https://github.com/simonrp84/pycoxmunk
Python package for estimating sea surface reflectance.
https://github.com/simonrp84/pycoxmunk
earth-observation oceanography reflectance remote-sensing satellite
Last synced: about 1 year ago
JSON representation
Python package for estimating sea surface reflectance.
- Host: GitHub
- URL: https://github.com/simonrp84/pycoxmunk
- Owner: simonrp84
- License: gpl-3.0
- Created: 2020-10-01T10:59:19.000Z (over 5 years ago)
- Default Branch: main
- Last Pushed: 2025-03-21T09:34:07.000Z (about 1 year ago)
- Last Synced: 2025-04-11T11:48:08.976Z (about 1 year ago)
- Topics: earth-observation, oceanography, reflectance, remote-sensing, satellite
- Language: Python
- Homepage: https://pycoxmunk.readthedocs.io/en/latest/
- Size: 4.82 MB
- Stars: 9
- Watchers: 2
- Forks: 2
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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PyCoxMunk
=========
### A python library for simulating satellite-viewed sea surface reflectances.
The pycoxmunk library computes the sea surface reflectance, using the
[Cox-Munk method](https://doi.org/10.1364/JOSA.44.000838), as expected to be seen from space based on the satellite and
weather conditions at the sea surface. A description of the algorithm and this library are given in
[this paper](https://joss.theoj.org/papers/10.21105/joss.05074.pdf), published in the
[Journal of Open Source Software](https://joss.theoj.org/).
The main purpose of this library is to enable an easy method for computing sea surface reflectance that is applicable
to a wide variety of low-earth and geostationary orbit satellites and sensors. The library is closely linked to the
[satpy library](https://github.com/pytroll/satpy) that loads, calibrates and produces projection information for many
satellites. Under the hood, pycoxmunk uses [dask](https://github.com/dask/dask) and
[xarray](https://github.com/pydata/xarray) to store and process data. Results are made available via the same `satpy`
scene used to provide the input satellite images.
As well as computing the sea surface reflectance, pycoxmunk can also compute the four BRDF terms, which can be useful in
radiative transfer, cloud and aerosol properties retrieval codes, and other uses such as cloud or sea ice detection.
Installation
------------
pycoxmunk can be installed from PyPI with pip:
```bash
pip install pycoxmunk
```
It is also available from `conda-forge` for conda installations:
```bash
conda install -c conda-forge pycoxmunk
```
Tests
-----
pycoxmunk comes with a set of scripts for testing the functionality of the library. These are located in the `./Tests/`
subdirectory and can be run using `pytest`:
```
cd Tests
pytest .
```
This will run all tests. You can also run just a single test script with, for example, `pytest test_CMCalcs.py`.
You may first need to install pytest via: `conda install pytest`.
Once testing is complete, a summary will be displayed. This should indicate that all tests have passed, as well as
potentially listing some warnings depending on your environment and the current status of `pycoxmunk`'s dependencies.
Credits
-------
This code was written by Simon Proud and is based on a fortran implementation of the Cox-Munk algorithm written by
Greg McGarragh as part of the [ORAC algorithm](https://github.com/ORAC-CC/orac).
Feedback and Contribution
-------------------------
Feedback, suggestions, bug reports or any other type of contribution is welcome.
If you encounter any problems with this code or the documentation then please file an
[issue](https://github.com/simonrp84/PyCoxMunk/issues).
It may help in debugging any problems to enable satpy's debug mode:
```python
from satpy import debug_on
debug_on()
```
This will print additional log and diagnostic information.
Suggestions for new features are welcome, but may not always be possible for me to code due to limited time. You can
also submit your own [pull requests](https://github.com/simonrp84/PyCoxMunk/pulls) that add features of fix bugs.
This is the recommended way to change the library code, rather than emailing me your updates.
By submitting a pull request please ensure that your code changes and additions are documented and, where appropriate,
covered by tests.