3.3.4 LTSK10
Mosaicking Algorithm
A. Algorithm Outline(1) Algorithm Code:LTSK 10(2) Product Code:L2A_LC(3) PI names:

G18 Dr. Alfredo Huete(4) Overview of algorithm(Standard level)
Algorithm objectives
The algorithm has the following objective:
To mosaic and composite the normalized, at-sensor radiances (apparent reflectance) on 16 day
for the 1km;•

1,5,8,13,15,17,19,24,26,27, 28,29 (VIS, NIR, and SWIR bands)•

30, 31, 34, 35, 36 (MTIR bands)

B. Theoretical Description(1) Methodology and Logic Flow
A general flowchart describing the mosaicking algorithm is presented in figure 1. Atsensor radiances are radiometrically calibrated at the GAIT processing facility to produce Level1B data. Cloud detectio n and screening algorithm (ATSK1 and CTSK1) produces cloud flags on
a pixel basis (CLFD_p). Precise geographic registration follows, with the LTSKG algorithm, toproduce the Level-1B+CLFD_p gridded 1km resolution. The mosaicking algorithm connects tothe data stream at this level (Fig. 1).
The mosaicking (also called compositing) algorithm ingests the gridded Level- 1B+cloudmask data and produces the 16-day and monthly surface reflectance composites. The
mosaicking algorithm selects the best value over a composite period, based on cloudiness andatmospheric contamination. The constraint view angle maximum value composite (CV-MVC)
technique (figure 2) is used to generate these composites. The algorithm works mostly withinteger data types, hence requiring less CPU time.

GLI Algorithm Description

3.3.4-1

Figure 1: Flow diagram for Mosaicking (LTSK 10)

GLI Algorithm Descri ption

3.3.4-2

Figure 2: Mosaicking algorithm data flow; Constraint view angle Maximum Value composite(2) Physical and Mathematical Aspects of the Algorithms
Compositing Algorithm
Although simple in theory, compositing surface reflectance data is a crucial step in
producing long-term, stable time series data, to be used in the detection of land surface changes
and vegetation dynamics (Tucker et al., 1985, Holben, 1986). In practice, compositing remotesensing data is complicated owing to the intrinsic behavior of the sensor, surface bi-directionalreflectance factors with sun and sensor view angle interactions, and contamination of the spectral
response by the presence of residual and persistent clouds, as well as atmospheric aerosols,
which are highly variable in space and time (figure 3).

GLI Algorithm Descri ption

3.3.4-3

a)b)
Figure 3a,b: The daily data is very noisy; owing to many factors including clouds,
vegetation change over time, viewing geometry. Compositing tries to capture the best possible
conditions (minimum cloud, minimum aerosol, best viewing geometry) and generates one value
representative of the composite cycle.
Over the composite period, one 'cloud-free' image will be reconstructed out of thetemporal period. With GLI four -day global coverage, the sensor will measure every pixel on theearth at least once every four days. With this frequency of observation and the ubiquitouspresence of clouds, a practical 16-day compositing period was chosen to match the ADEOS-2
orbital period. Compositing quasi-daily data also entails its reduction to more manageable
GLI Algorithm Descri ption

3.3.4-4

volumes. Moreover, processing and disk volume limitations mandated the compositing
algorithm take place before atmosphere corrections. This scheme fits the research findings andrecommendations about the advantages of applying the constraint view maximum value
composite (MVC/CV-MVC) on non-atmospherically corrected data (Cihlar et al., 1994a).
2.1 Constraint View Maximum value composite (CV-MVC)
The currently adopted procedure for generation of composited AVHRR-NDVI products
is the maximum value composite (MVC) technique (Holben, 1986). The MVC selects the
maximum NDVI value on a per pixel basis over a set compositing period and is designed tominimize atmospheric effects, including residual clouds. The compositing procedure generally
includes cloud screening and data quality checks (Goward et al., 1994; Eidenshink and Faundeen,1994).
The MVC works nicely over near-Lambertian surfaces where the primary source of pixel
variations within a composite cycle is associated with atmosphere contamination and path length.
However, the MVC has problems associated with sun-sensor geometries and surface anisotropies
and the maximum NDVI value becomes dependent on both the BRDF properties of the vegetatedcanopies and varying atmospheric conditions. It tends to overestimate NDVI values by selectionof off-nadir pixels in the forward scatter direction resulting in an overestimation of vegetation
biophysical parameters. The MVC bias toward selection of off- nadir pixels also results in lessaccurate atmospheric corrections (longer atmosphere path length). The bi- directional spectral
behavior of numerous, “global” land cover types and terrestrial surface conditions have been
widely documented and shown to be highly anisotropic due to canopy structure, shadowing, andbackground contributions (Kimes et al., 1985; van Leeuwen et al., 1994; Vierling et al., 1997).
Vegetation indices do no t remove surface anisotropy due to BRDF - spectral dependencies with
the NIR reflectance response generally more anisotropic than the red reflectance response(Walter-Shea et al., 1997; Gutman, 1991; Roujean et al., 1992).
The MVC approach becomes even less appropriate with atmospherically-corrected data,
since the anisotropic behavior of surface reflectances and vegetation indices is stronger (Cihlar etal., 1994b). For these reasons, the MVC method has been found to work best for data
uncorrected for atmosphere (Cihlar et al., 1994a), although inconsistencies remain since the
MVC favors cloud free pixels, but does not necessarily pick the pixel closest to nadir or with theleast atmospheric contamination (Gutman, 1991; Goward et al., 1991, 1994; Cihlar et al., 1994b,
1997). Many studies and experiments have shown the maximum NDVI approach to select pixels
with large view and sun angles which are not always cloud-free or atmospherically clear(Goward et al., 1991; Moody and Strahler, 1994; Cihlar et al., 1994b, 1997).
In order to simultaneously remove angular and atmospheric effects from compositedreflectances, both atmosphere and BRDF models are needed. Aerosol retrievals and correction
have yet to be implemented on an operational basis. MODIS and MISR will have aerosol
corrections but either over much coarser grid sizes or over only limited areas. GLI does not
currently plan aerosol correction over land, although there may be some limited areas corrected
as the aerosol product over land becomes available. Furthermore, there is no planned BRDF
GLI Algorithm Descri ption

3.3.4-5

product for the GLI sensor, and this is further complicated by the 4-day repeat coverage, which
greatly limits the number of angular measurements from which one could implement a BRDF
model.
Our goal with the compositing scheme is to implement an approach that is known to be
operational (MVC) with added improvements, which can be shown to be practical with current
prototype data sets. One improvement, which is within these goals, is the 'constrained view
angle' – MVC, in which the maximum NDVI within a limited range of viewing angles is selected.
This is similar to a threshold technique to prevent the selection of extreme off- nadir pixels.
Current and ongoing work with MODIS data indicates that in order to red uce the BRDF effects
and offset the off- nadir MVC behavior, a simple constraint of the view angle will greatly
enhance the results. The constraint view angle (CV-MCV ) technique works very much the same
way as the classical MVC; After computing the NDVI for each pixel and for each observation
the algorithm only retains ‘N’ pixel with highest NDVI values (N is usually 2 to 4 observations)and chooses the value with the lowest view angle. This will guarantee the elimination of clouds(MVC) and reduces the bias to off- nadir viewing (figure 4).

Figure 4: Performance of the CV-MVC and the MVC algorithms. The off-nadir bias is reducedwith CV-MVC, (source TBRS non-published research).
We envisage the possibility of post-launch development of additional atmospheric and angularcorrection improvements, which are considered experimental at this time. Lastly, we strive tokeep the final product as close to the actual measurements as possible so that we may keep trackof error and precision and be able to 'validate' the final product.

GLI Algorithm Descri ption

3.3.4-6

In summary, the main GLI Vegetation Index compositing goals are to:• provide accurate and cloud- free vegetation index (VI) imagery at a preset temporalintervals (16 days),• maximize global and temporal land coverage at the finest spatial and temporal
resolutions possible,• standardize variable sensor view and sun angles,• ensure the quality and consistency of the composited data,• depict and reconstruct phonological variations,• accurately discriminate inter-annual variations in vegetation.
Critical to the quality of the composited vegetation index product will be the co-registrationof the different bands used in computing the vegetation indices (red, NIR, and blue), spectral
stability of the channels, pixel registration (Townshend et al., 1992) and calibration over time
(Price, 1987). Actual day-to-day registration accuracy over a set composite period (16 days) will
be determined post- launch.
2.2 Compositing period
Variable composite periods have been used to obtain cloud free NDVI data on a global scale.
The minimum compositing period is limited by cloud cover frequency and may vary from every
5 days at higher latitudes to as long as 30 days or more in some humid tropical areas. NDVI
composite periods have varied among 7, 9, 10, 11 and 14 days and monthly intervals withvariable spatial resolutions (Townshend, 1994). The composite period depends on the final dataapplication and the availability of cloud free data on a global scale. Shorter compositing period
will pick up more dynamic land cover changes and allows one to combine compositing periods
to monthly or bi- weekly periods. However, the shorter the compositing period, the greater the
likelihood of cloud-affected or missing pixels in the composited image. The 16-day period
seems appropriate since it gives the possibility to avoid clouds and to cover all latitudes withinsmall viewing angles, providing the best spatial resolution, and the most accurate atmosphericcorrection. The monthly compositing cycle is based on demand and heritage of the user
community. Current work with MODIS (TBRS internal research) indicates that capturing
vegetation change and reducing atmospheric effects on a global basis could be achieved with the
16 days composite period (figure 5).
GLI Algorithm Descri ption

3.3.4-7

Figure 5: Composited EVI in relation to the daily EVI, the noise is due to atmosphere conditions,clouds, and viewing geometry and vegetation growth. The 16 days EVI composite captures thevegetation change and reduces this noise.
2.3 GLI data stream
The GLI data stream (calibration of top of the atmosphere (TOA) radiance; cloud mask;
land and water mask; mosaicking/compositing; atmospheric correction and surface reflectance;
vegetation indices) is set up to flow into the product algorithms in a predetermined order (figure1). For a tractable and best solution to the compositing algorithm it was logical to use the nonatmospherically corrected reflectance data in combination with the cloud mask as input and use
as many "good" observations as possible during a composite period. As depicted in figure 1 and
2, the compositing algorithm processes multi-day gridded data on a pixel basis, for each pixel the
MVC/CV-MVC approach is applied and one day (one pixel) is chosen to represent the composite
period. The final product of this algorithm will be used to generate the corrected surface
reflectance and to compute the vegetation indices.
C. Practical Considerations
In this section we shall explore some of the proposed algorithms aspects with respect toimplementatio n, programming and data interfacing.
(1) Programming, Procedural, Running Considerations
1.1 Compositing Algorithm Implementation
Due to computational limitations we adopted an algorithm scheme as diagrammed in figure 1.
We note that atmospheric correction will be posterior to the compositing algorithm, based on the
GLI Algorithm Descri ption

3.3.4-8

disk and CPU load savings. Reflectance data processed at the GAIT facility and cloud coverfrom the atmospheric (ATSK algorithms) and/or the cryosphere (CTSK algorithms) group willserve as the input for this algorithm. Gridded and tiled Global Coverage Data (GLI land tiles:
1600x1600 km ) is processed after precise geographic positioning to generate the composited
surface reflectance data.

GLI Algorithm Descri ption

3.3.4-9

1.2 Numerical and computation considerations
GAIT requires that all GLI production algorithms be modular and written in C, or
FORTRAN (ANSI). All data types (input and output) are in short integer format. For accuracy
reasons the reflectance data will be scaled to 10,000 and vegetation index computation will be
carried in floating point format. As with any other project of this magnitude, a sound system of
input/output specification is devised to optimize algorithm performance and reduce processing
requirements. A data structure is used to record the different characteristics of a pixel as it isprocessed at the different stages (location, date, viewing angles, geometric angles, cloud cover,etc.). This data structure generated at LTSKG algorithm level is built upon to accommodate
attributes required by or generated by subsequent algorithms. Overall, this structure is updated
by the processing algorithm discarding redundant data based on downstream algorithm
requirement. The final product is stored in the HDF files (Hierarchical Data Format), which is
widely adopted by the remote sensing community. Developed to be self-describing and portableformat, the HDF will allow data sharing between the different algorithms without the need todescribe it. Description data (called meta-data) will be stored with every layer of information toprovide accurate and efficient exchange.
As part of the algorithm implementation, each pixel will have a set QA/QC flags thatreflect the data quality and capture processing and ingestion problems. Various other attributes
can be added to the above format if required by the higher order GLI algorithms and products.
(2) Calibration and Validation(3) Quality Control and Diagnostic Information
Once proper attributes are present, the LTSK 1 algorithm will execute and replace thenormalized apparent reflectances (generated by LTSK 9) with the atmospherically correctedreflectances. Both LTSK 1 and LTSK 9 outputs are temporary, but the use of quality assurance
and quality control flags assures the proper interfacing between the science parts of thealgorithms. The algorithm generates diagnostics messages that capture production errors and therequired actions to correct them.
(4) Exception Handling
Exception handling is the technique of accounting for unforeseen situations. Missing
data, missing ancillary data, floating point error during computation, data out of range, should be
GLI Algorithm Descri ption

3.3.4-10

all properly ingested and documented during the run. In a generic approach, any time thealgorithm does not perform as designed, the code will generate an exception handler to beprocessed separately by the algorithm. During the run the algorithm uses a success flag to trackthe execution of the different parts. Anytime an exception handler is generated the pixel value
will not be updated and a pre-determined fill- value (-3000, -2000, etc…) will be appended
instead. All pixels will contain data at the end of the process, and the user will be able to identify
the bad pixel by the presence of fill values along with QA/QC flags.
(5) Constraints, Limitations, Assumptions
As proposed in phase one, we implemented an CV-MVC compositing algorithm that operates on
uncorrected surface reflectance. This compositing algorithm has some constraint andassumptions:•

Does not account for surface reflectance angular dependencies (BRDF)•

Assumes the cloud-screening algorithm performs well. Cloud flags are used to filter dataprior to compositing, hence their accuracies are crucial.

•

Day to day accurate geolocation and registration is crucial to the compositing, us uallyquarter to half a pixel error is acceptable•

Band to band registration is another issue to be analyzed post launch

Because of the number (17 bands) and spectral nature of bands (VIS, NIR, SWIR, and Thermal)
used during the compositing, the resulting product is only optimized for vegetation index
generation. Composited thermal band in this case is not an accurate product, rather it only
represents a by-product of compositing land bands (red, NIR, and blue). Other bands might
suffer from the same problem too, due to their spectral location. Hence, it is crucial to realizethat this product is based on optimizing the land bands, and only bands used in computing thevegetation index. All other bands are only kept in the data set and are by no means properly
composited. Other techniques for compositing thermal bands exist but no used here due to thenature and goals of the algorithm. The same problem is also noted when performing atmospherecorrection, LTSK 1 (atmosphere correction algorithm) is designed to work better with the landchannels, and it only performs Rayleigh and Ozone correction, which are not appropriate forthermal bands. The presence of 18 bands in
GLI Algorithm Descri ption

the composite product (L2A_LC) is the
3.3.4-11

result of a GAIT requirement that aimed at making them available for downstream products.
(6) Suggestions and Recommendations
Our involvement with the MODIS project helped us learn more about the performance of
the CV-MVC compositing algorithm. For instance, analysis of the MODIS compositing
algorithm indicates that the CV-MVC is superior to the classical and simple MVC. Moreover,the GLI adoption of compositing normalized surface reflectance, rather than correctedreflectance, will serve two goals:•

Reduced CPU and disk space requirements, by means of o nly correcting the data
once every composite period (once every 16 days) as opposed to every day (caseof MODIS).

•

MVC and subsequently CV-MVC is shown to perform better when performed on
non-atmospherically corrected data.

Our main suggestions are to separate between the land and thermal bands compositing.
Compositing thermal bands with CV-MVC is not the proper scheme, and will only result in theirmisappropriate interpretation. Actually this separation can farther reduce the CPU and disk
usage.
As expla ined in previous GLI report, a wealth of SeaWiFS data was acquired by ourgroup for the purpose of testing and prototyping the GLI algorithms. We aimed at thoroughly
establishing the soundness of our atmosphere correction and vegetation index compositing
strategies. As stated above, both MVC and CV-MVC along with a generic cloud mask werecarried with success, we were able to generate global VI prototypes under different scenario.
Moreover, the availability of MODIS data (since April 2000) made it possible to further test andprototype our algorithm. The current CV-MVC performed well either with a cloud mask or not.
We caution however, that the presence of a cloud mask is critical to the application of an
MVC/CV-MVC scheme. The algorithm is flexible eno ugh with respect to data structure,
number of bands (which could be reduced) and the size of the spatial units (tiles), it performed
very well when tested with AVHRR, SeaWiFS and currently with MODIS data.
D. References
Ackerman, S., K. Strabala, P. Menzel1, R. Frey, C. Moeller, L. Gumley, B. Baum, C. Schaaf, G. Riggs, R. Welch,
1996. Discriminating Clear-sky from Cloud with Modis Algorithm Theoretical Basis Document V3,

GLI Algorithm Descri ption

3.3.4-12

http://eospso.gsfc.nasa.gov/atbd/modistables. html.
Agbu, P.A., and James, M.E., 1994. The NOAA/NASA Pathfinder AVHRR Land Data Set User’s Manual. Goddard
Distributed Active Archive Center, NASA, Goddard Space Flight Center, Greenbelt.
Asano, S. and Uchiyama, A. (1987), Application of an extended ESFT method to calculation of solar heating rates
by water vapor absorption, J.Quant. Spectrosc. Radiat. Transfer, 38. 147-158.
Asrar, G., Fuchs, M., Kanemasu, E.T. and Hatfield, J.L., 1984, Estimating absorbed photosynthetic radiation andleaf area index from spectral reflectance in wheat, Agron. J., 76:300—306.
Asrar, G., Myneni, R.B., and Choudhury, B.J., 1992, Spatial heterogeneity in vegetation canopies and remotesensing of absorbed photosynthetically active radiation: a modeling study. Remote Sens. Environ. 41:85-101.
Baret, F., and Guyot, G., 1991. Potentials and limits of vegetation indices for LAI and APAR assessment. Remote
Sens. Environ. 35: 161-173.
Bohren, C.F. and Huffman,D.R. (1983), Absorption and Scattering of Light by Small Particles, Wiley-Interscience,
New York.
Chandrasekhar, S. (1960), Radiative Transfer, Dover, New York.Coulson, K.L., (1988) Polarization and Intensity ofLight in the Atmosphere, A. Deepak, Hampton, Virginia.
Choudhury, B.J., 1987, Relationships between vegetation indices, radiation absorption, and net photosynthesis
evaluated by a sensitivity analysis, Remote Sens. Environ. 22:209-233.
Cihlar, J., Manak, D., and D’Iorio, M., 1994b. Evaluation of Compositing Algorithms for AVHRR Data over Land.
IEEE Trans. Geosc. Remote Sens., 32:427-437.
Cihlar, J., Manak, D., and Voisin, N., 1994a. AVHRR Bidirectional Reflectance Effects and Compositing. Remote
Sens. Environ., 48:77-88
Cihlar, J.C., H. Ly, Z. Li, J. Chen, H. Pokrant, and F. Huang, 1997. Multitemporal, Multichannel AVHRR data setsfor Land Biosphere Studies— Artifacts and Corrections. Remote Sens. Environ. 60:35-57.
Deering, D.W. and Eck, T.E., 1987. Atmospheric optical depth effects on angular anisotropy of plant canopy
reflectance. Int. J. Remote Sensing, 8(6), 893-916.
Deering, D.W. and Leone, 1986. A sphere-scanning radiometer for rapid directional measurements of sky andground radiance, Remote Sens. Environ., 19:1-24.
Eidenshink, J.C. and Faundeen, J.L., 1994, "The 1km AVHRR global land data set: first stages in implementation,"
Int. J. Remote Sensing, 15(17), pp. 3443-3462.
Fung, Y., Tucker, C.J. and Prentice, K.C., 1987. Application of Advanced Very High Resolution Radiometer
Vegetation Index to Study Atmosphere-Biosphere Exchange of CO2 . J. Geoph. Res., 92, 2999-3015.
Giver, L.P., Benner, D.C., Tomasko, M.G., Fink, U. and Kerola, D.X.(1990), Gaussian quadrature exponential sum
modeling of near infrared methane laboratory spectra obtained at temperatures from 106 to 297 K.,in FirstInternational Conference on Laboratory Research for Planetary Atmospheres, pp. 147-156. NASA CP 3077.
Goody, R.M. and Yung, Y.L. (1989), Atmospheric Radiation: Theoretical Basis, Second Edition, Oxford University
Press, New York.
Gordon, H.R., Brown, J.W., and Evans, R. H. (1988), Exact Rayleigh scattering calculations for use with the
Nimbus-7 Coastal Zone Color Scanner, Applied Optics, 27, 862-871.
Goward, D.G., Turner, S., Dye, D.G., and Liang, J., 1994. University of Maryland improved Global Vegetation
Index. Int. J. Remote Sensing, 15(17), 3365-3395.
Goward, S.N. Dye, D.G., Turner, S., and Yang, J., 1993. Objective assessment of the NOAA global vegetation index
data product. Int. J. Remote Sensing, 14, 3365-3394.
Goward, S.N., and Huemmrich, K.F., 1992, Vegetation canopy PAR absorptance and the normalized difference
vegetation index: an assessment using the SAIL model, Remote Sens. Environ. 39:119-140.
Goward, S.N., B. Markham, D.G. Dye, W. Dulaney, J. Yang, 1991, “Normalized difference vegetation index

GLI Algorithm Descri ption

3.3.4-13

measurements from the Advanced Very High Resolution Radiometer”, Remote Sens. Environ., 35:257-277.
Gutman, G.G., 1991, Vegetation indices from AVHRR: an update and future prospect, Remote Sens. Environ.,
35:121-136
Hansen, J.E. (1971), Multiple scattering of polarized light in planetary atmospheres. Part II. Sunlight reflected byterrestrial water clouds, J. Atmos. Sci., 28, 1400-1426.
Hansen, J.E. and Travis, L.D. (1974), Light scattering in planetary atmospheres, Space Sci. Rev., 16, 527 -610.
Herman, B.M. and Browning, S.R. (1965), A numerical solution to the equation of radiative transfer, J. Atmos. Sci.,
22, 559-566.
Holben, B.N. 1986. Characterization of maximum value composites from temporal AVHRR data. Int. J. Remote
Sensing, 7:1417-1434.
Huete, A.R. ,1988, A soil adjusted vegetation index (SAVI), Remote Sens. Environ. 25 :295-309.
Huete, A.R., Justice, C.O., van Leeuwen, W.J.D., 1996. “MODIS vegetation Index, Algorithm Theoretical Basis
Document,” http://eospso.gsfc.nasa.gov/atbd/modistables. html.
Huete, A.R., Liu, H.Q., Batchily, K., and van Leeuwen, W., 1997, A comparison of vegetation indices over a global
set of TM images for EOS-MODIS, Remote Sens. Environ., 59:440-451.
Iqbal, M. (1983), Introduction to Solar Radiation, Academic Press, New York.
James, M.D., and Kalluri, S.N.V. (1994), The Pathfinder AVHRR land data set: An improved coarse resolution data
set for terrestrial monitoring, Int. J. Remote Sensing, 15(17):3347-3363.
Justice, C.O., Townshend, J.R.G., Holben, B.N. and Tucker, C.J.,1985, “Analysis of the phenology of global
vegetation using meteorological satellite data”, Int. J. Remote Sensing, 6:1271-1318.
Kaufman, Y.J. and Tanré, D., 1992, Atmospherically resistant vegetation index (ARVI) for EOS-MODIS, IEEE
Trans. Geosci. Remote Sensing, 30:261-270.
Kerola, D. X. (1994), Near-Infrared Spectroscopic Studies of the Troposphere of Saturn, Ph.D. Dissertation The
University of Arizona.
Kerola, D. X. (1996), Polarization and intensity of sunlight in the Earth's atmosphere: revisiting the Gauss-Seidel
modeling approach, Bull. Amer. Astron. Soc., 28, 1158.
Kerola, D. X., Larson, H. P ., and Tomasko, M. G. (1997), Analysis of the near-IR spectrum of Saturn: acomprehensive radiative transfer model of its middle and upper troposphere, Icarus, 127, 190-212.
Kimes, D.S., Holben, B.N., Tucker, C.J., and Newcomb, W.W., 1984. Optimal directional view angles for remotesensing missions. Int. J. Remote Sensing 5(6), 887-908.
Kimes, D.S., Newcomb, N.W., Tucker, C.J., Zonneveld, I.S., Van Wijngaar-den, W. De Leeuw, J. and Epema,
G.F.(1985), Directional reflectance factor distributions for cover types of northern Africa, Remote Sens.
Environ., 17, 1-19.
Kimes, D.S., Newcomb, W.W., Tucker, C.J., Zonneveld, I.S., Van Wijngaarden, W., De Leeuw, J., and Epema, G.F.,
1985. Directional Reflectance Factor Distribution for Cover Types of Northern Africa. Remote Sens. Environ.
18:1-19.
th

Kuchler, A.W., 1995. Natural vegetation map, In: Rand McNally Goode’s World Atlas; 19 edition. Eds.
Espenshade E.B. Hudson, J.C., Morrison, J.L.. p 18-19.
Lacis, A. (1990), private communication. Leckner, B. (1978), The spectral distribution of solar radiation at theearth's surface - elements of a model, Solar Energy, 20(2), 143-150.
Leeuwen van, W.J.D. A.R. Huete, S. Jia, C.L Walthall, 1996. Comparison of Vegetation Index Compositing
Scenarios: BRDF Vers us Maximum VI Approaches. IEEE- IGARSS'96, Lincoln Nebraska, Vol.3, 1423-1425.
Leeuwen van, W.J.D., A.R. Huete, J. Duncan, J. Franklin, 1994. Radiative transfer in shrub savanna sites in Niger -preliminary results from HAPEX -II-Sahel: 3. Optical dynamics and vegetation index sensitivity to biomass andplant cover. Agricultural and Forest Meteorology
69, 267-288.

GLI Algorithm Descri ption

3.3.4-14

Leeuwen van, W.J.D., A.R. Huete., K. Didan and T. Laing, 1997a. Modeling bi-directional reflectance factors forthdifferent land cover types and surface components to standardize vegetation indices. 7 Int. Symp. Phys.
Measurements and Signatures in Remote Sensing, Courcheval. (pp 1-8; in press)
Leeuwen van, W.J.D., Trevor W. Laing, and Alfredo R. Huete, 1997b. Quality Assurance of Global Vegetation
Index Compositing Algorithms Using AVHRR Data. IEEE- IGARSS'97, Singapore (pp 1-3; in press)
Liou, K.N. (1980), An Introduction to Atmospheric Radiation, Academic Press, San Diego.
Liu, H.Q., and Huete, A.R., 1995, “A feedback based modification of the NDVI to minimize canopy backgroundand atmospheric noise”, IEEE Trans. Geosci. Remote Sensing, 33:457-465.
Marchuk, G.I. (1980), The Monte Carlo Methods in Atmospheric Optics, Springer-Verrlag, New York.
Mercer, R.D., Dunkelman, L., Shaw, G. E., Larson, S. M. and Kerola, D. X. (1997), Solar spectral radiancemeasurements: connectivity across time and place, Proceedings of the NEWRAD'97 conference, held in Tucson,
Arizona, October 27-29, 1997.
Meyer, D., Verstraete, M. and Pinty, B., 1995. The effect of surface anisotropy and viewing geometry on theestimation of NDVI from AVHRR. Remote Sensing Reviews, 12:3-27.
Miura T., A. Huete, van Leeuwen. W.J.D., K. Didan, 1997. Vegetation Detection Through Smoke Filled AVIRIS
images: An assessment using MODIS band passes. J. Geoph. Res.. Submitted
Moody, A. and Strahler, A.H., 1994, Characteristics of composited AVHRR data and problems in their classification.
Int. J. Remote Sensing, 15(17), 3473-3491.
Myneni, R.B. and Asrar, G., 1993, Atmospheric effects and spectral vegetation indices, Remote Sens. Environ.
Myneni, R.B., Hall, F.G., Sellers, P.J., and Marshak, A.L., 1995, “The interpretation of spectral vegetation indices”,
IEEE Trans. Geosci. Remote Sens.
Nemani, R., Pierce, L., Running, S., and Band, L., 1993. Forest ecosystem processes at the watershed scale:sensitivity to remotely-sensed Leaf Area Index estimates. Int. J. Remote Sens. 14(13): 2519-2534.
Price, J.C., 1987. Calibration of Satellite Radiometers and the Comparison of Vegetation Indices. Rem. Sensing
Environ., 21:419-422.
Prince, S.D., 1991, A model of regional primary production for use with coarse resolution satellite data.
International Journal of Remote Sensing, 12(6):1313-1330.
Privette, J.L. Myneni, R.B., Emery, W.J. and Hall, F.G., 1996a. Optimal sampling conditions for estimating
grassland parameters via reflectance model inversions. IEEE Trans. Geosci. Remote Sens. Vol. 34(1):272-284.
Privette, J.L., Deering, D.W., and Eck, T.E., 1996b. Estimating albedo and nadir reflectance through inversion of
simple BRDF models with AVHRR/MODIS-like data. J. Geoph. Res. BOREAS special issue, submitted.
Qi, J. and Kerr, Y., 1997. On Current Compositing Algorithms. Remote Sensing Reviews, 15:235-256.
Qi, J., Huete, A.R., Hood, J., and Kerr, Y., 1994c. Compositing Multi-temporal remote sensing data sets. PECORA
11, Symposium on land information systems, Sioux Falls August 1993. p. 206-213.
Rahman, H., Pinty, B. and Verstraete, M.M., 1993. Coupled surface-atmosphere reflectance (CSAR) model 2. Semiempirical surface model usable with NOAA AVHRR. J. Geophys. Res. 89(D11):20791-20801.
Raich, J.W., and Schlesinger, W.H., 1992, The global carbon dioxide flux in soil respiration and its relationship tovegetation and climate, Tellus 44B:81-99.
Roujean, J.L., Leroy, M., Podaire, A., and Deschamps, P.Y., 1992. Evidence of surface reflectance bidirectional
effects from a NOAA/AVHRR multi-temporal data set. Int. J. Remote Sensing 13(4), 685 -698.
Roy, D.P., 1997. Investigation of the maximum normalized difference Vegetation Index (NDVI) and the Maximum
surface temperature (Ts) AVHRR compositing procedures for the extraction of NDVI and Ts over forest. Int. J.
Remote Sens. 18(11):2383-2401.
Running, S.W., and Nemani, R.R, 1988, Relating seasonal patterns of the AVHRR vegetation index to simulated

GLI Algorithm Descri ption

3.3.4-15

photosynthesis and transpiration of forest in different climates, Remote Sens. Environ. 24:347-367.
Running, S.W., Justice, C., Salomonson, V., Hall, D., Barker, J., Kaufmann, Y., Strahler, A., Huete, A., Muller, J.P.,
VanderBilt, V.,Wan, Z.M., Teillet, P. and Carneggie, D., 1994. Terrestrial Remote Sensing Science andAlgorithms planned for EOS/MODIS. Int. J. Remote Sensing, Vol. 15, 17:3587-3620.
Salomonson, V.V., Barnes, W.L., Maymon, P.W., Montgomery, H.E. and Ostrow, H., 1989, MODIS: advanced
facility instrument for studies of the earth as a system, IEEE Trans. Geosci. Remote Sens. 27:145-153.
Sellers, P.C. , 1985, Canopy Reflectance, photosynthesis and transpiration, Int. J. Remote Sens. 6:1335-1372
Sellers, P.J., Tucker, C.J., Collatz, G.J., Los S., Justice, C.O., Dazlich, D.A., and Randall, D.A., 1994, “A global 1°* 1° NDVI data set for climate studies. Part 2 - The adjustment of the NDVI and generation of global fields ofterrestrial biophysical parameters”, Int. J. Remote Sensing, 15:3519-3545.
Stowe, L.L., E.P. McClain, R. Carey, P. Pellegrino, G.G. Gutman, P. Davis, C. Long, and S. Hart. 1991. Global
distribution of cloud cover derived from NOAA/AVHRR operational satellite data. Advances in Space
Research, 3:51-54.
Strahler, A, et al., 1996. MODIS BRDF/Albedo Product : ATBD version 4.
http://eospso.gsfc.nasa.gov/atbd/modistables. html.
Tans, P.P., Fung, I.Y., and Takahashi, T. (1990), Observational constraints on the global atmosphere CO2 budget.
Science 247:1431-1438.
Teillet, P.M., Staenz, K., and Willams, D.J., 1997. Effects of Spectral, Spatial , and Radiometric Characteristics on
Remote Sensing Vegetation Indices of Forested Regions. Remote Sens. Environ. 61:139-149.
Thome, K.J., Gellman, D.I., Parada, R.J., Biggar, S.F., Slater, P.N., and Moran, M.S. (1993), In-flight radiometriccalibration of Landsat-5 Thematic Mapper from 1984 to present, in Proceedings of SPIE, vol. 1938, 126-130.
Tomasko, M.G. and Doose, L.R. (1989), private communication.
Townshend, J.R.G., 1994, Global data sets for land applications from the AVHRR: an introduction. Int. J. Remote
Sensing, 15:3319-3332.
Townshend, J.R.G., C. Justice, W. Li, C. Gurney, and J. McManus, 1991, “Global land cover classification byremote sensing: present capabilities and future possibilities”, Remote Sens. Environ., 35:243-256.
Townshend, J.R.G., Justice, C.O., Gurney, C. and McManus, J., 1992, The impact of misregistration on thedetection of changes in land cover, IEEE Trans. Geosci. Remote Sens., 30(5):1054-1060.
Tucker, C.J. and Sellers, P.J., 1986, Satellite remote sensing of primary productivity, International Journal of
Remote Sensing, 7:1395-1416.
Vermote, E., Remer, L.A., Justice, C.O., Kaufman, Y.J., and Tanré, 1995, ATBD Atmosphere correction algorithm:
Spectral reflectances (MOD09), Version 2.
Vermote, E., Tanré, D., Deuzé, J.L., Herman, M., and Mocrette, J.J., 1997, Second Simulation of the Satellite Signal
in the Solar Spectrum: an overview. IEEE Trans. Geosc. Remote Sens., 35(3):675-686.
Vermote, E.F., Tanre, D., Deuze, J.L., Herman, M. and Morcrette, JJ. (1997), Second simulation of the satellite
signal in the solar spectrum, 6S: an overview, IEEE Transactions on Geoscience and Remote Sensing, 35, No. 3,
675-686.
Vierling, L.A., Deering, D.W., Eck, T.F., 1997. Differences in Arctic Tundra Vegetation Type and Phenology as
Seen Using Bidirectional Radiometry in the Early Growing Season. Remote Sens. Environ. 60:71-82.
Viovy, N., Arino, O. and Belward, A.S., 1992. The best index slope extraction (BISE): A method for reducing noise
in NDVI time series. Int. J. Rem. Sensing, Vol:13, 8:1585-1590.
Walter-Shea, E.A., Privette, J., Cornell, D., Mesarch, M.A., Hays, C.J., 1997, Relationship between directional
spectral vegetation indices and leaf area and absorbed radiation in alfalfa. Remote Sens. Environ. (In press)
Walthall, C.L., Norman, J.M., Welles, J.M., Campbell, G., and Blad, B.L., 1985, Simple equation to approximate
the bi-directional reflectance from vegetative canopies and bare soil surfaces," Applied Optics, 24(3), pp. 383-

GLI Algorithm Descri ption

3.3.4-16387.
Wanner W., A.H. Strahler, B. Hu, P. Lewis, J.-P. Muller, X.. Li, C.L. Barker Schaaf, and M.J. Barnsley, 1997.
Global retrieval of bidirectional reflectance and albedo over land from EOS MODIS and MISR data: Theory
and algorithm. J. Geoph. Res., 102, D14, 17143-17161.
Wanner W., X.. Li, A.H. Strahler, 1995. On the Derivation of kernel-driven models of bidirectional reflectance. J.
Geoph. Res., 100, D10, 21077-21089
Wolfe. R., D. Roy. Georegistration of MODIS data (level 2G), to be submitted
Wu, A., Li, Z. and Cihlar, 1995, Effects of land cover type and greenness on advanced very high resolution
radiometer bidirectional reflectances: Analysis and removal. J. Geophys. Res., 100(D5), 9179-9192.

GLI Algorithm Descri ption

3.3.4-17