64°

66°

68°

70°

38°

38°

72°

72°

36°

34°

70°

32°

68°

30°

64°

62°

30°

32°

34°

62°

66°

74°

74°

36°

Hematite as thin coating

Hematite, fine-grained

Hematite, medium-grained

Hematite, coarse-grained

Iron hydroxide

Goethite, fine-grained

Goethite, medium-grained

Goethite, coarse-grained

Goethite and jarosite

Jarosite

Maghemite

Ferrihydrite

Epidote

Chlorite

Fe3+, type 1

Fe3+, type 2

Fe2+, type 1

Fe2+, type 2

Fe2+ and Fe3+, type 1

Fe2+ and Fe3+, type 2

Green vegetation

Dry vegetation

Snow/ice

Wet soils

Water

Cloud or cloud shadow

Not classified

Classification affected by wet soils

Province boundary—For name of

province see index map 

COLOR COMPARISON CHART

Hematite as thin coating

Hematite, fine-grained

Hematite, medium-grained

Hematite, coarse-grained

Iron hydroxide

Goethite, fine-grained

Goethite, medium-grained

Goethite, coarse-grained

Green vegetation

Dry vegetation

Ferrihydrite

Fe3+, Type 1

Fe3+, Type 2

Fe2+ and Fe3+, type 1

Fe2+ and Fe3+, type 2

Fe2+, Type 2

Fe2+, Type 1

Goethite and jarosite

Maghemite

Jarosite

Epidote

Chlorite

Snow/ice

Not classified

Wet soils

Water

Cloud or cloud shadow

DATA SUMMARY

This map shows the distribution of selected iron-bearing minerals and other 

materials derived from analysis of HyMap imaging spectrometer data of 

Afghanistan. Using a NASA (National Aeronautics and Space Administration) 

WB-57 aircraft flown at an altitude of ~15,240 meters (m) or ~50,000 feet (ft), 

218 flight lines of data were collected over Afghanistan between August 22 

and October 2, 2007 (Kokaly and others, 2008). The grayscale background 

image, visible in the area of no data around the country’s perimeter, is from 

Landsat Enhanced Thematic Mapper Plus (ETM+) (Davis, 2007). 

The HyMap imaging spectrometer measures 128 channels of reflected 

sunlight at wavelengths between 0.4 and 2.5 μm (Cocks and others, 1998). 

The HyMap data were converted to apparent surface reflectance using 

ACORN (Atmospheric CORrection Now) version 6lx software (developed by 

ImSpec LLC), then further empirically adjusted using ground-based reflectance 

measurements. Each flight line was georeferenced to Landsat base imagery in 

Universal Transverse Mercator projection (Davis, 2007). 

HyMap reflectance data were processed using MICA (Material Identification 

and Characterization Algorithm), a module of the U.S. Geological Survey 

PRISM (Processing Routines in IDL for Spectroscopic Measurements) software 

(Kokaly, 2011). Subsequently, the classified HyMap results were projected to a 

Transverse Mercator projection and mosaicked, with 23-m pixel spacing. 

Using MICA, the reflectance spectrum of each pixel of HyMap data was 

compared to the spectral features of reference entries in a spectral library of 

minerals, vegetation, water, ice, and snow. For each pixel, MICA determined 

the reference material having the best matching spectrum. MICA analysis 

resulted in a “Not classified” determination for a pixel when comparisons with 

reference spectra produced no viable match (see Kokaly, 2011). 

This map shows the spatial distribution of iron-bearing minerals and other 

materials having diagnostic absorptions at visible and near-infrared wavelengths. These absorptions result from electronic processes in the minerals. A 

complementary map showing the distribution of selected carbonates, phyllosilicates, sulfates, altered minerals, and other materials having diagnostic absorptions in the shortwave infrared wavelengths has also been created (Kokaly and 

others, 2011).

Several criteria, including (1) the reliability of detection and discrimination of 

minerals using the HyMap spectrometer data, (2) the relative abundance of 

minerals, and (3) the importance of particular minerals to studies of 

Afghanistan’s natural resources, guided the selection of entries in the reference 

spectral library and, therefore, guided the selection of mineral classes shown 

on this map. Minerals occurring abundantly at the surface and those having 

unique spectral features were easily detected and discriminated. Minerals 

having similar spectral features were less easily discriminated, especially where 

the minerals were not particularly abundant and (or) where vegetation cover 

reduced the absorption strength of mineral features. Thus, some identified 

classes consist of several minerals having similar spectra, such as Fe3+, type 1, 

and Fe3+, type 2, in which the primary difference between them is the width of 

the absorption feature. In addition, some identified minerals were grouped to 

reduce the total number of classes, in order to allow the assignment of discernible map colors.

Complications in reflectance calibration also affected the detection and 

identification of minerals. Complications arose because of (1) the large magnitude of the imaging spectrometer dataset, which covers an area over 480,000 

km2, (2) surface elevations that range from 280 to 5,642 m (920–18,510 ft), 

and (3) a protracted data-collection period lasting 43 days. In addition, variations in daily weather, in solar angles, and in airborne dust levels affected 

reflectance calibrations. In cloud-contaminated areas, it was not possible to 

completely remove atmospheric effects in the conversion of the data to reflectance because of significant variation in water-vapor content of the atmosphere. As a result, some differences in identified material classes exist between 

adjacent flight lines that differed in extent of cloud cover. Similarly, minor 

differences in identified material classes occur between adjacent flight lines 

having large differences in solar illumination angles (see Kokaly and others 

(2008) for flight-line data collection times and dates). On October 2, 2007, the 

last day of data collection, a large dust storm blanketed southern Afghanistan 

(see Kokaly and others, 2008). No correction for airborne dust, beyond extension of the ground calibration factor, was applied to the data collected along 

these lines. 

Hatched lines identify areas where mineral identification was adversely 

affected by soil moisture. Material classification in these areas is less accurate 

due to the distortion of reflectance spectra by the strong absorption of light in 

the infrared wavelengths by the water in the soil. In some limited areas, the 

MICA analysis misidentified wet, bright soils as snow, because both are highly 

reflective in the visible wavelengths but are strongly absorbing at longer wavelengths. Pixels classified initially as snow-covered for land surfaces whose 

elevation is lower than a threshold elevation of 3,150 m (10,334 ft) were 

reclassified as wet soils.

The 27 classes in the list of materials represent a necessary compromise 

between striving for the highly focused level of detail achievable in the bestcalibrated flight lines, on the one hand, and, on the other hand, seeking identifications that were reliably detected across the entire country and that occurred 

in large enough areas to be perceptible at a national map scale. Each class 

name specifies a mineral, or several minerals, whose reflectance spectra match 

the HyMap data. However, minerals having slightly different chemical compositions may have very similar reflectance spectra. Therefore, the material 

classes represented on this map should be evaluated giving consideration to the 

effects of mineral chemical composition on reflectance spectra. 

REFERENCES CITED

Cocks, T., Jenssen, R., Stewart, A., Wilson, I., and Shields, T., 1998, The 

HyMap airborne hyperspectral sensor; the system, calibration and performance, in Schaepman, M., Schläpfer, D., and Itten, K.I., eds., Proceedings 

of the First EARSeL Workshop on Imaging Spectroscopy, October 6–8, 

1998, Zurich, Switzerland: Paris, EARSeL, p. 37–43.

Davis, P.A., 2007, Landsat ETM+ false-color image mosaics of Afghanistan: 

U.S. Geological Survey Open-File Report 2007–1029, 22 p., available at 

http://pubs.usgs.gov/of/2007/1029/.

Kokaly, R.F., 2011, PRISM: Processing Routines in IDL for Spectroscopic 

Measurements (installation manual and user’s guide, version 1.0): U.S. 

Geological Survey Open-File Report 2011–1155, available at http://pubs. 

usgs.gov/of/2011/1155/.

Kokaly, R.F., King, T.V.V., Hoefen, T.M., Dudek, K.B., and Livo, K.E., 2011, 

Surface materials map of Afghanistan: carbonates, phyllosilicates, sulfates, 

altered minerals, and other materials: U.S. Geological Survey Scientific 

Investigations Map 3152–A, one sheet, scale 1:1,100,000. Also available 

at http://pubs.usgs.gov/sim/3152/A/.

Kokaly, R.F., King, T.V.V., and Livo, K.E., 2008, Airborne hyperspectral 

survey of Afghanistan 2007; flight line planning and HyMap data collection: 

U.S. Geological Survey Open-File Report 2008–1235, 14 p., available at 

http://pubs.usgs.gov/of/2008/1235/.

EXPLANATION OF MATERIAL CLASSES

OTHER SYMBOLS

[Some material classes which have small areal extent 

may not be visible at the publication scale of this map]

SCALE 1: 1 100 000

25 0 25 50 75 100 MILES

25 0 25 50 75 100 125 150 175 200 KILOMETERS

U.S. DEPARTMENT OF THE INTERIOR

U.S. GEOLOGICAL SURVEY

Base image from Landsat (Davis, 2007)

Political boundaries from digital files from 

Afghanistan Information Management Service (AIMS) 

Web site (http://www.aims.org.af)

Transverse Mercator projection, WGS 84 Datum; 

scale factor 0.9996; longitude of central meridian 

66°00’ E.; latitude of origin 34°00’ N.

SURFACE MATERIALS MAP OF AFGHANISTAN: IRON-BEARING MINERALS AND OTHER MATERIALS

By

Trude V.V. King, Raymond F. Kokaly, Todd M. Hoefen, Kathleen B. Dudek, and Keith E. Livo

2011

Prepared in cooperation with the

AFGHANISTAN MINISTRY OF MINES AND INDUSTRIES

SCIENTIFIC INVESTIGATIONS MAP 3152–B

USGS Afghanistan Project Product No. 191

Any use of trade, product, or firm names in this 

publication is for descriptive purposes only and does 

not imply endorsement by the U.S. Government

Printed on recycled paper 

INDEX MAP SHOWING PROVINCES OF AFGHANISTAN

0 50 100

0 50 100 MILES

150 200 KILOMETERS

FARAH

HELMAND

KANDAHAR

NIMROZ

ZABUL

GHAZNI KHOST

PAKTIKA

KABUL

LOGAR

PAKTYA

BADGHIS

HERAT

TAKHAR

JAWZJAN

BALKH

KUNDUZ

FARYAB BAGHLAN

GHOR NANGARHAR

NURISTAN

PARWAN

LAGHMAN KUNAR

KAPISA

BADAKHSHAN

SAMANGAN

BAMYAN

WARDAK

URUZGAN

DAYKUNDI

SAR-E PUL

PANJSHER

64°

66°

68°

70°

38°

72°

36°

34°

70°

32°

68°

30°

64°

62°

30°

32°

34°

62°

66°

74°

36°