{"type": "FeatureCollection", "features": [{"id": "10.1016/j.geoderma.2019.114061", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:17:00Z", "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.14039385", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:23:23Z", "type": "Dataset", "title": "Maps of topsoil (0-30 cm) properties of Tuscany (Italy)", "description": "Open AccessThe internal EJP SOIL project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national, and European scales. The topsoil (0-30 cm) properties maps are prepared to evaluate soil ecosystem services in SERENA/EJP-Soil and for applying SOC loss Cookbook and SOIL Loss Cookbook. In particular Soil Organic Carbon content map was directly considered as an application of SOC loss Cookbook (DOI: 10.5281/zenodo.13951265\u00a0Version 3). They are based on Tuscany Region soil database available at Geoscopio (https://www502.regione.toscana.it/geoscopio/pedologia.html) and on point soil data not freely available (Lamma Consortium). More information and requests to:\u00a0info@lamma.toscana.it. In accordance with the methodology reported in the Soil Organic Carbon Mapping Cookbook (Yigini et al., 2018), the following soil properties were mapped for all Tuscany Region: soil organic carbon content (dag/kg), soil organic carbon stock (t/ha), textural fractions (sand, silt and clay, USDA limits, dag/kg), rock fragments (vol/vol), pH in water, bulk density (g/cm3). They were obtained through Digital Soil Mapping (DSM) approach, based on correlations with numerous environmental factors and using Random Forest algorithm. All the maps have a 100 m spatial resolution.", "keywords": ["silt", "bulk density", "pH", "soil organic carbon content", "sand", "clay", "Grant n. 862695", "Digital Soil Mapping", "textural fractions", "Italy", "topsoil properties", "Tuscany", "soil organic carbon stock", "EJP-SOIL", "SERENA Project"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14039385"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14039385", "name": "item", "description": "10.5281/zenodo.14039385", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14039385"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-11-05T00:00:00Z"}}, {"id": "10.5281/zenodo.14936177", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:23:42Z", "type": "Dataset", "title": "Precision Liming Soil Datasets (LimeSoDa) Zenodo Repository", "description": "Overview Precision Liming Soil Datasets (LimeSoDa) is a collection of 31 datasets from a field- and farm-scale soil mapping context. These datasets are 'ready-to-use' for modeling purposes, as they include target soil properties and features in a tidy tabular format. Three target soil properties are present in every dataset: (1) soil organic matter (SOM) or soil organic carbon (SOC), (2) pH, and (3) clay content, while the features for modeling are dataset-specific. The primary goal of `LimeSoDa` is to enable more reliable benchmarking of machine learning methods in digital soil mapping and pedometrics. All the associated materials and data from LimeSoDa can be downloaded in this data repository. However, for a more in-depth analysis, we refer to the published paper 'LimeSoDa: A Dataset Collection for Benchmarking of Machine Learning Regressors in Digital Soil Mapping' by Schmidinger et al. (2025). You may also use our R\u00a0and Python package likewise called LimeSoDa. \u00a0 Citation Upon usage of datasets from LimeSoDa, please cite our associated paper: Schmidinger, J., Vogel, S., Barkov, V., Pham, A.-D., Gebbers, R., Tavakoli, H., Correa, J., Tavares, T.R., Filippi, P., Jones, E. J., Lukas, V., Boenecke, E., Ruehlmann, J., Schroeter, I., Kramer, E., Paetzold, S., Kodaira, M., Wadoux, A.M.J.-C., Bragazza, L., Metzger, K., Huang, J., Valente, D.S.M., Safanelli, J.L., Bottega, E.L., Dalmolin, R.S.D., Farkas, C., Steiger, A., Horst, T. Z., Ramirez-Lopez, L., Scholten, T., Stumpf, F., Rosso, P., Costa, M.M., Zandonadi, R.S., Wetterlind, J. & Atzmueller, M. (2025). LimeSoDa: A Dataset Collection for Benchmarking of Machine Learning Regressors in Digital Soil Mapping.", "keywords": ["Environmental sciences", "Soil Organic Carbon", "Pedometrics", "pH", "Soil Organic Matter", "Clay", "Remote sensing", "Digital Soil Mapping"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14936177"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14936177", "name": "item", "description": "10.5281/zenodo.14936177", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14936177"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.3591992", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:24:16Z", "type": "Dataset", "title": "Organic matter content (om) soil maps of the Upper Colorado River Basin", "description": "UPDATE: WE FOUND A RENDERING ERROR IN MANY AREAS OF THE 5 CM MAP. WE HAVE RECREATED THE MAP AND INCLUDED IN A NEW VERSION OF THE REPOSITORY. Repository includes maps of organic matter content (% wt) as defined by United States soil survey program. These data are preliminary or provisional and are subject to revision. They are being provided to meet the need for timely best science. The data have not received final approval by the U.S. Geological Survey (USGS) and are provided on the condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from the authorized or unauthorized use of the data. This data should be used in combination with a soil depth or depth to restriction layer map (both layers that will be released soon as part of this project) to eliminate areas mapped at deeper depths than the soil actually goes. This is a limitation of this data which will hopefully be updated in future updates. The creation and interpretation of this data is documented in the following article. Please note this article has not been reviewed yet and this citation will be updated as the peer review process proceeds. Nauman, T. W., Duniway, M. C., In Preparation. Predictive reconstruction of soil survey property maps for field scale adaptive land management. Soil Science Society of America Journal. File Name Details: ACCURACY!! Please see manuscript and Github repository (https://github.com/naumi421/SoilReconProps) for full details on accuracy. We do provide cross validation (CV) accuracy plots in this repository for both the overall sample (_CV_plots.tif). These plots compare CV predictions with observed values relative to a 1:1 line. Values plotted near the 1:1 line are more accurate. Note that values are plotted in hex-bin density scatter plots because of the large number of observations (most are >3000). Predictions are also evaluated with the U.S. soil survey laboratory database soil organic carbon (SOC) data. The SOC measurements were coverted to OM matter values using the common 1.724 conversion factor. The converted OM values are compared to predicted OM values using an accuracy plot (OM_SOC_plots.tif). Elements are separated by underscore (_) in the following sequence: property_r_depth_cm_geometry_model_additional_elements.extension Example: om_r_0_cm_2D_QRF_bt.tif Indicates soil organic matter content (om) at 0 cm depth using a 2D model (separate model for each depth) employing a quantile regression forest. This file is the raster prediction map for this model. There may be additional GIS files associated with this file (e.g. pyramids) that have the same file name, but different extensions. The _bt indicates that the map has been back transformed from ln or sqrt transformation used in modeling. The following elements may also exist on the end of filenames indicating other spatial files that characterize a given model's uncertainty (see below). _95PI_h: Indicates the layer is the upper 95% prediction interval value. _95PI_l: Indicates the layer is the lower 95% prediction interval value. _95PI_relwidth: Indicates the layer is the 95% relative prediction interval (RPI). The RPI is a standardization of the prediction interval that indicates that model is constraining uncertainty relative to the original sample. RPI values less than one represent uncertainty is being improved by the model relative to the original sample, and values less than 0.5 indicate low uncertainty in predictions. See paper listed above and also Nauman and Duniway (In revision) for more details on RPI. References Nauman, T. W., and Duniway, M. C., In Revision, Relative prediction intervals reveal larger uncertainty in 3D approaches to predictive digital soil mapping of soil properties with legacy data: Geoderma", "keywords": ["2. Zero hunger", "13. Climate action", "soil organic matter", "digital soil mapping", "15. Life on land", "6. Clean water", "predictive soil mapping", "soil property mapping"], "contacts": [{"organization": "Nauman, Travis", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.3591992"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.3591992", "name": "item", "description": "10.5281/zenodo.3591992", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.3591992"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-01-28T00:00:00Z"}}, {"id": "10.5281/zenodo.8089699", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:24:41Z", "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": "9b81642374175d90e0b717deca64ff67", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:29:13Z", "type": "Report", "title": "Satellite time series contribution to organic carbon mapping in cultivated soils at various regional scales", "description": "Open AccessLe carbone organique du sol (COS) dans les zones agricoles joue un r\u00f4le cl\u00e9 dans la s\u00e9curit\u00e9 alimentaire et l'att\u00e9nuation du changement climatique. La quantification du COS est n\u00e9cessaire pour mettre en \u0153uvre des techniques et des pratiques de stockage. Cependant, l'\u00e9chantillonnage du COS dans un monde qui couvre environ 1,5 milliard d'hectares de sols agricoles est un v\u00e9ritable d\u00e9fi. C'est pourquoi l'utilisation de technologies telles que les capteurs satellitaires constitue une alternative prometteuse pour quantifier et cartographier le COS dans diff\u00e9rents types d'agro\u00e9cosyst\u00e8mes \u00e0 travers le monde. L'objectif de cette th\u00e8se est d'\u00e9valuer le potentiel des images satellitaires Sentinel-2 (S2) et Sentinel-1 (S1) pour la cartographie du COS dans les agro-\u00e9cosyst\u00e8mes de la France m\u00e9tropolitaine en utilisant des mod\u00e8les spectraux et spatio-spectraux. Le chapitre 1 aborde l'\u00e9tat d'avancement de la cartographie du COS en France et pr\u00e9sente les principales limitations et m\u00e9thodes actuellement utilis\u00e9es avec les donn\u00e9es d'images satellitaires pour la pr\u00e9diction du COS. Le chapitre 2 pr\u00e9sente les zones d'\u00e9tude situ\u00e9es dans les r\u00e9gions Bretagne, Occitanie et Centre Val de Loire. De plus, les principaux ensembles de donn\u00e9es utilis\u00e9s sont d\u00e9crits et une analyse pr\u00e9liminaire de l'une des zones d'\u00e9tude est pr\u00e9sent\u00e9e. Le troisi\u00e8me chapitre \u00e9value le potentiel des images S2 et des produits d\u00e9riv\u00e9s de S1 et S2 pour pr\u00e9dire le SOC \u00e0 l'aide d'images \u00e0 date unique. Dans ce chapitre comme dans le second, des limitations li\u00e9es principalement aux conditions de surface du sol ont \u00e9t\u00e9 observ\u00e9es ; et les meilleures dates d'image pour d\u00e9tecter le SOC ont \u00e9t\u00e9 identifi\u00e9es. Dans la quatri\u00e8me au lieu d'images \u00e0 date unique, l'utilisation de mosa\u00efques temporelles S2 de sol nu (S2Bsoil) par p\u00e9riodes est abord\u00e9e comme l'utilisation de covariables d\u00e9riv\u00e9es de l'imagerie satellitaire et du terrain. Ce chapitre traite de l'importance de la s\u00e9lection des p\u00e9riodes de production de S2Bsol et de l'utilisation de covariables pertinentes pour comprendre la variabilit\u00e9 spatiale du COS \u00e0 l'\u00e9chelle r\u00e9gionale. Enfin, le dernier chapitre aborde les principaux constats et perspectives \u00e0 envisager dans un futur proche.", "keywords": ["[SDV.SA.AGRO] Life Sciences [q-bio]/Agricultural sciences/Agronomy", "[SDE.MCG] Environmental Sciences/Global Changes", "S\u00e9ries satellitaires Sentinel", "Digital soil mapping", "Soil organic carbon", "Carbone organique du sol", "Bare soil", "Sentinel time series", "Sol nu", "Croplands", "Terres agricoles", "[SDV.SA.SDS] Life Sciences [q-bio]/Agricultural sciences/Soil study", "Cartographie num\u00e9rique des sols"], "contacts": [{"organization": "Urbina Salazar, Diego Fernando", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/9b81642374175d90e0b717deca64ff67"}, {"rel": "self", "type": "application/geo+json", "title": "9b81642374175d90e0b717deca64ff67", "name": "item", "description": "9b81642374175d90e0b717deca64ff67", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/9b81642374175d90e0b717deca64ff67"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-01-01T00:00:00Z"}}, {"id": "PMC9846495", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:29:53Z", "type": "Journal Article", "created": "2023-01-04", "title": "A genome wide association study to dissect the genetic architecture of agronomic traits in Andean lupin (Lupinus mutabilis)", "description": "

Establishing Lupinus mutabilis as a protein and oil crop requires improved varieties adapted to EU climates. The genetic regulation of strategic breeding traits, including plant architecture, growing cycle length and yield, is unknown. This study aimed to identify associations between 16 669 single nucleotide polymorphisms (SNPs) and 9 agronomic traits on a panel of 223 L. mutabilis accessions, grown in four environments, by applying a genome wide association study (GWAS). Seven environment-specific QTLs linked to vegetative yield, plant height, pods number and flowering time, were identified as major effect QTLs, being able to capture 6 to 20% of the phenotypic variation observed in these traits. Furthermore, two QTLs across environments were identified for flowering time on chromosome 8. The genes FAF, GAMYB and LNK, regulating major pathways involved in flowering and growth habit, as well as GA30X1, BIM1, Dr1, HDA15, HAT3, interacting with these pathways in response to hormonal and environmental cues, were prosed as candidate genes. These results are pivotal to accelerate the development of L. mutabilis varieties adapted to European cropping conditions by using marker-assisted selection (MAS), as well as to provide a framework for further functional studies on plant development and phenology in this species.Genomic prediction with special weight of major genes is a valuable tool to populate bio-digital resource centers.

Abstract

Phenotypic information of crop genetic resources is a prerequisite for an informed selection that aims to broaden the genetic base of the elite breeding pools. We investigated the potential of genomic prediction based on historical screening data of plant responses against the Barley yellow mosaic viruses for populating the bio-digital resource center of barley. Our study includes dense marker data for 3838 accessions of winter barley, and historical screening data of 1751 accessions for Barley yellow mosaic virus (BaYMV) and of 1771 accessions for Barley mild mosaic virus (BaMMV). Linear mixed models were fitted by considering combinations for the effects of genotypes, years, and locations. The best linear unbiased estimations displayed a broad spectrum of plant responses against BaYMV and BaMMV. Prediction abilities, computed as correlations between predictions and observed phenotypes of accessions, were low for the marker-assisted selection approach amounting to 0.42. In contrast, prediction abilities of genomic best linear unbiased predictions were high, with values of 0.62 for BaYMV and 0.64 for BaMMV. Prediction abilities of genomic prediction were improved by up to\uffe2\uff80\uff89~\uffe2\uff80\uff895% using W-BLUP, in which more weight is given to markers with significant major effects found by association mapping. Our results outline the utility of historical screening data and W-BLUP model to predict the performance of the non-phenotyped individuals in genebank collections. The presented strategy can be considered as part of the different approaches used in genebank genomics to valorize genetic resources for their usage in disease resistance breeding and research.

", "keywords": ["Genetic Markers", "0301 basic medicine", "2. Zero hunger", "0303 health sciences", "Genotype", "Chromosome Mapping", "Genetic Variation", "Hordeum", "Genomics", "Potyviridae", "Linkage Disequilibrium", "Plant Breeding", "03 medical and health sciences", "Phenotype", "Databases", " Genetic", "Original Article", "Genetic Association Studies", "Disease Resistance", "Plant Diseases"]}, "links": [{"href": "https://link.springer.com/content/pdf/10.1007/s00122-021-03815-0.pdf"}, {"href": "https://doi.org/10.1007/s00122-021-03815-0"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Theoretical%20and%20Applied%20Genetics", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1007/s00122-021-03815-0", "name": "item", "description": "10.1007/s00122-021-03815-0", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1007/s00122-021-03815-0"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-03-25T00:00:00Z"}}, {"id": "10.1007/s10980-020-00984-z", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:15:26Z", "type": "Journal Article", "created": "2020-03-10", "title": "Global vulnerability of soil ecosystems to erosion", "description": "Abstract Context

Soil erosion is one of the main threats driving soil degradation across the globe with important impacts on crop yields, soil biota, biogeochemical cycles, and ultimately human nutrition.

Objectives

Here, using an empirical model, we present a global and temporally explicit assessment of soil erosion risk according to recent (2001\uffe2\uff80\uff932013) dynamics of rainfall and vegetation cover change to identify vulnerable areas for soils and soil biodiversity.

Methods

We used an adaptation of the Universal Soil Loss Equation together with state of the art remote sensing models to create a spatially and temporally explicit global model of soil erosion and soil protection. Finally, we overlaid global maps of soil biodiversity to assess the potential vulnerability of these soil communities to soil erosion.

Results

We show a consistent decline in soil erosion protection over time across terrestrial biomes, which resulted in a global increase of 11.7% in soil erosion rates. Notably, soil erosion risk systematically increased between 2006 and 2013 in relation to the baseline year (2001). Although vegetation cover is central to soil protection, this increase was mostly driven by changes in rainfall erosivity. Globally, soil erosion is expected not only to have an impact on the vulnerability of soil conditions but also on soil biodiversity with 6.4% (for soil macrofauna) and 7.6% (for soil fungi) of these vulnerable areas coinciding with regions with high soil biodiversity.

Conclusions

Our results indicate that an increasing proportion of soils are degraded globally, affecting not only livelihoods but also potentially degrading local and regional landscapes. Similarly, many degraded regions coincide with and may have impacted high levels of soil biodiversity.\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.geodrs.2024.e00801", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:17:01Z", "type": "Journal Article", "created": "2024-04-20", "title": "National-scale digital soil mapping performances are related to covariates and sampling density: Lessons from France", "description": "Accurate soil property and class predictions through spatial modelling necessitate a thoughtful selection of explanatory variables and sample size, as their choice greatly impacts model performance. Within the framework of Global Soil Nutrient and Nutrient Budgets maps (GSNmap), the FAO Global Soil Partnership (GSP) launched a country-driven digital soil mapping (DSM) approach. The GSP asked the countries if they could implement the DSM prediction of ten soil properties, using their national point data and a set of widely available covariates (GSP_Cov). In this study, we examined the effect of including additional national-based covariates and soil observations on the performance of the prediction models using mainland France as a pilot. The learning soil dataset was based on a systematic 16-to-16\u202fkm grid. For a subset of soil properties, we also assessed using repeated k-fold cross-validation the effect of adding to this dataset many other irregularly spread measurements. The GSP_Cov included common widely available covariates that represented information about terrain, climate, and organisms. The second set of covariates consisted of the GSP_Cov, extended to extra covariates available at a national level, such as previously existing soil maps, geological maps, remote sensing products and others. Random Forest approach in combination with the Boruta selection method was employed for mapping ten soil properties: soil organic carbon (SOC), pH (water), total nitrogen (N), available phosphorus (P), available potassium (K), cation exchange capacity (CEC), bulk density (BD), and texture (clay, silt, and sand). The results revealed noteworthy enhancements in prediction performance for more than half of the properties, although, for some of them, the improvements were negligible. The most significant improvements were obtained for pH, CEC and texture, where geological variables and a previous pH map significantly contributed to the increase in accuracy. Adding numerous points (around 25,000) to the learning dataset improved the performance of soil particle-size fractions predictions. By broadening the spectrum of covariates and better covering the feature and geographical spaces considered in soil prediction models, this research underscores the importance of implementing a more diverse range of covariates at a national scale and of densifying soil information to enlarge the feature and geographical spaces of multidimensional soil/covariates combinations. This information should be taken into account in national and continental digital soil mapping endeavours.", "keywords": ["2. Zero hunger", "Soil", "Digital soil mapping", "13. Climate action", "[SDU.STU] Sciences of the Universe [physics]/Earth Sciences", "[SDU.STU]Sciences of the Universe [physics]/Earth Sciences", "Spatial sampling", "[SDV.SA.SDS]Life Sciences [q-bio]/Agricultural sciences/Soil study", "15. Life on land", "[SDV.SA.SDS] Life Sciences [q-bio]/Agricultural sciences/Soil study", "Covariates", "Modelling", "Random forest"], "contacts": [{"organization": "Suleymanov, Azamat, Richer-De-Forges, Anne C, Saby, Nicolas P. A., Arrouays, Dominique, Martin, Manuel P, Bispo, Antonio,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.1016/j.geodrs.2024.e00801"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Geoderma%20Regional", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.geodrs.2024.e00801", "name": "item", "description": "10.1016/j.geodrs.2024.e00801", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.geodrs.2024.e00801"}, {"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": "10.1016/j.isprsjprs.2017.10.016", "type": "Feature", "geometry": null, "properties": {"license": "Closed Access", "updated": "2026-06-23T16:17:03Z", "type": "Journal Article", "created": "2017-11-06", "title": "Estimation And Mapping Of Above-Ground Biomass Of Mangrove Forests And Their Replacement Land Uses In The Philippines Using Sentinel Imagery", "description": "Abstract The recent launch of the Sentinel-1 (SAR) and Sentinel-2 (multispectral) missions offers a new opportunity for land-based biomass mapping and monitoring especially in the tropics where deforestation is highest. Yet, unlike in agriculture and inland land uses, the use of Sentinel imagery has not been evaluated for biomass retrieval in mangrove forest and the non-forest land uses that replaced mangroves. In this study, we evaluated the ability of Sentinel imagery for the retrieval and predictive mapping of above-ground biomass of mangroves and their replacement land uses. We used Sentinel SAR and multispectral imagery to develop biomass prediction models through the conventional linear regression and novel Machine Learning algorithms. We developed models each from SAR raw polarisation backscatter data, multispectral bands, vegetation indices, and canopy biophysical variables. The results show that the model based on biophysical variable Leaf Area Index (LAI) derived from Sentinel-2 was more accurate in predicting the overall above-ground biomass. In contrast, the model which utilised optical bands had the lowest accuracy. However, the SAR-based model was more accurate in predicting the biomass in the usually deficient to low vegetation cover non-forest replacement land uses such as abandoned aquaculture pond, cleared mangrove and abandoned salt pond. These models had 0.82\u20130.83 correlation/agreement of observed and predicted value, and root mean square error of 27.8\u201328.5\u202fMg\u202fha \u22121 . Among the Sentinel-2 multispectral bands, the red and red edge bands (bands 4, 5 and 7), combined with elevation data, were the best variable set combination for biomass prediction. The red edge-based Inverted Red-Edge Chlorophyll Index had the highest prediction accuracy among the vegetation indices. Overall, Sentinel-1 SAR and Sentinel-2 multispectral imagery can provide satisfactory results in the retrieval and predictive mapping of the above-ground biomass of mangroves and the replacement non-forest land uses, especially with the inclusion of elevation data. The study demonstrates encouraging results in biomass mapping of mangroves and other coastal land uses in the tropics using the freely accessible and relatively high-resolution Sentinel imagery.", "keywords": ["land use change", "580", "sentinel imagery", "mangrove", "biomass", "550", "Philippines", "0211 other engineering and technologies", "04 agricultural and veterinary sciences", "02 engineering and technology", "15. Life on land", "biomass mapping", "13. Climate action", "0401 agriculture", " forestry", " and fisheries"]}, "links": [{"href": "https://doi.org/10.1016/j.isprsjprs.2017.10.016"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/ISPRS%20Journal%20of%20Photogrammetry%20and%20Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.isprsjprs.2017.10.016", "name": "item", "description": "10.1016/j.isprsjprs.2017.10.016", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.isprsjprs.2017.10.016"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-12-01T00:00:00Z"}}, {"id": "10.1016/j.iswcr.2024.10.002", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:17:04Z", "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.mex.2024.102943", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-23T16:17:14Z", "type": "Journal Article", "created": "2024-08-31", "title": "A simple method to map pollination ecosystem services potential in urban lawns", "description": "Urban areas have detrimental impacts on the ecosystems. Nevertheless, they still supply many ecosystem services (ES), such as Pollination, in different urban green spaces (UGS). Lawns are among the most degraded UGS due to very high human impact. Still, flowers such as Dandelions (Taraxacum officinalis) live in these spaces. These flowers are considered a suitable habitat for pollinators. In this work, we develop a methodology to map Pollination ES potential in urban lawns using an Unmanned Aerial Vehicle. A detailed protocol was developed using high-resolution images, consisting of orthomosaic creation, flower vectorisation, field validation, and finally, Pollination ES potential mapping using Kernel and Point Density. This method can be applied to urban lawns and grasslands in Spring and Summer.\u2022A novel method was developed to map pollination potential in lawns.\u2022Dandelions (Taraxacum officinale) were mapped using UAV high-resolution images.\u2022The method is helpful to identify areas with pollination potential in urban lawns.", "keywords": ["Science", "Q", "Environmental Science", "Pollination potential mapping", "0211 other engineering and technologies", "02 engineering and technology", "01 natural sciences", "0105 earth and related environmental sciences"], "contacts": [{"organization": "Paulo Pereira, Marius Kalinauskas, Luis Valenca Pinto, Egle Baltranaite, Damia Barcelo, Wenwu Zhao, Miguel Inacio,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.1016/j.mex.2024.102943"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/MethodsX", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.mex.2024.102943", "name": "item", "description": "10.1016/j.mex.2024.102943", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.mex.2024.102943"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-12-01T00:00:00Z"}}, {"id": "10.1016/j.soilbio.2018.08.014", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:17:40Z", "type": "Journal Article", "created": "2018-09-19", "title": "Environmental drivers of the geographical distribution of methanotrophs: Insights from a national survey", "description": "Closed AccessM.D-B. acknowledges support from the Marie Sklodowska-Curie Actions of the Horizon 2020 Framework Programme H2020-MSCA-IF-2016 under REA grant agreement n\u00b0 702057. The B.K.S. team was supported by Australian Research Council grants (DP 170104634).", "keywords": ["PmoA", "2. Zero hunger", "0301 basic medicine", "0303 health sciences", "Spatial modelling", "spatial ecology", "niche (ecology)", "15. Life on land", "333", "03 medical and health sciences", "methanotrophs", "Methanotrophs", "Mapping", "Biogeography", "Niche partitioning", "13. Climate action", "XXXXXX - Unknown", "11. Sustainability", "mapping", "biogeography"]}, "links": [{"href": "https://doi.org/10.1016/j.soilbio.2018.08.014"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Soil%20Biology%20and%20Biochemistry", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.soilbio.2018.08.014", "name": "item", "description": "10.1016/j.soilbio.2018.08.014", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.soilbio.2018.08.014"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-12-01T00:00:00Z"}}, {"id": "10.1016/j.srs.2024.100118", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:17:42Z", "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": "10.1016/j.still.2020.104672", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:17:53Z", "type": "Journal Article", "created": "2020-05-15", "title": "Can pedotransfer functions based on environmental variables improve soil total nutrient mapping at a regional scale?", "description": "Abstract Numerous pedotransfer functions (PTFs) have been developed to predict the soil properties of interest from other soil properties and, less commonly, from environmental variables. However, only a few PTFs have been developed to predict soil nutrients using environmental variables and to extrapolate them to characterize spatial soil variations at a regional scale. In this study, we attempted to develop PTFs for the total nitrogen (TN), total phosphorus (TP) and total potassium (TK) concentrations in three typical pedo-climatic areas of China (Fujian Province, Jiangsu Province and Qilian Mountains) with diverse climate, terrain and soil types. A series of linear PTFs were developed to quantify the effect of terrain and climate on the predictive relations between the soil nutrients and other measured soil properties and environmental variables. In addition, digital soil mapping (DSM) based on the random forest (RF) technique was performed to test the hypothesis that the best-fit PTFs could be extrapolated, based on soil maps and environmental variables, to describe regional soil variations in the soil nutrients. The root mean square errors (RMSEs) of the best-fit PTFs for TN, TP and TK ranged from 0.21 to 0.79 g kg\u22121, 0.20 to 0.58 g kg\u22121, and 3.68 to 5.00 g kg\u22121, respectively. Different RMSEs were produced by DSM, namely 0.37-1.89 g kg\u22121, 0.19\u22120.56 g kg\u22121 and 3.79-4.83 g kg\u22121 for TN, TP and TK, respectively. PTFs provided a sound basis for database compilation if the soil properties were highly correlated. However, the extrapolation of best-fit PTFs to regional scales yielded greater errors than those produced by DSM. The comparison results reveal the limitations of PTFs and suggest that their performance could be improved by using environmental covariates or by fitting data in areas with relatively homogeneous soil landscapes. The DSM techniques may provide satisfactory alternatives to predict soil data at both regional and plot scales.", "keywords": ["Digital soil mapping", "Total phosphorus", "Total potassium", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "Total nitrogen", "15. Life on land", "Regression analysis", "01 natural sciences", "Random forest", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.1016/j.still.2020.104672"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Soil%20and%20Tillage%20Research", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.still.2020.104672", "name": "item", "description": "10.1016/j.still.2020.104672", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.still.2020.104672"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-08-01T00:00:00Z"}}, {"id": "10.1186/s12870-019-1831-x", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-23T16:20:05Z", "type": "Journal Article", "created": "2019-05-30", "title": "Construction of a high-density genetic map and identification of loci controlling purple sepal trait of flower head in Brassica oleracea L. italica", "description": "Some broccoli (Brassica oleracea L. italic) accessions have purple sepals and cold weather would deepen the purple color, while the sepals of other broccoli lines are always green even in cold winter. The related locus or gene is still unknown. In this study, a high-density genetic map was constructed based on specific locus amplified fragment (SLAF) sequencing in a doubled-haploid segregation population with 127 individuals. And mapping of the purple sepal trait in flower heads based on phenotypic data collected during three seasons was performed.A genetic map was constructed, which contained 6694 SLAF markers with an average sequencing depth of 81.37-fold in the maternal line, 84-fold in the paternal line, and 15.76-fold in each individual population studied. In all of the annual data recorded, three quantitative trait loci (QTLs) were identified that were all distributed within the linkage group (LG) 1. Among them, a major locus, qPH.C01-2, located at 36.393\u2009cM LG1, was consistently detected in all analysis. Besides this locus, another two minor loci, qPH.C01-4 and qPH.C01-5, were identified near qPH.C01-2, based on the phenotypic data from spring of 2018.The purple sepal trait could be controlled by a major single locus and two minor loci. The genetic map and location of the purple sepal trait of flower heads provide an important foundation for mapping other compound traits and the identification of the genes related to purple sepal trait in broccoli.", "keywords": ["0301 basic medicine", "0303 health sciences", "QTL", "Pigmentation", "Broccoli", "Quantitative Trait Loci", "Botany", "Chromosome Mapping", "Brassica", "03 medical and health sciences", "Genetic map", "QK1-989", "Broccoli; Genetic map; Purple sepal; QTL; SLAF; Brassica; Chromosome Mapping; Inflorescence; Pigmentation; Quantitative Trait Loci", "Purple sepal", "Inflorescence", "SLAF", "Research Article"]}, "links": [{"href": "https://www.iris.unict.it/bitstream/20.500.11769/378740/2/Construction.pdf"}, {"href": "http://link.springer.com/content/pdf/10.1186/s12870-019-1831-x.pdf"}, {"href": "https://doi.org/10.1186/s12870-019-1831-x"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/BMC%20Plant%20Biology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1186/s12870-019-1831-x", "name": "item", "description": "10.1186/s12870-019-1831-x", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1186/s12870-019-1831-x"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-05-30T00:00:00Z"}}, {"id": "10.1021/acs.est.1c08789", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-23T16:18:02Z", "type": "Journal Article", "created": "2022-04-18", "title": "Stabilization of Ferrihydrite and Lepidocrocite by Silicate during Fe(II)-Catalyzed Mineral Transformation: Impact on Particle Morphology and Silicate Distribution", "description": "Open AccessISSN:0013-936X", "keywords": ["Minerals", "magnetite", "Silicates", "elemental mapping", "Water", "Ferric Compounds", "01 natural sciences", "Catalysis", "Ferrosoferric Oxide", "atom exchange", "Soil", "iron", "redox", "goethite", "Oxidation-Reduction", "crystal morphology", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://pubs.acs.org/doi/pdf/10.1021/acs.est.1c08789"}, {"href": "https://doi.org/10.1021/acs.est.1c08789"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Environmental%20Science%20%26amp%3B%20Technology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1021/acs.est.1c08789", "name": "item", "description": "10.1021/acs.est.1c08789", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1021/acs.est.1c08789"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-04-18T00:00:00Z"}}, {"id": "10.1093/jxb/erab082", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-23T16:19:05Z", "type": "Journal Article", "created": "2021-03-05", "title": "A common bean truncated CRINKLY4 kinase controls gene-for-gene resistance to the fungus Colletotrichum lindemuthianum", "description": "Abstract

Identifying the molecular basis of resistance to pathogens is critical to promote a chemical-free cropping system. In plants, nucleotide-binding leucine-rich repeat constitute the largest family of disease resistance (R) genes, but this resistance can be rapidly overcome by the pathogen, prompting research into alternative sources of resistance. Anthracnose, caused by the fungus Colletotrichum lindemuthianum, is one of the most important diseases of common bean. This study aimed to identify the molecular basis of Co-x, an anthracnose R gene conferring total resistance to the extremely virulent C. lindemuthianum strain 100. To that end, we sequenced the Co-x 58 kb target region in the resistant JaloEEP558 (Co-x) common bean and identified KTR2/3, an additional gene encoding a truncated and chimeric CRINKLY4 kinase, located within a CRINKLY4 kinase cluster. The presence of KTR2/3 is strictly correlated with resistance to strain 100 in a diversity panel of common beans. Furthermore, KTR2/3 expression is up-regulated 24 hours post-inoculation and its transient expression in a susceptible genotype increases resistance to strain 100. Our results provide evidence that Co-x encodes a truncated and chimeric CRINKLY4 kinase probably resulting from an unequal recombination event that occurred recently in the Andean domesticated gene pool. This atypical R gene may act as a decoy involved in indirect recognition of a fungal effector.

", "keywords": ["Phaseolus", "2. Zero hunger", "0301 basic medicine", "anthracnose", "0303 health sciences", "[SDV]Life Sciences [q-bio]", "610", "Chromosome Mapping", "Genes", " Plant", "Phaseolus vulgaris", "630", "NLR", "[SDV] Life Sciences [q-bio]", "03 medical and health sciences", "disease resistance gene", "Colletotrichum", "[SDV.BV]Life Sciences [q-bio]/Vegetal Biology", "CRINKLY4 kinase", "[SDV.BV] Life Sciences [q-bio]/Vegetal Biology", "Common bean", "Common bean", " Phaseolus vulgaris", " NLR", " disease resistance gene", " CRINKLY4 kinase", " anthracnose", " unequal crossing-over", "unequal crossing-over", "Plant Diseases"]}, "links": [{"href": "http://academic.oup.com/jxb/article-pdf/72/10/3569/37799399/erab082.pdf"}, {"href": "https://doi.org/10.1093/jxb/erab082"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Experimental%20Botany", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1093/jxb/erab082", "name": "item", "description": "10.1093/jxb/erab082", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1093/jxb/erab082"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-03-06T00:00:00Z"}}, {"id": "10.1038/nature02052", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:18:20Z", "type": "Journal Article", "created": "2003-10-08", "title": "Loss Of Omi Mitochondrial Protease Activity Causes The Neuromuscular Disorder Of Mnd2 Mutant Mice", "description": "The mouse mutant mnd2 (motor neuron degeneration 2) exhibits muscle wasting, neurodegeneration, involution of the spleen and thymus, and death by 40 days of age. Degeneration of striatal neurons, with astrogliosis and microglia activation, begins at around 3 weeks of age, and other neurons are affected at later stages. Here we have identified the mnd2 mutation as the missense mutation Ser276Cys in the protease domain of the nuclear-encoded mitochondrial serine protease Omi (also known as HtrA2 or Prss25). Protease activity of Omi is greatly reduced in tissues of mnd2 mice but is restored in mice rescued by a bacterial artificial chromosome transgene containing the wild-type Omi gene. Deletion of the PDZ domain partially restores protease activity to the inactive recombinant Omi protein carrying the Ser276Cys mutation, suggesting that the mutation impairs substrate access or binding to the active site pocket. Loss of Omi protease activity increases the susceptibility of mitochondria to induction of the permeability transition, and increases the sensitivity of mouse embryonic fibroblasts to stress-induced cell death. The neurodegeneration and juvenile lethality in mnd2 mice result from this defect in mitochondrial Omi protease.", "keywords": ["Male", "0301 basic medicine", "0303 health sciences", "Binding Sites", "Cell Death", "Science", "Homozygote", "Molecular Sequence Data", "Caseins", "Chromosome Mapping", "Mice", " Transgenic", "High-Temperature Requirement A Serine Peptidase 2", "Mitochondria", "Mitochondrial Proteins", "Mice", "Mice", " Neurologic Mutants", "03 medical and health sciences", "Animals", "Humans", "Calcium", "Female", "Amino Acid Sequence", "Cells", " Cultured", "Crosses", " Genetic"]}, "links": [{"href": "https://doi.org/10.1038/nature02052"}, {"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": "10.1038/nature02052", "name": "item", "description": "10.1038/nature02052", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1038/nature02052"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2003-10-01T00:00:00Z"}}, {"id": "10.1038/nature24668", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:18:21Z", "type": "Journal Article", "created": "2017-12-08", "title": "Fire frequency drives decadal changes in soil carbon and nitrogen and ecosystem productivity", "description": "Fire frequency is changing globally and is projected to affect the global carbon cycle and climate. However, uncertainty about how ecosystems respond to decadal changes in fire frequency makes it difficult to predict the effects of altered fire regimes on the carbon cycle; for instance, we do not fully understand the long-term effects of fire on soil carbon and nutrient storage, or whether fire-driven nutrient losses limit plant productivity. Here we analyse data from 48 sites in savanna grasslands, broadleaf forests and needleleaf forests spanning up to 65 years, during which time the frequency of fires was altered at each site. We find that frequently burned plots experienced a decline in surface soil carbon and nitrogen that was non-saturating through time, having 36 per cent (\u00b113 per cent) less carbon and 38 per cent (\u00b116 per cent) less nitrogen after 64 years than plots that were protected from fire. Fire-driven carbon and nitrogen losses were substantial in savanna grasslands and broadleaf forests, but not in temperate and boreal needleleaf forests. We also observe comparable soil carbon and nitrogen losses in an independent field dataset and in dynamic model simulations of global vegetation. The model study predicts that the long-term losses of soil nitrogen that result from more frequent burning may in turn decrease the carbon that is sequestered by net primary productivity by about 20 per cent of the total carbon that is emitted from burning biomass over the same period. Furthermore, we estimate that the effects of changes in fire frequency on ecosystem carbon storage may be 30 per cent too low if they do not include multidecadal changes in soil carbon, especially in drier savanna grasslands. Future changes in fire frequency may shift ecosystem carbon storage by changing soil carbon pools and nitrogen limitations on plant growth, altering the carbon sink capacity of frequently burning savanna grasslands and broadleaf forests.", "keywords": ["2. Zero hunger", "Carbon Sequestration", "Time Factors", "Nitrogen", "carbon", "Geographic Mapping", "Phosphorus", "15. Life on land", "Grassland", "01 natural sciences", "nitrogen", "Carbon", "Wildfires", "Soil", "Spatio-Temporal Analysis", "13. Climate action", "XXXXXX - Unknown", "Potassium", "carbon cycle (biogeochemistry)", "Calcium", "ecosystems", "soils", "fire", "Ecosystem", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.1038/nature24668"}, {"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": "10.1038/nature24668", "name": "item", "description": "10.1038/nature24668", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1038/nature24668"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-12-11T00:00:00Z"}}, {"id": "10.1038/s41467-022-31540-9", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:18:23Z", "type": "Journal Article", "created": "2022-07-01", "title": "Global stocks and capacity of mineral-associated soil organic carbon", "description": "Abstract

Soil is the largest terrestrial reservoir of organic carbon and is central for climate change mitigation and carbon-climate feedbacks. Chemical and physical associations of soil carbon with minerals play a critical role in carbon storage, but the amount and global capacity for storage in this form remain unquantified. Here, we produce spatially-resolved global estimates of mineral-associated organic carbon stocks and carbon-storage capacity by analyzing 1144 globally-distributed soil profiles. We show that current stocks total 899 Pg C to a depth of 1\uffe2\uff80\uff89m in non-permafrost mineral soils. Although this constitutes 66% and 70% of soil carbon in surface and deeper layers, respectively, it is only 42% and 21% of the mineralogical capacity. Regions under agricultural management and deeper soil layers show the largest undersaturation of mineral-associated carbon. Critically, the degree of undersaturation indicates sequestration efficiency over years to decades. We show that, across 103 carbon-accrual measurements spanning management interventions globally, soils furthest from their mineralogical capacity are more effective at accruing carbon; sequestration rates average 3-times higher in soils at one tenth of their capacity compared to soils at one half of their capacity. Our findings provide insights into the world\uffe2\uff80\uff99s soils, their capacity to store carbon, and priority regions and actions for soil carbon management.Spatially resolved estimates of change in soil organic carbon (SOC) stocks are necessary for supporting national and international policies aimed at achieving land degradation neutrality and climate change mitigation. In this work we report on the development, implementation and application of a data\uffe2\uff80\uff90driven, statistical method for mapping SOC stocks in space and time, using Argentina as a pilot. We used quantile regression forest machine learning to predict annual SOC stock at 0\uffe2\uff80\uff9330\uffe2\uff80\uff89cm depth at 250\uffe2\uff80\uff89m resolution for Argentina between 1982 and 2017. The model was calibrated using over 5,000 SOC stock values from the 36\uffe2\uff80\uff90year time period and 35 environmental covariates. We preprocessed normalized difference vegetation index (NDVI) dynamic covariates using a temporal low\uffe2\uff80\uff90pass filter to allow the SOC stock for a given year to depend on the NDVI of the current as well as preceding years. Predictions had modest temporal variation, with an average decrease for the entire country from 2.55 to 2.48\uffe2\uff80\uff89kg\uffe2\uff80\uff89C\uffe2\uff80\uff89m\uffe2\uff88\uff922 over the 36\uffe2\uff80\uff90year period (equivalent to a decline of 211 Gg C, 3.0% of the total 0\uffe2\uff80\uff9330\uffe2\uff80\uff89cm SOC stock in Argentina). The Pampa region had a larger estimated SOC stock decrease from 4.62 to 4.34\uffe2\uff80\uff89kg\uffe2\uff80\uff89C\uffe2\uff80\uff89m\uffe2\uff88\uff922 (5.9%) during the same period. For the 2001\uffe2\uff80\uff932015 period, predicted temporal variation was seven\uffe2\uff80\uff90fold larger than that obtained using the Tier 1 approach of the Intergovernmental Panel on Climate Change and United Nations Convention to Combat Desertification. Prediction uncertainties turned out to be substantial, mainly due to the limited number and poor spatial and temporal distribution of the calibration data, and the limited explanatory power of the covariates. Cross\uffe2\uff80\uff90validation confirmed that SOC stock prediction accuracy was limited, with a mean error of 0.03\uffe2\uff80\uff89kg\uffe2\uff80\uff89C\uffe2\uff80\uff89m\uffe2\uff88\uff922 and a root mean squared error of 2.04\uffe2\uff80\uff89kg\uffe2\uff80\uff89C\uffe2\uff80\uff89m\uffe2\uff88\uff922. In spite of the large uncertainties, this work showed that machine learning methods can be used for space\uffe2\uff80\uff93time SOC mapping and may yield valuable information to land managers and policymakers, provided that SOC observation density in space and time is sufficiently large.

Highlights

We tested the use of machine learning for space\uffe2\uff80\uff93time mapping of soil organic carbon (SOC) stock.

Predictions for Argentina from 1982 to 2017 showed a 3% decrease of the topsoil SOC stock over time.

The machine learning model predicted a greater temporal variation than the IPCC Tier 1 approach.

Accurate machine learning SOC stock prediction requires dense soil sampling in space and time.

There is increasing recognition that soils fulfil many functions for society. Each soil can deliver a range of functions, but some soils are more effective at some functions than others due to their intrinsic properties. In this study we mapped four different soil functions on agricultural lands across the European Union. For each soil function, indicators were developed to evaluate their performance. To calculate the indicators and assess the interdependencies between the soil functions, data from continental long\uffe2\uff80\uff90term simulation with the DayCent model were used to build crop\uffe2\uff80\uff90specific Bayesian networks. These Bayesian Networks were then used to calculate the soil functions' performance and trade\uffe2\uff80\uff90offs between the soil functions under current conditions. For each soil function the maximum potential was estimated across the European Union and changes in trade\uffe2\uff80\uff90offs were assessed. By deriving current and potential soil function delivery from Bayesian networks a better understanding is gained of how different soil functions and their interdependencies can differ depending on soil, climate and management.

Highlights

When increasing a soil function, how do trade\uffe2\uff80\uff90offs affect the other functions under different conditions?

Bayesian networks evaluate trade\uffe2\uff80\uff90offs between soil functions and estimate their maximal delivery.

Maximizing a soil function has varied effects on other functions depending on soil, climate and management.

Differences in trade\uffe2\uff80\uff90offs make some locations more suitable for increasing a soil function then others.

White lupin is an old crop with renewed interest due to its seed high protein content and high nutritional value. Despite a long domestication history in the Mediterranean basin, modern breeding efforts have been fairly scarce. Recent sequencing of its genome has provided tools for further description of genetic resources but detailed characterization of genomic diversity is still missing. Here, we report the genome sequencing of 39 accessions that were used to establish a white lupin pangenome. We defined 32\uffe2\uff80\uff89068 core genes that are present in all individuals and 14\uffe2\uff80\uff89822 that are absent in some and may represent a gene pool for breeding for improved productivity, grain quality, and stress adaptation. We used this new pangenome resource to identify candidate genes for alkaloid synthesis, a key grain quality trait. The white lupin pangenome provides a novel genetic resource to better understand how domestication has shaped the genomic variability within this crop. Thus, this pangenome resource is an important step towards the effective and efficient genetic improvement of white lupin to help meet the rapidly growing demand for plant protein sources for human and animal consumption.

", "keywords": ["0301 basic medicine", "white lupin", "pangenome", "[SDV.BIO]Life Sciences [q-bio]/Biotechnology", "http://aims.fao.org/aos/agrovoc/c_49985", "630", "diversit\u00e9 g\u00e9n\u00e9tique (comme ressource)", "Domestication", "domestication", "03 medical and health sciences", "ressource g\u00e9n\u00e9tique v\u00e9g\u00e9tale", "[SDV.BV]Life Sciences [q-bio]/Vegetal Biology", "[SDV.BV] Life Sciences [q-bio]/Vegetal Biology", "http://aims.fao.org/aos/agrovoc/c_37418", "http://aims.fao.org/aos/agrovoc/c_37419", "http://aims.fao.org/aos/agrovoc/c_3224", "http://aims.fao.org/aos/agrovoc/c_33952", "Research Articles", "ressource g\u00e9n\u00e9tique animale", "2. Zero hunger", "0303 health sciences", "g\u00e9nome", "phytog\u00e9n\u00e9tique", "http://aims.fao.org/aos/agrovoc/c_27583", "Chromosome Mapping", "600", "s\u00e9quence nucl\u00e9otidique", "15. Life on land", "variation g\u00e9n\u00e9tique", "plant diversity", "[SDV.BIO] Life Sciences [q-bio]/Biotechnology", "Lupinus", "Plant Breeding", "http://aims.fao.org/aos/agrovoc/c_15975", "Genome", " Plant"]}, "links": [{"href": "https://onlinelibrary.wiley.com/doi/pdf/10.1111/pbi.13678"}, {"href": "https://doi.org/10.1111/pbi.13678"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Plant%20Biotechnology%20Journal", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1111/pbi.13678", "name": "item", "description": "10.1111/pbi.13678", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1111/pbi.13678"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-06-22T00:00:00Z"}}, {"id": "10.1111/tgis.12257", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-06-23T16:19:53Z", "type": "Journal Article", "created": "2016-12-12", "title": "Completeness and classification correctness of features on topographic maps: An analysis of the estonian basic map", "description": "Abstract

In an increasingly GIS\uffe2\uff80\uff90literate world, the availability of quality topographic maps and map databases is critical for the numerous users of spatial data. Particularly governmental agencies, first responders, and utility and transportation services, rely on the completeness and classification correctness of these maps. Estonia has systematically updated its topographic Basic Map in digital form over the past 15 years. An analysis of the Estonian production process in the period 2003\uffe2\uff80\uff902006 provides a useful case study of both error types and error frequencies encountered in topographic mapping. Errors of completeness and classification correctness of topographic features are analyzed at two levels of specificity: in general, across all map sheets, and in detail according to the field\uffe2\uff80\uff90workers who performed the mapping. The structure of errors at the two levels was different by geometry and error types; however, both systematic and individual errors were evident. The systematic errors indicated a need for revision and improvement of the data capture specifications, which was accomplished. The individual errors were addressed by additional training for the field\uffe2\uff80\uff90workers involved.

", "keywords": ["classification correctness", " completeness", " error analysis", " field verification", " topographic mapping", "0211 other engineering and technologies", "02 engineering and technology", "01 natural sciences", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://onlinelibrary.wiley.com/wol1/doi/10.1111/tgis.12257/fullpdf"}, {"href": "https://doi.org/10.1111/tgis.12257"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Transactions%20in%20GIS", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1111/tgis.12257", "name": "item", "description": "10.1111/tgis.12257", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1111/tgis.12257"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2016-12-12T00:00:00Z"}}, {"id": "10.3390/rs12244118", "type": "Feature", "geometry": null, "properties": {"updated": "2026-06-23T16:21:51Z", "type": "Journal Article", "created": "2020-12-17", "title": "Integrating Remote Sensing and Landscape Characteristics to Estimate Soil Salinity Using Machine Learning Methods: A Case Study from Southern Xinjiang, China", "description": "

Soil salinization, one of the most severe global land degradation problems, leads to the loss of arable land and declines in crop yields. Monitoring the distribution of salinized soil and degree of salinization is critical for management, remediation, and utilization of salinized soil; however, there is a lack of thorough assessment of various data sources including remote sensing and landscape characteristics for estimating soil salinity in arid and semi-arid areas. The overall goal of this study was to develop a framework for estimating soil salinity in diverse landscapes by fusing information from satellite images, landscape characteristics, and appropriate machine learning models. To explore the spatial distribution of soil salinity in southern Xinjiang, China, as a case study, we obtained 151 soil samples in a field campaign, which were analyzed in laboratory for soil electrical conductivity. A total of 35 indices including remote sensing classifiers (11), terrain attributes (3), vegetation spectral indices (8), and salinity spectral indices (13) were calculated or derived and correlated with soil salinity. Nine were used to model and estimate soil salinity using four predictive modelling approaches: partial least squares regression (PLSR), convolutional neural network (CNN), support vector machine (SVM) learning, and random forest (RF). Testing datasets were divided into vegetation-covered and bare soil samples and were used for accuracy assessment. The RF model was the best regression model in this study, with R2 = 0.75, and was most effective in revealing the spatial characteristics of salt distribution. Importance analysis and path modeling of independent variables indicated that environmental factors and soil salinity indices including digital elevation model (DEM), B10, and green atmospherically resistant vegetation index (GARI) showed the strongest contribution in soil salinity estimation. This showed a great promise in the measurement and monitoring of soil salinity in arid and semi-arid areas from the integration of remote sensing, landscape characteristics, and using machine learning model.

", "keywords": ["2. Zero hunger", "soil salinity; remote sensing; machine learning; predictive mapping", "soil salinity", "remote sensing", "machine learning", "13. Climate action", "Science", "Q", "0401 agriculture", " forestry", " and fisheries", "predictive mapping", "04 agricultural and veterinary sciences", "15. Life on land", "6. Clean water"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/12/24/4118/pdf"}, {"href": "https://doi.org/10.3390/rs12244118"}, {"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/rs12244118", "name": "item", "description": "10.3390/rs12244118", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs12244118"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-12-16T00:00:00Z"}}], "links": [{"rel": "self", "type": "application/geo+json", "title": "This document as GeoJSON", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=mapping&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=mapping&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=mapping&", "hreflang": "en-US"}, {"rel": "next", "type": "application/geo+json", "title": "items (next)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=mapping&offset=50", "hreflang": "en-US"}], "numberMatched": 329, "numberReturned": 50, "distributedFeatures": [], "timeStamp": "2026-06-24T03:06:49.984307Z"}