Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
96
datasets available to search
ShareScore release 0.7.1
Dataset results
96 results for “soil mapping”
A probabilistic map of Costa Rican peatlands based on vegetation, ecosystem, and soil inventories
<p>There are the GIS files that accompany a peer-reviewed article.</p> <p>This is the first published effort aimed at developing a peatland map for Costa Rica. A probabilistic approach using vegetation, ecosystem, and soil datasets was used to predict the distribution and extent of peatlands below 700 m in elevation. High-elevation sites found in the Talamanca Mountains were visually identified using satellite imagery; those peatlands are small in size (< 0.05 km<sup>2</sup>). Our analysis produced an estimated low-elevation peatland extent of 1433 km<sup>2</sup> and a high-elevation peatland extent of 23.08 km<sup>2</sup>, yielding an estimated total extent of 1456 km<sup>2</sup> for Costa Rica. This figure is in line with previously published extent estimates for this country (577-2670 km<sup>2</sup>). As for all maps, we stress that the accuracy of this product is ultimately limited by data availability and quality, as well as ground-referencing information. Still, the new map can provide guidance for land management, policymaking, and future science endeavors.</p>
Global maps of soil water characteristics parameters developed using the random forest in a Covariate-based GeoTransfer Functions (CoGTF) framework at 1 km resolution
<p>The global soil water characteristics parameters (<em>α, n, θ<sub>r</sub>, and θ<sub>s</sub></em>) maps based on van Genuchten (vG) model at 1 km resolution was developed by harnessing the technological advances in machine learning and availability of remotely sensed surrogate information such as terrain, climate, vegetation, and soil covariates. We merge concepts of predictive soil mapping with a large data set of vG parameters and local information (soil, vegetation, climate) into "Covariate-based GeoTransfer Functions'' (CoGTFs) to generate global estimates of vG parameters (to highlight the impact of Geo-referenced covariates including various remote sensing maps, we use the term Geotransfer Function GTF and not pedotransfer function PTF; in the latter case, typically only soil properties are used to predict vG parameters).</p> <p>The vG parameters (<em>α, n, θ<sub>r</sub>, and θ<sub>s</sub></em>) dataset is provided in GeoTIFF format. A total of 16 files that represent different soil depths (0, 30, 60, and 100 cm) are provided for each parameter.</p> <table> <caption>Description of vG parameters and their units</caption> <tbody> <tr> <td>vG Parameters</td> <td>Description</td> <td>units</td> </tr> <tr> <td><em>α</em></td> <td>Inverse air entry pressure</td> <td>Log<sub>10</sub><em>α (m<sup>-1</sup>)</em></td> </tr> <tr> <td><em>n</em></td> <td>Shape parameter</td> <td>Log<sub>10</sub><em>n </em>(dimensionless)</td> </tr> <tr> <td><em>θ<sub>r</sub></em></td> <td>Residual water content</td> <td>m<sup>3</sup>/m<sup>3</sup></td> </tr> <tr> <td><em>θ<sub>s</sub></em></td> <td>Saturated water content</td> <td>m<sup>3</sup>/m<sup>3</sup></td> </tr> </tbody> </table> <p> </p> <p>The Global vG training dataset used for this study is available here:</p> <p><a href="https://doi.org/10.5281/zenodo.5547338">10.5281/zenodo.5547338</a></p> <p>For more details / to cite this dataset please use:</p> <ul> <li>Gupta, S., Papritz, A., Lehmann, P., Hengl, T., Bonetti, S., & Or, D. (2022). Global Mapping of Soil Water Characteristics Parameters—Fusing Curated Data with Machine Learning and Environmental Covariates. <strong><em>Remote Sensing</em></strong>, <em>14</em>(8), 1947.</li> </ul> <p>The study was supported by ETH Zurich (Grant ETH-18 18-1). We thank Zhongwang Wei, Associate professor at Sun Yat-Sen University, for helping to collect the datasets and for insightful discussions. We would like to thank Andrea Carmintai, Professor at ETH Zurich, for the insightful discussions.</p>
Maps of soil organic carbon stocks in Brazil
<p>This database was created by Gustavo Vieira Veloso and Lucas Carvalho Gomes 04/06/2022. <br> Contact: gustavo.v.veloso@gmail.com and lucascarvalhogomes15@hotmail.com <br> ------------------------------------------------------------------------------------</p> <p>Maps of soil organic carbon (SOC) stocks in Brazil of the article: "Modeling and mapping soil organic carbon stocks in Brazil" (doi: 10.1016/j.geoderma.2019.01.007)</p> <p>The dataset is composed of five folders of SOC stocks maps at the standard depths (0–5, 5–15, 15–30, 30–60, and 60–100 cm). The maps are in Geotif format (EPSG 102015) with a spatial resolution of approximately 1 km and include the mean SOC stocks, standard deviation (SD), coefficient of variation (CV), 0.05 and 0.95 quantiles.</p> <p>The maps are free to use and please cite also the article:<br> Gomes, L.C., Faria, R.M., de Souza, E., Veloso, G.V., Schaefer, C.E.G., & Fernandes Filho, E.I. (2019). Modeling and mapping soil organic carbon stocks in Brazil. Geoderma, 340, 337-350.</p> <p> </p>
Disaggregated soil map of the Designation of Origin Campo de Borja
<p>Disaggregated soil map of the Designation of Origin Campo de Borja in GeoPackage format. It contains a single-polygon vector layer and a table of the map legend.</p>
Three-dimensional mapping of carbon, nitrogen, and phosphorus in soil microbial biomass and their stoichiometry at the global scale
<p>R code, raw datasets, and predicted global maps of soil microbial biomass C, N, and P and their stoichiometric ratios at 0-30 cm depth.</p> <p>When using any of these layers, please cite: Gao et al., Three-dimensional mapping of carbon, nitrogen, and phosphorus in soil microbial biomass and their stoichiometry at the global scale (2022). Global Change Biology. DOI: 10.1111/gcb.16374</p>
Soil moisture maps of Ukraine based on SMAP satellite data, vegetation season 2018
<p>A set of soil moisture maps of Ukraine based on SMAP satellite data</p> <p>Product: SMAP Enhanced L3 Radiometer Global Daily 9 km EASE-Grid Soil Moisture V001</p> <p>Vegetation season 2018</p> <p> </p>
Mapping Soil Organic Carbon in the World's Largest Arid Mangrove Forest (Indus Delta, Pakistan): A Multi-Sensor Remote Sensing and Machine Learning Approach
<p>Mangrove forests play a crucial role in carbon sequestration, especially in arid regions where their ability to store carbon in soil is vital for mitigating climate change. The Indus Delta in Pakistan, the world’s largest arid mangrove forest system, lacks spatially explicit data on Soil Organic Carbon (SOC) despite its importance for conservation and carbon budgeting. This study aims to establish a baseline SOC map 2020 at 10 m spatial resolution using Sentinel-1 (Synthetic Aperture Radar) and Sentinel-2 (MultiSpectral Instrument) satellite imagery, integrated with in-situ soil sampling. SOC predictions were made using a Classification and Regression Tree (CART) machine learning model within the Google Earth Engine platform, leveraging 40 predictor variables, including spectral bands and derived indices. A total of 53 topsoil (0-10 cm) samples were collected in February 2020 across the Indus Delta, and SOC was analyzed using the Walkley-Black method. The results showed an average SOC value of 65.88 Mg C ha⁻¹ with substantial spatial variability, ranging from 15.06 Mg C ha⁻¹ to 138.03 Mg C ha⁻¹ with a total of 0.91 Pg C. The CART model demonstrated high accuracy, with an R² of 0.95 and an RMSE of 9.18 Mg C ha⁻¹. However, the region faces challenges such as seawater intrusion and salinity, which threaten its ability to sequester carbon. With the first high-resolution SOC map for the Indus Delta, this study provides valuable insights for ecosystem management, conservation planning, and carbon budgeting. These findings of this study have the potential to significantly influence initiatives like REDD+ and Blue Carbon projects, which aim to enhance carbon sequestration while addressing the ecological challenges facing Pakistan’s mangroves</p>
Soil Survey of Scotland Staff (1970-1987). Soil maps of Scotland (partial coverage) at a scale of 1:25 000. Digital phase 8 release.
<p>This is the digital dataset which was created by digitising the Soils of Scotland 1:25,000 Soil maps and the Soils of Scotland 1:25,000 Dyeline Masters. The Soils of Scotland 1:25,000 Soil maps were the source documents for the production of the Soils of Scotland 1:63,360 and 1:50,000 published map series. Where no 1:25,000 published maps exist 1:63,360 maps have been digitised for this data set, the field SOURCE_MAP describes the source of the data. The classification is based on Soil Associations, Soil Series and Phases which reflect parent material, major soil group, and soil sub-groups, drainage and (for phases), texture, stoniness, land use, rockiness, topography and organic matter. Phases are not always mapped. In general terms this dataset primarily covers the cultivated land of Scotland but also includes some upland areas. This data set is undergoing a phased revision, the latest (phase 8) was released in August 2021. The digitising of the recently added data was funded by the Rural & Environment Science & Analytical Services Division of the Scottish Government. The data can also be downloaded from or viewed at <a href="https://www.hutton.ac.uk/soil-maps/">https://www.hutton.ac.uk/soil-maps/ </a>or viewed at <a href="https://map.environment.gov.scot/Soil_maps/?layer=2">Scotland's Soils - soil maps (environment.gov.scot). </a> This map should be cited as: 'Soil Survey of Scotland Staff (1970-1987). Soil maps of Scotland (partial coverage) at a scale of 1:25 000. Digital phase 8 release. James Hutton Institute, Aberdeen. DOI 10.5281/zenodo.5159133.</p>
Mapping soil microbial residence time at the global scale
<p>Soil microbes ultimately drive the mineralization of soil organic carbon and thus ecosystem functions. We compiled a dataset of the seasonality of microbial biomass carbon (MBC) and developed a semi-mechanistic model to map monthly MBC across the globe. MBC exhibits an equatorially symmetric seasonality between the Northern and Southern Hemispheres. In the Northern Hemisphere, MBC peaks in autumn and is minimal in spring at low latitudes (<25° N), peaks in the spring and is minimal in autumn at mid-latitudes (25°-50° N), while peaks in autumn and is minimal in spring at high latitudes (>50° N). This latitudinal shift of MBC seasonality is attributed to an interaction of soil temperature, soil moisture, and substrate availability. The MBC seasonality is inconsistent with patterns of heterotrophic respiration, indicating that MBC as a proxy for microbial activity is inappropriate at this resolution. This study highlights the need to explicitly represent microbial physiology in microbial models. The interactive controls of environments and substrate on microbial seasonality provide insights for better representing microbial mechanisms in simulating ecosystem functions at the seasonal scale.</p>
Spatial soil properties maps for Switzerland at 30 m resolution
<p>The Swiss Soil Property Map (SSPM) was developed using the quantile random forest machine learning algorithm and remotely sensed surrogate information such as terrain, climate, vegetation, and soil covariates. The SSPM dataset provides maps at 30 m resolution for different soil depths (0, 30, 60, and 100 cm) in GeoTIFF format. The mean and respective uncertainty information is provided for each map. Please note that the phosphorus spatial map is only available for the topsoil (0-20 cm) due to the unavailability of the dataset at deeper depths.</p> <table> <caption>Description of soil properties (SP) and their units</caption> <tbody> <tr> <td>SP</td> <td>Description</td> <td>units</td> </tr> <tr> <td>Sand </td> <td>Sand content</td> <td>%</td> </tr> <tr> <td>Clay</td> <td>Clay content</td> <td>%</td> </tr> <tr> <td>OC</td> <td>Organic carbon content </td> <td>%</td> </tr> <tr> <td>N</td> <td>Nitrogen content</td> <td>% </td> </tr> <tr> <td>P</td> <td>Phosphorus content</td> <td>mg/kg</td> </tr> </tbody> </table> <p>For more details / to cite this dataset please use:</p> <ul> <li><strong>Gupta, S. </strong>, Hasler, K. J., Alewell, C.: Mapping soil properties of Switzerland using remote sensing datasets and machine learning approach. Manuscript <strong>submitted</strong>, <strong>Geoderma Regional</strong>, 2023</li> </ul> <p> </p>
Data, research code, and metadata related to the Soils of the Upper Part of Itatiaia National Park (INP) for Pedology, Hyperspectral Soil Mapping, and Spectral studies
<p>Data, research code (in R language), and metadata related to the Soils of the Upper Part of Itatiaia National Park (INP) for Hyperspectral Soil Mapping</p> <p>Up to this point, this data, and code were used in the Doctoral thesis of Elias Mendes Costa and Yuri Andrei Gelsleichter, and the following publications.</p> <p>------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>Dados, código de pesquisa (em linguagem R) e metadados relacionados aos Solos e ao Mapeamento da Parte Superior do Parque Nacional de Itatiaia (PNI) para o Mapeamento Hiperespectral de Solos</p> <p>Até este momento, esses dados, e códigos foram utilizados nas teses de doutorado de Elias Mendes Costa e de Yuri Andrei Gelsleichter e nas seguintes publicações.</p> <p> </p> <p>Gelsleichter, Y. A., Costa, E. M., dos Anjos, L. H. C., & Marcondes, R. A. T. (2023). Enhancing soil mapping with hyperspectral subsurface images generated from soil lab vis-SWIR spectra tested in southern Brazil. Geoderma Regional, e00641.<br> doi: https://doi.org/10.1016/j.geodrs.2023.e00641<br> link: https://www.sciencedirect.com/science/article/pii/S2352009423000378</p> <p>Costa, E. M., Pinheiro, H. S. K., Anjos, L. H. C. dos, Marcondes, R. A. T., & Gelsleichter, Y. A. (2020). Mapping soil properties in a poorly-accessible area. Revista Brasileira de Ciência Do Solo, 44.<br> doi: https://doi.org/10.36783/18069657rbcs20190107<br> link: https://www.rbcsjournal.org/article/mapping-soil-properties-in-a-poorly-accessible-area/<br> <br> Costa, E. M., dos Anjos, L. H. C., Pinheiro, H. S. K., Gelsleichter, Y. A., & Marcondes, R. A. T. (2020). Spatial Bayesian belief networks: A participatory approach for mapping environmental vulnerability at the Itatiaia National Park, Brazil. Environmental Earth Sciences, 79, 1–13.<br> doi: https://doi.org/10.1007/s12665-020-09099-9<br> link: https://link.springer.com/article/10.1007/s12665-020-09099-9#citeas</p> <p>The code is arranged to deliver the outputs in their respective folders.</p>
Spatial mapping of root systems reveals diverse strategies of soil exploration and resource contest in grassland plants
Open the record for dataset details and reuse information.
Mapping soil microbial residence time at the global scale
Open the record for dataset details and reuse information.
SensRes dataset for downscaling soil maps
<p>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).</p> <p> It 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. </p> <p>The SensRes project developed a framework for downscaling soil maps, which was published as an R package (<a href="https://github.com/anbm-dk/soilscaler/tree/main">https://github.com/anbm-dk/soilscaler/tree/main</a>), 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. </p>
UAS dataset for soil moisture mapping
<p>This dataset is obtained from a UAS survey on 13-14 June 2019 on the field site MFC2 located in Monteforte Cilento (SA, Italy), within the Harmonious COST action CA16219 - Harmonization of UAS techniques for agricultural and natural ecosystems monitoring-, activities framework. The general aim was soil moisture mapping using the thermal inertia method and vegetation-temperature triangle model method. A multispectral camera (tetracam) and a thermal one (FLIR tau2) were used. The NDVI was calculated from the red and near-infrared reflectance from the multispectral imagery, and the land surface temperature was generated from the thermal imagery</p>
Soil Survey of Scotland Staff (1970-1987). Soil maps of Scotland (partial coverage). Digital phase 10 release.
<p>This is the digital dataset which was created by digitising the Soils of Scotland 1:25,000 Soil maps and the Soils of Scotland 1:25,000 Dyeline Masters. The Soils of Scotland 1:25,000 Soil maps were the source documents for the production of the Soils of Scotland 1:63,360 and 1:50,000 published map series. Where no 1:25,000 published maps exist 1:63,360 maps have been digitised for this data set, the field SOURCE_MAP describes the source of the data. The mapping is based on Soil Associations, Soil Series and Phases which reflect parent material, major soil group, and soil sub-groups, drainage and (for soil phases), texture, stoniness, land use, rockiness, topography and organic matter. Phases are not always mapped. In general terms this dataset primarily covers the cultivated land of Scotland but also includes some upland areas. This data set is undergoing a phased revision, the latest (phase 8) was released in August 2021. The digitising of the recently added data was funded by the Rural & Environment Science & Analytical Services Division of the Scottish Government. The data can also be downloaded from or viewed at <a href="https://www.hutton.ac.uk/learning/natural-resource-datasets/soilshutton/soils-maps-scotland/download"><span><span> </span>https://www.hutton.ac.uk/soil-maps/ </span></a>or viewed at <a href="https://map.environment.gov.scot/Soil_maps/?layer=2">Scotland's Soils - soil maps (environment.gov.scot). </a> This map should be cited as: 'Soil Survey of Scotland Staff (1970-1987). Soil maps of Scotland (partial coverage). Digital phase 10 release. James Hutton Institute, Aberdeen. DOI 10.5281/zenodo.6908156 .</p>
Map legend of the conventional soil map of the Designation of Origin Campo de Borja
<p>Soil Taxonomic Units in the map legend are named with the following convention: the four first letters abbreviate the taxon up to Subgroup level, a number differentiate among similar Subgroups, and the last two characters are linked to a lithostratigraphic unit. If defined, series are shown between brackets (Information source: Gómez-Miguel et al. 2015).</p>
Improved global soil salinity and sodicity mapping through Box-Cox-based sample transformation and feature optimization
<p>We introduce a novel framework that leverages the Box-Cox transformation to address the skewed distribution of samples and incorporates additional critical predictors to enhance the accuracy of soil salinity (measured as electrical conductivity of the saturated soil extract, ECe) and sodicity (measured as exchangeable sodium percentage, ESP) estimates.</p> <p>We provide high-resolution (1 km × 1 km) global maps of soil salinity and sodicity from 1980 to 2022 in GeoTIFF file format for each year.</p> <p>Note: the scale factor for ECe and ESP maps is 0.001</p>
Maps for Soil loss by water from climate change scenarios for Austria
<p><span>The 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.</span></p> <p><span>This dataset contains the change of modelled annual soil loss rates for changing R-factor according to RCP4.5 and RPC8.5 climate scenarios, relative to modelled soil loss in the base scenario, using R-factor calculated for the 1990-2021 period. For each climate scenario, four periods were considered: 1991-2020, 2021-2040, 2041-2060 and 2061-2080. The RUSLE-based soil loss calculations were done according to the SERENA/EJP-Soil soil erosion cookbook and are described in the respective project deliverables D3.3 and D3.4.</span></p>
Data from: Using digital soil maps to infer edaphic affinities of plant species in Amazonia: problems and prospects
Open the record for dataset details and reuse information.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.