Anamika Shreevastava, Glynn Hulley, James Thompson

Algorithms for Detecting Sub‐Pixel Elevated Temperature Features for the NASA Surface Biology and Geology (SBG) Designated Observable

  • Paleontology
  • Atmospheric Science
  • Soil Science
  • Water Science and Technology
  • Ecology
  • Aquatic Science
  • Forestry
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AbstractOne of the top priorities of the Surface Biology and Geology (SBG) Earth Observing System is the detection and retrieval of elevated temperature features (ETF) usually found in the vicinity of active fires and volcanic activity. We test the ability of currently proposed midwave (MIR: 3–5 μm) and thermal infrared (TIR: 8–12 μm) bands to detect ETF within the 400–1200 K range. Specifically, our investigation aims to compare and contrast the use of the 4 and 4.8 μm MIR bands. We use land surface temperature data obtained by the airborne Hyperspectral Thermal Emission Spectrometer (HyTES) instrument over active fire and lava flows to model at‐sensor SBG radiances in the 3–12 μm range. This is achieved using the Temperature Emissivity Uncertainty Simulator (TEUSim) with the designated/proposed SBG MIR and TIR band characteristics. For ETF detection, we applied the Normalized Thermal Index (NTI) and Enhanced Thermal Index (ETI) to determine a suitable threshold for a wide range of ETF sizes and temperatures. We find that combining an NTI threshold of −0.7 followed by an ETI threshold of 0.02 accurately identifies ETFs at a 97% rate. Sensor noise up to 0.5 K has negligible effects on ETF detection in the 400–1200 K range. The currently proposed SBG MIR and TIR bands are sufficient to detect unsaturated ETFs caused by wildfire and volcanic activities at a ∼3 day revisit and subpixel ETF area of ∼9 m2 (at 500 K) that is unattainable by current satellite TIR instruments.

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