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
This package should be installable through pip:
pip install flagitThe following script will install miniconda and setup the environment on a UNIX
like system. Miniconda will be installed into $HOME/miniconda.
wget https://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh -O miniconda.sh
bash miniconda.sh -b -p $HOME/miniconda
export PATH="$HOME/miniconda/bin:$PATH"
git clone git@github.com:TUW-GEO/flagit.git flagit
cd flagit
conda env create -f environment.yml
source activate flagitThis script adds $HOME/miniconda/bin temporarily to the PATH to do this
permanently add export PATH="$HOME/miniconda/bin:$PATH" to your .bashrc
or .zshrc
The last line in the example activates the flagit environment.
After that you should be able to run:
python setup.py testto 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 (https://ismn.geo.tuwien.ac.at) 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: 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
We are happy if you want 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.
For Development we also recommend a conda environment. You can create one
including test dependencies and debugger by running
conda env create -f environment.yml. This will create a new
ismn environment which you can activate by using
source activate ismn.
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/.