natural resources canada canada centre for remote sensing miami 2002 g. fedosejevs geocomp-n natural...

Natural Resources Canada

CANADA CENTRE FOR REMOTE SENSINGMiami 2002G. Fedosejevs

GeoComp-nGeoComp-n

Natural Resources

Canada

Natural ResourcesCanada

Ressources naturellesCanada

GeoComp-nGeoComp-n

Gunar FedosejevsCanada Centre for Remote Sensing



GeoComp - n, an advanced GeoComp - n, an advanced system for generating products system for generating products from coarse and medium from coarse and medium resolution optical satellite data. resolution optical satellite data. Part 1: System characterisationPart 1: System characterisation



GeoComp – nGeoComp – nPart 1: System characterisationPart 1: System characterisation

Canada Centre for Remote Sensing (CCRS) Geocoding and Compositing system (GeoComp) has been processing the Advanced Very High Resolution Radiometer (AVHRR) data from the United States National Oceanic and Atmospheric Administration (NOAA) series of satellites since 1992.




• Original GeoComp system built on Digital VAX platform by MacDonald Detwiller and Associates.

Reference:Robertson, B., A. Erickson, J. Friedel, B. Guindon, T.

Fisher, R. Brown, P. Teillet, M. D'Iorio, J. Cihlar, and A. Sancz. 1992. “GeoComp, a NOAA AVHRR geocoding and compositing system”, Proceedings of the ISPRS Conference, Commission 2, pp. 223-228, August 1992, Washington, D.C..




• GeoComp-n (“next generation”) includes a modular system architecture, a fully functional operator GUI and a revamped data product format.

• GeoComp-n was built by PCI Geomatics of Richmond Hill, Ontario and was delivered to the Manitoba Centre for Remote Sensing in 1999.




GeoComp-n supports four primary functions:

1) AVHRR data input

2) pre-processing

3) geocoding and resampling

4) composite product generation




• GeoComp-n employs plain text auxiliary databases for system operation and product generation.

• The radiometric calibration coefficient auxiliary file must be updated annually as it contains both time- and satellite-dependant parameters.

• The product coefficient auxiliary file contains product processing parameters and scaling coefficients.

• Other databases include the image chip database, digital elevation model, land cover map of Canada, and seasonal NDVI database for cloud removal.




• Composite products are generated as a flat raster image file with an associated product metadata file describing in plain-text the algorithm and processing parameters used.

• GeoComp-n can generate browse imagery in JPEF format and catalogue update files (CUFs) of the composite products for the CCRS Earth Observation Catalogue (CEOCat) database.

• The browse images consist of three operator-specified channels of the 1 km resolution data at an operator-specified reduced spatial resolution.




• Input data consists of High Resolution Picture Transmission (HRPT) AVHRR data with a nominal ground resolution of 1.1 km at nadir in CEOS format as produced by the CCRS NOAA AVHRR Transcription and Archive System (NATAS) at the Prince Albert Satellite Station (PASS) or Level 1B (L1B) data produced by any number of commercial AVHRR receiving systems.

• The data can be read from Exabyte tape, CD-ROM or downloaded over the network to a local disk system.




Data Pre-processing• Replace the missing lines for L1B data from the NOAA

Selective Active Archive (SAA). • Noisy lines can be detected using header information

supplied with the raw image files and/or automatically using simple heuristics.

• Line replacement algorithm can replace a noisy scan line with the line below or above, or by the average of the two if the scan line is not at the beginning or end of the file.

• Noisy lines are recorded in the Quality Control (QC) bit mask as noisy pixels/lines (0=bad or missing pixel, 1=clear or good pixel).




Data Pre-processing (continued)• A cloud detection algorithm using raw count thresholds

identifies cloudy pixels for the QC mask. • The raw data are calibrated to top-of-atmosphere (TOA)

radiance using onboard calibration data for the thermal channels or calibration parameters provided in the radiometric calibration coefficient file for the visible and near-infrared channels.

• The output of the pre-processing is a PCIDSK format file (16-bit unsigned integer) containing the scaled raw or calibrated radiance data for AVHRR channels 1 to 5.

• The pre-processed file contains a fully determined orbit model.




GeoCoding and Resampling

• An orbit model refinement process is employed in geocoding where the satellite position and attitude are modelled using TBUS ephemeris information and refined using GCPs that are automatically matched against an image chip database using image correlation.

• The absolute 2-dimensional residual error for 1 km resolution geocoded products shall be 0.55 +/- 0.07 % RMSE of the IFOV, or better; that is, 95% of geocoded AVHRR products shall have a measured absolute 2-dimensional error of 750 m or less.




GeoCoding and Resampling (continued)• While radiometric resampling may employ a variety of

algorithms; the damped Kaiser method is typically applied for maximum radiometric fidelity and geometric accuracy.

• While GeoComp-n supports a wide selection of map projections; the default projection used for CCRS products is the Lambert Conic Conformal (LCC) projection.

• The output consists of a precision geocoded product in a PCIDSK format file containing the calibrated radiance data for AVHRR channels 1 to 5, the solar zenith and azimuth angles, and the satellite zenith and azimuth angles.

• All data layers are scaled according to scaling coefficients provided in the radiometric coefficient auxiliary file.




Composite Product Generation• Pixels are selected from overlapping geocoded full swath

images based on maximum NDVI and/or minimum satellite zenith angle.

• With maximum NDVI, the least contaminated pixels are chosen from a series of images collected over several days.

• With minimum satellite zenith angle, near-nadir pixels have reduced atmospheric effects and footprint size.

• Pixels may also be masked out from the final composite product by applying a water mask.

• The product layers are scaled according to scaling coefficients provided in the product coefficient auxiliary file.




Basic Composite Product Layers• TOA radiance and brightness temperature• TOA reflectance, surface reflectance, BRDF-corrected

surface reflectance and associated NDVI• Satellite and sun zenith/azimuth angles• Quality control (QC) mask• Input scene mask• Pixel count mask• Relative date• Residual geometric error mask




Advanced Composite Product Layers• Pixel contamination (CECANT) mask• Land surface temperature• Leaf area index (LAI) • Instantaneous FPAR • Daily Mean FPAR• Instantaneous APAR Daily Total APAR• Composite Mean APAR • Fire (hot spot) mask • PAR albedo




Manitoba Remote Sensing Centre Products• MRSC in Winnipeg generates composite products on a

routine basis for CCRS and assorted composites for MRSC clients by subscription.

• Composite products are shipped on Exabyte tapes, CD-ROMs and DVDs or delivered electronically.

• Daily and multi-day composite products are produced from April 1 to October 31.

• For the 10-day products, the composite periods for any given month are from the 1st to 10th, 11th to 20th and 21st to end of the month.




Future Improvements• GeoComp-n processing time of one hour to geocode an AVHRR orbit

containing 5700 lines using a 16-point damped sin x/x resampling function on a 450MHz NT PC will decrease with faster CPUs.

• With the advent of large capacity RAID systems, disk space is becoming less of an issue. Typical disk requirements are such that one month’s worth of data consumes approximately 110 gigabytes of disk space if all the intermediate products (raw, pre-processed and geocoded) remain on-line.

• A series of Practical Extraction and Report Language (Perl) scripts, which are layered on top of the GeoComp-n software, can automate the import of raw data, geocoding and compositing processes.

• The GCP chip database will be augmented to include more GCPs to cover most of North America.




Conclusions• GeoComp-n can deliver geometric and radiometric accuracies

in composite products unprecedented in other operational AVHRR data processing systems.

• GeoComp-n supports a broad spectrum of operational and experimental products from AVHRR data, but expansion to other data types and new products is possible once their characteristics are established and the required algorithms defined.

• The higher-level products are being developed and validated for use in boreal and temperate ecosystems in Canada for applications such as forest fire management, crop forecasting and environmental monitoring.



GeoComp – nGeoComp – nSolar zenith angle product for the period August 11-20, 2000Solar zenith angle product for the period August 11-20, 2000



GeoComp – nGeoComp – nSatellite zenith angle product for the period August 11-20, 2000Satellite zenith angle product for the period August 11-20, 2000



GeoComp – nGeoComp – nGeometric error magnitude product for August 8, 2000Geometric error magnitude product for August 8, 2000



GeoComp – nGeoComp – nGeometric error direction product for August 8, 2000Geometric error direction product for August 8, 2000



GeoComp – n, an advanced GeoComp – n, an advanced system for the processing of system for the processing of coarse and medium coarse and medium resolution satellite data. Part 2: resolution satellite data. Part 2: Biophysical products for Biophysical products for northern ecosystemsnorthern ecosystems



GeoComp – nGeoComp – nPart 2: Biophysical productsPart 2: Biophysical products

• Much of the research work at CCRS has focused on vegetation dynamics, in view of the role of boreal ecosystems in the global carbon cycle.

• The GeoComp-n products are used to drive or validate vegetation process or global climate models.




Sensor Calibration• Because of post-launch sensor degradation and the absence of

onboard calibration for AVHRR channels 1 and 2, time-dependent calibration coefficients have been derived from vicarious calibration data provided by NOAA and other investigators. Radiometric calibration of channels 1 and 2 raw data counts into radiance uses the piece-wise linear calibration coefficients as recommended by CCRS (Cihlar and Teillet, 1995).

• The TOA radiance can be converted to TOA reflectance using the method of Teillet and Holben (1994).

• The thermal data in AVHRR channels 3, 4 and 5 are converted to TOA radiance and/or brightness temperature using onboard calibration data with the Kidwell (1998) method.




Atmospheric Correction• TOA reflectance is converted to surface reflectance by applying the Simplified

Method for Atmospheric Correction (SMAC) radiative transfer code (Rahman and Dedieu, 1994).

• This simplified version of the code to Simulate the Satellite Signal in the Solar Spectrum (5S) (Tanre et al., 1990) is much faster because it uses semi-empirical formulations and coefficients, which depend on the sensor spectral band of interest.

• Based on the analysis of AEROCAN data (Bokoye et al., 2002), Fedosejevs et al. (2000) found that aerosol optical depth of 0.06 at 550 nm is an acceptable value for clear-sky conditions across Canada.

• Nominal values of 2.3 gm cm-2 for column water vapour content and 319 Dobson Units for ozone are used in SMAC.

• The accuracy of SMAC decreases if solar zenith and viewing (satellite) zenith angles are above 60o and 50o, respectively. Such cases can occur over the Canadian landmass.




BRDF Correction• AVHRR observations at northern latitudes are not

sufficient to reconstruct a bi-directional reflectance distribution function (BRDF) on a per-pixel basis but are best used by grouping a complete season of surface reflectance observations according to the land cover type.

• AVHRR data are corrected to a standard viewing geometry with sun zenith angle of 45 and nadir viewing angle.

• A delta term was introduced to the modified Roujean BRDF model as a proxy for the effect of the changing leaf area during the growing season (Latifovic, Cihlar and Chen, 2002).




Identification of Contaminated Pixels• Despite the selection of a 10-day composite period for boreal ecosystems

(consistent with the IGBP specification, the resulting composite products still contain some contaminated pixels.

• A procedure for Cloud Elimination from Composites using Albedo and NDVI Trend (CECANT) was developed that takes advantage of the effect of the atmospheric noise on NDVI over land.

• Threshold coefficients were derived from a reference seasonal NDVI data set.

• An adjustment was applied for near real time applications because of possible shifts in NDVI distribution among years.

• CECANT was further refined by decreasing the number of thresholds from three to two (deviation measure and surface reflectance ) per composite period and by making the threshold coefficients dependent on land cover type (Cihlar et al., 2002).




Leaf Area Index• Leaf area index (LAI) is a vegetation structural parameter of

fundamental importance for quantitative assessment of physical and biological processes in vegetation canopies.

• LAI provides the key input for process-based terrestrial carbon cycle modelling (Liu et al., 1999).

• The LAI algorithm, which is land cover type dependent, was based on the simple ratio (SR) of Landsat 5 TM NIR to red bands after atmospheric and BRDF corrections, and was validated against ground LAI measurements acquired in eight Landsat TM scenes selected from across Canada (Chen et al., 2002).

• A spectral adjustment factor of 1.27 was applied to the algorithm for NOAA-11 AVHRR data.




Fraction of Photosythetically Active Radiation (FPAR) • FPAR determines the proportion of available PAR that a green

canopy absorbs.• In terrestrial carbon cycle estimation, FPAR is used to drive some

empirical photosynthetic models or simple process models. • The acuracy for canopy-level photosynthesis estimation can be

improved through the use of LAI and a vegetation clumping index (Chen et al., 1999a), where the clumping index can be derived from multi-angle remote sensing (Chen et al., 1999b; Lacaze et al., 2002).

• Instantaneous and daily mean (computed for the solar zenith angle at noon) FPAR are produced by GeoComp-n for use in computing APAR absorbed by the green canopy and for the consistency with similar products in other parts of the world.




Absorbed Photosynthetically Active Radiation (APAR)• APAR denotes the total PAR (incident solar energy between 400 and 700 nm)

absorbed by the surface canopy/soil layers. APAR can be converted to PAR reaching the top of a canopy with knowledge of the surface PAR albedo, or to PAR absorbed by canopy only with knowledge of the FPAR.

• APAR is one of the most important variables affecting the net primary productivity of vegetation.

• Cloud is the main modulator of APAR, followed by Raleigh scattering and absorption due to aerosols, ozone and other gases.

• GeoComp-n can generate instantaneous, daily total, and composite period mean APAR products.

• Determination of Instantaneous APAR consists of three steps: angular correction of channel 1 TOA reflectance to TOA albedo using the ERBE angular model, spectral adjustment of channel 1 TOA albedo to PAR TOA albedo (Li and Moreau, 1996), and conversion of PAR TOA albedo to Instantaneous APAR.




PAR surface albedo• PAR surface albedo is derived from surface reflectance in channel 1, NDVI, solar

zenith angle and surface cover type, following an integration based on the surface BRDF.

• GeoComp-n generates a LUT of integrated BRDF values according to the double integral in zenith and azimuth direction accomplished by a summation of BRDF values over an angle range of 0 to 90º at increments of 1o. This integration is repeated for NDVI values (computed from surface reflectance) from 0 to 1.0 at increments of 0.05, for each sun zenith angle from 0 to 90o at increments of 1o, and for each of 14 land cover types.

• Nearest neighbor sampling of the LUT is employed for the actual land cover type, NDVI value and sun zenith angle for a given pixel.

• The current implementation assumes an inherent surface albedo corresponding to black-sky conditions when all solar radiation comes from one particular direction as a collimated beam without diffuse component. The presence of diffuse component may affect the magnitude of the albedo, especially in cloudy conditions.




Fire Processing • Wildfire represents a dominant disturbance to Canadian boreal forests,

burning an annual average 1% of the national forested area. • About 97% of this burning is caused by crown fires consuming > 1000

ha each. Thus, fire exerts a major control on landscape successional patterns, stand age distribution, and carbon storage within the boreal forest.

• A satellite-based algorithm to detect actively burning boreal fires (as small as 0.1% of a pixel) has been developed at CCRS (Li et al., 2000). The hotspot algorithm for NOAA 14 identifies active fires using brightness temperature in the mid-infrared channel (3B) and uses a series of threshold tests to eliminate false hotspots caused by highly reflective clouds or warm surfaces such as cropland or recently burnt areas. Single fire pixels that are not surrounded by neighbouring fire pixels are assumed to be caused by sun-glint and are eliminated.




Fire Processing• Raw data from the NATAS data file server are imported into

GeoComp-n, geocoded using a nearest neighbour resampling algorithm and composited into daily products.

• A daily colour Tagged Image File Format (TIFF) image of the composite is also produced with the fire hotspots overlaid on the image.

• Both the TIFF image and the fire map are sent electronically to the Canadian Forestry Service (CFS) in Edmonton where they are imported into a geographic information system (GIS), overlain with map information and made available to forest fire managers via an Internet based system.

• The system is also used as a research tool for developing burnt area and smoke detection algorithms.




Future productsFire Smoke• In 2000, the threshold algorithm of Li et al. (2001) was applied to create

daily Canada-wide smoke masks for the boreal forests in near real time as part of the Fire M3 Project.

Cumulative Burnt Areas• An algorithm is under development, which is designed to compute a daily

cumulative burnt area mask during the forest fire season based on the observation that burnt boreal forest exhibits a strong increase in the mid-infrared spectral band (AVHRR channel 3A).

• A pixel is labelled as burnt if it satisfies four conditions: (1) it is cloud-free, (2) it is classified as forest, (3) the pixel is spatially connected to an active fire or previously identified burnt pixel, and (4) the pixel has an elevated channel 3 response similar to that of the adjoining cluster of active hotspot/burnt pixels.




Future products (continued)

Net Primary Productivity

• The Boreal Ecosystem Productivity Simulator (BEPS) (Liu et al., 1999) can be run within GeoComp-n to produce daily net primary productivity (NPP) values per pixel and accumulate them throughout the year to obtain the annual NPP distribution. The prerequisites are: (i) to compute daily NPP values before and after the growing season based on LAI ; and (ii) to provide GeoComp-n with daily gridded meteorological data.




Future products (continued)Evapotranspiration • BEPS can also be run to produce daily evapotranspiration (ET)

distributions. As ET is calculated based on the Penman-Monteith method, daily meteorological data, LAI and clumping index are needed for ET calculations. ET and NPP products can therefore be generated simultaneously.

Runoff • Runoff maps can be produced in two stages: (i) pixel-level excess

water estimation based on the water balance for each pixel, and (ii) routing of the excess water for large watersheds. However, a detailed hydrological model for simulating discharge rates of the major rivers in Canada is not feasible within GeoComp-n in the near future.




Quality Assurance• A step towards overcoming the spatial limitation has been taken in LAI

validation (Chen et al., 2002) by sampling the Canada-wide product. Such extensive validation studies may be required for other products.

• However, another strategy is needed to ensure that the algorithm performance does not change over time (Cihlar, Chen and Li, 1997).

• In addition, steps need to be taken to maintain consistency in the input data characteristics. The diverging effects may be of different origin, including spectral sensor characteristics even for nominally the same sensor type (Trishchenko, Cihlar and Li, 2002), calibration drift, or random effects in the down-linked signal and its initial processing.

• To compensate for sensor degradation, the most likely calibration coefficients are predicted for the current season based on the time history (Cihlar and Teillet, 1995).




Quality Assurance (continued)• As techniques for generation of automatic quality reports are under

development and are not implemented as part of GeoComp-n; the product quality assessment thus remains the responsibility of the analyst.

• While near real time higher-level products have much greater potential value; they are not likely to be as accurate as products generated in delayed mode such as at the end of the year when all measurements can be analyzed together.

• In case of AVHRR products from GeoComp-n, the two main potential causes of errors are sensor calibration and the identification/replacement of contaminated pixels.

• It should be noted that better near real time screening of contaminated pixels will be possible for other sensors like MODIS with its larger number of spectral bands at key wavelengths.




Summary and Conclusions• GeoComp-n can create a suite of higher-level products for

the monitoring and assessment of the terrestrial biosphere. • As CCRS has developed/validated products for application

in the boreal and temperate forest environments; the higher-level products are more-or-less biome-specific and should not be generated for areas outside of Canada without a thorough validation procedure.

• Research in product error (e.g. LAI) assessment, including the development of automated product quality assessment methods, is presently underway.




References:Adair, M., J. Cihlar, B. Park, G. Fedosejevs, A. Erickson, R. Keeping, D.

Stanley, and P. Hurlburt. 2001. “GeoComp - n, an advanced system for generating products from coarse and medium resolution optical satellite data. Part 1: System characterization”, Canadian Journal of Remote Sensing, vol. 28, no. 1, pp. 1-20.

Cihlar, J., J. Chen, Z. Li, G. Fedosejevs, M. Adair, W. Park, R. Fraser, A. Trishchenko, B. Guindon, and D. Stanley. 2001. “GeoComp-n, an advanced system for the processing of coarse and medium resolution satellite data. Part 2: biophysical products for the northern ecosystem”, Canadian Journal of Remote Sensing, vol. 28, no. 1, pp. 21-44.




References (continued):Bokoye, A.I., A. Royer, N.T. O’Neill, G. Fedosejevs, P.M. Teillet and B. McArthur.

2002. “Characterization of atmospheric aerosols across Canada from a ground-based sunphotometer network: AEROCAN”, Atmosphere-Ocean, Vol. 39, pp. 429-456).

Chen, J.M., J. Liu, J. Cihlar and M.L. Goulden. 1999a. “Daily canopy photosynthesis model through temporal and spatial scaling for remote sensing applications”, Ecological Modelling, Vol. 124, pp. 99-119.

Chen, J.M., R. Lacaze, S.G. Leblanc, J.-L., Roujean, and J. Liu. 1999b. “POLDER BRDF and photosynthesis: an angular signature useful for ecological applications”, Abstract to 2nd international workshop on multi-angular measurements and models, Ispra, Italy.

Chen, J., G. Pavlic, L. Brown, J. Cihlar, S.G. Leblanc, P. White, R.J. Hall, D. Peddle, D.J. King, J.A. Trofymow, E. Swift, J. van der Sanden, and P. Pellikka. 2002. “Derivation and validation of Canada-wide coarse resolution leaf area index maps using high resolution satellite imagery and ground measurements. Remote Sensing of Environment, Vol. 80, pp. 165-184.




References (continued):Cihlar, J., and P.M. Teillet. 1995. “Forward piecewise linear model for quasi-real

time processing of AVHRR data”, Canadian Journal of Remote Sensing, Vol. 21, pp. 22-27.

Cihlar, J., J. Chen, and Z. Li. 1997b. “On the validation of satellite-derived products for land applications”, Canadian Journal of Remote Sensing, Vol. 23, pp. 381-389.

Cihlar, J., Latifovic, R., Chen, J., Trishchenko, A., Du, Y., Fedosejevs, G., and Guindon, B. 2002. “Systematic corrections of AVHRR image composites for temporal studies”, Remote Sensing of Environment (in press).

Fedosejevs, G., N.T. O’Neill, A. Royer, P.M. Teillet, A.I. Bokoye and B. McArthur. 2000. “Aerosol optical depth for atmospheric correction of AVHRR composite data”, Canadian Journal of Remote Sensing, Vol. 26, pp. 273-284.

Kidwell, K. B. (Ed). 1998. “NOAA Polar Orbiter Data User’s Guide”, NOAA-NESDIS, Washington, D.C. <http://www2.ncdc.noaa.gov/docs/podug/>.




References (continued):Lacaze, R., J.M. Chen, J-L. Roujean, and S.G. Leblanc. 2002. “Retrieval of vegetation

clumping index using hotspot signatures measured by multi-angular POLDER instrument” Remote Sensing of Environment, Vol. 79, pp. 84-95.

Latifovic, R., J. Cihlar, and J. Chen. 2002. “A comparison of BRDF models for the normalisation of satellite optical data to a standard sun-target-sensor geometry”, IEEE Transaction for Geoscience and Remote Sensing (in press).

Li, Z., and L. Moreau. 1996b. “A new approach for remote sensing of canopy-absorbed photosynthetically active radiation. I: Total surface absorption”, Remote Sensing of Environment, Vol. 55, pp. 175-191.

Li, Z., S. Nadon, and J. Cihlar. 2000. “Satellite detection of Canadian boreal forest fires: Development and application of an algorithm”, International Journal of Remote Sensing, Vol. 21, pp. 3057-3069.

Liu, J., J.M. Chen, J. Cihlar, and W. Chen. 1999. “Net primary productivity distribution in the BOREAS study region from a process model driven by satellite and surface data”, Journal of Geophysical Research, Vol. 104, No. D22, pp. 27,735-27,754.




References (continued):Rahman, H. and G. Dedieu. 1994. “SMAC: A Simplified Method for the

Atmospheric Correction of Satellite Measurements in the Solar Spectrum”, International Journal of Remote Sensing, Vol. 15, pp. 123-143.

Tanre, D., C. Deroo, P. Duhaut, M. Herman, J.J. Morcrette, J. Perbos, and P.Y. Deschamps. 1990. “ Description of a computer code to simulate a satellite signal in the solar spectrum: the 5S code”, International Journal for Remote Sensing, Vol. 14, pp. 659-668.

Teillet, P.M. and B.N. Holben. 1994. “Towards operational radiometric calibration of NOAA AVHRR imagery in the visible and infrared channels”, Canadian Journal of Remote Sensing, Vol. 20, pp. 1-10.

Trishchenko, A.P., J. Cihlar, and Z. Li. 2002. “Effects of spectral response function on the surface reflectance and NDVI measured with moderate resolution sensors”, Remote Sensing of Environment, Vol. 80, pp. 1-18.



GeoComp – nGeoComp – nBRDF-corrected surface reflectance for AVHRR channel 1 BRDF-corrected surface reflectance for AVHRR channel 1

August 11-20, 2000August 11-20, 2000



GeoComp – nGeoComp – nAverage APAR product for the period August 11-20, 2000Average APAR product for the period August 11-20, 2000



GeoComp – nGeoComp – nPAR albedo product for August 8, 2000PAR albedo product for August 8, 2000



Useful Web SitesUseful Web Sites• CCRS GeoComp-n

http://www.ccrs.nrcan.gc.ca/ccrs/rd/ana/geocomp/geocomp_e.html• NATAS http://ceocat.ccrs.nrcan.gc.ca/client_acc/guides/avhrr/ch4.html• NOAA Reception at CCRS

http://www.ccrs.nrcan.gc.ca/ccrs/data/satsens/sats/noaa_e.html• CCRS CalVal

http://www.ccrs.nrcan.gc.ca/ccrs/rd/ana/calval/calhome_e.html• CEOCAT

http://ceocat.ccrs.nrcan.gc.ca/cgi-bin/client_acc/ceocate/holdings.phtml

• MRSC http://www.gov.mb.ca/conservation/geomatics/remote_sensing/index.html

• Fire M3 http://fms.nofc.cfs.nrcan.gc.ca/FireM3



ContactContactGunar FedosejevsGunar Fedosejevs

Data Acquisition DivisionData Acquisition Division

Canada Centre for Remote SensingCanada Centre for Remote Sensing

588 Booth Street588 Booth Street

Ottawa, Ontario, Canada K1A 0Y7Ottawa, Ontario, Canada K1A 0Y7

E-mail: [email protected]: [email protected]

URL: URL: http://www.ccrs.nrcan.gc.ca/ccrs/http://www.ccrs.nrcan.gc.ca/ccrs/

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