Use of a green channel in remote sensing of global vegetation from EOS-MODIS

Anatoly A. Gitelson; Yoram J. Kaufman; Mark N. Merzlyak

doi:10.1016/s0034-4257(96)00072-7

journal article Dec 01, 1996

Use of a green channel in remote sensing of global vegetation from EOS-MODIS

Anatoly A. Gitelson Yoram J. Kaufman Mark N. Merzlyak

Remote Sensing of Environment Vol. 58 No. 3 pp. 289-298 · Elsevier BV

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References

33

[1]

Andrieu "Indirect methods of estimating crop structure from optical measurements" (1993)

[2]

Baret "Potential and limits of vegetation indexes for LAI and APAR assessment" Remote Sens. Environ. (1991) 10.1016/0034-4257(91)90009-u

[3]

Baret "Modeled analysis of the biophysical nature of spectral shift and comparison with information content of broad bands" Remote Sens. Environ. (1992) 10.1016/0034-4257(92)90073-s

[4]

Buschmann "In vivo spectroscopy and internal optics of leaves as basis for remote sensing of vegetation" Int. J. Remote Sens. (1993) 10.1080/01431169308904370

[5]

Carter "Responses of leaf spectral reflectance to plant stress" Am. J. Bot. (1993) 10.2307/2445346

[6]

Carter "Ratios of leaf reflectances in narrow wavebands as indicators of plant stress" Int. J. Remote Sens. (1994) 10.1080/01431169408954109

[7]

Curran "The effect of a red leaf pigment on the relationship between red edge and chlorophyll concentration" Remote Sens. Environ. (1991) 10.1016/0034-4257(91)90066-f

[8]

Fraser "The relative importance of scattering and absorption in remote sensing" IEEE Trans. Geosci. Remote Sens. (1985) 10.1109/tgrs.1985.289380

[9]

Fukshansky "Optical properties of plants" (1981)

[10]

Gitelson "Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. Spectral features and relation to chlorophyll estimation" J. Plant Physiol. (1994) 10.1016/s0176-1617(11)81633-0

[11]

Gitelson "Quantitative estimation of chlorophyll-a using reflectance spectra: experiments with autumn chestnut and maple leaves" J. Photochem. Photobiol. (B) (1994) 10.1016/1011-1344(93)06963-4

[12]

Gitelson "Signature analysis of leaf reflectance spectra: algorithm development for remote sensing of chlorophyll" J. Plant Physiol. (1996) 10.1016/s0176-1617(96)80284-7

[13]

Gitelson "Novel algorithms for remote sensing of chlorophyll content in higher plant leaves" (1996)

[14]

Horler "The red edge of plant leaf reflectance" Int. J. Remote Sens. (1983) 10.1080/01431168308948546

[15]

Holben "Characteristics of maximum value composite images for temporal AVHRR data" Int. J. Remote Sens. (1986) 10.1080/01431168608948945

[16]

Huete "A soil-adjusted vegetation index (SAVI)" Remote Sens. Environ. (1988) 10.1016/0034-4257(88)90106-x

[17]

Huete "An error and sensitivity analysis of the atmospheric and soil correcting variants of the NDVI for the MODIS-EOS" IEEE Trans. Geosci. Remote Sens. (1994) 10.1109/36.298018

[18]

Huete "Development of vegetation and soil indices for MODIS-EOS" Remote Sens. Environ. (1994) 10.1016/0034-4257(94)90018-3

[19]

Kaufman "Atmospherically resistance vegetation index (ARVI) for EOS-MODIS" IEEE Trans. Geosci. Remote Sens. (1992) 10.1109/36.134076

[20]

Lichtenthaler "Chlorophyll and carotenoids: pigments of photosynthetic biomembranes" Meth. Enzym. (1987)

[21]

Lichtenthaler "Non-destructive determination of chlorophyll content of leaves of a green and an aurea mutant of tobacco by reflectance measurements" J. Plant Physiol. (1996) 10.1016/s0176-1617(96)80283-5

[22]

Merzlyak "Why and what for the leaves are yellow in autumn? On the interpretation of optical spectra of senescing leaves (Acer platanoides L.)" J. Plant Physiol. (1995) 10.1016/s0176-1617(11)81896-1

[23]

Moss "Analysis of red edge spectral characteristics and total chlorophyll values for red spruce (Picea rubens) branch segments from Mt. Moosilauke, NH, U.S.A." (1991)

[24]

Sellers "Canopy reflectance, photosynthesis and transpiration" Int. J. Remote Sens. (1985) 10.1080/01431168508948283

[25]

Sellers "Canopy reflectance, photosynthesis and transpiration. II. The role of biophysics in the linearity of their interdependence" Remote Sens. Environ. (1987) 10.1016/0034-4257(87)90051-4

[26]

Tanner "Veranderungen der spectralen Eigenschaften der Blatter der Buche (Fagus silvatica L.) von Laubaustrieb bis Laubfall" Allg. Forst. Jagdztg. (1986)

[27]

Tanré "Description of a computer code to simulate the satellite signal in the solar spectrum: 5S code" Int. J. Remote Sens. (1990) 10.1080/01431169008955048

[28]

Thomas "Leaf reflectance vs. leaf chlorophyll and cartoenoid concentration for eight crops" Agron. J. (1977) 10.2134/agronj1977.00021962006900050017x

[29]

Tucker "Red and photographic infrared linear combination for monitoring vegetation" Remote Sens. Environ. (1979) 10.1016/0034-4257(79)90013-0

[30]

Vermote "Second simulation of the satellite signal in the solar spectrum: an overview" IEEE Trans. Geosci. Remote Sens. (1995)

[31]

Vogelman "Plants as light traps" Physiol. Plant. (1986) 10.1111/j.1399-3054.1986.tb03421.x

[32]

Vogelman "Red edge spectral measurements from sugar maple leaves" Int. J. Remote Sens. (1993) 10.1080/01431169308953986

[33]

Yoder "The normalized difference vegetation index of small Douglas-fir canopies with varying chlorophyll concentrations" Remote Sens. Environ. (1994) 10.1016/0034-4257(94)90061-2

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Published: Dec 01, 1996
Vol/Issue: 58(3)
Pages: 289-298
License: View

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Cite This Article

Anatoly A. Gitelson, Yoram J. Kaufman, Mark N. Merzlyak (1996). Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment, 58(3), 289-298. https://doi.org/10.1016/s0034-4257(96)00072-7

Use of a green channel in remote sensing of global vegetation from EOS-MODIS

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