SpiPy

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SpiPy is a Python implementation of SPIRES (Snow Property Inversion From Remote Sensing), originally implemented in MATLAB (SPIRES GitHub repository).

Overview

SPIRES retrieves snow properties (grain size, dust concentration, fractional snow-covered area) from satellite multispectral imagery by inverting reflectance spectra using lookup tables generated from Mie-scattering theory.

Key features:

Hybrid Python/C++ implementation for performance (3000x speedup over pure Python)
Support for MODIS, Sentinel-2, and Landsat data
SWIG bindings for optimized interpolation and optimization routines
NLopt-based nonlinear optimization

Installation

Quick Install (PyPI)

pip install spires

Note: Pre-built binary wheels are available for Linux and macOS (Python 3.9-3.14). For other platforms or to build from source, see below.

Install from Source

Important: Use conda-forge for all dependencies. The apt version of nlopt does not include required C++ headers.

# Install build tools and nlopt (required)
conda install -c conda-forge swig gxx gcc nlopt

# Install all dependencies (recommended)
conda install -c conda-forge numpy h5py scipy xarray netCDF4 gdal geopandas matplotlib tox sphinx dask jupyterlab pyproj

Git LFS

This repository uses Git LFS for test data. Install Git LFS before cloning:

# macOS
brew install git-lfs

# Linux
sudo apt install git-lfs

# Initialize
git lfs install

Build and Install

# Build SWIG extensions
python3 setup.py build_ext --inplace

# Install package
pip install .

# Or install with optional dependencies
pip install ".[dev,test,docs]"

Usage

See the examples/ folder for Jupyter notebooks with detailed use cases.

Basic usage:

import spires

# Load lookup table
interpolator = spires.LutInterpolator(
    lut_file='tests/data/lut_sentinel2b_b2to12_3um_dust.mat'
)

# Process imagery to get fractional snow-covered area
fsca = spires.get_fsca(...)

Development

Building Wheels

Build a wheel for the active Python interpreter:

pip install build
python -m build --wheel

Build wheels for multiple Python versions using tox:

tox -e py39,py310,py311,py312

Note: When using pyenv, wheels for Python 3.9 may incorrectly build for x86 instead of arm64 on M1 Macs. Use a conda environment to build correctly.

Building C++ Extensions Manually

The setuptools build process handles SWIG bindings automatically. To build manually:

cd spires
make

Or specify paths explicitly:

NUMPY_INCLUDE=$(python -c "import numpy; print(numpy.get_include())")
g++ -shared -o spires_module.so spires.cpp -I$NUMPY_INCLUDE

Testing

Run doctests:

pytest --doctest-modules

Documentation

Install documentation dependencies:

pip install ".[docs]"

Build documentation:

cd doc/
make html

Lookup Tables and Test Data

Lookup Tables

Simulated Mie-scattering snow reflectance lookup tables are available on Zenodo:

MODIS: LUT_MODIS.mat (537 MB)
Sentinel-2: lut_sentinel2b_b2to12_3um_dust.mat (70 MB)

Download using the helper script:

python scripts/download_test_data.py --luts

Or download directly:

curl -L -o LUT_MODIS.mat https://zenodo.org/records/18701286/files/LUT_MODIS.mat
curl -L -o lut_sentinel2b_b2to12_3um_dust.mat https://zenodo.org/records/18701286/files/lut_sentinel2b_b2to12_3um_dust.mat

Note: The Sentinel-2 LUT is also included in the repository via Git LFS. Landsat lookup tables are planned.

Test Data

Full-resolution test imagery for validation is available on Zenodo:

Sentinel-2 reflectance: sentinel_r.nc (1.4 GB, 921×1347 pixels)
Background reflectance: sentinel_r0.nc (705 MB)

Small subsets suitable for CI/testing are included in the repository via Git LFS. See tests/data/README.md for details.

Performance

The C++ optimizations provide significant speedups over pure Python:

Interpolation: 3000x faster (1.07 ms → 309 ns)

Pure Python RegularGridInterpolator: 1.07 ms
Vectorized Python: 143 μs
SWIG C++ (vectorized): 5.58 μs
SWIG C++ (index lookup): 309 ns

Spectrum Difference: 1000x faster (1.1 ms → 1 μs)

Pure Python: 1.1 ms
With optimized interpolator: 3.8 μs
C++ implementation: 1 μs

Full Optimization: 3000x faster (165 ms → 43 μs)

Scipy optimization: 165 ms
With optimized interpolator: 4.94 ms
With C++ spectrum difference: 3.5 ms
NLopt in C++: 43 μs

Known Issues

SLSQP solver doesn't work in the C++ implementation; using COBYLA instead
SWIG interpolator and scipy's RegularGridInterpolator behave differently when coordinates aren't linspace
COBYLA in scipy can't set rhobeg per dimension individually, requiring problem scaling

Roadmap

Optimize inversion for single location over multiple timesteps (keep R_0 constant)
Support xarray inputs for interpolator and spectra
Add Landsat lookup tables
Improve cloud masking workflows

License

See LICENSE file for details.

Citation

If you use this software, please cite the algorithm paper, software implementation, and any datasets you use:

Algorithm:

@article{bair2021spires,
  title={Snow Property Inversion From Remote Sensing (SPIReS): A Generalized Multispectral Unmixing Approach With Examples From MODIS and Landsat 8 OLI},
  author={Bair, E. H. and Stillinger, T. and Dozier, J.},
  journal={IEEE Transactions on Geoscience and Remote Sensing},
  volume={59},
  number={9},
  pages={7270--7284},
  year={2021},
  doi={10.1109/TGRS.2020.3040328}
}

Software:

@software{bair2026spipy,
  title={SpiPy: Python implementation of SPIRES snow property inversion},
  author={Bair, Edward H. and Griessbaum, Niklas},
  year={2026},
  url={https://github.com/NiklasPhabian/SpiPy},
  version={0.2.2},
  doi={10.5281/zenodo.XXXXXXX},
  note={DOI will be updated after Zenodo release. See CITATION.cff for full metadata}
}

Lookup Tables (if used):

@dataset{bair2026spires_luts,
  author       = {Bair, Edward and Dozier, Jeff},
  title        = {{SPIRES} Snow Reflectance Lookup Tables},
  year         = 2026,
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.18701286},
  url          = {https://doi.org/10.5281/zenodo.18701286}
}

Test Data (if used):

@dataset{griessbaum2026sentinel2_testdata,
  author       = {Griessbaum, Niklas},
  title        = {Sentinel-2 reflectance data for testing the {SpiPy} implementation of the {SPIRES} algorithm},
  year         = 2026,
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.18704072},
  url          = {https://doi.org/10.5281/zenodo.18704072}
}

Alternatively, see CITATION.cff or use GitHub's "Cite this repository" feature.

Funding

Development of this software was supported by:

Contract: W913E523C0002 Program: "Climate and natural hazards, snow-covered and mountain environment sensing research" Sponsor: Broad Agency Announcement Program, Cold Regions Research and Engineering Laboratory Monitored by: U.S. Army Engineer Research and Development Center, Hanover, NH 03755

Distribution Statement: Approved for public release; distribution is unlimited.

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The following attestation bundles were made for spires-0.2.8-cp39-cp39-macosx_11_0_arm64.whl:

Publisher: publish-pypi.yml on NiklasPhabian/SpiPy

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

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0.2.8 This release

Feb 23, 2026 9 files

0.2.5

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0.2.2

Feb 23, 2026 9 files

spires 0.2.8

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