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96 results for “soil mapping”

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zenodo44/100

Global topsoil SOC stock from 1981 to 2018 estimated by combining process-based model and space-for-time digital soil mapping

This dataset include the topsoil (0-30cm) soil organic carbon (SOC) stocks in mineral soils under major land classes (forest, grassland, shrub land, savannas, cropland, cropland/natural vegetation mosaic, and sparely vegetated land) from 1981 to 2018. The long-time series of SOC stocks were estimated by using a space-for-time digital soil mapping (DSMst) model where the RothC-simulated SOC stocks were incorporated as one of the dynamic covariates of the DSMst model. The detail information on the products were given below: Name: DSMst-RothC 5-km global topsoil SOC stock products Period: 1981-2018 Spatial resolution: 0.041666667 degree Temporal resolution: 1 year CRS: geographic latitude/longitude (EPSG:4326 - WGS 84 – Geographic) Extent: -180°, -90°: 180°, 90° Data format: GeoTIFF Compression: LZW Data type: Float32 Unit: t C ha-1

opencc-by-4.0Jun 2021View details →

zenodo44/100

ELABORATION OF THE ITALIAN PORTION OF THE GLOBAL SOIL ORGANIC CARBON MAP (GSOCMAP)

The Global Soil Organic Carbon map (GSOCmap) published by the Food and Agriculture Organization constitutes a baseline estimation of soil organic carbon stock (CS, ton ha–1) from 0 to 30 cm, on a grid at 30 arc-seconds resolution (approximately 1 x 1 km). It has been produced for the Italian territory by the Italian Soil Partnership (ISP): a national hub of institutions dealing with soils, either academic/research institutions, and regional soil services (RSS). The RSS are the main soil data owners in Italy and play a central role in the elaboration of policies for soil management. The RSS adhering to the ISP are: Calabria, Campania, Emilia Romagna, Friuli Venezia Giulia, Liguria, Lombardia, Marche, Piemonte, Puglia, Sicilia, Toscana, and Veneto. A national soil database is maintained by the Consiglio per la Ricerca e l'Analisi dell'Economia Agraria (CREA). The RSS contributed with soil data, with mean density of 1 point per 50 square kilometres, selecting data analysed for soil organic carbon content (SOC, dag kg-1), which were representative and well distributed for the following environmental covariates: land use, geomorphology, and climate. The data were selected inbetween 1990 al 2013. This was necessary in order to exclude the effect of the new soil protection policies of the Rural Development Programme 2014-2020. For the RSS not included in the ISP, the data were selected from the national soil database. 6748 point data were finally selected. SOC values obtained with the Springer and Klee and flash combustion elemental analyser methods were retained for elaborations, because the 2 methods, were found to give statistically equivalent results. SOC values obtained with Walkey and Black method were, instead, corrected with an empirical factor of 1.3. 2292 of the 6748 point data had also measured bulk density (BD, Mg m–3). Pedotransfer functions were calibrated to estimate BD were measured BD were missing, with the following as auxiliary variables: land use, soil regions, texture, and SOC. The carbon stock (CS, ton ha–1) was calculated by multiplying: 0.3 (m) * SOC (dag kg-1) * fine earth fraction (1 - skeletal content expressed as daL m–3) * BD (Mg m–3). CS of the first 30 cm depth was calculated as depth-weighted average. A spatial statistics method was used for the CS interpolation. The following auxiliary variables were used: soil regions, soil subregions, Corine land cover 2006, lithology, soils affected by natural constrains (gleyic, histic, vertic, coarse, shallow, arenic, sodic, and acid), sand content, silt content, 30-m aster-DEM, distance from coast, distance from relieves, soil aridity index, annual mean precipitations, mean annual air temperature, soil inorganic carbon, and soil depth. For the soil region of Po valley, the land units at 1:250,000 scale were also used. The interpolation method was a general linear regression for the soil regions of Po valley, and a radial basis function for the remaining Italian territory. The 6748 point data were divided, by spatial random sampling, into 10 subsets. Ten interpolations were produced, each time leaving out 1/10 of the dataset. Average (fig. 1), standard deviation and confidence intervals of these 10 interpolations were calculated. Mean Absolute Errors (MAE) and Root Mean Squared Errors (RMSE) were respectively 25.5 and 36.4 Mg/ha. A.85 Italy Map source: Country submission Point data Number of samples: 6748 Sampling period: 1990-2013 SOC analysis method: SOC values obtained with the Springer and Klee and ’flash combustion elemental analyser’ methods were retained for elaborations. Uncorrected values obtained by the Walkey and Black method were corrected with an empirical linear equation, based on previous studies and as recommended by the Italian official methods. BD analysis method: Undisturbed sampling, core method and pit method Mapping method Mapping method details: Neural Networks and GLM, according to soil region Validation statistics: Mean Error (ME) of the prediction is 1.688 Mg/ha, MAE 25.57 Mg/ha, Root Mean Squared Error (RMSE) is 36.24 Mg/ha. Contact Data Holder: Research centre for agriculture and environment Contact: CREA Consiglio per la ricerca in agricoltura e l’analisi dell’economia agraria edoardo.costantini@crea.gov.it

opencc-by-4.0Dec 2018View details →

zenodo44/100

Spatial models of topsoil properties in Romania using digital soil mapping techniques

The database includes the selected spatial models of soil variables for the Romanian territory derived by digital soil mapping techniques, accepted for publication in: Cristian Valeriu Patriche, Bogdan Roșca, Radu Gabriel Pîrnău, Ionuț Vasiliniuc, Spatial modelling of topsoil properties in Romania using geostatistical methods and machine learning, PLOS ONE, 2023 The database includes the selected spatial models of soil variables for the Romanian territory derived by digital soil mapping techniques. The file names indicate the soil variable and the method used for interpolation (RK – regression - kriging, EML – ensemble machine learning, GWR_OK – Geographically Weighted Regression – Ordinary kriging). The raster data is classified and saved in tif format with a resolution of 100 x 100 m. The spatial reference is Stereographic projection 1970 (Pulkovo_1942_Adj_58_Stereo_70). The soil variables are classified as follows: <table> <tbody> <tr> <td> Variable </td> <td> Classes </td> </tr> <tr> <td> 1 </td> <td> 2 </td> <td> 3 </td> <td> 4 </td> <td> 5 </td> <td> 5 </td> <td> 7 </td> </tr> <tr> <td> pH </td> <td> ≤ 5 (strongly acid) </td> <td> 5.1 – 5.8 (moderately acid) </td> <td> 5.9 – 6.8 (weakly acid) </td> <td> 6.9 – 7.2 (neutral) </td> <td> 7.3 – 8.4 (weakly alkaline) </td> <td> 8.5 – 8.8 (moderately alkaline) </td> <td>   </td> </tr> <tr> <td> EC (mS m-1) </td> <td> ≤ 12.75 </td> <td> 12.76 – 16.49 </td> <td> 16.50 – 20.04 </td> <td> 20.05 – 24.18 </td> <td> 24.19 – 29.11 </td> <td> 29.12 – 35.23 </td> <td> ≤ 35.24 </td> </tr> <tr> <td> OC (g kg-1) </td> <td> < 7.5 (very low) </td> <td> 7.5 – 17.4 (low) </td> <td> 17.4 – 37.8 (moderate) </td> <td> 37.8 – 61.0 (high) </td> <td> > 61 (very high) </td> <td>   </td> <td>   </td> </tr> <tr> <td> CaCO3 (g kg-1) </td> <td> 0 (no carbonates) </td> <td> 1 – 10 (low) </td> <td> 11 – 40 (medium 1) </td> <td> 41 – 80 (medium 2) </td> <td> 81 – 107 (medium 3) </td> <td>   </td> <td>   </td> </tr> <tr> <td> P (mg kg-1) </td> <td> < 4 (extremely low) </td> <td> 4 – 8 (very low) </td> <td> 8 – 18 (low) </td> <td> 18 – 36 (medium) </td> <td> 36 – 72 (high) </td> <td> > 72 (very high) </td> <td>   </td> </tr> <tr> <td> N (g kg-1) </td> <td> ≤ 1 (very low) </td> <td> 1.1 – 1.4 (low) </td> <td> 1.5 – 2.0 (medium 1) </td> <td> 2.1 – 2.7 (medium 2) </td> <td> 2.8 – 6.0 (high) </td> <td> > 6 (very high) </td> <td>   </td> </tr> <tr> <td> K (mg kg-1) </td> <td> ≤ 40 * (extremely low) </td> <td> 41 – 65 * (very low) </td> <td> 66 – 130 (low) </td> <td> 131 – 200 (medium) </td> <td> 201 – 300 (high) </td> <td> > 300  (very high) </td> <td>   </td> </tr> <tr> <td> Clay (%) </td> <td> ≤ 25  (low 1) </td> <td> 26 – 32 (low 2) </td> <td> 33 – 40 (medium 1) </td> <td> 41 – 45 (medium 2) </td> <td> ≥ 46  (high) </td> <td>   </td> <td>   </td> </tr> <tr> <td> Silt (%) </td> <td> < 25 (medium 1) </td> <td> 25 – 32 (medium 2) </td> <td> 33 – 40 (high 1) </td> <td> 41 – 50 (high 2) </td> <td> > 50 (high 3) </td> <td>   </td> <td>   </td> </tr> <tr> <td> Sand (%) </td> <td> < 15 (low 1) </td> <td> 15 – 25 (low 2) </td> <td> 26 – 35 (low 3) </td> <td> 36 – 56 (medium) </td> <td> > 56 (high) </td> <td>   </td> <td>   </td> </tr> </tbody> </table>   * classes not present on the Romanian territory

opencc-by-4.0Jul 2023View details →

zenodo40/100

Data and R code for the revised manuscript "Downscaling digital soil maps using electromagnetic induction and aerial imagery"

Data and R code for the revised manuscript "Downscaling digital soil maps using electromagnetic induction and aerial imagery". This is the code for the revised version of the manuscript, after adressing comments from reviewers. The data and code for the preprint, before submission to peer review (Møller et al., 2020), is available at <a href="https://doi.org/10.5281/zenodo.3699130">https://doi.org/10.5281/zenodo.3699130</a>. The R code was written for R version 3.6.3. References Møller, A.B., Koganti, T., Beucher, A., Iversen, B.V. and Greve, M.H., 2020. Downscaling digital soil maps using electromagnetic induction and aerial imagery. EarthArXiv. <a href="http://dx.doi.org/10.31223/osf.io/a7xz6">http://dx.doi.org/10.31223/osf.io/a7xz6</a>. [preprint]

opencc-by-4.0Jul 2020View details →

zenodo40/100

Data: Spatio-temporal prediction of soil moisture using soil maps, topographic indices and SMAP retrievals

Data used in: Schönauer, M., Prinz, R., Väätäinen, K., Astrup, R., Pszenny, D., Lindeman, H., et al. (2022). Spatiotemporal prediction of soil moisture using soil maps, topographic indices and SMAP retrievals. International Journal of Applied Earth Observation and Geoinformation, 102730. doi: 10.1016/j.jag.2022.102730

opencc-by-4.0Feb 2021View details →

zenodo40/100

Probability maps of exceeding six soil thickness in mainland France

This is the dataset of probability maps of exceeding six soil thickness (i.e. 5, 15, 30, 60, 100, 200 cm) in mainland France produced in the paper "Probability mapping of soil thickness by random survival forest at a national scale" by Chen et al. (2019). Manuscript citation: Chen, S., Mulder, V.L., Martin, M.P., Walter, C., Lacoste, M., Richer-de-Forges, A.C., Saby, N.P., Loiseau, T., Hu, B. and Arrouays, D., 2019. Probability mapping of soil thickness by random survival forest at a national scale. Geoderma, 344, 184-194. When using the data, please cite repositories as well as the original manuscript. For any questions on the data, please contact Dr. Songchao Chen (chensongchao@zju.edu.cn).

opencc-by-4.0Jun 2019View details →

zenodo40/100

Prince Edward Island (Canada) Predictive Soil Mapping data set (30 m)

Prince Edward Island (Canada) Predictive Soil Mapping data set. Training points include: <ul> <li>soil organic matter (624 points): unit: percent, g/100g ) samples from the topsoil (0-23 cm depth),</li> <li>soil types (672 points),</li> </ul> Covariate layers include: <ul> <li>DEM derivatives (Channel_Network_Base_Level.tif, MRRTF.tif, Relative_Slope_Position.tif, Slope.tif, TWI.tif, Valley_Depth.tif, Vertical_Distance_To_Channel_Network.tif),</li> <li>landcover_2016_reclassify.tif (categorical values),</li> <li>DSS_soil_polygons.tif (soil polygons),</li> </ul> Some covariates are type numeric, some type factor. To use the pre-processed data download only the RDS file. E.g. "PEI100m.soil.rds" contains all covariates layers resampled to 100 m resolution and all training points.

opencc-by-nc-sa-4.0May 2019View details →

zenodo40/100

Map sheet 25-132 Lipník n. B. - Results of soil chemical analyses and pH of soil leachate

Results of soil chemical analyses and pH of soil leachate from the 25-132 Lipník nad Bečvou map sheet

opencc-by-4.0Sep 2024View details →

zenodo40/100

SOIL-WATERGRIDS v1, mapping dynamic changes in soil moisture and depth of water table from 1970 to 2014, dataset and modelling

SOIL-WATERGRIDS is a comprehensive data product of the monthly estimates of volumetric soil water content at three depths within the root zone and the depth of the water table globally gridded at a resolution of 0.25x025 degree per grid cell from 1970 to 2014. The SOIL-WATERGRIDS data product also provides the full-scale global model (BRTSim, https://sites.google.com/site/thebrtsimproject/home) that allows third party users to assess the entire volumetric soil water content and water table dynamics from land surface to 50 m depth.  This package includes a Technical Documentation with the details about the use of the data product.

opencc-by-4.0Dec 2020View details →

zenodo40/100

Impact of Schistosomiasis, Soil-Transmitted Helminthiasis and Anaemia on preschool and school-age children's health condition: post treatment predictive mapping in Benin Republic.

This dataset provides information about the epidemiology of schistosomiasis, soil transmitted helminthiasis and anemia alongside malnutrition among preschool and school age children in Ouake and Bembereke districts of donga and borgou departments in Benin republic.

opencc-by-4.0Mar 2023View details →

zenodo40/100

Soil texture dataset from the publication: "Machine learning applied for Antarctic soil mapping: Spatial prediction of soil texture for Maritime Antarctica and Northern Antarctic Peninsula'

Clay, silt and sand distribution in Antarctic soils modeled and predicted through Machine Learning approaches, legacy soil data and environmental covariates. The coefficient of variation and quantile data represent the spatial uncertainty of the predictions. For more information about the methodology used, users are referred to the article:  Siqueira, R.G., Moquedace, C.M., Francelino, M.R., Schaefer, C.E.G.R., Fernandes-Filho, E.I., 2023. Machine learning applied for Antarctic soil mapping: Spatial prediction of soil texture for Maritime Antarctica and Northern Antarctic Peninsula. Geoderma 432, 116405. https://doi.org/10.1016/j.geoderma.2023.116405 The .zip file has the following folders: 1) soil_texture_antarctica: soil texture information containing clay, silt and sand contents 2) soil_texture_coefficient_variation: uncertainty from the coefficient of variation of the soil texture prediction 3) soil_texture_prediction_interval: uncertainty from the prediction interval 90% (Q95% - Q5%) of the soil texture prediction 4) soil_texture_quantile05: quantile 5% of the soil texture prediction 5) soil_texture_quantile95: quantile 95% of the soil texture prediction

opencc-by-4.0Sep 2023View details →

dryad40/100

Data from: Large, climate-sensitive soil carbon stocks mapped with pedology-informed machine learning in the North Pacific coastal temperate rainforest

Open the record for dataset details and reuse information.

publicOct 2024View details →

edi40/100

High frequency soil sensor data for SOM input - Complex drivers of riparian soil oxygen variability revealed using self-organizing maps

The provided datasets contain the original (non-normalized) high-frequency soil and meteorological observations that were fed to the Self-Organizing Map (SOM) in order to identify ranges of values associated with low and high soil O2 conditions. For the Champlain Valley (CV) site we used the natural breaks algorithm to subset the data into high and low O2 datasets. O2 values were consistently low at the Green Mountains (GM) site, so we ran a single SOM for all O2 values at this site. The original values were then range-normalized before they were fed to the SOM.

openCC (other)Nov 2021View details →

edi40/100

Hubbard Brook Experimental Forest: Soil Profile Maps and Horizon Thicknesses on Watershed 5, 1983-1998

We sampled soils prior to the whole-tree harvest of watershed 5 at Hubbard Brook Experimental Forest in 1983, and again in 1986, 1991, and 1998, using the quantitative soil pit method. Here we report horizon thicknesses and present hand drawn maps of the sides of a subset of the 239 sampling pits excavated over the four sampling years. Note that U.S. standard soil horizon nomenclature changed between 1983 and 1986. In nearly all cases, the 1983 horizon designations have the following equivalencies: A2 = E, Bhir = Bs, Bir = Bs1, B23 and B23+ = Bs2. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station. An analysis of these data has been published in: Johnson, C.E., A.H. Johnson, T.G. Huntington, and T.G. Siccama. 1991. Whole-tree clear-cutting effects on soil horizons and organic-matter pools. Soil Science Society of America Journal. 55:497-502. https://doi.org/10.2136/sssaj1991.03615995005500020034x

openCC (other)Jul 2021View details →

edi40/100

Detailed USDA SSURGO soil map data for Accomack and Northampton Counties, VA, 2008-2010.

This dataset contains detailed USDA SSURGO soil information and mapped soil extents for the soils of Accomack and Northampton Counties on Virginia's Eastern Shore, including those areas of focused study by the Virginia Coast Reserve LTER project. This USDA soil data is collected and combined here to make it more accessible to VCRLTER researchers and students, in a more GIS-friendly format, and to supersede previous digitized versions of more generalized soil maps created by the VCRLTER and included as part of the 1995 VCRLTER-Northampton County GIS data archive (dataset VCR14219). Data was downloaded in Jan. 2014 and tabular data for both counties was imported into a MS Access database using the provided standard SSURGO US 2003 template. Spatial data for the two counties was merged together into a single ArcGIS shapefile and selected fields from the MAPUNIT and MUAGGATT tables were joined to the final shapefile's attribute table. Each polygon represents all or part of a SSURGO "mapunit", which may contain multiple component soils; usually very similar soils that grade together or else so heterogeneously mixed together at fine spatial scales to make mapping the component soils individually impractical. Also, each soil typically has multiple vertical soil horizons, each with its own distinct composition (mineral, textural, etc.) and other characteristics. Detailed information about component soils (including typical soil moisture, dry albedo, erodibility indices, taxonomic nomenclature, flooding and ponding characteristics, engineering, crop, forest, and habitat suitability indices and yield tables, and geomorphic descriptions) and component horizons (including horizon depths, grain size distributions, sand/silt/clay fractions, mineral and organic content, and pore space characteristics) is included in the MS Access database but NOT in the combined ArcGIS shapefile. Users interested in exploring or displaying component or horizon information may use the report and que

openCustomJan 2014View details →

zenodo36/100

soilmap_simple: a simplified and standardized derivative of the digital soil map of the Flemish Region

The data source <code>soilmap_simple</code> is a simplified and standardized derived form of the '<a href="https://www.dov.vlaanderen.be/geonetwork/srv/dut/catalog.search#/metadata/5c129f2d-4498-4bc3-8860-01cb2d513f8f">digital soil map of the Flemish Region</a>' (the shapefile of which we named <code>soilmap</code>, for analytical workflows in R) published by 'Databank Ondergrond Vlaanderen’ (DOV). It is a GeoPackage that contains a spatial polygon layer ‘<code>soilmap_simple</code>’ in the Belgian Lambert 72 coordinate reference system (EPSG-code <a href="https://epsg.io/31370">31370</a>), plus a non-spatial table ‘<code>explanations</code>’ with the meaning of category codes that occur in the spatial layer. Further documentation about the digital soil map of the Flemish Region is available in Van Ranst & Sys (2000) and Dudal et al. (2005). This version of <code>soilmap_simple</code> was derived from version '<code>soilmap_2017-06-20</code>' (<a href="https://doi.org/10.5281/zenodo.3387008">Zenodo DOI</a>) as follows: <ul> <li>all attribute variables received English names (purpose of standardization), starting with prefix <code>bsm_</code> (referring to the 'Belgian soil map');</li> <li>attribute variables were reordered;</li> <li>the values of the morphogenetic substrate, texture and drainage variables (<code>bsm_mo_substr</code>, <code>bsm_mo_tex</code> and <code>bsm_mo_drain</code> + their <code>_explan</code> counterparts) were filled for most features in the 'coastal plain' area. <ul> <li>To derive morphogenetic texture and drainage levels from the geomorphological soil types, a conversion table by Bruno De Vos & Carole Ampe was applied (for earlier work on this, see Ampe 2013).</li> <li>Substrate classes were copied over from <code>bsm_ge_substr</code> into <code>bsm_mo_substr</code> (<code>bsm_ge_substr</code> already followed the categories of <code>bsm_mo_substr</code>).</li> </ul> These steps coincide with the approach that had been taken to construct the <code>Unitype</code> variable in the <code>soilmap</code> data source;</li> <li>only a minimal number of variables were selected: those that are most useful for analytical work.</li> </ul> See R-code in the GitHub repository <a href="https://github.com/inbo/n2khab-preprocessing/tree/b3c6696/src/generate_soilmap_simple">'n2khab-preprocessing' at commit b3c6696</a> for the creation from the <code>soilmap</code> data source. A reading function to return <code>soilmap_simple</code> (this data source) or <code>soilmap</code> in a standardized way into the R environment is provided by the R-package <a href="https://inbo.github.io/n2khab/">n2khab</a>. The attributes of the spatial polygon layer <code>soilmap_simple</code> can have <code>mo_</code> in their name to refer to the Belgian Morphogenetic System: <ul> <li><code>bsm_poly_id</code>: unique polygon ID (numeric)</li> <li><code>bsm_region</code>: name of the region</li> <li><code>bsm_converted</code>: boolean. Were morphogenetic texture and drainage variables (<code>bsm_mo_tex</code> and <code>bsm_mo_drain</code>) derived from a conversion table (see above)? Value <code>TRUE</code> is largely confined to the 'coastal plain' areas.</li> <li><code>bsm_mo_soilunitype</code>: code of the soil type (applying morphogenetic codes within the coastal plain areas when possible, just as for the following three variables)</li> <li><code>bsm_mo_substr</code>: code of the soil substrate</li> <li><code>bsm_mo_tex</code>: code of the soil texture category</li> <li><code>bsm_mo_drain</code>: code of the soil drainage category</li> <li><code>bsm_mo_prof</code>: code of the soil profile category</li> <li><code>bsm_mo_parentmat</code>: code of a variant regarding the parent material</li> <li><code>bsm_mo_profvar</code>: code of a variant regarding the soil profile</li> </ul> The non-spatial table <code>explanations</code> has following variables: <ul> <li><code>subject</code>: attribute name of the spatial layer: either <code>bsm_mo_substr</code>, <code>bsm_mo_tex</code>, <code>bsm_mo_drain</code>, <code>bsm_mo_prof</code>, <code>bsm_mo_parentmat</code> or <code>bsm_mo_profvar</code></li> <li><code>code</code>: category code that occurs as value for the corresponding attribute in the spatial layer</li> <li><code>name</code>: explanation of the value of <code>code</code></li> </ul>

opencc-by-4.0Mar 2020View details →

zenodo36/100

Global soil saturated hydraulic conductivity map using random forest in a Covariate-based GeoTransfer Functions (CoGTF) framework at 1 km resolution

The global Ksat map 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 Ksat measurements and local information (soil, vegetation, climate) into covariate-based “Geo Transfer Functions'' (CoGTFs) to generate global estimates of Ksat values (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 estimate Ksat). The Ksat dataset is provided in GeoTIFF format. A total of 4 files that represent different soil depths (0, 30, 60, and 100 cm) are provided. The Ksat values are log-transformed (log10 Ksat) and cm/day was selected as a standardized unit. The Global Ksat training dataset used for this study is available here: <a href="https://doi.org/10.5281/zenodo.3752721">https://doi.org/10.5281/zenodo.3752721</a> The R code used for this study is available here: <a href="https://github.com/ETHZ-repositories/Ksat_mapping_2020">https://github.com/ETHZ-repositories/Ksat_mapping_2020</a> For more details / to cite this dataset please use: <ul> <li>Gupta, S., Lehmann, P., Bonetti, S., Papritz, A., and Or, D., (2020): Global prediction of soil saturated hydraulic conductivity using random forest in a Covariate-based Geo Transfer Functions (CoGTF) framework. Journal of Advances in Modeling Earth Systems, 13(4), e2020MS002242. https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020MS002242</li> </ul> Other datasets related to this project: The Global vG training dataset  is available here: <a href="https://doi.org/10.5281/zenodo.5547338">10.5281/zenodo.5547338</a> Examples of using this dataset to generate van Genuchten parameters maps can be found in <a href="https://doi.org/10.5281/zenodo.6343570">10.5281/zenodo.6343570</a>. The study was supported by ETH Zurich (Grant ETH-18 18-1). We would like to thank Zhongwang Wei, Samuel Bickel and Simone Fatichi (ETH Zurich) for insightful discussions.

opencc-by-4.0Dec 2019View details →

dryad36/100

Spatial mapping of root systems reveals diverse strategies of soil exploration and resource contest in grassland plants

1. When foraging and competing for belowground resources, plants have to coordinate the behaviour of thousands of root tips in a manner similar to that of eusocial animal colonies. While well described in animals, we know little about the spatial behaviour of plants, particularly at the level of individual roots. 2. Here, we employed statistical methods previously used to describe animal ranging behaviour to examine root system overlap and the efficiency of root positioning in eight grassland species grown in monocultures and mixtures along a gradient of neighbour densities. 3. Species varied widely in their ability to distribute roots efficiently, with the majority of species showing significant root aggregation at very fine spatial scales. Extensive root system overlap was observed in species mixtures, indicating a lack of territoriality at the level of whole root systems. However, with increasing density of competitors, several species withdrew roots from the periphery of foraging ranges and increased intraplant root aggregation in the remaining area, which may indicate consolidation of foraging areas under competitive pressure. 4. Several species exhibited responses consistent with resource contest in species mixtures where encounters with competitors' roots triggered increased root aggregation at the expense of foraging efficiency. Such responses only occurred in mixtures of species with comparable competitive abilities but were absent in asymmetric species combinations. 5. Synthesis. Combining fine-scale measurement of plant root distributions with spatial statistics yields new insights into plant behavioural strategies with significant potential to impact resource foraging efficiency and productivity.

opencc-zeroOct 2020View details →

zenodo36/100

Mapping Global Nitrogen Mineralization Rates: A Climate-Soil Perspective

The file "Ecosystem_β.tif" represents the spatial distribution of nitrogen mineralization rates in global ecosystems (cropland, grassland, and forest) under different climate models (SSP1-2.6, SSP2-4.5, SSP5-8.5). "Cropland_β.tif" represents the spatial distribution of cropland ecosystems across various SSPβ scenarios. "Grassland_β.tif" represents the spatial distribution of grassland ecosystems under different SSPβ scenarios. "Forest_β.tif" represents the spatial distribution of forest ecosystems across different SSPβ scenarios.

opencc-by-4.0Nov 2024View details →

zenodo36/100

High-resolution mapping of soil carbon stocks in the western Amazon

<h2>Dear Researchers and Interested Parties,</h2>  It is with great enthusiasm that we share our page on Zenodo, where we provide detailed maps (30 m resolution) of soil carbon stocks in Rondônia, Brazil. These maps were generated using machine learning techniques, using the Random Forest model implemented in the caret package. This initiative aims to provide a deeper and more accurate understanding of the spatial distribution of carbon in the soil, contributing significantly to environmental studies and climate change mitigation strategies in the region. <h2>Available resources:</h2> <h3>High Resolution Maps:</h3> We provide detailed maps of the estimates and uncertainties of soil carbon stocks at different depths (0-5; 5-15; 15-30; 30-60 and 60-100 cm). The maps include mean values (Mg ha-1), quantiles (Mg ha-1) and coefficients of variation (%), all in "tif" format, with a spatial resolution of 30 m and SAD 1969 Lambert South America projection system (<a href="https://epsg.io/102015">EPSG :102015</a>). The entire process was conducted in open source (R Language). The codes and database used can be found in the <a href="https://github.com/moquedace/ro_soil_carbon_stock" target="_blank" rel="noopener">GitHub repository</a>, and more information about the methodology is available in the following publication: Moquedace, C. M., Baldi, C. G. O., Siqueira, R. G., Cardoso I. M., Souza, E. F. M., Fontes, R. L. F., Francelino, M. R., Gomes, L. C., Fernandes-Filho, E. I. High-resolution mapping of soil carbon stocks in the Western Amazon. Geoderma Regional, v. 36, p. e00773, 2024. DOI: <a href="https://doi.org/10.1016/j.geodrs.2024.e00773">10.1016/j.geodrs.2024.e00773</a> <h2>Availability objectives:</h2> <h3>Promote scientific collaborations:</h3> We encourage researchers, scientists, and organizations to explore and use this data to enrich their own research and projects related to soil carbon and climate change. <h3>Enhance environmental understanding:</h3> By providing open access to these maps, we aim to contribute to a deeper understanding of environmental processes in Rondônia, Brazil and, by extension, enable the implementation of sustainable strategies. <h3>Stimulating innovation:</h3> We believe that sharing this data will stimulate innovation in modeling and spatial analysis methods, driving advances in the prediction of soil carbon stocks, especially in the Amazon. <h2>Thank you in advance for your interest and collaboration. Together, we can advance knowledge and the search for sustainable solutions to important environmental challenges.</h2>

opencc-by-4.0Jan 2024View details →

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