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opencc-by-sa-4.0May 2022View details →

Soil organic carbon content [g/kg] for continental Europe at 30 m spatial resolution for period 2000-2020: Open Soil Data Cube for Europe

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. The short description of currently available soil properties: log organic carbon [g/kg] to back-transform use exp(x/10)-1; Soil properties were predicted at fixed depths:     Surface soil = s0..0cm,     Subsoil 1 = s30..30cm,     Subsoil 2 = s60..60cm,     Subsoil 3 = s100..100cm. To produce estimates for depth intervals e.g. 0–30 cm, 0–100 cm best use the trapezoidal rule formula. Periods: 2000 (2000–2003), 2004 (2004–2007), 2008 (2008–2011), 2012 (2012–2015), 2016 (2016–2019), 2020; To back-transform the log.oc maps use formula: exp(x/10)-1. These are examples of back-transformed values:     log.oc = 15 → 0.3% SOC;     log.oc = 20 → 0.6% SOC;     log.oc = 25 → 1.1% SOC;     log.oc = 30 → 1.9% SOC;     log.oc = 35 → 3.2% SOC;     log.oc = 40 → 5.3% SOC;     log.oc = 50 → 14.8% SOC;

Soil Organic Carbon Content estimations over the Lithuanian pilot area (2022)

In the context of the EU-funded project DIONE (No. 870378), Soil Organic Carbon Content (SOC) estimations have been released as outputs of novel machine learning algorithms which combined the point measurements (i.e. soil properties detected by the Soil Scanning Systems) with temporal EO multispectral imagery and other ancillary variables, enabling end-users, and for the DIONE case, the national paying agency of Lithuania (National Paying Agency - NPA) to mine meaningful information about overall soil health and the effects applied agricultural practices. The dataset is delivered in a single-banded GeoTIFF file (DIONE_SOC_estimations_LT_2022.tif- EPSG:4326) containing the SOC content (SOC %) labelled as Band 1.

Soil Organic Carbon Content estimations over the Cypriot pilot area (2022)

In the context of the EU-funded project DIONE (No. 870378), Soil Organic Carbon Content (SOC) estimations have been released as outputs of novel machine learning algorithms which combined the point measurements (i.e. soil properties detected by the Soil Scanning Systems) with temporal EO multispectral imagery and other ancillary variables, enabling end-users, and for the DIONE case, the national paying agency of Cyprus (Cyprus Agricultural Payments Organisation - CAPO) to mine meaningful information about overall soil health and the effects applied agricultural practices. The dataset is delivered in a single-banded GeoTIFF file (DIONE_SOC_estimations_CY_2022.tif- EPSG:4326) containing the SOC content (SOC %) labelled as Band 1.

Soil Organic Carbon Content estimations over the Cypriot pilot area (2021)

In the context of the EU-funded project DIONE (No. 870378), Soil Organic Carbon Content (SOC) estimations have been released as outputs of novel machine learning algorithms which combined the point measurements (i.e. soil properties detected by the Soil Scanning Systems) with temporal EO multispectral imagery and other ancillary variables, enabling end-users, and for the DIONE case, the national paying agency of Cyprus (Cyprus Agricultural Payments Organisation - CAPO) to mine meaningful information about overall soil health and the effects applied agricultural practices at a parcel level. The dataset is delivered in a shapefile format (DIONE_SOC_estimations_CY_2021.shp - EPSG: 4326) containing the SOC content (SOC %) labeled as SOC.

Soil Organic Carbon Content estimations over the Lithuanian pilot area (2021)

In the context of the EU-funded project DIONE (No. 870378), Soil Organic Carbon Content (SOC) estimations have been released as outputs of novel machine learning algorithms which combined the point measurements (i.e. soil properties detected by the Soil Scanning Systems) with temporal EO multispectral imagery and other ancillary variables, enabling end-users, and for the DIONE case, the national paying agency of Lithuania (National Paying Agency - NPA) to mine meaningful information about overall soil health and the effects applied agricultural practices at a parcel level. The dataset is delivered in a shapefile format (DIONE_SOC_estimations_LT_2021.shp - EPSG:3346) containing the SOC content (SOC %) labeled as SOC.

opencc-by-sa-4.0Oct 2018View details →

Soil organic carbon content in x 5 g / kg at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution

Soil organic carbon content in × 5 g / kg (to convert to % divide by 2) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution. The maps are provided using Byte type to significantly reduce file size. Predicted from a global compilation of soil points. Also available for download: soil organic stock maps in in kg / m2 (<a href="https://doi.org/10.5281/zenodo.1475453">https://doi.org/10.5281/zenodo.1475453</a>) and bulk density maps in kg / m3 (<a href="https://doi.org/10.5281/zenodo.1475970">https://doi.org/10.5281/zenodo.1475970</a>). Processing steps are described in detail <a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil">here</a>. Antarctica is not included. To access and visualize maps use:  <a href="http://www.openlandmap.org/">OpenLandMap.org</a> If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels: <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> All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention: <ul> <li>sol = theme: soil,</li> <li>organic.carbon = variable: soil organic carbon content in x 5 g / kg,</li> <li>usda.6a1c = 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>

edi48/100

Fire Self-Limitation (FiSL) Experiment: Quantifying Wildfire Carbon Combustion Losses in boreal Deciduous and Mixed Forests in Interior Alaska and the Boreal Cordillera IV: Organic Soil Carbon and Nitrogen Content from Organic Soil Samples 2022

This dataset contains lab-quantified (and some field-measured) characteristics for post-fire residual organic soil samples collected in the field for plots in 8 fire scars in Interior Alaska and the Yukon. Data was collected in the summer of 2022. Fire scars sampled included Shovel Creek (2019), Aggie Creek (2015), Hess Creek (2019), Baker (2015), Munson Creek (2021), Isom Creek (2020), 2019MA014 (2019), and 2019BC005 (2019). Lab analyses were conducted in summer and fall of 2022 at UAF and NAU.

openOpenOct 2025View details →

edi48/100

Plant Carbon and Nitrogen Content at the Kellogg Biological Station, Hickory Corners, MI (1989 to 2018)

Dataset Abstract This dataset includes total carbon and total nitrogen measured for plants from the LTER Main Site and the Successional and Forested sites. Total C and N are analyzed at peak biomass for a given treatment. In some systems with multiple harvests or complex communities that have peaks occurring at different times of the year, measurements are taken at multiple times per year. Samples are combusted and measured on a CHN analyzer. original data source http://lter.kbs.msu.edu/datasets/32

openCustomJul 2020View details →

edi48/100

Stable isotope content (carbon, nitrogen) for epibenthic suspension feeders and Macrocyctis pyrifera at in the Santa Barbara Channel, 2010-2011

This dataset includes measurments of stable carbon, nitrogen isotopes in tissues of eight epibenthic suspension feeders and Macrocystis pyrifera (giant kelp) at three reefs in the Santa Barbara Channel during 2010 and 2011, as part of a study to (a) determine the contributions of phytoplankton and giant kelp detritus to the pool of suspended reef particulate organic matter (POM), and usage by benthic susupension feeders and (b) whether POM composition varies with distance from kelp forests. Species sampled include Chaceia ovoidea (bivalve), Aglaophenia spp (ostrich plume hydrozoan), Cucumaria piperata (echinoderm), Muricea californica (cnidarian), Phragmatopoma californica (tube worm), Parapholas californica (bivalve), Pachythyone rubra (echinoderm), and Styela montereyensis (tunicate). Samples were collected at three core SBC LTER research reefs: Arroyo Quemado, Arroyo Burro, and Mohawk. Potentially important food sources to benthic suspension-feeders on shallow subtidal reefs include phytoplankton-dominated seston, kelp-derived detritus, and for locations adjacent to sources of freshwater runoff, terrestrially-derived material. Our data will be input to mixing models to estimate the contribution of each source to the reef food web.

openCC (other)Oct 2022View details →

zenodo44/100

Gridded spatial information on soil organic carbon content, density and stock in Hungary for 1992 and 2000

Predictive soil organic carbon (SOC) content, density, and stock maps, along with the associated prediction uncertainty, are provided for the years 1992 and 2000, for the entire territory of Hungary. The maps refer to the topsoils (0–30 cm) with a spatial resolution of 100⨯100 m. The uncertainty associated with the SOC property maps is expressed by the lower and upper limits of the 90% prediction interval (PI), the range of values within which the true value is expected to occur 9 times out of 10. This means that there are two maps to each SOC property map, quantifying its prediction uncertainty. It should be added that all maps have been masked with open water bodies, as these areas are not relevant for soils. For more details / to cite this dataset please use: <a href="https://doi.org/10.1038/s41597-024-04158-3">Szatmári, G., Laborczi, A., Mészáros, J., Takács, K., Benő, A., Koós, S., Bakacsi, Z., & Pásztor, L. (2024). Gridded, temporally referenced spatial information on soil organic carbon for Hungary. Scientific Data 11, 1312.</a> Custom code used for digital soil mapping and validation is available on GitHub: <a href="https://github.com/GaborSzatmari/HU-SOC-mapping" target="_blank" rel="noopener">https://github.com/GaborSzatmari/HU-SOC-mapping</a> Description of the files: The resulting maps are shared as GeoTIFF files. The coordinate reference system is the Hungarian Unified National Projection System (HD72/EOV; EPSG: 23700) (<a href="https://epsg.io/23700" target="_blank" rel="noopener">https://epsg.io/23700</a>). The table below provides further information on the published maps. Note that the first file (00_Overview.jpg) gives an overview of the SOC property maps. <table> <tbody> <tr> <td> SOC property maps </td> <td> Unit </td> <td> Year </td> <td> Filename </td> </tr> <tr> <td> SOC content map </td> <td> [g ∙ kg-1] </td> <td> 1992 </td> <td> SOCc_0_30cm_1992_pred.tif </td> </tr> <tr> <td> SOC content, lower limit of the 90% PI </td> <td> [g ∙ kg-1] </td> <td> 1992 </td> <td> SOCc_0_30cm_1992_q05.tif </td> </tr> <tr> <td> SOC content, upper limit of the 90% PI </td> <td> [g ∙ kg-1] </td> <td> 1992 </td> <td> SOCc_0_30cm_1992_q95.tif </td> </tr> <tr> <td> SOC density map </td> <td> [kg ∙ m-3] </td> <td> 1992 </td> <td> SOCd_0_30cm_1992_pred.tif </td> </tr> <tr> <td> SOC density, lower limit of the 90% PI </td> <td> [kg ∙ m-3] </td> <td> 1992 </td> <td> SOCd_0_30cm_1992_q05.tif </td> </tr> <tr> <td> SOC density, upper limit of the 90% PI </td> <td> [kg ∙ m-3] </td> <td> 1992 </td> <td> SOCd_0_30cm_1992_q95.tif </td> </tr> <tr> <td> SOC stock map </td> <td> [tons ∙ ha-1] </td> <td> 1992 </td> <td> SOCs_0_30cm_1992_pred.tif </td> </tr> <tr> <td> SOC stock, lower limit of the 90% PI </td> <td> [tons ∙ ha-1] </td> <td> 1992 </td> <td> SOCs_0_30cm_1992_q05.tif </td> </tr> <tr> <td> SOC stock, upper limit of the 90% PI </td> <td> [tons ∙ ha-1] </td> <td> 1992 </td> <td> SOCs_0_30cm_1992_q95.tif </td> </tr> <tr> <td> SOC content map </td> <td> [g ∙ kg-1] </td> <td> 2000 </td> <td> SOCc_0_30cm_2000_pred.tif </td> </tr> <tr> <td> SOC content, lower limit of the 90% PI </td> <td> [g ∙ kg-1] </td> <td> 2000 </td> <td> SOCc_0_30cm_2000_q05.tif </td> </tr> <tr> <td> SOC content, upper limit of the 90% PI </td> <td> [g ∙ kg-1] </td> <td> 2000 </td> <td> SOCc_0_30cm_2000_q95.tif </td> </tr> <tr> <td> SOC density map </td> <td> [kg ∙ m-3] </td> <td> 2000 </td> <td> SOCd_0_30cm_2000_pred.tif </td> </tr> <tr> <td> SOC density, lower limit of the 90% PI </td> <td> [kg ∙ m-3] </td> <td> 2000 </td> <td> SOCd_0_30cm_2000_q05.tif </td> </tr> <tr> <td> SOC density, upper limit of the 90% PI </td> <td> [kg ∙ m-3] </td> <td> 2000 </td> <td> SOCd_0_30cm_2000_q95.tif </td> </tr> <tr> <td> SOC stock map </td> <td> [tons ∙ ha-1] </td> <td> 2000 </td> <td> SOCs_0_30cm_2000_pred.tif </td> </tr> <tr> <td> SOC stock, lower limit of the 90% PI </td> <td> [tons ∙ ha-1] </td> <td> 2000 </td> <td> SOCs_0_30cm_2000_q05.tif </td> </tr> <tr> <td> SOC stock, upper limit of the 90% PI </td> <td> [tons ∙ ha-1] </td> <td> 2000 </td> <td> SOCs_0_30cm_2000_q95.tif </td> </tr> </tbody> </table>

opencc-by-4.0Aug 2024View details →

openCC (other)Dec 2022View details →

Isotopes and content of carbon, nitrogen, and sulfur of eelgrass (Zostera marina) from West Falmouth Harbor from 2005 through 2019

West Falmouth Harbor (West Falmouth, MA, USA) has been experiencing a dramatic increase in nitrogen loading from an upgradient municipal wastewater treatment facility since the early 2000’s. As part of a long-term study into the effects of this nitrogen enrichment, we have been assessing changes in the extent and health of the eelgrass (Zostera marina) community within the harbor. Eelgrass shoots were collected during the summer and run for carbon (C), nitrogen (N), and sulfur (S) content and isotopic composition. Samples were analyzed from 7 stations in the more well-flushed outer harbor (OH), 4 stations in the middle of the harbor (MH), and 6 stations in the inner portion of the harbor in close proximity to a high groundwater nitrate source (Snug Harbor, SH). Carbon data is available from 2011 through 2018, nitrogen data from 2006 through 2018, and sulfur data from 2005 through 2019 with a few samples from 2021. Sulfur results have been published in Haviland et al. 2022 (doi: 10.1002/lno.12025).

openOpenMar 2009View details →

Nitrogen cycling at treeline. III. Total Soil Carbon and Nitrogen Content

We studied spatial and temporal patterns of nitrogen pools and fluxes in soils at treeline and forested sites within three mountain ranges across a 785 km transect in Alaska during 2001- 2002. We measured pools of soil mineral (ammonium and nitrate) and organic (amino acid and microbial biomass) nitrogen, in situ rates of net mineralization, net nitrification, net amino acid production, and decomposition, as well as soil carbon turnover in a laboratory incubation experiment. A complete characterization of the study can be found in Loomis et al. (2006).

openCustomJan 2020View details →

Forest floor carbon and nitrogen content from 9 Hillslope Project sites in Macon County, North Carolina, within the Upper Little Tennessee River Basin

Forest floor samples were collected at 9 hillslope sites representing a gradient of development, including forested, valley agriculture, and mountain housing developments in Macon County, NC. Forest floor was divided into two categories: forest floor (i.e., Oi, Oe, & Oa layers) and wood <10 cm diameter. Afterwards, the combined depth of the Oe and Oa was measured in the middle of 3 sides of the subplot. Forest floor collections were later dried and weighed before being processed for C and N analyses.