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/
pip install spicy-snowfrom 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 spicy_snow.retrieval import retrieve_snow_depth
from spicy_snow.IO.user_dates import get_input_dates
# 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 = get_input_dates('2021-04-01') # run on all s1 images from (2020-08-01, 2021-04-01) in this example
spicy_ds = retrieve_snow_depth(area = area, dates = dates,
work_dir = Path('~/Desktop/spicy-test/').expanduser(),
job_name = f'testing_spicy',
existing_job_name = 'testing_spicy',
debug=False,
outfp=out_nc)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.
from shapely import geometry
from itertools import product
for lon_min, lat_min in product(range(-117, -113), range(43, 46)):
area = shapely.geometry.box(lon_min, lat_min, lon_min + 1, lat_min + 1)
out_nc = Path(f'~/Desktop/spicy-test/swath_{lon_min}-{lon_min + 1}_{lat_min}-{lat_min + 1}.nc').expanduser()
if out_nc.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.
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!
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature) - Commit your Changes (
git commit -m 'Add some AmazingFeature') - Push to the Branch (
git push origin feature/AmazingFeature) - Open a Pull Request
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.
python -m coverage run -m unittest discover -s ./tests
python -m coverage reportDistributed under the MIT License. See LICENSE for more information.
Readme template: https://github.com/othneildrew/Best-README-Template
Title image: https://openai.com/dall-e-2/
Zach Hoppinen: [email protected]
Project Link: https://github.com/SnowEx/spicy-snow
Sentinel 1 Download: https://github.com/ASFHyP3/hyp3-sdk https://github.com/asfadmin/Discovery-asf_search
IMS Download: https://github.com/tylertucker202/tibet_snow_man/blob/master/tutorial/Tibet_snow_man_blog_entry.ipynb https://github.com/guidocioni/snow_ims
PROBA-V FCF Download: https://zenodo.org/record/3939050/files/PROBAV_LC100_global_v3.0.1_2019-nrt_Tree-CoverFraction-layer_EPSG-4326.tif
Xarray: https://github.com/pydata/xarray
Rioxarray: https://github.com/corteva/rioxarray