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Gérard Cubaka
Gérard Cubaka

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🛰️ How to Detect Minerals (Au, Cu, Li, Fe, etc.) Using Multispectral & Hyperspectral Remote Sensing

#data #datascience #science #tutorial

Gérard Cubaka — Geospatial / AI / Earth Observation

🌍 Introduction

Mineral exploration has been revolutionized by remote sensing. Instead of relying only on fieldwork, geologists can now detect mineralization zones from satellite imagery.

The key idea is simple:

Each mineral interacts with electromagnetic radiation differently — creating a unique spectral signature across Visible, NIR, SWIR, and TIR wavelengths.

This article explains:

  • How to detect key minerals (Au, Cu, Fe, Li, etc.) using multispectral data
  • The band ratios and indices used in practice
  • How hyperspectral data unlocks precise mineral identification

🧪 1. Core Principle: Spectral Signatures

Every mineral:

  • Reflects certain wavelengths
  • Absorbs others

For example:

  • Iron oxides → strong in visible (red)
  • Clays → absorption near 2.2 µm (SWIR)
  • Quartz → better detected in thermal infrared (TIR)

🛰️ 2. Multispectral Mineral Detection

Multispectral sensors (e.g., ASTER, Landsat 8/9) use broad bands, so we rely on:

  • Band ratios
  • Indices
  • PCA (Principal Component Analysis)

🔴 2.1 Iron Oxides (Fe, Mn, Ti)

These are the easiest to detect.

Characteristics:

  • Absorb blue
  • Reflect red

📊 Band Ratios:

  • Landsat 8: 

B4 / B2
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  • ASTER: 

B2 / B1
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Interpretation:

  • Bright areas → Hematite / Goethite (gossans)
  • Often indicate oxidized ore zones

🟡 2.2 Hydrothermal Alteration Minerals (Au, Cu, Ag, Zn, Ni, Co)

You rarely detect the metal directly. Instead, detect alteration halos:

  • Clays (Al-OH)
  • Carbonates (Mg-OH)
  • Sulfate alteration (Jarosite, Alunite)

📊 Key Indices:

🧱 Clay Index (Al-OH)

  • ASTER: 

B4 / B6
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  • Landsat 8: 

B6 / B7
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👉 Indicates:

  • Kaolinite, Illite
  • Strong indicator of gold (Au) and copper (Cu) systems

🪨 Carbonate / Chlorite Index (Mg-OH)

  • ASTER: 

B8 / B9
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👉 Useful for:

  • Copper (Cu)
  • Zinc (Zn)

🟤 Jarosite Index (Gold Indicator)

  • ASTER: 

B4 / B3
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👉 Indicates:

  • Acidic alteration → often linked to gold deposits

⚪ 2.3 Silica & Quartz (SiO₂)

⚠️ Problem:
Quartz has no strong SWIR absorption

👉 Solution: Use Thermal Infrared (TIR)

📊 Silica Index:

  • ASTER: 

(B11 * B11) / (B10 * B12)
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👉 Detects:

  • Quartz veins
  • Silicification zones (important for Au)

🟢 2.4 Mafic & Ultramafic Rocks (Ni, Co, Diamond Indicators)

We detect the host rocks, not the minerals directly.

📊 Indices:

Mafic Index:

  • ASTER: 

B12 / B13
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Ultramafic Index:

  • ASTER: 

(B1 + B3) / B2
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👉 Indicates:

  • Kimberlites → Diamond exploration
  • Ultramafic intrusions → Nickel, Cobalt

🟣 2.5 Pegmatites (Li, Be, Nb)

No direct detection ❌

👉 Strategy:

  • Detect associated minerals:
  • Micas (Lepidolite)
  • Feldspars

👉 Use:

  • Clay/mica indices (Al-OH absorption ~2.2 µm)

⚫ 2.6 Special Cases

💎 Diamond

  • Not directly detectable
  • Target: Kimberlite pipes
  • Look for:
  • Mg-rich signatures
  • Circular anomalies

☢️ Uranium (U)

  • Detect indirectly:
  • Clay alteration
  • Redox boundaries (Fe²⁺ / Fe³⁺)

👉 Note:

  • Radiometric surveys are often better than optical

🎨 3. RGB Composite Strategy

For fast visual interpretation:

Example (Gold/Copper exploration):

Channel Data
🔴 Red Iron Oxide Ratio
🟢 Green Clay Index
🔵 Blue Carbonate Index

👉 Result:

  • Bright composite zones = high exploration targets

🧠 4. Hyperspectral Data (The Game Changer)

Multispectral = ~10 bands

Hyperspectral = 100–300 narrow bands

👉 This allows true mineral identification

🔧 4.1 Preprocessing

  • Atmospheric correction:
  • ATREM
  • QUAC
  • Noise filtering:
  • Savitzky-Golay

🧩 4.2 Endmember Extraction

  • Pixel Purity Index (PPI)
  • n-D Visualizer

👉 Goal:
Extract pure mineral signatures

🔍 4.3 Spectral Matching Algorithms

🧭 SAM (Spectral Angle Mapper)

  • Measures similarity between spectra

📐 SFF (Spectral Feature Fitting)

  • Matches absorption features precisely

⚖️ Linear Unmixing

  • Determines proportions: 

Pixel = 30% Quartz + 70% Iron Oxide
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🎯 4.4 Targeting Specific Minerals

Mineral Strategy
Li, Be, Nb Shift in mica absorption (~2200 nm)
U Redox zones (Fe²⁺ vs Fe³⁺)
Diamond Indicator minerals (pyrope, magnesite)
Cu Malachite spectral signature
Au Hydrothermal alteration zones

🛠️ 4.5 Recommended Tools

  • ENVI → Industry standard
  • QGIS + EnMAP-Box → Open-source alternative

🚀 Conclusion

Multispectral remote sensing is powerful for:

  • Mapping alteration zones
  • Identifying exploration targets

But:

Hyperspectral data enables direct mineral identification.

🔑 Key Takeaways

  • You rarely detect metals directly — focus on alteration minerals
  • SWIR (2.1–2.4 µm) is critical for clays and hydrothermal systems
  • Use band ratios to enhance signals
  • Combine indices into RGB composites
  • Use hyperspectral data for high-precision exploration

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