BOREAS TE-17 Production Efficiency Model Images

Summary

A BOREAS version of the Global Production Efficiency Model 
(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
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 TE-17 Production Efficiency Model

1.2 Data Set Introduction

The Boreal Forest Production Efficiency Model (Boreal-PEM) is composed of a 
suite of models that provide estimates of the variables needed to drive a 
production efficiency model (i.e., one based on restrictions in the conversion 
"efficiency" of absorbed photosynthetically active radiation (APAR) in terms of 
unstressed gross primary production (GPP) through short-term environmental 
physiology).

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 gradients of land use intensity and climate. In 
addition to NPP and GPP, Boreal-PEM provided the data 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 using 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 stomatal 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-sensor 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-1a 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-1a 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

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 
(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
sg21@umail.umd.edu

Contact 3:
Stephen D. Prince
Dept. of Geography
University of Maryland
College Park, MD 
sp43@umail.umd.edu

Contact 4:
Kevin Czajkowski
Dept. of Geography
University of Maryland
College Park, MD 
kczajkow@geog.umd.edu

Contact 5:
Ralph O. Dubayah
Dept. of Geography
University of Maryland
College Park, MD 
rdubayah@geog.umd.edu

Contact 6:
Andrea Papagno
Raytheon ITSS
NASA GSFC
Greenbelt, MD
(301) 286-3134
(301) 286-0239 (fax)
Andrea.Papagno@gsfc.nasa.gov

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 Plem (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 documentation 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.

4.2.1.1 Tolerance

See associated data set documentation in Section 1.6.

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 al., 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-km resolution using image mapping techniques developed at CCRS 
(called GEOCOMP) and validated at the University of Maryland (Czajkowski et al., 
1997b).

The corners 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     -108.51193  50.97184  
  175.000 1000.000     -107.87384  59.94373  
  975.000    0.000      -97.31119  50.13370  
  975.000 1000.000      -93.92214  58.89897  

The total area covered by the model runs was 800 km by 1,000 km, corresponding 
to the BOREAS study region within the 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).

Image coordinates (upper left origin) are easily related to BORIS grid 
coordinates (lower left origin):  
grid_X = image_pixel
grid_Y = 1000 - image_line
For example, the NSA old jack pine tower site (T7Q8T) with BORIS grid 
coordinates Y=617, X=769 has image coordinates of line=383, pixel=769. 

7.1.2 Spatial Coverage Map

Not available.

7.1.3 Spatial Resolution

All data were resampled by CCRS to 1 km 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:     111.000W  51.000N 
Standard Parallels:    52  30' 00"N
                       58  30' 00"N
Units of Measure:  kilometers

7.1.5 Grid Description

Lines and pixels increase from the upper left corner 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 7-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.

7.3 Input Data Characteristics

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

Boreal-PEM input variables
7.3.1 
Input 
Parameter/
Variable
7.3.2 Variable 
Description/
Definition
7.3.3 
Unit of 
Measurement
7.3.4 
Data Source
7.3.5 
Data Range
visible
spectral 
exoatmospheric 
reflectance
%
AVHRR
channel 1
0, 25
near 
infrared
spectral 
exoatmospheric 
reflectance
%
AVHRR 
channel 2
0, 50
T4
thermal emission 
(brightness 
temperature)
degrees C
AVHRR 
channel 4
0, 50o
T5
thermal emission
(brightness 
temperature)
degrees C
AVHRR 
channel 5
0, 50o
NDVI
normalized 
difference 
vegetation index
unitless
(ch2-ch1) / 
(ch2+ch1)
0, 1
mean Ta
climatological mean 
air temperature
degrees C
Leemans and 
Cramer
0, 50o
incident 
PAR
incident 
photosynthetically 
active radiation
MJ/day
GOES (Eric 
Smith/
BORIS)
0, 12
e
surface spectral 
emissivity
unitless
Prabhakara 
and Dalu
0, 1

 

7.4  Output Data Characteristics

Model output is image format, binary single byte-per-pixel (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 variables.

7.4.1 
Output 
Parameter/
Variable
7.4.2 Variable 
Description/Definit
ion
7.4.3 
Unit of 
Measurement
7.4.4 
Data Source
7.4.5 
Data Range
Ts
surface radiometric 
temperature
degrees C
modeled
0, 50o
Ta
ambient air 
temperature
degrees C
modeled
0, 50o
VPD
vapor pressure 
deficit
millibars 
(mb)
modeled
0, 50
CSI
cumulative surface 
wetness index
unitless
modeled
-5, 5
APAR
absorbed 
photosynthetically 
active radiation
megajoules 
(MJ)
modeled
0, 12 / 
day
100, 1100 
/ yr
W
standing above-
ground biomass
kg / m2
modeled
0, 40
GPP*
gross primary 
production
gC/m2
modeled
0, 40 / 
day
0, 1900 / 
yr
Ra*
autotrophic 
respiration
gC/m2
modeled
0, 35 / 
day
0, 1100 / 
yr
NPP*
net primary 
production
gC/m2
modeled
0, 20 / 
day
0, 850 / 
yr
e*
carbon yield of 
APAR
gC/MJ
modeled
0, 1.25
* annual images provided to BORIS

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 ASCII header file and 6 image files.  Each 
image file contains 1000 records (image lines) that contain 800 one-byte pixel 
values.

File Num.  Description                Record Size   # Records  Bytes/Pixel
--------------------------------------------------------------------------
1          ASCII  header file            80            11       N/A
2          APAR Image                   800          1000        1
3          Biomass Image                800          1000        1
4          Gross Primary Production     800          1000        1
5          Light Use Efficiency         800          1000        1
6          Net Primary Production       800          1000        1
7          Autotrophic Respiration      800          1000        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

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 Gailly).  Gzip (GNU zip) uses the Lempel-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
                                                                          (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; 
                                                                          (2)
 

e* = grey-body emissivity for unvegetated surface
(Prabhakara et al., 1977).
and
e, e are terms to characterize between-band differences in e.

                                                                          (3)
Ta = a * 0.7 + b
where 
a = slope of Ts/NDVI relationship
b = intercept of Ts/NDVI relationship
and
Ta = intercept where NDVI = 0.7
a,b change with moving window contextural linear regression (TVX) approach.

VPD = vapor pressure deficit (D);
                                                                          (4)
 

where
Td = dewpoint temperature; 
                                                                          (5)
 
	and
	l = coefficient to adjust for latitude and season (Smith 1966).
U = atmospheric precipitable water content (cm);
                                                                          (6)

             (?T - 0.6831) 
 U = 17.32 * ------------- + 0.5456
             (TS - 291.97)

and DT = T4-T5

CSI = cumulative surface wetness index (Sg);
                                                                          (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,
                                                                          (8)
APAR = FPAR * PAR
where
FPAR = 1.67*NDVI - 0.08
NDVI = (ch2-ch1)/(ch2+ch1)
PAR = incident PAR from GOES (E. Smith/BORIS)

NPP = net primary production;
                                                                          (9)
NPP = GPP - Ra

GPP = gross primary production;
                                                                          (10a)
GPP = APAR * eg
where
eg = eg* (modified by multipliers that simulate stomatal physiology, Gs);
i.e., Ta, VPD, CSI)                                                       (10b)
 
? = quantum yield of photosynthesis (Collatz, et al.  1991)

en = carbon yield of APAR (g/MJ);
                                                                          (11)
en = NPP / APAR

Ra = autotrophic respiration;  
                                                                          (12)
 

W = standing aboveground biomass; 
                                                                          (13)
 
r = minimized visible reflectance (ch1) on an annual basis, cloud-screened and 
corrected for sun angle

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. 

 

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 et al. (1998).

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 journal 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). 

Validation of environmental components of Boreal-PEM.
Author(s)
Variables
Field Data
Czajkowski et al. (1997a)
Ta
BOREAS
Goetz (1997) 
Goetz et al. (1998)
CSI
CSI
FIFE
BOREAS
Goward et al. (1994)
Ta,  U, VPD, 
CSI
OTTER
Goward and Dye (1997)
Ta,  U, VPD, 
CSI
Global
Prihodko and Goward 
(1997)
Ta
FIFE
Prince and Goward (1995)
Ta, U, VPD, CSI
Global
Prince et al. (1998)
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 Boreal-PEM.
Author(s)
Variables
Field Data
Goetz and Prince (1996, 
1998)
en, eg, eg*, NPP, GPP, Ra, APAR
SNF
Goetz et al. (1998)
en, NPP, GPP, Ra
BOREAS
Goward et al. (1994)
FPAR, APAR
OTTER
Hanan et al. (1995, 1997)
en, gs, NPP, Ra, FPAR, APAR
HAPEX-Sahel
Prince and Goward (1995)
en, eg, NPP, GPP, Ra, FPAR, 
APAR
Global
Prince et al. (1995)
en, eg, NPP, Ra, APAR
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 Judgement

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).

10.2.3 Measurement Error for Variables

In quantitative terms, the results of model component validation work showed 
that Ts could be retrieved with roof 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 
km2 area made instantaneously, but 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.

11. 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.  

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

15.1  Contact for Data Center/Data Access Information

These BOREAS data are available from the Earth Observing System Data and 
Information System (EOS-DIS) Oak Ridge National Laboratory (ORNL) Distributed 
Active Archive Center (DAAC). The BOREAS contact at ORNL is:

ORNL DAAC User Services
Oak Ridge National Laboratory
(865) 241-3952
ornldaac@ornl.gov
ornl@eos.nasa.gov

15.2  Procedures for Obtaining Data

BOREAS data may be obtained through the ORNL DAAC World Wide Web site at 
http://www-eosdis.ornl.gov/ or users may place requests for data by telephone, electronic mail, or fax.

15.3  Output Products and Availability

Requested data can be provided electronically on the ORNL DAAC's anonymous FTP 
site or on various media including, CD-ROMs, 8-MM tapes, or diskettes.

The complete set of BOREAS data CD-ROMs, entitled "Collected Data of the Boreal 
Ecosystem-Atmosphere Study", edited by Newcomer, J., et al., NASA, 1999, are 
also available.

16. Output Products and Availability

16.1 Tape Products

None.

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 7.3, 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.

Czajkowski, K.P., T. Mulhern, S.N. Goward, and J. Cihlar. 1997a. Biospheric 
environmental monitoring at BOREAS with AVHRR observations. Journal of 
Geophysical Research, Journal of Geophysical Research, 102 (D24): 29,651-29,662.

Czajkowski, K.P., T. Mulhern, S.N. Goward, and J. Cihlar. 1997b. Validation of 
the geocoding and compositing system (GEOCOMP) using contextural analysis for 
AVHRR images. International Journal of Remote Sensing, 18 (14): 3055-3068.

Goetz, S.J. 1997. Multi-sensor analysis of NDVI, surface temperature, and 
biophysical variables at a mixed grassland site. International Journal of Remote 
Sensing, 18: 71-94.

Goetz, S.J. and S.D. Prince. 1996. Remote sensing of net primary production in 
boreal forest stands. Agricultural and Forest Meteorology, 78: 149-179.

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. 

Goetz, S.J. and S.D. Prince. 1999. Modeling terrestrial carbon exchange and 
storage: the evidence for and implications of functional convergence in light 
use efficiency.  Advances in Ecological Research, 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. Bgu. 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. Bgu. 1997. Modelling vegetation primary 
production during HAPEX-Sahel using production efficiency and canopy conductance 
model formulations. Journal of Hydrology, 188/189: 651-675.

Kumar, M. and J.L.Monteith. 1982. Remote sensing of plant growth. In: Plants and 
the Daylight Spectrum  (eds. H. Smith), pp. 133-144. Academic Press, London.  

Leemans, R. and W.P. Cramer. 1991. The IISAS database for mean monthly values of 
temperature, precipitation and cloudiness on a global terrestrial grid, 
Laxenburg, Austria International Institute for Applied Systems Analysis.

Potter, C.S., J.T. Randerson, C.B. Field, P.A. Matson, P.M. Vitousek, H.A. 
Mooney, and S.A. Klooster. 1993. Terrestrial ecosystem production: A process 
model based on global satellite and surface data. Global Biogeochemical Cycles, 
7: 811-841.

Prabhakara, C., G. Dalu, R.C. Lo, and R. Nath. 1977. Remote sensing of seasonal 
distribution of precipitable water over the oceans and inference of boundary 
layer structure. Monthly Weather Review, 107: 1388-1401.

Prihodko, L. and S.N. Goward. 1997. Estimation of air temperature from remotely 
sensed observations. Remote Sensing of Environment, 60: 335-346.

Prince, S.D. and S.J. Goward. 1995. Global primary production: a remote sensing 
approach. Journal of Biogeography, 22: 815-835.

Prince, S.D., S.J. Goetz, and S.N. Goward. 1995. Monitoring primary production 
from Earth observing satellites. Water, Air, and Soil Pollution, 82: 509-522.

Prince, S.D., S.J. Goetz, K. Czajkowski, R. Dubayah, and S.N. Goward. 1998. 
Inference of surface and air temperature, atmospheric precipitable water and 
vapor pressure deficit using AVHRR satellite observations: validation of 
algorithms. Journal of Hydrology, 213 (1-4), 230-249.

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

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

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

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

Sellers, P.J., F. Hall, H. Margolis, B. Kelly, D. Baldocchi, G. den Hartog, J. 
Cihlar, M.G. Ryan, B. Goodison, P. Crill, K.J. Ranson, D. Lettenmaier, and D.E. 
Wickland. 1995. The Boreal Ecosystem-Atmosphere Study (BOREAS): An overview and 
early results from the 1994 field year. Bull. Am. Meteorol. Soc. 76:1549-1577.

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. Halliwell, D. Williams, B. Goodison, 
D.E. Wickland, and F.E. Guertin.  1997.  BOREAS in 1997: Experiment overview, 
scientific results, and future directions. Journal of Geophysical Research, 102 
(D24): 28,731-28,769.

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.

18. Glossary of Terms

CSI          - Cumulative surface wetness index
Gs           - Stomatal conductance
?            - Carbon yield of APAR
Ra           - Autotrophic respiration
Rh           - Heterotrophic respiration
Ta           - Ambient air temperature
Ts           - Surface radiometric temperature
U            - Atmospheric precipitable water vapor amount
W            - Standing above-ground biomass

19. List of Acronyms

APAR         - Absorbed Photosynthetically Active Radiation
ASCII        - American Standard Code for Information Interchange
AVHRR        - Advanced Very High Resolution Radiometer
Boreal-PEM   - Boreal Forest Production Efficiency Model
BOREAS       - BOReal Ecosystem-Atmosphere Study
BORIS        - BOREAS Information System 
CASA         - Carnegie Ames Stanford Approach
CCRS         - Canada Centre Remote Sensing
CD-ROM       - Compact Disk-Read-Only memory
DAAC         - Distributed Active Archive Center
EASI         - Engineering Analysis and Scientific Interface
EOS          - Earth Observing System
EOSDIS       - EOS Data and Information System
FIFE         - First International Satellite Land Surface Climatology Field
		   Experiment
FPAR         - Fraction of incident PAR intercepted or absorbed
GEOCOMP      - Geocoding and Compositioning System
GEWEX        - Global Water Energy and Water Cycle Experiment
GOES         - Geostationary Operational Environmental Satellite
GLO-PEM      - Global Production Efficiency Model
GPP          - Gross Primary Production
GSFC         - Goddard Space Flight Center (NASA)
HAPEX-Sahel  - Hydrology Atmosphere Pilot Experiment  in the Sahel
HTML         - HyperText Markup Language
IFC          - Intensive Field Campaign
LAC          - Local Area Coverage (of AVHRR)
NASA         - National Aeronautics and Space Administration
NAD83        - North American Datum of 1983
NDVI         - Normalized Difference Vegetation Index
NEP          - Net Ecosystem Production
NPP          - Net Primary Production
NOAA         - National Oceanic and Atmospheric Administration
NSA          - Northern Study Area
OTTER        - Oregon Transect Experiment
ORNL         - Oak Ridge National Laboratory
PANP         - Prince Albert National Park
PAR          - Photosynthetically Active Radiation
PEM          - Production Efficiency Model
RMS          - Root Mean Square (Error)
SNF          - Superior National Forest, Minnesota
SSA          - Southern Study Area
TE           - Terrestrial Ecology
TF           - Tower Flux 
URL          - Uniform Resource Locator
UTM          - Universal Transverse Mercator
VPD          - Vapor Pressure Deficit

20. Document Information

20.1 Document Revision Date

Written:         25-Jun-97
Last Updated:    05-May-1999

20.2 Document Review Date(s)

BORIS Review:    04-May-1999
Science Review:  26-Oct-1998

20.3 Document ID

20.4 Data Set Citation

Image products on BOREAS CD-ROM described by Goetz et al. (1998).

20.5 Document Curator

20.6 Document URL

Keywords:

APAR
Carbon Flux
Carbon Storage
Ecophysiology
FPAR
Light Use Efficiency
Primary Production
Respiration
Stomatal Control

TE17_PEM_Images.doc.doc
06/09/99