library(fluxnet)
#> ! Use of data downloaded by fluxnet requires you abide by FLUXNET data policies: <
#> ℹ Citations for individual sites' datasets are returned by `
library(dplyr)
#>
#> Attaching package: 'dplyr'
#>
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#>
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, unionDiscovering what is available for download
flux_listall() is an R wrapper around a command line
program, fluxnet-shuttle,
which requires Python. The first time you run
flux_listall(), a Python virtual environment will be
created and fluxnet-shuttle will be installed into it. If
you don’t have an appropriate version of Python installed, you may be
prompted with tips on how to install it.
list <- flux_listall()#> virtualenv: fluxnet
#> File list is expired, downloading the latest versionBy default, the results of flux_listall() are saved in a
user cache, so when you run it again it’ll pull the results from there
unless they are older than cach_age. To ignore the cache,
use use_cache = FALSE. To invalidate the cache
(and replace it with an updated one), use
cache_age = -Inf.
# Don't use cached results:
list <- flux_listall(use_cache = FALSE)
# Invalidate and replace cached results:
list <- flux_listall(cache_age = -Inf)The list returned by flux_listall() contains metadata on
the available sites including, importantly, citations for site-level
data attribution which is required by FLUXNET.
colnames(list)
#> [1] "data_hub" "site_id" "site_name" "location_lat" "location_long"
#> [6] "igbp" "network" "team_member_name" "team_member_role" "team_member_email"
#> [11] "first_year" "last_year" "download_link" "fluxnet_product_name" "product_citation"
#> [16] "product_id" "oneflux_code_version" "product_source_network"
list %>% select(site_id, product_citation)
#> # A tibble: 767 × 2
#> site_id product_citation
#> <chr> <chr>
#> 1 AR-Bal Maria Isabel Gassmann, Natalia Edith Tonti (2026), AmeriFlux FLUXNET-1F AR-Bal Balcarce BA, Ver. v1.3_r1, AmeriFlux AMP, (Data…
#> 2 AR-CCa Gabriela Posse (2026), AmeriFlux FLUXNET-1F AR-CCa Carlos Casares agriculture, Ver. v1.3_r1, AmeriFlux AMP, (Dataset). https:/…
#> 3 AR-CCg Gabriela Posse (2025), AmeriFlux FLUXNET-1F AR-CCg Carlos Casares grassland, Ver. v1.3_r1, AmeriFlux AMP, (Dataset). https://d…
#> 4 AR-TF1 Lars Kutzbach (2025), AmeriFlux FLUXNET-1F AR-TF1 Rio Moat bog, Ver. v1.3_r1, AmeriFlux AMP, (Dataset). https://doi.org/10.171…
#> 5 AR-TF2 Lars Kutzbach (2025), AmeriFlux FLUXNET-1F AR-TF2 Rio Pipo bog, Ver. v1.3_r1, AmeriFlux AMP, (Dataset). https://doi.org/10.171…
#> 6 BR-CST Antonio Antonino (2025), AmeriFlux FLUXNET-1F BR-CST Caatinga Serra Talhada, Ver. v1.3_r1, AmeriFlux AMP, (Dataset). https://d…
#> 7 BR-ITA Joannès Guillemot (2026), AmeriFlux FLUXNET-1F BR-ITA MataFLUX: Multi-species tree restoration planting, Ver. v1.3_r1, AmeriFl…
#> 8 BR-Ji3 Alessandro Araujo (2026), AmeriFlux FLUXNET-1F BR-Ji3 Ji-Paraná/RO - Reserva Biológica do Jaru (RBJ), Ver. v1.3_r1, AmeriFlux …
#> 9 BR-Ma2 Alessandro Araujo, Celso von Randow, Antonio Manzi, Antonio Nobre, Gilberto Pastorello, Julie Andrews de França e Silva, Leila…
#> 10 BR-Ma3 Alessandro Araujo (2026), AmeriFlux FLUXNET-1F BR-Ma3 ZF3, Colosso farm, Ver. v1.3_r1, AmeriFlux AMP, (Dataset). https://doi.o…
#> # ℹ 757 more rowsDownloading data
There are a few paths to downloading FLUXNET data. If you just want
to download everything available, simply run
flux_download(). You can download just specific sites with
the site_ids argument, or you can filter the results of
flux_listall() and pass those in.
# Download everything available.
flux_download()
# Download just certain sites
flux_download(site_ids = c("AR-CCg", "AR-TF1", "BR-CST"))
# Filter list and download
list_wet <- list %>% filter(igbp == "WET")
flux_download(file_list_df = list_wet)#> Downloading data from all available sites.Extracting data from .zip files
flux_extract() allows you to unzip only desired files
from all or some of the downloaded site .zip files.
# Extract everything (not recommended!)
flux_extract()
# Extract just annual and monthly data
flux_extract(resolutions = c("y", "m"))
# Extract hourly data for just certain sites
flux_extract(site_ids = c("AR-CCg", "AR-TF1"), resolutions = "h")
# Don't extract BIF and BIFVARINFO CSVs
flux_extract(extract_varinfo = FALSE)Discovering what data you have extracted
flux_discover_files() is used to create a “manifest” of
the data available to read in. You must create this manifest
(and optionally filter it) to pass into flux_read().
manifest <- flux_discover_files()
manifest#> MM / ERA5 → 5 sites, 225 site-years across 5 files
#> MM / FLUXMET → 5 sites, 59 site-years across 5 files
#> YY / ERA5 → 5 sites, 225 site-years across 5 files
#> YY / FLUXMET → 5 sites, 59 site-years across 5 files
#> # A tibble: 35 × 23
#> path product_source_network site_id dataset time_resolution first_year last_year oneflux_code_version release_version
#> <fs::path> <chr> <chr> <chr> <chr> <int> <int> <chr> <chr>
#> 1 …2015-2024_v1.3_r1.csv ICOS CZ-Lnz BIF <NA> 2015 2024 v1.3 r1
#> 2 …2015-2024_v1.3_r1.csv ICOS CZ-Lnz BIFVAR… MM 2015 2024 v1.3 r1
#> 3 …2015-2024_v1.3_r1.csv ICOS CZ-Lnz BIFVAR… YY 2015 2024 v1.3 r1
#> 4 …1981-2025_v1.3_r1.csv ICOS CZ-Lnz ERA5 MM 1981 2025 v1.3 r1
#> 5 …1981-2025_v1.3_r1.csv ICOS CZ-Lnz ERA5 YY 1981 2025 v1.3 r1
#> 6 …2015-2024_v1.3_r1.csv ICOS CZ-Lnz FLUXMET MM 2015 2024 v1.3 r1
#> 7 …2015-2024_v1.3_r1.csv ICOS CZ-Lnz FLUXMET YY 2015 2024 v1.3 r1
#> 8 …2015-2020_v1.3_r1.csv JPF MN-Hst BIF <NA> 2015 2020 v1.3 r1
#> 9 …2015-2020_v1.3_r1.csv JPF MN-Hst BIFVAR… MM 2015 2020 v1.3 r1
#> 10 …2015-2020_v1.3_r1.csv JPF MN-Hst BIFVAR… YY 2015 2020 v1.3 r1
#> # ℹ 25 more rows
#> # ℹ 14 more variables: download_time <dttm>, data_hub <chr>, site_name <chr>, location_lat <dbl>, location_long <dbl>, igbp <chr>,
#> # network <chr>, team_member_name <chr>, team_member_role <chr>, team_member_email <chr>, download_link <chr>,
#> # fluxnet_product_name <chr>, product_citation <chr>, product_id <chr>You can visualize the sites you have data for with
flux_map_sites().
flux_map_sites(manifest)
Map of downloaded sites
Reading in data
You can read in data by passing a manifest to
flux_read(). You can read in data for just select sites
with the site_ids argument, but for more complex filtering
you can simply subset the manifest object first.
# Read all available annual data
annual <- flux_read(manifest, resolution = "y")
#> Reading 10 files.
## NOT RUN ##
# Read in hourly data from specific sites
hourly <- flux_read(
manifest,
resolution = "h",
site_ids = c("AR-CCg", "AR-TF1")
)
# Read in only ERA5 data
annual_era5 <- flux_read(manifest, resolution = "y", datasets = "ERA5")
# Filter manifest to just sites with "WET" for IGBP
n_wet_manifest <-
left_join(manifest, list, by = join_by(site_id)) %>%
filter(igbp == "WET", location_lat > 0)
n_wet_monthly <- flux_read(n_wet_manifest, resolution = "m")QC flagging
Fluxnet data can be gapfilled to varying degrees.
flux_qc() allows you to flag overly-gapfilled data using
your choice of variables and gapfilling threshold(s).
Example 1: Flag rows where NEE_VUT_REF is more than 30%
gapfilled
flagged_nee <- flux_qc(annual, qc_vars = "NEE_VUT_REF", threshold = 0.3)
flagged_nee %>%
filter(qc_flagged) %>%
select(qc_flagged, p_gapfilled, NEE_VUT_REF_QC, everything())
#> # A tibble: 5 × 344
#> qc_flagged p_gapfilled NEE_VUT_REF_QC site_id dataset time_resolution YEAR TA_ERA TA_ERA_NIGHT TA_ERA_NIGHT_SD TA_ERA_DAY TA_ERA_DAY_SD
#> <lgl> <dbl> <dbl> <chr> <chr> <chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 TRUE 0.412 0.588 NL-Loo FLUXMET YY 2019 10.8 9.61 1.75 11.7 2.40
#> 2 TRUE 0.352 0.648 US-KFS FLUXMET YY 2009 12.1 11.2 2.62 13.0 3.13
#> 3 TRUE 0.305 0.695 US-KFS FLUXMET YY 2014 11.9 10.7 2.80 12.9 3.29
#> 4 TRUE 0.337 0.663 US-KFS FLUXMET YY 2017 13.9 12.8 2.65 14.9 3.3
#> 5 TRUE 0.419 0.581 US-KFS FLUXMET YY 2019 12.6 11.7 2.51 13.4 3.05
#> # ℹ 332 more variables: SW_IN_ERA <dbl>, LW_IN_ERA <dbl>, VPD_ERA <dbl>, PA_ERA <dbl>, P_ERA <dbl>, WS_ERA <dbl>, TA_F_MDS <dbl>,
#> # TA_F_MDS_QC <dbl>, TA_F_MDS_NIGHT <dbl>, TA_F_MDS_NIGHT_SD <dbl>, TA_F_MDS_NIGHT_QC <dbl>, TA_F_MDS_DAY <dbl>, TA_F_MDS_DAY_SD <dbl>,
#> # TA_F_MDS_DAY_QC <dbl>, TA_F <dbl>, TA_F_QC <dbl>, TA_F_NIGHT <dbl>, TA_F_NIGHT_SD <dbl>, TA_F_NIGHT_QC <dbl>, TA_F_DAY <dbl>,
#> # TA_F_DAY_SD <dbl>, TA_F_DAY_QC <dbl>, SW_IN_F_MDS <dbl>, SW_IN_F_MDS_QC <dbl>, SW_IN_F <dbl>, SW_IN_F_QC <dbl>, LW_IN_F_MDS <dbl>,
#> # LW_IN_F_MDS_QC <dbl>, LW_IN_F <dbl>, LW_IN_F_QC <dbl>, LW_IN_JSB <dbl>, LW_IN_JSB_QC <dbl>, LW_IN_JSB_ERA <dbl>, LW_IN_JSB_F <dbl>,
#> # LW_IN_JSB_F_QC <dbl>, VPD_F_MDS <dbl>, VPD_F_MDS_QC <dbl>, VPD_F <dbl>, VPD_F_QC <dbl>, PA_F <dbl>, PA_F_QC <dbl>, P_F <dbl>,
#> # P_F_QC <dbl>, WS_F <dbl>, WS_F_QC <dbl>, USTAR <dbl>, USTAR_QC <dbl>, NETRAD <dbl>, NETRAD_QC <dbl>, PPFD_IN <dbl>, …Notice that in this case p_gapfilled is just 1 -
NEE_VUT_REF_QC.
Example 2: Flag rows where NEE_VUT_REF or TA_F are more than 30% gapfilled
flagged_2 <- flux_qc(
annual,
qc_vars = c("NEE_VUT_REF", "TA_F"),
threshold = 0.3
)
flagged_2 %>%
filter(qc_flagged) %>%
select(qc_flagged, p_gapfilled, NEE_VUT_REF_QC, TA_F_QC, everything())
#> # A tibble: 11 × 344
#> qc_flagged p_gapfilled NEE_VUT_REF_QC TA_F_QC site_id dataset time_resolution YEAR TA_ERA TA_ERA_NIGHT TA_ERA_NIGHT_SD TA_ERA_DAY
#> <lgl> <dbl> <dbl> <dbl> <chr> <chr> <chr> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 TRUE 0.345 0.946 0.655 CZ-Lnz FLUXMET YY 2019 11.8 10.8 2.02 12.5
#> 2 TRUE 0.378 NA 0.622 CZ-Lnz FLUXMET YY 2022 11.5 10.4 2.01 12.3
#> 3 TRUE 0.471 NA 0.529 MN-Hst FLUXMET YY 2015 2.13 0.584 3.35 3.47
#> 4 TRUE 0.504 0.951 0.496 NL-Loo FLUXMET YY 2004 9.93 8.81 1.70 10.7
#> 5 TRUE 0.434 0.588 0.566 NL-Loo FLUXMET YY 2019 10.8 9.61 1.75 11.7
#> 6 TRUE 0.454 NA 0.546 US-KFS FLUXMET YY 2007 13.3 12.3 2.59 14.2
#> 7 TRUE 0.352 0.648 0.728 US-KFS FLUXMET YY 2009 12.1 11.2 2.62 13.0
#> 8 TRUE 0.305 0.695 0.836 US-KFS FLUXMET YY 2014 11.9 10.7 2.80 12.9
#> 9 TRUE 0.337 0.663 0.909 US-KFS FLUXMET YY 2017 13.9 12.8 2.65 14.9
#> 10 TRUE 0.419 0.581 0.920 US-KFS FLUXMET YY 2019 12.6 11.7 2.51 13.4
#> 11 TRUE 0.457 NA 0.543 US-Me7 FLUXMET YY 2022 7.84 6.27 2.89 9.10
#> # ℹ 332 more variables: TA_ERA_DAY_SD <dbl>, SW_IN_ERA <dbl>, LW_IN_ERA <dbl>, VPD_ERA <dbl>, PA_ERA <dbl>, P_ERA <dbl>, WS_ERA <dbl>,
#> # TA_F_MDS <dbl>, TA_F_MDS_QC <dbl>, TA_F_MDS_NIGHT <dbl>, TA_F_MDS_NIGHT_SD <dbl>, TA_F_MDS_NIGHT_QC <dbl>, TA_F_MDS_DAY <dbl>,
#> # TA_F_MDS_DAY_SD <dbl>, TA_F_MDS_DAY_QC <dbl>, TA_F <dbl>, TA_F_NIGHT <dbl>, TA_F_NIGHT_SD <dbl>, TA_F_NIGHT_QC <dbl>, TA_F_DAY <dbl>,
#> # TA_F_DAY_SD <dbl>, TA_F_DAY_QC <dbl>, SW_IN_F_MDS <dbl>, SW_IN_F_MDS_QC <dbl>, SW_IN_F <dbl>, SW_IN_F_QC <dbl>, LW_IN_F_MDS <dbl>,
#> # LW_IN_F_MDS_QC <dbl>, LW_IN_F <dbl>, LW_IN_F_QC <dbl>, LW_IN_JSB <dbl>, LW_IN_JSB_QC <dbl>, LW_IN_JSB_ERA <dbl>, LW_IN_JSB_F <dbl>,
#> # LW_IN_JSB_F_QC <dbl>, VPD_F_MDS <dbl>, VPD_F_MDS_QC <dbl>, VPD_F <dbl>, VPD_F_QC <dbl>, PA_F <dbl>, PA_F_QC <dbl>, P_F <dbl>,
#> # P_F_QC <dbl>, WS_F <dbl>, WS_F_QC <dbl>, USTAR <dbl>, USTAR_QC <dbl>, NETRAD <dbl>, NETRAD_QC <dbl>, PPFD_IN <dbl>, …Now p_gapfilled is 1 - the smaller of
NEE_VUT_REF_QC and TA_F_QC.
Example 3: Flag rows where NEE_VUT_REF is more than 30% gapfilled and TA_F is more than 10% gapfilled
flagged_3 <- flux_qc(
annual,
qc_vars = c("NEE_VUT_REF", "TA_F"),
threshold = c(0.3, 0.1),
operator = "all"
)
flagged_3 %>%
filter(qc_flagged) %>%
select(qc_flagged, p_gapfilled, NEE_VUT_REF_QC, TA_F_QC, everything())
#> # A tibble: 3 × 344
#> qc_flagged p_gapfilled NEE_VUT_REF_QC TA_F_QC site_id dataset time_resolution YEAR TA_ERA TA_ERA_NIGHT TA_ERA_NIGHT_SD TA_ERA_DAY
#> <lgl> <dbl> <dbl> <dbl> <chr> <chr> <chr> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 TRUE 0.412 0.588 0.566 NL-Loo FLUXMET YY 2019 10.8 9.61 1.75 11.7
#> 2 TRUE 0.272 0.648 0.728 US-KFS FLUXMET YY 2009 12.1 11.2 2.62 13.0
#> 3 TRUE 0.164 0.695 0.836 US-KFS FLUXMET YY 2014 11.9 10.7 2.80 12.9
#> # ℹ 332 more variables: TA_ERA_DAY_SD <dbl>, SW_IN_ERA <dbl>, LW_IN_ERA <dbl>, VPD_ERA <dbl>, PA_ERA <dbl>, P_ERA <dbl>, WS_ERA <dbl>,
#> # TA_F_MDS <dbl>, TA_F_MDS_QC <dbl>, TA_F_MDS_NIGHT <dbl>, TA_F_MDS_NIGHT_SD <dbl>, TA_F_MDS_NIGHT_QC <dbl>, TA_F_MDS_DAY <dbl>,
#> # TA_F_MDS_DAY_SD <dbl>, TA_F_MDS_DAY_QC <dbl>, TA_F <dbl>, TA_F_NIGHT <dbl>, TA_F_NIGHT_SD <dbl>, TA_F_NIGHT_QC <dbl>, TA_F_DAY <dbl>,
#> # TA_F_DAY_SD <dbl>, TA_F_DAY_QC <dbl>, SW_IN_F_MDS <dbl>, SW_IN_F_MDS_QC <dbl>, SW_IN_F <dbl>, SW_IN_F_QC <dbl>, LW_IN_F_MDS <dbl>,
#> # LW_IN_F_MDS_QC <dbl>, LW_IN_F <dbl>, LW_IN_F_QC <dbl>, LW_IN_JSB <dbl>, LW_IN_JSB_QC <dbl>, LW_IN_JSB_ERA <dbl>, LW_IN_JSB_F <dbl>,
#> # LW_IN_JSB_F_QC <dbl>, VPD_F_MDS <dbl>, VPD_F_MDS_QC <dbl>, VPD_F <dbl>, VPD_F_QC <dbl>, PA_F <dbl>, PA_F_QC <dbl>, P_F <dbl>,
#> # P_F_QC <dbl>, WS_F <dbl>, WS_F_QC <dbl>, USTAR <dbl>, USTAR_QC <dbl>, NETRAD <dbl>, NETRAD_QC <dbl>, PPFD_IN <dbl>, …Because we’ve set operator = "all",
p_gapfilled is now 1 - the larger of
NEE_VUT_REF_QC and TA_F_QC and all of the
values are greater than the smaller of the threshold
values.