flagit
ISMN quality control procedures for in situ soil moisture time series
Citation
If you use the software in a publication then please cite:
Dorigo, W.A. , Xaver, A. Vreugdenhil, M. Gruber, A., Hegyiova, A. Sanchis-Dufau, A.D., Zamojski, D. , Cordes, C., Wagner, W., and Drusch, M. (2013). Global Automated Quality Control of In situ Soil Moisture data from the International Soil Moisture Network. Vadose Zone Journal, 12, 3, doi:10.2136/vzj2012.0097
Please also cite the correct version of this package. Click on the badge below, select the correct version and copy the text under “Cite as”.
Installation
For installation we recommend Miniconda. So please install it according to the official instructions. As soon
as the conda command is available in your shell you can continue:
conda install -c conda-forge pandas scipy numpy
This following command will install the flagit pip package:
pip install flagit
To create a full development environment with conda, the environment.yml file in this repository can be used:
git clone git@github.com:TUW-GEO/flagit.git flagit
cd flagit
conda create -n flagit python=3.10 # or any supported python version
conda activate flagit
conda env update -f environment.yml -n flagit
python setup.py develop
After that you should be able to run:
python setup.py test
to run the test suite.
Description
The International Soil Moisture Network (ISMN) quality control procedures are used to detect implausible and dubious measurements in hourly situ soil moisture time series. When downloading data at ISMN all variable-data are provided with additional tags in column “qflag”, which can be one of three main categories: C (exceeding plausible geophysical range), D (questionable/dubious) or G (good).
code |
description |
ancillary data required |
|---|---|---|
C01 |
soil moisture < 0 m³/m³ |
|
C02 |
soil moisture > 0.60 m³/m³ |
|
C03 |
soil moisture > saturation point (based on HWSD) |
HWSD sand, clay and organic content |
D01 |
negative soil temperature (in situ) |
in situ soil temperature |
D02 |
negative air temperature (in situ) |
in situ air temperature |
D03 |
negative soil temperature (GLDAS) |
GLDAS soil temperature |
D04 |
rise in soil moisture without precipitation (in situ) |
in situ precipitation |
D05 |
rise in soil moisture without precipitation (GLDAS) |
GLDAS precipitation |
D06 |
spikes |
|
D07 |
negative breaks (drops) |
|
D08 |
positive breaks (jumps) |
|
D09 |
constant low values following negative break |
|
D10 |
saturated plateaus |
|
G |
good |
At ISMN, ancillary data sets are used for flags C03, D01 - D05 (see table above). Since we do not provide ancillary data, we kindly ask users to either provide their own ancillary in situ and GLDAS data (including a soil moisture saturation value for flag C03) in the input (pandas.DataFrame), or accept the limitation of the quality control to flags without ancillary requirements.
We hope to update the functionality of this package to facilitate the inclusion of ancillary data.
For a detailed description of the quality control procedures see paper on Global Automated quality control.
Contribute
We would be happy if you would like to contribute. Please raise an issue explaining what is missing or if you find a bug. We will also gladly accept pull requests against our main branch for new features or bug fixes.
Guidelines
If you want to contribute please follow these steps:
Fork the flagit repository to your account
Clone the repository
make a new feature branch from the flagit main branch
Add your feature
Please include tests for your contributions in one of the test directories. We use unittest so a simple function called test_my_feature is enough
submit a pull request to our main branch
Note
This project has been set up using PyScaffold 3.2.3. For details and usage information on PyScaffold see https://pyscaffold.org/.
Run ISMN flag procedures
This example program shows how to initialize the Interface and run the flagging procedures.
As Input a pandas.DataFrame of the following format is required:
soil_moisture |
soil_temperature |
air_temperature |
precipitation |
gldas_soil_temperature |
gldas_precipitation |
|
|---|---|---|---|---|---|---|
utc |
||||||
2017-01-27 00:00:00 |
5.0 |
-4.7 |
-13.6 |
0.0 |
-8.4 |
0.0 |
2017-01-27 01:00:00 |
4.9 |
-4.9 |
-13.4 |
0.0 |
-8.6 |
0.0 |
2017-01-27 02:00:00 |
4.9 |
-5.1 |
-14.0 |
0.0 |
-8.8 |
0.0 |
2017-01-27 03:00:00 |
4.9 |
-5.1 |
-13.2 |
0.0 |
-8.9 |
0.0 |
2017-01-27 04:00:00 |
4.9 |
-4.9 |
-11.2 |
0.0 |
-9.1 |
0.0 |
2017-01-27 05:00:00 |
4.9 |
-4.6 |
-10.1 |
0.0 |
-9.2 |
0.0 |
2017-01-27 06:00:00 |
5.0 |
-4.5 |
-8.9 |
0.0 |
-9.4 |
0.0 |
from flagit import flagit
import pandas as pd
# read from CSV file
file_path = '/path_to_dataframe/*.csv'
df = pd.read_csv(file_path, index_col='utc', parse_dates=True)
# initialize interface and run all flagging procedures
flag = flagit.Interface(df)
result_df = flag.run(sat_point = 42.7) # Saturation Point in % vol
# alternatively: choose only specific procedures by providing a list as name:
flag = flagit.Interface(df)
result_df = flag.run(name = ['D06', 'D07', 'D09'])
result_df = flag.run(name = ['C01'])
# get flag-descriptions
flag = flagit.Interface(df)
flag.get_flag_description()