Remote Sensing Specialist

Satellite imagery from sensor physics to plume detection. Learn the electromagnetic spectrum, optical/IR/SAR sensor types, and the GOES-R ABI band suite. The capstone is a working thermal plume detector that operates on real NOAA GOES NetCDF files — the same data feed LaunchDetect uses in production.

What you'll learn

• Map the electromagnetic spectrum to sensor bands and understand what each band sees.
• Read Landsat / Sentinel-2 imagery and compute NDVI and false-color composites.
• Navigate GOES-R ABI's 16 bands, especially Band 7 (3.9 µm) for thermal-emissive sensing.
• Convert raw radiance to brightness temperature and run a hotspot detection.
• Georeference fixed-grid GOES imagery to lat/lon, accounting for parallax.

Prerequisites

Orbital Analyst track or equivalent. Familiarity with raster data and basic radiometric concepts.

Tools you'll use

xarray · rasterio · netCDF4 · satpy · pyresample

Weekly curriculum (5 weeks)

• Week 11 EM spectrum, sensor types, and radiometry
• Week 12 Landsat / Sentinel-2: bands, NDVI, false color
• Week 13 GOES-R ABI: full-disk, CONUS, mesoscale
• Week 14 Thermal IR Band 7: brightness temperature and hotspots
• Week 15 Georeferencing GOES and parallax (Capstone 3 week) Capstone 3

Capstone 3: Thermal Plume Detector

Detect a real rocket plume from a real NOAA GOES NetCDF.

Build a Python tool that, given a GOES-18 ABI Band 7 NetCDF file and a known launch event (date, location, vehicle), outputs detection records `(timestamp_UTC, lat, lon, brightness_temp_K, area_km²)` for each detected plume pixel cluster. Apply parallax correction. Apply false-positive filtering (mask known wildfires from the FIRMS dataset). Produce a Folium heatmap visualization showing detected hotspots overlaid on a basemap, with hover-popups showing the (t, T_b) for each detection.