Readers for the data from the International Soil Moisture Database (ISMN).
- The following tutorials are available in
If you use the software in a publication then please cite it using the Zenodo DOI. Be aware that this badge links to the latest package version.
Please select your specific version at https://doi.org/10.5281/zenodo.855308 to get the DOI of that version. You should normally always use the DOI for the specific version of your record in citations. This is to ensure that other researchers can access the exact research artefact you used for reproducibility.
You can find additional information regarding DOI versioning at http://help.zenodo.org/#versioning
This package should be installable through pip:
pip install ismn
matplotlib packages are only needed when creating data visualisations.
They can be installed separately with:
conda install -c conda-forge matplotlib conda install -c conda-forge cartopy
Example installation script
The following script will install miniconda and setup the environment on a UNIX
like system. Miniconda will be installed into
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 email@example.com:TUW-GEO/ismn.git ismn cd ismn conda env create -f environment.yml source activate ismn
This script adds
$HOME/miniconda/bin temporarily to the
PATH to do this
export PATH="$HOME/miniconda/bin:$PATH" to your
The second to last line in the example activates the
After that you should be able to run:
to run the test suite.
ISMN data can be downloaded for free after creating an account on the ISMN Website
ISMN data can be downloaded in two different formats:
Variables stored in separate files (CEOP formatted)
this format is supported 100% and should work with all examples
Variables stored in separate files (Header+values)
this format is supported 100% and should work with all examples
If you downloaded ISMN data in one of the supported formats in the past it can be that station names are not recognized correctly because they contained the ‘_’ character which is supposed to be the separator. If you experience problems because of this please download new data from the ISMN since this issue should be fixed.
The ISMN data comes with information about landcover classification from the ESA CCI land cover project (years 2000, 2005 and 2010) as well as from in-situ measurements. To use ESA CCI land cover variables for filtering the data in the get_dataset_ids function, set the keyword parameters (landcover_2000, landcover_2005 or landcover_2010) to the corresponding integer values (e.g. 10) in the list below. To get a list of possible values for filtering by in-situ values (keyword parameter: “landcover_insitu”), call the get_landcover_types method of your ISMN_Interface object and set landcover=’landcover_insitu’.
10: Cropland, rainfed
11: Cropland, rainfed / Herbaceous cover
12: Cropland, rainfed / Tree or shrub cover,
20: Cropland, irrigated or post-flooding,
30: Mosaic cropland (>50%) / natural vegetation (tree, shrub, herbaceous,
40: Mosaic natural vegetation (tree, shrub, herbaceous cover) (>50%) / cropland (<50%),
50: Tree cover, broadleaved, evergreen, Closed to open (>15%),
60: Tree cover, broadleaved, deciduous, Closed to open (>15%),
61: Tree cover, broadleaved, deciduous, Closed (>40%),
62: Tree cover, broadleaved, deciduous, Open (15-40%),
70: Tree cover, needleleaved, evergreen, closed to open (>15%),
71: Tree cover, needleleaved, evergreen, closed (>40%),
72: Tree cover, needleleaved, evergreen, open (15-40%),
80: Tree cover, needleleaved, deciduous, closed to open (>15%),
81: Tree cover, needleleaved, deciduous, closed (>40%),
82: Tree cover, needleleaved, deciduous, open (15-40%),
90: Tree cover, mixed leaf type (broadleaved and needleleaved),
100: Mosaic tree and shrub (>50%) / herbaceous cover (<50%),
110: Mosaic herbaceous cover (>50%) / tree and shrub (<50%),
121: Shrubland / Evergreen Shrubland,
122: Shrubland / Deciduous Shrubland,
140: Lichens and mosses,
150: Sparse vegetation (tree, shrub, herbaceous cover) (<15%),
152: Sparse vegetation (tree, shrub, herbaceous cover) (<15%) / Sparse shrub (<15%),
153: Sparse vegetation (tree, shrub, herbaceous cover) (<15%) / Sparse herbaceous cover (<15%),
160: Tree cover, flooded, fresh or brakish water,
170: Tree cover, flooded, saline water,
180: Shrub or herbaceous cover, flooded, fresh/saline/brakish water,
190: Urban areas,
200: Bare areas,
201: Consolidated bare areas,
202: Unconsolidated bare areas,
220: Permanent snow and ice,
The ISMN data comes with information about climate classification from the Koeppen-Geiger Climate Classification (2007) as well as in-situ measurements. To use Koeppen-Geiger variable for filtering the data in the get_dataset_ids function, set the keyword parameter “climate” to the corresponding keys (e.g. ‘Af’) in the list below. To get a list of possible values for filtering by in-situ values (keyword parameter: “climate_insitu”), call the get_climate_types method of your ISMN_Interface object and set climate=’climate_insitu’.
Af: Tropical Rainforest
Am: Tropical Monsoon
As: Tropical Savanna Dry
Aw: Tropical Savanna Wet
BWk: Arid Desert Cold
BWh: Arid Desert Hot
BWn: Arid Desert With Frequent Fog
BSk: Arid Steppe Cold
BSh: Arid Steppe Hot
BSn: Arid Steppe With Frequent Fog
Csa: Temperate Dry Hot Summer
Csb: Temperate Dry Warm Summer
Csc: Temperate Dry Cold Summer
Cwa: Temperate Dry Winter, Hot Summer
Cwb: Temperate Dry Winter, Warm Summer
Cwc: Temperate Dry Winter, Cold Summer
Cfa: Temperate Without Dry Season, Hot Summer
Cfb: Temperate Without Dry Season, Warm Summer
Cfc: Temperate Without Dry Season, Cold Summer
Dsa: Cold Dry Summer, Hot Summer
Dsb: Cold Dry Summer, Warm Summer
Dsc: Cold Dry Summer, Cold Summer
Dsd: Cold Dry Summer, Very Cold Winter
Dwa: Cold Dry Winter, Hot Summer
Dwb: Cold Dry Winter, Warm Summer
Dwc: Cold Dry Winter, Cold Summer
Dwd: Cold Dry Winter, Very Cold Winter
Dfa: Cold Dry Without Dry Season, Hot Summer
Dfb: Cold Dry Without Dry Season, Warm Summer
Dfc: Cold Dry Without Dry Season, Cold Summer
Dfd: Cold Dry Without Dry Season, Very Cold Winter
ET: Polar Tundra
EF: Polar Eternal Winter
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
conda 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 pytest so a simple function called test_my_feature is enough
submit a pull request to our master branch
Release new version
To release a new version of this package, make sure all tests are passing on the master branch and the CHANGELOG.rst is up-to-date, with changes for the new version at the top.
Then draft a new release at https://github.com/TUW-GEO/ismn/releases.
Create a version tag following the
This will trigger a new build on GitHub and should push the packages to pypi after
all tests have passed.
If this does not work (tests pass but upload fails) you can download the
dist packages for each workflow run from
https://github.com/TUW-GEO/ismn/actions (Artifacts) and push them manually to
https://pypi.org/project/ismn/ (you need to be a package maintainer on pypi for that).
In any case,
pip install ismn should download the newest version afterwards.