In cooperation with U.S. Agency for International Development 

Calibrated Landsat ETM+ Nonthermal-Band Image 

Mosaics of Afghanistan 

By Philip A. Davis 

Open-File Report 2006-1345

U.S. Department of the Interior 

U.S. Geological Survey 

 
U.S. Department of the Interior 

DIRK KEMPTHORNE, Secretary

U.S. Geological Survey 

Mark D. Myers, Director 

U.S. Geological Survey, Reston, Virginia 20192 

2006

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Davis, P. A., 2006, Calibrated Landsat ETM+ nonthermal-band image mosaics of Afghanistan, U.S. 

Geological Survey Open-File Report 2006-1345, 18 p. [http://pubs.usgs.gov/of2006/1345/ ]. 

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copyright owners to reproduce any copyrighted material contained within this report. 

2
Contents 

Introduction .........................................................................................................................................................................4 

Characteristics of Landsat ETM+ Satellite Image Data ..............................................................................................4 

Processing of Landsat ETM+ Satellite Image Data......................................................................................................5 

Conversion of Raw Satellite Data to Relative Reflectance.....................................................................................6

Correction of Scan Line Calibration Errors ................................................................................................................6

Countrywide Image-to-Image Reflectance Calibration...........................................................................................7

Noise Correction of Panchromatic Image Data........................................................................................................8

Resoultion Enhancement of the 28.5-m Reflectance Image Data .........................................................................8

Reprojection of Landsat Scenes along UTM Zone Borders ...................................................................................9

Mosaicking of Landsat Scenes into Map Quadrangles ..........................................................................................9

References Cited ................................................................................................................................................................9

Figures 

1. Landsat ETM+ scene outlines superposed on a map of Afghanistan. "P" indicates Path (red) and 

"R" indicates Row (blue).....................................................................................................................................12

2. Panchromatic image of an agricultural area in southern Afghanistan showing (a) dense, random 

noise present in the original orthorectified Landsat panchromatic images obtained from the 

EROS Data Center archive, and (b) the corresponding image after application of the adaptive 

sigma filter and an edge enhancement filter.................................................................................................13 

3. Landsat false-color image at (a) original 28.5-m resolution and (b) enhanced 14.25-m resolution. 

Landsat bands 7, 4, and 1 are displayed as red, green, and blue, respectively.................................... 14

4. Map tile scheme for the Afghanistan project with each 1o x 2o map quadrangle designated by its 

lower left corner latitude and longitude. ...................................................................................................... 15

Tables 

1. Characteristics of Landsat ETM+ Image Data. .......................................................................................................16

2. Landsat ETM+ Image Data Used in the Countrywide Image Mosaic.................................................................17

3
Calibrated Landsat ETM+ Nonthermal-Band Image 

Mosaics of Afghanistan 

By Philip A. Davis1

Introduction 

In 2005, the U.S. Agency for International Development and the U.S. Trade and 

Development Agency contracted with the U.S. Geological Survey to perform assessments of the 

natural resources within Afghanistan. The assessments concentrate on the resources that are related 

to the economic development of that country. Therefore, assessments were initiated in oil and gas, 

coal, mineral resources, water resources, and earthquake hazards. All of these assessments require 

geologic, structural, and topographic information throughout the country at a finer scale and better 

accuracy than that provided by the existing maps, which were published in the 1970’s by the 

Russians and Germans. The very rugged terrain in Afghanistan, the large scale of these 

assessments, and the terrorist threat in Afghanistan indicated that the best approach to provide the 

preliminary assessments was to use remotely sensed, satellite image data, although this may also 

apply to subsequent phases of the assessments. Therefore, the first step in the assessment process 

was to produce satellite image mosaics of Afghanistan that would be useful for these assessments. 

This report discusses the production and characteristics of the fundamental satellite image 

databases produced for these assessments, which are calibrated image mosaics of all six Landsat 

nonthermal (reflected) bands. 

Characteristics of Landsat ETM+ Satellite Image Data 

Landsat Enhanced Thematic Mapper Plus (ETM+) data were used to construct the image 

mosaics of Afghanistan. These data consist of six reflected (nonthermal) bands at 28.5-m spatial 

resolution, two thermal infrared bands at 57.0-m spatial resolution, and a panchromatic band at 

14.25-m spatial resolution. The wavelength range for each of these bands is listed in Table 1. The 

thermal infrared bands include a high- and low-gain image, but these two broad wavelength 

thermal images are not generally useful for geologic applications and were therefore not included 

in our image mosaics. The wavelength band of the panchromatic image covers most of the visible 

wavelength region (Table 1). Although not useful for mapping surface material, due to its broad 

wavelength range, its higher spatial resolution provides more detailed morphologic information 

than the other lower-resolution reflected wavelength band images. 

All of the Landsat scenes used to build the image mosaics are listed in Table 2. The scene 

ID for each Landsat ETM+ image listed in Table 2 (e.g., ELP149R034_7T20010930) provides the 

Path (“P”) and Row (“R”) of the satellite during image acquisition, the “7T” indicates that the 

 

1

 U.S. Geological Survey, 2255 North Gemini Drive, Flagstaff, AZ 86001 

4
scene is Landsat 7 Thematic Mapper, and the “20010930” indicates the date of acquisition, which 

is September 30, 2001 in this example. Figure 1 shows the Landsat ETM+ Path and Row grid for 

Afghanistan. These scenes were selected from NASA’s Landsat Orthorectified ETM+ Imagery 

Archive that resides at the EROS Data Center in South Dakota. Landsat scenes within that archive 

meet NASA’s criteria for orthorectification, which included acquisition date, cloud percentage, 

data quality, and best available phenology (Kalluri et al., 2000). The raw Landsat images were 

orthorectified by Earth Satellite Corporation to a positional accuracy of 50 meters. The image data 

are provided in Universal Transverse Mercator (UTM) map projection using the WGS84 datum. 

Afghanistan has three UTM zones (41, 42, and 43); zone 42 lies between 66o and 72o East 

longitude. We performed an additional selection process on the NASA archive because there are 

replicate image acquisitions for some scenes. Our selection process attempted to choose scenes 

that were acquired (1) within the same year to avoid surface changes between scenes, (2) near 

summer solstice to provide high solar elevation angles and minimize mountain shadows, and (3) 

with minimal cloud and snow/ice cover because most of our resource assessments (except water 

resources) need to see the rock and soil surfaces. Most of the scenes used in the mosaic were 

acquired in 2001, some in 2000, and only two in 1999. 

Although there are more sophisticated satellites now in Earth’s orbit (e.g., ASTER, 

HYPERION), Landsat ETM+ provides a larger field of view (about 32,400 km2 per scene) and 

spectral distinction among major rock, soil, and vegetation types. For example, Landsat ETM+ 

bands 1, 2, and 3 cover the blue, green, and red wavelength regions of the visible spectrum and can 

be used to make a natural color image of the surface. Iron oxides, consisting of ferric (Fe+3) and 

ferrous (Fe+2) iron, absorb in these visible wavelength regions and within the near-infrared band 4 

(due to intervalence charge transfer and crystal field transitions) and, therefore, ratios of these 

bands can indicate relative amounts of surface iron oxide (Hunt and Salisbury, 1970; Hunt et al., 

1971a). Landsat ETM+ band 4 occurs in the near-infrared (NIR) wavelength region in which 

vegetation has the highest spectral reflectance because chlorophyll absorbs in most of the other 

Landsat ETM+ band wavelengths. Thus, the band ratio 4/3 or the Normalized Difference 

Vegetation Index [NDVI = (4-3)/(4+3)] is normally used to indicate the presence and health of 

surface vegetation. Landsat ETM+ bands 5 and 7 occur within the shortwave infrared region where 

absorptions occur due to molecular vibrations. Water bound in certain minerals (e.g., clays) 

experience H-O-H dipolar stretching and OH-

 bending, whose overtones and combination tones 

cause absorptions within band 5 and band 7 (Hunt and Salisbury, 1970). Surface materials 

containing CO3=

 (carbonates) and SO4= (sulfates) cause absorptions within band 7 due to lattice 

vibrations (Hunt and Salisbury, 1971; Hunt et al., 1971b). Thus, the shortwave infrared bands are 

very useful for mapping certain minerals and rock types containing those mineral groups. 

However, there are a number of different mineral groups that produce absorptions within the same 

ETM+ band and, therefore, the broad shortwave-infrared bands do not provide unambiguous 

identification of surface mineralogy or rock type. 

Processing of Landsat ETM+ Satellite Image Data 

Several steps were required to transform the raw 8-bit (numbers 0-255) Landsat ETM+ 

band data into relative reflectance data that is consistent image to image throughout the large area 

of Afghanistan. In each step every effort was made to retain the integrity of the original data to 

allow accurate, wide-area mapping for a variety of natural resources. The different steps consisted 

of (1) conversion of stored 8-bit data to relative reflectance data; (2) correction for any image errors 

(calibration errors in specific image scan lines); (4) image-to-image adjustment of reflectance 

5
values to obtain consistent data throughout the country; (5) noise reduction of the panchromatic 

image data; (6) resolution enhancement of the 28.5-m band data to produce 14.25-m band data; (7) 

reprojection of images along UTM zone boundaries; and (8) mosaicking scenes into our designated 

1o latitude x 2o longitude map quadrangles. 

Conversion of Raw Satellite Data to Relative Reflectance 

During satellite image acquisition, the radiance (mW/cm2• sterdian • μm) observed by each 

Landsat band’s charged coupled device (CCD) is converted to an 8-bit number (0-255) for efficient 

storage using each band’s unique gain and bias values. The gain and bias for each CCD optimizes 

stored spectral sensitivity without saturation. The sensitivity of the stored 8-bit values is between 

0.13% and 0.29% reflectance. 

The atmosphere scatters a fraction of the solar incidence flux back to the satellite detectors 

before interacting with the Earth’s surface. This scattering can be Rayleigh, which involves 

atmospheric water molecules, or Mie, which involves aerosols. The fraction of solar incidence flux 

scattered by the atmosphere increases from the near-infrared wavelength (Landsat band 4) to the 

blue wavelength (Landsat band 1) in a 1/λ

n

 relation, where n can range from 4.0 for pure Rayleigh 

scattering to 1.0 for Mie scattering. Thus, the amount of scattering within the near-infrared band is 

very small compared to that within the blue band. This scattering has to be removed from ETM+ 

bands 1 through 4 in order to (1) produce consistent reflectance scene to scene, (2) allow the use of 

band ratios in mapping surface materials, and (3) produce reflectance spectra of surface materials 

that can be directly compared to laboratory spectral libraries of minerals, rocks, and vegetation. 

The FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes) algorithm 

within ENVI image processing software was used to convert the stored 8-bit band values to their 

radiance values and then to remove the atmosphere and its scattering effects from the Landsat band 

images (RSI, 2004). This algorithm has been tested and shown to be accurate by Matthew et al. 

(2003). FLAASH incorporates the MODTRAN4 radiation transfer code and corrects for adjacency 

effects, which is ground-to-ground scattering of surface-reflected radiance that results in picture 

element (pixel) spectral mixing. Estimates of the water vapor column and the aerosol amount 

within each scene were obtained from periodic MODIS data; these data were supplied to FLAASH 

to produce a final corrected reflectance image for each Landsat band. The reflectance data 

produced by the FLAASH algorithm are multiplied by 10,000 and converted to 16-bit values to 

conserve disk space. However, the 16-bit files are still very large for creating a countrywide 

mosaic, especially at our preferred 14.25-m resolution. Therefore, the band reflectance values were 

converted to 8-bit values by multiplying their 16-bit values by (255/10,000). This conversion 

reduces the spectral sensitivity from 0.13% reflectance to 0.39% reflectance; the mountain shaded 

and snow/ice surfaces did not allow a less constrained 8-bit conversion. The calibrated 16-bit 

scenes are preserved for more detailed assessment requirements and are stored on our internal 

website (http://gisdata.usgs.net/website/afghan). 

Correction of Scan Line Calibration Errors 

Two of the 54 selected Landsat scenes had a few scan lines with anomalously high or low 

reflectance values in some of the bands. Replacement scenes without this problem had even larger 

problems associated with low sun angle, clouds, or snow cover. Therefore, a least squares method 

was devised to correct the few anomalous scan lines. Fortunately, the anomalous scan lines were 

not coincident in the different reflectance bands within a scene. Therefore, data from an adjacent 

6
wavelength band could be used to adjust the anomalous scan-line reflectance values to correspond 

to adjacent “normal” reflectance. Data within areas on both sides of the anomalous scan lines were 

compared by linear least squares analysis to corresponding data in adjacent wavelength bands to 

determine which adjacent band most correlated with the band having the anomalous scan lines and 

to determine the linear reflectance relation between the two bands. The most linear relation was 

used to adjust the adjacent band data to the band data having the anomalous scan lines and the 

resulting “synthesized” data replaced the anomalous scan lines. The results were visually inspected 

to ensure the quality of the adjustment by digitally stretching the “synthesized” image area to 

determine if there were residual brightness discontinuities. The process worked very well. 

Countrywide Image-to-Image Reflectance Calibration 

No image calibration can completely correct large scene-to-scene differences in solar 

incidence angle, such as exists within our Landsat scene collection (38o to 65o; Table 2). 

Therefore, an image-to-image least-squares adjustment was implemented to adjust the band 

reflectance within a scene to the corresponding band reflectance of an adjacent scene. This process 

required a scene or set of scenes to serve as the standard lighting condition. The scenes within Path 

155 were selected as the standard set of scenes for normalization of all other scenes for the country 

mosaics. The scenes within Path 155 were all acquired on the same day close to summer solstice 

when the solar elevation angle was relatively high and therefore surface reflectance was the closest 

to surface normal reflectance that can be obtained from the Sun-synchronous Landsat satellite. 

This Path also covers most of the latitude range within Afghanistan and it is located in the middle 

of the country (Figure 1). Examination of the reflectance data within regions of overlap between 

each pair of scenes within Path 155 showed the reflectance values to be very consistent scene to 

scene. 

In order to derive least squares relations between two scenes it is necessary to remove from 

consideration surface areas that had changed between the two scene acquisition dates. These 

changes were generally vegetation, snow, ice, and clouds. Therefore, the initial step in the 

adjustment process was to construct image masks for clouds, snow, and ice. The least-squares 

algorithm used NDVI values to determine and exclude vegetation and used the masks to exclude 

snow, ice, and clouds. The algorithm determined the region of overlap between two scenes using 

their UTM coordinates, masked out the vegetation, snow, ice, and clouds, and then preformed a 

linear least-squares determination for each Landsat band that related a band’s reflectance to the 

band reflectance of the standard scene. The least-squares algorithm produced a two-dimensional 

plot of the pixel-to-pixel correspondence in reflectance for each band; these plots were examined 

and points were edited to exclude anomalies. If segments of the plotted data were not linear, the 

band data within the nonlinear range were examined to determine the reason and make 

adjustments. Nonlinearity was usually caused by incomplete removal of surfaces that had changed 

between the two scenes. The set of band linear equations were then applied to the scene to adjust 

its band reflectance values to that of the standard scene. 

The process started with scenes adjacent to Path 155 and moved east and west, using the 

adjusted scenes as the standards for scenes further east and west. After each scene was adjusted, 

the adjusted scene was mosaicked with adjacent “standard” scenes and the overlap borders were 

examined by applying a very hard digital stretch to each 400-pixel segment along the border seam. 

If a discontinuity was detected, the least squares data and equation were re-examined, revised, and 

re-applied until there were no visible brightness discontinuities along overlap borders. The linear 

7
adjustment to the scenes not only compensated for different solar illuminations, but also 

compensated for incomplete removal of atmospheric scattering between scenes. 

Noise Correction of Panchromatic Image Data 

The Landsat panchromatic image data at 14.25-m spatial resolution provide more 

morphologic details than the 28.5-m Landsat multispectral bands. However, inspection of the 

panchromatic images revealed a high density of random noise (Figure 2a), which was introduced 

into the raw data by the modulation transfer function applied by the EROS Data Center during their 

ephemerid rectification of the scenes. The random nature and variable brightness levels of the 

noise made its removal by conventional digital filters impossible without serious degradation of the 

true signal information. However, the adaptive sigma filter designed by Eliason and McEwen 

(1990) removed most of the noise with the least effect on the true signal data. This filter was 

applied to each panchromatic image using a filter dimension of 3x3 and a 2.7 standard deviation 

tolerance. This filter did reduce the sharpness of the image somewhat, so a subsequent 5x5 highpass filter was applied using a 95 percent addback of the original image data (also known as an 

edge enhancement filter). The result of this process on the panchromatic data is demonstrated by 

Figure 2b. 

Resolution Enhancement of the 28.5-m Reflectance Image Data 

The 14.25-m panchromatic images contain more morphologic information on bedding, 

faults, surface texture, cultural features, and small streams than the 28.5-m multispectral image 

data. In order to make use of this higher resolution texture and the spectral information contained 

in the multispectral data, a resolution enhancement algorithm was applied to both data sets to 

produce a multispectral dataset with 14.25-m resolution. There have been many different 

algorithms developed over the past decade for resolution enhancement, for each of which the 

earliest and latest references is provided: color normalized (Hallada and Cox, 1983), intensity-huesaturation merge (Haydn et al., 1982; Chavez et al., 1991), principal component analysis (Chavez 

et al., 1991), wavelet decomposition (Yocky, 1996; Gonzalez-Audicana, 2004), modulation transfer 

functions (Goforth, 1998; Alazzi et al., 2006), pixel unmixing (Zhukov et al., 1999), and 

SPARKLE (unpublished ERIM proprietary algorithm). Vrabel (1996; 2000) statistically examined 

many of these techniques to determine which technique provided the most improvement in ground 

sample distance while retaining most of the original spectral information. These studies indicated 

that the color normalized and the SPARKLE approach performed the best, although I have found 

through experimentation that the color normalized approach does not retain spectral fidelity and 

therefore the SPARKLE algorithm was implemented on our Landsat multispectral and “corrected” 

panchromatic image data. 

The basic concept of the SPARKLE algorithm is that a higher resolution panchromatic 

image can be filtered to duplicate the effect of the modulation transfer function of the lower 

resolution multispectral sensor. Once accomplished the ratio of the original panchromatic image to 

the filtered panchromatic image is multiplied by each scene’s multispectral bands and the highfrequency information in the panchromatic image will be transferred to the multispectral bands 

with no change in the relative spectral relations for any particular pixel. I have found that is true 

except along abrupt vegetation borders and I, therefore, test each pixel’s result to determine if the 

reflectance relations change by more than one percent from the original relations and, if it did, the 

original pixel values are retained in the final product. An example of the SPARKLE resolution 

enhancement is shown for a small area in Afghanistan in Figure 3. 

8
Reprojection of Landsat Scenes along UTM Zone Borders 

Along the borders of the UTM zones within Afghanistan the Landsat scenes were 

orthorectified to the UTM zone containing the majority of the scene. Thus, scene projections 

change from zone 42 in the north to zone 43 in the south along the boundary of those two zones, as 

well as along the zone 42-43 boundary. Therefore, scenes straddling the zone boundaries had to be 

reprojected into the other zone to make our UTM image mosaics. Investigation of different 

methods (RST, Triangulation, Polynomial) and degrees of map reprojection showed that the 

method that retained the most control was a second-order polynomial transformation using an x and 

y control-point grid of 90 x 90 (i.e., 8100 control points) for the 28.5-m scenes. The enhanced 

14.25-m scenes required a 180 x 180 control-point grid. 

Mosaicking of Landsat Scenes into Map Quadrangles 

For map production purposes in the Afghanistan project, the country was divided into 1o x 

2o tiles and each tile is labeled by the latitude and longitude values of its lower left corner (Figure 

4). Thus, tile 3468 has a lower left corner at 34oN of latitude and 68oE of longitude. Some of the 

calibrated Landsat scenes required some manual trimming before mosaicking in order to minimize 

cloud, snow, and ice cover, and to avoid areas that had otherwise changed within the region of 

overlap of two scenes. In addition, some degree of feathering was applied during the mosaicking 

process, especially in desert areas that have very low contrast. Feathering is an image processing 

procedure whereby the digital values of two images are proportionally blended (from 0 to 100 

percent) over a specified distance perpendicular to their overlap border. As the process proceeds 

away from the border, the proportion used of underlying image decreases from 100 percent and that 

of the overlying image increases to 100 percent. To maintain the original 50-m positional accuracy 

of the image data, each map tile was constructed separately only using the Landsat scenes (or 

portions of scenes) that covered the map tile. Once completed, each tile was trimmed to provide 

100 meter outside border to provide overlap with all adjacent map tiles. Each band in the tile was 

then converted to an embedded geotiff with a world file and all of the band tiffs and world files for 

a map tile were compressed into a single digital file whose file name corresponded to the map tile 

number. 

All of the 28.5-m and 14.25-m map tiles containing the calibrated nonthermal Landsat 

bands for Afghanistan can be downloaded from http://gisdata.usgs.net/website/afghan. 

References Cited 

Alazzi, B., Alparone, L., Baronti, S., Garzelli, A., and Selva, M., 2006, MTF-tailored multiscale 

fusion of high-resolution MS and pan imagery. Photogrammetric Engineering and Remote 

Sensing, v. 72, p. 591-596. 

Chavez, P. S., Sides, S. C., and Anderson, J. A., 1991, Comparison of three different methods to 

merge multiresolution and multispectral data: Landsat TM and SPOT panchromatic. 

Photogrammetric Engineering and Remote Sensing, v. 57, p. 295-303. 

Eliason, E. M. and McEwen, A. S., 1990, Adaptive box filters for removal of random noise from 

digital images. Photogrammetric Engineering and Remote Sensing, v. 56, p. 453-458. 

9
Goforth, M. A., 1998, Multispectral image sharpening with multiresolution analysis and the MTF. 

Proceedings of the SPIE Conference on Algorithms for Multispectral and Hyperspectral Imagery 

IV, SPIE Volume 3372, (Shen, S. S. and Descour, M. R., Eds.), p. 123-131. 

Gonzalez-Audicana, M., Saleta, J. L., Catalan, R. G., and Garcia, R., 2004, Fusion of multispectral 

and panchromatic images using improved HIS and PCA mergers based on wavelet 

decomposition. IEEE Transactions on Geoscience and Remote Sensing, v. 42, p. 1291-1299. 

Hallada, W. A., and Cox, S., 1983, Image sharpening for mixed spatial and spectral resolution 

satellite systems. Proceedings Seventh International Symposium on Remote Sensing of 

Environment, Environmental Research Institute of Michigan, Ann Arbor, p. 1023-1032. 

Haydn, R., Dalke, G. W., Henkel, J., and Bare, J. E., 1982, Application of the HIS color transform 

to the processing of multisensor data and image enhancement. Proceedings International 

Symposium on Remote Sensing of Environment, First Thematic Conference: Remote Sensing of 

Arid and Semi-arid Lands, Environmental Research Institute of Michigan, Ann Arbor, p. 599-

616. 

Hunt, G., R., and Salisbury, J. W., 1970, Visible and near-infrared spectra of minerals and rocks: I. 

Silicate minerals. Modern Geology, v. 1, p. 283-300. 

Hunt, G. R., and Salisbury, J. W., 1971, Visible and near-infrared spectra of minerals and rocks: II. 

Carbonates. Modern Geology, v. 2, p. 23-30. 

Hunt, G. R., Salisbury, J. W., and Lenoff, C. J., 1971a, Visible and near-infrared spectra of 

minerals and rocks: III. Oxides and hydroxides. Modern Geology, v. 2, p. 195-205. 

Hunt, G. R., Salisbury, J. W., and Lenoff, C. J., 1971b, Visible and near-infrared spectra of 

minerals and rocks: IV. Sulphides and sulphates. Modern Geology, v. 3, p. 1-14. 

Kalluri, S., Grant, D., Tucker, C., Policelli, F., Dykstra, J., and Bearden, P., 2000,, NASA creates 

global archive of ortho-rectified Landsat data. EOS, Transactions, American Geophysical Union, 

v. 81, no. 50, p. 609, 617-618. 

Matthew, M.W., Adler-Golden, S. M., Berk, A., Felde, G., Anderson, G. P., Gorodetzky, D., 

Paswaters, S., and Shippert, M., 2003, Atmospheric correction of spectral imagery: evaluation of 

the FLAASH algorithm with AVIRIS data. SPIE Proceeding, (Shen, S. S., and Lewis, P. E., 

Eds.), Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery 

IX, v. 5093. pp. 474-482. 

RSI, 2004, FLAASH User’s Guide, Version 4.1, 80 p. 

Vrabel, J., 1996, Multispectral imagery band sharpening study, Photogrammetric Engineering and 

Remote Sensing, v. 62, p. 1075-1083. 

Vrabel, J., 2000, Multispectral imagery advanced band sharpening study. Photogrammetric 

Engineering and Remote Sensing, v. 66, p. 73-79. 

10
Yocky, D. A., 1996, Multiresolution wavelet decomposition image merger of Landsat Thematic 

Mapper and SPOT panchromatic data. Photogrammetric Engineering and Remote Sensing, v. 62, 

p. 1067-1074. 

Zhukov, B., Oertel, D., Lanzl, F., and Reinhackel, G., 1999, Unmixing-based multisensor 

multiresolution image fusion. IEEE Transactions on Geosceince and Remote Sensing, v. 37, 

p.1212-1226. 

11
Figure 1. Landsat ETM+ scene outlines superposed on a map of Afghanistan. “P” indicates Path 

(red) and “R” indicates Row (blue). 

12
Figure 2. Panchromatic image of an agricultural area in southern Afghanistan showing (a) dense, 

random noise present in the original orthorectified Landsat panchromatic images obtained from the 

EROS Data Center archive, and (b) the corresponding image after application of the adaptive sigma 

filter and an edge enhancement filter. 

13
Figure 3. Landsat false-color image at (a) original 28.5-m resolution and (b) enhanced 14.25-m 

resolution. Landsat bands 7, 4, and 1 are displayed as red, green, and blue, respectively.

14
Figure 4. Map tile scheme for the Afghanistan project (on a natural-color image base) with each 

1o x 2o map quadrangle designated by its lower left corner latitude and longitude. 

15
Table 1. Characteristics of Landsat ETM+ Image Data. 

ETM+ Band Number Wavelength Range 

(μm) Wavelength Region Resolution (m) 

1 0.45-0.52 Blue 28.50 

2 0.52-0.60 Green 28.50 

3 0.63-0.69 Red 28.50 

4 0.76-0.90 Near-Infrared 28.50 

5 1.55-1.75 Shortwave Infrared 28.50 

6H (high gain) 10.40-12.50 Thermal Infrared 57.00 

6L (low gain) 10.40-12.50 Thermal Infrared 57.00 

7 2.08-2.35 Shortwave Infrared 28.50 

8 0.52-0.90 Visible Panchromatic 14.25 

16
Table 2. Landsat ETM+ Image Data Used in the Countrywide Image Mosaic 

Landsat Scene ID Acquisition Time 

(GMT) 

Solar Elevation 

Angle (degrees) Center Latitude Center Longitude 

ELP149R034_7T20010930 5:23:22 45.18 37.4721 N 75.5768 E 

ELP149R035_7T20010930 5:23:46 46.28 36.0378 N 75.1547 E 

ELP150R034_7T19990815 5:34:02 58.69 37.4797 N 74.0922 E 

ELP150R035_7T19990916 5:34:24 51.28 36.0485 N 73.6861 E 

ELP151R034_7T20010928 5:35:44 45.83 37.4723 N 72.4802 E 

ELP151R035_7T20001027 5:38:24 37.77 36.0463 N 72.1177 E 

ELP151R036_7T20010928 5:36:34 47.96 34.6110 N 71.6543 E 

ELP151R037_7T20001011 5:38:54 45.14 33.1761 N 71.2544 E 

ELP152R033_7T20000916 5:43:49 48.72 38.9082 N 71.4102 E 

ELP152R034_7T20010802 5:42:40 60.52 37.4782 N 70.9474 E 

ELP152R035_7T20010802 5:43:04 61.11 36.0439 N 70.5284 E 

ELP152R036_7T20011005 5:42:36 45.77 34.6130 N 70.1216 E 

ELP152R037_7T20001018 5:44:55 42.94 33.1784 N 69.7880 E 

ELP152R038_7T20001018 5:45:28 44.09 31.7436 N 69.3987 E 

ELP153R033_7T20010708 5:48:42 63.58 38.9110 N 69.8427 E 

ELP153R034_7T20010708 5:49:05 64.07 37.4774 N 69.4083 E 

ELP153R035_7T20010708 5:49:28 64.49 36.0482 N 68.9902 E 

ELP153R036_7T20000822 5:51:32 58.19 34.6113 N 68.5849 E 

ELP153R037_7T20001025 5:51:10 40.76 33.1808 N 68.2271 E 

ELP153R038_7T20001009 5:51:41 46.87 31.7457 N 67.7766 E 

ELP153R039_7T20001009 5:52:04 47.96 30.3087 N 67.3968 E 

ELP153R040_7T20001025 5:52:23 44.24 28.8720 N 67.0830 E 

ELP154R034_7T20010731 5:55:03 60.87 37.4771 N 67.8560 E 

ELP154R035_7T20010629 5:55:48 65.26 36.0489 N 67.4779 E 

ELP154R036_7T20010629 5:56:00 65.59 34.6182 N 67.0698 E 

ELP154R037_7T20000525 5:59:03 66.41 33.1810 N 66.6676 E 

ELP154R038_7T20010629 5:56:50 66.00 31.7496 N 66.2818 E 

ELP154R039_7T20010629 5:57:14 66.08 30.3110 N 65.9008 E 

ELP154R040_7T20010629 5:57:38 66.07 28.8760 N 65.5294 E 

ELP155R034_7T20010503 6:02:08 60.59 37.4830 N 66.3074 E 

ELP155R035_7T20010503 6:02:33 61.32 36.0541 N 65.8877 E 

ELP155R036_7T20010503 6:02:58 62.01 34.6160 N 63.9870 E 

ELP155R037_7T20010503 6:03:21 62.63 33.1878 N 65.0803 E 

17
Landsat Scene ID Acquisition Time 

(GMT) 

Solar Elevation 

Angle (degrees) Center Latitude Center Longitude 

ELP155R038_7T20010503 6:03:45 63.24 31.7511 N 64.6912 E 

ELP155R039_7T20010503 6:04:08 63.70 30.3176 N 64.3117 E 

ELP155R040_7T20010503 6:04:33 64.14 28.8790 N 63.9390 E 

ELP156R034_7T20010627 6:07:35 65.00 37.4809 N 64.8165 E 

ELP156R035_7T20010627 6:07:58 65.40 36.0471 N 64.3954 E 

ELP156R036_7T20010627 6:08:23 65.72 34.6160 N 63.9870 E 

ELP156R037_7T20010627 6:08:47 65.96 33.1843 N 63.5895 E 

ELP156R038_7T20010510 6:09:50 64.52 31.7493 N 63.1516 E 

ELP156R039_7T20010510 6:10:14 64.94 30.3158 N 62.7724 E 

ELP156R040_7T20010510 6:10:38 65.29 28.8768 N 62.4000 E 

ELP157R034_7T20010517 6:14:18 63.44 37.4821 N 63.2390 E 

ELP157R035_7T20010517 6:14:42 64.06 36.0532 N 62.8194 E 

ELP157R036_7T20010517 6:15:05 64.62 34.6180 N 62.4094 E 

ELP157R037_7T20010517 6:15:30 65.11 33.1864 N 62.0113 E 

ELP157R038_7T20010720 6:15:21 64.12 31.7445 N 61.5978 E 

ELP157R039_7T20000919 6:16:55 53.77 30.3107 N 61.2710 E 

ELP157R040_7T20010704 6:16:12 65.74 28.8766 N 60.8759 E 

ELP158R035_7T20010711 6:20:23 64.18 36.0457 N 61.2592 E 

ELP158R036_7T20000708 6:22:59 65.23 34.6103 N 60.8681 E 

ELP158R037_7T20010625 6:21:10 66.09 33.1829 N 60.4928 E 

ELP158R038_7T20010625 6:21:35 66.25 31.7459 N 60.1046 E 

18