**THE CONTERMINOUS UNITED STATES AND ALASKA WEEKLY AND BIWEEKLY AVHRR
COMPOSITES**

TABLE OF CONTENTS

[INTRODUCTION 1](#introduction)

[DATA SET CHARACTERISTICS 1](#data-set-characteristics)

[DATA PROCESSING 2](#data-processing)

[*Scene Selection* 2](#scene-selection)

[*Satellite and Solar Viewing Geometry*
3](#satellite-and-solar-viewing-geometry)

[*Radiometric Calibration* 3](#radiometric-calibration)

[*Atmospheric Correction* 5](#atmospheric-correction)

[*Scaling the Reflectance and Temperature Data*
6](#scaling-the-reflectance-and-temperature-data)

[*Geometric Registration* 6](#geometric-registration)

[*Normalized Difference Vegetation Index (NDVI)*
7](#normalized-difference-vegetation-index-ndvi)

[*Date of Acquisition* 8](#date-of-acquisition)

[*Compositing* 8](#compositing)

[*Cloud Screening* 8](#cloud-screening)

[CONTACT INFORMATION 9](#contact-information)

[REFERENCES 9](#references)

### INTRODUCTION

In 1987, the U.S. Geological Survey\'s National Center for Earth
Observation and Science (EROS) in Sioux Falls, South Dakota, began
receiving Advanced Very High Resolution Radiometer (AVHRR) data from
NOAA polar-orbiting satellites. The central United States location of
EROS enables direct reception of all AVHRR overpasses of the lower 48
States, as well as much of Canada and Mexico. In 1989, EROS started
acquiring afternoon AVHRR 1-km resolution daily observations to produce
weekly and biweekly maximum normalized difference vegetation index
(NDVI) composites of the conterminous United States and Alaska
(Eidenshink, 1992). The objective of the vegetation mapping program is
to compile, annually, a comprehensive series of calibrated,
georegistered, maximum NDVI composites. These data are being published
on CD-ROM for distribution of the data set. These data sets can be used
in environmental monitoring and global climate change studies.

The vegetation diversity of the United States provides opportunities for
using both AVHRR data and the NDVI for monitoring vegetation condition
in several different ecosystems, including forests, agricultural crops,
and grasslands. The data set provides a comprehensive growing season
profile of these ecosystems, is extremely useful for assessing seasonal
variations in vegetation conditions, and provides a foundation for
studying long-term changes resulting from human or natural factors.

### DATA SET CHARACTERISTICS

The conterminous United States data set is composed of twenty-six 14-day
and fifty-two 7-day NDVI composites annually since 1989. The Alaska data
set generally includes fourteen 14-day and twenty-eight weekly
composites for the period April trough October each year since 1990.
Each composite includes 14 bands of information (described in Table 1).

Table 1. Band description of composite images

\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

Band Description
\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

1 Top of atmosphere Channel 1 reflectance (Calibrated, no atmospheric
correction)

2 Top of atmosphere Channel 2 reflectance (Calibrated, no atmospheric
correction)

3 Radiance

4 Temperature

5 Temperature

6 NDVI

7 Satellite zenith

8 Solar zenith

9 Relative azimuth

10 Atmospherically corrected Channel 1 surface reflectance

11 Atmospherically corrected Channel 2 surface reflectance

12 QA/QC

13 Date index

14 Cloud mask

The image dimensions of the conterminous United States data set are
2,889 lines and 4,587 samples per band. The Alaska data set is 1,992
lines by 2,512 samples per band.

### DATA PROCESSING

The following information describes the data processing flow that was
used to create the composite data set. All image processing was
conducted using Land Analysis System (Ailts and others, 1990) software.

### *Scene Selection*

Cloud-free AVHRR observations of the land surface are necessary for
monitoring the vegetation conditions. A single AVHRR overpass is seldom
completely cloud free. Holben (1986) showed that compositing AVHRR data
acquired over several days produces spatially continuous cloud-free
images over large areas with sufficient temporal resolution to study
green vegetation dynamics. The duration of consecutive daily
observations is called the compositing period. On a daily basis during a
composite period, each observation over the conterminous United States
was evaluated for cloud cover. Typically, there are two satellite
overpasses per day, over the conterminous United States, one over the
eastern portion of North America and a second pass over the western part
of the continent. There are several overpasses of Alaska each day due
the location in the northern latitudes. Every image that provided a
clear observation of a large ground surface area at reasonable nadir
viewing angles is included in the composite. On an average, 30 daily
observations per biweekly period are included in the composite.

### *Satellite and Solar Viewing Geometry*

The availability of the viewing geometry information allows studies on
the effects of off-nadir viewing and the investigation of potential data
correction techniques. The viewing geometry is used during the
calibration and atmospheric correction.

The computation of the solar and satellite geometry is a process that
derives the satellite zenith, solar zenith, and relative azimuth angle
for each image pixel. The relative azimuth is the absolute difference
between the satellite and solar azimuth angles. The computed angles do
not exceed 180 degrees. A separate image band is created for each of
these three angle computations.

The satellite zenith angle is computed in degrees, in which nadir is
represented as 90 degrees. Therefore, values less than 90 degrees
represent view angles in the back scattered (easterly) direction and
values greater than 90 represent the forward scatter (westerly)
direction. Note that the effective field of view of the satellite is
approximately 55 degrees each side of nadir, but computed satellite
zenith angles can exceed 55 degrees because of the curvature of the
Earth.

The relative azimuth angle is computed as the absolute difference
between the solar azimuth and the satellite azimuth angles. The computed
values are in the 0 - 180 range. The relative azimuth angle is computed
instead of separate azimuth angles because only the absolute difference
between the azimuth angles is required for atmospheric correction
algorithms. Also, saving only the computed relative azimuth angle
requires only one band in a daily observation and composite image
instead of two, which reduces the image storage requirements.

### *Radiometric Calibration*

Radiometric calibration is fundamental to understanding the quantitative
measurements of a sensor. Data from NOAA-11, 14, 16, and 17 AVHRR
sensors have been used to produce the conterminous United States data
set. The source of the calibration coefficients for each sensor varies
(Table 2), but the basic method used to develop and apply the
coefficients is very similar. The calibration coefficients and
methodology for the thermal channels 3a, 3b, 4, and 5 are well
documented by NOAA (Kidwell 1998, 2000). It is widely recognized that
the calibration of channels1 and 2, the visible and near-infrared bands,
must include an accounting for sensor degradation (Rao *et al.* 1993;
Kaufman and Holben 1993; Brest and Rossow 1992).

Table 2. A summary of the calibration sources and the valid timeframes
used to produce the conterminous United States data set.

Satellite Start Date End Date Source

NOAA 11 09/26/1988 03/26/1989 prelaunch

NOAA 11 03/27/1989 01/01/2020 Teillet and Holben (1994)

NOAA 14 12/30/1994 06/30/1995 prelaunch

NOAA-14 06/31/1995 01/01/2020 Vermote and Kaufman (1995)

NOAA 16 09/01/2000 06/24/2003 prelaunch

NOAA-16 06/25/2003 01/01/2020 NOAA

NOAA-17 01/01/2004 01/01/2020 NOAA

Teillet and Holben (1994) provide a comprehensive evaluation of the
calibration coefficients for NOAA-11 AVHRR channels 1 and 2, and their
report provides the basis for the derivation of time variant calibration
coefficients for NOAA 7, 9, and 11. They derive the coefficients from
the results obtained from the desert validation approach of Kaufman and
Holben (1993). Teillet and Holben (1994) developed the time-dependent
coefficients based on two methods: a polynomial fit and a piecewise
linear (PWL) fit of the observations over desert areas. They recommended
a PWL fit to describe the trend of the gain or offset (*y*) with time
(*x*). This function consists of a series of straight-line segments,
joined to each other at points where values can be reliably estimated or
at times designed to coincide with the start of the northern hemisphere
growing season. The PWL is suitable for operational use because, unlike
polynomial fits, the PWL will not change retroactively when new data are
added to the end of the time series. This approach assumes that the
degradation trend is linear between measurements of sensor performance
of AVHRR channels 1 and 2. The equations for time-dependent radiometric
calibration to radiance and reflectance for the visible and
near-infrared channels of NOAA 7, 9, and 11 are described by Teillet and
Holben (1994).

NOAA-14 was launched into an afternoon ascending orbit in December 1995.
Shortly after launch, Vermote and Kaufman (1995) described the
postlaunch calibration of AVHRR channels 1 and 2. Their coefficients
were derived from ocean and cloud observations. Their work provided the
first postlaunch calibration coefficients for NOAA-14, which was used by
the USGS in the operational processing of AVHRR NOAA-14 data.

NOAA-16 became the operational system in January 2001. Initially
prelaunch coefficients were used for calibration. In June 2003, NOAA
began providing monthly updates of the calibration coefficients. The
updates were based on analysis of NOAA-16 data using the desert
calibration approach. The postlaunch calibration coefficients for
NOAA-16 can be found at
http://noaasis.noaa.gov/NOAASIS/ml/calibration.html.

In December 2003, NOAA-16 developed a problem with the scan motor that
rendered the data useless for greenness mapping. In January 2004, USGS
began to use NOAA-17 data for greenness mapping. Post launch calibration
coefficients provided by NOAA are being used for the calibration of
NOAA-17 data (<http://noaasis.noaa.gov/NOAASIS/ml/calibration.html>).

### *Atmospheric Correction*

The most substantial improvement to the conterminous United States and
Alaska AVHRR data sets has been the application of an atmospheric
correction for ozone, water vapor absorption, and Rayleigh scattering.
In 2001, the entire existing time series was reprocessed to include the
atmospheric correction, which has been applied routinely since 2001.

Water vapor absorption affects measurements in the near-infrared band
(channel 2) by reducing the reflectance by 10-30 percent, depending on
the viewing geometry (Tanre *et al.* 1992). Rayleigh scattering and
ozone absorption affect measurements in the red band (channel 1) by
increasing the reflectance by 1-2 percent, depending on the viewing
geometry. The combination of these effects results in significant error
in the computation of NDVI and other vegetation indices.

The second simulation of the satellite signal in the solar spectrum (6S)
radiative transfer model is used to quantify the difference between the
radiance measured at the satellite and Earth-leaving radiance (Vermote
*et al.* 1997). The length of the path and direction that the signal
travels are very important. The viewing geometry factors that affect the
path length and direction are the orientation of the sun and the sensor
relative to the target. The viewing geometry is provided as part of the
conterminous United States and Alaska data sets. Bandpass calculations
of 0.005-micrometer spacing or better are recommended for the simulation
(Teillet 1992). The 6S radiative transfer model includes the use of
parameterized look up tables (LUTs) and interpolation. LUTs are
generated from the radiative transfer code using four key parameters:
solar zenith, satellite zenith, relative azimuth, and terrain elevation.

The corrections for ozone absorption and Rayleigh scattering are
straightforward (Teillet 1991). Appropriate Rayleigh scattering
correction must include an adjustment for atmospheric pressure that can
be derived from the elevation of the target. Recommended reference
values for Rayleigh optical depths for standard pressure and temperature
conditions are available (Teillet 1990; El Saleous *et al.* 1994). The
local elevation adjustment can be derived using digital terrain data.
The correction for ozone absorption is based on concentration values
from actual measurements derived from the Total Ozone Mapping
Spectrometer (TOMS) or other appropriate sensors (El Saleous *et al.*
1994).

DeFelice *et al.* (2003) describe the correction for water vapor.
Atmospheric water vapor concentrations are available from at least two
sources. The NASA Goddard Space Flight Center distributes the TIROS
Operational Vertical Sounder (TOVS) level 3 geophysical parameters
derived using the physical retrieval algorithm designated as the
so-called Path A and Path B schemes (Suskind *et al.* 1997). The archive
data products consist of 1-degree-by-1-degree global fields of the
three-dimensional, temperature-moisture structure of the atmosphere. The
second source is the NOAA National Centers for Environmental Prediction
(NCEP).

### *Scaling the Reflectance and Temperature Data*

Reflectance values for channels 1 and 2 were converted to byte data,
where the range 0 - 254 represents 0 to 63.5 percent reflectance. The
value 255 corresponds to reflectance greater than 63.5 percent. Any
feature with greater than 63 percent reflectance is considered to be
bright and non-vegetative.

Energy is converted to brightness temperature using the inverse of
Planck\'s radiation function. The brightness temperatures are
represented in Kelvin units. A scaling factor was used to convert the
brightness temperatures to byte data. A scaling factor of 202.5 is
subtracted from the brightness temperature value and the difference is
multiplied by 2 to maintain one half percent accuracy (i.e., a
brightness temperature of 280 becomes 155).

### *Geometric Registration*

The compositing process requires that each daily overpass be precisely
registered to a common map projection to ensure that each daily 1-km
pixel is referenced to the correct ground location. Past experiments
with image registration have shown that image-to-image registration
provides the precision needed for temporal data sets, and the use of
digital image correlation techniques produced consistent image-to-image
registration results. An evaluation of AVHRR image-to-image registration
using automated correlation techniques showed an improvement in
geometric accuracy (root-mean-square error of less than 1.0 pixel)
compared to traditional image-to-map procedures (Kelly and Hood, 1991).

The geometric registration of an observation is a two-step process.
First, a systematic correction is applied to the calibrated channel 2
(near-infrared) data. The near-infrared channel is used because water
bodies that are commonly used as control points have the most contrast
to land in the near-infrared channel. The systematic correction is based
on a satellite platform model that yields an error of approximately
three pixels. The second step is to develop the precision correction
using several hundred ground control points (GCPs) selected from the
systematically corrected image and a corresponding precision registered
base image. Each GCP is centered in a chip that is an image segment 32
lines by 32 samples. The locations of the GCP chips from the systematic
image are correlated with the chips from the base image using gray-scale
correlation. A polynomial transformation is derived based on a
least-square fit of a set of GCPs and corresponding image (pixel)
locations. The control points must be well distributed throughout the
image. If substantial cloud cover exists throughout the image, the
registration process will fail because no point within the chip will
correlate with the base image. If a significant number of control
correlations fail, the image is not used in the composite. The
polynomial transformation derived from the correlation of channel 2 is
then used to georeference all of the remaining bands associated with an
observation. The map projection used for the conterminous United States
data set is Lambert Azimuthal Equal Area. Table 3 provides details on
projection parameters of the conterminous United States. Table 4
provides details on the projection parameters of Alaska.

Table 3. Lambert Azimuthal Equal Area (LAZEA) projection of the
conterminous United States

\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

Parameters:

Radius of sphere 6,370,997.0 meters

Longitude of central meridian 100 00 00 West

Latitude of origin 45 00 00 North

False easting 0

False northing 0

Units of measure meters

Pixel size 1,000 meters

For the conterminous United States (2000)

Center of pixel (1,1) ( -2050000, 752000 )

Number of lines 2,889

Number of samples 4,587

LAZEA minimum bounding rectangle:

In projection meters:

Lower left ( -2050500, -2136500 )

Upper left ( -2050500, 752500 )

Upper right ( 2536500, 752500 )

Lower right ( 2536500, -2136500 )

In decimal degrees of longitude and latitude:

Lower left ( -119.9722899 23.5837576 )

Upper left ( -128.5300591 48.4030555 )

Upper right ( -65.3946489 46.7048989 )

Lower right ( -75.4163527 22.4793919 )

In degrees, minutes, and seconds of longitude and latitude:

Lower left ( -119 58 20 23 35 02 )

Upper left ( -128 31 48 48 24 11 )

Upper right ( -65 23 41 46 42 18 )

Lower right ( -75 24 59 22 28 46 )

Table 4. Albers Equal-Area Conic projection of Alaska

\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

Parameters:

First standard parallel 55 00 00 N

Second standard parallel 65 00 00 N

Longitude of central meridian -154 00 00 W

Latitude of origin 50 00 00 N

False easting 0

False northing 0

Units of measure Meters

Pixel size 1,000 meters

Center of pixel (1,1) ( -977000, 2422000 )

Number of lines 1,992

Number of samples 2,512

Albers meters for minimum bounding rectangle:

Lower Left ( -977000, 431000 )

Upper Left ( -977000, 2422000 )

Upper Right ( 1534000, 2422000 )

Lower Right ( 1534000, 431000 )

Geographic decimal degrees:

Lower Left ( -168.5970, 52.9222 )

Upper Left ( -179.8476 70.0416 )

Upper Right ( -116.0057 67.6962 )

Lower Right ( -131.5953 51.5372 )

Geographic degrees, minutes, and seconds:

Lower Left ( -168 35\' 49\" 52 55\' 20\" )

Upper Left ( -179 50\' 51\" 70 02\' 30\" )

Upper Right ( -116 00\' 21\" 67 41\' 46\" )

Lower Right ( -131 35\' 43\" 67 32\' 14\" )

\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

### *Normalized Difference Vegetation Index (NDVI)*

The NDVI is the difference of near-infrared (channel 2) and visible

(channel 1) reflectance values normalized over the sum of channels 1 and
2

(NIR-VIS)/(NIR+VIS). The NDVI equation produces values in the range of
-1.0 to 1.0, where increasing positive values indicate increasing green
vegetation and negative values indicate nonvegetated surface features
such as water, barren, ice, snow, or clouds. The NDVI can be derived at
several points in the processing flow. To retain the most precision, the
NDVI is derived after calibration of channels 1 and 2, prior to scaling
to byte range. Computation of the NDVI must precede geometric
registration and resampling to maintain precision in this calculation.

To scale the computed NDVI results to byte data range, the NDVI computed
value, which ranges from -1.0 to 1.0, is scaled to the range of 0 to
200, where computed -1.0 equals 0, computed 0 equals 100, and computed
1.0 equals 200. As a result, NDVI values less than 100 now represent
clouds, snow, water, and other nonvegetative surfaces and values equal
to or greater than

100 represent vegetative surfaces.

### *Date of Acquisition*

The date of acquisition images are provided to allow a user to identify
the specific daily observation (satellite scene number) used for each
pixel. The date images for each composite identify each daily image as a
unique value. The unique value is linked to an inventory of the daily
observations. A complete list of daily observations used in each
composite period is on this CD-ROM in files with a .log extension used
for each pixel.

### *Compositing*

The greenness data set is a time series of weekly and bi-weekly maximum
NDVI composites of the conterminous United States and Alaska. The
maximum NDVI compositing process determines which pixels of each daily
pass are included in the composite (Holben, 1986). Selection of the
maximum NDVI is assumed to represent the maximum vegetation
\"greenness", a measure of photosynthetic activity, and also serves to
reduce the number of cloud-contaminated pixels. The NDVI values are
examined pixel by pixel for each of the observations comprising a weekly
period to determine which pixel has the maximum value. For the
conterminous United States, this is about 10 to 15 daily passes per
week.

The output of the compositing process is a 14-band data set with one
band containing the maximum NDVI value for each pixel selected from the
daily overpasses. The remaining 13 bands are the data values that are
coincident with the observation value selected as the maximum NDVI
value. They include channels 1 to 5, satellite viewing geometry data
(three bands), corrected channels 1 and 2 reflectance, a quality-control
band indicting the origin of the water vapor and ozone values, a band
that contains a pointer to the scene identification number for each
pixel selected from the same daily pass as the maximum NDVI value, and
the cloud mask.

### *Cloud Screening*

In spite of the maximum NDVI compositing process, clouds are present in
the composites. Several factors contribute to the cloud contamination.
Often persistent seasonal weather and cloud patterns, such as the
monsoon season in the southwest or early spring conditions in the
northern portion of the conterminous United States, limit the number of
cloud-free observations. The clouds are more prevalent in weekly rather
than biweekly composites simply because of the number of observations
available. Snow and clouds in Alaska are particular problems. As was
mentioned in the section on geometric registration, it is not possible
to accurately geometrically register an image if too many clouds are
present.

Clouds have low NDVI values because the channel 1 and channel 2 values
are nearly equal. However, sub- pixel clouds produce an NDVI that is a
blend of clouds and clear surface. Cloud presence can confound the
measurement of vegetation condition. The worse case example is sub-pixel
clouds over a very green surface condition where the resulting NDVI is
reduced and appears to be affected by drought.

All of the conterminous United States and Alaska composites have been
cloud screened using an adaptation of the cloud clearing of AVHRR data
(CLAVR) algorithm developed by NOAA (Stowe *et al.* 1999). CLAVR uses a
series of tests of reflectance, temperature, land cover, and geographic
location to identify clouds. Overcast clouds are relatively easy to
detect, but sub pixel clouds are much more difficult. The occurrence of
sub-pixel warm clouds over warm surfaces that often occurs during the
summer, or cold clouds over cold surfaces in the winter, is difficult to
detect. Therefore, sub-pixel cloud contamination can be present in the
composites even after cloud screening. Band 14 of a composite includes
the cloud mask derived from CLAVR. The cloud mask has values from 0 to
200. The individual values are keyed to which test was ultimately used
to determine if the pixel is clear or cloudy. Values less than 100 are
clear, values 100 or greater are clouds.

### CONTACT INFORMATION

For more information please contact Customer Services, EROS, U.S.

Geological Survey, Sioux Falls, SD 57198, (605)594-6151, FAX
(605)594-6589.

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