| Title: | Compute Soil Carbon Fluxes for the National Ecological Observatory Network Sites |
|---|---|
| Description: | Acquires and synthesizes soil carbon fluxes at sites located in the National Ecological Observatory Network (NEON). Provides flux estimates and associated uncertainty as well as key environmental measurements (soil water, temperature, CO2 concentration) that are used to compute soil fluxes. |
| Authors: | John Zobitz [aut, cre]  ,
Edward Ayres [aut] ,
Katie O'Rourke [ctb] (LinkedIn,
<https://www.linkedin.com/in/katie-o-rourke-5a898b194/>),
Zoey Werbin [ctb],
Lajntxiag Lee [ctb],
Ridwan Abdi [ctb] (LinkedIn,
<https://www.linkedin.com/in/ridwaan-cabdi/>),
Dijonë Mehmeti [ctb] (LinkedIn,
<https://www.linkedin.com/in/dijon%C3%AB-mehmeti/>),
Ly Xiong [ctb] (LinkedIn,
<https://www.linkedin.com/in/ly-xiong-bb83ba1b2/>) |
| Maintainer: | John Zobitz <[email protected]> |
| License: | GPL (>= 3) |
| Version: | 1.0.0 |
| Built: | 2025-02-21 04:45:31 UTC |
| Source: | https://github.com/jmzobitz/neonsoilflux |
Given a site code and dates, apply the neonUtilities package to download the data from NEON API
acquire_neon_data( site_name, download_date, time_frequency = "30_minute", provisional = FALSE )acquire_neon_data( site_name, download_date, time_frequency = "30_minute", provisional = FALSE )
site_name |
Required. NEON code for a particular site (a string) |
download_date |
Required. Date where we end getting NEON data. Format: YYYY-MM (can't specify day). So "2020-05" means it will grab data for the entire 5th month of 2020. (a string). Downloads data for a given month only |
time_frequency |
Required. Will you be using 30 minute ("30_minute") or 1 minute ("1_minute") recorded data? Defaults to 30 minutes. |
provisional |
Required. Should you use provisional data when downloading? Defaults to FALSE. See NEON Data Releases. Defaults to FALSE (similar to include.provisional in loadByProduct). |
A list containing stacked environmental data ('site_data') and soil properties ('site_megapit').
John Zobitz [email protected]
out_env_data <- acquire_neon_data("SJER","2022-06")out_env_data <- acquire_neon_data("SJER","2022-06")
Given a data measurement, determine when a monthly mean is used and the bulk QF flags. Helps to determine when the environmental measurements produced a QF value and to be used in subsequent flux calculations.
check_qf_flags(measurement_name, data)check_qf_flags(measurement_name, data)
measurement_name |
The name of the measurement (staPres, soilTemp, VSWC, soilCO2Concentration) |
data |
Data used to check the qf flags |
A data frame of startDateTime, horizontalPosition, and the associated QF flag.
# changelog and author contributions / copyrights
John Zobitz [email protected]
Given a measurement of co2, convert it from ppm to umol m-3 based on temperature and pressure. Also compute associated error via quadrature.
co2_to_umol( temperature, pressure, co2, temperature_err, pressure_err, co2_err, zOffset )co2_to_umol( temperature, pressure, co2, temperature_err, pressure_err, co2_err, zOffset )
temperature |
Required. Soil temperature (degrees C) |
pressure |
Required. Barometric air pressure (kilopascal) |
co2 |
Carbon dioxide in ppm |
temperature_err |
Required. Reported Soil temperature error (degrees C) |
pressure_err |
Required. Reported Barometric air pressure error (kilopascal) |
co2_err |
Required. Carbon dioxide in ppm error |
zOffset |
Required. Surface depth (m). Reported as a negative number. |
A value of the converted co2
John Zobitz [email protected]
co2_to_umol(31,96.3,654,.15,.05,9,-.05)co2_to_umol(31,96.3,654,.15,.05,9,-.05)
Given a NEON measurement data frame calculate the monthly mean values across all horizontal and vertical locations. Based off code from Zoey Werbin.
compute_monthly_mean( NEON_data, position_columns = c("horizontalPosition", "verticalPosition") )compute_monthly_mean( NEON_data, position_columns = c("horizontalPosition", "verticalPosition") )
NEON_data |
Required. Input vector of neon measurements for a month |
position_columns |
Optional. Do we group by horizontalPosition, verticalPosition, and? Default is both. Added this option in case we just want to average across a given dimension. |
A data frame that reports for each horiztonal and vertical position the computed mean and standard deviation from sampling (similar to a bootstrap method) as well as the sample mean and sample standard deviation
John Zobitz [email protected]
Zoey Werbin (@zoey-rw): original author https://github.com/zoey-rw/microbialForecasts/blob/caa7b1a8aa8a131a5ff9340f1562cd3a3cb6667b/data_construction/covariate_prep/soil_moisture/clean_NEON_sensor_moisture_data.r
# Download the NEON data directly - here this would be soil moisture NEON_moist_30m_orig <- neonUtilities::loadByProduct( dpID = "DP1.00094.001", site = "WREF", startdate = "2022-06", enddate = "2022-06", timeIndex = "30", package = "expanded", check.size = FALSE, include.provisional = TRUE ) # Then correct the swc site_swc <- swc_correct(NEON_moist_30m_orig, "WREF","2022-06") # Select the columns and the time frequency time_frequency <- "30_minute" column_selectors <- c("Mean", "Minimum", "Maximum", "ExpUncert", "StdErMean") swc <- site_swc |> purrr::pluck(paste0("SWS_",time_frequency)) |> dplyr::select(tidyselect::all_of(c("domainID","siteID", "horizontalPosition","verticalPosition","startDateTime","VSWCFinalQF")), tidyselect::matches(stringr::str_c("VSWC",column_selectors))) # Determine a data frame of the different horizontal and vertical positions swc_positions <- site_swc |> purrr::pluck(paste0("sensor_positions_","00094")) # Add on the positions for swc swc <- determine_position(swc_positions,swc)# Download the NEON data directly - here this would be soil moisture NEON_moist_30m_orig <- neonUtilities::loadByProduct( dpID = "DP1.00094.001", site = "WREF", startdate = "2022-06", enddate = "2022-06", timeIndex = "30", package = "expanded", check.size = FALSE, include.provisional = TRUE ) # Then correct the swc site_swc <- swc_correct(NEON_moist_30m_orig, "WREF","2022-06") # Select the columns and the time frequency time_frequency <- "30_minute" column_selectors <- c("Mean", "Minimum", "Maximum", "ExpUncert", "StdErMean") swc <- site_swc |> purrr::pluck(paste0("SWS_",time_frequency)) |> dplyr::select(tidyselect::all_of(c("domainID","siteID", "horizontalPosition","verticalPosition","startDateTime","VSWCFinalQF")), tidyselect::matches(stringr::str_c("VSWC",column_selectors))) # Determine a data frame of the different horizontal and vertical positions swc_positions <- site_swc |> purrr::pluck(paste0("sensor_positions_","00094")) # Add on the positions for swc swc <- determine_position(swc_positions,swc)
Given a site filename (from acquire_neon_data), process and compute fluxes. This file takes a saved data file from acquire: 1) Takes the needed components (QF and measurement flags) for soil water, temperature, co2, binding them together in a tidy data frame 2) Interpolates across the measurements 3) Merges air pressure data into this data frame 4) Does a final QF check so we should have only timeperiods where all measurements exist 5) Adds in the megapit data so we have bulk density, porosity measurements at the interpolated depth. 6) Saves the data
compute_neon_flux(input_site_env, input_site_megapit)compute_neon_flux(input_site_env, input_site_megapit)
input_site_env |
Required. Input list of environmental data. Usually given from acquire_neon_data |
input_site_megapit |
Required. Input list of environmental soil data. Usually given from acquire_neon_data |
Data frame of fluxes and gradient from the timeperiod
John Zobitz [email protected] based on code developed by Edward Ayres [email protected]
# First acquire the NEON data at a given NEON site out_env_data <- acquire_neon_data("SJER","2020-05") # Then process and compute the fluxes: out_flux <- compute_neon_flux(input_site_env = sjer_env_data_2022_06, input_site_megapit = sjer_megapit_data_2022_06)# First acquire the NEON data at a given NEON site out_env_data <- acquire_neon_data("SJER","2020-05") # Then process and compute the fluxes: out_flux <- compute_neon_flux(input_site_env = sjer_env_data_2022_06, input_site_megapit = sjer_megapit_data_2022_06)
Computation function. Given a measurement of the co2 and diffusive flux at different levels, return the surface flux
compute_surface_flux(input_data)compute_surface_flux(input_data)
input_data |
Required. A data frame containing zOffsets, diffusivity, and co2 (umol mol-1) and their associated errors |
A value of the surface CO2 flux (umol m-2 s-1)
John Zobitz [email protected]
This file takes data frame from acquire_neon_data and: 1) Takes the needed components (QF and measurement flags) for soil water, temperature, co2, binding them together in a tidy data frame 2) Interpolates across the measurements 3) Merges air pressure data into this data frame
correct_env_data(input_data)correct_env_data(input_data)
input_data |
Required. Nested data frame from acquire_neon_data. |
List of all QF flags over time period and Data frame of environmental measurements for flux computation
John Zobitz [email protected]
# Note: you may need to first aqcuire the NEON data using acquire_neon_data # Now correct existing environmental data: corrected_data <- correct_env_data(sjer_env_data_2022_06)# Note: you may need to first aqcuire the NEON data using acquire_neon_data # Now correct existing environmental data: corrected_data <- correct_env_data(sjer_env_data_2022_06)
Given zOffsets, diffusivity, and co2 (umol mol-1) and their associated errors, compute the surface flux. This is done by estimating the surface concentration (through linear regression) and doing a gradient calculation. Modified from De Jong and Schappert (1972).
dejong_shappert_flux(zOffset, co2, co2_err, diffusive, diffusive_err)dejong_shappert_flux(zOffset, co2, co2_err, diffusive, diffusive_err)
zOffset |
Required. depths below surface - assumed to be positive in value. Important for directionality! |
co2 |
Required. co2 at depth (umol m–1) |
co2_err |
Required. Associated errors with that value of co2 |
diffusive |
Required. diffusivity at each depth |
diffusive_err |
Required Associated errors with diffusivity |
Data frame of fluxes associated error
John Zobitz [email protected]
Jong, E. De, and H. J. V. Schappert. 1972. “Calculation of Soil Respiration and Activity from CO2 Profiles in the Soil.” Soil Science 113 (5): 328.
Maier, M., and H. Schack-Kirchner. 2014. “Using the Gradient Method to Determine Soil Gas Flux: A Review.” Agricultural and Forest Meteorology 192–193 (July):78–95. https://doi.org/10.1016/j.agrformet.2014.03.006.
[hirano_flux()], [tang_2003_flux()], [tang_2005_flux()] for other ways to compute surface fluxes.
Definition function. Linearly interpolate a measurement across the different measurement depths
depth_interpolate( input_measurements, measurement_name, measurement_interpolate )depth_interpolate( input_measurements, measurement_name, measurement_interpolate )
input_measurements |
Required. Nested data frame (tibble of a months worth of data of co2, temperature, swc, pressure) |
measurement_name |
Required. Names of measurements we are interpolating. Currently only does one column at a time. |
measurement_interpolate |
Required. Names of measurement whose depth is used to interpolate (typically co2) |
A nested data frame with interpolated measurements.
John Zobitz [email protected] based on code developed by Edward Ayres [email protected]
Given a NEON measurement data frame and measurement depth, pull out the measurement depth for a measurement - because it may vary in time based on if a sensor is replaced.
determine_position(input_positions, input_measurement)determine_position(input_positions, input_measurement)
input_positions |
Required. Input vector of measurements depths |
input_measurement |
Required. Input vector of measurements. |
A data frame that reports the measurement depth for and associated environmental measurement.
John Zobitz [email protected]
Given a tidied data frame of soil measurements (from interpolate.R), compute the diffusivity in a given soil layer
diffusivity( temperature, soil_water, pressure, temperature_err, soil_water_err, pressure_err, zOffset, porVol2To20 )diffusivity( temperature, soil_water, pressure, temperature_err, soil_water_err, pressure_err, zOffset, porVol2To20 )
temperature |
Required. Soil temperature (degrees C) |
soil_water |
Required. Soil water content |
pressure |
Required. Barometric air pressure (kilopascal) |
temperature_err |
Required. Reported Soil temperature error (degrees C) |
soil_water_err |
Required. Reported Soil water content error |
pressure_err |
Required. Reported Barometric air pressure error (kilopascal) |
zOffset |
Required. Measurement level in cm. |
porVol2To20 |
Required. Porosity of the 0-20 mm fraction (cm3 cm-3). Assumes no pores within rocks. |
A value of the computed diffusivity
John Zobitz [email protected]
diffusivity(31,0.0102,96.3,.15,.2135,.05,-.05,0.45)diffusivity(31,0.0102,96.3,.15,.2135,.05,-.05,0.45)
Given a flux measurement data frame, show when the environmental measurements produced a QF value
env_fingerprint_plot(input_fluxes)env_fingerprint_plot(input_fluxes)
input_fluxes |
data frame of computed fluxes |
A ggplot graph where we have ordered factors showing the QA values a given environmental measurement
John Zobitz [email protected]
# Make a fingerprint plot for environmental variables: env_fingerprint_plot(sjer_flux_2022_06)# Make a fingerprint plot for environmental variables: env_fingerprint_plot(sjer_flux_2022_06)
Definition function. Linearly interpolate a measurement across the different measurement depths
fit_function( input_depth, input_value, input_value_err, input_value_qf, interp_depth, measurement_special )fit_function( input_depth, input_value, input_value_err, input_value_qf, interp_depth, measurement_special )
input_depth |
Required. Vector of measurement depths. |
input_value |
Required. Vector of measured values (i.e. soil temperature and soil water) measured at depths input_depth |
input_value_err |
Required. Vector or reported measurement errors. Used to compute prediction error (via quadrature) when linear interpolation is used. |
input_value_qf |
Required. Vector of qf values from the smoothing 0 = no smoothing, 1 = mean value used (smoothing), 2 = NA value |
interp_depth |
Depths of the sensors required for interpolation |
measurement_special |
Flag if we want to just do linear interpolation for a given measurement |
A data frame of the depth and the measured column for the measurements and reported error
John Zobitz [email protected] based on code developed by Edward Ayres [email protected]
Given a flux measurement data frame, show when the flux and diffusivity measurements produced a QF value
flux_fingerprint_plot(input_fluxes)flux_fingerprint_plot(input_fluxes)
input_fluxes |
data frame of computed fluxes |
A ggplot graph where we have ordered factors showing the QA values a given flux computation
John Zobitz [email protected]
# Make a fingerprint plot for computed flux values: flux_fingerprint_plot(sjer_flux_2022_06)# Make a fingerprint plot for computed flux values: flux_fingerprint_plot(sjer_flux_2022_06)
Given zOffsets, diffusivity, and co2 (umol mol-1) and their associated errors, compute the surface flux. This is done by estimating the surface concentration through linear regression and linear extrapolation of the bottom and top surface fluxes. Modified from Hirano et al (2005).
hirano_flux(zOffset, co2, co2_err, diffusive, diffusive_err)hirano_flux(zOffset, co2, co2_err, diffusive, diffusive_err)
zOffset |
Required. depths below surface - assumed to be positive in value. Important for directionality! |
co2 |
Required. co2 at depth (umol m–1) |
co2_err |
Required. Associated errors with that value of co2 |
diffusive |
Required. diffusivity at each depth |
diffusive_err |
Required Associated errors with diffusivity |
Data frame of fluxes associated error
John Zobitz [email protected]
Hirano, Takashi, Honghyun Kim, and Yumiko Tanaka. 2003. “Long-Term Half-Hourly Measurement of Soil CO2 Concentration and Soil Respiration in a Temperate Deciduous Forest.” Journal of Geophysical Research: Atmospheres 108 (D20). https://doi.org/10.1029/2003JD003766.
Maier, M., and H. Schack-Kirchner. 2014. “Using the Gradient Method to Determine Soil Gas Flux: A Review.” Agricultural and Forest Meteorology 192–193 (July):78–95. https://doi.org/10.1016/j.agrformet.2014.03.006.
[dejong_shappert_flux()], [tang_2003_flux()], [tang_2005_flux()] for other ways to compute surface fluxes.
Given a site measurement and monthly mean, insert in the monthly mean value when the QF flag fails.
insert_mean(data, monthly_mean, measurement_name)insert_mean(data, monthly_mean, measurement_name)
data |
Required. input data to use |
monthly_mean |
Required. monthly mean of input data to use |
measurement_name |
Required. name of measurement |
Nested data frame of measurements
John Zobitz [email protected]
Determine if a YYYY-MM string is inside a interval
inside_interval(start, end, reference_time)inside_interval(start, end, reference_time)
start |
starting interval time |
end |
ending interval time |
reference_time |
time we are comparing to - YYYY-MM string |
Logical indicating whether or not the reference time is inside the interval. We need this when working with downloaded NEON data which usually comes in a YYYY-MM string
John Zobitz [email protected]
# Define starting and ending dates: start_date <- as.POSIXct("2021-06-01 09:30:00",tz="UTC") end_date <- as.POSIXct("2023-06-01 20:00:00",tz="UTC") # Test, should return TRUE inside_interval(start_date,end_date,"2022-06") # Test, should return FALSE inside_interval(start_date,end_date,"2020-06")# Define starting and ending dates: start_date <- as.POSIXct("2021-06-01 09:30:00",tz="UTC") end_date <- as.POSIXct("2023-06-01 20:00:00",tz="UTC") # Test, should return TRUE inside_interval(start_date,end_date,"2022-06") # Test, should return FALSE inside_interval(start_date,end_date,"2020-06")
Given a merged data frame of co2, water, and temperature: 1) Filters on QF measurement flags 2) Filters if we have at least 2 soil h20 and temperature measurements, 3 co2 measurements at each time, horizontal position, and vertical depth 3) Filters if we have at least 3 distinct measurements at each time and horizontal position 4) Returns the resulting data frame.
This internal function is created to speed up data processing.
measurement_detect(input_data)measurement_detect(input_data)
input_data |
Required. Nested data frame of merged soil water, temperature, co2, and pressure across different NEON locations and depths |
Data frame of fluxes from the timeperiod
John Zobitz [email protected] based on code developed by Edward Ayres [email protected]
Internal function. Given a set of environmental measurements, create a merged data frame of measurements and positions where the QF_flag exists (either observed or a smoothed mean measurement).
measurement_merge( neon_data, data_code, data_product_id, measurement_name, qf_name )measurement_merge( neon_data, data_code, data_product_id, measurement_name, qf_name )
neon_data |
Required. A list of NEON data downloaded from the utilities |
data_code |
Required. Names of data product we are interpolating. (SWS = soil water, ST = soil temperature, SCO2C = soil CO2) |
data_product_id |
Name of the data product 00094 = soil water, 00041 = soil temperature, 00095 = soil CO2 |
measurement_name |
Required. Names of column we are grabbing. (VSWCMean = soil water, soilTempMean = soil temperature, soilCO2concentrationMean = soil CO2) |
qf_name |
Required. Names of qf column we are grabbing. (VSWCFinalQF = soil water, finalQF = soil temperature, finalQF = soil CO2) |
A data frame of the requested data.
John Zobitz [email protected]
Assume a derived quantity y is a function of inputs x_i: y = f(x_1,x_2,x_3, ...)
Given uncertainties (x_err) for each x_i, then this function will compute the corresponding y_err via quadrature. Inputs are the vector of partial derivaties df/dx_i, evaluated at (x_1,x_2,x_3,...).
Resulting y_err is the square root of the sum of (df/dx_1)^2 * (x_err)^2 + (df/dx_2)^2 * (x_err)^2 + (df/dx_3)^2 * (x_err)^2 ...
quadrature_error(x_pd, x_err)quadrature_error(x_pd, x_err)
x_pd |
Required. Input vector of partial derivatives for y = f(x), evaluated at x_i |
x_err |
Required. Error vector of measurements |
A value of quadrature error
John Zobitz [email protected]
# Let's say we have 5 temperature measurements w/ error:: temperature <- c(31.108, 30.689, 30.463, 30.381, 30.250) temperature_error <- c(0.1508,0.1507,0.1497,0.1496,0.1497) # The sample mean is the sum of all measurements divided by the average: sum(temperature)/5 # (Can also be computed with mean(temperature)) # The vector of partial derivatives is just 1/n for each measurement: temperature_pd <- c(1/5,1/5,1/5,1/5,1/5) quadrature_error(temperature_pd,temperature_error) # Note: quadrature_error(1/5,temperature_error) is also allowed.# Let's say we have 5 temperature measurements w/ error:: temperature <- c(31.108, 30.689, 30.463, 30.381, 30.250) temperature_error <- c(0.1508,0.1507,0.1497,0.1496,0.1497) # The sample mean is the sum of all measurements divided by the average: sum(temperature)/5 # (Can also be computed with mean(temperature)) # The vector of partial derivatives is just 1/n for each measurement: temperature_pd <- c(1/5,1/5,1/5,1/5,1/5) quadrature_error(temperature_pd,temperature_error) # Note: quadrature_error(1/5,temperature_error) is also allowed.
A nested dataset containing environmental variables at the SJER site from June 2022. A convenience data frame to make processing fluxes easier for testing. Computed with the function acquire_neon_data. All the variables can be used to compute fluxes (along with megapit data).
data(sjer_env_data_2022_06)data(sjer_env_data_2022_06)
A nested 3 item list
measurement: Name of environmental measurement
data: Nested list of data corresponding to a measurement
monthly_mean: Monthly mean of measurement, computed in 'compute_monthly_mean'
A nested dataset containing computed fluxes at the SJER site from June 2022. Fluxes were computed with using compute_neon_flux(sjer_env_data_2022_06,sjer_megapit_data_2022_06).
data(sjer_flux_2022_06)data(sjer_flux_2022_06)
A nested data frame item list with 7200 rows and 8 columns
startDateTime: Time period of measurement (as POSIXct)
horizontalPosition: Sensor location where flux is computed
flux_compute: A nested tibble with variables (1) flux, flux_err, and method (one of 4 implemented)
diffusivity: Computation of surface diffusivity
VSWCMeanQF: QF flag for soil water content across all vertical depths at the given horizontal position: 0 = no issues, 1 = monthly mean used in measurement, 2 = QF fail
soilTempMeanQF: QF flag for soil temperature across all vertical depths at the given horizontal position: 0 = no issues, 1 = monthly mean used in measurement, 2 = QF fail
soilCO2concentrationMeanQF: QF flag for soil CO2 concentration across all vertical depths at the given horizontal position: 0 = no issues, 1 = monthly mean used in measurement, 2 = QF fail
staPresMeanQF: QF flag for atmospheric pressure across all vertical depths at the given horizontal position: 0 = no issues, 1 = monthly mean used in measurement, 2 = QF fail
A nested dataset containing soil data properties at the SJER site from June 2022. A convenience data frame to make processing fluxes easier for testing. Computed with using the function acquire_neon_data. Essentially what is returned when data product DP1.00096.001 is loaded from loadByProduct.
data(sjer_megapit_data_2022_06)data(sjer_megapit_data_2022_06)
A nested 10 item list
citation_00096_RELEASE-2024: BibTex reference
issueLog_00096: Listing of known issues
mgp_perarchivesample: Archived sample information
mgp_perbiogeosample: Data collected on biogeochemistry sample
mgp_perbulksample: Data collected on bulk density sample
mgp_perhorizon: Per soil horizon metadata
mgp_permegapit: Data collected per megapit
readme_00096: Data product description, issue log, and other metadata about the data product
validation_00096: Description of data validation applied at the points of collection and ingest
variables_00096: Description and units for each column of data in data tables
From https://data.neonscience.org/data-products/DP1.00096.001
Given the expanded SWC data, return a corrected version based on the values below
swc_correct(input_swc, curr_site, reference_time)swc_correct(input_swc, curr_site, reference_time)
input_swc |
Required. input soil water content data from acquire_neon_data (as a list) |
curr_site |
Current site we are working with |
reference_time |
Current month we are working with |
A revised list of corrected soil water content and depths.
John Zobitz [email protected]
# Download the soil water content data: site_swc <- neonUtilities::loadByProduct( dpID="DP1.00094.001", site="SJER", startdate="2020-05", enddate="2020-05", timeIndex = "30", package="expanded", check.size = FALSE, include.provisional = TRUE ) # Then correct the swc: site_swc <- swc_correct(site_swc,"SJER","2020-05")# Download the soil water content data: site_swc <- neonUtilities::loadByProduct( dpID="DP1.00094.001", site="SJER", startdate="2020-05", enddate="2020-05", timeIndex = "30", package="expanded", check.size = FALSE, include.provisional = TRUE ) # Then correct the swc: site_swc <- swc_correct(site_swc,"SJER","2020-05")
A dataset containing corrected NEON sensor location depths for soil water content data
swc_correctionsswc_corrections
A data frame with 2161 rows and 6 variables
domainID (ecological domain of site)
siteID NEON code to refer to site
HOR.VER 3 digit code to refer to the horizontal and vertical position of measuring location.
sensorDepth depth below surface (m)
startDateTime time measurement was started
endDateTime time measurement ended
<hhttps://data.neonscience.org/data-products/DP1.00094.001>
Given zOffsets, diffusivity, and co2 (umol mol-1) and their associated errors, compute the surface flux. This is done by computing the gradient of co2 (slope from linear regression) and linear extrapolation of surface diffusivity. Modified from Tang et al (2003).
tang_2003_flux(zOffset, co2, co2_err, diffusive, diffusive_err)tang_2003_flux(zOffset, co2, co2_err, diffusive, diffusive_err)
zOffset |
Required. depths below surface - assumed to be positive in value. Important for directionality! |
co2 |
Required. co2 at depth (umol m–1) |
co2_err |
Required. Associated errors with that value of co2 |
diffusive |
Required. diffusivity at each depth |
diffusive_err |
Required Associated errors with diffusivity |
Data frame of fluxes associated error
John Zobitz [email protected]
Tang, Jianwu, Dennis D Baldocchi, Ye Qi, and Liukang Xu. 2003. “Assessing Soil CO2 Efflux Using Continuous Measurements of CO2 Profiles in Soils with Small Solid-State Sensors.” Agricultural and Forest Meteorology 118 (3): 207–20. https://doi.org/10.1016/S0168-1923(03)00112-6.
Maier, M., and H. Schack-Kirchner. 2014. “Using the Gradient Method to Determine Soil Gas Flux: A Review.” Agricultural and Forest Meteorology 192–193 (July):78–95. https://doi.org/10.1016/j.agrformet.2014.03.006.
[dejong_shappert_flux()], [hirano_flux()], [tang_2005_flux()] for other ways to compute surface fluxes.
Given zOffsets, diffusivity, and co2 (umol mol-1) and their associated errors, compute the surface flux. This is done by linear extrapolation of surface fluxes. Modified from Tang et al (2005).
tang_2005_flux(zOffset, co2, co2_err, diffusive, diffusive_err)tang_2005_flux(zOffset, co2, co2_err, diffusive, diffusive_err)
zOffset |
Required. depths below surface - assumed to be positive in value. Important for directionality! |
co2 |
Required. co2 at depth (umol m–1) |
co2_err |
Required. Associated errors with that value of co2 |
diffusive |
Required. diffusivity at each depth |
diffusive_err |
Required Associated errors with diffusivity |
Data frame of fluxes associated error
John Zobitz [email protected]
Tang, Jianwu, Laurent Misson, Alexander Gershenson, Weixin Cheng, and Allen H. Goldstein. 2005. “Continuous Measurements of Soil Respiration with and without Roots in a Ponderosa Pine Plantation in the Sierra Nevada Mountains.” Agricultural and Forest Meteorology 132 (3): 212–27. https://doi.org/10.1016/j.agrformet.2005.07.011.
Maier, M., and H. Schack-Kirchner. 2014. “Using the Gradient Method to Determine Soil Gas Flux: A Review.” Agricultural and Forest Meteorology 192–193 (July):78–95. https://doi.org/10.1016/j.agrformet.2014.03.006.
[dejong_shappert_flux()], [hirano_flux()], [tang_2003_flux()] for other ways to compute surface fluxes.