SmartSMEAR

An R package for accessing SmartSMEAR API data.

SmartSMEAR is a data visualization and download tool for atmospheric, flux, soil, tree physiological, and water quality measurements from SMEAR research stations of the University of Helsinki and the University of Eastern Finland.

Installation

# Install from GitHub:devtools::install_github("JonGretar/SmartSMEAR-R")

Basic Usage

library(SmartSMEAR)
# Get list of all stationsstations<- search_stations()
# Get metadata for a specific measurement tablehyytiala_meta<- search_tables("HYY_META")
# Search for specific variablespar_vars<- search_variables(description="PAR")
hyy_meta_vars<- search_variables(table="HYY_META")
# Get time series datadata<- get_timeseries(
variables= c("HYY_META.PAR", "HYY_META.PAR2", "HYY_META.NDVI"),
start_time="2023-01-01 00:00:00",
end_time="2023-01-02 00:00:00"
)
# Result has a 'time' column in POSIXct format:
head(data)
# time HYY_META.PAR HYY_META.PAR2 HYY_META.NDVI# 1 2023-01-01 00:00:00 0.00 0.00 0.00# 2 2023-01-01 00:01:00 0.00 0.00 0.00# 3 2023-01-01 00:02:00 0.00 0.00 0.00# 4 2023-01-01 00:03:00 0.00 0.00 0.00# 5 2023-01-01 00:04:00 0.00 0.00 0.00# 6 2023-01-01 00:05:00 0.00 0.00 0.00

Data Timing and Resolution

• Most data is recorded at 1-min or 30-min intervals
• Timestamps are in local standard time (UTC+2)
• Timestamps indicate the beginning of measurement/aggregation interval
• The API supports temporal aggregation between 1-60 minutes
• Time values are returned as POSIXct in EET timezone

Data Quality

• Processing level flags indicate automatic vs human-checked data
• Additional quality flags describe observation accuracy
• Use processing_level = "CHECKED" to get only quality-checked data

Variable Aggregation

Different variables require different aggregation methods:

• ARITHMETIC: Standard measurements (default)
• SUM: Cumulative measurements (e.g., precipitation)
• CIRCULAR: Wind direction
• NONE: Original data without aggregation

Example with aggregation:

# Get hourly averageshourly_data<- get_timeseries(
variables= c("HYY_META.PAR", "HYY_META.PAR2", "HYY_META.NDVI"),
start_time="2023-01-01 00:00:00",
end_time="2023-01-02 00:00:00",
interval=30,
aggregation="ARITHMETIC"
)
head(hourly_data)
# time HYY_META.PAR HYY_META.PAR2 HYY_META.NDVI# 1 2023-01-01 00:00:00 0.00 0.00 0.00# 2 2023-01-01 00:01:00 0.00 0.00 0.00# 3 2023-01-01 00:02:00 0.00 0.00 0.00# 4 2023-01-01 00:03:00 0.00 0.00 0.00# 5 2023-01-01 00:04:00 0.00 0.00 0.00# 6 2023-01-01 00:05:00 0.00 0.00 0.00

Cleanup functions

There are a few convenience related functions available. clear_column_prefix remove the table prefixes from the column names.

Note that it does not do any checks of duplicity. So if a dataframes has both "SII1_EDDY.F_c_LI72" and "SII2_EDDY.F_c_LI72" it will result in loss of data.

hyy_df<- get_timeseries(
variables= c("SII1_EDDY.NEE","SII1_EDDY.F_c_LI72", "SII1_EDDY.F_c_LI70"),
start_time="2023-01-01 00:00:00", end_time="2023-01-02 00:00:00"
)
# time SII1_EDDY.F_c_LI72 SII1_EDDY.F_c_LI70 SII1_EDDY.NEE# 1 2023-01-01 00:00:00 0.25421 NaN 0.25421# 2 2023-01-01 00:30:00 NaN 0.22673 0.21579# 3 2023-01-01 01:00:00 -0.19346 NaN -0.20133hyy_df<- clear_column_prefix(hyy_df)
# time F_c_LI72 F_c_LI70 NEE# 1 2023-01-01 00:00:00 0.25421 NaN 0.25421# 2 2023-01-01 00:30:00 NaN 0.22673 0.21579# 3 2023-01-01 01:00:00 -0.19346 NaN -0.20133

Another helper function is merge_device_columns which looks for columns ending with c("_PIC", "_LI77", "_LI72", "_LI70", "_LGR") and merges into a single column. For value it uses the first non-NaN value it finds.

Optionally it saves the device whose value was used in the _device postfix.

merge_device_columns(hyy_df, record_device=TRUE)
# time NEE F_c F_c_device# 1 2023-01-01 00:00:00 0.25421 0.25421 LI72# 2 2023-01-01 00:30:00 0.21579 0.22673 LI70# 3 2023-01-01 01:00:00 -0.20133 -0.19346 LI72

Working with Time Data

The time series data is returned with a time column that is already in POSIXct format with the correct timezone (EET). This makes it easy to use with time series analysis packages and plotting libraries:

# Filter data for daytime onlydaytime<- subset(data, format(time, "%H") >="06"& format(time, "%H") <="18")
# Get daily averages using base Rdaily_avg<- aggregate(PAR~ as.Date(time), data=data, FUN=mean)
# Or use with dplyr
library(dplyr)
daily_avg<-data %>%
group_by(date= as.Date(time)) %>%
summarize(PAR= mean(PAR))

API Documentation

Full API documentation is available at: https://smear-backend.2.rahtiapp.fi/q/openapi-ui/

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.