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: 111.000ºW 51.000ºN Standard Parallels: 52º 30' 00"N 58º 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. Bégué. 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. Bégué. 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