{"type": "FeatureCollection", "features": [{"id": "10.1016/j.still.2024.106125", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-31T06:58:03Z", "type": "Journal Article", "created": "2024-04-26", "title": "On the impact of soil texture on local scale organic carbon quantification: From airborne to spaceborne sensing domains", "description": "Soil organic carbon (SOC) distribution and interaction with light is influenced by soil texture parameters (clay, silt and sand), which makes SOC prediction complicated, especially in samples with considerable pedological variability. Hence, understanding the relationship between SOC and soil texture is important within the context of SOC prediction using remote sensing data. The main objective of this study was to find the impact of soil texture on the performance of local SOC prediction models that were developed on Sentinel-2 (S2) multispectral and CASI/SASI (CS) hyperspectral airborne data as the main predictor variables. One approach to that objective was to lower the texture variance by stratification of the samples. Therefore, soil samples collected from four agricultural sites in the Czech Republic were segregated based on the i) site-based and ii) texture-based stratification strategies. Random forest (RF) models were then developed on all stratified classes with and without considering the soil texture parameters as predictor variables and results were compared with those obtained by the RF models developed on the non-stratified (NS) samples. Both stratification strategies provided more homogeneous classes, which enhanced the accuracy of SOC prediction, compared to using the NS samples. In addition, the texture-based RF models yielded higher accuracy predictions than the site-based ones. Except for sand, adding texture parameters to the main predictors improved the accuracy of the models, so that the highest prediction performance was obtained by a texture-based model developed on clay-added CS data. Overall, texture-based stratification could significantly enhance the SOC prediction, when the texture parameters were added to the S2 and CS data as the main predictor variables.", "keywords": ["EJP SOIL", "550", "Airborne hyperspectral data", "STEROPES", "Soil organic carbon", "Soil texture", "EJPSOIL", "Sentinel-2", "Stratification"]}, "links": [{"href": "https://doi.org/10.1016/j.still.2024.106125"}, {"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.2024.106125", "name": "item", "description": "10.1016/j.still.2024.106125", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.still.2024.106125"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-09-01T00:00:00Z"}}, {"id": "10.1109/jstars.2024.3422494", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-31T06:59:19Z", "type": "Journal Article", "created": "2024-07-03", "title": "Soil Texture and pH Mapping Using Remote Sensing and Support Sampling", "description": "Soil pH and texture are valuable information for agriculture, supporting the achievement of high productivity and low environmental impact, which is the basis for sustainable agricultural production. In this study, we present novel soil mapping techniques that integrate high-spatial-resolution satellite and ground data, surpassing traditional methods in precision and reliability. By synergizing remote sensing data, including polarimetric synthetic aperture and multispectral imagery, with climate and terrain information, alongside coarse-resolution soil data, we achieved high accuracy, with an average error of less than 6&#x0025;, in predicting soil pH and texture parameters. Notably, the approach allows for detailed mapping at the pixel level, revealing nuanced variability within 10&#x00D7;10 m field pixels. Considering the accuracy, the method establishes itself as a benchmark for field management guidelines integrating a precision sampling approach, offering actual and high spatial resolution information crucial for sustainable agricultural practices. This holistic approach allows new opportunities to revolutionize soil management practices, facilitating variable rate applications, soil moisture, and fertilization mapping and ultimately enhancing agri-environmental sustainability.", "keywords": ["2. Zero hunger", "precision agriculture", "STEROPES", "soil health", "QC801-809", "Geophysics. Cosmic physics", "Machine learning (ML)", "04 agricultural and veterinary sciences", "Remote sensing", "15. Life on land", "01 natural sciences", "soil mapping", "12. Responsible consumption", "Machine Learning", "Ocean engineering", "remote sensing", "13. Climate action", "0401 agriculture", " forestry", " and fisheries", "TC1501-1800", "0105 earth and related environmental sciences"], "contacts": [{"organization": "Y\u00fcz\u00fcg\u00fcll\u00fc, Onur, Fajraoui, Noura, Liebisch, Frank,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.1109/jstars.2024.3422494"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/IEEE%20Journal%20of%20Selected%20Topics%20in%20Applied%20Earth%20Observations%20and%20Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1109/jstars.2024.3422494", "name": "item", "description": "10.1109/jstars.2024.3422494", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1109/jstars.2024.3422494"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-01-01T00:00:00Z"}}, {"id": "10.2139/ssrn.5039431", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-05-31T07:01:13Z", "type": "Report", "created": "2024-12-09", "title": "Soil Organic Carbon and Clay Prediction and Mapping Using EnMAP Data: A Sensor-and Domain-based Performance Comparison", "description": "Environmental Mapping and Analysis Program (EnMAP) hyperspectral sensor\u2019s data was employed for the prediction and mapping of SOC and clay in agricultural soils. Results were compared with those obtained from the Landsat 8-OLI (L08-OLI) multispectral and Sentinel-2 (S2) superspectral satellites data. The CASI/SASI (CS) airborne hyperspectral data was also used as the reference. Overall, EnMAP data showed enough promise, higher than satellite-based L08-OLI and S2 multispectral sensors, for prediction and mapping of SOC and clay in the agricultural topsoil.  The manuscript is about to be submitted after the final approval of all authors.", "keywords": ["spaceborne sensors", "EJP SOIL", "STEROPES", "modeling and prediction", "EnMAP", "soil parameters", "hyperspectral airborne", "bare soil selection"], "contacts": [{"organization": "Khosravi, Vahid, Gholizadeh, Asa, Saberioon, Mohammadmehdi, \u017d\u00ed\u017eala, Daniel, Chapman Agyeman, Prince, Kode\u0161ov\u00e1, Radka, Ju\u0159icov\u00e1, Anna, Klement, Ale\u0161, N\u011bme\u010dek, Karel, Dematt\u00ea, Jos\u00e9 Alexandre Melo, Bor\u016fvka, Lubo\u0161,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.2139/ssrn.5039431"}, {"rel": "self", "type": "application/geo+json", "title": "10.2139/ssrn.5039431", "name": "item", "description": "10.2139/ssrn.5039431", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.2139/ssrn.5039431"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-01-01T00:00:00Z"}}, {"id": "10.2139/ssrn.5042274", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-31T07:01:13Z", "type": "Report", "created": "2024-12-09", "title": "Impact of Different Supervised Bare Soil Pixels Retrieval Approaches on Prediction of the Soil Organic Carbon", "description": "This study was to compare the performance of the index-based and unmixing-based classification approaches as well as their integration on discrimination of the bare soil pixels on Sentinel-2 (S2) and Landsat 8-OLI (L08-OLI) single-date scenes from dry and green vegetation within four local agricultural sites, in the Czech Republic. In conclusion, classification of soil cover using the integrated approach led to more accurate extraction of bare soil and higher performance SOC prediction models, on both types of satellite data. Considering all approaches, results obtained on S2 data were more accurate than those delivered on L08-OLI.\u00a0  The manuscript is about to be submitted after the final approval of all authors.", "keywords": ["Linear spectral unmixing", "EJP SOIL", "STEROPES", "Spectral indices", "Soil organic carbon", "Soil cover classification", "Airborne and satellite data"], "contacts": [{"organization": "Khosravi, Vahid, Gholizadeh, Asa, Castaldi, Fabio, Saberioon, Mohammadmehdi, Chapman Agyeman, Prince, \u017d\u00ed\u017eala, Daniel, Kode\u0161ov\u00e1, Radka, Bor\u016fvka, Lubo\u0161,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.2139/ssrn.5042274"}, {"rel": "self", "type": "application/geo+json", "title": "10.2139/ssrn.5042274", "name": "item", "description": "10.2139/ssrn.5042274", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.2139/ssrn.5042274"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.10118119", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-05-31T07:02:40Z", "type": "Dataset", "title": "Soil grid data for 4 agricultural fields in PT (ECe; soil organic carbon, pH)", "description": "Soil data collected in an agricultural area with annual crops in Portugal (Lez\u00edria Grande). The data refers to soil properties of 63 soil samples collected at a depth of 0-20 cm, considering a regular sampling grid, in four fields with varying soil salinity (field areas between 2 and 34 ha). The samples were collected at a period when the soil was bare, following the harvest of the annual crops, and pictures of the soil surface were taken for eventual correction of corresponding remote sensing imaging. The data includes: soil organic carbon (SOC) (Walkley-Black method), soil water content, electric conductivity of the saturated soil paste (ECe), EC1:5, and \u00a0pH1:5.\u00a0 The data may be representative of the soil conditions of the area, which is a highly productive agricultural low land, prone to the development of soil salinity as a result of the rise of saline groundwater and/or irrigation. The data can be used to establish relations between soil salinity (ECe) and other soil properties as well as build prediction models of the soil properties from remote sensing namely, for developing models for SOC prediction under the STEROPES project (WP5 (WP5-T3) and WP2 (WP2-T3)).The aim of the collected dataset was to be able to analyze the influence of soil salinity in SOC prediction from remote sensing. Data in the form of MS Excel files (xlsx), pictures of the soil surface in jpg. format.", "keywords": ["2. Zero hunger", "soil organic carbon", "Soil salinity", "STEROPES", "EJPSOIL", "15. Life on land"], "contacts": [{"organization": "Paz, A. M., Farzamian, M., Antunes, Jo\u00e3o,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.10118119"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.10118119", "name": "item", "description": "10.5281/zenodo.10118119", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.10118119"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-11-13T00:00:00Z"}}, {"id": "10.5281/zenodo.10118120", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-05-31T07:02:40Z", "type": "Dataset", "title": "Soil grid data for 4 agricultural fields in PT (ECe; soil organic carbon, pH)", "description": "Soil data collected in an agricultural area with annual crops in Portugal (Lez\u00edria Grande). The data refers to soil properties of 63 soil samples collected at a depth of 0-20 cm, considering a regular sampling grid, in four fields with varying soil salinity (field areas between 2 and 34 ha). The samples were collected at a period when the soil was bare, following the harvest of the annual crops, and pictures of the soil surface were taken for eventual correction of corresponding remote sensing imaging. The data includes: soil organic carbon (SOC) (Walkley-Black method), soil water content, electric conductivity of the saturated soil paste (ECe), EC1:5, and \u00a0pH1:5.\u00a0 The data may be representative of the soil conditions of the area, which is a highly productive agricultural low land, prone to the development of soil salinity as a result of the rise of saline groundwater and/or irrigation. The data can be used to establish relations between soil salinity (ECe) and other soil properties as well as build prediction models of the soil properties from remote sensing namely, for developing models for SOC prediction under the STEROPES project (WP5 (WP5-T3) and WP2 (WP2-T3)).The aim of the collected dataset was to be able to analyze the influence of soil salinity in SOC prediction from remote sensing. Data in the form of MS Excel files (xlsx), pictures of the soil surface in jpg. format.", "keywords": ["2. Zero hunger", "soil organic carbon", "Soil salinity", "STEROPES", "EJPSOIL", "15. Life on land"], "contacts": [{"organization": "Paz, A. M., Farzamian, M., Antunes, Jo\u00e3o,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.10118120"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.10118120", "name": "item", "description": "10.5281/zenodo.10118120", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.10118120"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-11-13T00:00:00Z"}}, {"id": "10.5281/zenodo.14055260", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-05-31T07:03:11Z", "type": "Report", "title": "Influence of soil texture on the estimation of soil organic carbon from Sentinel-2 temporal mosaics at 34 European sites", "description": "The aim of this study was to investigate the influence of soil texture on SOC predictions using Sentinel-2 temporal mosaics. The study analysed how local within-site variability and correlations in SOC and soil texture influence the possibility of predicting SOC from satellite data using local models at sites in different pedo-climatic zones across Europe. Analyses of 34 individual sites in 10 European countries were carried out within the framework of the STEROPES project of the European Joint H2020 Programme, EJP SOIL.  The manuscript is curently under review in European journal of Soil Science.", "keywords": ["EJP SOIL", "remote sensing", "STEROPES", "satellite", "SOC", "clay", "soil moisture", "time series", "field scale"], "contacts": [{"organization": "Wetterlind, J, Simmler, M, Castaldi, F, Bor\u016fvka, L, Gabriel, J.L., Gomes, L.C., Khosravi, V, K\u0131vrak, C, Koparan, M.H., L\u00e1zaro-L\u00f3pez, A, Liebisch, F, Rodriguez, J.A., Sava\u015f, A.\u00d6., Stenberg, B, Tun\u00e7ay, T, Vinci, I, Volungevi\u010dius, J, \u017dydelis, R, Vaudour, E,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14055260"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14055260", "name": "item", "description": "10.5281/zenodo.14055260", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14055260"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-11-08T00:00:00Z"}}, {"id": "10.5281/zenodo.14137626", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-05-31T07:03:13Z", "type": "Dataset", "title": "STEROPES_TURKIYE_DATASET", "description": "Two agricultural areas, Dalaman and Kocas , were chosen in T\u00fcrkiye regarding their different climatic conditions, soil characteristics, SOC content, soil and crop management.  Dalaman site is located in the south-western part of T\u00fcrkiye near the Aegean Sea in alluvial deposits. The climate of the region is classified as Hot-summer Mediterranean climate\u00a0(Csa) with an annual precipitation of 1030\u00a0mm and mean temperature of 18.1\u00a0\u00b0C (mean values over the 30\u00a0years period). Common soil type in the study area is Calcaric\u00a0Fluvisols\u00a0(Drainic). In the sampling area both\u00a0corn\u00a0(Zea mays L.)\u00a0-wheat (Triticum aestivum\u00a0L.) or wheat-\u00a0sunflower\u00a0(Helianthus annuus L.) crops are raised in the same year.  Kocas study area is located in the alluvial deposits of the Konya Closed Basin, 30 km southeast of Salt Lake (Tuz G\u00f6l\u00fc). The climate is classified as a Cold semi-arid climate (Bsk) with an average annual precipitation of 362 mm and temperature of 12.2 \u00b0C (mean values over the 30 years period). The common soil types in this area are Calcaric Fluvisols (Drainic) and Eutric Vertisols. The main crops are irrigated corn and wheat with annual rotation.", "keywords": ["STEROPES", "EJPSOIL"], "contacts": [{"organization": "Koparan, Muhammed Halil, K\u0131vrak, Cantekin, Savas, Ayse \u00d6zge, Tuncay, Tulay, Kececi, Mehmet,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14137626"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14137626", "name": "item", "description": "10.5281/zenodo.14137626", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14137626"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-11-13T00:00:00Z"}}, {"id": "10.5281/zenodo.14056687", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-05-31T07:03:12Z", "type": "Report", "title": "STEROPES synthesis report", "description": "STEROPES, named after a cyclop from the Ancient Greek mythology, stands for \u201cstimulating novel technologies from earth remote sensing to predict European soil carbon\u201d. The overall context of this project (36 months duration plus 6 months-extension, started on 1st February 2021) stems from the need to spatially estimate and monitor soil properties, and especially soil organic carbon (SOC) content, for decision support and land management, notably regarding carbon balance assessment and the feasibility of carbon credits, and more generally in relation to soil health and ecosystem services assessment.  The main aim of STEROPES was to elaborate maps from satellite time series, such as those\u00a0made available by the European Space Agency through the Copernicus data portal. The STEROPES consortium was composed of 19 public institutes from 14 countries spread over Europe, hence with highly diverse agropedoclimatic contexts.  The first phase of the project consisted of constructing reflectance image spectra of optical satellite series, notably Sentinel-2 (ESA), based on a number of diversified areas for which SOC samples were already available. Over the course of the project, several datasets have been collected focusing on small regions of some hundreds of km\u00b2 or on detailed scales of farms or catchments of some km\u00b2, for which SOC samples were already available with an areal density higher than 1-3 samples/km\u00b2.  The second phase of the project was dedicated to analysing the influence of various factors on SOC prediction performance: soil moisture, texture, green and dry vegetation due to management practices, salinity. Then, for the sites where satellite information may not enable to derive acceptable predictions, other ancillary data such as gamma-ray data layers were considered.  The third phase of the project included field campaigns that were carried out in order to complement or to provide datasets; the processing of the data and the launch of an additional work package dedicated to emerging research gaps.  This final report compiles the overall results of the work packages and their joint analysis in WP6 of\u00a0STEROPES.", "keywords": ["EJP SOIL", "remote sensing", "STEROPES", "satellite", "SOC", "time series"], "contacts": [{"organization": "Vaudour, Emmanuelle, Wetterlind, Johanna, van Egmond, Fenny, Bor\u016fvka, Lubo\u0161, Castaldi, Fabio, Dik, Pim, Farzamian, Mohammad, Fouad, Youssef, Liebisch, Frank, Lopatka, Artur, Michot, Didier, Nino, Pasquale, Saberioon, Mehdi, Simmler, Michael, Vanderhasselt, Adrian,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14056687"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14056687", "name": "item", "description": "10.5281/zenodo.14056687", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14056687"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-11-08T00:00:00Z"}}, {"id": "10.5281/zenodo.14137627", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-05-31T07:03:13Z", "type": "Dataset", "title": "STEROPES_TURKIYE_DATASET", "description": "Two agricultural areas, Dalaman and Kocas , were chosen in T\u00fcrkiye regarding their different climatic conditions, soil characteristics, SOC content, soil and crop management.  Dalaman site is located in the south-western part of T\u00fcrkiye near the Aegean Sea in alluvial deposits. The climate of the region is classified as Hot-summer Mediterranean climate\u00a0(Csa) with an annual precipitation of 1030\u00a0mm and mean temperature of 18.1\u00a0\u00b0C (mean values over the 30\u00a0years period). Common soil type in the study area is Calcaric\u00a0Fluvisols\u00a0(Drainic). In the sampling area both\u00a0corn\u00a0(Zea mays L.)\u00a0-wheat (Triticum aestivum\u00a0L.) or wheat-\u00a0sunflower\u00a0(Helianthus annuus L.) crops are raised in the same year.  Kocas study area is located in the alluvial deposits of the Konya Closed Basin, 30 km southeast of Salt Lake (Tuz G\u00f6l\u00fc). The climate is classified as a Cold semi-arid climate (Bsk) with an average annual precipitation of 362 mm and temperature of 12.2 \u00b0C (mean values over the 30 years period). The common soil types in this area are Calcaric Fluvisols (Drainic) and Eutric Vertisols. The main crops are irrigated corn and wheat with annual rotation.", "keywords": ["STEROPES", "EJPSOIL"], "contacts": [{"organization": "Koparan, Muhammed Halil, K\u0131vrak, Cantekin, Savas, Ayse \u00d6zge, Tuncay, Tulay, Kececi, Mehmet,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14137627"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14137627", "name": "item", "description": "10.5281/zenodo.14137627", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14137627"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-11-13T00:00:00Z"}}, {"id": "10.5281/zenodo.14065908", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-05-31T07:03:12Z", "type": "Report", "title": "STEROPES D7.1 - D7.3. Satellite-based (Sentinel-2) soil sampling strategies for SOC estimation at field- and farm-scale", "description": "Report for Task 7.3 SATELLITE-BASED SAMPLING STRATEGIES of the EJPSOIL - STEROPES PROJECT\u00a0  Within this report we will focus on three broad research questions:    Research question 1: Is it possible to reduce the plot-scale (10mx10m) subsampling costs by reducing the number of subsamples currently used in the protocol of the Flemish Cmon monitoring network and used for the original ENVISION sampling campaign (i.e. 16 subsamples), without drastically reducing the precision of the SOC content estimation?  Research question 2 : Can the Sentinel-2 spectral bands (satellite-based) or pre-existing SOC maps (map-based \u2013 here ENVISION PyCaret SOC map for the region of Flanders) provide us with adequate knowledge on topsoil SOC variability at field- and farm-scale to help optimize soil sampling strategies?  Research question 3: Is it possible to meaningfully increase the accuracy of the ENVISION TensorFlow SOC map at field-scale by its local (i.e. farm-scale) calibration?", "keywords": ["sampling", "STEROPES", "Satellite", "EJPSOIL", "SENTINEL2", "soil"], "contacts": [{"organization": "Castaldi, Fabio, Vanderhasselt, Adriaan, Saberioon, Mohammadmehdi, Callens, Bert, Berckvens, Nick, Ilias, Panos, Quataert, Paul, Ruysschaert, Greet, Del Pr\u00e0, Aldo,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14065908"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14065908", "name": "item", "description": "10.5281/zenodo.14065908", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14065908"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.14065909", "type": "Feature", "geometry": null, "properties": {"license": "unspecified", "updated": "2026-05-31T07:03:12Z", "type": "Report", "title": "STEROPES D7.1 - D7.3. Satellite-based (Sentinel-2) soil sampling strategies for SOC estimation at field- and farm-scale", "description": "Report for Task 7.3 SATELLITE-BASED SAMPLING STRATEGIES of the EJPSOIL - STEROPES PROJECT\u00a0  Within this report we will focus on three broad research questions:    Research question 1: Is it possible to reduce the plot-scale (10mx10m) subsampling costs by reducing the number of subsamples currently used in the protocol of the Flemish Cmon monitoring network and used for the original ENVISION sampling campaign (i.e. 16 subsamples), without drastically reducing the precision of the SOC content estimation?  Research question 2 : Can the Sentinel-2 spectral bands (satellite-based) or pre-existing SOC maps (map-based \u2013 here ENVISION PyCaret SOC map for the region of Flanders) provide us with adequate knowledge on topsoil SOC variability at field- and farm-scale to help optimize soil sampling strategies?  Research question 3: Is it possible to meaningfully increase the accuracy of the ENVISION TensorFlow SOC map at field-scale by its local (i.e. farm-scale) calibration?", "keywords": ["sampling", "STEROPES", "Satellite", "EJPSOIL", "SENTINEL2", "soil"], "contacts": [{"organization": "Castaldi, Fabio, Vanderhasselt, Adriaan, Saberioon, Mohammadmehdi, Callens, Bert, Berckvens, Nick, Ilias, Panos, Quataert, Paul, Ruysschaert, Greet, Del Pr\u00e0, Aldo,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14065909"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14065909", "name": "item", "description": "10.5281/zenodo.14065909", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14065909"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.14076347", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-05-31T07:03:12Z", "type": "Dataset", "title": "Soil characteristics and spectral reflectance data of six agricultural fields in Switzerland", "description": "Soil characteristics and spectral reflectance data of six agricultural fields in Switzerland collected within the EJP Soil project STEROPES. The data in the .csv files is organized as relational database with the database schema depicted in DB_Schema.pdf. The file headers (marked with #) contain metadata.", "keywords": ["soil organic carbon", "Soil", "STEROPES", "EJPSOIL"], "contacts": [{"organization": "Simmler, Michael, Liebisch, Frank,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14076347"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14076347", "name": "item", "description": "10.5281/zenodo.14076347", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14076347"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-11-22T00:00:00Z"}}, {"id": "10.5281/zenodo.14134736", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-31T07:03:12Z", "type": "Dataset", "title": "Soil grid dataset of agricultural sites in the Czech Republic", "description": "The current dataset includes 320 topsoil samples (0\u201320\u202fcm depth) collected from four agricultural sites in the Czech Republic. The samples were gathered from P\u0159estavlky, Klu\u010dov, Nov\u00e1 Ves nad Popelkou, and Udrnice (80 samples from each site) in June 2021. It contains sample coordinates and some soil parameters including SOC and texture, prepared and stored in MS Excel (.xlsx) format. The data were used in STEROPES WP1 (basic local model development), WP3 (effect of texture), and WP4 (effect of vegetation and plant residues).", "keywords": ["EJP SOIL", "STEROPES", "Soil Organic Carbon", "Soil sampling", "SOC", "Texture", "Agricultural sites"], "contacts": [{"organization": "Czech University of Life Sciences Prague", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14134736"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14134736", "name": "item", "description": "10.5281/zenodo.14134736", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14134736"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-11-13T00:00:00Z"}}, {"id": "10.5281/zenodo.14134737", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-05-31T07:03:12Z", "type": "Dataset", "title": "Soil grid dataset of agricultural sites in the Czech Republic", "description": "The current dataset includes 320 topsoil samples (0\u201320\u202fcm depth) collected from four agricultural sites in the Czech Republic. The samples were gathered from P\u0159estavlky, Klu\u010dov, Nov\u00e1 Ves nad Popelkou, and Udrnice (80 samples from each site) in June 2021. It contains sample coordinates and some soil parameters including SOC and texture, prepared and stored in MS Excel (.xlsx) format. The data were used in STEROPES WP1 (basic local model development), WP3 (effect of texture), and WP4 (effect of vegetation and plant residues).", "keywords": ["EJP SOIL", "STEROPES", "Soil Organic Carbon", "Soil sampling", "SOC", "Texture", "Agricultural sites"], "contacts": [{"organization": "Czech University of Life Sciences Prague", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14134737"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14134737", "name": "item", "description": "10.5281/zenodo.14134737", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14134737"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-11-13T00:00:00Z"}}, {"id": "10.5281/zenodo.14137529", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-05-31T07:03:13Z", "type": "Dataset", "title": "Soil laboratory spectra of agricultural sites in the Czech Republic", "description": "The dataset includes VNIR-SWIR spectra of dried, ground, and sieved (< 2 mm) soil samples collected from three agricultural sites in the Czech Republic. The spectra\u00a0were recorded in the laboratory using an\u00a0ASD FieldSpec 3 instrument and under\u00a0the standard protocol.\u00a0The samples were gathered from Klu\u010dov, Nov\u00e1 Ves nad Popelkou, and Udrnice sites (80 samples from each site) in June 2021. The dataset also contains sample coordinates and some parameters including SOC and texture, prepared and stored in MS Excel (.xlsx) format. The data were used in STEROPES WP1 (basic local models development).", "keywords": ["EJP SOIL", "Soil spectra", "Field scale", "STEROPES", "Soil Organic Carbon", "SOC", "Texture"], "contacts": [{"organization": "Czech University of Life Sciences Prague", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14137529"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14137529", "name": "item", "description": "10.5281/zenodo.14137529", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14137529"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-11-13T00:00:00Z"}}, {"id": "10.5281/zenodo.14137530", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-05-31T07:03:13Z", "type": "Dataset", "title": "Soil laboratory spectra of agricultural sites in the Czech Republic", "description": "The dataset includes VNIR-SWIR spectra of dried, ground, and sieved (< 2 mm) soil samples collected from three agricultural sites in the Czech Republic. The spectra\u00a0were recorded in the laboratory using an\u00a0ASD FieldSpec 3 instrument and under\u00a0the standard protocol.\u00a0The samples were gathered from Klu\u010dov, Nov\u00e1 Ves nad Popelkou, and Udrnice sites (80 samples from each site) in June 2021. The dataset also contains sample coordinates and some parameters including SOC and texture, prepared and stored in MS Excel (.xlsx) format. The data were used in STEROPES WP1 (basic local models development).", "keywords": ["EJP SOIL", "Soil spectra", "Field scale", "STEROPES", "Soil Organic Carbon", "SOC", "Texture"], "contacts": [{"organization": "Czech University of Life Sciences Prague", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14137530"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14137530", "name": "item", "description": "10.5281/zenodo.14137530", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14137530"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-11-13T00:00:00Z"}}, {"id": "10.5281/zenodo.14143563", "type": "Feature", "geometry": null, "properties": {"license": "unspecified", "updated": "2026-05-31T07:03:13Z", "type": "Report", "created": "2024-12-09", "title": "Impact of Different Supervised Bare Soil Pixels Retrieval Approaches on Prediction of the Soil Organic Carbon", "description": "This study was to compare the performance of the index-based and unmixing-based classification approaches as well as their integration on discrimination of the bare soil pixels on Sentinel-2 (S2) and Landsat 8-OLI (L08-OLI) single-date scenes from dry and green vegetation within four local agricultural sites, in the Czech Republic. In conclusion, classification of soil cover using the integrated approach led to more accurate extraction of bare soil and higher performance SOC prediction models, on both types of satellite data. Considering all approaches, results obtained on S2 data were more accurate than those delivered on L08-OLI.\u00a0  The manuscript is about to be submitted after the final approval of all authors.", "keywords": ["Linear spectral unmixing", "EJP SOIL", "STEROPES", "Spectral indices", "Soil organic carbon", "Soil cover classification", "Airborne and satellite data"], "contacts": [{"organization": "Khosravi, Vahid, Gholizadeh, Asa, Castaldi, Fabio, Saberioon, Mohammadmehdi, Chapman Agyeman, Prince, \u017d\u00ed\u017eala, Daniel, Kode\u0161ov\u00e1, Radka, Bor\u016fvka, Lubo\u0161,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14143563"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14143563", "name": "item", "description": "10.5281/zenodo.14143563", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14143563"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.14161808", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-05-31T07:03:13Z", "type": "Dataset", "title": "SensRes dataset for downscaling soil maps", "description": "This dataset contains soil information and sensor data from agricultural fields in Denmark, Lithuania, Northern Ireland, the Netherlands, and Turkey to downscale coarse-resolution maps to high resolution in the SensRes project (EJP SOIL).  \u00a0It includes 1455 soil samples with data on soil organic carbon and soil texture fractions (clay, silt, and sand). For each sampling site, the dataset also contains rasters of Sentinel-2 bare soil images, aerial images (RGB), and maps from Electromagnetic Induction and Gamma sensors. The soil data is provided in .txt file format, while the sensor data is available in .tif format. There are also shapefiles from each field in .shp format.\u00a0  The SensRes project developed a framework for downscaling soil maps, which was published as an R package (https://github.com/anbm-dk/soilscaler/tree/main), and this dataset contains the required local inputs to apply the downscaling process. Part of the soil information present in this dataset has also been used in the STEROPES EJP SOIL project.", "keywords": ["soil organic carbon", "STEROPES", "SensRes", "High resolution maps", "Sentinel 2", "soil texture"], "contacts": [{"organization": "Carvalho Gomes, Lucas, M\u00f8ller, Anders, Koganti, Triven, Higgins, Suzanne, \u017dydelis, Renaldas, Volungevi\u010dius, Jonas, Kavaliauskas, Ardas, van Egmond, Fenny, Kramer, Henk, Teuling, Kees, \u00c7inkaya, \u0130smail, Greve, Mogens H,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14161808"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14161808", "name": "item", "description": "10.5281/zenodo.14161808", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14161808"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-11-14T00:00:00Z"}}, {"id": "10.5281/zenodo.14161809", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-05-31T07:03:13Z", "type": "Dataset", "title": "SensRes dataset for downscaling soil maps", "description": "This dataset contains soil information and sensor data from agricultural fields in Denmark, Lithuania, Northern Ireland, the Netherlands, and Turkey to downscale coarse-resolution maps to high resolution in the SensRes project (EJP SOIL).  \u00a0It includes 1455 soil samples with data on soil organic carbon and soil texture fractions (clay, silt, and sand). For each sampling site, the dataset also contains rasters of Sentinel-2 bare soil images, aerial images (RGB), and maps from Electromagnetic Induction and Gamma sensors. The soil data is provided in .txt file format, while the sensor data is available in .tif format. There are also shapefiles from each field in .shp format.\u00a0  The SensRes project developed a framework for downscaling soil maps, which was published as an R package (https://github.com/anbm-dk/soilscaler/tree/main), and this dataset contains the required local inputs to apply the downscaling process. Part of the soil information present in this dataset has also been used in the STEROPES EJP SOIL project.", "keywords": ["soil organic carbon", "STEROPES", "SensRes", "High resolution maps", "Sentinel 2", "soil texture"], "contacts": [{"organization": "Carvalho Gomes, Lucas, M\u00f8ller, Anders, Koganti, Triven, Higgins, Suzanne, \u017dydelis, Renaldas, Volungevi\u010dius, Jonas, Kavaliauskas, Ardas, van Egmond, Fenny, Kramer, Henk, Teuling, Kees, \u00c7inkaya, \u0130smail, Greve, Mogens H,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14161809"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14161809", "name": "item", "description": "10.5281/zenodo.14161809", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14161809"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-11-14T00:00:00Z"}}, {"id": "10.5281/zenodo.14163614", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-05-31T07:03:13Z", "type": "Dataset", "title": "STEROPES_dataset-29", "description": "Dataset 29: Multispectral UAV data, ILVO\u00a0\u00a0  Origin: Multispectral UAV remote sensing data of soil surface were collected across the ILVO field trails (s15 - BOPACT & S2 - Gavere) in Flanders (Belgium). \u00a0  Purpose: Used in WP 4.\u00a0\u00a0  Format: raster files.\u00a0\u00a0  Data utility:\u00a0 For fusing with sentinel-2 data in order to improve the spatial resolution of sentinel-2 images in evaluating the impact of plant residual on SOC prediction.", "keywords": ["EJP SOIL", "remote sensing", "STEROPES", "UAV", "SENTINEL2"], "contacts": [{"organization": "De Boever, Maarten, saberioon, mohammadmehdi, Van Beek, Jonathan, Callens, Bert,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14163614"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14163614", "name": "item", "description": "10.5281/zenodo.14163614", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14163614"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-11-14T00:00:00Z"}}, {"id": "10.5281/zenodo.14169022", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-05-31T07:03:14Z", "type": "Dataset", "title": "STEROPES - Dataset 19: Collected soil data, ILVO", "description": "Dataset 19: Collected soil data, ILVO (Belgium)  Origin: First field trial on BOPACT LTE (S15) and on field trial S2 in Gavere, 16 samples from topsoil 0-10 cm; in 5m-radius around center of each plot): soil carbon and soil moisture). Second field trial, 20 samples from top soil 0-10 cm during 2021 and 2022 collected and soil organic carbon measured.\u00a0\u00a0  Purpose and objectives: to analyse the input of plant residue in SOC prediction from remote sensing data under WP4.\u00a0  Format: Data in the format of CSV .\u00a0\u00a0  Data utility: Data is collected based on the experiment where each plot has different amount of plant residual from without plant residual till completely covered with plant residual.", "keywords": ["EJP SOIL", "sampling", "remote sensing", "STEROPES", "SENTINEL2", "soil"], "contacts": [{"organization": "De Boever, Maarten, saberioon, mohammadmehdi,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14169022"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14169022", "name": "item", "description": "10.5281/zenodo.14169022", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14169022"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-11-14T00:00:00Z"}}], "links": [{"rel": "self", "type": "application/geo+json", "title": "This document as GeoJSON", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=STEROPES&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=STEROPES&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=STEROPES&", "hreflang": "en-US"}, {"rel": "last", "type": "application/geo+json", "title": "items (last)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=STEROPES&offset=22", "hreflang": "en-US"}], "numberMatched": 22, "numberReturned": 22, "distributedFeatures": [], "timeStamp": "2026-05-31T12:03:56.500377Z"}