How to re-photograph Apollo landing sites

 The project involves using a 3U CubeSat, “Arkiv-1,” with a 40 m resolution hyperspectral imager to re-photograph Apollo landing sites at the same solar azimuth and elevation as the Apollo 11 photograph AS11-37-5447, aiming to detect terrain disturbances in iron-oxide absorption bands. The data will be downlinked to a rented container in the Sahara Desert. Below is a detailed plan to execute this, tailored to your specifications and the remote setup.

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1. Data Acquisition with Arkiv-1The task is to acquire hyperspectral imagery from the Arkiv-1 3U CubeSat, designed to re-photograph Apollo landing sites (e.g., Apollo 11 at 0.674°N, 23.473°E) under specific lighting conditions to match AS11-37-5447, focusing on iron-oxide absorption bands.

• Arkiv-1 Specifications:
  • Platform: 3U CubeSat (~10x10x30 cm, ~4 kg).
  • Cost: ~$85k build + $25k launch (e.g., SpaceX Transporter rideshare).
  • Instrument: Hyperspectral imager with 40 m spatial resolution, covering iron-oxide absorption bands (0.9–1.0 µm, near-infrared) with spectral resolution of ~10–20 nm.
  • Orbit: Likely low Earth orbit (LEO, ~500 km) with a steerable imager for lunar observations. Lunar orbits are possible but cost-prohibitive for a 3U CubeSat.
• Task Requirements:
  • Solar Geometry: AS11-37-5447 was taken on July 20, 1969, at Tranquility Base with a solar elevation of ~10–15° and azimuth of ~90° (east). Use NASA’s SPICE toolkit or Lunar Reconnaissance Orbiter (LRO) ephemeris data to calculate equivalent solar angles for each Apollo site (Apollo 11, 12, 14, 15, 16, 17) during Arkiv-1 overpasses.
  • Target: Detect terrain disturbances (e.g., lander blast zones, ~10–20 m) visible in iron-oxide absorption bands due to regolith disruption (e.g., compaction, exposure of subsurface material).
  • Sites: Six Apollo landing sites, requiring multiple imaging passes to match solar conditions.
• Acquisition Steps:
  1. Orbit Planning:
     • Use Systems Tool Kit (STK) or FreeFlyer to schedule lunar overpasses when solar azimuth/elevation matches AS11-37-5447. A 500 km LEO orbit provides periodic lunar visibility (e.g., every ~2 weeks for specific sites).
     • Task Arkiv-1 to point its imager at each Apollo site during optimal passes. Ensure the CubeSat’s attitude control system (e.g., reaction wheels) can achieve precise pointing (±0.1°).
  2. Imaging:
     • Capture hyperspectral data cubes (x, y, wavelength) covering 0.4–1.0 µm to include iron-oxide bands. Each image may be ~1 GB due to high spectral resolution (e.g., 100 bands).
     • Verify the imager’s signal-to-noise ratio (SNR >100) for reliable spectral analysis.
  3. Downlink:
     • Transmit raw data to the Sahara container via UHF (400–450 MHz) or S-band (2.2–2.3 GHz). Data rates may be low (10–100 kbps for UHF), requiring multiple passes to downlink large datasets.
     • Use a ground station receiver (e.g., 1–2 m parabolic antenna) in the container, paired with software like GNU Radio or SatNOGS to decode telemetry (e.g., AX.25 protocol).

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2. Sahara Container SetupThe rented container in the Sahara must support data reception, processing, and storage under harsh desert conditions.

• Power:
  • Solar Panels: Install 2–5 kW photovoltaic panels with lithium-ion battery storage (e.g., 10 kWh) to power the ground station, computers, and cooling. The Sahara’s high solar irradiance (~7 kWh/m²/day) supports this.
  • Backup: Use a diesel generator (5 kW) with fuel reserves for sandstorms or low sunlight periods.
• Cooling:
  • Equip the container with a 5–10 kW air conditioning unit to maintain <30°C internally, protecting electronics from temperatures up to 50°C. Use dust filters to block fine Saharan sand.
  • Insulate the container with reflective panels to reduce heat ingress.
• Ground Station:
  • Antenna: A steerable 1–2 m parabolic antenna for S-band or a Yagi antenna for UHF. Ensure tracking software aligns with Arkiv-1’s orbit (e.g., using TLE data from Celestrak).
  • Receiver: Software-defined radio (SDR) like USRP B210 for flexible frequency tuning. Store raw packets on 20 TB SSDs.
• Connectivity:
  • Use Starlink for internet access (50–150 Mbps) to upload processed data or download ephemeris files. Protect the Starlink dish from sand with a sealed enclosure.
  • Alternatively, use a VSAT terminal (~10 Mbps) as a backup.
• Hardware:
  • Workstation: 64 GB RAM, AMD Ryzen 9 or Intel Xeon, NVIDIA RTX A4000 GPU for hyperspectral processing.
  • Storage: 20–50 TB SSDs with RAID for redundancy.

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3. Processing WorkflowProcess the raw hyperspectral data to detect terrain disturbances in iron-oxide absorption bands.

1. Data Decoding:
   • Decode telemetry packets using GNU Radio or CubeSat-specific software to reconstruct hyperspectral data cubes (HDF5 or ENVI format).
2. Preprocessing:
   • Radiometric Calibration: Convert raw digital numbers to radiance (W/m²/sr/µm) using calibration coefficients from the imager’s manufacturer.
   • Geometric Correction: Georeference images to lunar coordinates (e.g., Mean Earth/Polar Axis system) using Arkiv-1’s attitude and position data. Use USGS ISIS for lunar projections.
   • Illumination Correction: Normalize for solar illumination variations using the solar zenith angle from AS11-37-5447. No atmospheric correction is needed (lunar surface).
3. Spectral Analysis:
   • Band Selection: Extract bands covering 0.9–1.0 µm for iron-oxide absorption (e.g., FeO, Fe₂O₃). Use Python’s spectral or hyperspy libraries.
   • Continuum Removal: Apply continuum-removed reflectance to enhance absorption features. Compare spectra from disturbed areas (e.g., lander zones) to undisturbed regolith.
   • Spectral Angle Mapping (SAM): Use ENVI or Python to quantify spectral differences between disturbed and undisturbed areas, highlighting iron-oxide anomalies.
4. Terrain Disturbance Detection:
   • Image Alignment: Overlay Arkiv-1 images with LRO Narrow Angle Camera (NAC) images (0.5 m resolution, available via USGS) to locate Apollo site features (e.g., lander, rover tracks).
   • Change Detection: At 40 m resolution, focus on large-scale disturbances (e.g., lander blast zones, ~10–20 m). Use difference imaging or SAM to identify altered albedo or spectral signatures.
   • Validation: Cross-reference findings with LRO data to compensate for Arkiv-1’s coarse resolution.
5. Output:
   • Generate GeoTIFF or ENVI files showing iron-oxide absorption anomalies for each Apollo site.
   • Produce spectral plots comparing disturbed vs. undisturbed regolith.
   • Store results on local SSDs and upload to a cloud platform (e.g., AWS S3) via Starlink for sharing.

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4. Challenges and Mitigations

• Resolution Limitation: 40 m resolution limits detection to large-scale disturbances. Supplement with LRO NAC imagery for finer details (e.g., footprints).
• Solar Angle Matching: Precise timing is critical. Use SPICE or STK to predict overpasses within ±1° of target solar angles. Multiple passes may be needed.
• Data Volume: Hyperspectral data (~1 GB per image) and slow downlink rates (10–100 kbps) may delay acquisition. Prioritize key bands (0.9–1.0 µm) to reduce data size.
• Sahara Environment: Protect equipment from heat (up to 50°C) and sandstorms (winds up to 100 km/h). Use sealed enclosures and regular maintenance.
• Logistics: Fuel and spare parts delivery to the Sahara is challenging. Stockpile supplies and train local staff for basic operations.

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5. Recommendations

• Start with Apollo 11: Focus initial imaging on Tranquility Base to validate the workflow before scaling to other sites.
• Leverage LRO Data: Freely available LRO NAC images (https://pds-imaging.jpl.nasa.gov/) provide high-resolution context to interpret Arkiv-1’s coarse data.
• Automate Processing: Use Python scripts to automate spectral analysis and change detection, reducing manual effort in the remote container.
• Collaborate: Partner with lunar science groups (e.g., NASA’s SSERVI) for ephemeris data or processing expertise.

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6. Tools and Resources

• Software:
  • USGS ISIS: Lunar image processing (https://isis.astrogeology.usgs.gov/).
  • ENVI/SNAP: Hyperspectral analysis (https://step.esa.int/main/toolboxes/snap/).
  • Python: spectral, rasterio, hyperspy for custom processing.
  • QGIS: Mapping and visualization (https://qgis.org/).
• Data:
  • LRO NAC images: https://pds-imaging.jpl.nasa.gov/
  • SPICE toolkit: https://naif.jpl.nasa.gov/naif/toolkit.html
• Ground Station:
  • GNU Radio: https://www.gnuradio.org/
  • SatNOGS: https://satnogs.org/