BOREAS RSS-07 Landsat TM LAI Images of the SSA and NSA

Summary

The BOREAS RSS-07 team used Landsat TM images processed at CCRS to produce 
images of LAI for the BOREAS study areas.  Two images acquired on June 6 and 
August 9, 1991 were used for the SSA, and one image acquired on June 9, 1994 was 
used for the NSA.  The LAI images are based on ground measurements and Landsat 
TM RSR images.  The data are stored in binary image-format files.

Table of Contents

   *   1 Data Set Overview
   *   2 Investigator(s)
   *   3 Theory of Measurements
   *   4 Equipment
   *   5 Data Acquisition Methods
   *   6 Observations
   *   7 Data Description
   *   8 Data Organization
   *   9 Data Manipulations
   *  10 Errors
   *  11 Notes
   *  12 Application of the Data Set
   *  13 Future Modifications and Plans
   *  14 Software
   *  15 Data Access
   *  16 Output Products and Availability
   *  17 References
   *  18 Glossary of Terms
   *  19 List of Acronyms
   *  20 Document Information

1. Data Set Overview

1.1 Data Set Identification

BOREAS RSS-07 Landsat TM LAI Images of the SSA and NSA

1.2 Data Set Introduction

These Leaf Area Index (LAI) images were generated in response to the need within 
the BOReal Ecosystem-Atmosphere Study (BOREAS) modeling community for adequate 
spatial and temporal coverage of estimated vegetation indices across the BOREAS 
region.  

1.3 Objective/Purpose

The objective of this study was to provide spatially referenced LAI maps for 
landscape and regional ecosystem analysis and modeling in the Southern Study 
Area (SSA) and Northern Study Area (NSA) of the BOREAS.  

1.4 Summary of Parameters

LAI images

1.5 Discussion

This data set was prepared as a part of continuous investigation by the Remote 
Sensing Science (RSS)-07 team.  LAI maps were calculated using Landsat Thematic 
Mapper (TM) images acquired on 06-Jun-1991 (SSA), 09-Aug-1991 (SSA), and 09-Jun-
1994 (NSA).  These LAI maps are produced with atmospherically corrected Landsat 
TM band 3, 4, and 5 data and field measurements of LAI.  The link between the 
image and field measurements is based on the correlation between the Reduced 
Simple Ratio (RSR) and the field LAI values.  RSR is the simple ratio (SR), 
near-infrared (NIR) reflectance/red reflectance, reduced by a factor based on 
the mid-infrared (MIR) reflectance (TM band 5), i.e.

	RSR = SR(1-(MIR-MIRmin)/(MIRmax-MIRmin))

where MIRmin and MIRmax are the minimum and maximum MIR reflectance in the 
image, determined from the histogram of each image.  They are 0.05 and 0.22 for 
the 06-Jun and 09-Aug images, and 0.06 and 0.24 for the 09-Jun image.  Generally 
the correlation takes the following form: 
     
	LAI = intercept + Slope * RSR 

In this LAI calculation, the following parameters were used for both the SSA and 
the NSA: intercept = 1.75, slope = 0.46.  Unlike our previous techniques (i.e., 
regional LAI maps from the Advanced Very High Resolution Radiometer (AVHRR), no 
land cover information was used in the LAI calculation.  This may reduce the 
error propagation caused by misclassification or grouping of spectral data and 
provides more details of spatial variation of LAI because of the sensitivity of 
the MIR band to vegetation cover.  The disadvantage of not using the land cover 
map is that in areas of low canopy density, the unknown background reflectance 
can affect the determination of LAI; however, this bias is very small.  For a 
small number of pixels, the calculated LAI is unreasonably high, and therefore 
an upper limit of an LAI of 6 is imposed.  The advantages of using RSR are 
discussed by Brown et al. (1999).

The theories of field LAI measurements are documented in the BOREAS RSS-07 LAI, 
Gap Fraction, and FPAR Data document.  The LAI data sets have been geometrically 
rectified.  The LAI maps were produced with encoded LAI range values. Image 
processing and penetration of LAI maps were conducted at the Canada Centre for 
Remote Sensing (CCRS). 

1.6 Related Data Sets

BOREAS Level-3b Landsat TM Imagery: At-sensor Radiance in BSQ Format
BOREAS RSS-07 LAI, Gap Fraction, and FPAR Data

2. Investigator(s)

2.1 Investigator(s) Name and Title

Dr. Jing M. Chen
Mr. Xiaoyuan Geng (Ph.D. Candidate)

2.2 Title of Investigation

Retrieval of Boreal Forest Leaf Area Index From Multiple Scale Remotely Sensed 
Vegetation Indices

2.3 Contact Information

Contact 1 
---------
Jing M. Chen
Canada Centre for Remote Sensing
Ottawa, Ontario
Canada
(613) 947-1266
(613) 947-1406 (fax)
jing.chen@ccrs.nrcan.gc.ca

Contact 2
---------
Mr. Xiaoyuan Geng
Canada Centre for Remote Sensing
Ottawa, Ontario
Canada
(613) 947-1231
(613) 947-1406 (fax)
xgeng@ccrs.nrcan.gc.ca 

Contact 3
--------------
Jaime Nickeson
Raytheon ITSS
NASA GSFC
Greenbelt, MD 
(301) 286-3373
(301) 286-0239 (fax)
Jaime.Nickeson@gsfc.nasa.gov

3. Theory of Measurements

The theory of Landsat TM has been discussed in the BOREAS documentation for the 
Landsat Level-3b data.  Relevant portions have been repeated throughout.

The Landsat TM sensor collects imagery of Earth in seven spectral bands ranging 
from the blue to the thermal IR portion of the electromagnetic spectrum.  
Multispectral classification of this imagery can be performed by identifying 
areas that are representative of land cover types to be classified.  Statistics 
can be computed in these spectral bands for each feature type.  A maximum 
likelihood classifier can then be applied by identifying the most likely land 
cover type for each pixel in the image based on the statistics of the training 
fields.

The following spectral bands are collected by the TM sensor:

Channel    Wavelength ()                   Primary Use
-------   ---------------     -----------------------------------------
 1         0.45 - 0.52        Coastal water mapping, soil vegetation
                              differentiation, deciduous/coniferous
                              differentiation.
 2         0.52 - 0.60        Green reflectance by healthy vegetation.
 3         0.63 - 0.69        Chlorophyll absorption for plant species
                              differentiation.
 4         0.76 - 0.90        Biomass surveys, water body delineation. 
 5         1.55 - 1.72        Vegetation moisture measurement, snow
                              cloud differentiation.
 6        10.4  - 12.5        Plant heat stress measurement, other
                              thermal mapping.
 7         2.08 -  2.35       Hydrothermal mapping.

4. Equipment

4.1 Sensor/Instrument Description

The TM sensor system records radiation from the seven bands in the 
electromagnetic spectrum described in Section 3.  It has a telescope that 
directs the incoming radiant flux obtained along a scan line through a scan line 
collector to the visible and NIR focal plane, or to the MIR and thermal-IR 
cooled focal plane.  The detectors for the visible and NIR bands (1 to 4) are 
four staggered linear arrays, each containing 16 silicon detectors. The two MIR 
detectors are 16 indium-antimonide cells in a staggered linear array, and the 
thermal-IR detector is a four-element array of mercury-cadmium-telluride cells.

4.1.1 Collection Environment

The Landsat satellite orbits Earth at an altitude of 705 km.

4.1.2 Source/Platform

Landsat TM satellite.

4.1.3  Source/Platform Mission Objectives

The Landsat TM is designed to respond to and measure both reflected and emitted 
Earth surface radiation to enable the investigation, survey, inventory, and 
mapping of Earth's natural resources.

4.1.4 Key Variables

Reflected radiation, emitted radiation, and temperature.

4.1.5 Principles of Operation

The TM is a scanning optical sensor operating in the visible and IR wavelengths. 
It contains a scan mirror assembly that directly projects the reflected Earth 
radiation onto detectors arrayed in two focal planes. The TM achieves better 
imagery resolution, sharper color separation, and greater in-flight geometric 
and radiometric accuracy for seven spectral bands simultaneously than the 
previous generation sensor, the Multispectral Scanner (MSS).  Data collected by 
the TM sensor are transmitted back to Earth receiving stations for processing.

4.1.6 Sensor/Instrument Measurement Geometry

The TM sensor depends on the forward motion of the spacecraft for the along-
track scan and uses a moving mirror assembly to scan in the cross-track 
direction (perpendicular to the spacecraft).  The instantaneous field of view 
(IFOV) for each detector from bands 1 through 5 and band 7 is equivalent to a 
30-m square when projected to the ground; band 6 (the thermal-IR band) has an 
IFOV equivalent to a 120-m square.

4.1.7 Manufacturer of Sensor/Instrument

NASA/GSFC
Greenbelt, MD 20771

Hughes Aircraft Corporation
Santa Barbara, CA

4.2 Calibration

The internal calibrator, a flex-pivot-mounted shutter assembly, is synchronized 
with the scan mirror, oscillating at the same 7-Hz frequency.  During the turn-
around period of the scan mirror, the shutter introduces the calibration source 
energy and a black direct-current restoration surface into the 100-detector 
field of view.

The calibration signals for bands 1 through 5 and band 7 are derived from three 
regulated tungsten-filament lamps.  The calibration source for band 6 is a 
blackbody with three temperature selections, commanded from the ground. The 
method for transmitting radiation to the moving calibration shutter allows the 
tungsten lamps to provide radiation independently and to contribute 
proportionately to the illumination of all detectors.

4.2.1 Specifications

               Band             Radiometric
                              Sensitivity [NE(dP)]*
               ----           --------------------
                1                  0.8%
                2                  0.5%
                3                  0.5%
                4                  0.5%
                5                  1.0%
                6                  0.5 K [NE(dT)]
                7                  2.4%

          Ground IFOV          30 m (Bands 1-5, 7)
                              120 m (Band 6)
          Avg. altitude       699.6 km
          Data rate              85 Mbps
          Quantization levels   256
          Orbit angle          8.15 degrees
          Orbital nodal
          Period              98.88 minutes
          Scan width            185 km
          Scan angle           14.9 degrees
          Image overlap         7.6 %

*  N.B. The radiometric sensitivities are the noise-equivalent  reflectance 
differences for the reflective channels expressed as percentages [NE(dP)] and 
temperature differences for the thermal-IR bands [NE(dT)].

4.2.1.1 Tolerance

The TM channels were designed for a noise-equivalent differential represented by 
the radiometric sensitivity shown in Section 4.2.1.

4.2.2 Frequency of Calibration

The absolute radiometric calibration between bands is maintained by using 
internal calibrators that are located between the telescope and the detectors 
and are sampled at the end of a scan.

4.2.3 Other Calibration Information

Relative within-band radiometric calibration, to reduce "striping," is provided 
by a scene-based procedure called histogram equalization. The absolute accuracy 
and relative precision of this calibration scheme assumes that any changes in 
the in the optics of the primary telescope or the "effective radiance" from the 
internal calibrator lamps are insignificant in comparison to the changes in 
detector sensitivity and electronic gain and bias with time and that the scene-
dependent sampling is sufficiently precise for the required within-scan 
destriping from histogram equalization. 

Each TM reflective band and the internal calibrator lamps were calibrated prior 
to launch using lamps in integrating spheres that were in turn calibrated 
against lamps traceable to calibrated National Bureau of Standards lamps.  
Sometimes the absolute radiometric calibration constants in the "short-term" and 
"long-term parameters" files used for ground processing have been modified after 
launch because of inconsistency within or between bands, changes in the inherent 
dynamic range of the sensors, or a desire to make quantized and calibrated 
values from one sensor match those from another.

5. Data Acquisition Methods

These data were acquired from the Landsat 5 TM sensor and received from CCRS who 
purchased it from the Earth Observation Satellite Corporation (EOSAT).  As 
received from CCRS, the image had been processed from raw telemetry to a 
systematically corrected product within the CCRS MOSAICS system.  Atmospheric 
correction was applied to the systematically corrected data. Surface reflectance 
was calculated using the Second Simulation of the Satellite Signal in the Solar 
Spectrum (6S) (Vermote et al., 1997) model, using inputs of a continental 
airmass, midlatitude summer, a uniform target, and 30 km atmospheric visibility. 
Modified SR was calculated using maximum IR reflectance of 22%  and 24%, and 
minimum IR reflectance of 5% and 6% for the SSA and NSA, respectively (Brown et 
al., submitted 1998).  Field LAI data were taken from the published data of Chen 
(1996) and Brown et al. (1999).  Detailed procedures are documented in Chen et 
al. (1997).

6. Observations

6.1 Data Notes

The radiometric quality of the imagery is acceptable.

6.2 Field Notes

None given.

7. Data Description

7.1 Spatial Characteristics

7.1.1 Spatial Coverage

The calculated LAI images cover portions of a full Landsat TM scene.  The data 
set boundaries have well-defined coordinates.  

The North American Datum of 1927 (NAD27) Universal Transverse Mercator (UTM) 
coordinates of the TM image of NSA acquired on 09-Jun-1994 are:

                      UTM         UTM
                    Easting     Northing
Northwest           501220.5    6239553.0
Southease           562660.5    6178113.5

The NAD27 UTM coordinates of the TM image of SSA acquired on 09-Aug-1991: 

                      UTM         UTM
                    Easting     Northing
Northwest            451000     6042000
Southease            556000     5937000

The NAD27 UTM coordinates of the TM image of SSA acquired on 06-Jun-1991: 

                      UTM         UTM
                    Easting     Northing
Northwest            471200     6016960
Southease            532640     5955520

7.1.2 Spatial Coverage Map

Not available.

7.1.3 Spatial Resolution

These data were gridded to a cell size of 30 meters from the original nominal 
resolution of 28.5 meters by the MOSAICS system.

7.1.4 Projection

Universal Transverse Mercator (UTM).

7.1.5 Grid Description

SSA:  UTM Zone 13, NAD27
NSA:  UTM Zone 14, NAD27

7.2 Temporal Characteristics

7.2.1 Temporal Coverage

The images used were acquired in 1991 for the SSA, and in 1994 for the NSA.

7.2.2 Temporal Coverage Map

	  Image 	   WRS*	Solar Elevation	Solar Azimuth	Radiometric
	  Date	(Path/Row)	   (degrees)	  (degrees)	  Quality
-----------     ----------    ----------------    -------------     -----------
06-Jun-1991	 37/22-23	    47.41	   139.83	   Good

09-Aug-1991	  37/22	    43.25	   141.88	   Good

09-Jun-1994	  33/21	    37.65	   140.81	   Good
------------------------------------------------------------------------------
	* WRS -- World Reference System

7.2.3 Temporal Resolution

The Landsat TM satellite revisit frequency is 16 days for each path/row; 
however, in the BOREAS region, the overlap between adjacent scene paths is about 
50%.  Thus, the frequency for some areas can be 8 days.

7.3 Data Characteristics

7.3.1 Parameter/Variable

Leaf Area Index (LAI)

7.3.2 Variable Description/Definition

The definition of LAI is one half the total leaf area per unit ground surface 
area.

The reduced simple ratio is:

RSR = SR * (1-(MIR-MIRmin)/(MIRmax-MIRmin))
where MIRmin and MIRmax are the minimum and maximum MIR reflectance in an image.

7.3.3 Unit of Measurement

LAI - square meters of leaf area / square meter of ground surface

7.3.4 Data Source

Landsat TM 5 sensor.

7.3.5 Data Range

LAI :  1 - 55 (0 for water) for LAI 0 - 5.4

7.4 Sample Data Record
    
Not applicable for image data.

8. Data Organization
   
8.1 Data Granularity

The smallest unit of data tracked by BORIS is a binary and TIFF image pair 
acquired on a single day. 

8.2 Data Format(s)

8.2.1 Uncompressed Files

This data set contains the following 6 files: 

File Description/Name	Format	Npix	Nlines	Block Size	File Size
 NSA_LAI_94JUNE.IMG	Raw Binary	2048	 2048	2048	 4Mb
 NSA_LAI_94JUNE.TIFF	TIFF			 512	 2Mb
 SSA_LAI_91JUNE.IMG	Raw Binary	2048	 2048	2048	 4Mb
 SSA_LAI_91JUNE.TIFF	TIFF			 512	 2Mb
 SSA_LAI_91AUG.IMG	Raw Binary	3500	 3500	3500	12Mb
 SSA_LAI_91AUG.TIFF	TIFF			 512	 6Mb

The *.IMG files are all 8-bit binary images.  The file architecture is the same 
as that of any common image file:  the first byte is the first pixel of the 
first line, the second byte is the second pixel of the first line,.(the first 
pixel in the second line of image 1 is the 2049th byte).  The *.TIFF files are 
TIFF versions of the *.IMG files and are for display purposes only.

8.2.2 Compressed CD-ROM Files

On the BOREAS CD-ROMs, the image files been compressed with the Gzip (GNU zip) 
compression program (file_name.gz).  These data have been compressed using gzip 
version 1.2.4 and the high compression (-9) option (Copyright (C) 1992-1993 
Jean-loup Gailly).  Gzip uses the Lempel-Ziv algorithm (Welch, 1994) also used 
in the zip and PKZIP programs.  The compressed files may be uncompressed using 
gzip (with the -d option) or gunzip.  Gzip is available from many Web sites (for 
example, the ftp site prep.ai.mit.edu/pub/gnu/gzip-*.*) for a variety of 
operating systems in both executable and source code form.  Versions of the 
decompression software for various systems are included on the CD-ROMs.

9. Data Manipulations

9.1 Formulae

9.1.1 Derivation Techniques and Algorithms

LAI maps were calculated using Landsat TM images acquired on 06-Jun-1991 (SSA), 
09-Aug-1991 (SSA), and 09-Jun-1994 (NSA).  These LAI maps are produced with 
atmospherically corrected Landsat TM band 3, 4, and 5 data and field 
measurements of LAI.  The link between the image and field measurements is based 
on the correlation between the RSR and the field LAI values.  RSR is the SR, NIR 
reflectance/red reflectance, reduced by a factor based on the MIR reflectance 
(TM band 5), i.e.

	RSR = SR(1-(MIR-MIRmin)/(MIRmax-MIRmin))

where MIRmin and MIRmax are the minimum and maximum MIR reflectance in the image 
determined from the histogram of each image.  They are 0.05 and 0.22 for the 06-
Jun and 09-Aug images, and 0.06 and 0.24 for the 09-Jun image.  Generally, the 
correlation takes the following form: 
     
	LAI = intercept + Slope * RSR 

In this LAI calculation, the following parameters were used for both the SSA and 
the NSA: intercept = 1.75, slope = 0.46.

9.2 Data Processing Sequence

9.2.1 Processing Steps

None given.

9.2.2 Processing Changes

None.

9.3 Calculations

9.3.1 Special Corrections/Adjustments

None.

9.3.2 Calculated Variables

	RSR = SR(1-(MIR-MIRmin)/(MIRmax-MIRmin))
	LAI = intercept + Slope * RSR 

9.4 Graphs and Plots

None.

10. Errors

10.1 Sources of Error

The sources of error in the LAI maps could result from a number of factors.  The 
correlation between LAI and modified SR is empirical.  Also, the use of MIR 
channel in the empirical relationship may be influenced by the surface water 
conditions.  Other potential sources of error, such as those caused by image 
correction and projection, are minor in comparison. 

10.2 Quality Assessment by Source

10.2.1  Data Validation by Source

Even though we have inspected the final LAI values for about 10 known sites on 
each image, no extensive validation has been done.  Since our source of RSR data 
came from the scenes of 06-Jun-1991 for the SSA and 09-Jun-1994 for the NSA, we 
believe LAI images of these two dates are most useful. 

10.2.2  Confidence Level/Accuracy Judgement

None given.

10.2.3  Measurement Error for Parameters

Ground LAI measurement errors are about 25% (Chen et al., 1997).  The RSR may be 
sensitive to rainfall events. In particular, the 09-Aug-1991 scene was not used 
in the RSR-LAI algorithm development, so it is less reliable. 

10.2.4  Additional Quality Assessment

Not applicable.

10.2.5  Data Verification by Data Center

11. Notes

11.1  Limitations of the Data

None given.

11.2 Known Problems with the Data

Clouds in the 06-Jun-1991 scene of the SSA show up in a diagonal strip across 
the image.  The areas under the clouds have LAI in the range of 2.5-3.0.  The 
09-Aug-1991 scene which covers the same area, is clear and is therefore provided 
here as a remedy for the cloud effect.  It is known, however, that summer scenes 
are less reliable for LAI retrieval because of the increased background effect, 
which overestimates LAI by about 15-25%, and reduces the spatial variation of 
LAI. 

For jack pine stands, the LAI in the 09-Aug-1991 scene of the SSA seems to be 
about 20% high because of understory development, while this background effect 
in other stand types is more suppressed by the use of the MIR (TM band 5) data. 
   
11.3 Usage Guidance

Spring scenes (06-Jun-1991 and 09-Jun-1991) are the most suitable for LAI 
mapping in the SSA and NSA (Chen and Cihlar, 1996).  However, in the SSA, the 
06-Jun-1991 scene is affected by clouds. The 09-Aug-1991 scene is therefore 
provided as a reference to remove the cloud effects.  The LAI change is small 
for conifer stands and higher for deciduous and grassland.
 
Before uncompressing the Gzip files on CD-ROM, be sure that you have enough disk 
space to hold the uncompressed data files.  Then use the appropriate 
decompression program provided on the CD-ROM for your specific system.

11.4 Other Relevant Information

None.

12. Application of the Data Set

These images can be used to investigate the spatial distribution of LAI over the 
mapped areas.

13. Future Modifications and Plans

None given.

14. Software

14.1 Software Description

Gzip (GNU zip) uses the Tempel-Ziv algorithm (Welch, 1994) used in the zip and 
PKZIP commands.

14.2 Software Access

Gzip is available from many web sites across the net (for example) ftp site 
prep.ai.mit.edu/pub/gnu/gzip-*.*) for a variety of operating systems in both 
executable and source code form. Versions of the decompression software for 
various systems are included on the CD-ROMs.

15. Data Access 

15.1 Contact Information

For BOREAS data and documentation please contact: 

ORNL DAAC User Services
Oak Ridge National Laboratory
Oak Ridge, TN 
Phone:  (423) 241-3952
Fax:    (423) 574-4665
E-mail:  ornldaac@ornl.gov or ornl@eos.nasa.gov

15.2 Data Center Identification

Earth Observing System Data and Information System (EOSDIS) 
Oak Ridge National Laboratory (ORNL) 
Distributed Active Archive Center (DAAC) for Biogeochemical Dynamics
http://www-eosdis.ornl.gov/

15.3 Procedures for Obtaining Data

Users may obtain data directly through the ORNL DAAC online search and order 
system [http://www-eosdis.ornl.gov/] and the anonymous FTP site [ftp://www-
eosdis.ornl.gov/data/] or by contacting User Services by electronic mail, 
telephone, or fax. 

15.4 Data Center Status/Plans

The ORNL DAAC is the primary source for BOREAS field measurement, image, GIS, 
and hardcopy data products. The BOREAS CD-ROM and data referenced or listed in 
inventories on the CD-ROM are available from the ORNL DAAC. 

16. Output Products and Availability

16.1 Tape Products

The data can be made available on 8-mm or Digital Archive Tape (DAT) media. 

16.2 Film Products

None.

16.3 Other Products

These data are available on the BOREAS CD-ROM series.

17. References

17.1   Satellite/Instrument/Data Processing Documentation

PACE Image Analysis Kernal Version 6.2. 1997. PCI, Inc. Richmond Hill, Ontario.

Welch, T.A. 1984. A Technique for High Performance Data Compression. IEEE 
Computer, Vol. 17, No. 6, pp. 8-19.

17.2   Journal Articles and Study Reports

Brown L., J.M. Chen, S.G. Leblanc, and J. Cihlar. 1999. Application of the 
shortwave infrared for LAI retrieval in boreal forests: an image and model 
analysis. Submitted to Remote Sens. Environ.

Chen, J. M. 1996. Evaluation of vegetation indices and modified simple  ratio 
for Boreal applications. Canadian Journal of Remote Sensing 22:229-242.

Chen, J.M. and J. Cihlar. 1996. Retrieving leaf area index of boreal  conifer 
forests using Landsat TM images. Remote Sens. Environ. 55:153-162.

Chen, J.M., P.M. Rich, S.T. Gower, J.M. Norman, S. and Plummer. 1997. Leaf area 
index of boreal forests: Theory, techniques, and measurements.  Journal of 
Geophysical Research. 102(D24): 29,429-29,443.

Sellers, P. and F. Hall. 1994. Boreal Ecosystem-Atmosphere Study: Experiment 
Plan. Version 1994-3.0, NASA BOREAS Report (EXPLAN 94). 

Sellers, P. and F. Hall. 1996. Boreal Ecosystem-Atmosphere Study: Experiment 
Plan. Version 1996-2.0, NASA BOREAS Report (EXPLAN 96). 

Sellers, P., F. Hall, and K.F. Huemmrich. 1996. Boreal Ecosystem-Atmosphere 
Study: 1994 Operations. NASA BOREAS Report (OPS DOC 94). 

Sellers, P., F. Hall, and K.F. Huemmrich. 1997. Boreal Ecosystem-Atmosphere 
Study: 1996 Operations. NASA BOREAS Report (OPS DOC 96). 

Sellers, P., F. Hall, H. Margolis, B. Kelly, D. Baldocchi, G. den Hartog, J. 
Cihlar, M.G. Ryan, B. Goodison, P. Crill, K.J. Ranson, D. Lettenmaier, and D.E. 
Wickland. 1995. The boreal ecosystem-atmosphere study (BOREAS): an overview and 
early results from the 1994 field year. Bulletin of the American Meteorological 
Society. 76(9): 1549-1577. 

Sellers, P.J., F.G. Hall, R.D. Kelly, A. Black, D. Baldocchi, J. Berry, H. 
Margolis, M. Ryan, J. Ranson, P.M. Crill, D.P. Lettenmeier, J. Cihlar, J. 
Newcomer, D. Halliwell, D. Fitzjarrald, P.G. Jarvis, S.T. Gower, D. Williams, B. 
Goodison, D.E. Wickland, and F.E. Guertin. 1997.  BOREAS in 1997: Scientific 
results, experiment overview and future directions.Journal of Geophysical 
Research, 102(D24): 28,731-28,770. 

Vermote, E., D. Tanre, and J. Morcrette. 1997.  Second simulation of the 
satellitesignal in the solar spectrum, 6S: an overview. IEEE Trans. Geosci. 
Remote Sens., vol. 35, no. 3, pp. 675.

17.3 Archive/DBMS Usage Documentation

None.

18. Glossary of Terms

None given.

19. List of Acronyms

    6S      - Second Simulation of the Satellite Signal in the Solar Spectrum
    ASCII   - American Standard Code for Information Interchange
    AVHRR   - Advanced Very High Resolution Radiometer
    BOREAS  - BOReal Ecosystem-Atmosphere Study
    BORIS   - BOREAS Information System
    BSQ     - Band Sequential
    CCRS    - Canada Centre for Remote Sensing
    CD-ROM  - Compact Disk-Read-Only Memory
    DAAC    - Distributed Active Archive Center
    DAT     - Digital Archive Tape
    DN      - Digital Number
    EOS     - Earth Observing System
    EOSAT   - Earth Observation Satellite Corporation
    EOSDIS  - EOS Data and Information System
    EROS    - Earth Resources Observation System
    FPAR    - Fraction of PAR absorbed by plant canopies
    GSFC    - Goddard Space Flight Center
    IFC     - Intensive Field Campaign
    IFOV    - Instantaneous Field-of-View
    IR      - Infrared
    ISLSCP  - International Satellite Land Surface Climatology Project
    LAI     - Leaf Area Index
    LCC     - Lambert Conformal Conic
    Mbps    - Megabytes per second
    MIR     - Mid-Infrared
    MRSC    - Manitoba Remote Sensing Centre
    MSS     - Multispectral Scanner
    NAD83   - North American Datum of 1983
    NASA    - National Aeronautics and Space Administration
    NDVI    - Normalized Difference Vegetation Index
    NEdT    - Noise Equivalent Differential Temperature
    NIR     - Near-Infrared
    NOAA    - National Oceanic and Atmospheric Administration
    NSA     - Northern Study Area
    ORNL    - Oak Ridge National Laboratory
    PANP    - Prince Albert National Park
    PAR     - Photosynthetically Active Radiation
    PASS    - Prince Albert Satellite Station
    RSS     - Remote Sensing Science
    RSR     - Reduced Simple Ratio
    SR      - Simple Ratio
    SSA     - Southern Study Area
    TM      - Thematic Mapper
    URL     - Uniform Resource Locator
    UTM     - Universal Transverse Mercator
    WRS     - World Reference System

20. Document Information

20.1 Document Revision Date 

     Written:         11-Jan-1998
     Last Updated:    05-May-1999

20.2 Document Review Date(s)

     BORIS Review:    25-Feb-1999
     Science Review:  

20.3 Document ID 

20.4 Citation 

Any publication of these data should acknowledge the source of the data as 
the authors and CCRS. 

If using data from the BOREAS CD-ROM series, also reference the data as:
   Chen, Jing and X. Geng,"Retrieval of Boreal Forest Leaf Area Index From 
   Multiple Scale Remotely Sensed Vegetation Indices" in Collected Data of The 
   Boreal Ecosystem-Atmosphere Study.  Eds. J. Newcomer, D. Landis, S. Conrad, 
   S. Curd, K. Huemmrich, D. Knapp, A.Morrell, J. Nickeson, A. Papagno, D. 
   Rinker, R. Strub, T. Twine, F. Hall, and P. Sellers.  CD-ROM. NASA, 2000.

Also, cite the BOREAS CD-ROM set as:
	Newcomer, J., D. Landis, S. Conrad, S. Curd, K. Huemmrich, D. Knapp, A. 
Morrell, J. Nickeson, A. Papagno, D. Rinker, R. Strub, T. Twine, F. Hall, 
and P. Sellers, eds. Collected Data of The Boreal Ecosystem-Atmosphere 
Study. CD-ROM. NASA, 2000.

20.5 Document Curator 

20.6 Document URL 

Keywords:

    AVHRR-LAC
    LAI
    NOAA
    REFLECTANCE
RSS07_TM_LAI.doc.doc
06/09/99