flagit 0.2

ISMN quality control procedures for in situ soil moisture time series

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

• https://github.com/TUW-GEO/flagit

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 [e-mail]: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/.