTF, and highlight the ability, or inability, of this technique to adequately reproduce the observed spatial variability of soil hydraulic properties and simulated water fluxes.", "keywords": ["S1 Agriculture (General) / mez\u0151gazdas\u00e1g \u00e1ltal\u00e1ban", "Physical geography", "QE1-996.5", "Water retention function", "Hydrus-1D", "saturated hydraulic conductivity", "0208 environmental biotechnology", "0207 environmental engineering", "Geology", "02 engineering and technology", "15. Life on land", "Semi-variogram", "S590 Soill / Talajtan", "Saturated hydraulic conductivity", "6. Clean water", "GB3-5030", "Kriging", "semi-variogram", "functional evaluation", "water retention function", "Functional evaluation", "kriging", "water retention function", " saturated hydraulic conductivity", " semi-variogram", " kriging", " functional evaluation", " Hydrus-1D"]}, "links": [{"href": "https://doi.org/11588/856948"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Hydrology%3A%20Regional%20Studies", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "11588/856948", "name": "item", "description": "11588/856948", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/11588/856948"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-10-01T00:00:00Z"}}, {"id": "1871.1/505fa0c0-6587-48f4-a8b1-4f1ad19d6bb8", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:24:50Z", "type": "Journal Article", "created": "2022-11-10", "title": "Forest foliage fuel load estimation from multi-sensor spatiotemporal features", "description": "Foliage fuel is the most flammable component in crown fires. Spatiotemporal dynamics of foliage fuel load (FFL) are important for fire managers to assess fire risk. Here, we integrated optical data from the Landsat 8 Operational Land Imager (OLI) with synthetic aperture radar (SAR) data from Sentinel-1 to estimate FFL. We first reconstructed seamless time series from the Landsat 8 and Sentinel-1 imagery by accounting for unequal time intervals between image observations and outliers. We then extracted temporal features that are proxies of the intra- and inter-annual dynamics from these time series. In addition, we derived spatial features from the imagery that quantify spatial context and therefore used varying window sizes. The random forest regression was implemented to assess the importance of the spatiotemporal features, reduce errors, and derive robust FFL estimates. The satellite estimates were validated against 96 field measurements from Pinus yunnanensis forests in the Liangshan Yi Autonomous Prefecture, Sichuan Province, China. Both the spatiotemporal features of SAR and optical data importantly contributed to FFL estimation. When only optical data was used, the model achieved a R2 of 0.75 (relative Root Mean Squared Error (rRMSE)\u00a0=\u00a025.3\u00a0%), while when only SAR data was used the R2 was 0.76 (rRMSE\u00a0=\u00a025.6\u00a0%). However, when optical and SAR data were combined, the R2 increased to 0.81 (rRMSE\u00a0=\u00a023.2\u00a0%). We also found that temporal features were more important predictors of FFL than features that captured spatial context. We demonstrated our FFL mapping method by a case study in the Chinese Sichuan Province, in relation to the occurrence of a fire. Our method needs additional validation over different tree species and forest types, yet has potential for mapping forest fuel loads and fire risk.", "keywords": ["Landsat 8", "Physical geography", "04 agricultural and veterinary sciences", "15. Life on land", "Fire risk", "01 natural sciences", "GB3-5030", "Spatiotemporal features", "Environmental sciences", "Forest foliage fuel load", "Sentinel-1", "0401 agriculture", " forestry", " and fisheries", "GE1-350", "SDG 14 - Life Below Water", "Random forest", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/1871.1/505fa0c0-6587-48f4-a8b1-4f1ad19d6bb8"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/International%20Journal%20of%20Applied%20Earth%20Observation%20and%20Geoinformation", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "1871.1/505fa0c0-6587-48f4-a8b1-4f1ad19d6bb8", "name": "item", "description": "1871.1/505fa0c0-6587-48f4-a8b1-4f1ad19d6bb8", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/1871.1/505fa0c0-6587-48f4-a8b1-4f1ad19d6bb8"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-12-01T00:00:00Z"}}, {"id": "3196546689", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:25:49Z", "type": "Journal Article", "created": "2021-09-03", "title": "Evaluation of pedotransfer functions for predicting soil hydraulic properties: A voyage from regional to field scales across Europe", "description": "Study region: Europe. A total of 660, 522, and 4940 soil samples belonging to GRIZZLY, HYPRES, and EU-HYDI databases, respectively, were used for parametric evaluation. Study focus: The soil water retention and hydraulic conductivity functions are crucial input information for land surface models. Determining these functions by using direct methods is hampered by excessive time and unaffordable costs required for field activities and laboratory analyses. Pedotransfer functions (PTFs) are widely-used indirect techniques enabling soil hydraulic properties to be predicted by using easily-retrievable soil information. In a parametric evaluation, the predictive capability of PTFs is examined by comparing measured and estimated soil water retention parameters and saturated hydraulic conductivity. Yet information about the performance of PTFs for specific modeling applications is mandatory to evaluate PTF effectiveness in greater depth. This approach is commonly defined as functional evaluation. New hydrological insights for the region: The best performing four PTFs selected in the parametric evaluations are tested under two functional evaluations. The first encompasses a spatial interpolation with a geostatistical technique, whereas the second employs Hydrus-1D to simulate the water balance components along an experimental transect. Our results reinforce and integrate the insights of previous studies about the use of a PTF, and highlight the ability, or inability, of this technique to adequately reproduce the observed spatial variability of soil hydraulic properties and simulated water fluxes.", "keywords": ["S1 Agriculture (General) / mez\u0151gazdas\u00e1g \u00e1ltal\u00e1ban", "Physical geography", "QE1-996.5", "Water retention function", "Hydrus-1D", "saturated hydraulic conductivity", "0208 environmental biotechnology", "0207 environmental engineering", "Geology", "02 engineering and technology", "15. Life on land", "Semi-variogram", "S590 Soill / Talajtan", "Saturated hydraulic conductivity", "6. Clean water", "GB3-5030", "Kriging", "semi-variogram", "functional evaluation", "water retention function", "Functional evaluation", "kriging", "water retention function", " saturated hydraulic conductivity", " semi-variogram", " kriging", " functional evaluation", " Hydrus-1D"]}, "links": [{"href": "https://doi.org/3196546689"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Hydrology%3A%20Regional%20Studies", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "3196546689", "name": "item", "description": "3196546689", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/3196546689"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-10-01T00:00:00Z"}}, {"id": "10029/623539", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:24:10Z", "type": "Journal Article", "created": "2019-06-12", "title": "Mapping Soil Biodiversity in Europe and the Netherlands", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Soil is fundamental for the functioning of terrestrial ecosystems, but our knowledge about soil organisms and the habitat they provide (shortly: Soil biodiversity) is poorly developed. For instance, the European Atlas of Soil Biodiversity and the Global Soil Biodiversity Atlas contain maps with rather coarse information on soil biodiversity. This paper presents a methodology to map soil biodiversity with limited data and models. Two issues were addressed. First, the lack of consensus to quantify the soil biodiversity function and second, the limited data to represent large areas. For the later issue, we applied a digital soil mapping (DSM) approach at the scale of the Netherlands and Europe. Data of five groups of soil organisms (earthworms, enchytraeids, micro-arthropods, nematodes, and micro-organisms) in the Netherlands were linked to soil habitat predictors (chemical soil attributes) in a regression analysis. High-resolution maps with soil characteristics were then used together with a model for the soil biodiversity function with equal weights for each group of organisms. To predict soil biodiversity at the scale of Europe, data for soil biological (earthworms and bacteria) and chemical (pH, soil organic matter, and nutrient content) attributes were used in a soil biodiversity model. Differential weights were assigned to the soil attributes after consulting a group of scientists. The issue of reducing uncertainty in soil biodiversity modelling and mapping by the use of data from biological soil attributes is discussed. Considering the importance of soil biodiversity to support the delivery of ecosystem services, the ability to create maps illustrating an aggregate measure of soil biodiversity is a key to future environmental policymaking, optimizing land use, and land management decision support taking into account the loss and gains on soil biodiversity.</p></article>", "keywords": ["2. Zero hunger", "Physical geography", "Soil multi-functionality", "soil biodiversity", "04 agricultural and veterinary sciences", "soil functions", "15. Life on land", "Soil functions", "Soil biodiversity", "GB3-5030", "Chemistry", "Digital soil mapping", "13. Climate action", "soil multi-functionality", "digital soil mapping", "Ecosystem services", "0401 agriculture", " forestry", " and fisheries", "ecosystem services", "Biology", "QD1-999"]}, "links": [{"href": "http://www.mdpi.com/2571-8789/3/2/39/pdf"}, {"href": "https://www.mdpi.com/2571-8789/3/2/39/pdf"}, {"href": "https://doi.org/10029/623539"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Soil%20Systems", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10029/623539", "name": "item", "description": "10029/623539", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10029/623539"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-06-12T00:00:00Z"}}, {"id": "11588/987830", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:24:42Z", "type": "Journal Article", "created": "2023-09-11", "title": "Research Evolution on the Impact of Agronomic Practices on Soil Health from 1996 to 2021: A Bibliometric Analysis", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>In the last two decades, there has been a significant shift in focus towards soil health by international institutions, organizations, and scholars. Recognizing the vital role of soil in sustaining agriculture, ecosystems, and mitigating climate change, there has been a concerted effort to study and understand soil health more comprehensively. In this study, a bibliometric analysis was performed in order to determine the research trend of the articles published in the Scopus database in the last 26 years on soil health experimental studies and agronomic practices conducted in field conditions on agricultural soils. It has been observed that, after 2013, there has been a significant increase in research articles on soil health, with the USA and India research institutions ranking as the most productive on this topic. There is an asymmetry in international cooperation among research institutions, as well as for scholars. In addition, the research topic is gradually shifting from the effects of soil management strategies, especially nutrient management, on soil organic carbon and yield to the study of the impact of soil management on biochemistry and microbiological soil activities and greenhouse gas emissions. Future research should focus into more integrated approaches to achieve soil indicators enabling to evaluate the impact of sustainable management practices (e.g., cropping practices) on soil health.</p></article>", "keywords": ["2. Zero hunger", "Physical geography", "agronomic practices", "soil health", "04 agricultural and veterinary sciences", "15. Life on land", "01 natural sciences", "12. Responsible consumption", "GB3-5030", "Chemistry", "bibliometric analysis", "13. Climate action", "0401 agriculture", " forestry", " and fisheries", "QD1-999", "0105 earth and related environmental sciences"], "contacts": [{"organization": "Mohamed Houssemeddine Sellami, Fabio Terribile,", "roles": ["creator"]}]}, "links": [{"href": "https://www.mdpi.com/2571-8789/7/3/78/pdf"}, {"href": "https://doi.org/11588/987830"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Soil%20Systems", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "11588/987830", "name": "item", "description": "11588/987830", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/11588/987830"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-09-11T00:00:00Z"}}, {"id": "21.11116/0000-0005-C54E-6", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-23T16:25:09Z", "type": "Report", "created": "2020-03-09", "title": "Mathematical Reconstruction of Land Carbon Models From Their Numerical Output: Computing Soil Radiocarbon From 12C Dynamics", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>         &amp;lt;p&amp;gt;Radiocarbon (&amp;lt;sup&amp;gt;14&amp;lt;/sup&amp;gt;C) is a powerful tracer of the global carbon cycle that is commonly used to assess carbon cycling rates in various Earth system reservoirs and as a benchmark to assess model performance. Therefore, it has been recommended that Earth System Models (ESMs) participating in the Coupled Model Intercomparison Project Phase 6 report predicted radiocarbon values for relevant carbon pools. However, a detailed representation of radiocarbon dynamics may be an impractical burden on model developers. Here, we present an alternative approach to compute radiocarbon values from the numerical output of an ESM that does not explicitly represent these dynamics. The approach requires computed &amp;lt;sup&amp;gt;12&amp;lt;/sup&amp;gt;C stocks and fluxes among all carbon pools for a particular simulation of the model. From this output, a time&amp;amp;#8208;dependent linear compartmental system is computed with its respective state&amp;amp;#8208;transition matrix. Using transient atmospheric &amp;lt;sup&amp;gt;14&amp;lt;/sup&amp;gt;C values as inputs, the state&amp;amp;#8208;transition matrix is then applied to compute radiocarbon values for each pool, the average value for the entire system, and component fluxes. We demonstrate the approach with ELMv1&amp;amp;#8208;ECA, the land component of an ESM model that explicitly represents &amp;lt;sup&amp;gt;12&amp;lt;/sup&amp;gt;C, and &amp;lt;sup&amp;gt;14&amp;lt;/sup&amp;gt;C in 7 soil pools and 10 vertical layers. Results from our proposed method are highly accurate (relative error &amp;lt;0.01%) compared with the ELMv1&amp;amp;#8208;ECA &amp;lt;sup&amp;gt;12&amp;lt;/sup&amp;gt;C and &amp;lt;sup&amp;gt;14&amp;lt;/sup&amp;gt;C predictions, demonstrating the potential to use this approach in CMIP6 and other model simulations that do not explicitly represent &amp;lt;sup&amp;gt;14&amp;lt;/sup&amp;gt;C.&amp;lt;/p&amp;gt;         </p></article>", "keywords": ["Physical geography", "Earth system models", "GC1-1581", "dynamical systems", "15. Life on land", "Oceanography", "compartmental systems", "01 natural sciences", "GB3-5030", "13. 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