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The PROFOUND database

Ramiro Silveyra Gonzalez, Christopher Reyer, Mats Mahnken, Florian Hartig

2020-03-29

Abstract

This vignette provides an overview of the Profound databse for benchmarking forest vegetation models, in particular database structure, content, data policy and an overview of each forest site contained in the database.

Overview

The PROFOUND database (PROFOUND DB) brings together data from a wide range of data sources to evaluate vegetation models and simulate climate impacts at the forest stand scale. It includes 9 forest sites across Europe, and provides for them a site description as well as soil, climate, CO2, Nitrogen deposition, tree-level, forest stand-level and remote sensing data. Moreover, for a subset of 5 sites, also time series of carbon fluxes, energy balances and soil water are available.

For more details, see the ProfoundData website, as well as Reyer et al, The PROFOUND database for evaluating vegetation models and simulating climate impacts on forests, Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2019-220, in review, 2019.

Data Policy

The PROFOUND Database (DB) is available under the Creative Commons Attribution-NonCommercial 4.0 International license (CC BY-NC 4.0). Further data policy statements of the individual data sets contained in the PROFOUND database are listed in the table below.

Table 1: Additional data policy statements specific to the individual datasets included in the PROFOUND database.
dataset dataPolicy
CLIMATE_ISIMIP2B Standard PROFOUND database policy
CLIMATE_ISIMIP2BLBC Standard PROFOUND database policy
CLIMATE_ISIMIP2A Standard PROFOUND database policy
CLIMATE_ISIMIPFT Standard PROFOUND database policy
NDEPOSITION_EMEP Please consider this data policy statement from EMEP: The Environment Agency - Austria allows the reproduction of its resources, with appropriate citation, for non-commercial purposes provided no other more specific rules apply. The officially reported emission data should be cited as: EMEP/CEIP 2014 Present state of emission data; http://www.ceip.at/webdab_emepdatabase/reported_emissiondata/ or http://www.ceip.at/status_reporting/2014_submissions/.
NDEPOSITION_ISIMIP2B Standard PROFOUND database policy
CO2_ISIMIP Standard PROFOUND database policy
ENERGYBALANCE Please consider this data policy statement from FLUXNET: This work used eddy covariance data acquired and shared by the FLUXNET community, including these networks: CarboEuropeIP, CarboItaly and ICOS. The FLUXNET eddy covariance data processing and harmonization was carried out by the ICOS Ecosystem Thematic Center, AmeriFlux Management Project and Fluxdata project of FLUXNET, with the support of CDIAC, and the OzFlux, ChinaFlux and AsiaFlux offices. Tier One data are open and free for scientific and educational purposes and their use will follow the fair use policy, stated here.  Data users describe the intended use of the data when they fill out the data-download form; this intended-use statement will be emailed to the data producer(s) and posted on the Fluxdata website (https://fluxnet.fluxdata.org).  The fair use policy dictates that (1) data producers are informed of who uses the data and for what purpose (which can be satisfied by the aforementioned mechanism) and (2) that proper acknowledgment and citations are given to all data used in a peer reviewed publication, via the following protocols: The data citation will be either a per-site DOI that is provided with the data download or a citation of a publication for each site.  Every publication should use the standard FLUXNET acknowledgment given below.  It is requested that every publication specify each site used with the FLUXNET-ID, data-years used, data DOI (in preparation), and brief acknowledgment for funding (if provided by FLUXNET PI) in the text or supplementary material.  Finally, all data providers should be informed of forthcoming publications.
FLUX Please consider this data policy statement from FLUXNET: This work used eddy covariance data acquired and shared by the FLUXNET community, including these networks: CarboEuropeIP, CarboItaly and ICOS. The FLUXNET eddy covariance data processing and harmonization was carried out by the ICOS Ecosystem Thematic Center, AmeriFlux Management Project and Fluxdata project of FLUXNET, with the support of CDIAC, and the OzFlux, ChinaFlux and AsiaFlux offices. Tier One data are open and free for scientific and educational purposes and their use will follow the fair use policy, stated here.  Data users describe the intended use of the data when they fill out the data-download form; this intended-use statement will be emailed to the data producer(s) and posted on the Fluxdata website (https://fluxnet.fluxdata.org).  The fair use policy dictates that (1) data producers are informed of who uses the data and for what purpose (which can be satisfied by the aforementioned mechanism) and (2) that proper acknowledgment and citations are given to all data used in a peer reviewed publication, via the following protocols: The data citation will be either a per-site DOI that is provided with the data download or a citation of a publication for each site.  Every publication should use the standard FLUXNET acknowledgment given below.  It is requested that every publication specify each site used with the FLUXNET-ID, data-years used, data DOI (in preparation), and brief acknowledgment for funding (if provided by FLUXNET PI) in the text or supplementary material.  Finally, all data providers should be informed of forthcoming publications.
METEOROLOGICAL Please consider this data policy statement from FLUXNET: This work used eddy covariance data acquired and shared by the FLUXNET community, including these networks: CarboEuropeIP, CarboItaly and ICOS. The FLUXNET eddy covariance data processing and harmonization was carried out by the ICOS Ecosystem Thematic Center, AmeriFlux Management Project and Fluxdata project of FLUXNET, with the support of CDIAC, and the OzFlux, ChinaFlux and AsiaFlux offices. Tier One data are open and free for scientific and educational purposes and their use will follow the fair use policy, stated here.  Data users describe the intended use of the data when they fill out the data-download form; this intended-use statement will be emailed to the data producer(s) and posted on the Fluxdata website (https://fluxnet.fluxdata.org).  The fair use policy dictates that (1) data producers are informed of who uses the data and for what purpose (which can be satisfied by the aforementioned mechanism) and (2) that proper acknowledgment and citations are given to all data used in a peer reviewed publication, via the following protocols: The data citation will be either a per-site DOI that is provided with the data download or a citation of a publication for each site.  Every publication should use the standard FLUXNET acknowledgment given below.  It is requested that every publication specify each site used with the FLUXNET-ID, data-years used, data DOI (in preparation), and brief acknowledgment for funding (if provided by FLUXNET PI) in the text or supplementary material.  Finally, all data providers should be informed of forthcoming publications.
SOILTS Please consider this data policy statement from FLUXNET: This work used eddy covariance data acquired and shared by the FLUXNET community, including these networks: CarboEuropeIP, CarboItaly and ICOS. The FLUXNET eddy covariance data processing and harmonization was carried out by the ICOS Ecosystem Thematic Center, AmeriFlux Management Project and Fluxdata project of FLUXNET, with the support of CDIAC, and the OzFlux, ChinaFlux and AsiaFlux offices. Tier One data are open and free for scientific and educational purposes and their use will follow the fair use policy, stated here.  Data users describe the intended use of the data when they fill out the data-download form; this intended-use statement will be emailed to the data producer(s) and posted on the Fluxdata website (https://fluxnet.fluxdata.org).  The fair use policy dictates that (1) data producers are informed of who uses the data and for what purpose (which can be satisfied by the aforementioned mechanism) and (2) that proper acknowledgment and citations are given to all data used in a peer reviewed publication, via the following protocols: The data citation will be either a per-site DOI that is provided with the data download or a citation of a publication for each site.  Every publication should use the standard FLUXNET acknowledgment given below.  It is requested that every publication specify each site used with the FLUXNET-ID, data-years used, data DOI (in preparation), and brief acknowledgment for funding (if provided by FLUXNET PI) in the text or supplementary material.  Finally, all data providers should be informed of forthcoming publications.
MODIS_MOD09A1 When using the data, please cite it as ORNL DAAC 2008 and include the following reference (ORNL DAAC 2008. MODIS Collection 5 Land Products Global Subsetting and Visualization Tool. ORNL DAAC, Oak Ridge, Tennessee, USA. Accessed June 25, 2016. Subset obtained for MOD09A1 product at various sites in Spatial Range: N=70.00N, S=35.00N, E=50.00E, W=10.00W, time period: 2000-02-18 to 2015-12-27, and subset size: 0.5 x 0.5 km. http://dx.doi.org/10.3334/ORNLDAAC/1241) in the reference list of your publication. Please also acknowledge the data in the following way: The MOD09A1 was (were) retrieved from MODISTools (Tuck et al., 2014), courtesy of the NASA EOSDIS Land Processes Distributed Active Archive Center (LP DAAC), USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota, https://lpdaac.usgs.gov/. For MODISTools see Tuck, Sean L. and Phillips, Helen R.P. and Hintzen, Rogier E. and Scharlemann, Jörn P.W. and Purvis, Andy and Hudson, Lawrence N. (2014) MODISTools – downloading and processing MODIS remotely sensed data in R. Ecology and Evolution, (4) 24, 4658–4668. http://dx.doi.org/10.1002/ece3.1273
MODIS_MOD11A2 When using the data, please cite it as ORNL DAAC 2008 and include the following reference (ORNL DAAC 2008. MODIS Collection 5 Land Products Global Subsetting and Visualization Tool. ORNL DAAC, Oak Ridge, Tennessee, USA. Accessed June 25, 2016. Subset obtained for MOD11A2 product at various sites in Spatial Range: N=70.00N, S=35.00N, E=50.00E, W=10.00W, time period: 2000-03-05 to 2015-12-27, and subset size: 1 x 1 km. http://dx.doi.org/10.3334/ORNLDAAC/1241) in the reference list of your publication. Please also acknowledge the data in the following way: The MOD11A2 was (were) retrieved from MODISTools (Tuck et al., 2014), courtesy of the NASA EOSDIS Land Processes Distributed Active Archive Center (LP DAAC), USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota, https://lpdaac.usgs.gov/. For MODISTools see Tuck, Sean L. and Phillips, Helen R.P. and Hintzen, Rogier E. and Scharlemann, Jörn P.W. and Purvis, Andy and Hudson, Lawrence N. (2014) MODISTools – downloading and processing MODIS remotely sensed data in R. Ecology and Evolution, (4) 24, 4658–4668. http://dx.doi.org/10.1002/ece3.1273
MODIS_MOD13Q1 When using the data, please cite it as ORNL DAAC 2008 and include the following reference (ORNL DAAC 2008. MODIS Collection 5 Land Products Global Subsetting and Visualization Tool. ORNL DAAC, Oak Ridge, Tennessee, USA. Accessed June 25, 2016. Subset obtained for MOD13Q1 product at various sites in Spatial Range: N=70.00N, S=35.00N, E=50.00E, W=10.00W, time period: 2000-02-18 to 2015-12-19, and subset size: 0.25 x 0.25 km. http://dx.doi.org/10.3334/ORNLDAAC/1241) in the reference list of your publication. Please also acknowledge the data in the following way: The MOD13Q1 was retrieved from MODISTools (Tuck et al., 2014), courtesy of the NASA EOSDIS Land Processes Distributed Active Archive Center (LP DAAC), USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota, https://lpdaac.usgs.gov/. For MODISTools see Tuck, Sean L. and Phillips, Helen R.P. and Hintzen, Rogier E. and Scharlemann, Jörn P.W. and Purvis, Andy and Hudson, Lawrence N. (2014) MODISTools – downloading and processing MODIS remotely sensed data in R. Ecology and Evolution, (4) 24, 4658–4668. http://dx.doi.org/10.1002/ece3.1273
MODIS_MOD15A2 When using the data, please cite it as ORNL DAAC 2008 and include the following reference (ORNL DAAC 2008. MODIS Collection 5 Land Products Global Subsetting and Visualization Tool. ORNL DAAC, Oak Ridge, Tennessee, USA. Accessed June 25, 2016. Subset obtained for MOD15A2 product at various sites in Spatial Range: N=70.00N, S=35.00N, E=50.00E, W=10.00W, time period: 2000-02-18 to 2015-12-19, and subset size: 0.25 x 0.25 km. http://dx.doi.org/10.3334/ORNLDAAC/1241) in the reference list of your publication. Please also acknowledge the data in the following way: The MOD15A2 was retrieved from MODISTools (Tuck et al., 2014), courtesy of the NASA EOSDIS Land Processes Distributed Active Archive Center (LP DAAC), USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota, https://lpdaac.usgs.gov/. For MODISTools see Tuck, Sean L. and Phillips, Helen R.P. and Hintzen, Rogier E. and Scharlemann, Jörn P.W. and Purvis, Andy and Hudson, Lawrence N. (2014) MODISTools – downloading and processing MODIS remotely sensed data in R. Ecology and Evolution, (4) 24, 4658–4668. http://dx.doi.org/10.1002/ece3.1273
MODIS_MOD17A2 When using the data, please cite it as ORNL DAAC 2008 and include the following reference (ORNL DAAC 2008. MODIS Collection 5 Land Products Global Subsetting and Visualization Tool. ORNL DAAC, Oak Ridge, Tennessee, USA. Accessed June 25, 2016. Subset obtained for MOD17A2 product at various sites in Spatial Range: N=70.00N, S=35.00N, E=50.00E, W=10.00W, time period: 2000-02-18 to 2015-12-19, and subset size: 0.25 x 0.25 km. http://dx.doi.org/10.3334/ORNLDAAC/1241) in the reference list of your publication. Please also acknowledge the data in the following way: The MOD17A2 was retrieved from MODISTools (Tuck et al., 2014), courtesy of the NASA EOSDIS Land Processes Distributed Active Archive Center (LP DAAC), USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota, https://lpdaac.usgs.gov/. For MODISTools see Tuck, Sean L. and Phillips, Helen R.P. and Hintzen, Rogier E. and Scharlemann, Jörn P.W. and Purvis, Andy and Hudson, Lawrence N. (2014) MODISTools – downloading and processing MODIS remotely sensed data in R. Ecology and Evolution, (4) 24, 4658–4668. http://dx.doi.org/10.1002/ece3.1273
TREE See site specific policy
STAND See site specific policy
SOIL See site specific policy
CLIMATE_LOCAL See site specific policy

Database structure

The PROFOUND database is a relational SQLite database and it is made of several independent tables (Fig. 1). From these tables views are created that can be accessed and downloaded by users with the ProfoundData package.

Figure 1: Overview on the PROFOUND database and the R package.

Figure 1: Overview on the PROFOUND database and the R package.

Site overview

The PROFOUND database includes 9 forest sites. They are listed in the table below.

Table 2: Forest sites included in the database.
site_id site lat lon epsg country aspect_deg elevation_masl slope_percent
3 bily_kriz 49.30 18.320 4326 Czech Republic 180.0 875 12.5
5 collelongo 41.85 13.588 4326 Italy 252.0 1560 10.0
12 hyytiala 61.85 24.295 4326 Finland 180.0 185 2.0
13 kroof 48.25 11.400 4326 Germany 1.8 502 2.1
14 le_bray 44.72 -0.769 4326 France 61 0.0
16 peitz 51.92 14.350 4326 Germany 50 0.0
20 solling_beech 51.77 9.570 4326 Germany 225.0 504 1.0
21 soro 55.49 11.645 4326 Denmark 40 0.0
25 solling_spruce 51.77 9.580 4326 Germany 90.0 508 1.0

There is an overview table to provide the information on which data is available for each site. The table is created by combining all existing tables in the database.

Table 3: Overview of sites and datasets
site_id site SITES TREE STAND SOIL CLIMATE_LOCAL CLIMATE_ISIMIP2B CLIMATE_ISIMIP2BLBC CLIMATE_ISIMIP2A CLIMATE_ISIMIPFT METEOROLOGICAL FLUX ATMOSPHERICHEATCONDUCTION SOILTS NDEPOSITION_EMEP NDEPOSITION_ISIMIP2B CO2_ISIMIP MODIS_MOD09A1 MODIS_MOD15A2 MODIS_MOD11A2 MODIS_MOD13Q1 MODIS_MOD17A2 MODIS
3 bily_kriz 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
5 collelongo 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
12 hyytiala 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
13 kroof 1 1 1 1 1 1 1 1 1 0 0 0 0 1 1 1 1 1 1 1 1 1
14 le_bray 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
16 peitz 1 1 1 1 1 1 1 1 1 0 0 0 0 1 1 1 1 1 1 1 1 1
20 solling_beech 1 1 1 1 1 1 1 1 1 0 0 0 0 1 1 1 1 1 1 1 1 1
21 soro 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
25 solling_spruce 1 1 1 1 1 1 1 1 1 0 0 0 0 1 1 1 1 1 1 1 1 1

Datasets

Dataset SITES

The sites parameters were provided by the local site data responsibles. The sites variables that we included in the database are listed in the table below.

Table 4: Description of SITES variables included in the database.
variable type units description
site TEXT adimensional Site name
site2 TEXT adimensional Additional site name
site_id INTEGER adimensional Site code as decimal number (01-99)
aspect_deg REAL degree Direction of slope inclination. Degrees against North. No Value indicates no exposition.
country TEXT adimensional Country
elevation_masl REAL m Elevation above sea level as recorded by PI
epsg INTEGER adimensional EPSG Coordinate System
lat REAL degree decimal Latitude
lon REAL degree decimal Longitude
natVegetation_code1 TEXT adimensional Code of the vegetation mapping unit group in the “Map of the Natural Vegetation of Europe”. BOHN, U.; GOLLUB, G. & HETTWER, C. (2000) Karte der natuerlichen Vegetation Europas. Massstab 1:2.500.000 Karten und Legende. Teil 1-3.. Bundesamt fuer Naturschutz, Bonn, Germany.
natVegetation_code2 TEXT adimensional Code of the vegetation mapping unit in the “Map of the Natural Vegetation of Europe”. BOHN, U.; GOLLUB, G. & HETTWER, C. (2000) Karte der natuerlichen Vegetation Europas. Massstab 1:2.500.000 Karten und Legende. Teil 1-3.. Bundesamt fuer Naturschutz, Bonn, Germany.
natVegetation_description TEXT adimensional Description of natVegetation_code2. BOHN, U.; GOLLUB, G. & HETTWER, C. (2000) Karte der natuerlichen Vegetation Europas. Massstab 1:2.500.000 Karten und Legende. Teil 1-3.. Bundesamt fuer Naturschutz, Bonn, Germany.
slope_percent REAL percent Mean slope within the plot

Dataset SITEDESCRIPTION

The sites description were provided by the local site data responsibles. The site description variables that we included in the database are listed in the table below.

Table 5: Description of SITEDESCRIPTION variables included in the database.
variable type units description
site TEXT adimensional Site name
site_id INTEGER adimensional Site code as decimal number (01-99)
description TEXT adimensional Ecological description of the site
reference TEXT adimensional Publications referring to the site description and the site datasets

Dataset TREE

The individual tree data were provided by the local site data responsibles. The tree variables that we included in the database are listed in the table below.

Table 6: Description of TREE variables included in the database.
variable type units description
record_id INTEGER adimensional Record ID as decimal number
site TEXT adimensional Site name
site_id INTEGER adimensional Site code as decimal number (01-99)
species TEXT adimensional Species name
species_id TEXT adimensional Species text code
year INTEGER YYYY Year with century as decimal number (0000-9999)
dbh1_cm REAL cm Diameter at breast height
height1_m REAL m Tree height
size_m2 REAL m2 Plot size

Dataset STAND

The stand data were provided by the local site data responsibles. In some cases, the data were derived using the function summarizeData included in this package. The stand variables that we included in the database are listed in the table below.

Table 7: Description of STAND variables included in the database.
variable type units description
record_id INTEGER adimensional Record ID as decimal number
site TEXT adimensional Site name
site_id INTEGER adimensional Site code as decimal number (01-99)
species TEXT adimensional Species name
species_id TEXT adimensional Species text code
year INTEGER YYYY Year with century as decimal number (0000-9999)
aboveGroundBiomass_kgha REAL kg ha-1 Above ground biomass
age INTEGER years Mean stand age
ba_m2ha REAL m2 ha-1 Basal area per hectare
branchesBiomass_kgha REAL kg ha-1 Branches biomass
dbhArith_cm REAL cm Arithmetic mean diameter
dbhBA_cm REAL cm Average diameter weighted by basal area calculated as dbhBA = (ba1dbh1 + ba2dbh2 + … + bak*dbhk) / (ba1 + ba2+ … + bak), where bai and dbhi are the basal area and dbh, respectively, of the tree i, and i = 1, 2, . . , k
dbhDQ_cm REAL cm Mean squared diameter or quadratic mean diameter calculated as dbhDQ = sqrt( (dbh12 + dbh22+ … + dbhk^2) / N), where dbhi is the diameter at breat height of tree i, i = 1, 2, . . , k, N is the total number of trees, and sqrt is the square root
density_treeha REAL tree ha-1 Number of tree per ha
foliageBiomass_kgha REAL kg ha-1 Foliage biomass
heightArith_m REAL m Arithmetic mean height
heightBA_m REAL m Average height weighted by basal area or Loreys height calculated as heightBA = (ba1h1 + ba2h2 + … + bak*hk) / (ba1 + ba2+ … + bak), where bai and hi are the basal area and height, respectively, of the tree i, and i = 1, 2, . . , k
lai REAL adimensional Leaf Area Index
rootBiomass_kgha REAL kg ha-1 Root biomass
stemBiomass_kgha REAL kg ha-1 Stem biomass
stumpCoarseRootBiomass_kgha REAL kg ha-1 Stump and coarse roots biomass

Dataset SOIL

The soil data were provided by the local site data responsibles. The variables that we included in the database are listed in the table below. The data is very heteregenous, therefore not all variables are available for each site.

Table 8: Description of SOIL variables included in the database.
variable type units description
record_id INTEGER adimensional Record ID as decimal number
site TEXT adimensional Site name
site_id INTEGER adimensional Site code as decimal number (1-99)
date TEXT adimensional Unformatted date of inventory as provided for the inventory. See site specific metadata for further information on date.
bs_percent REAL percent Percentage of alkaline and earth alkaline metals at CEC
cMax_percent REAL percent Maximum soil carbon content
cMin_percent REAL percent Minimum soil carbon content
cOrgSigma_percent REAL percent Soil organic carbon content error estimate as standard deviation
cOrg_gcm3 REAL g cm-3 Soil organic carbon content
cOrg_percent REAL percent Soil organic carbon content
cSigma_kgm2 REAL kg m-2 Soil carbon content error estimate as standard deviation
c_kgm2 REAL kg m-2 Soil carbon content
c_percent REAL percent Soil carbon content
cec_µeqg REAL µeq g-1 Soil cation exchange capacity
claySigma_percent REAL percent Soil clay particle content error estimate as standard deviation
clay_percent REAL percent Soil clay particle content
cn REAL adimensional Soil C:N ratio
densitySigma_gcm3 REAL g cm-3 Soil bulk density content error estimate as standard deviation
density_gcm3 REAL g cm-3 Soil bulk density
fcapv_percent REAL percent Soil field capacity
fineRoot_percent REAL percent Distribution of fine roots accross soil horizons
gravel_percent REAL percent Soil gravel particle content
horizon TEXT adimensional Name of soil horizon
humus_tCha REAL tC ha-1 Humus carbon content
hydCondSat_cmd1 REAL cm d-1 Soil hydraulic conductivity at saturation
layer_id INTEGER adimensional Layer code as decimal number (1-99)
lowerDepth_cm REAL cm Lower soil horizon limit
mbCSigma_mgg REAL mg C g-1 dry soil Soil microbial biomass carbon error estimate as standard deviation
mbC_mgg REAL mg C g-1 dry soil Soil microbial biomass carbon
mbNSigma_mgNg REAL mg N g-1 dry soil Soil microbial biomass nitrogen error estimate as standard deviation
mbN_mgNg REAL mg N g-1 dry soil Soil microbial biomass nitrogen
minRSigma_mgkgh REAL mg N kg-1 h-1 Soil mineralisation rate error estimate as standard deviation
minR_mgkgh REAL mg N kg-1 h-1 Soil mineralisation rate
nMax_percent REAL percent Maximum soil nitrogen content
nMin_percent REAL percent Minimum soil nitrogen content
nOrgSigma_percent REAL percent Soil organic nitrogen content error estimate as standard deviation
nOrg_percent REAL percent Soil organic nitrogen content
n_kgm2 REAL kg m-2 Soil nitrogen content
n_percent REAL percent Soil nitrogen content
ofhC_percent REAL percent The organic fermentative-humic (Ofh) subhorizon consists of forest litter (leaves, bark, twigs etc) showing considerable decay.
ofhN_percent REAL percent Carbon content in a gram of OFH sample
ofh_gDWm2 REAL g DW m-2 Litter layer (leaves not decomposed)
ol_gDWm2 REAL g DW m-2 Nitrogen content in a gram of OFH sample
phSigma_h2o REAL adimensional Soil pH determined with H2O error estimate as standard deviation
phSigma_kcl REAL adimensional Soil pH determined by KCl error estimate as standard deviation
ph_cacl2 REAL adimensional Soil pH determined with CaCl2
ph_h2o REAL adimensional Soil pH determined with H2O
ph_kcl REAL adimensional Soil pH deterimed with KCl
porosity_percent REAL percent Soil water content at saturation in the bulk soil
rainGroundWater REAL adimensional Whether the soil is mostly influenced by rain or ground water
sandSigma_percent REAL percent Soil sand particle content error estimate as standard deviation
sand_percent REAL percent Soil sand particle content
siltSigma_percent REAL percent Soil silt particle content error estimate as standard deviation
silt_percent REAL percent Soil silt particle content
table_id INTEGER adimensional Table code as decimal number (1-99)
texture TEXT adimensional Soil texture
thicknesSigma_cm REAL cm Soil thickness error estimate
thickness_cm REAL cm Soil thickness
type_fao TEXT adimensional Soil type after ISSS-ISRIC-FAO (1998) World reference basis for soil resources. World Soil Resources Reports 84. FAO, Rome. 92 p.
type_ka5 TEXT adimensional Soil type after AG Boden (2005) Bodenkundliche Kartieranleitung. Bundesanstalt für Geowissenschaften und Rohstoffe, Hannover
upperDepth_cm REAL cm Upper soil horizon limit
whcSigma_mm REAL mm Soil water holding capacity error estimate
whc_mm REAL mm Soil water holding capacity
whcp_percent REAL percent Water holding capacity for plant available water
wiltp_percent REAL percent Soil wilting point

Dataset CLIMATE

The climate data contains daily measurements of the following variables: min, max and mean temperature, precipitation, relative humidity, air pressure, global radiation and wind speed.

Table 9: Description of CLIMATE variables included in the database.
variable type units description
record_id INTEGER adimensional Record ID as decimal number
site TEXT adimensional Site name
site_id INTEGER adimensional Site code as decimal number (01-99)
date TEXT adimensional Date in format YYYY-MM-DD
year INTEGER YYYY Year with century as decimal number (0000-9999)
mo INTEGER MM Month as decimal number (01-12)
day INTEGER DD Day of the month as decimal number (01-31)
airpress_hPa REAL hPa Mean daily air pressure
p_mm REAL mm Total daily precipitation
rad_Jcm2day REAL J cm-2 day-1 Total daily global radiation
relhum_percent REAL percent Mean daily relative humidity
tmax_degC REAL degree Celsius Maximum daily temperature
tmean_degC REAL degree Celsius Mean daily temperature
tmin_degC REAL degree Celsius Minimum daily temperature
wind_ms REAL m s-1 Mean daily wind speed

Dataset CLIMATE_LOCAL

The CLIMATE LOCAL data refers to climate data measured at each forest site or meteorological stations close to the site of the forest site. For those forest sites for which the data has been derived from half-hourly FLUXNET2015 data, we also provide the original half-hourly data in the table METEOROLOGICAL.

When relative humidity was not part of the original data, we calculated it from the vapour pressure deficit and the daily temperatures as

\[ relhum\_percent = (1 - VPD\_F / es)*100 \]

where

\[ es = (es(tmax\_degC) - es(tmin\_degC))/2 \]

and

\[ es(Ta) = 0.6108e^{(17.27*Ta)/ (Ta + 237.3)} \] Besides the variables listed in Table 9, the CLIMATE_LOCAL dataset contains additional variables to indicate the data quality.

Table 10: Description of CLIMATE_LOCAL quality variables from FLUXNET included in the database.
variable type units description
site TEXT adimensional Site name
airpress_qc REAL adimensional fraction between 0-1; indicating percentage of measured and good quality gapfill half-hourly data used to create the daily value
p_qc REAL adimensional fraction between 0-1; indicating percentage of measured and good quality gapfill half-hourly data used to create the daily value
rad_qc REAL adimensional fraction between 0-1; indicating percentage of measured and good quality gapfill half-hourly data used to create the daily value
relhum_qc REAL adimensional fraction between 0-1; indicating percentage of measured and good quality gapfill half-hourly data used to create the daily value
tmax_qc REAL adimensional fraction between 0-1; indicating percentage of measured and good quality gapfill half-hourly data used to create the daily value
tmean_qc REAL adimensional fraction between 0-1; indicating percentage of measured and good quality gapfill half-hourly data used to create the daily value
tmin_qc REAL adimensional fraction between 0-1; indicating percentage of measured and good quality gapfill half-hourly data used to create the daily value
wind_qc REAL adimensional fraction between 0-1; indicating percentage of measured and good quality gapfill half-hourly data used to create the daily value

Dataset CLIMATE_ISIMIP

There are several climatic datasets based on the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP). For each forest site, we extracted the climate data from the corresponding gridcell in the ISIMIP data.

ISIMIP climatic datasets contain additionally one or both of the variables forcingCondition and forcingDataset.

Table 11: Description of CLIMATE_ISIMIP addtional variables included in the database.
variable type units description
site TEXT adimensional Site name
forcingCondition TEXT adimensional This category refers to the conditions underlying the climatic forcing, e.g. following historical CO2 time series, preindustrial picontrol runs or representative concentration pathways (rcp).
forcingDataset TEXT adimensional This category refers to data taken from bias-corrected general circulation models (e.g. hadgem) or historical global meteorological forcing data based on bias-corrected reanalysis data (e.g. watch)

Dataset NDEPOSITION

The nitrogen deposition data contain annual measurements of the variables listed in the table below.

Table 12: Description of NDEPOSITION variables included in the database.
variable type units description
record_id INTEGER adimensional Record ID as decimal number
site TEXT adimensional Name of the site
site_id INTEGER adimensional Site code as decimal number (01-99)
year INTEGER YYYY Year with century as decimal number (0000-9999)
nhx_gm2 REAL g m-2 Total deposition of reduced nitrogen (Dry+Wet RdN)
noy_gm2 REAL g m-2 Total deposition of oxidized nitrogen (Dry+Wet oxN)

Dataset NDEPOSITION_EMEP

The NDEPOSITION_EMEP data were obtained from EMEP/CEIP 2014 Present state of emissions as used in EMEP models (http://www.ceip.at/webdab_emepdatabase/emissions_emepmodels/).

Dataset NDEPOSITION_ISIMIP2B

The NDEPOSITION_EMEP data were extracted for for each forest site from the corresponding gridcell in the ISIMIP data described in Frieler K., R. Betts, E. Burke, P. Ciais, S. Denvil, D. Deryng, K. Ebi, T. Eddy, K. Emanuel, J. Elliott, E. Galbraith, S.N. Gosling, K. Halladay, F. Hattermann, T. Hickler, J. Hinkel, V. Huber, C. Jones, V. Krysanova, S. Lange, H.K. Lotze, H. Lotze-Campen, M. Mengel, I. Mouratiadou, H. Müller Schmied, S. Ostberg, F. Piontek, A. Popp, C.P.O. Reyer, J. Schewe, M. Stevanovic, T. Suzuki, K. Thonicke, H. Tian, D.P. Tittensor, R. Vautard, M. van Vliet, L. Warszawski, F. Zhao (accepted pending revisions) Assessing the impacts of 1.5°C global warming - simulation protocol of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b). Geoscientific Model Development (https://www.isimip.org/gettingstarted/#input-data-bias-correction). The dataset contains additionally the variable forcingCondition.

Table 13: Description of NDEPOSITION_ISIMIP2B addtional variables included in the database.
variable type units description source
forcingCondition TEXT adimensional This category refers to the conditions underlying the climatic forcing, e.g. following historical CO2 time series, preindustrial picontrol runs or representative concentration pathways (rcp). ISIMIP

Dataset CO2_ISIMIP

The CO2 dataset contains annual global concentrations of atmospheric CO2 for several forcing conditions.

Table 14: Description of CO2_ISIMIP variables included in the database.
variable type units description
record_id INTEGER adimensional Record ID as decimal number
site TEXT adimensional Site name
site_id INTEGER adimensional Site code as decimal number (01-99)
forcingCondition TEXT adimensional This category refers to the conditions underlying the climatic forcing, e.g. following historical CO2 time series or representative concentration pathways (rcp).
year INTEGER YYYY Year with century as decimal number (0000-9999)
co2_ppm REAL ppm CO2 mean global concentrations for the different different forcing conditions: RCP and historical values (1975-2013)

Dataset ATMOSPHERICHEATCONDUCTION

The ATMOSPHERICHEATCONDUCTION data contains half-hourly measurements of the variables listed in the table below and was obtained from FLUXNET2015 data.

Table 15: Description of ATMOSPHERICHEATCONDUCTION variables included in the database.
variable type units description
record_id INTEGER adimensional Record ID as decimal number
site_id INTEGER adimensional Site code as decimal number (01-99)
date TEXT adimensional Date in format YYYY-MM-DD hh:mm:ss. Derived from TIMESTAMP_START
year INTEGER YYYY Year with century as decimal number (0000-9999). Derived from TIMESTAMP_START
mo INTEGER MM Month as decimal number (01-12). Derived from TIMESTAMP_START
day INTEGER DD Day of the month as decimal number (01-31). Derived from TIMESTAMP_START
hCORRJOINTUNC_Wm2 REAL W m-2 Joint uncertainty estimation for h as sqrt(hRANDUNC2 + ((hCORR75 - hCORR25) / 1.349)2)
hCORR_Wm2 REAL W m-2 Sensible heat flux, corrected hFMDS by energy balance closure correction factor
hFMDS_Wm2 REAL W m-2 Sensible heat flux, gapfilled using MDS method
hFMDS_qc INTEGER adimensional Quality flag for hCORR. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor.
leCORRJOINTUNC_Wm2 REAL W m-2 Joint uncertainty estimation for le
leCORR_Wm2 REAL W m-2 Latent heat flux, corrected le_FMDS by energy balance closure correction factor
leFMDS_Wm2 REAL W m-2 Latent heat flux, gapfilled using MDS method
leFMDS_qc INTEGER adimensional Quality flag for leCORR. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor.
timestampEnd TEXT YYYYMMDDHHMM ISO timestamp end of averaging period - short format
timestampStart TEXT YYYYMMDDHHMM ISO timestamp start of averaging period - short format

Dataset FLUX

The FLUX data contains half-hourly measurements of the variables listed in the table below and was obtained from FLUXNET2015 data.

Table 16: Description of FLUX variables included in the database.
variable type units description
record_id INTEGER adimensional Record ID as decimal number
site_id INTEGER adimensional Site code as decimal number (01-99)
date TEXT adimensional Date in format YYYY-MM-DD hh:mm:ss. Derived from TIMESTAMP_START
year INTEGER YYYY Year with century as decimal number (0000-9999). Derived from TIMESTAMP_START
mo INTEGER MM Month as decimal number (01-12). Derived from TIMESTAMP_START
day INTEGER DD Day of the month as decimal number (01-31). Derived from TIMESTAMP_START
gppDtCutRef_umolCO2m2s1 REAL umolCO2 m-2 s-1 Gross Primary Production, from Daytime partitioning method, reference selected from GPP versions using a model efficiency approach. Based on corresponding NEE_CUT_XX version
gppDtCutSe_umolCO2m2s1 REAL umolCO2 m-2 s-1 Standard Error for Gross Primary Production, calculated as stdev(gppDtCut_XX) / sqrt(40). SE from 40 half-hourly gppDtCut_XX
gppDtVutRef_umolCO2m2s1 REAL umolCO2 m-2 s-1 Gross Primary Production, from Daytime partitioning method, reference version selected from GPP versions using a model efficiency approach. Based on corresponding neeVut_XX version
gppDtVutSe_umolCO2m2s1 REAL umolCO2 m-2 s-1 Standard Error for Gross Primary Production, calculated as stdev(gppDtVut_XX) / sqrt(40. SE from 40 half-hourly gppDtVut_XX
gppNtCutRef_umolCO2m2s1 REAL umolCO2 m-2 s-1 Gross Primary Production, from Nighttime partitioning method, reference selected from GPP versions using a model efficiency approach. Based on corresponding NEE_CUT_XX version
gppNtCutSe_umolCO2m2s1 REAL umolCO2 m-2 s-1 Standard Error for Gross Primary Production, calculated as stdev(gppNtCut_XX) / sqrt(40). SE from 40 half-hourly gppNtCut_XX
gppNtVutRef_umolCO2m2s1 REAL umolCO2 m-2 s-1 Gross Primary Production, from Nighttime partitioning method, reference version selected from GPP versions using a model efficiency approach. Based on corresponding neeVut_XX version
gppNtVutSe_umolCO2m2s1 REAL umolCO2 m-2 s-1 Standard Error for Gross Primary Production, calculated as (stdev(gppNtVut_XX) / sqrt(40)). SE from 40 half-hourly gppNtVut_XX
neeCutRefJointunc_umolCO2m2s1 REAL umolCO2 m-2 s-1 Joint uncertainty estimation for neeCutRef, including random uncertainty and USTAR filtering uncertainty [sqrt(neeCutRef_RANDUNC2 + ((NEE_CUT_84 - NEE_CUT_16) / 2)2)] for each half-hour
neeCutRef_qc INTEGER adimensional Quality flag for neeCutRef. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor.
neeCutRef_umolCO2m2s1 REAL umolCO2 m-2 s-1 Net Ecosystem Exchange, using Constant Ustar Threshold (CUT) across years, reference selected on the basis of the model efficiency
neeVutRefJointunc_umolCO2m2s1 REAL umolCO2 m-2 s-1 Joint uncertainty estimation for neeVutRef, including random uncertainty and USTAR filtering uncertainty [sqrt(neeVutRef_RANDUNC2 + ((neeVut_84 - neeVut_16) / 2)2)] for each half-hour
neeVutRef_qc INTEGER adimensional Quality flag for neeVutRef. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor.
neeVutRef_umolCO2m2s1 REAL umolCO2 m-2 s-1 Net Ecosystem Exchange, using Variable Ustar Threshold (VUT) for each year, reference selected on the basis of the model efficiency
recoDtCutRef_umolCO2m2s1 REAL umolCO2 m-2 s-1 Ecosystem Respiration, from Daytime partitioning method, reference selected from RECO versions using a model efficiency approach. Based on corresponding NEE_CUT_XX version
recoDtCutSe_umolCO2m2s1 REAL umolCO2 m-2 s-1 Standard Error for Ecosystem Respiration, calculated as stdev(recoDtCut_XX) / sqrt(40). SE from 40 half-hourly recoDtCut_XX
recoDtVutRef_umolCO2m2s1 REAL umolCO2 m-2 s-1 Ecosystem Respiration, from Daytime partitioning method, reference selected from RECO versions using a model efficiency approach. Based on corresponding neeVut_XX version
recoDtVutSe_umolCO2m2s1 REAL umolCO2 m-2 s-1 Standard Error for Ecosystem Respiration, calculated as stdev(recoDtVut_XX) / sqrt(40). SE from 40 half-hourly recoDtCut_XX
recoNtCutRef_umolCO2m2s1 REAL umolCO2 m-2 s-1 Ecosystem Respiration, from Nighttime partitioning method, reference selected from RECO versions using a model efficiency approach. Based on corresponding NEE_CUT_XX version
recoNtCutSe_umolCO2m2s1 REAL umolCO2 m-2 s-1 Standard Error for Ecosystem Respiration, calculated as stdev(recoNtCut_XX) / sqrt(40. SE from 40 half-hourly recoNtCut_XX
recoNtVutRef_umolCO2m2s1 REAL umolCO2 m-2 s-1 Ecosystem Respiration, from Nighttime partitioning method, reference selected from RECO versions using a model efficiency approach. Based on corresponding neeVut_XX version
recoNtVutSe_umolCO2m2s1 REAL umolCO2 m-2 s-1 Standard Error for Ecosystem Respiration, calculated as stdev(recoNtVut_XX) / sqrt(40. SE from 40 half-hourly recoNtCut_XX
timestampEnd TEXT YYYYMMDDHHMM ISO timestamp end of averaging period - short format
timestampStart TEXT YYYYMMDDHHMM ISO timestamp start of averaging period - short format

Dataset METEOROLOGICAL

The METEOROLOGICAL data contains half-hourly measurements of the variables listed in the table below and was obtained from FLUXNET2015 data.

Table 17: Description of METEOROLOGICAL variables included in the database.
variable type units description
record_id INTEGER adimensional Record ID as decimal number
site_id INTEGER adimensional Site code as decimal number (01-99)
date TEXT adimensional Date in format YYYY-MM-DD hh:mm:ss. Derived from TIMESTAMP_START
year INTEGER YYYY Year with century as decimal number (0000-9999). Derived from TIMESTAMP_START
mo INTEGER MM Month as decimal number (01-12). Derived from TIMESTAMP_START
day INTEGER DD Day of the month as decimal number (01-31). Derived from TIMESTAMP_START
timestampEnd TEXT YYYYMMDDHHMM ISO timestamp end of averaging period - short format
timestampStart TEXT YYYYMMDDHHMM ISO timestamp start of averaging period - short format
lwInFMDS_Wm2 REAL W m-2 Longwave radiation, incoming, gapfilled using MDS
lwInFMDS_qc REAL adimensional Quality flag for lwInFMDS. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor.
lwInF_Wm2 REAL W m-2 Longwave radiation, incoming, consolidated from lwInFMDS and lwInERA. lwInFMDS used if lwInFMDS_qc is 0 or 1.
lwInF_qc INTEGER adimensional Quality flag for lwInF. 0 = measured; 1 = good quality gapfill; 2 = downscaled from ERA
pF_mm REAL mm Precipitation consolidated from p and pERA
pF_qc INTEGER adimensional Quality flag for pF. 0 = measured; 2 = downscaled from ERA
p_mm REAL mm Precipitation.
paF_kPa REAL kPa Atmospheric pressure consolidated from pa and paERA
paF_qc INTEGER adimensional Quality flag for paF. 0 = measured; 2 = downscaled from ERA.
pa_kPa REAL kPa Atmospheric pressure
swInFMDS_Wm2 REAL W m-2 Shortwave radiation, incoming, gapfilled using MDS (negative values set to zero, e.g., negative values from instrumentation noise).
swInFMDS_qc REAL adimensional Quality flag for swInFMDS. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor.
swInF_Wm2 REAL W m-2 Shortwave radiation, incoming consolidated from swInFMDS and swInERA (negative values set to zero). swInFMDS used if swInFMDS_QC is 0 or 1
swInF_qc INTEGER adimensional Quality flag for swInF. 0 = measured; 1 = good quality gapfill; 2 = downscaled from ERA
taFMDS_degC REAL degree Celsius Air temperature, gapfilled using MDS method
taFMDS_qc INTEGER adimensional Quality flag for taFMDS. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor.
taF_degC REAL degree Celsius Air temperature, consolidated from taFMDS and taERA
taF_qc INTEGER adimensional Quality flag for taF. 0 = measured; 1 = good quality gapfill; 2 = downscaled from ERA.
vpdFMDS_hPa REAL hPa Vapor Pressure Deficit, gapfilled using MDS.
vpdFMDS_qc INTEGER adimensional Quality flag for vpdFMDS. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor.
vpdF_hPa REAL hPa Vapor Pressure Deficit consolidated from vpdFMDS and vpdERA. vpdFMDS used if vpdFMDS_qc is 0 or 1.
vpdF_qc INTEGER adimensional Quality flag for vpdF. 0 = measured; 1 = good quality gapfill; 2 = downscaled from ERA
wsF_ms1 REAL m s-1 Wind speed, consolidated from ws and wsERA. ws used if measured.
wsF_qc INTEGER adimensional Quality flag of wsF.0 = measured; 2 = downscaled from ERA.
ws_ms1 REAL m s-1 Wind speed

Dataset SOILTS

The soil time series data contains half-hourly measurements of the variables listed in the table below and was obtained from FLUXNET2015 data.

Table 18: Description of SOILTS variables included in the database.
variable type units description
record_id INTEGER adimensional Record ID as decimal number
site_id INTEGER adimensional Site code as decimal number (01-99)
date TEXT adimensional Date in format YYYY-MM-DD hh:mm:ss. Derived from TIMESTAMP_START
year INTEGER YYYY Year with century as decimal number (0000-9999). Derived from TIMESTAMP_START
mo INTEGER MM Month as decimal number (01-12). Derived from TIMESTAMP_START
day INTEGER DD Day of the month as decimal number (01-31). Derived from TIMESTAMP_START
timestampEnd TEXT YYYYMMDDHHMM ISO timestamp end of averaging period - short format
timestampStart TEXT YYYYMMDDHHMM ISO timestamp start of averaging period - short format
swcFMDS1_degC REAL percent Soil water content, gapfilled with MDS (numeric index “#” increases with the depth, 1 is shallowest)
swcFMDS1_qc INTEGER adimensional Quality flag for tsFMDS#. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor.
swcFMDS2_degC REAL percent Soil water content, gapfilled with MDS (numeric index “#” increases with the depth, 1 is shallowest)
swcFMDS2_qc INTEGER adimensional Quality flag for tsFMDS#. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor.
swcFMDS3_degC REAL percent Soil water content, gapfilled with MDS (numeric index “#” increases with the depth, 1 is shallowest)
swcFMDS3_qc INTEGER adimensional Quality flag for tsFMDS#. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor.
swcFMDS4_degC REAL percent Soil water content, gapfilled with MDS (numeric index “#” increases with the depth, 1 is shallowest)
swcFMDS4_qc INTEGER adimensional Quality flag for tsFMDS#. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor.
swcFMDS5_degC REAL percent Soil water content, gapfilled with MDS (numeric index “#” increases with the depth, 1 is shallowest)
swcFMDS5_qc INTEGER adimensional Quality flag for tsFMDS#. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor.
tsFMDS1_degC REAL degree Celsius Soil temperature, gapfilled with MDS (numeric index “#” increases with the depth, 1 is shallowest)
tsFMDS1_qc INTEGER adimensional Quality flag for tsFMDS#. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor.
tsFMDS2_degC REAL degree Celsius Soil temperature, gapfilled with MDS (numeric index “#” increases with the depth, 1 is shallowest)
tsFMDS2_qc INTEGER adimensional Quality flag for tsFMDS#. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor.
tsFMDS3_degC REAL degree Celsius Soil temperature, gapfilled with MDS (numeric index “#” increases with the depth, 1 is shallowest)
tsFMDS3_qc INTEGER adimensional Quality flag for tsFMDS#. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor.
tsFMDS4_degC REAL degree Celsius Soil temperature, gapfilled with MDS (numeric index “#” increases with the depth, 1 is shallowest)
tsFMDS4_qc INTEGER adimensional Quality flag for tsFMDS#. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor.
tsFMDS5_degC REAL degree Celsius Soil temperature, gapfilled with MDS (numeric index “#” increases with the depth, 1 is shallowest)
tsFMDS5_qc INTEGER adimensional Quality flag for tsFMDS#. 0 = measured; 1 = good quality gapfill; 2 = medium; 3 = poor.

Dataset MODIS

The original MODIS time series are available at the NASA Land Processes Distributed Archive Center (LP DAAC). The data were downloaded from the Land Product Subset web service of the Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC). Five different datasets are included in the database:

The data comprise surface reflectance, land surface temperature, vegetation indexes, Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR), GPP and Net Photosynthesis. The MODIS variables are listed the table below.

Table 19: Description of MODIS variables included in the database.
variable type units description source
record_id INTEGER adimensional Record ID as decimal number MOD09A1
site TEXT adimensional Site name MOD09A1
site_id INTEGER adimensional Site code as decimal number (01-99) MOD09A1
date TEXT adimensional Date in format YYYY-MM-DD MOD09A1
year INTEGER YYYY Year with century as decimal number (0000-9999) MOD09A1
mo INTEGER MM Month as decimal number (01-12) MOD09A1
day INTEGER DD Day of the month as decimal number (01-31) MOD09A1
aB01_rad REAL radian Angle in red. Spatial resolution: 0.5 km. Temporal resolution: 8-day composite. MOD09A1
aB02_rad REAL radian Angle in near infrared. Calculated with MOD09A1. Spatial resolution 0.5 km MOD09A1
aB05_rad REAL radian Angle in SWIR 1.Spatial resolution: 0.5 km. Temporal resolution: 8-day composite. MOD09A1
aB06_rad REAL radian Angle in SWIR 2. Spatial resolution: 0.5 km. Temporal resolution: 8-day composite. MOD09A1
evi8 REAL adimensional Enhance Vegetation Index. Spatial resolution: 0.5 km. Temporal resolution: 8-day composite MOD09A1
ndvi8 REAL adimensional Normalized Difference Vegetation Index. Spatial resolution: 0.5 km. Temporal resolution: 8-day composite MOD09A1
ndwi REAL adimensional Normalized Difference Water Index. Spatial resolution: 0.5 km. Temporal resolution: 8-day composite MOD09A1
reflB01_percent REAL percent reflectance Surface Reflectance Band 1 (620–670 nm) Red. Fill value: NA. Spatial resolution: 0.5 km. Temporal resolution: 8-day composite. MOD09A1
reflB02_percent REAL percent reflectance Surface Reflectance Band 2 (841–876 nm) NIR. Fill value: NA. Spatial resolution: 0.5 km. Temporal resolution: 8-day composite. MOD09A1
reflB03_percent REAL percent reflectance Surface Reflectance Band 3 (459–479 nm) Blue. Fill value: NA. Spatial resolution: 0.5 km. Temporal resolution: 8-day composite. MOD09A1
reflB04_percent REAL percent reflectance Surface Reflectance Band 4 (545–565 nm) Green. Fill value: NA. Spatial resolution: 0.5 km. Temporal resolution: 8-day composite. MOD09A1
reflB05_percent REAL percent reflectance Surface Reflectance Band 5 (1230–1250 nm) SWIR1. Fill value: NA. Spatial resolution: 0.5 km. Temporal resolution: 8-day composite. MOD09A1
reflB06_percent REAL percent reflectance Surface Reflectance Band 6 (1628–1652 nm) SWIR2. Fill value: NA. Spatial resolution: 0.5 km. Temporal resolution: 8-day composite. MOD09A1
reflB07_percent REAL percent reflectance Surface Reflectance Band 7 (2105–2155 nm) SWIR3. Fill value: NA. Spatial resolution: 0.5 km. Temporal resolution: 8-day composite. MOD09A1
refl_qc INTEGER adimensional Indicates the level of quality correction of the product (the seven bands) that is classified as follows: 0 = Highest quality, corrected product produced at ideal quality all bands; 2 = corrected product produced at less than ideal quality some or all bands, some bands could not be completely correct; 3 = interpolated, when corrected product has not been produced in one or some bands and they have been interpolated with the value Rt = (Rt-1 + Rt+1)/2; 4 = corrected product not produced, when product has not been completely corrected in one or some bands and could not be interpolated. Data may be wrong or filled with NA; 5 = Missing data, indicates that the product was not available for that date. Some of them correspond to specific continuous periods. All the columns filled with NA. MOD09A1
sani_rad REAL radian Shortwave Angle Normalized Index. Valid range: -3.14 - 3.14. Spatial resolution: 0.5 km. Temporal resolution: 8-day composite. MOD09A1
sasi_rad REAL radian Shortwave Angle Slope Index. Spatial resolution: 0.5 km. Temporal resolution: 8-day composite MOD09A1
record_id INTEGER adimensional Record ID as decimal number MOD15A2
site TEXT adimensional Site name MOD15A2
site_id INTEGER adimensional Site code as decimal number (01-99) MOD15A2
date TEXT adimensional Date in format YYYY-MM-DD MOD15A2
year INTEGER YYYY Year with century as decimal number (0000-9999) MOD15A2
mo INTEGER MM Month as decimal number (01-12) MOD15A2
day INTEGER DD Day of the month as decimal number (01-31) MOD15A2
fpar REAL adimensional Proportion of available radiation in the photosynthetically active wavelengths. Valid range: 0 - 1. Fill value: NA. Spatial resolution: 1 km. Temporal resolution: 8-day composite. MOD15A2
fpar_qc INTEGER adimensional Indicates the level of the product quality that is classified as follows: 0 = Good quality (main algorithm with or without saturation); 2 = Other quality (back-up algorithm or fill values) MOD15A2
lai REAL adimensional Leaf area index. Valid range: 0 - 10. Fill value: NA. Spatial resolution: 1 km. Temporal resolution: 8-day composite. MOD15A2
lai_qc INTEGER adimensional Indicates the level of the product quality that is classified as follows: 0 = Good quality (main algorithm with or without saturation); 2 = Other quality (back-up algorithm or fill values) MOD15A2
record_id INTEGER adimensional Record ID as decimal number MOD11A2
site TEXT adimensional Site name MOD11A2
site_id INTEGER adimensional Site code as decimal number (01-99) MOD11A2
date TEXT adimensional Date in format YYYY-MM-DD MOD11A2
year INTEGER YYYY Year with century as decimal number (0000-9999) MOD11A2
mo INTEGER MM Month as decimal number (01-12) MOD11A2
day INTEGER DD Day of the month as decimal number (01-31) MOD11A2
lstDay_degK REAL degree Kelvin Daytime land surface temperature. Valid range: 150 – 1310.7. Fill value: NA. Spatial resolution: 1 km. Temporal resolution: 8-day composite. MOD11A2
lstDay_qc INTEGER adimensional Indicates the level of quality of the product that is classified as follows: 0 = good quality; 2 = other quality; 3 = interpolated, 4 = pixel not produced (NA) MOD11A2
lstNight_degK REAL degree Kelvin Nighttime land surface temperature. Valid range: 150 – 1310.7. Fill value: NA. Spatial resolution: 1 km. Temporal resolution: 8-day composite. MOD11A2
lstNight_qc INTEGER adimensional Indicates the level of quality of the product that is classified as follows: 0 = good quality; 2 = other quality; 3 = interpolated, 4 = pixel not produced (NA) MOD11A2
record_id INTEGER adimensional Record ID as decimal number MOD13Q1
site TEXT adimensional Site name MOD13Q1
site_id INTEGER adimensional Site code as decimal number (01-99) MOD13Q1
date TEXT adimensional Date in format YYYY-MM-DD MOD13Q1
year INTEGER YYYY Year with century as decimal number (0000-9999) MOD13Q1
mo INTEGER MM Month as decimal number (01-12) MOD13Q1
day INTEGER DD Day of the month as decimal number (01-31) MOD13Q1
evi16 REAL adimensional Enhanced Vegetation Index. Valid range: -0.2 - 1. Fill value: NA. Spatial resolution: 250 meters. Temporal resolution: 16-day composite. MOD13Q1
evi16_qc REAL adimensional Indicates the level of the product quality that is classified as follows: 0 = good quality, index produced; 2 = other quality, index produced, but check other qc and index produced, but most probably cloudy; 3 = index not produced due to other reasons than cloud, thus fill values were substituted by an interpolated values when the previous and the following values were available index = (indext-1 + indext+1)/2. MOD13Q1
ndvi16 REAL adimensional Normalized Difference Vegetation Index. Valid range: -0.2 - 1. Fill value: NA. Spatial resolution: 250 meters. Temporal resolution: 16-day composite. MOD13Q1
ndvi16_qc REAL adimensional Indicates the level of the product quality that is classified as follows: 0 = good quality, index produced; 2 = other quality, index produced, but check other qc and index produced, but most probably cloudy; 3 = index not produced due to other reasons than cloud, thus fill values were substituted by an interpolated values when the previous and the following values were available index = (indext-1 + indext+1)/2. MOD13Q1
record_id INTEGER adimensional Record ID as decimal number MOD17A2
site TEXT adimensional Site name MOD17A2
site_id INTEGER adimensional Site code as decimal number (01-99) MOD17A2
date TEXT adimensional Date in format YYYY-MM-DD MOD17A2
year INTEGER YYYY Year with century as decimal number (0000-9999) MOD17A2
mo INTEGER MM Month as decimal number (01-12) MOD17A2
day INTEGER DD Day of the month as decimal number (01-31) MOD17A2
gpp_gCm2d REAL gC m-2 d Gross Primary Production. Valid range: -375 – 375. Fill value: NA. Spatial resolution 1 km. Temporal resolution: 8-day accumulation. MOD17A2
gpp_qc INTEGER adimensional Indicates the level of the product quality that is classified as follows: 0 = good quality, the estimates were done using the main algorithm with or without saturation; 2 = other quality, the estimates were done using back-up algorithm. MOD17A2
psNet_gCm2d REAL gC m-2 d Net Photosynthesis (GPP – maintenance respiration). Valid range: -375 – 375. Fill value: NA. Spatial resolution 1 km. Temporal resolution: 8-day accumulation. MOD17A2
psNet_qc INTEGER adimensional Indicates the level of the product quality that is classified as follows: 0 = good quality, the estimates were done using the main algorithm with or without saturation; 2 = other quality, the estimates were done using back-up algorithm. MOD17A2

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