Examples and Tutorials

This page links to example Jupyter notebooks demonstrating the usage of SpiPy for snow property inversion from satellite imagery.

Main Workflow Examples

1. Setup

• 01_define_regions.ipynb - Define regions of interest for processing

2. Data Preparation (Prerequisites for Inversion)

• 02_pansharpening.ipynb - Pansharpening to improve spatial resolution of Sentinel-2 bands

• 03_create_background_reflectance.ipynb - Generate R0 (background/snow-free) reflectance maps from snow-free periods

• 04_cloud_masking.ipynb - Cloud detection and masking for satellite imagery

3. Core Processing Pipeline

• 05_sentinel_snow_inversion.ipynb - Main workflow: Sentinel-2 snow property inversion using SPIRES

  • Loading preprocessed Sentinel-2 data

  • Applying SPIRES algorithm

  • Batch processing with Dask

  • Saving inversion results (fsca, grain size, dust concentration)

4. Postprocessing

• 06_postprocess_clouds.ipynb - Interpolate cloud gaps and fix sharpening artifacts in results

• 07_postprocess_trees.ipynb - Tree masking and inpainting using deep learning

5. Analysis and Visualization

• 08_create_animations.ipynb - Generate temporal animations of snow properties

Test Notebooks

These notebooks demonstrate specific functionality and can be used for testing:

• test_interpolator.ipynb - Test LUT interpolation functionality

• test_inversion.ipynb - Test basic inversion on single pixels/images

• test_spectrum_diff.ipynb - Test spectral difference calculations

Recommended Learning Path

For New Users

If you’re new to SpiPy, we recommend going through the notebooks in this order:

1. test_interpolator.ipynb - Understand the core LUT interpolation

2. test_inversion.ipynb - See basic inversion examples on single pixels

3. 05_sentinel_snow_inversion.ipynb - Full workflow with batch processing

Complete Workflow

For processing your own Sentinel-2 data, follow this sequence:

1. 01_define_regions.ipynb - Define your region of interest

2. 02_pansharpening.ipynb - Improve spatial resolution

3. 03_create_background_reflectance.ipynb - Generate R0 maps

4. 04_cloud_masking.ipynb - Apply cloud detection

5. 05_sentinel_snow_inversion.ipynb - Run SPIRES inversion

6. 06_postprocess_clouds.ipynb - Fill cloud gaps

7. 07_postprocess_trees.ipynb - (Optional) Remove tree artifacts

8. 08_create_animations.ipynb - Visualize temporal evolution

Requirements

Most notebooks require:

• SpiPy package installed (see Getting Started)

• Access to Sentinel-2 data (zarr format)

• Lookup tables (LUT) for snow reflectance

• Sufficient memory for processing satellite imagery

Some notebooks have additional requirements:

• postprocess_trees.ipynb requires PyTorch and simple-lama-inpainting

• Processing notebooks benefit from Dask for parallel computation

Data Notes

• Notebooks were authored against developer-specific data paths; you’ll need to edit the input/output paths near the top of each notebook to point at your own data

• Most workflows were developed for the UCSB region but can be adapted

• Large notebooks (>1MB) contain embedded outputs and visualizations

Development Notebooks

The examples/development/ directory contains notebooks used during development, kept for reference:

• legacy_speedy_invert.ipynb - Legacy MATLAB-style implementation comparison

• cobyla.ipynb - COBYLA optimizer testing

• invert2d.ipynb - 2D inversion experiments

• compress_nc.ipynb - NetCDF compression utilities

• spicy_snow_experiment.ipynb - Comparison experiment against the spicy-snow approach