Skip to content

README.md

Latest commit

 

History

History
85 lines (62 loc) · 3.38 KB

README.md

File metadata and controls

85 lines (62 loc) · 3.38 KB

SpiPy Examples

This directory contains example 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

Development Notebooks

The following notebooks were used during development and are 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

Data Files

  • UCSB_fsca.mp4 - Example animation output showing fractional snow-covered area

Requirements

Most notebooks require:

  • SpiPy package installed
  • 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

Getting Started

Quick Start

For new users, 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:

  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

Notes

  • Notebooks assume data is stored in /scratch/tristate/ or /data/sentinel2/ directories
  • Most workflows are designed for UCSB region but can be adapted
  • Large notebooks (>1MB) contain embedded outputs and visualizations