spicy-snow

Python module to use volumetric scattering at C-band to calculate snow depths from Sentinel-1 imagery using Lieven et al.'s 2021 technique.

The relevant papers for this repository technique are:

Lievens et al 2019 - https://www.nature.com/articles/s41467-019-12566-y

Lievens et al 2021 - https://tc.copernicus.org/articles/16/159/2022/

Example Installation

sh pip install spicy-snow

Example usage:

```python from pathlib import Path

Add main repo to path if you haven't added with conda-develop

import sys

sys.path.append('path/to/the/spicy-snow/')

from spicysnow.retrieval import retrievesnowdepth from spicysnow.IO.userdates import getinput_dates import shapely

change to your minimum longitude, min lat, max long, max lat

area = shapely.geometry.box(-113.2, 43, -113, 43.4)

this will be where your results are saved

out_nc = Path('~/Desktop/spicy-test/test.nc').expanduser()

this will generate a tuple of dates from the previous August 1st to this date

dates = getinputdates('2021-04-01') # run on all s1 images from (2020-08-01, 2021-04-01) in this example

spicyds = retrievesnowdepth(area = area, dates = dates, workdir = Path('~/Desktop/spicy-test/').expanduser(), jobname = f'testingspicy', existingjobname = 'testingspicy', debug=False, outfp=outnc) ```

Running over large areas/memory issues

If you are running out of memory or running over multiple degrees of latitude this code snippet should get you started on batch processing swathes.

```python from shapely import geometry from itertools import product for lonmin, latmin in product(range(-117, -113), range(43, 46)): area = shapely.geometry.box(lonmin, latmin, lonmin + 1, latmin + 1) outnc = Path(f'~/Desktop/spicy-test/swath{lonmin}-{lonmin + 1}{latmin}-{latmin + 1}.nc').expanduser() if outnc.exists(): continue

spicy_ds = retrieve_snow_depth(area = area, dates = dates, 
                            work_dir = Path('~/scratch/spicy-lowman-quadrant/data/').expanduser(), 
                            job_name = f'spicy-lowman-{lon_min}-{lon_min + 1}_{lat_min}-{lat_min + 1}', # v1
                            existing_job_name = f'spicy-lowman-{lon_min}-{lon_min + 1}_{lat_min}-{lat_min + 1}', # v1
                            debug=False,
                            outfp=out_nc)

```

Description of the output netcdf variables.

• wet_snow: layer showing layers flagged as wet snow (1 = wet, 0 = dry)
• snow_depth: derived snow depth in meters
• ims: snow coverage binary mask (2 = no snow, 4 = snow)
• fcf: forest coverage percentage
• s1: raw sentinel-1 with 3 bands for VV, VH backscatter in dB and incidence angle

All the other layers are intermediate layers for if you want to explore the processing pipeline.

Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

Coverage instructions

Run the following from the root directory of this project to get a coverage report.

You will need to have the dependencies and coverage packages available.

bash python -m coverage run -m unittest discover -s ./tests python -m coverage report

License

Distributed under the MIT License. See LICENSE for more information.

Acknowledgments

Readme template: https://github.com/othneildrew/Best-README-Template

Title image: https://openai.com/dall-e-2/

Contact

Zach Hoppinen: zacharyhoppinen@u.boisestate.edu

Project Link: https://github.com/SnowEx/spicy-snow

Links to relevant repos/sites

Sentinel 1 Download: https://github.com/ASFHyP3/hyp3-sdk https://github.com/asfadmin/Discovery-asf_search

IMS Download: https://github.com/tylertucker202/tibetsnowman/blob/master/tutorial/Tibetsnowmanblogentry.ipynb https://github.com/guidocioni/snow_ims

PROBA-V FCF Download: https://zenodo.org/record/3939050/files/PROBAVLC100globalv3.0.12019-nrtTree-CoverFraction-layerEPSG-4326.tif

Xarray: https://github.com/pydata/xarray

Rioxarray: https://github.com/corteva/rioxarray