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- Dual-sensor Earth-pointing payload for humanitarian impact
- Wildfire Prediction
Instruments/Sensors: Thermal Camera + Imager
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- https://technology.nasa.gov/patent/GSC-TOPS-138#:~:text=The%20need%20for%20such%20a,more%20robust%20missions%20for%20science.
- 3U for just this thermal camera
- Spectrometers
Uvsq-Sat NG (Nanosatellite for GHG monitoring)
Spectrometer occupies 1 U of payload volume
Physical dimensions: 10 × 10 × 10 cm³
Mass: 1–2 kg
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Payload Compatibility
| Feature | 3U CubeSat (Challenging) | 6U CubeSat (Ideal) |
| Payload Volume | Approx 1.5U to 2U max. | Approx 3U to 4U max. |
| Mass Budget | Max approx 1-2 kg for combined payload. | Max approx 4-7 kg for combined payload. |
| Power Budget | Low Orbit Average Power (OAP), typically 10-20 W max. | Higher OAP, typically 20-40 W max. |
| Instrument Performance | Must accept lower spectral/spatial resolution; potentially passive (non-cryocooled) thermal imaging. | Allows for higher resolution, larger optics, and active (cryocooled) thermal imaging. |
| Data Downlink | Lower rate (e.g., 9.6 kbps VHF/UHF), limiting data collected. | Higher rate (e.g., up to 4.3 Mbps S-band), supporting large science data files. |
Why it would be interesting to pursue:
Wildfires are a big threat to human and natural safety. We hope to use thermal and imaging cameras to better understand how wildfires start and predict various natural causes that would liken wildfire creation. Data could be easily found through controlled burnings in the U.S. and other countries, which would provide a good source of baseline data.
- Putting a sensor that outputs processable data
- Serious imaging with fun side mission like imaging big MIT sign
Finalized for Saturday, Nov. 15 meeting:
Design and launch a CubeSat with a Thermal Camera and a Spectrometer/Imager to correlate the primary environmental factors that lead to natural wildfires (drought stress and fuel flammability).
Data collected would be used for wildfire prediction models to benefit humanitarian safety and natural resource management.
Data Validation: Use data from known controlled burnings in the U.S. and other countries to provide reliable baseline data for model calibration.
Possible Correlation: Correlate Land Surface Temperature (LST) with Fuel Moisture Content (FMC) to improve ignition likelihood forecasts.