BOREAS RSS-07 Regional LAI and FPAR Images From Ten-Day AVHRR-LAC Composites Summary The BOREAS RSS-07 team focused their efforts on developing and validating procedures and algorithms that would allow the retrieval of LAI from remotely sensed vegetation indices. This data set contains images of LAI and FPAR that were produced from the AVHRR Level-4c ten-day composite NDVI images produced at CCRS for the three summer IFCs in 1994. The algorithms were developed based on ground measurements and Landsat TM images (Chen and Cihlar, 1996; Chen, 1996b). The data are provided in binary image format files. Table of Contents * 1 Data Set Overview * 2 Investigator(s) * 3 Theory of Measurements * 4 Equipment * 5 Data Acquisition Methods * 6 Observations * 7 Data Description * 8 Data Organization * 9 Data Manipulations * 10 Errors * 11 Notes * 12 Application of the Data Set * 13 Future Modifications and Plans * 14 Software * 15 Data Access * 16 Output Products and Availability * 17 References * 18 Glossary of Terms * 19 List of Acronyms * 20 Document Information 1. Data Set Overview 1.1 Data Set Identification BOREAS RSS-07 Regional LAI and FPAR Images From Ten-Day AVHRR-LAC Composites 1.2 Data Set Introduction These Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) images were generated in response to the need within the Boreal Ecosystem Atmosphere Study (BOREAS) modeling community for adequate spatial and temporal coverage of LAI and FPAR estimates across the BOREAS region. The ten-Day Advanced Very High Resolution Radiometer (AVHRR) composite Normalized Difference Vegetation Index NDVI images, produced after various processing steps to remove artifacts, are very suitable for this purpose. Although algorithms for some cover types for this region are not available, these parameter maps were produced as the best estimates at this stage. 1.3 Objective/Purpose The objective of this project was to provide quantitative spatial distribution of LAI and FPAR for the BOREAS region for the 1994 summer Intensive Field Campaigns (IFCs) for the purpose of modeling the carbon, water, energy and trace gas exchange between the boreal ecosystems and the atmosphere. 1.4 Summary of Parameters LAI FPAR absorbed by plant canopies 1.5 Discussion These LAI and FPAR images are based on AVHRR Level-4c NDVI products and a coregistered land cover map (Pokrant, 1991). The Level-4c product is based on the AVHRR Level-4b product, but is further processed to remove or mitigate some artifacts caused by the input data or the compositing process. The artifacts of concern are atmospheric contamination and bidirectional reflectance effects for AVHRR channels 1 and 2, and atmospheric and surface emissivity effects for AVHRR channel 4. The processing was carried out at the Canadian Centre for Remote Sensing (CCRS) using software and procedures developed in-house (see Section 9 for details). The spatial and temporal coverage of this product is identical to that of the Level-4b and -4c products. 1.6 Related Data Sets BOREAS RSS-07 Ground Measurements of LAI and FPAR BOREAS Level-4b AVHRR-LAC Ten-Day Composite Images: At-sensor Radiance BOREAS Level-4c AVHRR-LAC Ten-Day Composite Images: Surface Parameters 2. Investigator(s) 2.1 Investigator(s) Name and Title Dr. Jing M. Chen Dr. Josef Cihlar 2.2 Title of Investigation Retrieval of Boreal Forest Leaf Area Index from Multiple Scale Remotely Sensed Vegetation Indices 2.3 Contact Information Contact 1 --------- Jing M. Chen Canada Centre for Remote Sensing Ottawa, Ontario Canada (613) 947-1266 (613) 947-1406 (fax) jing.chen@ccrs.nrcan.gc.ca Contact 2 --------- Josef Cihlar Canada Centre for Remote Sensing Ottawa, Ontario Canada (613) 947-1265 (613) 947-1406 (fax) Josef.Cihlar@ccrs.nrcan.gc.ca 3. Theory of Measurements The theories of LAI and FPAR measurements are documented in the Remote Sensing Science (RSS)-07 ground LAI and FPAR data document. The theory of AVHRR measurements is given in the BOREAS Level-4c AVHRR images document, but the relevant portions are copied below. The AVHRR is a four- or five-channel scanning radiometer capable of providing global daytime and nighttime information about ice, snow, vegetation, clouds, and the sea surface. These data are obtained on a daily basis primarily for use in weather analysis and forecasting; however, a variety of other applications are possible. The AVHRR-Local Area Coverage(LAC) data collected for the BOREAS project were from instruments onboard National Oceanic and Atmospheric Administration (NOAA)-9, NOAA-11, and NOAA-12 polar orbiting platforms. The radiometers measured emitted and reflected radiation in the visible, near- infrared, one middle-infrared, and one or two thermal channels. The primary use of each channel and spectral regions and bandwidths on the respective NOAA platforms are given in the following tables: Channel Wavelength Primary Use [micrometers] ------- ------------------- --------------------------------------------- 1* 0.57 - 0.69 Daytime Cloud and Surface Mapping 2 0.72 - 0.98 Surface Water Delineation, Vegetation Cover 3 3.52 - 3.95 Sea Surface Temperature (SST), Nighttime Cloud Mapping 4** 10.3 - 11.4 Surface Temperature, Day/Night Cloud Mapping 5*** 11.4 - 12.4 Surface Temperature * Channel 1 wavelength for the Television and Infrared Observation Satellite (TIROS)-N flight model was 0.5 -0.90 micrometers. ** For NOAA-7 and-9, channel 4 was 10.3-11.3 micrometers. *** For TIROS-N, NOAA-6, -8, -10, and-12 Channel 5 duplicates Channel 4. The wavelength ranges at 50% relative spectral response (in micrometers) of the bands for the platform-specific instruments are: Band NOAA-9 NOAA-11 NOAA-12 NOAA-14 ---- --------------- --------------- --------------- --------------- 1 0.570 - 0.699 0.572 - 0.698 0.571 - 0.684 0.570 - 0.699 2 0.714 - 0.983 0.716 - 0.985 0.724 - 0.984 0.714 - 0.983 3 3.525 - 3.931 3.536 - 3.935 3.554 - 3.950 3.525 - 3.931 4 10.334 - 11.252 10.338 - 11.287 10.601 - 11.445 10.330 - 11.250 5 11.395 - 12.342 11.408 - 12.386 10.601 - 11.445 11.390 - 12.340 The AVHRR is capable of operating in both real-time and recorded modes. Direct readout data were transmitted to ground stations of the automatic picture transmission (APT) class at low resolution (4 x 4 km) and to ground stations of the high-resolution picture transmission (HRPT) class at high resolution (1 x 1 km). AVHRR HRPT data were received for the BOREAS region by the CCRS Prince Albert Satellite Station (PASS). The LAI and FPAR images in 1994 in this data set were produced using the NOAA-11 sensor. 4. Equipment 4.1 Sensor/Instrument Description The AVHRR is a cross-track scanning system featuring one visible, one near- infrared, one middle-infrared, and two thermal channels. The analog data output from the sensors are digitized onboard the satellite at a rate of 39,936 samples per second per channel. Each sample step corresponds to an angle of scanner rotation of 0.95 milliradians. At this sampling rate, there are 1.362 samples per instantaneous field of view (IFOV). A total of 2,048 samples is obtained per channel per Earth scan, which spans an angle of +/-55.4 degrees from nadir. 4.1.1 Collection Environment The NOAA satellites orbit Earth at an altitude of 833 km. From this space platform, the data are transmitted to a ground receiving station. 4.1.2 Source/Platform Launch and available dates for the TIROS-N series of satellites from CCRS are: Satellite Launch Date Date Range --------- ----------- -------------------------- TIROS-N 13-Oct-1978 19-Oct-1978 to 30-Jan-1980 NOAA-6 27-Jun-1979 21-Aug-1984 to 23-Jan-1986 NOAA-B 29-May-1980 Failed to achieve orbit NOAA-7 23-Jun-1981 24-Jul-1983 to 30-Dec-1984 NOAA-8 28-Mar-1983 24-Jul-1983 to 13-Aug-1985 NOAA-9 12-Dec-1984 16-Sep-1985 to 19-Mar-1995 NOAA-10 17-Sep-1986 11-Oct-1986 to 15-Nov-1993 NOAA-11 24-Sep-1988 28-Jun-1989 to 13-Sep-1994 NOAA-12 14-May-1991 11-Aug-1993 to present NOAA-14 30-Dec-1994 15-May-1995 to present 4.1.3 Source/Platform Mission Objectives The AVHRR is designed for multispectral analysis of meteorologic, oceanographic, and hydrologic parameters. The objective of the instrument is to provide radiance data for investigation of clouds, land water boundaries, snow and ice extent, ice or snow melt inception, day and night cloud distribution, temperatures of radiating surfaces, and SST. It is an integral member of the payload on the advanced TIROS-N spacecraft and its successors in the NOAA series, and as such contributes data required to meet a number of operational and research-oriented meteorological objectives. 4.1.4 Key Variables Emitted radiation. Reflected radiation. 4.1.5 Principles of Operation The AVHRR is a four- or five-channel scanning radiometer that detects emitted and reflected radiation from Earth in the visible, near-infrared, middle- infrared, and thermal-infrared regions of the electromagnetic spectrum. A fifth channel was added to the follow-on instrument designated AVHRR/2 and flown on NOAA-7, -9, -11, and -14 to improve the correction for atmospheric water vapor. 4.1.6 Sensor/Instrument Measurement Geometry The AVHRR is a cross-track scanning system. The IFOV of each sensor is approximately 1.4 milliradians, giving a spatial resolution of 1.1 km at the satellite subpoint. There is about a 36 percent overlap between IFOVs (1.362 samples per IFOV). The scanning rate of the AVHRR is six scans per second, and each scan spans an angle of +/-55.4 degrees from the nadir. 4.1.7 Manufacturer of Sensor/Instrument ITT Aerospace 4.2 Calibration No in-flight visible channel calibration is performed. 4.2.1 Specifications IFOV 1.4 mrad RESOLUTION 1.1 km ALTITUDE 833 km SCAN RATE 360 scans/min (1.362 samples per IFOV) SCAN RANGE -55.4 to 55.4 degrees SAMPLES/SCAN 2,048 samples per channel per Earth scan 4.2.1.1 Tolerance A signal-to-noise ratio of 3:1 at 0.5-percent albedo. 4.2.2 Frequency of Calibration The Naval Research Laboratory’s (NRL) TIROS-N calibration overlay performs the calibration on blocks of telemetry data. For LAC/HRPT acquisitions, a block consists of 20 scan lines. Calibration begins by reading the calibration parameters into memory. For each scan line of telemetry in a block, the following process takes place: (1) Telemetry data are extracted and unpacked. (2) Ramp calibration data for each of the five channels are decommutated. 5. Data Acquisition Methods The BOREAS Level-4c AVHRR-LAC images were acquired through the CCRS. Some radiometric and geometric corrections are applied to produce the imagery in a spatially corrected form (Lambert Conformal Conic (LCC) projection). A full Level-4c AVHRR-LAC image contains approximately 1,200 pixels in each of approximately 1,200 lines. Before any geometric corrections, the ground resolution ranges from 1.1 km at nadir to 2.5 km x 6.8 km at the scanning extremes. Each pixel value is stored in a 2-byte field starting with Level-4b products. The Level-4c images were processed through software developed at CCRS. The raw data are available from the CCRS PASS. 6. Observations 6.1 Data Notes None. 6.2 Field Notes None. 7. Data Description 7.1 Spatial Characteristics The characteristics are the same as for the BOREAS Level-4c AVHRR-LAC imagery, which covers the entire 1,000-km by 1,000-km BOREAS region. This contains both the Northern Study Area (NSA), the Southern Study Area (SSA), and the transect region between the SSA and NSA. The composite images contain 1,200 pixels in each of the 1200 lines covering the BOREAS region of Canada. 7.1.1 Spatial Coverage The North American Datum of 1983 (NAD83) corner coordinates of the BOREAS region are: Latitude Longitude -------- --------- Northwest 58.979°N 111.000°W Northeast 58.844°N 93.502°W Southwest 51.000°N 111.000°W Southeast 50.089°N 96.969°W The NAD83 corner coordinates of the SSA are: Latitude Longitude -------- --------- Northwest 54.321°N 106.228°W Northeast 54.225°N 104.237°W Southwest 53.515°N 106.321°W Southeast 53.420°N 104.368°W The NAD83 corner coordinates of the NSA are: Latitude Longitude -------- --------- Northwest 56.249°N 98.825°W Northeast 56.083°N 97.234°W Southwest 55.542°N 99.045°W Southeast 55.379°N 97.489°W The corners of the LAI and FPAR images are: Latitude Longitude ---------- ----------- Northwest (1,1) 59.36395°N 115.40859°W Northeast (1,1200) 61.01294°N 93.28553°W Southwest (1200,1) 48.83387°N 110.25229°W Southeast (1200,1200) 50.02993°N 93.73857°W The northwest corner has a distance (1109.76 km west, 7900.04 km north) from the origin (95°W and 0°N) of the LCC coordinate. The pixel size is exactly 1 km. This 1200 km by 1200 km area includes the four corners of the BOREAS region. 7.1.2 Spatial Coverage Map Not available. 7.1.3 Spatial Resolution Before any geometric corrections, the spatial resolution varies from 1.1 km at nadir to approximately 2.5 x 6.8 km at the extreme edges of the scan. The Level-4b composite AVHRR-LAC images have had geometric corrections applied so that the pixel size is 1 km in all bands. Only pixels with view zenith angles 57 degrees or less are used in Level-4c product. The LAI and FPAR images have the same 1.0-km spatial resolution 7.1.4 Projection The coordinate system is the LCC with the two standard parallels at 49°N and 77°N, respectively, and the meridian at 95°W. 7.1.5 Grid Description The LAI and FPAR images are the same as the Level-4 images, which are projected into the LCC projection described in Section 7.1.4 at a resolution of 1.0 km per pixel (grid cell) in both the X and Y directions. 7.2 Temporal Characteristics 7.2.1 Temporal Coverage The overall time period of AVHRR data acquisition in 1994 was from 09-Apr through 10-Sep. Ten-Day composite images of NDVI for this period were produced at Level-4c. Three NDVI composite images corresponding to the IFC periods were used for LAI and FPAR calculation (see Section 7.2.2). 7.2.2 Temporal Coverage Map The 1994 compositing periods from the Level-4c data set used for the LAI and FPAR parameter images were the following Ten-Day periods, one during each IFC: May 21-31 July 21-31 September 1-10 7.2.3 Temporal Resolution The daily images are composited into nominally cloud-free images over Ten-Day periods. One Ten-Day compositing period was selected to represent each 1994 IFC to create the three LAI and FPAR images for this data set. 7.3 Data Characteristics 7.3.1 Parameter/Variable LAI FPAR 7.3.2 Variable Description/Definition LAI is defined as one half the total leaf area per unit ground surface area. For four-sided spruce needles, two sides are included. To derive LAI from the digital counts in the image: LAI = (DN-1)/10 Green FPAR is the fraction of incident PAR that is absorbed by the green leaves in the canopy. It excludes the fraction reflected back to space and the fraction absorbed by the background (moss, soil and understory in forest, and soil in the crops), but it includes the small fraction that is reflected by the background and absorbed by the green leaves on the way back to space. To derive FPAR from the digital counts in the image: FPAR = (DN-1)/100 7.3.3 Unit of Measurement Both LAI and FPAR are unitless; for LAI, it can be expressed as m2 of leaf area/ m2 of ground surface area. 7.3.4 Data Source The Level-4c AVHRR data used to create this product was processed and provided by the CCRS. 7.3.5 Data Range Both LAI and FPAR images are 8-bit images; i.e., the digital values (DN) vary from 0 to 255. 7.4 Sample Data Record Not applicable to image data. 8. Data Organization 8.1 Data Granularity The smallest unit of data tracked by BORIS is the entire set of composited images and the coordinate conversion software. 8.2 Data Format(s) 8.2.1 Uncompressed Data Files The AVHRR LAI and FPAR data product contains the following eight files: File Description Recordsize ------ ------------------------------- --------------------- 1 Header/Readme 80 bytes (ASCII) 2 FPAR for 01-MAY-94 to 10-MAY-94 1200 bytes (binary) 3 FPAR for 21-JUL-94 to 31-JUL-94 1200 bytes (binary) 4 FPAR for 01-SEP-94 to 10-SEP-94 1200 bytes (binary) 5 LAI for 01-MAY-94 to 10-MAY-94 1200 bytes (binary) 6 LAI for 21-JUL-94 to 31-JUL-94 1200 bytes (binary) 7 LAI for 01-SEP-94 to 10-SEP-94 1200 bytes (binary) 8 lcc_1200.f (FORTRAN Source) 80 bytes (ASCII) The image files are all 8-bit binary images with 1200 pixels per line and 1200 lines. A FORTRAN program, 1lcc_1200.f', is also provided for the calculation of longitude and latitude for any given pixel and line in the image. When using the software, you need to input the pixel and line numbers relative to the top- left pixel, which is (1,1). A version of the lcc_1200 code written in C is available on request. 8.2.2 Compressed CD-ROM Files On the BOREAS CD-ROMs, the ASCII header file for this image is stored as ASCII text; however, files 2 through 7 have been compressed with the Gzip (GNU zip) compression program (file_name.gz). These data have been compressed using gzip version 1.2.4 and the high compression (-9) option (Copyright (C) 1992-1993 Jean-loup Gailly). Gzip uses the Lempel-Ziv algorithm (Welch, 1994) also used in the zip and PKZIP programs. The compressed files may be uncompressed using gzip (with the -d option) or gunzip. Gzip is available from many websites (for example, the ftp site prep.ai.mit.edu/pub/gnu/gzip-*.*) for a variety of operating systems in both executable and source code form. Versions of the decompression software for various systems are included on the CD-ROMs. 9. Data Manipulations 9.1 Formulae 9.1.1 Derivation Techniques and Algorithms 9.1.1.1 LAI Maps The NDVI was first converted to the simple ratio (SR) using SR = (1+NDVI)/(1-NDVI) In the calculation of LAI from SR, different algorithms were used for the following 10 land cover types: water, mixed wood, deciduous, conifer, transitional forest, tundra, barren lands, cropland, rangeland/pasture, and built-up areas. For water, barren lands, and built-up areas, LAI is set to zero. An algorithm for the cropland is formulated using available NDVI-LAI relationships in the literature (Holben et al., 1980; Gardner and Blad, 1986; Aase et al., 1986; Wiegand et al., 1992; Li et al., 1993). The algorithm for boreal conifer forests is derived from our recent work (Chen and Cihlar, 1996). The methodology for ground truth LAI measurements are described in Chen and Cihlar (1995a and 1995b) and Chen (1996a). The algorithm for deciduous forests is also based on our own work, but is less accurate because of the vegetation dynamics and insufficient field data for the seasonal coverage. Mixed wood and transitional forests are considered as the intermediate case between conifer and deciduous forests. Tundra and rangeland/pasture are treated the same as cropland because of lack of data. Summer images are less useful for determining the overstory NDVI in conifer canopies because of the understory effect (Chen and Cihlar, 1996); therefore, the IFC-2 and IFC-3 LAI values for conifer species were taken to be 1.12 and 1.05 times the IFC-1 values. A ceiling of LAI values was used for each IFC to remove ‘speckles’ (unreasonably large LAI values caused by noise in NDVI data). These ceilings were 5.5, 6.0, and 5.7 for IFC-1, IFC-2 and IFC-3, respectively. The algorithms for boreal forests were developed by correlating SR from Landsat- 5 Thematic Mapper (TM) with ground-based LAI measurements. In the application of the algorithm to AVHRR images, a factor of 1.10 was used to increase the NDVI of AVHRR to account for sensor differences. Caution should be taken when using these LAI values for the calculation of FPAR because some models only use LAI inputs, while FPAR is calculated from LAI. Because of the canopy architectural difference between the species, it is suggested that the LAI values should be reduced by 50% for conifers, 25% for deciduous and mixed wood, and 10% for agricultural crops and tundra in the calculation of radiation interception using Beer's Law. The equations used in the LAI algorithm are: IFC-1 and IFC-3: LAI=0.594(SR-2.781) For mixed wood LAI=0.475(SR-2.781) For deciduous forest LAI=0.792(SR-2.781) For transitional forest LAI=1.188(SR-2.781) For conifer forest LAI=0.325(SR-1.5) For tundra LAI=0.325(SR-1.5) For cropland LAI=0.325(SR-1.5) For rangeland and pasture LAI=0 For water, barren, and built-up areas IFC-2: LAI=0.493(SR-3.637) For mixed wood LAI=0.394(SR-3.637) For deciduous forest LAI=0.657(SR-3.637) For transitional forest LAI=1.12*LAI in IFC-1 For conifer forest LAI=0.325(SR-1.5) For tundra LAI=0.325(SR-1.5) For cropland LAI=0.325(SR-1.5) For rangeland and pasture LAI=0 For water, barren, and built-up areas As shown above, the background SR values are all the same because of the lack of field data. The background values were obtained through regression for the conifer species. The IFC-2 LAI values were calculated from IFC-1 values to minimize the background effect (Chen and Cihlar, 1996). 9.1.1.2. Daily Green FPAR Maps Green FPAR refers to the fraction absorbed by green leaves only after the removal of the contribution of the supporting woody material to the PAR absorption. The instantaneous green FPAR is integrated over the day with a weight equal to the cosine of the solar zenith angle to obtain the daily green FPAR presented in the map. The daily green FPAR can be used as a parameter to convert the daily absorbed PAR to daily total incident PAR. The dates for IFC- 1, IFC-2 and IFC-3 are the same as those for LAI maps. Similar to the calculation of LAI, different algorithms for the daily green FPAR were used for the 10 land cover types. FPAR for water, barren lands, and built- up areas is assumed to be zero. An algorithm for the cropland is formulated using measurements from crop fields (Chen, unpublished). The algorithm is similar to that of Asrar et al. (1984). The algorithms for boreal conifer forests are published in Chen (1996b). For deciduous cover, the algorithm is formulated using eight sites in the midsummer (Chen, unpublished). The algorithms are linear relationships between SR and FPAR. Tundra and rangeland/pasture are treated the same as cropland because of lack of data. The values for boreal forests within the map are the most reliable. FPAR of the overstory is calculated from the downwelling and upwelling PAR measurements at two levels: above and below the canopy, for over 30 stands. The below-canopy measurements were made using the Tracing Radiation and Architecture of Canopies (TRAC) instrument (BOREAS RSS-07 Ground Measurements of LAI and FPAR). Because the downwelling PAR through the overstory and upwelling PAR reflected from the forest floor are highly variable, average values were obtained from closely spaced measurements over long transects (50-340 m). Simultaneous measurements of the downwelling and upwelling PAR above the forests were made on micrometeorological towers established for BOREAS. The data were collected at the beginning, middle, and end of the growing season to consider the effect of seasonal vegetation dynamics and the change in solar zenith angle (Chen, 1996b). The equations used in the FPAR algorithm are: IFC-1 and IFC-3: FPAR=0.170(SR-2.044) For mixed wood FPAR=0.147(SR-2.044) For deciduous forest FPAR=0.176(SR-2.044) For transitional forest FPAR=0.221(SR-2.044) For conifer forest FPAR=0.138(SR-1.5) For tundra FPAR=0.138(SR-1.5) For cropland FPAR=0.138(SR-1.5) For rangeland and pasture FPAR=0 For water, barren, and built-up areas IFC-1 and IFC-3: FPAR=0.147(SR-3.074) For mixed wood FPAR=0.127(SR-3.074) For deciduous forest FPAR=0.154(SR-3.074) For transitional forest FPAR=1.05*FPAR in IFC-1 For conifer forest FPAR=0.138(SR-1.5) For tundra FPAR=0.138(SR-1.5) For cropland FPAR=0.138(SR-1.5) For rangeland and pasture FPAR=0 For water, barren, and built-up areas In the equations the background SR changes between IFCs. The background values are different from the LAI equations because the SR-FPAR relationship is not strictly linear. These values were found from the regressions for the conifer species. A refinement of the algorithm would be to use the Modified SR (MSR, Chen 1996c), which is more linearly related to FPAR. IFC-2 LAI values for conifers are directly related to IFC-1 values because the background effect in IFC-1 is smaller (Chen and Cihlar, 1996). This assumes the normal seasonal growth situation. Because of lack of complete field data for all cover types, the most reliable data are for conifer, transitional forest, and cropland. The images with quality quote 04 (on a scale of 05) will be made available upon request. Please notify the contacts listed in section 2.3 regarding any problems you encounter. 9.2 Data Processing Sequence The processing steps from NDVI to LAI and FPAR are summarized above (Section 9.1); the processing steps leading to NDVI are described in the document for BOREAS Level-4c AVHRR-LAC images. The relevant descriptions are copied below for convenience. The Level-4c processing sequence is called Atmosphere, Bidirectional and Contamination Corrections of CCRS (ABC3) and is described in more detail by Cihlar et al. (1997a, 1997b). 9.2.1 Processing Steps Step 1: Top-of-the-Atmosphere (TOA) reflectance TOA reflectance for channel 1 or 2 is calculated from the corrected TOA radiance, L*(new), with the formula given by Teillet (1992). Values of gain G and offset O were calculated with consideration of postlaunch sensor degradation (Teillet and Holben, 1994). Step 2: Atmospheric correction of AVHRR channels 1 and 2 The algorithm Simplified Method for Atmospheric Correction (SMAC) (Rahman and Dedieu, 1994) was used in the processing. The processing was carried out assuming a water content of 2.3 g/cm2 and ozone content of 0.319 cm-atm. A constant value of 0.05 was used for optical depth at 550 nm. The corrections were computed on a pixel basis using solar zenith, view zenith, and relative azimuth channels. Step 3: Identification of contaminated pixels A new procedure was developed to identify the 'contaminated' pixels; i.e., pixels where the surface vegetation or soil signal is obscured (Cihlar, 1996). The procedure, dubbed Cloud Elimination from Composites using Albedo and NDVI Trend (CECANT), is based on the high sensitivity of NDVI to the presence of clouds, aerosol, and snow. Three features of the annual surface reflectance trend are used: the high contrast between the albedo (represented by AVHRR channel 1) of land, especially when fully covered by green vegetation, and clouds or snow/ice; the average NDVI value (expected value for that pixel and compositing period); and the monotonic trend in NDVI. Four thresholds are required in CECANT to identify a partially contaminated pixel (i,j,t) where i and j are pixel coordinates and t is the compositing period: C1(t): The maximum channel 1 reflectance of a clear-sky, snow- or ice-free land pixel in the data set. Rmin(t): The maximum acceptable deviation of the measured value NDVI(i,j,t) below the estimated NDVIa(i,j,t). Rmax(t): The maximum acceptable deviation of the measured value NDVI(i,j,t) above the estimated NDVIa(i,j,t). Zmax(t): The maximum acceptable deviation of the measured value NDVI(i,j,t) above the estimated NDVImax(i,j,t). NDVImax(i,j,t) and NDVIa(i,j,t) were calculated using the Fourier-Adjustment, Solar Zenith Angle Corrected, Interpolated, Reconstructed (FASIR) model of Sellers et al. (1994), which approximates the seasonal NDVI curve with a third- order Fourier transform. Before the computation, missing NDVI values between first and last measurements were replaced through linear interpolation after finding the seasonal peak for each pixel, using the rationale and algorithm of Cihlar and Howarth (1994). A constant value of 0.30 was used for C1. The upper and lower limits for R and Z were determined separately for each composite period using R and Z histograms (Cihlar, 1996). Using these thresholds, a cloud mask was prepared for each composite period. Step 4: Corrections for bidirectional reflectance effects in channels 1 and 2 The model of Roujean et al. (1992) as modified by Wu et al. (1995) was used to characterize the seasonal bidirectional reflectance function for each cover type. Land cover-dependent model coefficients were derived (Li et al., 1995) using a map of Canada with pixel size of 1 km prepared with AVHRR data (Pokrant, 1991). Only cloud-free pixels were included in the derivation of the model coefficients, and no bidirectional corrections for snow- or ice-covered areas were made. The resulting models were used to compute channel 1 and 2 reflectance for view zenith of 0 degrees and solar zenith of 45 degrees. Step 5: Replacement of contaminated pixels for AVHRR channels 1 and 2 Two cases were recognized: pixels contaminated either during or at the end of the growing season. For pixels contaminated during the growing season, the new values were found through linear interpolation for both channels 1 and 2. At the end of the growing season, it was assumed that the annual trajectory for individual channels as well as for NDVI could be approximated by a second-degree polynomial. The polynomial was fitted to the plot of corrected reflectance for all clear-sky periods, starting with the first clear-sky composite period after 01-Aug. After the best-fit coefficients were determined, the new values were calculated using the polynomial coefficients to replace contaminated pixels in each channel prior to the first clear pixel or after the last such pixel. Step 6: NDVI processing Because of imperfections in the bidirectional corrections of channels 1 and 2, the NDVI values computed from atmospherically corrected NDVI were also retained. However, corrections for solar zenith angle were desirable in view of the known dependence of the NDVI on the solar zenith angle. The coefficients of Sellers et al. (1994) were used for the various land cover classes. The new set of NDVI values was then computed for a reference solar zenith angle of 45 degrees based on the equations of Sellers et al. (1994). The NDVI values for the missing or contaminated pixels were interpolated as in Step 5 above. BORIS personnel processed the data by reviewing the images and compressing them for CD-ROM publication. 9.2.2 Processing Changes None. 9.3 Calculations See Section 9.2.1. 9.3.1 Special Corrections/Adjustments See Section 9.2.1. 9.3.2 Calculated Variables See Section 9.2.1. 9.4 Graphs and Plots None. 10. Errors 10.1 Sources of Error The sources of errors in LAI and FPAR are: (1) ground measurements, (2) use of algorithms based on Landsat TM that include image geometric and radiometric corrections and atmospheric correction, (3) error in determining the response difference between TM and AVHRR sensors, (4) the error associated with the AVHRR NDVI product. The major sources of error in AVHRR NDVI are due to the inaccurate knowledge of atmospheric conditions during image acquisition (and thus the use of nominal values for atmospheric corrections) and imperfect modeling of the bidirectional effects. 10.2 Quality Assessment 10.2.1 Data Validation by Source None given. 10.2.2 Confidence Level/Accuracy Judgement The FPAR images are more accurate than the LAI images. The relative error in LAI is estimated to be 20% for conifers and transitional forests; 25% for deciduous and mixed covers; and 30% for cropland, grassland, and tundra. The relative error in daily green FPAR is estimated to be less than 10% for all cover types. 10.2.3 Measurement Error for Parameters See Section 10.2.2. 10.2.4 Additional Quality Assessments None given. 10.2.5 Data Verification by Data Center BORIS staff have displayed the LAI and FPAR images as a visual check that the images were what we expected. 11. Notes 11.1 Limitations of the Data The calculated LAI and FPAR maps should be considered the best estimate for this moderate resolution. The distribution patterns and the magnitude of LAI and FPAR values for each cover type are indeed very reasonable. The resampling scheme used for the original channel images makes the actual resolution coarser than 1 km. It is on the order of 2-3 km in reality (Cihlar et al., 1997b). Subpixel water bodies and mixture of different cover types in a pixel can incur errors in the calculated results. Lack of data makes the calculations for rangeland, tundra, and mixed wood types least reliable. 11.2 Known Problems with the Data None. 11.3 Usage Guidance Before uncompressing the Gzip files on CD-ROM, be sure that you have enough disk space to hold the uncompressed data files. Then use the appropriate decompression program provided on the CD-ROM for your specific system. 11.4 Other Relevant Information None. 12. Application of the Data Set None given. 13. Future Modifications and Plans None given. 14. Software 14.1 Software Description A Fortran program, 'lcc_1200.f', is provided for the calculation of longitude and latitude for any given pixel and line in the image. When using the software, you need to input the pixel and line numbers relative to the top-left pixel, which is (1,1). 14.2 Software Access A version of the lcc_1200 code written in C is available on request from contacts listed in section 2.3. 15. Data Access 15.1 Contact Information Ms. Beth Nelson NASA GSFC Greenbelt, MD (301) 286 4005 (301) 286 0239 (fax) beth@ltpmail.gsfc.nasa.gov 15.2 Data Center Identification See Section 15.1. 15.3 Procedures for Obtaining Data Users may place requests by telephone, electronic mail, or fax. 15.4 Data Center Status/Plans The RSS-07 AVHRR LAI and FPAR image data are available from the Earth Observing System Data and Information System (EOSDIS) Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC). The BOREAS contact at ORNL is: ORNL DAAC User Services Oak Ridge National Laboratory Oak Ridge, TN (423) 241-3952 ornldaac@ornl.gov ornl@eos.nasa.gov 16. Output Products and Availability 16.1 Tape Products The data can be made available on 8-mm or Digital Archive Tape (DAT) media. 16.2 Film Products None. 16.3 Other Products None. 17. References 17.1 Platform/Sensor/Instrument/Data Processing Documentation Aase, J.K., J.P. Millard, and B.S. Brown. 1986. Spectral Radiance Estimates of Leaf Area and Leaf Phytomass of Small Grains and Native Vegetation. IEEE Transactions on Geoscience and Remote Sensing 24: 685-692. Asrar, G., M. Fuchs, E.T. Kanemasu, and J.H. Hatfield. 1984. Estimating absorbed photosynthetic radiation and leaf area index from spectral reflectance in wheat. Agron. J. 76:300-306. Becker, F. and Z.L. Li. 1990. Towards a local split window method over land surface. International Journal of Remote Sensing 3: 369-393. Buffam, A. 1994. GEOCOMP User Manual. Internal Report, Canada Centre for Remote Sensing, Ottawa, Ontario. Chen, J.M. 1996a. Optically-based methods for measuring seasonal variation of leaf area index in boreal conifer stands. Agric. For. Meteorology 80:135-163. Chen, J.M. 1996b. Canopy Architecure and remote sensing of the fraction of photosynthetically active radiation absorbed by boreal forests. IEEE Trans. Geosci Remote Sens. 34:1353-1368. Chen, J.M. 1996c. Evaluation of vegetation indices and a modified simple ratio for boreal applications. Can. J. Remote Sens. 22:229-242. Chen, J.M. and J. Cihlar. 1995a. Plant canopy gap size analysis theory for improving optical measurements of leaf area index of plant canopies. Applied Optics 34:6211-6222. Chen, J.M. and J. Cihlar. 1995b. Quantifying the effect of canopy architecture on opticalmeasurements of leaf area index using two gap size analysis methods. IEEE Trans. Geosci. Remote Sens. 33:777-787. Chen, J.M. and J. Cihlar 1996. Retrieving leaf area index in boreal forests using Landsat TM images. Remote Sensing of Environment 55:153-162. Cihlar, J. 1996. Identification of contaminated pixels in AVHRR composite images for studies of land biosphere. Remote Sensing of Environment 56:149-163. Cihlar, J. and J. Howarth. 1994. Detection and removal of cloud contamination from AVHRR composite images. IEEE Transactions on Geoscience and Remote Sensing 32: 427-437. Cihlar, J., H. Ly, Z. Li, J. Chen, H. Pokrant, and F. Huang. 1997a. Multitemporal, multichannel data sets for land biosphere studies: artifacts and corrections. Remote Sensing of Environment 60:35-57. Cihlar, J., J. Chen, and Z. Li, 1997b. Seasonal AVHRR multi-channel data sets and products for scaling up biospheric processes. Journal of Geophysical Research, Special BOREAS Issue 102:29,625-29,640. Coll, C., V. Caselles, J.A. Sobrino, and E. Valor. 1994. On the atmospheric dependence of the split-window equation for land surface temperature. International Journal for Remote Sensing 15(1): 105-122. Gardner, B.R., and B.L. Blad. 1986. Evaluation of Spectral Reflectance Models To Estimate Corn Leaf Area While Minimizing the Influence of Soil Background Effects. Remote Sensing of Environment 20: 183-193. Holben, B.N., C.J. Tucker, and C-J. Fan. 1980. Spectral Assessment of Soybean Leaf Area andLeaf Biomass. Photogrammetric Engineering and Remote Sensing 46: 651-656. Kidwell, K. 1991. NOAA Polar Orbiter Data User's Guide, NCDC/SDSD. (Updated from original 1984 edition.) Li, Z., J. Cihlar, X. Zheng, L. Moreau, and H. Ly. 1996. The bidirectional effects of AVHRR measurements over northern regions. IEEE Transactions on Geoscience and Remote Sensing (accepted). Pokrant, H. 1991. Land cover map of Canada derived from AVHRR images. Manitoba Remote Sensing Centre, Winnipeg, Manitoba, Canada. Rahman, H. and G. Dedieu. 1994. SMAC: a simplified method for the atmospheric correction of satellite measurements in the solar spectrum. International Journal for Remote Sensing 15: 123-143. Roujean, J.-L., M. Leroy, and P.-Y. Deschamps. 1992. A bidirectional reflectance model of the Earth's surface for the correction of remote sensing data. Journal of Geophysical Research 97(D18): 20,455-20,468. Salisbury, J.W. and D.M. D'Aria. 1992. Emissivity of terrestrial materials in the 8-1 m atmospheric window. Remote Sensing of Environment 42: 83-106. 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.and F. Hall and K.F. Huemmrich. 1996. Boreal Ecosystem-Atmosphere Study: 1994 Operations. NASA BOREAS Report (OPS DOC 94). Sellers, P., F. Hall and K.F. Huemmrich. 1997. Boreal Ecosystem-Atmosphere Study: 1996 Operations. NASA BOREAS Report (OPS DOC 96). Sellers, P., 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. Bulletin of the American Meteorological Society. 76(9):1549-1577. Sellers, P.J., F.G. Hall, R.D. Kelly, A. Black, D. Baldocchi, J. Berry, M. Ryan, K.J. Ranson, P.M. Crill, D.P. Lettenmaier, H. Margolis, J. Cihlar, J. Newcomer, D. Fitzjarrald, P.G. Jarvis, S.T. Gower, D. 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 (JGR), BOREAS Special Issue, 102(D24), Dec. 1997, pp. 28731-28770. Sellers, P.J., S.O.Los, C.J. Tucker, C.O. Justice, D.A. Dazlich, G.A. Collatz, and D.A. Randall, 1994. A global 1o by 1 o NDVI data set for climate studies. Part 2: The generation of global fields of terrestrial biophysical parameters from the NDVI. International Journal of Remote Sensing. Sobrino, J.A., C. Coll, and V. Caselles. 1991. Atmospheric correction for land surface temperature using NOAA-11 AVHRR channels 4 and 5. Remote Sensing of Environment 38: 19-34. Suttles, J.T., R.N. Green, P. Minnis, G.L. Smith, W.F. Staylor, B.A. Wielicki, I.J. Walker, D.F. Young, V.R. Taylor, and L.L. Stowe 1989 Angular radiation models for Earth-atmosphere system, Vol. 1, Shortwave radiation, NASA Ref. Publ., 1184, 84 pp. Teillet, P.M. 1992. An algorithm for the radiometric and atmospheric correction of AVHRR data in the solar reflective channels. Remote Sensing of Environment 41: 185-195. Teillet, P.M. and B.N. Holben. 1994. Towards operational radiometric calibration of NOAA AVHRR imagery in the visible and near-infrared channels. Canadian Journal of Remote Sensing 20: 1-10. Townshend, J. (Ed.). 1995. Global data sets for the land from AVHRR. International Journal of Remote Sensing 15: 3315-3639 (special issue describing several program generating composite AVHRR image data sets). Van de Griend, A.A. and M. Owe. 1993. On the relationship between thermal emissivity and the normalized difference vegetation index for natural surfaces. International Journal of Remote Sensing 14(6): 1119-1131. Welch, T.A. 1984, A Technique for High Performance Data Compression, IEEE Computer, Vol. 17, No. 6, pp. 8 - 19. Wiegand, C.L., S.J. Maas, J.K. Aase, J.L. Hatfield, P.J. Pinter, Jr., R.D. Jackson, E.T.Kanemasu, and R.L. Lapitan 1992 Multisite Analyses of Spectral- Biophysical Data for Wheat. Remote Sensing of Environment 42: 1-21. Wu, A., Z. Li, and J. Cihlar. 1995. Effects of land cover type and greenness on AVHRR bidirectional reflectances: Analysis and removal J. Geophy. Res. (in press). 17.2 Journal Articles and Study Reports Cihlar, J. and P.M. Teillet. 1995. Forward piecewise linear calibration model for quasi-real time processing of AVHRR data. Canadian Journal of Remote Sensing 21: 22-27. Robertson, B., A. Erickson, J. Friedel, B. Guindon, T. Fisher, R. Brown, P. Teillet, M. D'Iorio, J. Cihlar, and A. Sancz. 1992. GEOCOMP, a NOAA AVHRR geocoding and compositing system. Proceedings of the ISPRS Conference, Commission 2, Washington, DC: 223-228. 17.3 Archive/DBMS Usage Documentation The raw data are archived by the CCRS at PASS. Processed Level-4c data are currently archived at NASA/GSFC. 18. Glossary of Terms None given. 19. List of Acronyms ABC3 - Atmosphere, Bidirectional and Contamination Corrections of CCRS AEAC - Albers Equal Area Conic APT - Automatic Picture Transmission ASCII - American Standard Code for Information Interchange AVHRR - Advanced Very High Resolution Radiometer BOREAS - BOReal Ecosystem-Atmosphere Study BORIS - BOREAS Information System BPI - Bytes per inch CCRS - Canada Centre for Remote Sensing CCT - Computer-Compatible Tape CECANT - Cloud Elimination from Composite Using Albedo and NDVI Trend CD-ROM - Compact Disk-Read-Only Memory DAAC - Distributed Active Archive Center DAT - Digital Archive Tape DN - Digital Number EOS - Earth Observing System EOSDIS - EOS Data and Information System EROS - Earth Resources Observation System FASIR - Fourier-Adjustment, Solar Zenith Angle corrected, Interpolated, Reconstructed FPAR - Fraction of PAR absorbed by plant canopies GAC - Global Area Coverage GEOCOMP - Geocoding and Compositing System GSFC - Goddard Space Flight Center HRPT - High Resolution Picture Transmission IFC - Intensive Field Campaign IFOV - Instantaneous Field-of-View ISLSCP - International Satellite Land Surface Climatology Project LAC - Local Area Coverage LAI - Leaf Area Index LCC - Lambert Conformal Conic MRSC - Manitoba Remote Sensing Centre MSR - Modified SR NAD83 - North American Datum of 1983 NASA - National Aeronautics and Space Administration NDVI - Normalized Difference Vegetation Index NEdT - Noise Equivalent Differential Temperature NOAA - National Oceanic and Atmospheric Administration NRL - Naval Research Laboratory NSA - Northern Study Area ORNL - Oak Ridge National Laboratory PAR - Photosynthetically Active Radiation PASS - Prince Albert Satellite Station PRT - Platinum Resistor Thermometer RSS - Remote Sensing Science SMAC - Simplified Method for Atmospheric Correction SR - Simple Ratio SSA - Southern Study Area SST - Sea Surface Temperature TIROS - Television and Infrared Observation Satellite TM - Thematic Mapper TOA - Top-of-the-Atmosphere TRAC - Tracing Radiation and Architecture of Canopies (a LAI and FPAR optical instrument) URL - Uniform Resource Locator 20. Document Information 20.1 Document Revision Date Written: 25-Sep-1997 Last Updated: 04-Aug-1998 20.2 Document Review Date(s) BORIS Review: 06-Oct-97 Science Review: 10-Jan-98 20.3 Document ID 20.4 Citation The AVHRR-LAC Level-4c composite images resulted from a joint effort between BOREAS staff at CCRS and NASA GSFC. The original data were acquired by CCRS and processed as Level-3b products by the MRSC in Winnipeg, Manitoba. The present Level-4c product was created by CCRS staff using a method developed at CCRS. The respective contributions of the above individuals and agencies to completing this data set are greatly appreciated. 20.5 Document Curator 20.6 Document URL Keywords AVHRR-LAC LAI FPAR NOAA REFLECTANCE NDVI RSS07_AVHRR_LAI_FPAR.doc 08/20/98