Until recently, imaging satellites used cameras that capture the Earth in a few frequency bands. Multispectral sources typically cover the optical red, green, and blue bands of visible light in addition to a few other frequency ranges.
But a revolution is underway. A growing number of satellites equipped with hyperspectral cameras are going to allow us to observe the Earth in extraordinary detail across a large frequency range — well beyond what the human eye can see, and in much more detail than existing multispectral sensors such as Landsat or Sentinel-2.
Hyperspectral imagery enables applications such as tree and plant species identification, monitoring of air quality (particulate and gaseous) and water quality (e.g. algal bloom detection), tree health observation, and more.
The visualization above shows one spectral band at a time. The amount of data contained in a single image can also be represented as a hyperspectral cube.
Different materials can often be identified by their characteristic spectral signature. Hyperspectral imaging sensors cover a larger frequency spectrum in narrow, continuous frequency bands. They enable the capture and identification of spectral profiles, such as of healthy (green-stage) and stressed (red-stage) conifers in California.
Manually comparing spectral profiles is inefficient at scale. Using machine learning we can train a model to identify the spectral characteristics of different materials. New imagery can then be classified more quickly and at scale.
Techniques for production of actual analyses are still in an early stage. Hyperspectral imagery is currently limited and ground truth and model training data is scarce.
The image below shows early results from our tree health study area in California. Here, we used hyperspectral imagery to distinguish between healthy (green-stage) conifers and conifers under stress (red-stage). These results are from an ML model that works on 224-band AVIRIS imagery.
NASA expects to see a major increase in the number of hyperspectral data sets in the coming years. Supported by an innovation research grant from NASA, Azavea created a reusable, open source data processing pipeline for serving and analyzing hyperspectral imagery.
Azavea creates software and data analytics for the web. We are a mission-driven company, using our twenty years of geospatial expertise to help our clients address complex civic, social, and environmental problems.
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See available HSI data and find out when more becomes available
|Launch date||Name||Provider||Platform||Constellation size now / planned||Wavelengths in nm||Spectral res.in nm||Spatial res.in m|
|2020-25||Satellogic Aleph-1||Satellogic||Satellite||26 / 200+||460-830||14-35||25|
|2022-23||Orbital Sidekick GHOSt||Orbital Sidekick||Satellite||6||400-2500||3-5||8|
|2023||Carbon Mapper (Tanager)||Planet/JPL/CarbonMapper Coalition||Satellite||2||5||30|
|2023||Kuva Hyperfield-1||Kuva Space||Satellite||450-1000||25|
|2029||CHIME (Sentinel 10)||ESA||Satellite||400-2500||10|
Launch dates and specifications may be changed or delayed. Last updated Mon Dec 12 2022.