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Tutorial: NEON → Corrected ENVI (BRDF + Topographic)

Canonical learning path

This older tutorial is retained for stable links. Use Correct NEON reflectance for the canonical module vignette.

This tutorial walks through converting NEON hyperspectral HDF5 files into physically corrected ENVI reflectance cubes. The output is the foundational product used in all downstream harmonization workflows (e.g., Landsat-style reflectance).

Overview

You will learn how to:

  1. download NEON directional reflectance tiles
  2. export them to ENVI format
  3. apply topographic correction
  4. apply BRDF correction
  5. inspect corrected outputs and QA artifacts

This tutorial assumes you have installed SpectralBridge and have run through the Quickstart.

1. Set up a working directory

BASE=output_neon_to_envi
mkdir -p "$BASE"

2. Run the pipeline for one flight line

Here we process a single NEON hyperspectral flight line at NIWO.

spectralbridge-pipeline \
  --base-folder "$BASE" \
  --site-code NIWO \
  --year-month 2023-08 \
  --product-code DP1.30006.001 \
  --flight-lines NEON_D13_NIWO_DP1_L020-1_20230815_directional_reflectance \
  --engine thread \
  --max-workers 2

The command will:

  • fetch the necessary HDF5 files
  • export directional reflectance to ENVI (*_envi.img/.hdr)
  • apply topographic correction
  • apply BRDF correction
  • write corrected ENVI reflectance (*_brdfandtopo_corrected_envi.img)
  • generate QA PNG, PDF, and JSON files

Re-running the command will skip completed stages.

3. What the corrected ENVI product contains

A corrected flight line directory contains files such as:

  • *_directional_reflectance_envi.img
  • *_topocorrected_envi.img
  • *_brdfandtopo_corrected_envi.img
  • *_qa.png
  • *_qa.pdf
  • *_qa.json

The key output is *_brdfandtopo_corrected_envi.img, which contains physically corrected reflectance values suitable for sensor harmonization or modeling.

4. Inspect the corrected ENVI cube

You can view the ENVI product using:

  • ENVI/IDL
  • QGIS (with raster bands exposed)
  • Python libraries such as rioxarray or spectral

Example in Python:

import rioxarray as rxr

cube = rxr.open_rasterio("path/to/..._brdfandtopo_corrected_envi.img")
cube

This loads the corrected reflectance cube into an xarray.DataArray.

5. Inspect QA outputs

Open the QA PNG:

open "$BASE/..._qa.png"

The QA panel includes:

  • reflectance range checks
  • brightness differences across correction stages
  • mask summary statistics
  • wavelength and band metadata
  • BRDF / topographic coefficient summaries

The PDF version includes multi-page diagnostics and expanded panels.

6. Next steps

Proceed to: