c3s_sm

Processing tools and tutorials for users of the C3S satellite soil moisture service ( https://doi.org/10.24381/cds.d7782f18 ). Written in Python.

Installation

The c3s_sm package and all required dependencies can be installed via

pip install c3s_sm

On macOS if you get ImportError: Pykdtree failed to import its C extension, then it might be necessary to install the pykdtree package from conda-forge

conda install -c conda-forge pykdtree

API Key

In order to download C3S soil moisture data from CDS, this package uses the CDS API (https://pypi.org/project/cdsapi/). You can either pass your credentials directly on the command line (which might be unsafe) or set up a .cdsapirc file in your home directory (recommended). Please see the description at https://cds.climate.copernicus.eu/how-to-api.

Quickstart

Download image data from CDS using the c3s_sm shell command

c3s_sm download /tmp/c3s/img -s 2023-09-01 -e 2023-10-31 -v v202212

... and convert them to time series

c3s_sm reshuffle /tmp/c3s/img /tmp/c3s/ts

Finally, in python, read the time series data for a location as pandas DataFrame.

>> from c3s_sm.interface import C3STs
>> ds = C3STs('/tmp/c3s/ts')
>> ts = ds.read(18, 48)

                  sm  sm_uncertainty  flag  ...  mode  sensor            t0
2023-09-01  0.222125        0.014661     0  ...     2     544  19601.100348
2023-09-02  0.213480        0.011166     0  ...     3   38432  19602.051628
2023-09-03  0.197324        0.014661     0  ...     3   33312  19602.945730
              ...             ...   ...  ...   ...     ...           ...
2023-10-29  0.265275        0.013192     0  ...     3   37408  19658.955236
2023-10-30  0.256964        0.011166     0  ...     3   38432  19660.085144
2023-10-31  0.241187        0.014661     0  ...     3   33312  19660.945730

Tutorials

We provide tutorials on using the C3S Soil Moisture data:

• Tutorial 1: DataAccess from CDS & Anomaly computation

These tutorials are designed to run on mybinder.org You can find the code for all examples in this repository.

Supported Products

At the moment this package supports C3S soil moisture data in netCDF format (reading and time series creation) with a spatial sampling of 0.25 degrees.

Build Docker image

For operational implementations, this package and be installed in a docker container.

• Check out the repo at the branch/tag/commit you want build
• Make sure you have docker installed and run the command (replace the tag latest with something more meaningful, e.g. a matching version number)

docker build -t c3s_sm:latest . 2>&1 | tee docker_build.log

This will execute the commands from the Dockerfile. I.e. install a new environment with the checked out version of the c3s_sm package.

To build and publish the image online, we have a GitHub Actions workflow in .github/workflows/docker.yml

Contribute

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.

Guidelines

If you want to contribute please follow these steps:

• Fork the c3s_sm repository to your account
• Clone the repository, make sure you use git clone --recursive to also get the test data repository.
• make a new feature branch from the c3s_sm master branch
• Add your feature
• Please include tests for your contributions in one of the test directories.
• submit a pull request to our master branch