NASA/TMM2000-209891, Vol. 171

Technical Report Series on the

Boreal Ecosystem-Atmosphere Study (BOREAS)

Forrest G. Hall and Andrea Papagno, Editors

Volume 171

BOREAS TE-17 Production

Efficiency Model Images

Scott J. Goetz, Samual N. Goward, Stephen D. Prince, Kevin Czajkowski, and Ralph O. Dubayah

Universi_' of Mat_.'land. College Park

National Aeronautics and

Space Administration

Goddard Space Flight Center

Greenbelt, Maryland 20771

October 2000

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BOREAS TE-17 Production Efficiency Model Images

Scott J. Goetz, Samual N. Goward, Stephen D. Prince, Kevin Czajkowski, Ralph Dubayah

Summary

A BOREAS version of the Global Production Efficiency Model (http://www.inform.umd.edu/glopem/)

was developed by TE- 17 to generate maps of gross and net primary production, autotrophic respiration,

and light use efficiency for the BOREAS region. This document provides basic information on the

model and how the maps were generated. The data generated by the model are stored in binary

image-format files.

Note that the files of this data set 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

I 1) Notes

12) Application of the Data Set

13) Future Modifications and Plans

14) Software

15) Data Access

16) Output Pr_vducts and Availability

17) References

18) Glossary of Terms

19) List of Acronyms

20) Document Inl"onnation

1. Data Set Overview

1.1 Data Set Idenlification

BOREAS TE-17 Production Efficiency Model Images

1.2 Data Set lnlr(_duclion

The Boreal Forc,t lh_luction Efficiency Model (BoreaI-PEM) is composed of a suite of models

that provide estimate, of the variables needed to drive a production efficiency model (i.e., one based

on restrictions in 1he ct_n_ersion "efficiency" of absorbed photosynthetically active radiation (APAR) in

terms of unstressed gross primary production (GPP) through short-term environmental physiology).

Page 1
1.3 Objective/Purpose

The purpose of the production efficiency modeling for the BOReal Ecosystem-Atmosphere Study

(BOREAS) was to use remotely sensed observations to estimate GPP and net primary production

(NPP) at the spatial resolution of the Advanced Very High Resolution Radiometer (AVHRR) Local

Area Coverage (LAC) for the entire BOREAS region. Higher resolution maps would be possible with,

e.g., Earth Observing System (EOS) instruments (launched 1998) or with sensors on aircraft

platforms. The advantage of satellite data was that, once the component models were validated,

consistent measurements could be made across the entire region. Thus, this approach captured

radients of land use intensity and climate. In addition to NPP and GPP, Boreal-PEM provided the

ata needed to model above-ground biomass and canopy conductance (e.g., maps of air temperature,

vapor pressure deficit (VPD), etc.). In combination with land cover and deforestation maps, the

remotely sensed measurements of NPP can yield estimates of the impact of land cover change on

carbon storage, which is a focus of the follow-on work.

1.4 Summary of Parameters

A number of surface variables required to implement Boreal-PEM are retrieved on a daily basis

usinz surface "parameter retrieval" algorithms. These include surface radiometric temperature (Ts),

ambient air temperature (Ta), atmospheric precipitable water vapor amount (U), surface absolute

humidity, VPD, fractional PAR absorption (FPAR), APAR, standing above-ground biomass, and a

cumulative surface wetness index (CSI). Other variables related to the efficiency of light utilization, or

the carbon yield of APAR, include the proportion of vegetation cover types that utilize the C3 or C4

photosynthetic pathways. This is derived using long-term climatological information and modeled

biomass.

1.5 Discussion

Boreal-PEM consists of linked models of canopy radiative transfer, canopy utilization of APAR,

and physical environmental variables that have a multiplicative effect on stomatai control. The model is

entirely driven with satellite-retrieved surface variables (e.g., APAR, air temperature, soil moisture,

absolute humidity, etc.). The resulting "stressed" GPP is reduced to NPP through carbon expenditures

associated with autotrophic respiration derived from standing above-ground biomass. The model

results closely approximated surface measurements of both physical and biological variables, including

NPP, within the BOREAS study areas and were clearly associated with land cover type (i.e., broadleaf

deciduous, needleleaf evergreen, etc.).

1.6 Related Data Sets

BOREAS Level-3b AVHRR-LAC Imagery: Scaled At-_nsor Radiance in LGSOWG Format

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

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

BOREAS RSS-04 1994 Southern Study Area Jack Pine LAI and FPAR Data

BOREAS RSS-07 Regional LAI and FPAR Images From Ten-Day AVHRR-LAC Composites

BOREAS RSS- 14 Level- 1 GOES-7 Visible, IR and Water-vapor Images

BOREAS RSS-14 Level-I a GOES-7 Visible, IR, and Water-vapor Images

BOREAS RSS-14 Level-2 GOES-7 Shortwave and Longwave Radiation Images

BOREAS RSS-14 Level-1 GOES-8 Visible, IR and Water-vapor Images

BOREAS RSS-14 Level-1 a GOES-8 Visible, IR and Water-vapor Images

2. Investigator(s)

2.1 Investigator(s) Name and Title

Samual N. Goward (PI), Professor and Chair

Stephen D. Prince, Professor

Scott J. Goetz, Research Scientist

Kevin Czajkowski, Research Scientist

Ralph O. Dubayah, Assoc. Professor

Page 2
2.2 Title of Investigation

Biospheric Dynamics in the Boreal Forest Ecotone

2.3 Contact Information

Contact 1:

Dr. Scott J. Goetz

Dept. of Geography

University of Maryland

College Park, MD 20742-8225

(301) 405-1297

(301) 314-9299 (fax)

sgoetz@geog.umd.edu.

Contact 2:

Samual N. Goward

Dept. of Geography

University of Maryland

College Park, MD 20742-8225

sg2 l@umail.umd.edu

Contact 3:

Stephen D. Prince

Dept. of Geography

University of Maryland

College Park, MD 20742-8225

sp43@umail.umd.edu

Contact 4:

Kevin Czajkowski

Dept. of Geography

University of Maryland

College Park, MD 20742-8225

kczajkow@geog.umd.edu

Contact 5:

Ralph O. Dubayah

Dept. of Geography

University of Maryland

College Park, MD 20742-8225

rdubayah@ geog.umd.edu

Contact 6:

Andrea Papagno

Raytheon ITSS

NASA GSFC

Code 923

Greenbelt, MD 20771

(301 ) 286-3134

(301) 286-0239 (fax)

Andrea.Papagno@ gsfc.nasa.gov

Page 3
3. Theory of Measurements

The model is driven primarily by vegetation light absorption, which determines potential

photosynthetic rates. The potential rates are reduced by stress terms, including Ta, VPD, and CSI,

which act on stomatal physiology (i.e., conductance, Gs). The "stressed" photosynthetic rates are used

to provide an actual value of daily carbon gain. The loss of carbon via autotrophic respiration (Ra) is

modeled based on a semi-empirical relationship with standing above-ground biomass, which, in turn,

is derived from monthly minimum visible reflectance. A contextural approach is used to derive

pixel-by-pixel maps of the environmental variables on a daily basis for all days when AVHRR scenes

were available (the BOREAS results were based on 35 acquisitions throughout the 1994 growing

season). GPP, NPP, Ra, and the amount of NPP per unit APAR on an annual basis are also generated

as output maps.

Variations of the PEM approach have been taken by various investigators, beginning with

correlative models first described by Kumar and Monteith (1982), Tucker et al. (1983), and Asrar et

al. (1985). The Carnegie Ames Stanford Approach (CASA) (Potter et al., 1993) was the first model to

use the PEM concept on a global scale, and Global PEM (GLO-PEM) was the first PEM to utilize

variables retrieved entirely with remotely sensed observations on a global scale. Descriptions of

individual model components and their performance have been reported in numerous journal

publications (see Section 10.2.1).

4. Equipment

4.1 Sensor/Instrument Description

See associated data set documentanon in Section 1.6.

4.1.1 Collection Environment

See associated data set documentation in Section 1.6.

4.1.2 Source/Platform

See associated data set documentation in Section 1.6.

4.1.3 Source/Platform Mission Objectives

See associated data set documentation in Section 1.6.

4.1.4 Key Variables

See associated data set documentation in Section 1.6.

4.1.5 Principles of Operation

See associated data set documentation in Section 1.6.

4.1.6 Sensor/Instrument Measurement Geometry

See associated data set documentation in Section 1.6.

4.1.7 Manufacturer of Sensor/Instrument

See associated data set documentation in Section 1.6.

4.2 Calibration

4.2.1 Specifications

See associated data set documentation in Section 1.6.

Page 4
4.2.1.1 Tolerance

Seeassociateddatasetdocumentationi Section1.6.

4.2.2 Frequency of Calibration

See associated data set documentation in Section 1.6.

4.2.3 Other Calibration Information

See associated data set documentation in Section 1.6.

5. Data Acquisition Methods

All data are recovered with algorithms driven by optical and thermal AVHRR observations, with the

exception of incident PAR, which is derived from the Geostationary Operational Environmental

Satellite (GOES) observations (provided by Eric Smith for BOREAS).

6. Observations

6.1 Data Notes

The AVHRR data as provided by the Canada Centre for Remote Sensing (CCRS) to BOREAS

were used for these analyses. No additional screening was necessary; however, Boreal-PEM utilizes a

series of routines to test for the presence of subpixel clouds, and this technique was improved using

the BOREAS data set (Czajkowski et ai., 1997a).

6.2 Field Notes

None given.

7. Data Description

7.1 Spatial Characteristics

7.1.1 Spatial Coverage

The nominal spatial resolution of the data was 1.1 km at nadir. All data were resampled to 1-kin

resolution using image mapping techniques developed at CCRS (called GEOCOMP) and validated at

the University of Maryland (Czajkowski et al., 1997b).

The comers of the total area covered by the model are defined by the following:

BOREAS Grid NAD83 Coordinates

X (KM) Y (KM) Longitude Latitude

175.000 0.000 I08.51193W 50.97184N

175.000 i000.000 i07.87384W 59.94373N

975.000 0.000 97.31119W 50.13370N

975.000 I000.000 93.922!4W 58.89897N

The total area covered by the model runs was 800 km by 1,000 km, corresponding to the BOREAS

study region within the BOREAS Information System (BORIS) X grid range of 175-975 km and the

BORIS Y grid range of 0-1000 km, encompassing both the Northern Study Area (NSA) and the

Southern Study Area (SSA).

Page 5
Imagecoordinates(upperleft origin)areeasilyrelatedtoBORISgridcoordinates(lowerleft

origin):

grid_X= image_pixel

grid_Y= 1000- image_line

Forexample,theNSAoldjackpinetowersite(T7Q8T)withBORISgridcoordinatesY=617,X=769

hasimagecoordinatesof 1ine=383,pixel=769.

7.1.2 Spatial Coverage Map

Not available.

7.1.3 Spatial Resolution

All data were resampled by CCRS to 1-kin pixels before being used as model input fields.

7.1.4 Projection

The area mapped is projected in the BOREAS grid projection, which is based on the ellipsoidal

version of the Albers Equal-Area Conic (AEAC) projection. The projection has the following

parameters:

Datum: NAD83

Ellipsoid: GRS80 or WGS84

Origin: III.000°W 51.000°N

Standard Parallels: 52 ° 30'

58 ° 30'

Units of Measure: kilometers

00"N

00"N

7.1.5 Grid Description

Lines and pixels increase from the upper left comer of the image.

7.2 Temporal Characteristics

7.2.1 Temporal Coverage

The 34 AVHRR acquisitions defined the observational period during the growing season. The

earliest image was 16-Apr-1994 and the latest was 07-Sep-1994. GPP, Ra, and NPP for periods

between acquisitions were interpolated to a daily interval using linear interpolation. Annual (growing

season) results are summed daily values.

7.2.2 Temporal Coverage Map

Not applicable.

7.2.3 Temporal Resolution

The model operates on a daily time-step. But the data represent annual values.

Page 6
7.3 Input Data Characteristics

Input data required by the model are summarized in the following table.

Table 1. BoreaI-PEM input variables

11=31

[[Input Parameter/

Variable

7.3.2

Variable Description/

Definition

near infrared

II

7.3.4

Data Source

visible spectral exoatmospheric reflectance AVHRR

channel 1

spectral exoatmospheric reflectance

T4 thermal emission

(brightness temperature)

thermal emission

(brightness temperature)

!7.3.3

Unit of

i Measurement

%

degrees C

i degrees C

normalised difference vegetation

index

T5

NDVI unitless

mean Ta

incident PAR

ile

degrees C

I MJ/day

climatological mean air

temperature

AVHRR

channel 2

AVHRR

channel 4

AVHRR

channel 5

(ch2-ch 1) /

(ch2+ch I )

Leemans and

Cramer

incident photosynthetically active GOES (Eric

radiation Smith/BORIS)

surface spectral emissivity unitless Prabhakara and

7.3.5 [

Data Range [

II

0.2 II

II

0, 50 [

II

0, 50 ° [

II

o, II

II

0, 50 ° I

II

0,12 ][

o,, ii

Dalu

....... PI

7.4 Output Data Characteristics

Model output is image format, binary single byte-per-pixei (i.e., 8-bit) values that range between

0-255. See Section 8.2 for data format, scaling, and coordinate information. The following table

describes the output _ ariablcs.

Table 2. BoreaI-PEM.output variables

17.4.1 7.4.2

]]Output Parameter _ ariable Description

Variable Definition

Ws

Ta

VPD

CSI

,urtace radiometric temperature

ambient air temperature

apor pressure deficit

7.4.3

Unit of

Measurement

APAR

II

absorbed photosynthetically active

radiation

L

degrees C

degrees C

millibars (mb)

7.4.4

Data Source

modeled

modeled

modeled

cumulative surface wemess index unitless modeled

megajoules modeled

(MJ)

II

Data Range ][

II

°,5°° ][

o. o II

II

,oo,,,oo/yrll

Page 7
W

GPP*

Ra*

NPP*

standing above-ground biomass

i gross primary production

autotrophic respiration

net primary production

e* carbon yield of APAR

* annual images provided to BORIS

i

kg / m 2

gC/m 2

gC/m 2

gC/m 2

gC/MJ

modeled

modeled

modeled

modeled

modeled

O, 40

0, 40 / day

0, 1900 / yr

0, 35 / day

0, 1100 / yr

0, 20 / day

0, 850 / yr

0, 1.25

r

7.5 Sample Data Record

Not applicable to image data.

8. Data Organization

8.1 Data Granularity

The smallest unit of data tracked by BORIS was the entire set of images.

8.2 Data Format(s)

8.2.1 Uncompressed Data Files

The entire data set contains one American Standard Code for Information Interchange (ASCII)

header file and six image files. Each image file contains 1,000 records (image lines) that contain 800

1-byte pixel values.

File Num. Description Record Size # Records Bytes/Pixel

1 ASCII header file 80 ii N/A

2 APAR Image 800 I000 1

3 Biomass Image 800 i000 1

4 Gross Primary Production 800 i000 1

5 Light Use Efficiency 800 i000 i

6 Net Primary Production 800 I000 1

7 Autotrophic Respiration 800 I000 1

The image files values must be multiplied or divided by a scaling factor to obtain physical units. The

appropriate factors are:

• apar - multiply image values by 4 for actual range of 0 - 900 gC/m2-yr

• biomass - multiply image values by 6 for actual range of 0 - 40 kg/m2

• gpp - multiply image values by 6 for actual range of 0 - 1500 gC/m2-yr

• lue - divide image values by 255 for actual range of 0 - 1 gC/MJ

• npp - multiply image values by 3 for actual range of 0 - 600 gC/m2-yr

• resp - multiply image values by 4 for actual range of 0 - 900 gC/m2-yr

Page 8
8.2.2 Compressed CD-ROM Files

On the BOREAS CD-ROMs, files 2 through 7 listed in Section 8.2.1 have been compressed with

the Gzip 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 Gaiily). Gzip (GNU

zip) uses the Lempei-Ziv algorithm (Welch, 1994) used in the zip and PKZIP programs. The

compressed files may be uncompressed using gzip (-d option) or gunzip. Gzip is available from many

Web sites (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.

9. Data Manipulations

9.1 Formulae

See Section 9.1.1.

9.1.1 Derivation Techniques and Algorithms

T, = 1.274 + (_-)T,; + (-_)T5 (1) (after Becker and Li, 1990)

where: T4 = apparent temperature in AVHRR channel 4 (K)

T5 = apparent temperature in AVHRR channel 5 (K)

e = surface spectral emissivity;

e _0.6_ _ l/ (2)

e* = 'grey-body' emissivity for unvegetated surface (Prabhakara et al., 1977). and e', e" are terms to

characterize between-band differences in e.

Ta = a * 0.7 + b (3)

where: a = slope of Ts/NDVI relationship

b = intercept of Ts/NDVI relationship

Ta = intercept

where NDVI = 0.7 a,b change with moving window contextural linear regression (TVX) approach.

VPD = vapor pressure deficit (D);

D= 0.61i* [exp(17.27*

where Td = dewpoint temperature;

and

%

+}37I-exp(l7.27*

T,i= IrtO,+l)+ in(U)- 0.I133

0.0393 (5)

Ta + 237 (4)

I = coefficient to adjust for latitude and season (Smith 1966).

U = atmospheric precipitable water content (cm);

Page 9
(DT - 0.6831)

U = 17.32* ................. + 0.5456 (6)

(TS- 291.97)

andDT = T4-T5

CSI= cumulativesurfacewetnessindex(Sg);

_g= Iyt-1+yt (7)

where gt = slope of Ts/NDVI at time (t) (i.e., a simple 'bucket' model in which gt, corrected for solar

zenith angle effects, varies relative to a normalized value, 0.5);

APAR = absorbed photosynthetically active radiation,

APAR = FPAR * PAR (8)

where: FPAR = 1.67*NDVI - 0.08

NDVI = (ch2-ch I )/(ch2+ch 1)

PAR = incident PAR from GOES (E. Smith/BORIS)

NPP = net primary production;

NPP = GPP - Ra

GPP = gross primary production;

GPP = APAR * eg

(9)

(10a)

where eg = eg* (modified by multipliers that simulate stomatal physiology, Gs); i.e., Ta, VPD, CSI)

eg = 55.2 c_ (10b)

_= quantum yield of photosynthesis (Collatz, et al. 1991)

en = carbon yield of APAR (g/MJ);

en = NPP / APAR ( 11)

Ra = autotrophic respiration;

_ 04

_lO_-_50)J (12)

W = standing aboveground biomass;

W= 7166.1 (p-2.e,) 13)

r = minimized visible reflectance (ch 1) on an annual basis, cloud-screened and corrected for sun angle

Page 10
9.2 Data Processing Sequence

9.2.1 Processing Steps

Each individual AVHRR image is input to the model, and the various component models derive

intermediate products that are then interpolated to a daily basis and integrated annually. The full

processing sequence is complex because of the availability of individual AVHRR acquisitions, the

number of individual component algorithms, and the exchange of variables between the different

model time-steps. A sense of the processing steps can be best acquired in Prince and Goward (1995),

Goetz et al. (1998), and the flowchart below.

BORIS staff copied the ASCII and compressed the binary files for release on CD-ROM.

i _s_le ii _a¢ bfra¢_ i

f

C

Vqeteliom Inde_ 1

2,)Phmologicel _ /"- Conte_ural "_

(csq

I_Cu_vPAR I

IlhenneJ hfrx_d IJ"lhemld kh_d

C _pi_ild_w )

Briollaess TelnFerelure

Diffemmce

I FlexEomeuic [

Suffece TemperxlMm

ffs)

I

I FieMnimized 1

Yisibie

T

Air Temperelare C_6_

(Ta) 0tPD)

_o,m-0mmd \" _ -J_ " "T" )

(NPP) (Ra)

9.2.2 Processing Changes

None given.

9.3 Calculations

9.3.1 Special Corrections/Adjustments

None.

9.3.2 Calculated Variables

See Table 2.

9.4 Graphs and Plots

For BOREAS-specific results see Goetz and Prince (1998).

Page 11
10. Errors

10.1 Sources of Error

There are several potential sources of error that can affect the model results. These include errors in

the data that drive the model (e.g., calibration and correction of the AVHRR reflectances and

temperatures, the GOES PAR maps and their time-integration), errors in the recovery of surface

variables within model component algorithms (e.g., Ts, Ta, U, VPD, FPAR, APAR, W, en, etc.),

and multiplicative or canceling errors in variables derived from other recovered variables and

parameters (e.g., APAR, Ra, GPP, NPP, etc.).

10.2 Quality Assessment

10.2.1 Model Validation by Source

Numerous joumal articles describe efforts to test, compare, and validate various component model

results. The validity of physical environmental components of the model (i.e., Ts, Ta, U, VPD, CSI)

have been assessed with different field experiment data (SNF, FIFE, HAPEX-Sahel, OTTER,

GEWEX, BOREAS) as listed in the following table (see acronym list, Section 19).

Author(s)

Czajkowski et al. (1997a)

Goetz (1997)

Goetz et al. (1998)

Goward et al. (1994)

Goward and Dye (1997)

Prihodko and Goward (1997)

Prince and Goward (1995)

Prince et al. (1998)

Variables Field Data

Ta BOREAS

CSI FIFE

CSI BOREAS

Ta, U, VPD, CSI OTTER

Ta, U, VPD, CSI Global

Ta FIFE

Ta, U, VPD, CSI Global

Ts, Ta, U, VPD FIFE, HAPEX, GEWEX, BOREAS

The biological components of the model have been assessed in the following journal publications:

Validation of biological components of BoreaI-PEM.

Author(s)

Goetz and Prince (1996, 1998)

Goetz et al. (1998)

Goward et al. (1994)

Hanan et al. (1995, 1997)

Prince and Goward (1995)

Prince et al. (1995)

Variables

en, eg, eg*, NPP, GPP, Ra, APAR

en, NPP, GPP, Ra

FPAR, APAR

en, g_, NPP, Ra, FPAR, APAR

en, eg, NPP, GPP, Ra, FPAR, APAR

en, e_, NPP, Ra, APAR

Field Data

SNF

BOREAS

O'ITER

HAPEX-Sahel

Global

SNF, Global

Numerous other articles have examined more general aspects of model components (particularly with

respect to Ts and U). A brief summary of results from the previous tables is provided below in

Sections 10.2.2-10.2.4.

10.2.2 Confidence Level/Accuracy Judgment

Confidence in the results is very high in terms of the spatial patterns and magnitudes within the

images and moderate to high in terms of the absolute values of variables recovered (see Sections

10.2.3 and 10.2.4). Comparisons showed a close correspondence between measured and inferred soil

moisture at the BOREAS sites. There was also good agreement between inferred and site

measurements of biomass and NPP, although the biomass values were underestimated compared to

those derived with an independent technique (i.e., Hall et al., 1995).

Page 12
10.2.3 Measurement Error for Variables

In quantitative terms, the results of model component validation work showed that Ts could be

retrieved with root mean square (RMS) errors of 3.5 °C for a range of 48 °C; Ta with 3.9 °C over a

range of 36 °C; U with 0.6 cm over a range of 3.6 cm; and VPD with 10.9 mb over a range of 58 mb

(Prince et al., 1998). There was some evidence of compounding errors in VPD because of the

integration of multiple retrieved variables (Ts, Ta, U). FPAR was recovered with an RMS error of

2.4% over a wide range of sites in Oregon (Goward et al., 1994). There was some evidence of a lag

between the CSI and soil moisture at depth at sites in Oregon. The CSI was found to predict surface

soil moisture at a grassland site in central Kansas with an RMS error of 3.2% (Goetz, 1997).

10.2.4 Additional Quality Assessments

Although the results sometimes had low absolute accuracies, the field data themselves are not

without error: although the inferences were usually for a >1 kin2 area made instantaneously, they were

compared with point field values generally not measured at exactly the same times in the day or

covering the same spatial area. Maps of retrieved variables had good relative accuracy and possibly

better absolute accuracy than the comparisons with point measurements suggest.

10.2.5 Data Verification by Data Center

Data were examined for general consistency and clarity.

1 1. Notes

11.1 Limitations of the Data

The model is probably limited in terms of Ra recovery; hence NPP, because of the potential

insensitivity of Ra to biomass in boreal forest stands (Goetz and Prince, 1998).

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

Contact Dr. Scott J. Goetz for a platform-independent version of the model that is available;

however, the model is highly system-tailored and requires 50 Gigabytes of space for a single run. If

someone really wants it, Dr. Goetz could discuss with them some collaborative effort to get it

functioning in another lab.

12. Application of the Data Set

The model may be operated with any remotely sensed observations that provide a measure of

vegetation amount (e.g., spectral vegetation indices) and thermal emission in more than one channel (in

order to get split-window surface radiometric temperature). The results are applicable to many studies,

from characterizing carbon flux and storage over large areas to monitoring changes in forest

productivity, stress, and management.

Page 13
13. Future Modifications and Plans

A heterotrophic respiration (Rh) component is being added to the model in order to simulate, when

combined with NPP, net ecosystem productivity (NEP). Simulated NEP is more directly comparable

with eddy correlation (e.g., tower) carbon flux measurements than is NPP because of the lack of

separability between the Ra and Rh components of measured soil CO2 efflux. Moreover, NEP is

important to characterize in order to quantify the direction of vegetation - atmosphere fluxes and to

address carbon budgets at the local to global scale.

14. Software

14.1 Software Description

Version 1 of the model was developed and operated under the PCI image processing package using

the Engineering Analysis and Scientific Interface (EASI) procedure language. The model has since

been exported, primarily for speed of processing and modularity, to the UNIX environment. Version 2

is written in the C programming language. Version 2 is operable and changes to the model are reported

in the upcoming publication Goetz et al. (1999a). BOREAS results with version 2 of the model are

reported in Goetz et al. (1999b).

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

commands.

14.2 Software Access

Contact Dr. Scott J. Goetz for a platform-independent version of the model that is available,

however the model is highly system-tailored and requires 50 Gigabytes of space for a single run. If

someone really wants it, Dr. Goetz could discuss with them some collaborative effort to get it

functioning in another lab.

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 production efficiency model 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@oml.gov or ornl@eos.nasa.gov

Page 14
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 FFP site [ftp://www-eosdis.oml.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.

16.1 Tape Products

None.

16. Output Products and Availability

16.2 Film Products

None.

16.3 Other Products

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

Output image products listed in Section 7.4, Table 2, and described herein, that are not included on

the BOREAS CD-ROM might be available on request.

A description of the model, summary of research papers, and results of model application at the

global scale are available at the following URL: http://www.geog.umd.edu/glopem/.

17. References

17.1 Platform/Sensor/Instrument/Data Processing Documentation

Thawley, M. and J. Small. 1997. The Global Production Efficiency Model, version 2.0

(GLO-PEM2).

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

Asrar, G., E.T. Kanemasu, R.D. Jackson, and P.J. Pinter. 1985. Estimation of total above ground

phytomass production using remotely sensed data. Remote Sensing of Environment, 17:211-220.

Becker, F. and Z.L. Li. 1990. Towards a local split window method over land surfaces. International

Journal of Remote Sensing, 11 : 1509-1522.

Collatz, G.J., J.T. Ball, C. Grivet, and J.A. Berry. 1991. Physiological and environmental regulation

of stomatal conductance, photosynthesis and transpiration: a model that includes a laminar boundary

layer. Agricultural and Forest Meteorology, 54: 107-136.

Page 15
Czajkowski,K.P.,T. Mulhern,S.N.Goward,J.Cihlar, R.O.Dubayah,andS.D.Prince.1997a.

Biosphericenvironmental monitoringatBOREASwithAVHRRobservations. Journalof Geophysical

Research,102(D24):29,651-29,662.

Czajkowski,K.P.,T. Mulhern,S.N.Goward,andJ.Cihlar.1997b.Validationof thegeocodingand

compositingsystem(GEOCOMP)usingcontexturalanalysisforAVHRRimages.International

Journalof RemoteSensing,18(14):3055-3068.

Goetz,S.J.1997.Multi-sensoranalysisof NDVI, surfacetemperature,andbiophysicalvariablesata

mixedgrasslandsite.InternationalJournalof RemoteSensing,18:71-94.

Goetz,S.J.andS.D.Prince.1996.Remotesensingof netprimaryproductionin borealforeststands.

AgriculturalandForestMeteorology,78:149-179.

Goetz,S.J.andS.D.Prince.1998.Variabilityincarbonexchangeandlight utilizationamongboreal

foreststands:implicationsforremotesensingof netprimaryproduction.CanadianJournalof Forest

Research28:375-389.

Goetz,S.J.andS.D.Prince.1999.Modelingterrestrialcarbonexchangeandstorage:theevidencefor

andimplicationsof functionalconvergenceinlight useefficiency.AdvancesinEcologicalResearch,

28: 57-92.

Goetz, S.J., S.D. Prince, M.M. Thawley, K.P. Czajkowski, and S.N. Goward. 1999. Mapping

regional patterns of canopy carbon exchange and storage with remote sensing. Journal of Geophysical

Research (in press).

Goetz, S.J., S.D. Prince, S.N. Goward, M. Thawley, and J. Small. 1999a. Satellite remote sensing

of primary production: an improved production efficiency modeling approach. Ecological Modeling (in

press).

Goetz, S.J., S.D. Prince, S.N. Goward, M.M. Thawley, J. Small, and A. Johnston. 1999b.

Mapping net primary production and related biophysical variables with remote sensing: application to

the BOREAS region. Journal of Geophysical Research (in press).

Goward, S.N. and D.G. Dye. 1997. Global biospheric monitoring with remote sensing. In: The use

of remote sensing in modeling forest productivity (eds. H.L. Gholtz, K. Nakane, and H. Shimoda).

pages 241-272, Kluwer Academic, New York.

Goward, S.N., R.H. Waring, and D.G. Dye. 1994. Ecological remote sensing at OTTER: Macroscale

satellite observations. Ecological Applications, 4: 322-343.

Hall, F.G., Y. Shimabukuro, and K.F. Huemmrich. 1995. Remote sensing of forest biophysical

structure in boreal stands of Picea mariana using mixture decomposition and geometric reflectance

models. Ecological Applications, 5: 993-1013.

Hanan, N.P., S.D. Prince, and A. B6gub. 1995. Estimation of absorbed photosynthetically active

radiation and vegetation net production efficiency using satellite data. Agricultural and Forest

Meteorology, 76: 259-276.

Hanan, N.P., S.D. Prince, and A. B_gu_. 1997. Modelling vegetation primary production during

HAPEX-Sahel using production efficiency and canopy conductance model formulations. Journal of

Hydrology, 188/189:651-675.

Page 16
Kumar,M. andJ.L.Monteith.1982.Remotesensingof plantgrowth.In: PlantsandtheDaylight

Spectrum(eds.H.Smith),pp. 133-144.AcademicPress,London.

Leemans,R. andW.P.Cramer.1991.TheIISASdatabasefor meanmonthlyvaluesof temperature,

precipitationandcloudinessonaglobalterrestrialgrid,Laxenburg,AustriaInternationalInstitutefor

AppliedSystemsAnalysis.

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-Atmosphere Study.NASA.CD-ROM.

Potter,C.S.,J.T.Randerson,C.B.Field,P.A.Matson,P.M.Vitousek,H.A. Mooney,andS.A.

Klooster.1993.Terrestrialecosystemproduction:A processmodelbasedonglobalsatelliteand

surfacedata.GlobalBiogeochemical Cycles,7:811-841.

Prabhakara,C.,G. Dalu,R.C.Lo, andR.Nath.1977.Remotesensingof seasonaldistributionof

precipitablewaterovertheoceansandinferenceof boundarylayerstructure.MonthlyWeather

Review,107:1388-1401.

Prihodko,L. andS.N.Goward.1997.Estimationof airtemperaturefromremotelysensed

observations.RemoteSensingof Environment,60:335-346.

Prince,S.D.andS.J.Goward.1995.Globalprimaryproduction:aremotesensingapproach.Journal

ofBiogeography,22:815-835.

Prince,S.D.,S.J.Goetz,andS.N.Goward.1995.Monitoringprimaryproductionfrom Earth

observingsatellites.Water,Air, andSoilPollution,82:509-522.

Prince,S.D.,S.J.Goetz,K. Czajkowski,R. Dubayah,andS.N.Goward.1998.Inferenceof surface

andairtemperature,atmosphericprecipitablewaterandvaporpressuredeficitusingAVHRRsatellite

observations:validationof algorithms.Journalof Hydrology,213(1-4),230-249.

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

1994-3.0,NASA BOREASReport(EXPLAN94).

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

1996-2.0,NASABOREASReport(EXPLAN96).

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

Operations.NASABOREASReport(OPSDOC94).

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

Operations.NASABOREASReport(OPSDOC96).

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

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

ecosystem-atmosphere study(BOREAS):anoverviewandearlyresultsfromthe 1994fieldyear.

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

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

in 1997:ExperimentOverview,ScientificResultsandFutureDirections.Journalof Geophysical

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

Page17
Smith, W.L. 1966. Note on the relationship between precipitable water and surface dew point. Journal

of Applied Meteorology, 5: 726-727.

Tucker, C.J., C.L. Vanpraet, E. Boerwinkel, and A. Gaston. 1983. Satellite remote sensing of total

dry matter accumulation in the Senegalese Sahel. Remote Sensing of Environment, 13:461-474.

17.3 Archive/DBMS Usage Documentation

None.

CSI

Gs

e

Ra

Rh

Ta

Ts

U

W

18. Glossary of Terms

- Cumulative surface wetness index

- Stomatal conductance

- Carbon yield of APAR

- Autotrophic respiration

- Heterotrophic respiration

- Ambient air temperature

- Surface radiometric temperature

- Atmospheric precipitable water vapor amount

- Standing above-ground biomass

APAR

ASCII

AVHRR

Boreal-PEM

BOREAS

BORIS

CASA

CCRS

CD-ROM

DAAC

EASI

EOS

EOSDIS

FIFE

FPAR

GEOCOMP

GEWEX

GIS

GLO-PEM

GOES

GPP

GSFC

HAPEX-Sahel

HTML

IFC

LAC

NAD83

NASA

NDVI

19. List of Acronyms

- Absorbed Photosynthetically Active Radiation

- American Standard Code for Information Interchange

- Advanced Very High Resolution Radiometer

- Boreal Forest Production Efficiency Model

- BOReal Ecosystem-Atmosphere Study

- BOREAS Information System

- Carnegie Ames Stanford Approach

- Canada Centre Remote Sensing

- Compact Disk-Read-Only memory

- Distributed Active Archive Center

- Engineering Analysis and Scientific Interface

- Earth Observing System

- EOS Data and Information System

- First International Satellite Land Surface Climatology Field

Experiment

- Fraction of incident PAR intercepted or absorbed

- Geocoding and Compositioning System

- Global Water Energy and Water Cycle Experiment

- Geographic Information System

- Global Production Efficiency Model

- Geostationary Operational Environmental Satellite

- Gross Primary Production

- Goddard Space Flight Center (NASA)

- Hydrology Atmosphere Pilot Experiment in the Sahel

- HyperText Markup Language

- Intensive Field Campaign

- Local Area Coverage (of AVHRR)

- North American Datum of 1983

- National Aeronautics and Space Administration

- Normalized Difference Vegetation Index

Page 18
NEP

NOAA

NPP

NSA

ORNL

OTTER

PANP

PAR

PEM

RMS

SNF

SSA

TE

TF

URL

UTM

VPD

- Net Ecosystem Production

- National Oceanic and Atmospheric Administration

- Net Primary Production

- Northern Study Area

- Oak Ridge National Laboratory

- Oregon Transect Experiment

- Prince Alhert National Park

- Photosynthetically Active Radiation

- Production Efficiency Model

- Root Mean Square (Error)

- Superior National Forest, Minnesota

- Southern Study Area

- Terrestrial Ecology

- Tower Flux

- Uniform Resource Locator

- Universal Transverse Mercator

- Vapor Pressure Deficit

20. Document Information

20.1 Document Revision Date

Written: 25-Jun- 1997

Last Updated: 24-Sep- 1999

20.2 Document Review Date(s)

BORIS Review: 04-May-1999

Science Review: 26-Oct- 1998

20.3 Document ID

20.4 Citation

When using these data, please include the following reference:

Goetz, S.J. and S.D. Prince. 1998. Variability in carbon exchange and light utilization among

boreal forest stands: implications for remote sensing of net primary production. Canadian Journal of

Forest Research 28: 375-389.

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

Goward. S.N.. S.D Prince. S.J. Goetz, K. Czajkowski, and R.O. Dubayah, "Biospheric

Dynamics in the Boreal F,rest Ecotone." In Collected Data of The Boreal Ecosystem-Atmosphere

Study. Eds. J. Nov, comer. D. Landis, S. Conrad, S. Curd, K. Huemmrich, D. Knapp, A. Morrell, J.

Nickeson. A. Papagn,. D. Rmker, 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. Morreil, J.

Nickeson, A. Papagno. D. Rinker, R. Strub, T. Twine, F. Hall, and P. Sellers, eds. Collected Data of

The Boreal EcoLvstcm-Atnlosphere Study. NASA. CD-ROM. NASA, 2000.

20.5 Document Curator

20.6 Document URL

Page 19
REPORT DOCUMENTATION PAGE I FormApproved

I

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 collectionof information. Send commentsregarding this burden estimate or any other aspect ofthis

collection of information, including suggestionsfor reducing this burden, to Washington Headquarters Services, Directoratefor Information Operations and Reports, 1215 Jefferson

Davis Highway, Suite 1204, Arlington, VA22202-4302, and to the Office of Management and Budget, PaperworkReduction Project(0704-0188), Washington, DC 20503.

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

October 2000 Technical Memorandum

4. h I LE AND SUBTITLE 5. FUNDING NUMBERS

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

BOREAS TE-17 Production Efficiency Model Images 923

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

Scott J. Goetz, Samual N. Goward, Stephen D. Prince, Kevin Czajkowski.

and Ralph O. Dubayah

Forrest G. Hall and Andrea Papa_no r Editors

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS (ES) 8. PEFORMING ORGANIZATION

REPORT NUMBER

Goddard Space Flight Center

Greenbelt, Maryland 20771 2000-03136-0

9. SPONSORING / MONITORING AGENCY NAME(S) AND ADDRESS (ES) 10. SPONSORING / MONITORING

AGENCY REPORT NUMBER

National Aeronautics and Space Administration TM----2000-209891

Washington, DC 20546-0001 Vol. 171

11. SUPPLEMENTARY NOTES

S. Goetz, S.N. Goward, S.D. Prince, K. Czajkowski, and R.O. Dubayah: University of Maryland,

College Park; A. Papagno: Raytheon ITSS, NASA Goddard Space Flight Center, Greenbelt, Maryland

12a. DISTRIBUTION / AVAILABILITY STATEMENT 12b. DISTRIBUTION CODE

Unclassified-Unlimited

Subject Category: 43

Report available from the NASA Center for AeroSpace Information,

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

13. ABSTRACT (Maximum 200 words)

A BOREAS version of the Global Production Efficiency Model (http://www.inform.umd.edu/glopem/

[Internet Link]) was developed by TE-17 to generate maps of gross and net primary production,

autotrophic respiration, and light use efficiency for the BOREAS region. This document provides

basic information on the model and how the maps were generated. The data generated by the model

are stored in binary image-format files.

14.SUBJECTTERMS

BOREAS, terrestrial ecology.

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

19

16. PRICE CODE

20. LIMITATION OF ABSTRACT

UL

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Prescribed by ANSI Std, Z3918

298-102