spicy-snow 0.3.8

Snow Depth Retrievals from Sentinel-1 Backscatter.

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/

Hoppinen et al 2024 - https://tc.copernicus.org/articles/18/5407/2024/

Example Installation

pip install spicy-snow

Example usage:

# Add main repo to path if not pip installed
# import sys
# sys.path.append('path/to/the/spicy-snow/')

from pathlib import Path
from spicy_snow.retrieval import retrieve_snow_depth

# change to your minimum longitude, min lat, max long, max lat
test_aoi = [-114.5, 43, -114, 44]

# can also be a point for point based retrievals
# test_aoi = [-114.2, 43.75]

# you will want to start before the snowfalls since this is a change detection method
dates = ['2020-10-01', '2021-05-01']

# this will be where your results are saved
out_nc = Path('./test.nc').resolve()

# resolution in meters to work at (100, 500, 1km all have been tested).
# default is 100 meters
spatial_resolution = 100

# data source can be either "opera" RTC or "hyp3" cloud processed
# opera is much faster and doesn't require hyp3 credits
# hyp3 uses gamma processing and may be cleaner
source = 'opera' # other option "hyp3"

spicy_ds = retrieve_snow_depth(aoi = test_aoi, dates = dates, 
                               work_dir = Path('./spicy-test/').resolve(), 
                               resolution = spatial_resolution,
                               debug=False,
                               source = source,
                               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
• snowcover: snow coverage binary mask (True = snow on, False = snow-free)
• fcf: forest coverage percentage
• vv: gamma0 sentinel-1 VV backscatter in dB
• vh: gamma0 sentinel-1 VH backscatter in dB

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.

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: [e-mail]

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

Data Sources

Sentinel-1 RTCs are downloaded from the Alaska Satellite Facility and processed by JPL's Opera Project. Forest cover within CONUS comes from the National Land Cover Database, otherwise from the Proba-V forest cover. Snow coverage is from the VIIRS snow cover product. See and use citations below.

Citations

Work using this repository should cite it and the following datasources NASA/JPL/OPERA. (2023). OPERA Co-registered Single Look Complex from Sentinel-1 validated product (Version 1) [Data set]. NASA Alaska Satellite Facility Distributed Active Archive Center. https://doi.org/10.5067/SNWG/OPERA_L2_CSLC-S1_V1 Date Accessed: 2025-11-18

Riggs, G. A., Hall, D. K. & Román, M. O. (2019). VIIRS/NPP Snow Cover Daily L3 Global 375m SIN Grid. (VNP10A1, Version 1). [Data Set]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/VIIRS/VNP10A1.001. [describe subset used if applicable]. Date Accessed 11-18-2025.

U.S. Geological Survey (USGS), 2024, Annual NLCD Collection 1 Science Products (ver. 1.1, June 2025): U.S. Geological Survey data release, https://doi.org/10.5066/P94UXNTS.

Links to relevant repos/sites

Opera S1 RTC Products: https://www.earthdata.nasa.gov/data/catalog/asf-opera-l2-cslc-s1-v1-1 https://asf.alaska.edu/datasets/daac/opera/

VIIRS Snow Fractional Coverage https://nsidc.org/data/vnp10a1/versions/1

Annual National Land Cover Database https://www.usgs.gov/centers/eros/science/annual-national-land-cover-database

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