NASA/TMm2000-209891, Vol. 52

Technical Report Series on the

Boreal Ecosystem-Atmosphere Study (BOREAS)

Forrest G. Hall and Jaime Nickeson, Editors

Volume 52

BOREAS RSS-7 Regional LAI and FPAR Images

From 10-Day AVHRR-LAC Composites

Jing Chen and Josef Cihlar

Canada Centre for Remote Sensing, Ottawa, Ontario, Canada

National Aeronautics and

Space Administration

Goddard Space Flight Center

Greenbelt, Maryland 20771

August 2000

https://ntrs.nasa.gov/search.jsp?R=20010022094 2018-05-14T18:54:14+00:00Z
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NASA/TMm2000-209891, Vol. 52

Technical Report Series on the

Boreal Ecosystem-Atmosphere Study (BOREAS)

Forrest G. Hall and Jaime Nickeson, Editors

Volume 52

BOREAS RSS-7 Regional LAI and FPAR Images

From 10-Day AVHRR-LAC Composites

Jing Chen and Josef Cihlar

Canada Centre for Remote Sensing, Ottawa, Ontario, Canada

National Aeronautics and

Space Administration

Goddard Space Flight Center

Greenbelt, Maryland 20771

August 2000
NASA Center for AeroSpace Information

7121 Standard Drive

Hanover, MD 21076-1320

Price Code: A17

Available from:

National Technical Information Service

5285 Port Royal Road

Springfield, VA 22161

Price Code: A10
BOREAS RSS-7 Regional LAI and FPAR Images From Ten-Day

AVHRR-LAC Composites

Jing M. Chen, Josef Cihlar

Summary

The BOREAS RSS-7 team collected various data sets to develop and validate an algorithm to allow the

retrieval of the spatial distribution of LAI from remotely sensed images. AVHRR level-4c 10-day

composite NDVI images produced at CCRS were used to produce images of LAI and the FPAR

absorbed by plant canopies for the three summer IFCs in 1994 across the BOREAS region. The

algorithms were developed based on ground measurements and Landsat TM images (Chen and Cihlar,

1996; Chen, 1996b). The data are stored in binary image format files.

Note that some of the data files on the BOREAS CD-ROMs have been compressed using the Gzip

program. See Section 8.2 for details.

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 Regional LAI and FPAR Images From Ten-Day AVHRR-LAC Composites

1.2 Data Set Introduction

These Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) images

were generated in response to the need within the BOReal Ecosystem-Atmosphere Study (BOREAS)

modeling community for adequate spatial and temporal coverage of LAI and FPAR estimates across

the BOREAS region. The 10-Day Advanced Very High Resolution Radiometer (AVHRR) composite

Normalized Difference Vegetation Index (NDVI) images, produced after various processing steps to

remove artifacts, are very suitable for this purpose. Although algorithms for some cover types for this

region are not available, these parameter maps were produced as the best estimates at this stage.

Page 1
1.3 Objective/Purpose

The objective of this project was to provide quantitative spatial distribution of LAI and FPAR for

the BOREAS region for the 1994 summer Intensive Field Campaigns (IFCs) for the purpose of

modeling the carbon, water, energy, and trace gas exchange between the boreal ecosystems and the

atmosphere.

1.4 Summary of Parameters

LAI and FPAR absorbed by plant canopies.

1.5 Discussion

These LAI and FPAR images are based on AVHRR level-4c NDVI products and a coregistered

land cover map (Pokrant, 1991). The level-4c product is based on the AVHRR level-4b product, but is

further processed to remove or mitigate some artifacts caused by the input data or the compositing

process. The artifacts of concern are atmospheric contamination and bidirectional reflectance effects for

AVHRR channels 1 and 2, and atmospheric and surface emissivity effects for AVHRR channel 4. The

processing was carried out at the Canadian Centre for Remote Sensing (CCRS) using software and

procedures developed in-house (see Section 9 for details). The spatial and temporal coverage of this

product is identical to that of the level-4b and -4c products.

1.6 Related Data Sets

BOREAS RSS-07 LAI, Gap Fraction, and FPAR Data

BOREAS Level-4c AVHRR-LAC Ten-Day Composite Images: Surface Parameters

BOREAS Level-4b AVHRR-LAC Ten-Day Composite Images: At-sensor Radiance

2. Investigator(s)

2.1 Investigator(s) Name and Title

Dr. Jing M. Chen

Dr. Josef Cihlar

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

588 Booth Street, 4th Floor

Ottawa, Ontario

K1AOY7 Canada

(613) 947-1266

(613) 947-1406 (fax)

jing.chen@ ccrs.nrcan.gc.ca

Page 2
Contact 2"

JosefCihlar

CanadaCentrefor RemoteSensing

588BoothStreet,4thFloor

Ottawa,Ontario

K1AOY7Canada

(613)947-1265

(613)947-1406(fax)

Josef.Cihlar@ccrs.nrcan.gc.ca

Contact 3:

JaimeNickeson

RaytheonITSS

NASA GSFC

Code923

Greenbelt,MD 20771

(301)286-3373

Jaime.Nickeson@gsfc.nasa.gov

3. Theory of Measurements

The theories of LAI and FPAR measurements are documented in the Remote Sensing Science

(RSS)-07 ground LAI and FPAR data document. The theory of AVHRR measurements is given in the

BOREAS level-4c AVHRR images document, but the relevant portions are copied below.

The AVHRR is a four- or five-channel scanning radiometer capable of providing global daytime

and nighttime information about ice, snow, vegetation, clouds, and the sea surface. These data are

obtained on a daily basis primarily for use in weather analysis and forecasting; however, a variety of

other applications are possible. The AVHRR-Local Area Coverage (LAC) data collected for the

BOREAS project were from instruments onboard National Oceanic and Atmospheric Administration

(NOAA)-9, NOAA-11, and NOAA-12 polar orbiting platforms. The radiometers measured emitted and

reflected radiation in the visible, near-infrared, one middle-infrared, and one or two thermal channels.

The primary use of each channel and spectral regions and bandwidths on the respective NOAA

platforms are given in the following tables:

Wavelength

Channel [micrometers]

1" 0.57 - 0.69

2 0.72 - 0.98

3 3.52 - 3.95

4** 10.3 - 11.4

5*** 11.4 - 12.4

Primary Use

Cloud and Surface Mapping

Surface Water Delineation, Vegetation Cover

Sea Surface Temperature (SST), Nighttime Cloud Mapping

Surface Temperature, Day/Night Cloud Mapping

Surface Temperature

* Channel 1 wavelength for the Television and Infrared Observation Satellite (TIROS)-N flight model

was 0.5 -0.90 micrometers.

** For NOAA-7 and-9, channel 4 was 10.3-11.3 micrometers.

*** For TIROS-N, NOAA-6, -8, -10, and-12 Channel 5 duplicates Channel 4.

Page 3
Thewavelengthrangesat50%relativespectralresponse(in micrometers)of thebandsfor the

platform-specificinstrumentsare:

Band NOAA-9 NOAA-II NOAA-12 NOAA-14

1 0.570 - 0.699 0.572 - 0.698

2 0.714 - 0.983 0.716 - 0.985

3 3.525 - 3.931 3.536 - 3.935

4 10.334 - 11.252 10.338 - 11.287

5 11.395 - 12.342 11.408 - 12.386

0 571 -

0 724 -

3 554 -

i0 601 -

i0 601 -

0.684 0.570 - 0.699

0.984 0.714 - 0.983

3.950 3.525 - 3.931

11.445 10.330 - 11.250

11.445 11.390 - 12.340

The AVHRR is capable of operating in both real-time and recorded modes. Direct readout data were

transmitted to ground stations of the automatic picture transmission (APT) class at low resolution (4 x

4 km) and to ground stations of the high-resolution picture transmission (HRPT) class at high

resolution (1 x 1 kin). AVHRR HRPT data were received for the BOREAS region by the CCRS

Prince Albert Satellite Station (PASS). The LAI and FPAR images in 1994 in this data set were

produced using the NOAA- 11 sensor.

4. Equipment

4.1 Sensor/Instrument Description

The AVHRR is a cross-track scanning system featuring one visible, one near-infrared, one

middle-infrared, and two thermal channels. The analog data output from the sensors are digitized

onboard the satellite at a rate of 39,936 samples per second per channel. Each sample step corresponds

to an angle of scanner rotation of 0.95 milliradians. At this sampling rate, there are 1.362 samples per

instantaneous field of view (IFOV). A total of 2,048 samples is obtained per channel per Earth scan,

which spans an angle of +/-55.4 degrees from nadir.

4.1.1 Collection Environment

The NOAA satellites orbit Earth at an altitude of 833 kin. From this space platform, the data are

transmitted to a ground receiving station.

4.1.2 Source/Platform

Launch and available dates for the TIROS-N series of satellites from CCRS are:

Satellite Launch Date Date Range

TIROS-N 13-Oct-1978 19-Oct-1978 to 30-Jan-1980

NOAA-6 27-Jun-1979 21-Aug-1984 to 23-Jan-1986

NOAA-B 29-May-1980 Failed to achieve orbit

NOAA-7 23-Jun-1981 24-Jui-1983 to 30-Dec-1984

NOAA-8 28-Mar-1983 24-Jui-1983 to 13-Aug-1985

NOAA-9 12-Dec-1984 16-Sep-1985 to 19-Mar-1995

NOAA-10 17-Sep-1986 ii-Oct-1986 to 15-Nov-1993

NOAA-II 24-Sep-1988 28-Jun-1989 to 13-Sep-1994

NOAA-12 14-May-1991 ll-Aug-1993 to present

NOAA-14 30-Dec-1994 15-May-1995 to present

4.1.3 Source/Platform Mission Objectives

The AVHRR is designed for multispectral analysis of meteorologic, oceanographic, and

hydrologic parameters. The objective of the instrument is to provide radiance data for investigation of

clouds, land water boundaries, snow and ice extent, ice or snow melt inception, day and night cloud

distribution, temperatures of radiating surfaces, and SST. It is an integral member of the payload on

Page 4
theadvancedTIROS-Nspacecraftandits successorsin theNOAA series,andassuchcontributesdata

requiredto meetanumberof operationalandresearch-orientedmeteorologicalobjectives.

4.1.4 Key Variables

Emittedradiationandreflectedradiation.

4.1.5 Principles of Operation

The AVHRR is a four- or five-channel scanning radiometer that detects emitted and reflected

radiation from Earth in the visible, near-infrared, middle-infrared, and thermal-infrared regions of the

electromagnetic spectrum. A fifth channel was added to the follow-on instrument designated

AVHRR/2 and flown on NOAA-7, -9, -11, and -14 to improve the correction for atmospheric water

vapor.

4.1.6 Sensor/Instrument Measurement Geometry

The AVHRR is a cross-track scanning system. The IFOV of each sensor is approximately 1.4

milliradians, giving a spatial resolution of 1.1 km at the satellite subpoint. There is about a 36 percent

overlap between IFOVs (1.362 samples per IFOV). The scanning rate of the AVHRR is six scans per

second, and each scan spans an angle of +/-55.4 degrees from the nadir.

4.1.7 Manufacturer of Sensor/Instrument

ITT Aerospace

4.2 Calibration

No in-flight visible channel calibration is performed.

4.2.1 Specifications

IFOV 1.4 mrad

RESOLUTION i.i km

ALTITUDE 833 km

SCAN RATE 360 scans/min

SCAN RANGE -55.4 to 55.4

SAMPLES/SCAN 2,048 samples

(1.362 samples per IFOV)

degrees

per channel per Earth scan

4.2.1.1 Tolerance

A signal-to-noise ratio of 3:1 at 0.5-percent albedo.

4.2.2 Frequency of Calibration

The Naval Research Laboratory's (NRL's) TIROS-N calibration overlay performs the calibration

on blocks of telemetry data. For LAC/HRPT acquisitions, a block consists of 20 scan lines.

Calibration begins by reading the calibration parameters into memory. For each scan line of telemetry

in a block, the following process takes place:

• Telemetry data are extracted and unpacked.

• Ramp calibration data for each of the five channels are decommutated.

4.2.3 Other Calibration Information

None given.

Page 5
5. Data Acquisition Methods

The BOREAS level-4c AVHRR-LAC images were acquired through the CCRS. Some radiometric and

geometric corrections are applied to produce the imagery in a spatially corrected form (Lambert

Conformal Conic (LCC) projection). A full level-4c AVHRR-LAC image contains approximately

1,200 pixels in each of approximately 1,200 lines. Before any geometric corrections, the ground

resolution ranges from 1.1 km at nadir to 2.5 km x 6.8 km at the scanning extremes. Each pixel value

is stored in a 2-byte field starting with level-4b products. The level-4c images were processed through

software developed at CCRS. The raw data are available from the CCRS PASS.

6. Observations

6.1 Data Notes

None.

6.2 Field Notes

None.

7. Data Description

7.1 Spatial Characteristics

7.1.1 Spatial Coverage

The spatial characteristics for these LAI and FPAR images are the same as for the BOREAS

level-4c AVHRR-LAC imagery, which covers the entire 1,000-km by 1,000-km BOREAS region.

This contains the Northern Study Area (NSA), the Southern Study Area (SSA), and the transect region

between the SSA and NSA.

The North American Datum of 1983 (NAD83) coordinates for the comers of the LAI and FPAR

images are:

Latitude Long itude

Northwest (i,i) 59.36395°N I15.40859°W

Northeast (1,1200) 61.01294°N 93.28553°W

Southwest (1200,1) 48.83387°N II0.25229°W

Southeast (1200,1200) 50.02993°N 93.73857°W

The northwest comer has a distance (1109.76 km west, 7900.04 km north) from the origin (95°W and

0°N) of the LCC coordinate. The pixel size is exactly 1 km.

The NAD83 corner coordinates of the BOREAS region are:

Latitude Longitude

Northwest 59.979°N III.000°W

Northeast 58.844°N 93.502°W

Southwest 51.000°N III.000°W

Southeast 50.089°N 96.970°W

Page 6
The NAD83 corner coordinates of the SSA are

Latitude Longitude

Northwest 54.319°N I06.227°W

Northeast 54.223°N I04.236°W

Southwest 53.513°N I06.320°W

Southeast 53.419°N I04.368°W

The NAD83 corner coordinates of the NSA are

Latitude Longitude

Northwest 56.249°N 98.824°W

Northeast 56.083°N 97.241°W

Southwest 55.542°N 99.045°W

Southeast 55.379°N 97.489°W

7.1.2 Spatial Coverage Map

Not available.

7.1.3 Spatial Resolution

Before any geometric corrections, the spatial resolution varies from 1.1 km at nadir to

approximately 2.5 x 6.8 km at the extreme edges of the scan. The level-4b composite AVHRR-LAC

images have had geometric corrections applied so that the pixel size is 1 km in all bands. Only pixels

with view zenith angles 57 degrees or less are used in level-4c product. The LAI and FPAR images

have the same 1.0-kin spatial resolution.

7.1.4 Projection

The coordinate system is the LCC with the two standard parallels at 49 degrees north and 77

degrees north, respectively, and the meridian at 95 degrees west.

7.1.5 Grid Description

The LAI and FPAR images are the same as the level-4 images, which are projected into the LCC

projection described in Section 7.1.4 at a resolution of 1.0 km per pixel (grid cell) in both the X and Y

directions.

7.2 Temporal Characteristics

7.2.1 Temporal Coverage

The overall time period of AVHRR data acquisition in 1994 was from 09-Apr through 10-Sep.

Ten-day composite images of NDVI for this period were produced at level-4c. Three NDVI composite

images corresponding to the IFC periods were used for LAI and FPAR calculation (see Section 7.2.2).

7.2.2 Temporal Coverage Map

The 1994 compositing periods from the level-4c data set used for the LAI and FPAR parameter

images were the following 10-day periods, one during each IFC:

May 21-31

July 21-31

September l- l0

Page 7
7.2.3 Temporal Resolution

The daily images are composited into nominally cloud-free images over 10-day periods. One

10-day compositing period was selected to represent each 1994 IFC to create the three LAI and FPAR

images for this data set.

7.3 Data Characteristics

7.3.1 Parameter/Variable

LAI

FPAR

7.3.2 Variable Description/Definition

LAI is defined as one half the total leaf area per unit ground surface area. For four-sided spruce

needles, two sides are included. To derive LAI from the digital numbers (DN) in the image, use the

following formula:

LAI = (DN-1)/10

Green FPAR is the fraction of incident PAR that is absorbed by the green leaves in the canopy. It

excludes the fraction reflected back to space and the fraction absorbed by the background (moss, soil

and understory in forest, and soil in the crops), but it includes the small fraction that is reflected by the

background and absorbed by the green leaves on the way back to space. To derive FPAR from the

DNs in the image, use the following formula:

FPAR = (DN- 1)/100

7.3.3 Unit of Measurement

Both LAI and FPAR are unitless; for LAI, it can be expressed as m 2 of leaf area/m2 of ground

surface area.

7.3.4 Data Source

The level-4c AVHRR data used to create this product was processed and provided by the CCRS.

7.3.5 Data Range

Both the LAI and FPAR images are 8-bit images; i.e., the DNs vary from 0 to 255.

7.4 Sample Data Record

Not applicable to image data.

8. Data Organization

8.1 Data Granularity

The smallest unit of data tracked by the BOREAS Information System (BORIS) is the entire set of

AVHRR parameter image files, the readme file, and the coordinate conversion code.

8.2 Data Format(s)

Page 8
8.2.1 Uncompressed Data Files

This data product consists of the following data files:

logical

file description record size

1 Header/Readme 80

2 FPAR AVHRR IFCI 94.IMG 1200

3 FPAR AVHRR IFC2 94.IMG 1200

4 FPAR AVHRR IFC3 94.IMG 1200

5 LAI AVHRR IFCI 94.IMG 1200

6 LAI AVHRR IFC2 94.IMG 1200

7 LAI AVHRR IFC3 94.IMG 1200

8 icc 1200.f 80

The header file contains descriptive information about the files in the product.

Each of the composite images contain 1,200 8-bit (1-byte) pixels in each of the 1,200 lines. The

image file format is the same as that of any band sequential (BSQ) image file.

The FORTRAN program, "lcc_1200.f", is provided for the calculation of longitude and latitude for

any given pixel and line in the image. When using the software, you need to input the pixel and line

numbers relative to the top-left pixel, which is (1,1). A version of the lcc_1200 code written in C is

available on request from Jing M. Chen at CCRS.

8.2.2 Compressed CD-ROM Files

On the BOREAS CD-ROMs, the ASCII header file for this image is stored as ASCII text;

however, the image files have 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

9.1.1.1 LAI Maps

The NDVI was first converted to the simple ratio (SR) using

SR = (1 +NDVI)/(1-NDVI)

In the calculation of LAI from SR, different algorithms were used for the following 10 land cover

types: water, mixed wood, deciduous, conifer, transitional forest, tundra, barren lands, cropland,

rangeland/pasture, and built-up areas. For water, barren lands, and built-up areas, LAI is set to zero.

An algorithm for the cropland is formulated using available NDVI-LAI relationships in the literature

(Holben et al., 1980; Gardner and Blad, 1986; Aase et al., 1986; Wiegand et al., 1992; Li et al.,

1993). The algorithm for boreal conifer forests is derived from our recent work (Chen and Cihlar,

1996). The methodology for ground truth LAI measurements is described in Chen and Cihlar (1995a

and 1995b) and Chen (1996a). The algorithm for deciduous forests is also based on our own work,

Page 9
butis lessaccuratebecauseof thevegetationdynamicsandinsufficientfield datafortheseasonal

coverage.Mixed woodandtransitionalforestsareconsideredastheintermediatecasebetweenconifer

anddeciduousforests.Tundraandrangeland/pasturearetreatedthesameascroplandbecauseof lack

of data.Summerimagesarelessusefulfor determiningtheoverstoryNDVI in conifercanopies

becauseof theunderstoryeffect(ChenandCihlar,1996);therefore,theIFC-2andIFC-3LAI values

for coniferspeciesweretakento be 1.12and1.05timestheIFC-1values.A ceilingof LAI valueswas

usedfor eachIFC to remove'speckles'(unreasonablylargeLAI valuescausedby noisein NDVI

data).Theseceilingswere5.5, 6.0,and5.7for IFC-1,IFC-2,andIFC-3,respectively.

Thealgorithmsfor borealforestsweredevelopedby correlatingSRfrom Landsat-5Thematic

Mapper(TM) with ground-basedLAI measurements. In theapplicationof thealgorithmtoAVHRR

images,afactorof 1.10wasusedto increasetheNDVI of AVHRR to accountfor sensordifferences.

CautionshouldbetakenwhenusingtheseLAI valuesforthecalculationof FPARbecausesome

modelsonly useLAI inputs,whileFPARis calculatedfrom LAI. Becauseof thecanopyarchitectural

differencebetweenthespecies,it is suggestedthattheLAI valuesshouldbereducedby 50%for

conifers,25%for deciduousandmixedwood,and 10%for agriculturalcropsandtundrain the

calculationofradiationinterceptionusingBeer'sLaw.

Theequationsusedin theLAI algorithmare:

IFC-I and

LAI=0

LAI=0

LAI=0

LAI=I

LAI=0

LAI=0

LAI=0

LAI=0

IFC-3 :

594 (SR-2.781)

475 (SR-2 781)

792 (SR-2 781)

188 (SR-2 781)

325 (SR-I 5)

325 (SR-I 5)

325 (SR-I 5)

For mixed wood

For deciduous forest

For transitional forest

For conifer forest

For tundra

For cropland

For rangeland and pasture

For water, barren, and built-up areas

IFC-2 :

LAI=0

LAI:0

LAI:0

LAI:I

LAI:0

LAI:0

LAI:0

LAI:0

493(SR-3.637) For mixed wood

394(SR-3.637) For deciduous forest

657(SR-3.637) For transitional forest

12*LAI in IFC-I For conifer forest

325(SR-1.5) For tundra

325(SR-1.5) For cropland

325(SR-1.5) For rangeland and pasture

For water, barren, and built-up areas

As shown above, the background SR values are all the same because of the lack of field data. The

background values were obtained through regression for the conifer species. The IFC-2 LAI values

were calculated from IFC-1 values to minimize the background effect (Chen and Cihlar, 1996).

9.1.1.2 Daily Green FPAR Maps

Green FPAR refers to the fraction absorbed by green leaves only after the removal of the

contribution of the supporting woody material to the PAR absorption. The instantaneous green FPAR

is integrated over the day with a weight equal to the cosine of the solar zenith angle to obtain the daily

green FPAR presented in the map. The daily green FPAR can be used as a parameter to convert the

daily absorbed PAR to daily total incident PAR. The dates for IFC- 1, IFC-2, and IFC-3 are the same

as those for LAI maps.

Similar to the calculation of LAI, different algorithms for the daily green FPAR were used for the

10 land cover types. FPAR for water, barren lands, and built-up areas is assumed to be zero. An

algorithm for the cropland is formulated using measurements from crop fields (Chen, unpublished).

The algorithm is similar to that of Asrar et al. (1984). The algorithms for boreal conifer forests are

published in Chen (1996b). For deciduous cover, the algorithm is formulated using eight sites in the

Page 10
midsummer(Chen,unpublished).ThealgorithmsarelinearrelationshipsbetweenSRandFPAR.

Tundraandrangeland/pasturearetreatedthesameascroplandbecauseof lackof data.

Thevaluesfor borealforestswithin themaparethemostreliable.FPARof theoverstoryis

calculatedfromthedownwellingandupwellingPARmeasurementsattwolevels:aboveandbelowthe

canopy,for over30stands.Thebelow-canopymeasurementsweremadeusingtheTracingRadiation

andArchitectureof Canopies(TRAC)instrument(BOREASRSS-07GroundMeasurementsof LAI

andFPAR).BecausethedownwellingPARthroughtheoverstoryandupwellingPARreflectedfrom

theforestfloor arehighly variable,averagevalueswereobtainedfrom closelyspacedmeasurements

overlongtransects(50-340m).Simultaneousmeasurementsof thedownwellingandupwellingPAR

abovetheforestsweremadeonmicrometeorological towersestablishedforBOREAS.Thedatawere

collectedatthebeginning,middle,andendof thegrowingseasonto considertheeffectof seasonal

vegetationdynamicsandthechangein solarzenithangle(Chen,1996b).

Theequationsusedin theFPARalgorithmare:

IFC-I

FPAR=0

FPAR=0

FPAR=0

FPAR=0

FPAR=0

FPAR=0

FPAR=0

FPAR=0

and IFC-3 :

170 (SR-2.044)

147 (SR-2 044)

176 (SR-2 044)

221 (SR-2 044)

138 (SR-I 5)

138 (SR-I 5)

138 (SR-I 5)

For mixed wood

For deciduous forest

For transitional forest

For conifer forest

For tundra

For cropland

For rangeland and pasture

For water, barren, and built-up areas

IFC-I

FPAR=0

FPAR=0

FPAR=0

FPAR=I

FPAR=0

FPAR=0

FPAR:0

FPAR:0

and IFC-3 :

147 (SR-3.074) For mixed wood

127 (SR-3.074) For deciduous forest

154 (SR-3.074) For transitional forest

05*FPAR in IFC-I For conifer forest

138 (SR-I.5) For tundra

138 (SR-I.5) For cropland

138 (SR-I.5) For rangeland and pasture

For water, barren, and built-up areas

In the equations the background SR changes between IFCs. The background values are different

from the LAI equations because the SR-FPAR relationship is not strictly linear. These values were

found from the regressions for the conifer species. A refinement of the algorithm would be to use the

Modified SR (MSR, Chen 1996c), which is more linearly related to FPAR. IFC-2 LAI values for

conifers are directly related to IFC-1 values because the background effect in IFC-1 is smaller (Chen

and Cihlar, 1996). This assumes the normal seasonal growth situation.

Because of lack of complete field data for all cover types, the most reliable data are for conifer,

transitional forest, and cropland. The images with quality quote 04 (on a scale of 05) will be made

available upon request. Please notify the contacts listed in Section 2.3 regarding any problems you

encounter.

9.2 Data Processing Sequence

The processing steps from NDVI to LAI and FPAR are summarized above (Section 9.1); the

processing steps leading to NDVI are described in the document for BOREAS level-4c AVHRR-LAC

images. The relevant descriptions are copied below for convenience.

The level-4c processing sequence is called Atmosphere, Bidirectional and Contamination

Corrections of CCRS (ABC3) and is described in more detail by Cihlar et al. (1997a, 1997b).

Page 11
9.2.1 Processing Steps

Step 1: Top-of-the-Atmosphere (TOA) reflectance

TOA reflectance for channel 1 or 2 is calculated from the corrected TOA radiance, L*(new), with

the formula given by Teillet (1992). Values of gain G and offset O were calculated with consideration

of postlaunch sensor degradation (Teillet and Holben, 1994).

Step 2: Atmospheric correction of AVHRR channels 1 and 2

The algorithm Simplified Method for Atmospheric Correction (SMAC) (Rahman and Dedieu,

1994) was used in the processing. The processing was carried out assuming a water content of 2.3

g/cm2 and ozone content of 0.319 cm-atm. A constant value of 0.05 was used for optical depth at 550

nm. The corrections were computed on a pixel basis using solar zenith, view zenith, and relative

azimuth channels.

Step 3: Identification of contaminated pixels

A new procedure was developed to identify the 'contaminated' pixels; i.e., pixels where the

surface vegetation or soil signal is obscured (Cihlar, 1996). The procedure, dubbed Cloud Elimination

from Composites using Albedo and NDVI Trend (CECANT), is based on the high sensitivity of NDVI

to the presence of clouds, aerosol, and snow. Three features of the annual surface reflectance trend are

used: the high contrast between the albedo (represented by AVHRR channel 1) of land, especially

when fully covered by green vegetation, and clouds or snow/ice; the average NDVI value (expected

value for that pixel and compositing period); and the monotonic trend in NDVI. Four thresholds are

required in CECANT to identify a partially contaminated pixel (i,j,t) where i and j are pixel coordinates

and t is the compositing period:

Iic (t : The maximum channel 1 reflectance of a clear-sky, II

II snow- or ice-free land pixel in the data set. II

I1 II

IIRmin(t): The maximum acceptable deviation of the measured II

"ll value NDVI(i,j,t) below the estimated NDVIa(i,j,t). II

I1 II

IIRmax(t): The maximum acceptable deviation of the measured II

"ll value NDVI(i,j,t) above the estimated NDVIa(i,j,t). II

IIzmax(t : The maximum acceptable deviation of the measured II

value NDVI(i,j,t) above the estimated NDVImax(i,j,t). II II I[

NDVImax(i,j,t) and NDVIa(i,j,t) were calculated using the Fourier-Adjustment, Solar Zenith

Angle Corrected, Interpolated, Reconstructed (FASIR) model of Sellers et al. (1994), which

approximates the seasonal NDVI curve with a third-order Fourier transform. Before the computation,

missing NDVI values between first and last measurements were replaced through linear interpolation

after finding the seasonal peak for each pixel, using the rationale and algorithm of Cihlar and Howarth

(1994). A constant value of 0.30 was used for C1. The upper and lower limits for R and Z were

determined separately for each composite period using R and Z histograms (Cihlar, 1996). Using these

thresholds, a cloud mask was prepared for each composite period.

Step 4: Corrections for bidirectional reflectance effects in channels 1 and 2

The model of Roujean et al. (1992) as modified by Wu et al. (1995) was used to characterize the

seasonal bidirectional reflectance function for each cover type. Land cover-dependent model

coefficients were derived (Li et al., 1995) using a map of Canada with pixel size of 1 km prepared with

AVHRR data (Pokrant, 1991). Only cloud-free pixels were included in the derivation of the model

coefficients, and no bidirectional corrections for snow- or ice-covered areas were made. The resulting

models were used to compute channel 1 and 2 reflectance for view zenith of 0 degrees and solar zenith

of 45 degrees.

Page 12
Step5: Replacementof contaminatedpixelsfor AVHRR channels1and2

Twocaseswererecognized:pixelscontaminatedeitherduringor attheendof thegrowingseason.

Forpixelscontaminatedduringthegrowingseason,thenewvalueswerefoundthroughlinear

interpolationfor bothchannels1and2.At theendof thegrowingseason,it wasassumedthatthe

annualtrajectoryfor individualchannelsaswell asfor NDVI couldbeapproximatedby a

second-degreepolynomial.Thepolynomialwasfitted to theplotof correctedreflectancefor all

clear-skyperiods,startingwith thefirst clear-skycompositeperiodafter01-Aug.After thebest-fit

coefficientsweredetermined,thenewvalueswerecalculatedusingthepolynomialcoefficientsto

replacecontaminatedpixelsin eachchannelpriorto thefirst clearpixel or afterthelastsuchpixel.

Step6: NDVI processing

Becauseof imperfectionsin thebidirectionalcorrectionsof channels1and2, theNDVI values

computedfrom atmosphericallycorrectedNDVI werealsoretained.However,correctionsfor solar

zenithangleweredesirablein view of theknowndependenceof theNDVI onthesolarzenithangle.

Thecoefficientsof Sellersetal. (1994)wereusedfor thevariouslandcoverclasses.Thenew setof

NDVI valueswasthencomputedfor areferencesolarzenithangleof 45degreesbasedonthe

equationsofSellerset al.(1994).TheNDVI valuesfor themissingor contaminatedpixelswere

interpolatedasin Step5 above.

Step7: Compression

BORISstaffcopiedtheASCII andcompressedthebinaryfiles forreleaseonCD-ROM.

9.2.2 Processing Changes

None.

9.3 Calculations

See Section 9.2.1.

9.3.1 Special Corrections/Adjustments

See Section 9.2.1.

9.3.2 Calculated Variables

See Section 9.2.1.

9.4 Graphs and Plots

None.

10. Errors

10.1 Sources of Error

The sources of errors in LAI and FPAR are: (1) ground measurements, (2) use of algorithms based

on Landsat TM that include image geometric and radiometric corrections and atmospheric correction,

(3) error in determining the response difference between TM and AVHRR sensors, (4) the error

associated with the AVHRR NDVI product.

The major sources of error in AVHRR NDVI are due to the inaccurate knowledge of atmospheric

conditions during image acquisition (and thus the use of nominal values for atmospheric corrections)

and imperfect modeling of the bidirectional effects.

10.2 Quality Assessment

10.2.1 Data Validation by Source

None given.

Page 13
10.2.2 Confidence Level/Accuracy Judgment

The FPAR images are more accurate than the LAI images. The relative error in LAI is estimated to

be 20% for conifers and transitional forests; 25% for deciduous and mixed covers; and 30% for

cropland, grassland, and tundra. The relative error in daily green FPAR is estimated to be less than

10% for all cover types.

10.2.3 Measurement Error for Parameters

See Section 10.2.2.

10.2.4 Additional Quality Assessments

None given.

10.2.5 Data Verification by Data Center

BORIS staff displayed the LAI and FPAR images as a visual check that the images were what was

expected.

11. Notes

11.1 Limitations of the Data

The calculated LAI and FPAR maps should be considered the best estimate for this moderate

resolution. The distribution patterns and the magnitude of LAI and FPAR values for each cover type

are indeed very reasonable. The resampling scheme used for the original channel images makes the

actual resolution coarser than 1 km. It is on the order of 2-3 km in reality (Cihlar et al., 1997b).

Subpixel water bodies and mixture of different cover types in a pixel can incur errors in the calculated

results. Lack of data makes the calculations for rangeland, tundra, and mixed wood types least reliable.

11.2 Known Problems with the Data

None.

11.3 Usage Guidance

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.

None given.

12. Application of the Data Set

None given.

13. Future Modifications and Plans

Page 14
14. Software

14.1 Software Description

A Fortran program, "lcc_1200.f", is provided for the calculation of longitude and latitude for any

given pixel and line in the image. When using the software, you need to input the pixel and line

numbers relative to the top-left pixel, which is (1,1). Gzip (GNU zip) uses the Lempel-Ziv algorithm

(Welch, 1994) used in the zip and PKZIP commands.

14.2 Software Access

A version of the lcc_1200 code written in C is available on request from contacts listed in Section

2.3. Gzip is available from many Web sites across the Internet (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

The RSS-07 regional LAI and FPAR images are available from the Earth Observing System Data and

Information System (EOSDIS) Oak Ridge National Laboratory (ORNL) Distributed Active Archive

Center (DAAC).

15.1 Contact Information

For BOREAS data and documentation please contact:

ORNL DAAC User Services

Oak Ridge National Laboratory

P.O. Box 2008 MS-6407

Oak Ridge, TN 37831-6407

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, fax, letter, or personal visit using the contact

information in Section 15.1.

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.

Page 15
16. Output Products and Availability

16.1 Tape Products

The data can be made available on 8-ram 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 Platform/Sensor/Instrument/Data Processing Documentation

Buffam, A. 1994. GEOCOMP User Manual. Internal Report, Canada Centre for Remote Sensing,

Ottawa, Ontario.

Cihlar, J. 1996. Identification of contaminated pixels in AVHRR composite images for studies of land

biosphere. Remote Sensing of Environment 56:149-163.

Cihlar, J. and J. Howarth. 1994. Detection and removal of cloud contamination from AVHRR

composite images. IEEE Transactions on Geoscience and Remote Sensing 32: 427-437.

Cihlar, J., H. Ly, Z. Li, J. Chen, H. Pokrant, and F. Huang. 1997a. Multitemporal, multichannel

data sets for land biosphere studies: artifacts and corrections. Remote Sensing of Environment

60:35-57.

Cihlar, J., J. Chen, and Z. Li. 1997b. Seasonal AVHRR multichannel data sets and products for

studies of surface-atmosphere interactions. Journal of Geophysical Research

102(D24):29,625-29,640.

Kidwell, K. 1991. NOAA Polar Orbiter Data User's Guide, NCDC/SDSD. (Updated from original

1984 edition.)

Li, Z., J. Cihlar, X. Zheng, L. Moreau, and H. Ly. 1996. The bidirectional effects of AVHRR

measurements over northern regions. IEEE Transactions on Geoscience and Remote Sensing

(accepted).

Pokrant, H. 1991. Land cover map of Canada derived from AVHRR images. Manitoba Remote

Sensing Centre, Winnipeg, Manitoba, Canada.

Rahman, H. and G. Dedieu. 1994. SMAC: a simplified method for the atmospheric correction of

satellite measurements in the solar spectrum. International Journal for Remote Sensing 15: 123-143.

Robertson, B., A. Erickson, J. Friedel, B. Guindon, T. Fisher, R. Brown, P. Teillet, M. D'Iorio, J.

Cihlar, and A. Sancz. 1992. GEOCOMP, a NOAA AVHRR geocoding and compositing system.

Proceedings of the ISPRS Conference, Commission 2, Washington, DC: 223-228.

Roujean, J.-L., M. Leroy, and P.-Y. Deschamps. 1992. A bidirectional reflectance model of the

Earth's surface for the correction of remote sensing data. Journal of Geophysical Research 97(D 18):

20,455-20,468.

Page 16
Salisbury,J.W.andD.M. D'Aria. 1992.Emissivityof terrestrialmaterialsin the8-1m atmospheric

window.RemoteSensingof Environment42: 83-106.

Sellers,P.J.,S.O.Los, C.J.Tucker,C.O.Justice,D.A. Dazlich,G.A. Collatz, andD.A. Randall,

1994.A global lo by 1o NDVI datasetfor climatestudies.Part2: Thegenerationof globalfieldsof

terrestrialbiophysicalparametersfrom theNDVI. InternationalJournalof RemoteSensing.

Sobrino,J.A., C. Coll, andV. Caselles.1991.Atmosphericcorrectionfor landsurfacetemperature

usingNOAA-11AVHRR channels4 and5. RemoteSensingof Environment38: 19-34.

Suttles,J.T., R.N. Green,P. Minnis, G.L. Smith, W.F. Staylor,B.A. Wielicki, I.J. Walker,D.F.

Young,V.R.Taylor,andL.L. Stowe.1989Angularradiationmodelsfor Earth-atmospheresystem,

Vol. 1,Shortwaveradiation,NASA Ref.Publ., 1184,84pp.

Teillet,P.M. 1992.An algorithmfortheradiometricandatmosphericorrectionofAVHRR datain the

solarreflectivechannels.RemoteSensingofEnvironment41:185-195.

Teillet,P.M.andB.N. Holben.1994.Towardsoperationalradiometriccalibrationof NOAA AVHRR

imageryin thevisibleandnear-infraredchannels.CanadianJournalof RemoteSensing20: 1-10.

Townshend,J.(Ed.).1995.Globaldatasetsfor thelandfrom AVHRR. InternationalJournalof

RemoteSensing15:3315-3639(specialissuedescribingseveralprogramgeneratingcomposite

AVHRR imagedatasets).

VandeGriend,A.A. andM. Owe. 1993.On therelationshipbetweenthermalemissivityandthe

normalizeddifferencevegetationindexfor naturalsurfaces.InternationalJournalof RemoteSensing

14(6):1119-1131.

Welch,T.A. 1984.A Techniquefor High PerformanceDataCompression.IEEEComputer,Vol. 17,

No. 6, pp. 8-19.

Wiegand,C.L., S.J.Maas,J.K. Aase,J.L.Hatfield, P.J.Pinter,Jr., R.D. Jackson,E.T. Kanemasu,

andR.L. Lapitan.1992.MultisiteAnalysesof Spectral-Biophysical Datafor Wheat.RemoteSensing

of Environment42: 1-21.

Wu, A., Z. Li, andJ.Cihlar. 1995.Effectsof landcovertypeandgreennessonAVHRR bidirectional

reflectances:AnalysisandremovalJ.Geophy.Res.(inpress).

17.2 Journal Articles and Study Reports

Aase,J.K., J.P.Millard, andB.S.Brown. 1986.SpectralRadianceEstimatesof LeafAreaandLeaf

Phytomassof SmallGrainsandNativeVegetation.IEEETransactionsonGeoscienceandRemote

Sensing24: 685-692.

Asrar, G.,M. Fuchs,E.T.Kanemasu,andJ.H.Hatfield. 1984.Estimatingabsorbedphotosynthetic

radiationandleafareaindexfrom spectralreflectancein wheat.Agron.J.76:300-306.

Becker,F. andZ.L. Li. 1990.Towardsalocal splitwindowmethodoverlandsurface.International

Journalof RemoteSensing3: 369-393.

Chen,J.M. 1996a.Optically-basedmethodsfor measuringseasonalvariationof leafareaindexin

borealconiferstands.Agric. For.Meteorology80:135-163.

Chen,J.M. 1996b.CanopyArchitectureandremotesensingof thefractionof photosynthetically

activeradiationabsorbedby borealforests.IEEETrans.GeosciRemoteSens.34:1353-1368.

Page17
Chen,J.M. 1996c.Evaluationof vegetationindicesandamodifiedsimpleratiofor boreal

applications.Can.J.RemoteSens.22:229-242.

Chen,J.M. andJ.Cihlar. 1995a.Plantcanopygapsizeanalysistheoryfor improvingoptical

measurementsof leafareaindexof plantcanopies.AppliedOptics34:6211-6222.

Chen,J.M. andJ.Cihlar. 1995b.Quantifyingtheeffectof canopyarchitectureonoptical

measurementsof leafareaindexusingtwo gapsizeanalysismethods.IEEETrans.Geosci.Remote

Sens.33:777-787.

Chen,J.M. andJ.Cihlar 1996.Retrievingleafareaindexin borealforestsusingLandsatTM images.

RemoteSensingof Environment55:153-162.

Cihlar,J.andP.M.Teillet. 1995.Forwardpiecewiselinearcalibrationmodelfor quasi-realtime

processingof AVHRR data.CanadianJournalof RemoteSensing21:22-27.

Coll, C.,V. Caselles,J.A.Sobrino,andE.Valor. 1994.Ontheatmosphericdependenceof the

split-windowequationfor landsurfacetemperature.InternationalJournalfor RemoteSensing15(1):

105-122.

Gardner,B.R.,andB.L. Blad.1986.Evaluationof SpectralReflectanceModelsToEstimateCorn

LeafAreaWhile MinimizingtheInfluenceofSoilBackgroundEffEcts.RemoteSensingof

Environment20:183-193.

Holben,B.N., C.J.Tucker,andC-J.Fan. 1980.SpectralAssessmentof SoybeanLeafAreaandLeaf

Biomass.PhotogrammetricEngineeringandRemoteSensing46:651-656.

Li Z, T.H. Demetriadesshah,E.T.Kanemasu,J.K. Shultis,andM.B. Kirkham. 1993.UseOf 2nd

DerivativesOf CanopyReflectanceForMonitoringPrairieVegetationOverDiffErentSoil

Backgrounds.RemoteSensingOf Environment44:(1) 81-87APR 1993.

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, andP.Sellers,eds.2000.CollectedDataof The

BorealEcosystem-AtmosphereStudy.NASA. CD-ROM.

Sellers,P.andF.Hall. 1994.BorealEcosystem-AtmosphereStudy:ExperimentPlan.Version

1994-3.0,NASA BOREASReport(EXPLAN 94).

Sellers,P.andF.Hall. 1996.BorealEcosystem-AtmosphereStudy:ExperimentPlan.Version

1996-2.0,NASA BOREASReport(EXPLAN 96).

Sellers,P.,F. Hall, andK.F. Huemmrich.1996.BorealEcosystem-AtmosphereStudy:1994

Operations.NASA BOREASReport(OPSDOC94).

Sellers,P.,F. Hall, andK.F. Huemmrich.1997.BorealEcosystem-AtmosphereStudy:1996

Operations.NASA BOREASReport(OPSDOC96).

Sellers,P.,F. Hall, H. Margolis,B. Kelly, D. Baldocchi,G. denHartog,J.Cihlar, M.G. Ryan,B.

Goodison,P.Crill, K.J.Ranson,D. Lettenmaier,andD.E. Wickland. 1995.Theboreal

ecosystem-atmospherestudy(BOREAS):anoverviewandearlyresultsfrom the1994field year.

Bulletinof theAmericanMeteorologicalSociety.76(9):1549-1577.

Page18
Sellers,P.J.,F.G.Hall, R.D.Kelly, A. Black, D. Baldocchi,J. Berry, M. Ryan,K.J. Ranson,P.M.

Crill, D.P.Lettenmaier,H. Margolis,J.Cihlar, J.Newcomer,D. Fitzjarrald,P.G.Jarvis,S.T.

Gower,D. Halliwell, D. Williams, B. Goodison,D.E.Wickland, andF.E.Guertin.1997.BOREAS

in 1997:ExperimentOverview,ScientificResultsandFutureDirections.Journalof Geophysical

Research102(D24):28,731-28,770.

17.3 Archive/DBMS Usage Documentation

None.

None given.

18. Glossary of Terms

19. List of Acronyms

ABC3

AEAC

APT

ASCII

AVHRR

BOREAS

BORIS

BPI

BSQ

CCRS

CCT

CD-ROM

CECANT

DAAC

DAT

DN

EOS

EOSDIS

EROS

FASIR

FPAR

GAC

- Atmosphere, Bidirectional and Contamination Corrections of CCRS

- Albers Equal-Area Conic

- Automatic Picture Transmission

- American Standard Code for Information Interchange

- Advanced Very High Resolution Radiometer

- BOReal Ecosystem-Atmosphere Study

- BOREAS Information System

- Bytes Per Inch

- Band Sequential

- Canada Centre for Remote Sensing

- Computer-Compatible Tape

- Compact Disk-Read-Only Memory

- Cloud Elimination from Composite Using Albedo and NDVI Trend

- Distributed Active Archive Center

- Digital Archive Tape

- Digital Number

- Earth Observing System

- EOS Data and Information System

- Earth Resources Observation System

- Fourier-Adjustment, Solar Zenith Angle corrected, Interpolated,

Reconstructed

- Fraction of PAR absorbed by plant canopies

- Global Area Coverage

GEOCOMP - Geocoding and Compositing System

GIS

GSFC

HRPT

IFC

IFOV

ISLSCP

LAC

LAI

LCC

MRSC

MSR

NAD83

NASA

- Geographic Information System

- Goddard Space Flight Center

- High-Resolution Picture Transmission

- Intensive Field Campaign

- Instantaneous Field-of-View

- International Satellite Land Surface Climatology Project

- Local Area Coverage

- Leaf Area Index

- Lambert Conformal Conic

- Manitoba Remote Sensing Centre

- Modified SR

- North American Datum of 1983

- National Aeronautics and Space Administration

Page 19
NDVI - Normalized Difference Vegetation Index

NEdT - Noise Equivalent Differential Temperature

NOAA - National Oceanic and Atmospheric Administration

NRL - Naval Research Laboratory

NSA - Northern Study Area

ORNL - Oak Ridge National Laboratory

PAR - Photosynthetically Active Radiation

PASS - Prince Albert Satellite Station

PRT - Platinum Resistor Thermometer

RSS - Remote Sensing Science

SMAC - Simplified Method for Atmospheric Correction

SR - Simple Ratio

SSA - Southern Study Area

SST - Sea Surface Temperature

TIROS - Television and Infrared Observation Satellite

TM - Thematic Mapper

TOA - Top-of-the-Atmosphere

TRAC - Tracing Radiation and Architecture of Canopies

optical instrument)

URL - Uniform Resource Locator

(a LAI and FPAR

20. Document Information

20.1 Document Revision Date

Written: 25-Sep- 1997

Last Updated: 26-Jul- 1999

20.2 Document Review Date(s)

BORIS Review: 06-Oct-97

Science Review: 10-Jan-98

20.3 Document ID

20.4 Citation

When using these data, please include the following acknowledgment as well as citations of

relevant papers in Section 17.2:

The AVHRR-LAC level-4c composite images resulted from a joint effort between BOREAS staff

at CCRS and NASA GSFC. The original data were acquired by CCRS and processed as level-3b

products by the MRSC in Winnipeg, Manitoba. The present level-4c product was created by CCRS

staff using a method developed at CCRS. The respective contributions of the above individuals and

agencies to completing this data set are greatly appreciated.

If using data from the BOREAS CD-ROM series, also reference the data as:

Chen, J.M. and J. Cihlar, "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. NASA. CD-ROM. NASA, 2000.

Page 20
20.5 Document Curator

20.6 Document URL

Page 21
REPORT DOCUMENTATION PAGE FormApproved

OMB No. 0704-0188

Public reporting burden for this collection of informationis estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources,

gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this

collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson

Davis Highway, Suite 1204, Arlington, VA 22202-4302, and to the Office of Management and Budget, Paperwork Reduction Project (0704-0188), Washington, DC 20503.

1. AGENCY USE ONLY (Leave blank) 2. REPORT DATE 3. REPORT TYPE AND DATES COVERED

August 2000 Technical Memorandum

4. TITLE AND SUBTITLE 5. FUNDING NUMBERS

Technical Report Series on the Boreal Ecosystem-Atmosphere Study (BOREAS)

BOREAS RSS-7 Regional LAI and FPAR hnages From 10-Day 923

AVHRR-LAC Composites

6. AUTHOR(S) RTOP: 923-462-33-01

Jing Chen and Josef Cihlar

Forrest G. Hall and Jaime Nickeson, Editors

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS (ES)

Goddard Space Flight Center

Greenbelt, Maryland 20771

9. SPONSORING / MONITORING AGENCY NAME(S) AND ADDRESS (ES)

National Aeronautics and Space Administration

Washington, DC 20546-0001

8. PEFORMING ORGANIZATION

REPORT NUMBER

2000-03136-0

10. SPONSORING / MONITORING

AGENCY REPORT NUMBER

TM--2000-209891

Vol. 52

11. SUPPLEMENTARY NOTES

J. Chen and J. Cihlar: Canada Centre for Remote Sensing; Jaime Nickeson: Raytheon ITSS

12a. DISTRIBUTION / AVAILABILITY STATEMENT

Unclassifie_Unlimited

Subject Category: 43

Report available from the NASA Center for AeroSpace Information,

7121 Standard Drive, Hanover, MD 21076-1320. (301) 621-0390.

12b. DISTRIBUTION CODE

13. ABSTRACT (Maximum 200 words)

The BOREAS RSS-7 team collected various data sets to develop and validate an algorithm to allow

the retrieval of the spatial distribution of LAI from remotely sensed images. AVHRR level-4c 10-day

composite NDVI images produced at CCRS were used to produce images of LAI and the FPAR

absorbed by plant canopies for the three summer IFCs in 1994 across the BOREAS region. The

algorithms were developed based on ground measurements and Landsat TM images (Chen and Cihlar,

1996; Chen, 1996b). The data are stored in binary image format files.

14. SUBJECT TERMS

BOREAS, remote sensing science, LAI, FPAR.

17. SECURITY CLASSIFICATION

OF REPORT

Unclassified

NSN 7540-01-280-5500

18. SECURITY CLASSIFICATION

OF THIS PAGE

Unclassified

19. SECURITY CLASSIFICATION

OF ABSTRACT

Unclassified

15. NUMBER OF PAGES

21

16. PRICE CODE

20. LIMITATION OF ABSTRACT

UL

Standard Form 298 (Rev. 2-89)

Prescribed by ANSI Std. Z39.18

298-102