The Eye That Doesn't Exist Yet

Understanding how terrestrial and aquatic ecosystems respond to climate change, where mineral resources and hazards are located, and how soil and water quality vary across landscapes requires measuring reflected sunlight in hundreds of contiguous spectral bands from 380 to 2500 nm (visible through shortwave infrared) at 30-60 m spatial resolution globally. The Earth Science decadal survey designated Surface Biology and Geology (SBG) as a priority measurement, but no satellite has combined the spectral coverage, spectral resolution (~10 nm), spatial resolution (30 m), signal-to-noise ratio, and global revisit time needed. The engineering challenge is building a spaceborne imaging spectrometer that maintains radiometric calibration to <2% across 200+ spectral channels while scanning the full ~185 km Landsat swath width, producing 5+ terabytes/day of data that must be atmospherically corrected, spectrally unmixed, and delivered to users within hours.

Imaging spectroscopy uniquely identifies the chemical composition of surfaces: chlorophyll absorption features diagnose plant photosynthetic function, mineral absorption features map geological composition, and water absorption features reveal soil moisture and water quality. Multispectral sensors (Landsat, Sentinel-2) sample only 6-12 broad bands and cannot distinguish between spectrally similar materials. Wildfires, droughts, invasive species, harmful algal blooms, and mineral exploration all benefit from the diagnostic specificity of full imaging spectroscopy. NASA's EMIT instrument on the ISS (launched 2022) demonstrated that spaceborne imaging spectroscopy at 60 m resolution is feasible and has already discovered that mineral dust in the atmosphere is more absorptive than models assumed — a finding with direct implications for climate radiative forcing estimates. But EMIT covers only a narrow range of latitudes from the ISS orbit and lacks the global, systematic coverage needed for Earth system science.

Airborne imaging spectrometers (AVIRIS, AVIRIS-NG) have demonstrated the science at spatial resolutions of 1-20 m since the 1980s, but airborne campaigns cover tiny areas (~thousands of km² per campaign) and cost ~$50-100/km² — global coverage is economically impossible. The EO-1/Hyperion satellite (2000-2017) carried a 220-band imaging spectrometer but had low signal-to-noise ratio, a narrow 7.7 km swath, and no atmospheric correction pipeline, limiting its scientific utility. ESA's PRISMA (2019) and the Italian-German EnMAP (2022) demonstrate spaceborne imaging spectroscopy but with 30 km swaths, making global systematic coverage impractical. The core engineering challenge is maintaining spectral calibration stability across thermal cycling in orbit, stray light rejection across a wide field of view, and the detector technology needed for high-SNR measurements in the SWIR (1000-2500 nm) where InGaAs or HgCdTe detector arrays are required. The data processing challenge is equally formidable: atmospheric correction of hyperspectral data requires pixel-by-pixel estimation of water vapor, aerosols, and surface adjacency effects using the spectral data itself — a computationally demanding inverse problem.

A Dyson or Offner imaging spectrometer design that achieves low spectral smile and keystone distortion across a wide (~185 km) swath while maintaining SNR >300:1 in the VSWIR. Advances in large-format HgCdTe detector arrays for the SWIR regime, with improved uniformity and lower dark current for space operation. Onboard or near-real-time atmospheric correction processing using physics-informed ML models trained on radiative transfer simulations. Data fusion approaches that combine hyperspectral (high spectral resolution, moderate spatial resolution) with multispectral (moderate spectral, high temporal) data to achieve both spectral specificity and daily-to-weekly revisit.

A student team could process publicly available EMIT or EnMAP hyperspectral data to map a specific surface property (vegetation biochemistry, mineral type, or water quality) in their region, developing and validating atmospheric correction and spectral unmixing workflows using ground truth measurements. Alternatively, a team could design and build a low-cost hyperspectral camera (pushbroom spectrometer with a linear detector array) for drone or ground-based measurements, calibrating it against known spectral standards. Relevant disciplines: optical engineering, remote sensing, signal processing, ecology, geology.