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ISMN quality control procedures for in situ soil moisture time series


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
  • https://github.com/TUW-GEO/flagit


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=3.7 # 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.


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.


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 master branch for new features or bug fixes.


If you want to contribute please follow these steps:

  • Fork the ismn repository to your account
  • Clone the repository
  • make a new feature branch from the ismn master 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 master branch


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
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)

# alternatively: choose only specific procedures by providing a list or string 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)

Indices and tables