Landsat / Sentinel-2: bands, NDVI, false color

Landsat and Sentinel-2 are the two great open civilian Earth-observation programs. Landsat (NASA + USGS) has flown continuously since 1972 — 50+ years of consistent imagery — and Sentinel-2 (ESA) provides 10-meter, 5-day-revisit coverage of the entire globe. Together they are the workhorses of every academic, NGO, and commercial Earth observation workflow.

Landsat 9: the heritage program

Landsat 9 launched in 2021 and is the operational US Earth-observation flagship. Key specs:

• Orbit: sun-synchronous, 705 km, ~99° inclination, 10:00 AM mean local time descending node.
• Revisit: 16 days (the same orbit returns over a given point every 16 days). With Landsat 8 also in orbit, the combined revisit is 8 days.
• Sensors: OLI-2 (Operational Land Imager 2) and TIRS-2 (Thermal Infrared Sensor 2).
• Bands: 11 spectral bands ranging from coastal aerosol (0.443 µm) to thermal IR (12 µm).
• Resolution: 30 m for most bands, 15 m for the panchromatic band, 100 m for thermal.

Sentinel-2: the European twin-pair

Sentinel-2A (launched 2015) and Sentinel-2B (launched 2017) fly the same orbit on opposite sides of the planet, giving a 5-day revisit at the equator and 2–3 days at mid-latitudes. Specs:

• Orbit: sun-synchronous, 786 km, ~98.6° inclination.
• Sensor: MSI (Multi-Spectral Instrument).
• Bands: 13 spectral bands.
• Resolution: 10 m for the visible + NIR bands (Bands 2, 3, 4, 8), 20 m for red-edge and SWIR bands, 60 m for atmospheric correction bands.

NDVI: the most-used vegetation index

NDVI (Normalized Difference Vegetation Index) is:

NDVI = (NIR - Red) / (NIR + Red)

Healthy vegetation reflects NIR strongly (chlorophyll's mesophyll layer is highly reflective at ~0.85 µm) but absorbs visible red (which is what chlorophyll uses for photosynthesis). The difference between NIR and Red, normalized by their sum, is high for vegetation (0.4–0.9), low for bare soil (0.1–0.2), and near zero or negative for water and built surfaces.

import rasterio
import numpy as np

with rasterio.open('s2_b08_nir.tif') as src:
    nir = src.read(1).astype(float)
with rasterio.open('s2_b04_red.tif') as src:
    red = src.read(1).astype(float)
ndvi = (nir - red) / (nir + red + 1e-10)

False-color composites

A natural-color image puts R=Red, G=Green, B=Blue — what your eye would see. A "false-color" composite swaps in NIR. The classic false-color infrared (R=NIR, G=Red, B=Green) renders healthy vegetation as bright red, water as black, and bare soil as gray-blue. It's the fastest way to scan a scene for vegetation patterns.

Where to get the data

Both Landsat and Sentinel-2 are freely available from multiple cloud-native catalogs:

• AWS Open Data — Landsat: s3://usgs-landsat/collection02/; Sentinel-2: s3://sentinel-s2-l2a/ (free, requester-pays). Both serve Cloud-Optimized GeoTIFF (COG) format, so you can range-request just the bands and tiles you need without downloading entire scenes.
• Microsoft Planetary Computer — a STAC catalog with both datasets, free access with a free account.
• Google Earth Engine — both datasets indexed, but tied to the Earth Engine compute platform.

The lab

You'll download a recent Sentinel-2 L2A scene over Cape Canaveral (Florida) — 5-day-revisit means you can almost always find a cloud-free one within the past month. Compute NDVI. Mask out clouds using the L2A cloud probability layer (it ships with the scene). Output a styled PNG showing vegetation cover around the launch facilities, with the Kennedy and Cape Canaveral pads annotated.

This is the same workflow used for environmental impact monitoring around launch sites — a topic that gets significant attention as Starbase, in particular, expands operations into ecologically sensitive Texas Gulf Coast wetlands.