This data set is a compilation of soil respiration rates (g C m-2 yr-1) from terrestrial and wetland ecosystems reported in the literature prior to 1992. These rates were measured in a variety of ecosystems to examine rates of microbial activity, nutrient turnover, carbon cycling, root dynamics, and a variety of other soil processes. In this summary, only those data based on most or all of one full year of measurements were used so that annual rates of soil respiration could be estimated. Data from soil cores were excluded because the sample coring modifies root respiration. Also included in the data set are biome type, vegetation type, locality, and geographic coordinates, based on information from the original paper. Mean annual temperature and precipitation were based on the original paper; where those data were not included, they were estimated from a gridded global climate database [0.5 degree resolution; Legates D.R. and C.J. Willmott. 1988. Global Air Temperature and Precipitation Data Archive. Department of Geography, University of Delaware, Newark, Delaware, USA).

Landscape carbon (C) flux estimates are necessary for assessing the ability of terrestrial ecosystems to buffer further increases in anthropogenic carbon dioxide (CO2) emissions. Advances in remote sensing have allowed for coarse-scale estimates of gross primary productivity (GPP) (e.g., MODIS 17), yet efforts to assess spatial patterns in respiration lag behind those of GPP. Here, we demonstrate a method to predict growing season soil respiration at a regional scale in a forested ecosystem. We related field measurements (n=144) of growing season soil respiration across subalpine forests in the Southern Rocky Mountains ecoregion to a suite of biophysical predictors with a Random Forest model (30 m pixel size). We found that Landsat Enhanced Vegetation Index (EVI), growing season AI, temperature, precipitation, elevation, and slope aspect explained spatiotemporal variability in soil respiration. Our model had a psuedo-r2 of 0.45 and root mean squared error (RMSE) of roughly one-quarter of the mean value of respiration. Predicted growing season soil respiration across the region was remarkably consistent across 2004, 2005 and 2006 (150-d averages of 542.8, 544.3, and 536.5 g C m-2, respectively). Yet, we observed substantial variability in spatial patterns of soil respiration predictions that varied between years, suggesting that our method is sensitive to changes in respiration drivers. We compared our estimates to MODIS GPP and nocturnal net ecosystem exchange (NEE) derived from eddy covariance towers as a proxy for ecosystem respiration. Averaged across the predictive region, mean predicted growing season soil respiration was 73% of MODIS GPP, while predicted soil respiration was generally within 20% of nocturnal NEE from eddy covariance towers. This study demonstrated that geospatial and remotely-sensed datasets can be used in a statistical modeling framework to estimate soil respiration at landscape scales.

This data set is a compilation of soil respiration rates (g C m-2 yr-1) from terrestrial and wetland ecosystems reported in the literature prior to 1992. These rates were measured in a variety of ecosystems to examine rates of microbial activity, nutrient turnover, carbon cycling, root dynamics, and a variety of other soil processes. Also included in the data set are biome type, vegetation type, locality, and geographic coordinates.

This dataset provides six global gridded products at 1-km resolution of predicted annual soil respiration (Rs) and associated uncertainty, maps of the lower and upper quartiles of the prediction distributions, and two derived annual heterotrophic respiration (Rh) maps. A machine learning approach was used to derive the predicted Rs and uncertainty data using a quantile regression forest (QRF) algorithm trained with observations from the global Soil Respiration Database (SRDB) version 3 spanning from 1961 to 2011. The two Rh maps were derived from the predicted Rs with two different empirical equations. These products were produced to support carbon cycle research at local- to global-scales, and highlight the immense spatial variability of soil respiration and our ability to predict it across the globe.

This data set provides (1) carbon (C) and nitrogen (N) concentration measurements of two soil aggregate fractions (250-2000 micon, small macro-aggregates (SMAG)), and (53-250 micron (micro-aggregates (mico)) and (2) in situ soil respiration measurements (January-March 2003) on sand and clay soils from a Eucalyptus plantation and an adjacent primary forest. The soils for fractionation were sampled in July 2001 from 0-20 cm and 30-50 cm depths. The research site was on the property of Jari Celulose, Monte Dourado, Para, Brazil. There are two files with this data set in comma-delimited (.csv) format.

This dataset provides global gridded estimates of annual soil respiration (Rs) and soil heterotrophic respiration (Rh) and associated uncertainties at 1 km resolution. Mean soil respiration was estimated using a quantile regression forest model utilizing data from the global Soil Respiration Database Version 5 (SRDB-V5) and covariates of mean annual temperature, seasonal precipitation, and vegetative cover. The SRDB holds results of field studies of soil respiration from around the globe. A total of 4,115 records from 1,036 studies were selected from SRDB-V5. SRDB-V5 features more soil respiration data published in Russian and Chinese scientific literature for better global spatio-temporal coverage and improved global climate-space representation. These soil respiration records were combined with global meteorological, land cover, and topographic data and then evaluated with variable selection using random forests. The standard deviation and coefficient of variation of Rs are included and were also derived from the same model. Global heterotrophic respiration was calculated from Rs estimates. The data are produced in part from SRDB-V5 inputs that cover the period 1961-2016.

This dataset provides estimates of total organic soil carbon (SOC), pyrogenic (PyC), particulate (POC), and other organic soil carbon (OOC) fractions in 473 surface layer soil samples collected from stratified-sampling locations in Colorado, Kansas, New Mexico, and Wyoming, USA. Terrain, climate, soil, fire, and land cover data used to predict and map SOC, PyC, POC, and OOC at 1 km resolution throughout the study region are also included. The estimates were derived using a best random forest regression model and cover the period 2007-05-01 to 2010-10-01.

In the Soil Carbon Dioxide Flux study, a prototype gas exchange system and sensor were used to determine the soil surface flux of CO2 and associated parameters at the three FIFE supersites. The goal of this investigation was to characterize fluxes of carbon dioxide from the surface of the soil for a representative portion of the FIFE study area. These measurements are required to understand the carbon budget of the prairie and necessary for comparing vegetation models of photosynthesis with CO2 flux measurements by micrometeorological methods. The flux of the carbon dioxide from the surface of the soil is an important component of the carbon budget of a prairie ecosystem. The results from this study indicate that a soil chamber can be used to obtain reasonable estimates of soil surface carbon dioxide fluxes when operated in a closed system that is ported to the free atmosphere. Further, the flux of carbon dioxide from the soil surface of a grassland can be a large part of the carbon budget and should never be assumed to be negligible. Both soil temperature and soil water content are critical parameters for predicting soil surface CO2 flux, and leaf area index is a surrogate for the plant contribution through root respiration.

The data set consists of a southern African subset of the "Global Annual Soil Respiration Data (Raich and Schlesinger 1992)" data set. The data file is in ASCII text format and contains four observations. This data set is a compilation of soil respiration rates (g C m -2 yr -1) from terrestrial and wetland ecosystems reported in the literature prior to 1992. These rates were measured in a variety of ecosystems to examine rates of microbial activity, nutrient turnover, carbon cycling, root dynamics, and a variety of other soil processes. In this summary, only those data based on most or all of one full year of measurements were used so that annual rates of soil respiration could be estimated. Data from soil cores were excluded because the sample coring modifies root respiration. Also included in the data set are biome type, vegetation type, locality, and geographic coordinates, based on information from the original paper. Mean annual temperature and precipitation were based on the original paper; where those data were not included, they were estimated from a gridded global climate database (0.5-degree resolution; Legates, D. R., and C. J. Willmott. 1988. Global Air Temperature and Precipitation Data Archive. Department of Geography, University of Delaware, Newark, Delaware, U.S.A.).