fetchHenry: Download Data from the Henry Mount Soil Temperature and Water Database

Description

This function is a front-end to the REST query functionality of the Henry Mount Soil Temperature and Water Database.

Usage

fetchHenry(what='all', usersiteid = NULL, project = NULL, sso = NULL,
gran = "day", start.date = NULL, stop.date = NULL, 
pad.missing.days = TRUE, soiltemp.summaries = TRUE)

Arguments

what

type of data to return: 'sensors': sensor metadata only | 'soiltemp': sensor metadata + soil temperature data | 'soilVWC': sensor metadata + soil moisture data | 'airtemp': sensor metadata + air temperature data | 'waterlevel': sensor metadata + water level data |'all': sensor metadata + all sensor data

usersiteid

(optional) filter results using a NASIS user site ID

project

(optional) filter results using a project ID

sso

(optional) filter results using a soil survey office code

gran

data granularity: "day", "week", "month", "year"; returned data are averages

start.date

(optional) starting date filter

stop.date

(optional) ending date filter

pad.missing.days

should missing data ("day" granularity) be filled with NA? see details

soiltemp.summaries

should soil temperature ("day" granularity only) be summarized? see details

Value

a list containing:

sensors

a SpatialPointsDataFrame object containing site-level information

soiltemp

a data.frame object containing soil temperature timeseries data

soilVWC

a data.frame object containing soil moisture timeseries data

airtemp

a data.frame object containing air temperature timeseries data

waterlevel

a data.frame object containing water level timeseries data

Details

Filling missing days with NA is useful for computing and index of how complete the data are, and for estimating (mostly) unbiased MAST and seasonal mean soil temperatures. Summaries are computed by first averaging over Julian day, then averaging over all days of the year (MAST) or just those days that occur within "summer" or "winter". This approach makes it possible to estimate summaries in the presence of missing data. The quality of summaries should be weighted by the number of "functional years" (number of years with non-missing data after combining data by Julian day) and "complete years" (number of years of data with >= 365 days of non-missing data).

Examples

Run this code

# NOT RUN {
if(requireNamespace("curl") &
    curl::has_internet() &
    require(lattice)) {
    
  # get CA630 data as daily averages
  x <- fetchHenry(project='CA630', gran = 'day')
  
  # inspect data gaps
  levelplot(factor(!is.na(sensor_value)) ~ doy * factor(year) | name, 
  data=x$soiltemp, col.regions=c('grey', 'RoyalBlue'), cuts=1, 
  colorkey=FALSE, as.table=TRUE, scales=list(alternating=3), 
  par.strip.text=list(cex=0.75), strip=strip.custom(bg='yellow'), 
  xlab='Julian Day', ylab='Year')

}
# }