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Tutorial: NEON -> Landsat-Style Reflectance

This tutorial shows how SpectralBridge turns physically corrected NEON ENVI reflectance into Landsat-style sensor products using sensor spectral response functions (SRFs).

The output is a set of ENVI cubes and Parquet tables representing NEON reflectance in target sensor bandspaces such as Landsat TM, ETM+, OLI, and OLI-2.

Prerequisites

Before running this tutorial, complete:

You will need the *_brdfandtopo_corrected_envi.img product. If it already exists and validates, the pipeline will reuse it and continue to sensor resampling.

1. Select a corrected ENVI cube

Your corrected data should live inside a per-flightline folder:

output_neon_to_envi/
└── NEON_D13_NIWO_DP1_L020-1_20230815_directional_reflectance/
    └── NEON_D13_NIWO_DP1_L020-1_20230815_directional_reflectance_brdfandtopo_corrected_envi.img

This corrected ENVI product is the input to sensor bandpass convolution.

2. Run the pipeline

Sensor resampling runs automatically after BRDF + topographic correction. To generate or refresh Landsat-style products, re-run the standard pipeline for the same flight line. Completed earlier stages are skipped.

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 \
  --max-workers 2 \
  --engine thread

Outputs include sensor-specific files such as:

*_landsat_tm_envi.img
*_landsat_tm_envi.hdr
*_landsat_tm_envi.parquet
*_landsat_oli_envi.img
*_landsat_oli_envi.hdr
*_landsat_oli_envi.parquet

3. Understanding Landsat bandpass integration

The convolution stage integrates each corrected NEON spectrum against the target sensor SRFs. For Landsat OLI-style products, the output bands include coastal/aerosol, blue, green, red, NIR, SWIR1, and SWIR2 where supported by the sensor definition.

This process produces reflectance values aligned to Landsat definitions, enabling:

  • direct comparison to satellite imagery
  • validation exercises
  • cross-scale modeling
  • harmonized vegetation index calculations

4. Inspecting Landsat-style ENVI outputs

Example in Python:

import rioxarray as rxr

landsat = rxr.open_rasterio("path/to/..._landsat_oli_envi.img")
landsat

Band order, wavelengths, and metadata are stored in the matching ENVI header.

5. Checking QA outputs

The QA JSON and QA PNG summarize:

  • brightness adjustments used in harmonization
  • bandwise statistics after convolution
  • wavelength alignment checks
  • mask and no-data diagnostics

Use these artifacts to verify successful harmonization before downstream analysis.

6. Using the Parquet tables

Sensor Parquet files use one row per pixel-band observation. A quick DuckDB summary looks like:

import duckdb

duckdb.query("""
    SELECT wavelength_nm, AVG(reflectance) AS mean_reflectance
    FROM '..._landsat_oli_envi.parquet'
    GROUP BY wavelength_nm
    ORDER BY wavelength_nm
""").df()

For analysis across all products, start with the merged table: *_merged_pixel_extraction.parquet.

Next steps