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openCC (other)Mar 2025View details →

Soil respiration rates, biogeochemical pools, and mineral-associated organic matter from high organic matter and high mineral content coastal wetland soils in Apalachicola, Florida, 2022

This data set was used to observe how the application of dredged sediment would impact soil respirations rates, biogeochemical pools, mineral associated organic matter of coastal wetland soils from Apalachicola, Florida. To achieve this, a combination of intact core and bottle incubations were used, comparing a high organic matter coastal wetland soil to a high mineral content wetland soil which were collected in June, 2022. All laboratory analysis was conducted at the University of Central Florida in Orlando, Florida.

openCC BY-NCJan 2026View details →

Limno-STOICH a comprehensive database linking the elemental content of organisms with inland, aquatic habitats (2025-12-11)

The Limnology Stoichiometric Traits of Organisms In their Chemical Habitats (Limno-STOICH) contains >51,000 observations of organismal elemental content fro >3,100 rivers, lakes, wetlands, and other aquatic ecosystem sites on seven continents. The data are derived from 190+ sources including author contributed collections, novel NEON-related data, and published datasets. The database also includes extensive spatial and temporal metadata to link elemental stoichiometry with ecosystem type, trophic status, etc., and information on organismal data (body size, taxonomic classifications, stable isotope composition) and water physicochemical parameters, as available. Users are encouraged to read the associated manuscript (Corman et al.) for further information.

openCC (other)Dec 2021View details →

Soil organic matter content sampled in and around harvester ant nests in three habitats at the Jornada Basin LTER site, 1987

This dataset contains soil organic matter content measurements from harvester ant nests and reference soils in three nesting habitats at the Jornada Basin LTER site in 1987. The purpose of this investigation was to answer three general questions: 1. How does the modification of soil properties and the ratios of resources (e.g., water-N) by ants alter species assemblages of winter annual plants at the edge of the ant nests? 2. How does the "spring cleaning", clipping, predation or herbivory by ants affect success of the winter annual plants at the edge of ant nests? 3. Are there significant differences in the floristic assemblage and belowground standing crop (root biomass) between the edge of ant nest and the surrounding unaffected soils? Five ant nests and paired (non-nest) reference soils were sampled in three study locations. This dataset contains percent (%) organic matter content measured by mass-loss on combustion in a muffle furnace. This study was completed in 1987.

openCustomAug 2022View details →

Crab Burrows, Soil Nutrients, and Spartina alterniflora : organic content in Brownsville, VA 1992

The effect of Crab Burrows on Soil Nutrients and Spartina alterniflora by Winli Lin This study investigated the effects of fiddler crab (Uca pugnax) burrows on soil nutrients and the marsh grass Spartina alterniflora. Tall-form Spartina alterniflora (1-2m tall) typically dominates the marsh area that is flooded daily by tides. The short-form S. alterniflora(<0.5m tall) generally occupies the higher tidal heights (Bertness 1985). These short-form S. alterniflora are charterized by reduced soil drainage (Mendelssohn and Senecs 1980; Howes et al. 1981; Mendelssohn et al. 1981) and increased soil sulfide levels (King et al. 1982). From comparing control areas devoid of burrrows to those with burrows added, an increase in above-ground Spartina alterniflora production has been observed along with an increase of soil drainage rates and redox potential levels (Bertness 1985). Others have looked at how nutrient availability (Mendelssohn 1979) and sulfide accumulation (King et al. 1982; Howarth and Giblin 1983) may be the primary limiting factors controlling the production and success of S. alterniflora. While soil water movement has been shown to influence the soil parameters, (i.e., sulfide concentration and redox potential) that directly affect cordgrass production (King et al. 1982; Koch et al. 1990), little has been studied on how biotic modifications, such as crab burrows, mediate these physical factors. The Uca pugnax, are burrowing deposit-feeders that excavate and maintain semi-permanent burrows in the marsh surface. They have been found to not only oxygenate marsh soils (Howes et al 1981) and modify sediment meiofaunal abundance, they could also provide a suitable environment for continued burrowing and, as a byproduct, increase the marsh grass production and maintain the tall-form S. alterniflora. Uca pugnax, the mud fiddler crab, is the dominant form of crab seen in Brownsville, VA. Their burrows are primarily restricted to areas of tall-form S. alterniflora, due

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>

openOpenOct 2025View details →

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.

Benthic chlorophyll, density, porosity, and organic content concentrations and gross oxygenic photosynthesis rates in surficial estuarine intertidal sediments at sites on Sapelo Island and near the Satilla River from January, April, June and July 2001

Seasonal patterns of estuarine creek-bank and intertidal marsh benthic chlorophyll, density, porosity, and organic content were investigated at several sites on Sapelo Island and the Satilla River in coastal Georgia. Benthic chlorophyll, density, porosity, and organic content were measured in the bulk surface centimeter depth of sediment. Several relatively pristine sites on Sapelo Island (Moses Hammock and Dean Creek) and a presumably heavily impacted site (Dover Bluff) show similar levels of chlorophyll concentration across bank and marsh zones with higher photosynthetic biomass in the spring season.

Benthic chlorophyll, density, porosity, and organic content concentrations in surficial estuarine intertidal sediments at sites on Sapelo Island and near the Satilla River from June and August 2002

Fall 2000 soil organic content survey -- ash-free dry weight analysis for soil samples from 10 GCE LTER sampling sites

Soil core samples were collected from the permanent plots at 10 GCE LTER sampling sites in October, 2000, to survey the fractional organic content in marsh sediments. Surveys will be conducted annually to assess changes in soil organic content in response to environmental factors documented by other GCE monitoring efforts.

Soil salinity and organic content at GCE-LTER vegetation monitoring plots in October 2009

Soil samples were collected in conjunction with Fall 2009 plant monitoring at half of the permanent vegetation monitoring plots in the creekbank and midmarsh zones at 10 GCE study sites. Pore-water salinity was determined by analysis of supernatant salinity in dried soil samples hydrated with a measured volume of deionized water. Organic content was measured gravimetrically by comparing ash-free dry weight and total weight of soil samples.

Soil salinity and organic content at GCE-LTER vegetation monitoring plots in October 2011

Soil samples were collected in conjunction with Fall 2011 plant monitoring at half of the permanent vegetation monitoring plots in the creekbank and midmarsh zones at 10 GCE study sites. Pore-water salinity was determined by analysis of supernatant salinity in dried soil samples hydrated with a measured volume of deionized water. Organic content was measured gravimetrically by comparing ash-free dry weight and total weight of soil samples.