Slopes for each flux — slopes30

Slopes of C(t) for each flux with 30 seconds end cut, with quality flags.

Usage

slopes30_flag

Format

A tibble with 1251 rows and 36 variables

datetime: Datetime at which CO2 concentration was recorded.
temp_air: Air temperature inside the flux chamber in Celsius.
temp_soil: Ground temperature inside the flux chamber in Celsius.
f_conc: CO2 concentration in ppm.
PAR: Photosynthetically active radiation inside the chamber in micromol/s/sqm.
turfID: Unique ID of the turf in which the measurement took place.
type: Type of measurement: ecosystems respiration (ER) or net ecosystem exchange (NEE).
start: Datetime at which the measurement was started.
end: Datetime at which the measurement ended.
f_fluxID: Unique ID for each flux.
n_conc: Number of data point per flux.
ratio: Ratio of n_conc over length of the measurement (in seconds).
flag: Data quality flags.
f_time: Time variable of the flux in seconds.
f_cut: Indicating if the measurement should be kept (keep) or discarded (cut).
Cm_est: Estimation of the Cm parameter.
a_est: Estimation of the a parameter.
b_est: Estimation of the b parameter.
tz_est: Estimation of the tz parameter.
Cz: Cz parameter of the C(t) function.
Cm: Cm parameter of the C(t) function, calculated by optim() with Cm_est as starting point.
a: a parameter of the C(t) function, calculated by optim() with a_est as starting point.
f_b: b parameter of the C(t) function, calculated by optim() with b_est as starting point.
tz: tz parameter of the C(t) function, calculated by optim() with tz_est as starting point.
f_slope: Slope of C(t) at tz
f_fit: C(t), modeled CO2 concentration as a function of time.
fit_slope: Output of linear model of CO2 concentration passing by C(tz) and a slope of slope_tz.
start_z: Datetime format of tz
f_cor_coef: coefficient of correlation between gas concentration and time
f_RMSE: RMSE of the exponential fit and the measured data
f_start_error: flagging if measurement started outside of the possible ambient concentration
f_fit_quality: flagging bad fit
f_correlation: flagging if there is a correlation between gas concentration and time
f_quality_flag: quality flag advising if the slope has to be replaced by 0 or NA
f_slope_corr: slope corrected according to quality flag

Examples

slopes30_flag
#> # A tibble: 1,251 × 39
#>    f_datetime          temp_air temp_soil f_conc   PAR turfID       type 
#>    <dttm>                 <dbl>     <dbl>  <dbl> <dbl> <chr>        <chr>
#>  1 2022-07-28 23:43:35    NA         NA     447. NA    156 AN2C 156 ER   
#>  2 2022-07-28 23:43:36     7.22      10.9   447.  1.68 156 AN2C 156 ER   
#>  3 2022-07-28 23:43:37    NA         NA     448. NA    156 AN2C 156 ER   
#>  4 2022-07-28 23:43:38    NA         NA     449. NA    156 AN2C 156 ER   
#>  5 2022-07-28 23:43:39    NA         NA     449. NA    156 AN2C 156 ER   
#>  6 2022-07-28 23:43:40    NA         NA     450. NA    156 AN2C 156 ER   
#>  7 2022-07-28 23:43:41    NA         NA     451. NA    156 AN2C 156 ER   
#>  8 2022-07-28 23:43:42    NA         NA     451. NA    156 AN2C 156 ER   
#>  9 2022-07-28 23:43:43    NA         NA     453. NA    156 AN2C 156 ER   
#> 10 2022-07-28 23:43:44    NA         NA     453. NA    156 AN2C 156 ER   
#> # ℹ 1,241 more rows
#> # ℹ 32 more variables: f_start <dttm>, f_end <dttm>, f_fluxID <dbl>,
#> #   n_conc <dbl>, ratio <dbl>, flag <lgl>, f_time <dbl>, f_cut <chr>,
#> #   Cm_est <dbl>, a_est <dbl>, b_est <dbl>, tz_est <dbl>, f_Cz <dbl>,
#> #   time_diff <dbl>, f_Cm <dbl>, f_a <dbl>, f_b <dbl>, f_tz <dbl>,
#> #   f_slope <dbl>, f_fit <dbl>, f_fit_slope <dbl>, f_start_z <dttm>,
#> #   f_n_conc <int>, f_ratio <dbl>, f_flag_ratio <chr>, f_start_error <chr>, …