AndreaBozzo/ceres-open-data-index huggingface dataset:
Standing Crop & Nitrogen Content (FIFE)

The FIFE Standing Crop and Nitrogen Content Data Set contains biomass and nitrogen concentration data for live and dead above-ground plant material collected along transects in watersheds within the FIFE study area. The transects were in watersheds that had undergone burning and grazing treatments. Point physical descriptors (elevation, slope, and soil depth) are also included in the data set. Substantial variation in biomass, and N accumulation occurred over time, with topography, and as a result of grazing and previous burning (Schimel et al. 1991a, Kittel et al. 1990, Turner et al. 1992, Davis et al. 1992).

Tags: earth-science-ecological-dynamics-biosphere-ecosystem-functions-biomass-dynamics, earth-science-soils-land-surface-nitrogen, earth-science-soils-land-surface-soil-depth, earth-science-topography-land-surface-terrain-elevation, earth-science-vegetation-biosphere-biomass, earth-science-vegetation-biosphere-nitrogen
Organization: National Aeronautics and Space Administration
ACT-America: Gridded Ensembles of Surface Biogenic Carbon Fluxes, 2003-2019

This data set provides gridded, model-derived gross primary productivity (GPP), ecosystem respiration (RECO), and net ecosystem exchange (NEE) of CO2 biogenic fluxes and their uncertainties at monthly and 3-hourly time scales over 2003-2019 on a 463-m spatial resolution grid for the conterminous United States (CONUS) and on both 5-km and half-degree spatial resolution grids for North America (NA). The biogeochemical model Carnegie Ames Stanford Approach (CASA) was used.

Tags: earth-science, nasa, remote-sensing
Organization: National Aeronautics and Space Administration
ACT-America: L2 In Situ CO2, CO, and CH4 Concentrations from Towers, Eastern USA

This dataset provides atmospheric carbon dioxide (CO2), carbon monoxide (CO), and methane (CH4) concentrations as measured on a network of instrumented communications towers operated by the Atmospheric Carbon and Transport-America (ACT-America) project. ACT-America's mission spans five years and includes five 6-week intensive field campaigns covering all 4 seasons and 3 regions of the central and eastern United States. Tower-based measurements began in early 2015 and are continuously collecting CO2, CO, and CH4 data to characterize ground-level (>100 m) carbon background conditions to support the periodic airborne measurement campaigns and transport modeling conducted by ACT-America. The towers are instrumented with infrared cavity ring-down spectrometer systems (CRDS; Picarro Inc.). Data are reported for the highest sampling port on each tower. The averaging interval standard deviation and uncertainty derived from periodic flask sample to in-situ measurement comparisons are provided. Complete tower location, elevation, instrument height, and date/time information are also provided.

Tags: earth-science, nasa, remote-sensing
Organization: National Aeronautics and Space Administration
A Global Data Set of Leaf Photosynthetic Rates, Leaf N and P, and Specific Leaf Area

This global data set of photosynthetic rates and leaf nutrient traits was compiled from a comprehensive literature review. It includes estimates of Vcmax (maximum rate of carboxylation), Jmax (maximum rate of electron transport), leaf nitrogen content (N), leaf phosphorus content (P), and specific leaf area (SLA) data from both experimental and ambient field conditions, for a total of 325 species and treatment combinations. Both the original published Vcmax and Jmax values as well as estimates at standard temperature are reported. 

The maximum rate of carboxylation (Vcmax) and the maximum rate of electron transport (Jmax) are primary determinants of photosynthetic rates in plants, and modeled carbon fluxes are highly sensitive to these parameters. Previous studies have shown that Vcmax and Jmax correlate with leaf nitrogen across species and regions, and locally across species with leaf phosphorus and specific leaf area, yet no universal relationship suitable for global-scale models is currently available. 

These data are suitable for exploring the general relationships of Vcmax and Jmax with each other and with leaf N, P and SLA. This data set contains one *.csv file.

Tags: earth-science, nasa, remote-sensing
Organization: National Aeronautics and Space Administration
A Global Database of Field-observed Leaf Area Index in Woody Plant Species, 1932-2011

This data set provides global leaf area index (LAI) values for woody species. The data are a compilation of field-observed data from 1,216 locations obtained from 554 literature sources published between 1932 and 2011. Only site-specific maximum LAI values were included from the sources; values affected by significant artificial treatments (e.g. continuous fertilization and/or irrigation) and LAI values that were low due to drought or disturbance (e.g. intensive thinning, wildfire, or disease), or because vegetation was immature or old/declining, were excluded (Lio et al., 2014). 
To maximize the generic applicability of the data, original LAI values from source literature and values standardized using the definition of half of total surface area (HSA) are included. Supporting information, such as geographical coordinates of plot, altitude, stand age, name of dominant species, plant functional types, and climate data are also provided in the data file. 
There is one data file in comma-separated (.csv) format with this data set and one companion file which provides the data sources.

Tags: earth-science, nasa, remote-sensing
Organization: National Aeronautics and Space Administration
A Global Database of Soil Respiration Data, Version 5.0

The Soil Respiration Database (SRDB) is a near-universal compendium of published soil respiration (Rs) data. The database encompasses published studies that report at least one of the following data measured in the field (not laboratory): annual soil respiration, mean seasonal soil respiration, a seasonal or annual partitioning of soil respiration into its source fluxes, soil respiration temperature response (Q10), or soil respiration at 10 degrees C. The SRDB's orientation is to seasonal and annual fluxes, not shorter-term or chamber-specific measurements, and the database is dominated by temperate, well-drained forest measurement locations. Version 5 (V5) is the compilation of 2,266 published studies with measurements taken between 1961-2017. 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. The database is also restructured to have better interoperability with other datasets related to carbon-cycle science.

Tags: earth-science, nasa, remote-sensing
Organization: National Aeronautics and Space Administration
A Global Database of Soil Phosphorus Compiled from Studies Using Hedley Fractionation

This data set provides concentrations of soil phosphorus (P) compiled from the peer-reviewed literature that cited the Hedley fractionation method (Hedley and Stewart, 1982). This database contains estimates of different forms of naturally occurring soil phosphorus, including labile inorganic P, organic P, occluded P, secondary mineral P, apatite P, and total P, based on the analyses of the various Hedley soil fractions.

The recent literature survey (Yang and Post, 2011) was restricted to studies of natural, unfertilized, and uncultivated soils since 1995. Ninety measurements of soil P fractions were identified. These were added to the 88 values from soils in natural ecosystems that Cross and Schlesinger (1995) had compiled. Cross and Schlesinger provided a comprehensive survey on Hedley P data prior to 1995. Measurement data are provided for studies published from 1985 through 2010. 

In addition to the Hedley P fraction measurement data Yang and Post (2011) also compiled information on soil order, soil pH, organic carbon and nitrogen content, as well as the geographic location (longitude and latitude) of the measurement sites.

Tags: earth-science, nasa, remote-sensing
Organization: National Aeronautics and Space Administration
Spatial Statistical Data Fusion (SSDF) Level 3: CONUS Near-Surface Vapor Pressure Deficit from SNPP CrIMSS and Aqua AIRS, V2 (SNDR13IML3SSDFCVPD)

The Spatial Statistical Data Fusion (SSDF) surface continental United States (CONUS) products, fuse data from the Atmospheric InfraRed Sounder (AIRS) instrument on the EOS-Aqua spacecraft with data from the Cross-track Infrared and Microwave Sounding Suite (CrIMSS) instruments on the Suomi-NPP spacecraft. The CrIMSS instrument suite consists of the Cross-track Infrared Sounder (CrIS) infrared sounder and the Advanced Technology Microwave Sounder (ATMS) microwave sounder. This data set provides an estimate of the vapor pressure deficit. It infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight. These are all daily products on a ¼ x ¼ degree latitude/longitude grid covering the continental United States (CONUS). The SSDF algorithm infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight. Performing the data fusion of two (or more) remote sensing datasets that estimate the same physical state involves four major steps: (1) Filtering input data; (2) Matching the remote sensing datasets to an in situ dataset, taken as a truth estimate; (3) Using these matchups to characterize the input datasets via estimation of their bias and variance relative to the truth estimate; (4) Performing the spatial statistical data fusion. We note that SSDF can also be performed on a single remote sensing input dataset. The SSDF algorithm only ingests the bias-corrected estimates, their latitudes and longitudes, and their estimated variances; the algorithm is agnostic as to which dataset or datasets those estimates, latitudes, longitudes, and variances originated from.

Tags: earth-science-atmospheric-water-vapor-atmosphere-water-vapor-indicators-vapor-pressure
Organization: National Aeronautics and Space Administration
Spatial Statistical Data Fusion (SSDF) Level 3: CONUS Near-Surface Atmospheric Temperature from Aqua AIRS, V2 (SNDRAQIL3SSDFCNSAT)

This data set provides an estimate of the surface air temperature. It infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight. The Spatial Statistical Data Fusion (SSDF) surface continental United States (CONUS) products, fuse data from the Atmospheric InfraRed Sounder (AIRS) instrument on the EOS-Aqua spacecraft with data from the Cross-track Infrared and Microwave Sounding Suite (CrIMSS) instruments on the Suomi-NPP spacecraft. The CrIMSS instrument suite consists of the Cross-track Infrared Sounder (CrIS) infrared sounder and the Advanced Technology Microwave Sounder (ATMS) microwave sounder. It infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight. These are all daily products on a ¼ x ¼ degree latitude/longitude grid covering the continental United States (CONUS). The SSDF algorithm infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight. Performing the data fusion of two (or more) remote sensing datasets that estimate the same physical state involves four major steps: (1) Filtering input data; (2) Matching the remote sensing datasets to an in situ dataset, taken as a truth estimate; (3) Using these matchups to characterize the input datasets via estimation of their bias and variance relative to the truth estimate; (4) Performing the spatial statistical data fusion. We note that SSDF can also be performed on a single remote sensing input dataset. The SSDF algorithm only ingests the bias-corrected estimates, their latitudes and longitudes, and their estimated variances; the algorithm is agnostic as to which dataset or datasets those estimates, latitudes, longitudes, and variances originated from.

Tags: earth-science-atmospheric-temperature-atmosphere-surface-temperature-air-temperature
Organization: National Aeronautics and Space Administration
Spatial Statistical Data Fusion (SSDF) Level 3: CONUS Near-Surface Atmospheric Temperature from SNPP CrIMSS and Aqua AIRS, V2 (SNDR13IML3SSDFCNSAT)

This data set provides an estimate of the surface air temperature. It infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight. The Spatial Statistical Data Fusion (SSDF) surface continental United States (CONUS) products, fuse data from the Atmospheric InfraRed Sounder (AIRS) instrument on the EOS-Aqua spacecraft with data from the Cross-track Infrared and Microwave Sounding Suite (CrIMSS) instruments on the Suomi-NPP spacecraft. The CrIMSS instrument suite consists of the Cross-track Infrared Sounder (CrIS) infrared sounder and the Advanced Technology Microwave Sounder (ATMS) microwave sounder. These are all daily products on a ¼ x ¼ degree latitude/longitude grid covering the continental United States (CONUS). The SSDF algorithm infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight. Performing the data fusion of two (or more) remote sensing datasets that estimate the same physical state involves four major steps: (1) Filtering input data; (2) Matching the remote sensing datasets to an in situ dataset, taken as a truth estimate; (3) Using these matchups to characterize the input datasets via estimation of their bias and variance relative to the truth estimate; (4) Performing the spatial statistical data fusion. We note that SSDF can also be performed on a single remote sensing input dataset. The SSDF algorithm only ingests the bias-corrected estimates, their latitudes and longitudes, and their estimated variances; the algorithm is agnostic as to which dataset or datasets those estimates, latitudes, longitudes, and variances originated from.

Tags: earth-science-atmospheric-temperature-atmosphere-surface-temperature-air-temperature
Organization: National Aeronautics and Space Administration
Spatial Statistical Data Fusion (SSDF) Level 3: CONUS Near-Surface Vapor Pressure Deficit from Aqua AIRS, V2 (SNDRAQIL3SSDFCVPD)

This data set provides an estimate of the vapor pressure deficit. It infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight. The Spatial Statistical Data Fusion (SSDF) surface continental United States (CONUS) products, fuse data from the Atmospheric InfraRed Sounder (AIRS) instrument on the EOS-Aqua spacecraft with data from the Cross-track Infrared and Microwave Sounding Suite (CrIMSS) instruments on the Suomi-NPP spacecraft. The CrIMSS instrument suite consists of the Cross-track Infrared Sounder (CrIS) infrared sounder and the Advanced Technology Microwave Sounder (ATMS) microwave sounder. These are all daily products on a ¼ x ¼ degree latitude/longitude grid covering the continental United States (CONUS). The SSDF algorithm infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight. Performing the data fusion of two (or more) remote sensing datasets that estimate the same physical state involves four major steps: (1) Filtering input data; (2) Matching the remote sensing datasets to an in situ dataset, taken as a truth estimate; (3) Using these matchups to characterize the input datasets via estimation of their bias and variance relative to the truth estimate; (4) Performing the spatial statistical data fusion. We note that SSDF can also be performed on a single remote sensing input dataset. The SSDF algorithm only ingests the bias-corrected estimates, their latitudes and longitudes, and their estimated variances; the algorithm is agnostic as to which dataset or datasets those estimates, latitudes, longitudes, and variances originated from.

Tags: earth-science-atmospheric-water-vapor-atmosphere-water-vapor-indicators-vapor-pressure
Organization: National Aeronautics and Space Administration
Spectral Ocean Radiance Transfer Investigation and Experiment (SORTIE) program

Measurements made under the SORTIE (Spectral Ocean Radiance Transfer Investigation and Experiment) program between 2007 and 2009.

Tags: earth-science-ocean-chemistry-oceans, earth-science-ocean-optics-oceans, earth-science-ocean-temperature-oceans, earth-science-salinity-density-oceans
Organization: National Aeronautics and Space Administration
Stream Flow Storm Data (FIFE)

The Storm Event Stream Flow Data Set were collected during storm events from five treatment areas within the Konza Prairie Long-Term Ecological Research (LTER) site located within the northwest quadrant of the FIFE study area. These data were recorded so that the hydrology of the streams draining the tallgrass prairie could be studied. Moreover, these data were collected to determine the effect of burn frequency of a watershed upon runoff. Data are available from June 14, 1985 through December 31, 1987. The V-throated flume and standpipes used at the LTER weirs operated on the principle that the height of the water level in the standpipe at a specific location within a weir of known dimensions can be converted to volume of water in the stream. The change of this instantaneous volume with time could then be used to compute volumetric stream flow. The V-notch, sharp-crested weir used in watershed 1D operated on the principle that water flowing past a point of known dimensions per unit time could be converted through standard equations to volumetric flow.

Tags: earth-science-surface-water-terrestrial-hydrosphere-surface-water-processes-measurements-discha, earth-science-surface-water-terrestrial-hydrosphere-surface-water-processes-measurements-stage
Organization: National Aeronautics and Space Administration
ABoVE/ASCENDS: Atmospheric Backscattering Coefficient Profiles from CO2 Sounder, 2017

This dataset provides atmospheric backscattering coefficient profiles collected during Active Sensing of CO2 Emissions over Nights, Days, and Seasons (ASCENDS) deployments from 2017-07-20 to 2017-08-08 over Alaska, U.S., and the Yukon and Northwest Territories of Canada. These profiles were measured by the CO2 Sounder Lidar instrument carried on a DC-8 aircraft. The airborne CO2 Sounder is a pulsed, multi-wavelength Integrated Path Differential Absorption lidar that estimates column-averaged dry-air CO2 mixing ratio (XCO2) in the nadir path from the aircraft to the scattering surface. In addition to XCO2, the lidar receiver recorded the time-resolved atmospheric backscatter signal strength as the laser pulses propagated through the atmosphere. Raw lidar data were converted to the atmospheric backscatter cross-section product and the two-way atmosphere transmission, also known as attenuated backscatter profiles. These ASCENDS flights were coordinated with the 2017 Arctic-Boreal Vulnerability Experiment (ABoVE) campaign and are provided in ICARTT format.

Tags: earth-science, nasa, remote-sensing
Organization: National Aeronautics and Space Administration
ABoVE: Active Layer Thickness from Airborne L- and P- band SAR, Alaska, 2017, Ver. 3

This dataset provides estimates of seasonal subsidence, active layer thickness (ALT), the vertical soil moisture profile, and uncertainties at a 30 m resolution for 51 sites across the ABoVE domain, including 39 sites in Alaska and 12 sites in Northwest Canada. The ALT and soil moisture profile retrievals simultaneously use L- and P-band synthetic aperture radar (SAR) data acquired by the NASA/JPL Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) instruments during the 2017 Arctic Boreal Vulnerability Experiment (ABoVE) airborne campaign. The data are provided in NetCDF Version 4 format along with a python script for estimating soil volumetric water content from data.

Tags: earth-science, nasa, remote-sensing
Organization: National Aeronautics and Space Administration
ABoVE: Active Layer Soil Characterization of Permafrost Sites, Northern Alaska, 2018

This dataset provides in situ soil measurements including soil dielectric properties, temperature, and moisture profiles, active layer thickness (ALT), and measurements of soil organic matter, bulk density, porosity, texture, and coarse root biomass. Samples were collected from the surface to permafrost table in soil pits at selected sites along the Dalton Highway in Northern Alaska. From North to South, the study sites include Franklin Bluffs, Sagwon, Happy Valley, Ice Cut, and Imnavait Creek. Measurements were made from August 22 to August 26, 2018. The purpose of the field campaign was to characterize the dielectric properties of permafrost active layer soils in support of the NASA Arctic and Boreal Vulnerability Experiment (ABoVE) Airborne Campaign.

Tags: earth-science, nasa, remote-sensing
Organization: National Aeronautics and Space Administration
ABoVE: Active Layer and Soil Moisture Properties from AirMOSS P-band SAR in Alaska

This dataset provides estimates of soil geophysical properties derived from Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) P-band polarimetric synthetic aperture radar (PolSAR) data collected in August and October of 2014, 2015, and 2017 over 12 study sites (with some exceptions) across Northern Alaska. Soil properties reported include the active layer thickness (ALT), dielectric constant, soil moisture profile, surface roughness, and their respective uncertainty estimates at 30-m spatial resolution over the 12 flight transects. Most of the study sites are located within the continuous permafrost zone and where the aboveground vegetation consisting mainly of dwarf shrub and tussock/sedge/moss tundra has a minimal impact on P-band radar backscatter.

Tags: earth-science, nasa, remote-sensing
Organization: National Aeronautics and Space Administration
ABoVE: Active Layer Thickness from Remote Sensing Permafrost Model, Alaska, 2001-2015

This dataset provides annual estimates of active layer thickness (ALT) at 1 km resolution across Alaska from 2001-2015. The ALT was estimated using a remote sensing-based soil process model incorporating global satellite data from Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) and snow cover extent (SCE), and Soil Moisture Active and Passive (SMAP) satellite soil moisture records. The study area covers the majority land area of Alaska except for areas of perennial ice/snow cover or open water. The ALT was defined as the maximum soil thawing depth throughout the year. The mean ALT and mean uncertainty from 2001 to 2015 are also provided.

Tags: earth-science, nasa, remote-sensing
Organization: National Aeronautics and Space Administration