Download and work with NEON Aquatic Instrument Data

This tutorial covers downloading NEON Aquatic Instrument System (AIS) data, using the neonUtilities R package, as well as basic instruction in beginning to explore and work with the downloaded data, including guidance in navigating data documentation, separating data using the horizontal location (HOR) variable, interpreting quality flags, and resampling time intervals.

The following material steps through the multiple considerations in interpreting NEON data, and ultimately achieves a data comparison between two different sensors at nearby locations that are published at different time intervals. This sort of data wrangling is useful for comparing different data streams, and/or preparing data into a consistent format for modeling.

Download Files and Load Directly to R: loadByProduct()

The most popular function in neonUtilities is loadByProduct(). This function downloads data from the NEON API, merges the site-by-month files, and loads the resulting data tables into the R environment, assigning each data type to the appropriate R class. This is a popular choice because it ensures you're always working with the most up-to-date data, and it ends with ready-to-use tables in R. However, if you use it in a workflow you run repeatedly, keep in mind it will re-download the data every time.

Before we get the NEON data, we need to install (if not already done) and load the neonUtilities R package, as well as other packages we will use in the analysis.

# Install neonUtilities package if you have not yet.
install.packages("neonUtilities")
install.packages("ggplot2")
install.packages("dplyr")
install.packages("padr")


# Set global option to NOT convert all character variables to factors
options(stringsAsFactors=F)

# Load required packages
library(neonUtilities)
library(ggplot2)
library(dplyr)
library(padr)

The inputs to loadByProduct() control which data to download and how to manage the processing. The following are frequently used inputs:

• dpID: the data product ID, e.g. DP1.20288.001
• site: defaults to "all", meaning all sites with available data; can be a vector of 4-letter NEON site codes, e.g. c("MART","ARIK","BARC").
• startdate and enddate: defaults to NA, meaning all dates with available data; or a date in the form YYYY-MM, e.g. 2017-06. Since NEON data are provided in month packages, finer scale querying is not available. Both start and end date are inclusive.
• package: either basic or expanded data package. Expanded data packages generally include additional information about data quality, such as individual quality flag test results. Not every NEON data product has an expanded package; if the expanded package is requested but there isn't one, the basic package will be downloaded.
• avg: defaults to "all", to download all data; or the number of minutes in the averaging interval. See example below; only applicable to IS data.
• savepath: the file path you want to download to; defaults to the working directory.
• check.size: T or F; should the function pause before downloading data and warn you about the size of your download? Defaults to T; if you are using this function within a script or batch process you will want to set this to F.
• token: this allows you to input your NEON API token to obtain faster downloads. Learn more about NEON API tokens in the Using an API Token when Accessing NEON Data with neonUtilities tutorial.

There are additional inputs you can learn about in the Use the neonUtilities R Package to Access NEON Data tutorial.

The dpID is the data product identifier of the data you want to download. The DPID can be found on the Explore Data Products page.

It will be in the form DP#.#####.###. For this tutorial, we'll use some data products collected in NEON's Aquatic Instrument System:

• DP1.20288.001: Water quality
• DP1.20033.001: Nitrate in surface water
• DP1.20016.001: Elevation of surface water

Now it's time to consider the NEON field site of interest. If not specified, the default will download a data product from all sites. The following are 4-letter site codes for NEON's 34 aquatics sites as of 2020:

ARIK = Arikaree River CO
BARC = Barco Lake FL
BIGC = Upper Big Creek CA
BLDE = Black Deer Creek WY
BLUE = Blue River OK
BLWA = Black Warrior River AL
CARI = Caribou Creek AK
COMO = Como Creek CO
CRAM = Crampton Lake WI
CUPE = Rio Cupeyes PR
FLNT = Flint River GA
GUIL = Rio Guilarte PR
HOPB = Lower Hop Brook MA
KING = Kings Creek KS
LECO = LeConte Creek TN
LEWI = Lewis Run VA
LIRO = Little Rock Lake WI
MART = Martha Creek WA
MAYF = Mayfield Creek AL
MCDI = McDiffett Creek KS
MCRA = McRae Creek OR
OKSR = Oksrukuyik Creek AK
POSE = Posey Creek VA
PRIN = Pringle Creek TX
PRLA = Prairie Lake ND
PRPO = Prairie Pothole ND
REDB = Red Butte Creek UT
SUGG = Suggs Lake FL
SYCA = Sycamore Creek AZ
TECR = Teakettle Creek CA
TOMB = Lower Tombigbee River AL
TOOK = Toolik Lake AK
WALK = Walker Branch TN
WLOU = West St Louis Creek CO

In this exercise, we will want data from only one NEON field site, Pringle Creek, TX (PRIN) from February, 2020.

Now let us download our data. If you are not using a NEON token to download your data, neonUtilities will ignore the token input. We set check.size = F
so that the script runs well but remember you always want to check your download size first.

# download data of interest - Water Quality
waq <- loadByProduct(dpID="DP1.20288.001", site="PRIN", 
                     startdate="2020-02", enddate="2020-02", 
                     package="expanded", 
                     token = Sys.getenv("NEON_TOKEN"),
                     check.size = F)

# download data of interest - Nitrate in Suface Water
nsw <-  loadByProduct(dpID="DP1.20033.001", site="PRIN", 
                      startdate="2020-02", enddate="2020-02", 
                      package="expanded", 
                      token = Sys.getenv("NEON_TOKEN"),
                      check.size = F)

# #1. 2.0 MiB
# #2. You can change check.size to True (T), and compare "basic" vs "expaneded"
# package types. The basic package is 37.0 KiB, and the expanded is 42.4 KiB. 


# download data of interest - Elevation of surface water
eos <- loadByProduct(dpID="DP1.20016.001", site="PRIN",
                     startdate="2020-02", enddate="2020-02",
                     package="expanded",
                     token = Sys.getenv("NEON_TOKEN"),
                     check.size = F)

Files Associated with Downloads

The data we've downloaded comes as an object that is a named list of objects. To work with each of them, select them from the list using the $ operator.

# view all components of the list
names(waq)

## [1] "readme_20288"           "sensor_positions_20288" "variables_20288"       
## [4] "waq_instantaneous"

# View the dataFrame
View(waq$waq_instantaneous)

We can see that there are four objects in the downloaded water quality data. One dataframe of data (waq_instantaneous) and three metadata files.

If you'd like you can use the $ operator to assign an object from an item in the list. If you prefer to extract each table from the list and work with it as independent objects, which we will do, you can use the list2env() function.

# unlist the variables and add to the global environment
list2env(waq, .GlobalEnv)

## <environment: R_GlobalEnv>

So what exactly are these four files and why would you want to use them?

• data file(s): There will always be one or more dataframes that include the primary data of the data product you downloaded. Multiple dataframes are available when there are related datatables for a single data product.
• readme_xxxxx: The readme file, with the corresponding 5 digits from the data product number, provides you with important information relevant to the data product and the specific instance of downloading the data. Here you can find manual flagging notes for all sites, locations, and time periods.
• sensor_postions_xxxxx: this file contains information about the coordinates of each sensor, relative to a reference location.
• variables_xxxxx: this file contains all the variables found in the associated data table(s). This includes full definitions, units, and other important information.

Let's perform the same thing for the surface water nitrate and elevation of surface water data products too:

list2env(nsw, .GlobalEnv)

## <environment: R_GlobalEnv>

list2env(eos, .GlobalEnv)

## <environment: R_GlobalEnv>

Note that a few more objects were added to the Global Environment, including:

• NSW_15_minute
• EOS_5_min
• EOS_30_min

The 15_minute name indicates the time-averaging intervals in a dataset. Other examples may include 5_min and 30_min in the same data product, such as elevation of surface water (DP1.20016.001). If only one time average interests you, you may specify the time interval of interest when downloading the data when calling neonUtilities::loadByProduct().

Data from Different Sensor Locations (HOR)

NEON often collects the same type of data from sensors in different locations. These data are delivered together but you will frequently want to plot the data separately or only include data from one sensor in your analysis. NEON uses the horizontalPosition variable in the data tables to describe which sensor data is collected from. The horizontalPosition is always a three digit number for AIS data. Non-shoreline HOR examples as of 2020 at AIS sites include:

• 101: stream sensors located at the upstream station on a monopod mount,
• 111: stream sensors located at the upstream station on an overhead cable mount,
• 131: stream sensors located at the upstream station on a stand alone pressure transducer mount,
• 102: stream sensors located at the downstream station on a monopod mount,
• 112: stream sensors located at the downstream station on an overhead cable mount
• 132: stream sensors located at the downstream station on a stand alone pressure transducer mount,
• 110: pressure transducers mounted to a staff gauge.
• 103: sensors mounted on buoys in lakes or rivers
• 130 and 140: sensors mounted in the littoral zone of lakes

You'll frequently want to know which sensor locations are represented in your data. We can do this by looking for the unique() position designations in horizontalPostions.

# which sensor locations exist for water quality, DP1.20288.001?
print("Water quality horizontal positions:")

## [1] "Water quality horizontal positions:"

unique(waq_instantaneous$horizontalPosition)

## [1] "101" "102"

We can see that there are two water quality sensor positions at PRIN in February 2020. As the locations of sensors can change at sites over time (especially with aquatic sensors as AIS sites undergo redesigns) it is a good idea to check horizontal positions when you're adding in new locations or a new date range to your analyses.

Let's check the HOR locations for surface water nitrate and elevation too:

# which sensor locations exist for other data products?
print("Nitrate in Surface Water horizontal positions: ")

## [1] "Nitrate in Surface Water horizontal positions: "

unique(NSW_15_minute$horizontalPosition)

## [1] "102"

print("Elevation of Surface Water horizontal positions: ")

## [1] "Elevation of Surface Water horizontal positions: "

unique(EOS_30_min$horizontalPosition)

## [1] "110" "132"

Now we can use this information to split water quality data into the two different sensor set locations: upstream and the downstream.

# Split data into separate dataframes by upstream/downstream locations.

waq_up <- 
  waq_instantaneous[(waq_instantaneous$horizontalPosition=="101"),]
waq_down <- 
  waq_instantaneous[(waq_instantaneous$horizontalPosition=="102"),]

# Note: The surface water nitrate sensor is only stationed at one location.

eos_up <- EOS_30_min[(EOS_30_min$horizontalPosition=="110"),]
eos_down <- EOS_30_min[(EOS_30_min$horizontalPosition=="132"),]

Plot Data

Now that we have our data separated into the upstream and downstream data, let's plot both of the data sets together. We want to create a plot of the measures of Dissolved Oxygen from the two different sensors.

First, let's identify the column names important for plotting - time and dissolved oxygen data:

# One option is to view column names in the data frame
colnames(waq_instantaneous)

##   [1] "domainID"                        "siteID"                         
##   [3] "horizontalPosition"              "verticalPosition"               
##   [5] "startDateTime"                   "endDateTime"                    
##   [7] "sensorDepth"                     "sensorDepthExpUncert"           
##   [9] "sensorDepthRangeQF"              "sensorDepthNullQF"              
##  [11] "sensorDepthGapQF"                "sensorDepthValidCalQF"          
##  [13] "sensorDepthSuspectCalQF"         "sensorDepthPersistQF"           
##  [15] "sensorDepthAlphaQF"              "sensorDepthBetaQF"              
##  [17] "sensorDepthFinalQF"              "sensorDepthFinalQFSciRvw"       
##  [19] "specificConductance"             "specificConductanceExpUncert"   
##  [21] "specificConductanceRangeQF"      "specificConductanceStepQF"      
##  [23] "specificConductanceNullQF"       "specificConductanceGapQF"       
##  [25] "specificConductanceSpikeQF"      "specificConductanceValidCalQF"  
##  [27] "specificCondSuspectCalQF"        "specificConductancePersistQF"   
##  [29] "specificConductanceAlphaQF"      "specificConductanceBetaQF"      
##  [31] "specificCondFinalQF"             "specificCondFinalQFSciRvw"      
##  [33] "dissolvedOxygen"                 "dissolvedOxygenExpUncert"       
##  [35] "dissolvedOxygenRangeQF"          "dissolvedOxygenStepQF"          
##  [37] "dissolvedOxygenNullQF"           "dissolvedOxygenGapQF"           
##  [39] "dissolvedOxygenSpikeQF"          "dissolvedOxygenValidCalQF"      
##  [41] "dissolvedOxygenSuspectCalQF"     "dissolvedOxygenPersistenceQF"   
##  [43] "dissolvedOxygenAlphaQF"          "dissolvedOxygenBetaQF"          
##  [45] "dissolvedOxygenFinalQF"          "dissolvedOxygenFinalQFSciRvw"   
##  [47] "dissolvedOxygenSaturation"       "dissolvedOxygenSatExpUncert"    
##  [49] "dissolvedOxygenSatRangeQF"       "dissolvedOxygenSatStepQF"       
##  [51] "dissolvedOxygenSatNullQF"        "dissolvedOxygenSatGapQF"        
##  [53] "dissolvedOxygenSatSpikeQF"       "dissolvedOxygenSatValidCalQF"   
##  [55] "dissOxygenSatSuspectCalQF"       "dissolvedOxygenSatPersistQF"    
##  [57] "dissolvedOxygenSatAlphaQF"       "dissolvedOxygenSatBetaQF"       
##  [59] "dissolvedOxygenSatFinalQF"       "dissolvedOxygenSatFinalQFSciRvw"
##  [61] "pH"                              "pHExpUncert"                    
##  [63] "pHRangeQF"                       "pHStepQF"                       
##  [65] "pHNullQF"                        "pHGapQF"                        
##  [67] "pHSpikeQF"                       "pHValidCalQF"                   
##  [69] "pHSuspectCalQF"                  "pHPersistenceQF"                
##  [71] "pHAlphaQF"                       "pHBetaQF"                       
##  [73] "pHFinalQF"                       "pHFinalQFSciRvw"                
##  [75] "chlorophyll"                     "chlorophyllExpUncert"           
##  [77] "chlorophyllRangeQF"              "chlorophyllStepQF"              
##  [79] "chlorophyllNullQF"               "chlorophyllGapQF"               
##  [81] "chlorophyllSpikeQF"              "chlorophyllValidCalQF"          
##  [83] "chlorophyllSuspectCalQF"         "chlorophyllPersistenceQF"       
##  [85] "chlorophyllAlphaQF"              "chlorophyllBetaQF"              
##  [87] "chlorophyllFinalQF"              "chlorophyllFinalQFSciRvw"       
##  [89] "turbidity"                       "turbidityExpUncert"             
##  [91] "turbidityRangeQF"                "turbidityStepQF"                
##  [93] "turbidityNullQF"                 "turbidityGapQF"                 
##  [95] "turbiditySpikeQF"                "turbidityValidCalQF"            
##  [97] "turbiditySuspectCalQF"           "turbidityPersistenceQF"         
##  [99] "turbidityAlphaQF"                "turbidityBetaQF"                
## [101] "turbidityFinalQF"                "turbidityFinalQFSciRvw"         
## [103] "fDOM"                            "rawCalibratedfDOM"              
## [105] "fDOMExpUncert"                   "fDOMRangeQF"                    
## [107] "fDOMStepQF"                      "fDOMNullQF"                     
## [109] "fDOMGapQF"                       "fDOMSpikeQF"                    
## [111] "fDOMValidCalQF"                  "fDOMSuspectCalQF"               
## [113] "fDOMPersistenceQF"               "fDOMAlphaQF"                    
## [115] "fDOMBetaQF"                      "fDOMTempQF"                     
## [117] "fDOMAbsQF"                       "fDOMFinalQF"                    
## [119] "fDOMFinalQFSciRvw"               "buoyNAFlag"                     
## [121] "spectrumCount"                   "publicationDate"                
## [123] "release"

# Alternatively, view the variables object corresponding to the data product for more information
View(variables_20288)

Quite a few columns in the water quality data product!

The time column we'll consider for instrumented systems is endDateTime because it approximately represents data within the interval on or before the endDateTime time stamp. Timestamp column choice matters for time-aggregated datasets, but should not matter for instantaneous data such as water quality.

When interpreting data, keep in mind NEON timestamps are always in UTC.

The data column we would like to plot is labeled dissolvedOxygen.

# plot
wqual <- ggplot() +
    geom_line(data = waq_up, 
              aes(endDateTime, dissolvedOxygen,color="a"), 
              na.rm=TRUE ) +
    geom_line(data = waq_down, 
              aes(endDateTime, dissolvedOxygen, color="b"), 
              na.rm=TRUE) +
    geom_line(na.rm = TRUE) +
    ylim(0, 20) + ylab("Dissolved Oxygen (mg/L)") +
    xlab(" ") +
  scale_color_manual(values = c("blue","red"),
                     labels = c("upstream","downstream")) +
  labs(colour = "") + # Remove legend title
  theme(legend.position = "top") +
  ggtitle("PRIN Upstream and Downstream DO")



wqual

Now let's try plotting fDOM. fDOM is only measured at the downstream location. NEON also provides uncertainty values for each measurement. Let's also consider measurement uncertainty in the plot.

The data columns we would like to plot are labeled fDOM and fDOMExpUncert.

# plot
fdomUcert <- ggplot() +
    geom_line(data = waq_down, 
              aes(endDateTime, fDOM), 
              na.rm=TRUE, color="orange") +
  geom_ribbon(data=waq_down, 
              aes(x=endDateTime, 
                  ymin = (fDOM - fDOMExpUncert), 
                  ymax = (fDOM + fDOMExpUncert)), 
              alpha = 0.4, fill = "grey75") +
    geom_line( na.rm = TRUE) +
    ylim(0, 200) + ylab("fDOM (QSU)") +
    xlab(" ") +
  ggtitle("PRIN Downstream fDOM with Expected Uncertainty Bounds") 

fdomUcert

# recall dataframes created in list2env() command, including NSW_15_minute

# which sensor locations?
unique(NSW_15_minute$horizontalPosition)

# what is the column name of the data stream of interest?
names(NSW_15_minute)

Using what you've learned above, plot nitrate in surface water.

# plot
plot_NSW <- ggplot(data = NSW_15_minute,
                   aes(endDateTime, surfWaterNitrateMean)) +
                   geom_line(na.rm=TRUE, color="blue") + 
                   ylab("NO3-N (uM)") + xlab(" ") +
                   ggtitle("PRIN Downstream Nitrate in Surface Water")

plot_NSW

Examine Quality Flagged Data

Data product quality flags fall under two distinct types:

• Automated quality flags, e.g. range, spike, step, null
• Manual science review quality flag

In instantaneous data such as water quality DP1.20288.001, the quality flag columns are denoted with "QF".

In time-averaged data, most quality flags have been aggregated into quality metrics, with column names denoted with "QM" representing the fraction of flagged points within the time averaging window.

waq_qf_names <- names(waq_down)[grep("QF", names(waq_down))]

print(paste0("Total columns in DP1.20288.001 expanded package = ", 
             as.character(length(waq_qf_names))))

## [1] "Total columns in DP1.20288.001 expanded package = 96"

# water quality has 96 data columns with QF in the name, 
# so let's just look at those corresponding to fDOM
print("fDOM columns in DP1.20288.001 expanded package:")

## [1] "fDOM columns in DP1.20288.001 expanded package:"

print(waq_qf_names[grep("fDOM", waq_qf_names)])

##  [1] "fDOMRangeQF"       "fDOMStepQF"        "fDOMNullQF"        "fDOMGapQF"        
##  [5] "fDOMSpikeQF"       "fDOMValidCalQF"    "fDOMSuspectCalQF"  "fDOMPersistenceQF"
##  [9] "fDOMAlphaQF"       "fDOMBetaQF"        "fDOMTempQF"        "fDOMAbsQF"        
## [13] "fDOMFinalQF"       "fDOMFinalQFSciRvw"

A quality flag (QF) of 0 indicates a pass, 1 indicates a fail, and -1 indicates a test that could not be performed. For example, a range test cannot be performed on missing measurements.

Detailed quality flags test results are all available in the package = 'expanded' setting we specified when calling neonUtilities::loadByProduct(). If we had specified package = 'basic', we wouldn't be able to investigate the detail in the type of data flag thrown. We would only see the FinalQF columns.

The AlphaQF and BetaQF represent aggregated results of various QF tests, and vary by a data product's algorithm. In most cases, an observation's AlphaQF = 1 indicates whether or not at least one QF was set to a value of 1, and an observation's BetaQF = 1 indicates whether or not at least one QF was set to value of -1.

Note that fDOM has a couple other data-stream specific QFs beyond the standard quality flags. These are specific to the algorithms used to correct raw fDOM readings using temperature and absorbance per Watras et al. (2011) and Downing et al. (2012).

Let's consider what types of fDOM quality flags were thrown.

waq_qf_names <- names(waq_down)[grep("QF", names(waq_down))]

print(paste0("Total QF columns: ",length(waq_qf_names)))

## [1] "Total QF columns: 96"

# water quality has 96 data columns with QF in the name, 
# so let us just look at those corresponding to fDOM
fdom_qf_names <- waq_qf_names[grep("fDOM",waq_qf_names)]

for(col_nam in fdom_qf_names){
  print(paste0(col_nam, " unique values: ", 
               paste0(unique(waq_down[,col_nam]), 
                      collapse = ", ")))
}

## [1] "fDOMRangeQF unique values: 0, -1"
## [1] "fDOMStepQF unique values: 0, 1, -1"
## [1] "fDOMNullQF unique values: 0, 1"
## [1] "fDOMGapQF unique values: 0, 1"
## [1] "fDOMSpikeQF unique values: 0, -1, 1"
## [1] "fDOMValidCalQF unique values: 0"
## [1] "fDOMSuspectCalQF unique values: 0"
## [1] "fDOMPersistenceQF unique values: 0"
## [1] "fDOMAlphaQF unique values: 0, 1"
## [1] "fDOMBetaQF unique values: 0, 1"
## [1] "fDOMTempQF unique values: 0, 1, -1"
## [1] "fDOMAbsQF unique values: 0, -1, 1, 2"
## [1] "fDOMFinalQF unique values: 0, 1"
## [1] "fDOMFinalQFSciRvw unique values: NA"

QF values generally mean the following:

• 0: Quality test passed
• 1: Quality test failed
• -1: Quality test could not be run
• 2: A special case for fDOMAbsQF

So what does fDOMAbsQF = 2 mean? The data product's variable descriptions may provide us some clues.

Recall we previously viewed the water quality variables object that comes with every NEON data download. Now let's print the description corresponding to the fDOMAbsQF field name.

print(variables_20288$description[which(variables_20288$fieldName == "fDOMAbsQF")])

## [1] "Quality flag indicating that fDOM absorbance corrections were applied = 0; unable to be applied = 1; absorbance values were high = 2; calculated correction factor was 1 (i.e. no absorbance correction was made) = 3"

So whenever fDOMAbsQF = 2, the absorbance values coming from the SUNA (surface water nitrate sensor) were high.

Now let's consider the total number of flags generated for each quality test:

# Loop across the fDOM QF column names. 
#  Within each column, count the number of rows that equal '1'.
print("FLAG TEST - COUNT")

## [1] "FLAG TEST - COUNT"

for (col_nam in fdom_qf_names){
  totl_qf_in_col <- length(which(waq_down[,col_nam] == 1))
  print(paste0(col_nam,": ",totl_qf_in_col))
}

## [1] "fDOMRangeQF: 0"
## [1] "fDOMStepQF: 770"
## [1] "fDOMNullQF: 233"
## [1] "fDOMGapQF: 218"
## [1] "fDOMSpikeQF: 71"
## [1] "fDOMValidCalQF: 0"
## [1] "fDOMSuspectCalQF: 0"
## [1] "fDOMPersistenceQF: 0"
## [1] "fDOMAlphaQF: 9997"
## [1] "fDOMBetaQF: 238"
## [1] "fDOMTempQF: 9"
## [1] "fDOMAbsQF: 9016"
## [1] "fDOMFinalQF: 9997"
## [1] "fDOMFinalQFSciRvw: 0"

# Let's also check out how many fDOMAbsQF = 2 exist
print(paste0("fDOMAbsQF = 2: ",
             length(which(waq_down[,"fDOMAbsQF"] == 2))))

## [1] "fDOMAbsQF = 2: 210"

print(paste0("Total fDOM observations: ", nrow(waq_down) ))

## [1] "Total fDOM observations: 41769"

Above lists the total fDOM QFs from a month of data at PRIN, as well as the total number of observation data points in the data file.

We see a notably higher quantity of fDOMAbsQF relative to other quality flags. Why is that? How do we know where to look?

The variables_20228 included in the download would be a good place to start. Let's check the description for fDOMAbsQF again.

print(variables_20288[which(variables_20288$fieldName == "fDOMAbsQF"),])

##                table fieldName
## 1: waq_instantaneous fDOMAbsQF
##                                                                                                                                                                                                              description
## 1: Quality flag indicating that fDOM absorbance corrections were applied = 0; unable to be applied = 1; absorbance values were high = 2; calculated correction factor was 1 (i.e. no absorbance correction was made) = 3
##          dataType units downloadPkg pubFormat primaryKey categoricalCodeName
## 1: signed integer  <NA>    expanded   integer       <NA>                  NA

So fDOMAbsQF = 1 means fDOM absorbance corrections were unable to be applied.

For specific details on the algorithms used to create a data product and it's corresponding quality tests, it's best to first check the data product's Algorithm Theoretical Basis Document (ATBD). For water quality, that is NEON.DOC.004931 listed as Documentation references in the README file and the data product's web page.

Are there any manual science review quality flags? If so, the explanation for flagging may also be viewed in the data product's README file or in the data product's web page on NEON's data portal.

Filtering (Some) Quality Flagged Observations

A simple approach to removing quality flagged observations is to remove data when the finalQF is raised. Let's view a plotting example using fDOM:

# Map QF label names forthe plot for the fDOMFinalQF grouping
group_labels <- c("fDOMFinalQF = 0", "fDOMFinalQF = 1")
names(group_labels) <- c("0","1")

# Plot fDOM data, grouping by the fDOMFinalQF value
ggplot2::ggplot(data = waq_down, 
                aes(x = endDateTime, y = fDOM, group = fDOMFinalQF)) +
  ggplot2::geom_step() +
  facet_grid(fDOMFinalQF ~ ., 
             labeller = labeller(fDOMFinalQF = group_labels)) +
  ggplot2::ggtitle("PRIN Sensor Set 102 fDOM final QF comparison")

The top panel corresponding to fDOMFinalQF = 0 represents all fDOM data that were not flagged. Conversely, the fDOMFinalQF = 1 represents all flagged fDOM data. Clearly, many spikes look like they were appropriately flagged. However, some flagged data look like they could be useful, such as the 2020 February 18-February 24 time range.

Let's inspect the quality flags during that time.

# Find row indices around February 22:
idxs_Feb22 <- base::which(waq_down$endDateTime > as.POSIXct("2020-02-22"))[1:1440]

print("FLAG TEST - COUNT")

## [1] "FLAG TEST - COUNT"

for (col_nam in fdom_qf_names){
  totl_qf_in_col <- length(which(waq_down[idxs_Feb22,col_nam] == 1))
  print(paste0(col_nam,": ",totl_qf_in_col))
}

## [1] "fDOMRangeQF: 0"
## [1] "fDOMStepQF: 8"
## [1] "fDOMNullQF: 0"
## [1] "fDOMGapQF: 0"
## [1] "fDOMSpikeQF: 0"
## [1] "fDOMValidCalQF: 0"
## [1] "fDOMSuspectCalQF: 0"
## [1] "fDOMPersistenceQF: 0"
## [1] "fDOMAlphaQF: 1440"
## [1] "fDOMBetaQF: 0"
## [1] "fDOMTempQF: 0"
## [1] "fDOMAbsQF: 1440"
## [1] "fDOMFinalQF: 1440"
## [1] "fDOMFinalQFSciRvw: 0"

Looks like all Feb 22, 2020 data were flagged with fDOMAbsQF, with a few step test quality flags as well.

Let's take a closer look at each fDOMAbsQF flag value by grouping data based on each fDOMAbsQF value:

ggplot2::ggplot(data = waq_down, 
                aes(x = endDateTime, y = fDOM, group = fDOMAbsQF)) +
  ggplot2::geom_step() +
  facet_grid(fDOMAbsQF ~ .) +
  ggplot2::ggtitle("PRIN Sensor Set 102 fDOMAbsQF comparison")

## Warning: Removed 233 row(s) containing missing values (geom_path).

The fDOMAbsQF = 1 is the most common quality flag from any single test. This means the absorbance correction could not be applied to fDOM data. This absorbance test also causes the final quality flag test fail, but some users may wish to ignore the absorbance quality test entirely.

Note the fDOMAbsQF = 1 time frame corresponds to the missing surface water nitrate data, as shown in the surface water nitrate plot we generated earlier. Here is a reminder of our nitrate data:

plot_NSW

Some types of automated quality flags may be worth ignoring. Rather than use the FinalQF column to omit any quality flags, let's create a custom final quality flag by ignoring thefDOMAbsQF column, allowing us omit quality flagged fDOM data regardless of absorbance correction status.

# Remove the absorbance and aggregated quality flag tests from list of fDOM QF tests:
fdom_qf_non_abs_names <- fdom_qf_names[which(!fdom_qf_names %in% c("fDOMAlphaQF","fDOMBetaQF","fDOMAbsQF","fDOMFinalQF"))]

# Create a custom quality flag column as the maximum QF value within each row
waq_down$aggr_non_abs_QF <- apply( waq_down[,fdom_qf_non_abs_names],1,max, na.rm = TRUE)
# The 'apply' function above allows us avoid a for-loop and more efficiently 
#  iterate over each row.

# Plot fDOM data, grouping by the custom quality flag column's value
ggplot2::ggplot(data = waq_down, 
                aes(x = endDateTime, y = fDOM, 
                    group = aggr_non_abs_QF)) +
  ggplot2::geom_step() +
  facet_grid(aggr_non_abs_QF ~ .) +
  ggplot2::ggtitle("PRIN Sensor Set 102 fDOM custom QF aggregation")

Using the custom quality flag aggregation that ignored fDOMAbsQF, the aggregated aggr_non_abs_QF column we created increases the quantity of data that could be used for further analyses.

Note that the automated quality flag algorithms are not perfect, and a few suspect data points may occasionally pass the quality tests.

Data Aggregation

Sensor data users commonly wish to aggregate data such that time stamps match across two different datasets. In the following example, we will show how to combine elevation of surface water (DP1.20016.001) and water quality (DP1.20288.001) data products into a single dataframe.

Water quality is published as an instantaneous record, which should be every minute at non-buoy sites such as PRIN. We know a data product does not come from the buoy if the HOR location is different from "103". Because elevation of surface water is already aggregated to 30-minute intervals, we want to aggregate the water quality data product to 30-minute intervals as well.

At PRIN in February 2020, the elevation of surface water sensor is co-located with the water quality sonde at horizontalPosition = "102", meaning the downstream sensor set. In this lesson, let's ignore the upstream data at HOR 101 and just aggregate water quality's downstream data from HOR 102.

Data can easily be aggregated in different forms, such as the mean, min, max, and sum. In the following code chunk, we'll aggregate the data values to 15 minutes as a mean, and the finalQF values as a max between 0 and 1. More complex functions may be needed for aggregating other types of data, such as the measurement uncertainty or special, non-binary quality flags like fDOMAbsQF.

# Recall we already created the downstream object for water quality, waq_down

# We first need to name each data stream within water quality. 
# One trick is to find all the variable names by searching for "BetaQF"
waq_strm_betaqf_cols <- names(waq_down)[grep("BetaQF",names(waq_down))]
print(paste0("BetaQF column names: ",
             paste0(waq_strm_betaqf_cols, collapse = ", ")))

## [1] "BetaQF column names: sensorDepthBetaQF, specificConductanceBetaQF, dissolvedOxygenBetaQF, dissolvedOxygenSatBetaQF, pHBetaQF, chlorophyllBetaQF, turbidityBetaQF, fDOMBetaQF"

# Now let's remove the BetaQF from the column name:
waq_strm_cols <- base::gsub("BetaQF","",waq_strm_betaqf_cols)
# To keep column names short, some variable names had to be shortened
# when appending "BetaQF", so let's add "uration" to "dissolvedOxygenSat"
waq_strm_cols <- base::gsub("dissolvedOxygenSat",
                            "dissolvedOxygenSaturation",waq_strm_cols)
print(paste0("Water quality sensor data stream names: ", 
             paste0(waq_strm_cols, collapse = ", ")))

## [1] "Water quality sensor data stream names: sensorDepth, specificConductance, dissolvedOxygen, dissolvedOxygenSaturation, pH, chlorophyll, turbidity, fDOM"

# We will also aggregate the final quality flags:
waq_final_qf_cols <- names(waq_down)[grep("FinalQF",names(waq_down))]

# Let's check to make sure our time column is in POSIXct format, which is 
# needed if you download and read-in NEON data files without using the 
# neonUtilities package.
if("POSIXct" %in% class(waq_down$endDateTime)){
  print("Time column in waq_down is appropriately in POSIXct format")
} else {
  print("Converting waq_down endDateTime column to POSIXct")
  waq_down$endDateTime <- as.POSIXct(waq_down$endDateTime, tz = "UTC")
}

## [1] "Time column in waq_down is appropriately in POSIXct format"

Now that we have the column names of data and quality flags which we wish to aggregate, we can now move to the aggregation steps! We're going to use some more advanced features using the dplyr and padr packages. Instead of looping by each column, let's employ the dplyr pipe operator, %>%, and call a function that acts on each data column of interest, which we've determined above.

# Aggregate water quality data columns to 30 minute intervals, 
# taking the mean of non-NA values within each 30-minute period. 
# We explain each step in the dplyr piping operation in code 
# comments:

waq_30min_down <- waq_down %>% 
              # pass the downstream data frame to the next function
              # padr's thicken function adds a new column, roundedTime, 
              # that shows the closest 30 min timestamp to
              # to a given observation in time

              padr::thicken(interval = "30 min",
                            by = "endDateTime",
                            colname = "roundedTime",
                            rounding = "down") %>%
              # In 1-min data, there should now be sets of 30 
              # corresponding to each 30-minute roundedTime
              # We use dplyr to group data by unique roundedTime 
              # values, and summarise each 30-min group
              # by the the mean, for all data columns provided 
              # in waq_strm_cols and waq_final_qf_cols

              dplyr::group_by(roundedTime) %>% 
                dplyr::summarise_at(vars(dplyr::all_of(c(waq_strm_cols, 
                                                  waq_final_qf_cols))), 
                                    mean, na.rm = TRUE)

# Rather than binary values, quality flags are more like "quality 
# metrics", defining the fraction of data flagged within an 
# aggregation interval.

Now, we have a new dataframe for water quality data and associated final quality flags aggregated to 30 minute time intervals. Now the downstream water quality data may be easily combined with the nearby, albeit non-co-located downstream 30-minute averaged elevation of surface water data.

The following code chunk merges the data:

# We have to specify the matching column from each dataframe
all_30min_data_down <- base::merge(x = waq_30min_down, 
                                   y = eos_down, 
                                   by.x = "roundedTime", 
                                   by.y = "endDateTime")

# Let's take a peek at the combined data frame's column names:
colnames(all_30min_data_down)

##  [1] "roundedTime"                     "sensorDepth"                    
##  [3] "specificConductance"             "dissolvedOxygen"                
##  [5] "dissolvedOxygenSaturation"       "pH"                             
##  [7] "chlorophyll"                     "turbidity"                      
##  [9] "fDOM"                            "sensorDepthFinalQF"             
## [11] "sensorDepthFinalQFSciRvw"        "specificCondFinalQF"            
## [13] "specificCondFinalQFSciRvw"       "dissolvedOxygenFinalQF"         
## [15] "dissolvedOxygenFinalQFSciRvw"    "dissolvedOxygenSatFinalQF"      
## [17] "dissolvedOxygenSatFinalQFSciRvw" "pHFinalQF"                      
## [19] "pHFinalQFSciRvw"                 "chlorophyllFinalQF"             
## [21] "chlorophyllFinalQFSciRvw"        "turbidityFinalQF"               
## [23] "turbidityFinalQFSciRvw"          "fDOMFinalQF"                    
## [25] "fDOMFinalQFSciRvw"               "domainID"                       
## [27] "siteID"                          "horizontalPosition"             
## [29] "verticalPosition"                "startDateTime"                  
## [31] "surfacewaterElevMean"            "surfacewaterElevMinimum"        
## [33] "surfacewaterElevMaximum"         "surfacewaterElevVariance"       
## [35] "surfacewaterElevNumPts"          "surfacewaterElevExpUncert"      
## [37] "surfacewaterElevStdErMean"       "sWatElevRangeFailQM"            
## [39] "sWatElevRangePassQM"             "sWatElevRangeNAQM"              
## [41] "sWatElevPersistenceFailQM"       "sWatElevPersistencePassQM"      
## [43] "sWatElevPersistenceNAQM"         "sWatElevStepFailQM"             
## [45] "sWatElevStepPassQM"              "sWatElevStepNAQM"               
## [47] "sWatElevNullFailQM"              "sWatElevNullPassQM"             
## [49] "sWatElevNullNAQM"                "sWatElevGapFailQM"              
## [51] "sWatElevGapPassQM"               "sWatElevGapNAQM"                
## [53] "sWatElevSpikeFailQM"             "sWatElevSpikePassQM"            
## [55] "sWatElevSpikeNAQM"               "validCalFailQM"                 
## [57] "validCalPassQM"                  "validCalNAQM"                   
## [59] "sWatElevAlphaQM"                 "sWatElevBetaQM"                 
## [61] "sWatElevFinalQF"                 "sWatElevFinalQFSciRvw"          
## [63] "publicationDate"                 "release"

We now have matching time stamps for water quality and any other 30-minute
averaged data product, such as elevation of surface water. The merged data frame facilitates direct comparison across different sensors.

Let's take a look with a plot of specific conductance versus water surface elevation:

ggplot(data = all_30min_data_down, 
       aes(x = surfacewaterElevMean, y = specificConductance)) +
  geom_point() + 
  ggtitle("PRIN specific conductance vs. surface water elevation") + 
  xlab("Elevation [m ASL]") + 
  ylab("Specific conductance [uS/cm]")

## Warning: Removed 5 rows containing missing values (geom_point).

Aggregating high frequency time series data is a useful tool for understanding relationships between variables collected at different time intervals, and may also be a required format for model inputs.

Now that you have the basic tools and knowledge on how to read and wrangle NEON AIS data, go have fun working on your scientific questions!

Citations

Watras, C. J., Hanson, P. C., Stacy, T. L., Morrison, K. M., Mather, J., Hu, Y. H., & Milewski, P. (2011). A temperature compensation method for CDOM fluorescence sensors in freshwater. Limnology and Oceanography: Methods, 9(7), 296-301.

Downing, B. D., Pellerin, B. A., Bergamaschi, B. A., Saraceno, J. F., & Kraus, T. E. (2012). Seeing the light: The effects of particles, dissolved materials, and temperature on in situ measurements of DOM fluorescence in rivers and streams. Limnology and Oceanography: Methods, 10(10), 767-775.