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12 results for “sand content”
Soil sand content [%] for continental Europe at 30 m spatial resolution for period 2000-2020: Open Soil Data Cube for Europe
<p>Predictions are based on the 3D Ensemble Machine Learning framework, as implemented in the R environment for statistical computing (Hengl & MacMillan, 2019; Hengl, et al., 2021). For each pixel we provide prediction errors as 1 standard deviation in either log or the original variable scale.</p> <p>The short description of currently available soil properties: sand.tot = sand content [percent];</p> <p>Soil properties were predicted at fixed depths:</p> <p> Surface soil = s0..0cm,<br> Subsoil 1 = s30..30cm,<br> Subsoil 2 = s60..60cm,<br> Subsoil 3 = s100..100cm.</p> <p>To produce estimates for depth intervals e.g. 0–30 cm, 0–100 cm best use the trapezoidal rule formula.</p> <p>Periods: 2000 (2000–2003), 2004 (2004–2007), 2008 (2008–2011), 2012 (2012–2015), 2016 (2016–2019), 2020;</p>
Sand content in % (kg / kg) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution
<p>Sand content in % (kg / kg) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution. Based on machine learning predictions from global compilation of soil profiles and samples. Processing steps are described in detail <strong><a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil">here</a></strong>. Antarctica is not included.</p> <p>To access and visualize maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>sol = theme: soil,</li> <li>sand.wfraction = variable: sand weight fraction,</li> <li>usda.3a1a1a = determination method: laboratory method code,</li> <li>m = mean value,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>b10..10cm = vertical reference: 10 cm depth below surface,</li> <li>1950..2017 = time reference: period 1950-2017,</li> <li>v0.2 = version number: 0.2,</li> </ul>
Observations of groundwater fluctuations and surface moisture content on a medium-grained, planar beach (Sand Engine, the Netherlands)
<p>These data are groundwater and beach surface moisture values collected during the MegaPex campaign between October 11 and 20, 2014 at the Sand Engine, The Hague, the Netherlands by MSc students and staff of the Coastal Research Group at Utrecht University, the Netherlands. The data were obtained at 8 locations in a cross-shore array on the intertidal and upper beach. During the measurements the beach was planar (1:30) and the median grain size was 0.365 mm. The data are supplemented with bed profiles along the instrument array. For further information and meta-data, please consult the readme.txt and the header of the individual text files in the zip-file.</p>
iSDAsoil: soil sand content (USDA system) for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths
<p>iSDAsoil dataset soil sand content in % predicted at 30 m resolution for 0–20 and 20–50 cm depth intervals. Data has been projected in WGS84 coordinate system and compiled as <a href="https://gdal.org/drivers/raster/cog.html">COG</a>. Predictions have been generated using multi-scale Ensemble Machine Learning with 250 m (MODIS, PROBA-V, climatic variables and similar) and 30 m (DTM derivatives, Landsat, Sentinel-2 and similar) resolution covariates. For model training we use a pan-African compilations of soil samples and profiles (<a href="https://www.isda-africa.com/national-soil-services/">iSDA points</a>, <a href="https://www.isric.org/projects/africa-soil-profiles-database-afsp">AfSPDB</a>, <a href="https://landpotential.org/data-portal/">LandPKS</a>, and other national and regional soil datasets). Cite as:</p> <p>Hengl, T., Miller, M.A.E., Križan, J. <em>et al.</em> African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. <em>Sci Rep</em> <strong>11, </strong>6130 (2021). <a href="https://doi.org/10.1038/s41598-021-85639-y">https://doi.org/10.1038/s41598-021-85639-y</a></p> <p>To open the maps in QGIS and/or directly compute with them, please use the <a href="https://gitlab.com/openlandmap/africa-soil-and-agronomy-data-cube"><strong>Cloud-Optimized GeoTIFF version</strong></a>.</p> <p>Layer description:</p> <ul> <li>sol_sand_tot_psa_m_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil sand content mean value,</li> <li>sol_sand_tot_psa_md_30m_*..*cm_2001..2017_v0.13_wgs84.tif = predicted soil sand content model (prediction) errors,</li> </ul> <p>Model errors were derived using bootstrapping: md is derived as standard deviation of individual learners from 5-fold cross-validation (using spatial blocking). The model 5-fold cross-validation (<a href="https://mlr.mlr-org.com/reference/makeStackedLearner.html">mlr::makeStackedLearner</a>) for this variable indicates:</p> <pre><code>Variable: sand_tot_psa R-square: 0.736 Fitted values sd: 22.8 RMSE: 13.7 Random forest model: Call: stats::lm(formula = f, data = d) Residuals: Min 1Q Median 3Q Max -80.626 -5.321 0.221 6.071 88.686 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 6.687471 24.022001 0.278 0.780714 regr.ranger 1.060521 0.003503 302.742 < 2e-16 *** regr.xgboost -0.018718 0.004910 -3.812 0.000138 *** regr.cubist 0.031749 0.003922 8.096 5.73e-16 *** regr.nnet -0.161127 0.422746 -0.381 0.703098 regr.cvglmnet -0.028217 0.004462 -6.323 2.57e-10 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 13.65 on 122261 degrees of freedom Multiple R-squared: 0.736, Adjusted R-squared: 0.736 F-statistic: 6.818e+04 on 5 and 122261 DF, p-value: < 2.2e-16</code></pre> <p>To submit an issue or request support please visit <a href="https://isda-africa.com/isdasoil"><strong>https://isda-africa.com/isdasoil</strong></a></p>
Data for low-clay content submarine slope failure flume experiments. Experiment 100% FG Sand 4.
<p>These video and photographic data support the following manuscripts:</p><p>Silver, M.M.W., Dugan, B., 2020, The influence of clay content on submarine slope failure: insights from laboratory experiments and numerical models, Geological Society of London, Special Publications, 500, 301-309, <a href="https://doi.org/10.1144/SP500-2019-186">https://doi.org/10.1144/SP500-2019-186</a>. </p><p>Silver, M.M.W., Dugan, B., 2023, Cohesion, permeability, and slope failure dynamics: implications for failure morphology and tsunamigenesis from benchtop flume experiments, Marine Geology, 462, <a href="https://doi.org/10.1016/j.margeo.2023.107079">https://doi.org/10.1016/j.margeo.2023.107079</a>.</p><p>Log sheets are included for each experiment file.</p>
Data for low-clay content submarine slope failure flume experiments. Experiment 100% FG Sand 2.
<p>These video and photographic data support the following manuscripts:</p><p>Silver, M.M.W., Dugan, B., 2020, The influence of clay content on submarine slope failure: insights from laboratory experiments and numerical models, Geological Society of London, Special Publications, 500, 301-309, <a href="https://doi.org/10.1144/SP500-2019-186">https://doi.org/10.1144/SP500-2019-186</a>. </p><p>Silver, M.M.W., Dugan, B., 2023, Cohesion, permeability, and slope failure dynamics: implications for failure morphology and tsunamigenesis from benchtop flume experiments, Marine Geology, 462, <a href="https://doi.org/10.1016/j.margeo.2023.107079">https://doi.org/10.1016/j.margeo.2023.107079</a>.</p><p>Log sheets are included for each experiment file.</p>
Data for low-clay content submarine slope failure flume experiments. Experiment 100% FG Sand 3.
<p>These video and photographic data support the following manuscripts:</p><p>Silver, M.M.W., Dugan, B., 2020, The influence of clay content on submarine slope failure: insights from laboratory experiments and numerical models, Geological Society of London, Special Publications, 500, 301-309, <a href="https://doi.org/10.1144/SP500-2019-186">https://doi.org/10.1144/SP500-2019-186</a>. </p><p>Silver, M.M.W., Dugan, B., 2023, Cohesion, permeability, and slope failure dynamics: implications for failure morphology and tsunamigenesis from benchtop flume experiments, Marine Geology, 462, <a href="https://doi.org/10.1016/j.margeo.2023.107079">https://doi.org/10.1016/j.margeo.2023.107079</a>.</p><p>Log sheets are included for each experiment file.</p>
Data for low-clay content submarine slope failure flume experiments. Experiment 100% FG Sand 1.
<p>These video and photographic data support the following manuscripts:</p><p>Silver, M.M.W., Dugan, B., 2020, The influence of clay content on submarine slope failure: insights from laboratory experiments and numerical models, Geological Society of London, Special Publications, 500, 301-309, <a href="https://doi.org/10.1144/SP500-2019-186">https://doi.org/10.1144/SP500-2019-186</a>. </p><p>Silver, M.M.W., Dugan, B., 2023, Cohesion, permeability, and slope failure dynamics: implications for failure morphology and tsunamigenesis from benchtop flume experiments, Marine Geology, 462, <a href="https://doi.org/10.1016/j.margeo.2023.107079">https://doi.org/10.1016/j.margeo.2023.107079</a>.</p><p>Log sheets are included for each experiment file.</p>
Organic matter and sand content for backbarrier marsh cores from Metompkin island, VA 2012
Cores collected: Summer of 2012 Organic matter and sand content for backbarrier marsh cores from Metompkin island. Cores were taken from 6 different locations in the backbarrier on the south end of Metompkin island, with varying along- and cross-shore distances. The core sites are labelled MA-MF (eg: Metompkin core A, Metompkin core B, etc.), and each has an associated set of latitude and longitude coordinates showing the location where the core was taken. Included is the data for the percent organic matter, measured at 5cm intervals in the core, via loss-on-ignition experiments. Also included is the data for the percent sand, measured at 10cm intervals in the core, using a LS 13 320 particle analyzer.
Data from: Sperm duct gland contents increase sperm velocity in the sand goby
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Very fine sand content (sandvf) soil maps of the Upper Colorado River Basin
<p>The data here were originally posted to facilitate timely and transparent peer review. The final public data release with formal metadata is now available from at the following location:</p> <p>Nauman, T.W., and Duniway, M.C., 2020, Predictive soil property maps with prediction uncertainty at 30 meter resolution for the Colorado River Basin above Lake Mead: U.S. Geological Survey data release,<a href="http://https//doi.org/10.5066/P9SK0DO2"> https://doi.org/10.5066/P9SK0DO2</a>.</p> <p>Associated publication:</p> <p>Nauman, T. W., and Duniway, M. C., 2020, A hybrid approach for predictive soil property mapping using conventional soil survey data: Soil Science Society of America Journal, v. 84, no. 4, p. 1170-1194. <a href="https://doi.org/10.1002/saj2.20080">https://doi.org/10.1002/saj2.20080</a>.</p> <p>Repository includes maps of very fine sand content (sandvf) as defined by United States soil survey program. Sandvf content is estimated by percent weight of the <2mm portion of the soil.</p> <p>These data are preliminary or provisional and are subject to revision. They are being provided to meet the need for timely best science. The data have not received final approval by the U.S. Geological Survey (USGS) and are provided on the condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from the authorized or unauthorized use of the data.</p> <p>This data should be used in combination with a soil depth or depth to restriction layer map (both layers that will be released soon as part of this project) to eliminate areas mapped at deeper depths than the soil actually goes. This is a limitation of this data which will hopefully be updated in future updates. </p> <p>The creation and interpretation of this data is documented in the following article. Please note this article has not been reviewed yet and this citation will be updated as the peer review process proceeds.</p> <p>Nauman, T. W., Duniway, M. C., In Preparation. Predictive reconstruction of soil survey property maps for field scale adaptive land management. Soil Science Society of America Journal.</p> <p>File Name Details:</p> <p>ACCURACY!! Please see manuscript and Github repository (https://github.com/naumi421/SoilReconProps) for full details on accuracy. We do provide cross validation (CV) accuracy plots in this repository for both the overall sample (NRCS field pedons plus NRCS laboratory pedons; file ending _CV_plots.tif) and for just the CV results at laboratory pedons (file ending _CV_SCD_plots.tif). These plots compare CV predictions with observed values relative to a 1:1 line. Values plotted near the 1:1 line are more accurate. Note that values are plotted in hex-bin density scatter plots because of the large number of observations (most are >3000).</p> <p>Elements are separated by underscore (_) in the following sequence:</p> <p>property_r_depth_cm_geometry_model_additional_elements.extension</p> <p>Example: sandvf_r_0_cm_2D_QRF.tif</p> <p>Indicates very fine sand content (sandvf) at 0 cm depth using a 2D model (separate model for each depth) employing a quantile regression forest. This file is the raster prediction map for this model. There may be additional GIS files associated with this file (e.g. pyramids) that have the same file name, but different extensions.</p> <p>The following elements may also exist on the end of filenames indicating other spatial files that characterize a given model's uncertainty (see below).</p> <p>_95PI_h: Indicates the layer is the upper 95% prediction interval value.</p> <p>_95PI_l: Indicates the layer is the lower 95% prediction interval value.</p> <p>_95PI_relwidth: Indicates the layer is the 95% relative prediction interval (RPI). The RPI is a standardization of the prediction interval that indicates that model is constraining uncertainty relative to the original sample. RPI values less than one represent uncertainty is being improved by the model relative to the original sample, and values less than 0.5 indicate low uncertainty in predictions. See paper listed above and also Nauman and Duniway (In revision) for more details on RPI.</p> <p>References</p> <p> Nauman, T. W., and Duniway, M. C., In Revision, Relative prediction intervals reveal larger uncertainty in 3D approaches to predictive digital soil mapping of soil properties with legacy data: Geoderma</p> <p> </p>
Fine sand content (sandfine; 0.10 to 0.25 mm) soil maps of the Upper Colorado River Basin
<p>The data here were originally posted to facilitate timely and transparent peer review. The final public data release with formal metadata is now available from at the following location:</p> <p>Nauman, T.W., and Duniway, M.C., 2020, Predictive soil property maps with prediction uncertainty at 30 meter resolution for the Colorado River Basin above Lake Mead: U.S. Geological Survey data release,<a href="http://https//doi.org/10.5066/P9SK0DO2"> https://doi.org/10.5066/P9SK0DO2</a>.</p> <p>Associated publication:</p> <p>Nauman, T. W., and Duniway, M. C., 2020, A hybrid approach for predictive soil property mapping using conventional soil survey data: Soil Science Society of America Journal, v. 84, no. 4, p. 1170-1194. <a href="https://doi.org/10.1002/saj2.20080">https://doi.org/10.1002/saj2.20080</a>.</p> <p>Repository includes maps of fine sand content (sandfine; 0.10 to 0.25 mm) as defined by United States soil survey program. Content is calculated on the fine earth fraction (<2mm).</p> <p>These data are preliminary or provisional and are subject to revision. They are being provided to meet the need for timely best science. The data have not received final approval by the U.S. Geological Survey (USGS) and are provided on the condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from the authorized or unauthorized use of the data.</p> <p>The creation and interpretation of this data is documented in the following article. Please note this article has not been reviewed yet and this citation will be updated as the peer review process proceeds.</p> <p>Nauman, T. W., Duniway, M. C., In Preparation. Predictive reconstruction of soil survey property maps for field scale adaptive land management. Soil Science Society of America Journal.</p> <p>File Name Details:</p> <p>ACCURACY!! Please see manuscript and Github repository (https://github.com/naumi421/SoilReconProps) for full details on accuracy. We do provide cross validation (CV) accuracy plots in this repository for both the overall sample (NRCS field pedons plus NRCS laboratory pedons; file ending _CV_plots.tif) and for just the CV results at laboratory pedons (file ending _CV_SCD_plots.tif). These plots compare CV predictions with observed values relative to a 1:1 line. Values plotted near the 1:1 line are more accurate. Note that values are plotted in hex-bin density scatter plots because of the large number of observations (most are >3000).</p> <p>Elements are separated by underscore (_) in the following sequence:</p> <p>property_r_depth_cm_geometry_model_additional_elements.extension</p> <p>Example: sandfine_r_0_cm_2D_QRF.tif</p> <p>Indicates fine sand content (sandfine) at 0 cm depth using a 2D model (separate model for each depth) employing a quantile regression forest. This file is the raster prediction map for this model. There may be additional GIS files associated with this file (e.g. pyramids) that have the same file name, but different extensions.</p> <p>The following elements may also exist on the end of filenames indicating other spatial files that characterize a given model's uncertainty (see below).</p> <p>_95PI_h: Indicates the layer is the upper 95% prediction interval value.</p> <p>_95PI_l: Indicates the layer is the lower 95% prediction interval value.</p> <p>_95PI_relwidth: Indicates the layer is the 95% relative prediction interval (RPI). The RPI is a standardization of the prediction interval that indicates that model is constraining uncertainty relative to the original sample. RPI values less than one represent uncertainty is being improved by the model relative to the original sample, and values less than 0.5 indicate low uncertainty in predictions. See paper listed above and also Nauman and Duniway (In revision) for more details on RPI.</p> <p>References</p> <p> Nauman, T. W., and Duniway, M. C., In Revision, Relative prediction intervals reveal larger uncertainty in 3D approaches to predictive digital soil mapping of soil properties with legacy data: Geoderma.</p>
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