Copper oxide and alteration zone outcrop mapping

SWIR and TIR spectroscopy from ASTER, EMIT and PRISMA identifies porphyry copper indicator minerals at outcrop scale, cutting field sampling costs in arid terrain where exposure is good but access is expensive.

Sensors

ASTER (Terra): Six SWIR bands at 30 m and five TIR bands at 90 m. The SWIR channels (1.6–2.5 µm) resolve Al-OH, Mg-OH and CO3 absorption features diagnostic of alunite, kaolinite, chlorite and calcite. TIR emissivity separates silica-rich from mafic lithologies. Archive runs from 2000; SWIR detector failed in 2008 but the archive remains the global standard for alteration mapping.
EMIT (ISS): Earth Surface Mineral Dust Source Investigation instrument. Hyperspectral VSWIR coverage from 380 to 2500 nm at roughly 60 m ground sampling distance, with ~285 contiguous bands. Launched 2022. Provides spectral fidelity well above ASTER's six SWIR channels, enabling sub-pixel spectral unmixing of mixed alteration assemblages. Coverage is non-systematic; tasking requests can be submitted through NASA.
ASI PRISMA: Italian Space Agency hyperspectral mission, 30 m spatial resolution, 400–2500 nm range across ~240 bands. Revisit roughly 29 days at nadir but programmable. Delivers full reflectance cubes suitable for matched-filter or spectral angle mapping against mineral reference libraries. Data available via ASI portal under research licence.
Landsat-9 OLI/TIRS: 30 m multispectral with two SWIR bands (1.61 µm and 2.20 µm) and a TIR channel at 100 m. Cannot resolve individual mineral species but band ratios (e.g. OLI band 6/7 for clay index, band 4/2 for iron oxide) provide a rapid, free, globally consistent screening layer. Revisit 16 days; archive from 1972 (earlier missions).
Sentinel-2 MSI: 10–20 m spatial resolution, three SWIR bands including a 2.19 µm channel. Free and 5-day revisit at mid-latitudes. Useful for iron oxide and broad clay mapping via band ratios, though the spectral resolution cannot distinguish alunite from kaolinite. Best used as a change-detection and spatial-context layer alongside hyperspectral data.

Why arid outcrops are the right place to start

Porphyry copper systems weather predictably. Sulphide oxidation produces a gossan cap rich in iron oxides, jarosite and hematite, underlain by a leached cap and then, deeper, a supergene enrichment blanket. The hydrothermal alteration halo around the intrusion carries potassic, phyllic and argillic zones, each with characteristic mineral assemblages: alunite and kaolinite in the argillic zone, sericite and pyrite in the phyllic, biotite and K-feldspar in the potassic core. These minerals have sharp, well-characterised absorption features in the SWIR between 2.0 and 2.5 µm, caused by Al-OH, Mg-OH and Fe-OH vibrational bonds.

Arid and semi-arid terrain is where remote sensing earns its keep. Sparse vegetation means the mineral signal reaches the sensor without being swamped by chlorophyll. Cloud cover is low and persistent. Field access is expensive: helicopter days in the Atacama or the Iranian plateau cost more than a season of satellite tasking. The economic logic of mapping first and walking second is straightforward.

What each sensor actually resolves, and what it cannot

ASTER's six SWIR bands were designed specifically for this application, and the published literature confirms they can distinguish alunite from kaolinite, and chlorite from epidote, using band ratio combinations or spectral angle mapping against the USGS spectral library. The 30 m pixel is adequate for mapping alteration zones at exploration scale, where the target is a zone hundreds of metres across rather than a single vein. The practical limit is mixed pixels at zone boundaries and the inability to separate minerals with closely spaced absorption features, such as muscovite and paragonite.

EMIT and PRISMA change the problem materially. With roughly 240 to 285 contiguous bands, sub-pixel spectral unmixing can estimate the fractional abundance of two or three minerals within a single 30–60 m pixel. Published EMIT results from arid mineral dust studies show detection of carbonate, gypsum and chlorite at sub-pixel fractions as low as 20–30 percent, though the exact floor depends on scene brightness and atmospheric correction quality. Neither sensor provides systematic daily revisit; cloud-free tasking over a specific prospect may take weeks.

Landsat-9 and Sentinel-2 are screening tools, not mapping tools. Band ratios flag anomalies worth investigating but cannot confirm mineral identity. A high clay index in OLI band 6/7 could indicate kaolinite, smectite or muscovite. Use them to prioritise which areas get hyperspectral tasking, not to make drill-targeting decisions.

From spectra to a mineral map: the processing chain

Raw radiance data must be converted to surface reflectance before any mineralogical interpretation. Atmospheric correction is the step where most errors enter. For ASTER SWIR, the USGS-published FLAASH and empirical line methods are well documented; for EMIT, NASA distributes Level 2A reflectance products with per-pixel uncertainty estimates. PRISMA Level 2D products include a reflectance cube corrected using the 6SV radiative transfer code.

Once in reflectance, three methods dominate the published literature. Spectral angle mapping (SAM) measures the angle between a pixel spectrum and a reference endmember from the USGS or ASTER spectral libraries; small angles indicate close matches. Matched filter unmixing estimates fractional abundance of a target mineral against a background. Constrained energy minimisation (CEM) is a variant that requires only the target spectrum, not a full background model, which is useful when the scene is spectrally complex. All three methods produce a confidence or abundance layer, not a binary classification, and all require validation against field spectra or hand samples before being used to commit drill-hole expenditure.

TIR emissivity data from ASTER adds a lithological dimension that SWIR alone cannot provide. Silica content correlates with emissivity in the 8–12 µm range; quartz-rich silicified zones show a distinctive reststrahlen feature near 8.6 µm. Combining SWIR alteration maps with TIR silicification maps narrows the search space considerably.

Honest limits: what the method will not tell you

Spectral mapping identifies surface mineralogy. It says nothing about depth, grade or continuity below the oxidised cap. A spectacular alunite anomaly at surface may sit above a barren intrusive or a porphyry system already mined out. The method is a targeting tool, not a resource estimate.

Vegetation cover above roughly 30 percent canopy closure degrades the mineral signal severely. Tropical and temperate porphyry belts, including parts of the Philippines, Papua New Guinea and British Columbia, are poor candidates for this approach without careful band selection and vegetation index masking. Similarly, alluvial cover, aeolian sand and caliche crusts can mask alteration entirely. The method is most reliable in the Atacama, the Iranian plateau, the Central Asian orogenic belts and parts of the American Southwest, where outcrop exposure is high and atmospheric water vapour is low.

Jarosite and hematite both produce strong iron oxide signatures, but jarosite indicates acid sulphate conditions associated with sulphide oxidation, while hematite can be sedimentary or metamorphic and unrelated to mineralisation. Distinguishing them requires the full SWIR spectrum, not just a broad iron oxide ratio. ASTER can do this with care; Landsat cannot.

Designing a practical campaign

A tiered approach reduces cost and ambiguity. Start with Landsat-9 and Sentinel-2 band ratios across the entire licence area to produce an iron oxide and clay anomaly map. This costs nothing in data and a few days of processing. Use that map to define priority polygons for ASTER archive extraction or EMIT/PRISMA tasking. ASTER SWIR archive scenes, where cloud-free coverage exists, can be obtained through NASA Earthdata at no charge. PRISMA tasking is available under ASI research agreements.

Field validation should follow the spectral map, not precede it. Sample the highest-confidence spectral anomalies first, collect hand spectra with a field spectrometer, and use those to refine the endmember library before running the full unmixing pass. This iterative loop, satellite to field to satellite, is standard practice in exploration remote sensing and is documented in published programmes across the Andes and the Tethyan belt.

Satellize can run the full processing chain on open-archive ASTER and Landsat data and coordinate commercial PRISMA or EMIT tasking on client licence.

Typical figures

Spatial resolution (SWIR)	30 m (ASTER, PRISMA, Landsat-9 OLI); ~60 m (EMIT); 20 m (Sentinel-2 SWIR bands)
Spatial resolution (TIR)	90 m (ASTER); 100 m (Landsat-9 TIRS)
Spectral bands (SWIR)	6 bands (ASTER); 2 bands (Landsat-9 OLI); 3 bands (Sentinel-2); ~240 bands (PRISMA); ~285 bands (EMIT)
SWIR spectral range	1.60–2.43 µm (ASTER); 1.57–2.29 µm (Landsat-9); 1.61–2.19 µm (Sentinel-2); 1.0–2.5 µm (PRISMA, EMIT)
Revisit (systematic)	16 days (Landsat-9); 5 days (Sentinel-2 at mid-latitudes); ~29 days nadir (PRISMA); non-systematic (EMIT, ASTER archive)
Archive depth	ASTER SWIR: 2000–2008 (detector failed 2008); Landsat: 1972–present; Sentinel-2: 2015–present; EMIT: 2022–present; PRISMA: 2019–present
Minimum mappable alteration zone	Approximately 1–2 ha at 30 m resolution for a spectrally pure zone; sub-pixel unmixing can detect minority mineral fractions down to ~20–30% within a pixel under good atmospheric conditions
Cloud and cover constraints	Method requires cloud-free, low-aerosol conditions; vegetation cover above ~30% canopy closure degrades mineral signal significantly
Data cost	ASTER archive and Landsat-9: free via NASA Earthdata; Sentinel-2: free via Copernicus Data Space; PRISMA: research licence via ASI; EMIT: free via NASA Earthdata (L2A reflectance)
Delivery formats	GeoTIFF mineral abundance rasters, vector alteration-zone polygons, spectral angle maps, field-sampling priority shapefiles

Analytics Satellize can run

Iron oxide and clay anomaly screening map	Band ratio indices (e.g. Landsat OLI bands 4/2 for iron oxide, 6/7 for Al-OH clay) applied to Landsat-9 and Sentinel-2 mosaics	GeoTIFF anomaly layer and ranked polygon shapefile covering the full licence area, delivered within 5 working days of data access
ASTER SWIR mineral classification map	Spectral angle mapping (SAM) against USGS spectral library endmembers for alunite, kaolinite, chlorite, jarosite and muscovite, with per-pixel confidence score	Multi-class GeoTIFF with confidence raster and PDF summary of zone extents and spectral match scores
Hyperspectral mineral abundance cube (EMIT or PRISMA)	Constrained spectral unmixing or matched-filter detection run on atmospherically corrected Level 2 reflectance cubes; endmembers derived from USGS or field-collected spectra	Per-mineral fractional abundance GeoTIFFs, spectral residual layer for quality control, and GIS-ready alteration zone polygons
TIR silicification index map	ASTER TIR emissivity decorrelation stretch and silica index (band ratio 14/12) to delineate quartz-rich silicified zones	GeoTIFF silicification layer co-registered with SWIR alteration map for combined targeting overlay
Drill-target priority ranking	Weighted overlay of iron oxide intensity, Al-OH mineral confidence, silicification index and structural lineament proximity (where lineament data is available from published geological maps)	Ranked point shapefile of recommended field-sampling localities with supporting spectral evidence summary per target
Field validation spectral comparison report	Comparison of field spectrometer measurements against satellite-derived spectra at sampled locations; endmember library refinement and re-run of classification	Updated mineral map and PDF report documenting spectral match statistics and revised target confidence levels

Sources

Who does the work

We can get this done for you. Satellize runs its own analyst desk and a strong science team. You do not buy a data feed and work out what it means; our people source the imagery, run the analysis described on this page, and hand you the answer with its confidence limits stated. Discuss this requirement.