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Earth System Science Office, NASA, Stennis Space Center, Mississippi 39529 USA; and Division of Biology, Kansas State University, Manhattan, Kansas 66506 USA
Received for publication March 7, 2000. Accepted for publication June 16, 2000.
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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Key Words: absorptance • chlorophyll • leaf optics • light • reflectance • stress • transmittance
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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; Coblentz, 1912
; Shull, 1929
). Investigation of such spectral characteristics has intensified greatly since the 1960s, along with the development of instrumentation and interest in the potential of remote sensing for stress detection. Largely as a result of interests in remote sensing, leaf reflectance has been studied more extensively than transmittance or absorptance responses to stress. Pioneering efforts in this field have been reviewed elsewhere (Myers et al., 1983
; Jackson, 1986
).
Throughout this research history, the extent to which differing causes of stress within a species may yield correspondingly different spectral signatures has remained in question. Also in question is the degree to which the spectral response to a particular stressor may vary among species. A mounting body of evidence, described here in part, indicates that leaf reflectance is altered by stress more consistently at visible wavelengths (
400–720 nm) than in the remainder of the incident solar spectrum (730–2500 nm) (Carter, 1993
, 1994
). These changes were spectrally similar among many common stressors and vascular plant species. Increased reflectance in the far-red 690–720 nm spectrum is a particularly generic response, providing an earlier or more consistent indication of stress than reflectance in other regions of the incident solar spectrum (Carter, 1993
, 1994
; Carter, Cibula, and Miller, 1996
).
It has long been suggested that alterations of reflectance in the visible spectrum by stress conditions result from the sensitivity of leaf chlorophyll concentrations to metabolic disturbance (Knipling, 1970
). Indeed, several studies have shown that indices based on reflectance in the far-red can precisely estimate leaf chlorophyll concentration (Chappelle, Kim, and McMurtrey, 1992
; Gitelson and Merzlyak, 1994
, 1996
, 1997
; McMurtrey et al., 1994;
Carter, Rebbeck, and Percy, 1995
; Gitelson, Merzlyak, and Lichtenthaler, 1996
; Lichtenthaler, Gitelson, and Lang, 1996
; Schepers et al., 1996
; Datt, 1998
, 1999
). Thus, leaf optical properties in a relatively narrow spectral band near 700 nm are crucial for plant stress detection and the estimation of leaf chlorophyll concentration.
The consistency with which leaf optical properties near 700 nm change in response to stress among causes of stress and species indicates a general mechanism by which such changes occur. The goal of this paper is to elucidate this mechanism by simulating general patterns in optical responses to stress in the 400–850 nm wavelength range. Leaves having unusual anatomical characteristics such as heavy pubescence or succulence or colors other than green in the healthy, mature state were not included. The objectives were to (1) use previously unpublished results and data from the literature to demonstrate that stress-induced changes in reflectance, transmittance, and absorptance tend to be greatest at wavelengths near 700 nm; (2) simulate this stress response for reflectance using chlorophyll in vitro; and (3) simulate reflectance, transmittance, and absorptance responses to stress using the natural range of chlorophyll concentrations found in senescent leaves of several species. This combination of results from a variety of approaches should provide a clearer understanding of the basis for changes in leaf optical properties that occur commonly with stress at visible and near-infrared wavelengths.
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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) and insufficient N fertilization in seedlings of radiata pine (P. radiata D. Don) (Thorn, 1993
) were used as examples of typical reflectance responses to stress. In the southern pine beetle (SPB) study, 500 current-year needles were sampled from the upper, sun-exposed canopy of each of several trees that were felled during an SPB outbreak in the woodlands of Stennis Space Center, Mississippi during June 1995. The trees represented several damage classes that ranged from uninfested to severely damaged. Here, we report data only for undamaged trees and trees that were recently infested but which still maintained green needles. Reflectance was measured for each of six composite needle samples representing three undamaged and three recently infested trees. A sample was arranged in a bundle and placed on a black platform under a high-intensity tungsten lamp as described previously (Carter et al., 1992
). Reflectance of each bundle was measured at 1 nm wavelength intervals using a spectroradiometer attached by a fiber optic cable to a telescope/microscope body (LI1800UW with LI1800-06 body, LI-COR, Lincoln, Nebraska, USA). Radiance reflected from the needle bundle was divided by radiance reflected from a white reference (Spectralon SRT-99-05, Labsphere, North Sutton, New Hampshire, USA) to compute reflectance in units of percentage. True spectral bandwidth produced by the 0.5-mm slitwidth of the monochromator was 4 nm. Data were recorded at 1-nm intervals throughout the 400–850 nm range.
In the N fertilization study of radiata pine, seedlings grown in 4-L pots in the greenhouse were exposed to a range of N fertilization treatments by applying ammonium nitrate to the soil as described earlier (Thorn, 1993
). In this paper, seedlings that received N at 0.5 mmol/L (controls) were compared only with seedlings that received no supplemental N. These treatments yielded mean total N concentrations of 10.34 and 5.54 mg/g needle dry mass, respectively. Reflectance was measured for an individual needle selected from each of three seedlings per treatment using procedures described previously in detail (Thorn, 1993
; Carter, Rebbeck, and Percy, 1995
). Briefly, each needle was placed atop a flat-black surface and irradiated with a tungsten lamp. A 7x microscope objective (LI1800–06E 7x objective) was attached to the same microscope body as described above and microscope aperture set to limit field-of-view within a single needle width. Once focused, light reflected from the needle travelled through the fiber optic to the spectroradiometer where spectral radiance was measured. Radiance reflected from the needle was multiplied by 100 and divided by reference radiance to obtain percentage reflectance.
Manipulation of chlorophyll concentration to simulate stress responses
Glass microfibre circular filters (Whatman GF/F, 25 mm diameter) and mixtures of chlorophylls a and b (Sigma Chemical Co., St. Louis, Missouri, USA) in 100% methanol were used to construct in vitro leaf models that simulated typical leaf reflectance responses to stress. Chlorophyll mixtures were added to a series of the white filter pads to simulate leaf total chlorophyll (a + b) concentrations of 380, 342, 304, 266, 228, and 190 µmol/m2. Reductions in the pigment content of model leaves were accomplished via dilution of stock solutions prior to applying a constant volume (0.22 mL) to all filters. The chlorophyll a/b ratio remained constant at 3.75 as total concentration was altered. The maximum pigment concentration of the filters approximated concentrations that are found commonly in well-nourished leaves (Porra, Thompson, and Kriedemann, 1989
).
Reflected radiance of the leaf models (N = 3 for each chlorophyll concentration) was measured immediately after pigment application using a spectroradiometer (Model 2000, Ocean Optics, Dunedin, Florida, USA). Reflected radiance was also measured for N = 5 reference filter pads to which only 0.22 ml of 100% methanol was added. A 1300-ms integration time optimized resolution among samples without saturating the detector. Data were output at intervals of 0.3 nm throughout the 400–850 nm range. Reflectances (in percentages) were computed by multiplying sample spectra by 100 and dividing by the mean reference spectrum. Mean reflectance spectra for each chlorophyll concentration (N = 3) were normalized to correct for scattering effects by dividing reflectance at all wavelengths by reflectance at 730 nm and again multiplying by 100. To evaluate the wavelengths at which the reflectance change was largest with a change in chlorophyll concentration, reflectance differences were computed by subtracting pad reflectance at 380 µmol/m2 chlorophyll from reflectance at each lesser chlorophyll concentration.
Use of senescent leaves to alter chlorophyll concentration
Leaf optical responses to a broad range in leaf chlorophyll concentration were examined also for leaves that were at various stages of senescence in five species. Leaves of sweetgum (Liquidambar styraciflua L.), red maple (Acer rubrum L.), wild grape (Vitis rotundifolia Michx.), switchcane (Arundinaria gigantea (Walter) Muhl.), and longleaf pine (Pinus palustris Miller) that ranged in color from green to yellow were collected from the woodlands of Stennis Space Center during December 1998 through February 1999. For N = 42 leaves per broadleaved species, leaf reflectance and transmittance were measured throughout the 400–850 nm spectrum using a spectroradiometer (model 1500, Geophysical Environmental Research Corp., Millbrook, New York, USA) attached via fiber optic to an integrating sphere (model LI1800-12S, LI-COR, Lincoln, Nebraska, USA) and methods described earlier (Daughtry, Biehl, and Ranson, 1989
). A leaf was clamped into position over the sample port on the sphere wall and a 1.65-cm2 leaf area was irradiated by the beam from a tungsten halogen lamp. Light reflected from the leaf was transmitted from the sphere interior through the fiber optic to the spectroradiometer for measurement of reflected spectral radiance. The spectroradiometer recorded data at wavelength intervals of 1.6 nm. Similar measurements were made for stray light caused by imperfect collimation of the lamp beam and light reflected from a white reference while the adaxial leaf surface faced the sphere interior (Spectralon SRT-05-99, Labsphere, North Sutton, New Hampshire, USA). Spectral reflectance was computed by subtracting stray light radiance from the radiances reflected by the leaf and reference, then dividing leaf reflected radiance by reference reflected radiance. This quantity was multiplied by 100 to yield units of percentages. Leaf transmittance was measured by illuminating the adaxial leaf surface such that light passed through the leaf into the integrating sphere. Radiance reflected from the white reference was measured while the abaxial surface faced the sphere interior. Transmitted radiance was multiplied by 100 and divided by reference radiance to yield percentage transmittance. For longleaf pine, reflectance and transmittance were measured for 42 samples. Each sample was composed of 5–6 needles spaced 1 mm apart and arranged in parallel across the sample port of the integrating sphere. Reflected and transmitted radiances were recorded as above. An additional transmittance scan was taken without needles in the sample holder to enable the correction of radiance values for light that passed between needles (Daughtry, Biehl, and Ranson, 1989
). In contrast to the earlier method, a high-resolution digital camera and image processing software (ENVI v. 3.1, Research Systems, Boulder, Colorado, USA) were used to determine the percentage of irradiance that was not intercepted by the needles. In all species, percentage leaf absorptance was computed as 100 - (reflectance + transmittance).
Chlorophyll extraction
After leaf optical properties were measured, chlorophyll concentrations of the same leaves were determined. Six circular disks, each 6.25 mm in diameter, were punched from the leaf portion for which optical properties were measured. The disks were placed immediately into 8 mL of 100% methanol, and pigments were allowed to extract in the dark at 30°C for 24 h. Absorbances of the clear extract at 652.0, 665.2, and 750 nm were recorded and concentrations of chlorophylls a, b, and a + b were computed after Porra, Thompson, and Kriedemann (1989)
. Chlorophyll concentration of the extract and the total disk surface area of 1.84 cm2 were used to compute leaf chlorophyll concentrations per unit projected area. Total projected leaf areas for computing chlorophyll concentration in pine needles were determined by the digital camera and image analysis.
Statistical analysis
Significant effects (P = 0.05) of SPB damage and N fertilization on reflectance at each 1 nm wavelength interval were determined by analysis of variance (ANOVA) (SAS 6.0, SAS Institute, Cary, North Carolina, USA). For the senescent leaves of five species, coefficients of determination (r2) were used to evaluate simple linear relationships of reflectance, transmittance, or absorptance with leaf total chlorophyll concentration at 1.6-nm wavelength intervals throughout the 400–850 nm spectrum. The reported r2 values were adjusted downward slightly to account for the number of model parameters and sample size (SAS 6.0; Table Curve 2D v. 4.0, SPSS, Chicago, Illinois, USA).
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RESULTS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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20 nm toward the blue spectrum compared with leaf data (Fig. 1) because the in vitro molecular environment differed from that of chloroplasts. With respect to wavelength, plotting r2 for relationships of reflectance, transmittance, or absorptance with leaf chlorophyll concentration against wavelength (Fig. 4) could simulate this pattern more accurately in the green–yellow and far-red spectra. In every case except reflectance in red maple and longleaf pine, the strongest linear relationships with total chlorophyll concentration occurred near 700 nm (Fig. 4). The absorptivity of chlorophyll approaches zero at wavelengths near 700 nm in organic solvents (Lichtenthaler, 1987
) and near 720 nm when it remains associated with chloroplast membranes (Rabideau, French, and Holt, 1946
). Thus, reflectance or transmittance may increase and absorptance may decrease significantly in the far-red when leaf chlorophyll concentrations are reduced by relatively small amounts. Indeed, the minor reflectance increase at 698 nm caused by the early stages of an SPB attack on mature loblolly pine (Fig. 1) represents a previsible change in optical properties of the type described earlier (Cibula and Carter, 1992
; Carter, Cibula, and Miller, 1996
).
Because reflectance generally increases at wavelengths near 700 nm with plant stress, the steep slope of the reflectance curve in the far-red to near-infrared transition spectrum tends to shift toward the blue spectrum (e.g., Fig. 1C). This has become widely known as the blue shift of the reflectance curve red edge and is quantified by the red-edge inflection point. The inflection point is located at the wavelength where the first derivative of the reflectance curve is maximum in the far-red spectrum. This shift of the red edge has long been known to occur with plant stress and corresponds strongly with leaf chlorophyll concentration (Gates et al., 1965
; Horler, Dockray, and Barber, 1983
; Rock, Hoshizaki, and Miller, 1988
; Curran, Dungan, and Gholz, 1990
; Buschmann and Nagel, 1993
; Vogelmann, Rock, and Moss, 1993
; Filella and Peñuelas, 1994
; Munden, Curran, and Catt, 1994
; Belanger, Miller, and Boyer, 1995
; Gitelson, Merzlyak, and Lichtenthaler, 1996
; Lichtenthaler, Gitelson, and Lang, 1996
; Pinar and Curran, 1996
).
With more severe loss of chlorophyll, the absorption spectrum of a leaf continues to narrow and reflectance increases over a broader portion of the visible spectrum (Gates, 1980
). The absorptivity of chlorophyll in the green, yellow, and orange spectra (535–640 nm) is greater than at 700 nm but relatively weak compared with the major chlorophyll absorption bands in the blue spectrum and near 680 nm (Rabideau, French, and Holt, 1946
; Moss and Loomis, 1952
; Lichtenthaler, 1987
). The chronic stress induced by N deficiency in radiata pine resulted in a much greater reflectance increase near 700 nm, but also increased reflectance in the blue–green through orange spectra and produced a yellow–green needle color (Thorn, 1993
). Several analyses, some of which have not addressed reflectance in the 700 nm region, concluded that reflectance near 550 nm or its ratio with the near-infrared provides the closest correlation with leaf chlorophyll content (e.g., Thomas and Gausman, 1977
; Tsay, Gjerstad, and Glover, 1982
; Buschmann and Nagel, 1993
). The sensitivity of reflectance near 550 nm to chlorophyll can be similar to that near 700 nm (Buschmann and Nagel, 1993
; Blackmer, Schepers, and Varvel, 1994
; Gitelson and Merzlyak, 1994
, 1997
; Blackmer et al., 1996
; Schepers et al., 1996
). However, reflectance at or near 550 nm appears to be less reliable as a stress indicator than reflectance near 700 nm (Cibula and Carter, 1992
; Carter, 1994
, 1998
; Carter and Miller, 1994
; Carter, Cibula, and Miller, 1996
). Lower standard deviations for mean wavelengths of peak locations, particularly for reflectance and transmittance, indicated a more precise optical response to stress in the far-red vs. green–yellow spectrum (Fig. 4).
As is often the case, reflectance, transmittance, and absorptance differences in the 400–500 nm, 670–680 nm, and near-infrared spectra tend to be low for stressed vs. healthy leaves. It appears that concentrations of carotenoids and other accessory pigments are usually high enough in stressed leaves that absorption in the 400–500 nm range remains similar to that in healthy leaves (e.g., Merzlyak et al., 1999
). In the 670–680 nm range, absorption may saturate due to the strong chlorophyll absorptivity such that relatively large amounts of chlorophyll must be lost from the leaves before a significant optical difference occurs. Beyond 730 nm in the near-infrared, reflectance is not affected by chlorophyll absorptivity and would be expected to change only if leaf anatomy or water content changed in response to stress (e.g., Sinclair, Schreiber, and Hoffer, 1973
).
In conclusion, the highly consistent changes in leaf reflectance, transmittance, and absorptance that occur commonly with plant stress, particularly in the far-red and green–yellow spectra, can be explained by stress-induced decreases in leaf chlorophyll concentration. Stress would generally be expected to result in chlorophyll loss (Hendry, Houghton, and Brown, 1987
). Thus, it is likely that stress due to a variety of causes and in most vascular plant species will induce changes in leaf optical properties that are spectrally similar to those described herein. This understanding should provide a basis for modeling responses of plant radiative properties to stress and for remote detection of stress at larger scales. The generality of leaf optical responses to stress implies that diagnosis of the cause of stress by remote sensing will not be possible in most cases. Continued investigation will be required to evaluate the extent to which exceptions to this generality will enable the identification of specific stressors by remote sensing.
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FOOTNOTES |
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2 Author for reprint requests.
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LITERATURE CITED |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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Blackmer, T. M., J. S. Schepers, and G. E. Varvel. 1994 Light reflectance compared with other nitrogen stress measurements in corn leaves. Agronomy Journal 86: 934–938
———, ———, ———, and E. A. Walter-Shea. 1996 Nitrogen deficiency detection using reflected shortwave radiation from irrigated corn canopies. Agronomy Journal 88: 1–5
Buschmann, C., and E. Nagel. 1993 In vivo spectroscopy and internal optics of leaves as basis for remote sensing of vegetation. International Journal of Remote Sensing 14: 711–722[CrossRef][Web of Science]
Carter, G. A. 1991 Primary and secondary effects of water content on the spectral reflectance of leaves. American Journal of Botany 78: 916–924[CrossRef][Web of Science]
———. 1993 Responses of leaf spectral reflectance to plant stress. American Journal of Botany 80: 239–243[CrossRef][Web of Science]
———. 1994 Ratios of leaf reflectances in narrow wavebands as indicators of plant stress. International Journal of Remote Sensing 15: 697–703[CrossRef][Web of Science]
———. 1998 Reflectance wavebands and indices for remote estimation of photosynthesis and stomatal conductance in pine canopies. Remote Sensing of Environment 63: 61–72[CrossRef][Web of Science]
———, W. G. Cibula, and R. L. Miller. 1996 Narrow-band reflectance imagery compared with thermal imagery for early detection of plant stress. Journal of Plant Physiology 148: 516–523
———, and D. C. McCain. 1993 Relationship of leaf spectral reflectance to chloroplast water content determined using NMR microscopy. Remote Sensing of Environment 46: 305–310[CrossRef][Web of Science]
———, and R. L. Miller. 1994 Early detection of plant stress by digital imaging within narrow stress-sensitive wavebands. Remote Sensing of Environment 50: 295–302
———, R. J. Mitchell, A. H. Chappelka, and C. H. Brewer. 1992 Response of leaf reflectance in loblolly pine to increased atmospheric ozone and precipitation acidity. Journal of Experimental Botany 43: 577–584
———, K. Paliwal, U. Pathre, T. H. Green, R. J. Mitchell, and D. H. Gjerstad. 1989 Effect of competition and leaf age on visible and infrared reflectance in pine foliage. Plant, Cell, and Environment 12: 309–315[CrossRef]
———, J. Rebbeck, and K. E. Percy. 1995 Leaf optical properties in Liriodendron tulipifera and Pinus strobus as influenced by increased atmospheric ozone and carbon dioxide. Canadian Journal of Forest Research 25: 407–412
———, and D. R. Young. 1993 Foliar spectral reflectance and plant stress on a barrier island. International Journal of Plant Sciences 154: 298–305[CrossRef]
Chappelle, E. W., M. S. Kim, and J. E. McMurtrey III. 1992 Ratio analysis of reflectance spectra (RARS): an algorithm for the remote estimation of the concentrations of chlorophyll a, chlorophyll b, and carotenoids in soybean leaves. Remote Sensing of Environment 39: 239–247[CrossRef][Web of Science]
Cibula, W. G., and G. A. Carter. 1992 Identification of a far-red reflectance response to ectomycorrhizae in slash pine. International Journal of Remote Sensing 13: 925–932[CrossRef][Web of Science]
Coblentz, W. W. 1912 The diffuse reflecting power of various substances. Bulletin of the Bureau of Standards 9: 283–325
Curran, P. J., J. L. Dungan, and H. L. Gholz. 1990 Exploring the relationship between reflectance red edge and chlorophyll content in slash pine. Tree Physiology 7: 33–48[Web of Science][Medline]
Datt, B. 1998 Remote sensing of chlorophyll a, chlorophyll b, chlorophyll a + b, and total carotenoid content in eucalyptus leaves. Remote Sensing of Environment 66: 111–121[CrossRef][Web of Science]
———. 1999 A new reflectance index for remote sensing of chlorophyll content in higher plants: tests using Eucalyptus leaves. Journal of Plant Physiology 154: 30–36[Web of Science]
Daughtry, C. S. T., L. L. Biehl, and K. J. Ranson. 1989 A new technique to measure the spectral properties of conifer needles. Remote Sensing of Environment 27: 81–91[CrossRef][Web of Science]
Entcheva, P. K., W. G. Cibula, and G. A. Carter. 1996 Spectral reflectance characteristics and remote sensing detection of southern pine beetle infestation. In Proceedings of the Eco-Informa Conference: 4–7 November, 1996, Lake Buena Vista, Florida. Technical Program Abstract F-12, Environmental Research Institute of Michigan, Ann Arbor, Michigan
Filella, I., and J. Peñuelas. 1994 The red edge position and shape as indicators of plant chlorophyll content, biomass and hydric status. International Journal of Remote Sensing 15: 1459–1470[CrossRef][Web of Science]
Gates, D. M. 1980 Biophysical ecology. Springer, New York, New York, USA
———, H. J. Keegan, J. C. Schleter, and V. R. Weidner. 1965 Spectral properties of plants. Applied Optics 4: 11–20
Gitelson, A. A., and M. N. Merzlyak. 1994 Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves: spectral features and relation to chlorophyll estimation. Journal of Plant Physiology 143: 286–292[Web of Science]
———, and ———. 1996 Signature analysis of leaf reflectance spectra: algorithm development for remote sensing of chlorophyll. Journal of Plant Physiology 148: 494–500[Web of Science]
———, and ———. 1997 Remote estimation of chlorophyll content in higher plant leaves. International Journal of Remote Sensing 18: 2691–2697[CrossRef][Web of Science]
———, ———, and H. K. Lichtenthaler. 1996 Detection of red edge position and chlorophyll content by reflectance measurements near 700 nm. Journal of Plant Physiology 148: 501–508[Web of Science]
Hendry, G. A. F., J. D. Houghton, and S. B. Brown. 1987 Tansley review no. 11: the degradation of chlorophyll—a biological enigma. New Phytologist 107: 255–302[CrossRef][Web of Science]
Horler, D. N. H., M. Dockray, and J. Barber. 1983 The red edge of plant leaf reflectance. International Journal of Remote Sensing 4: 273–288[CrossRef][Web of Science]
Jackson, R. D. 1986 Remote sensing of biotic and abiotic plant stress. Annual Review of Phytopathology 24: 265–287[CrossRef][Web of Science]
Knipling, E. B. 1970 Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation. Remote Sensing of Environment 1: 155–159
Lichtenthaler, H. K. 1987 Chlorophylls and carotenoids: pigments of photosynthetic biomembranes. Methods in Enzymology 148: 350–382[Web of Science]
———, A. Gitelson, and M. Lang. 1996 Non-destructive determination of chlorophyll content of leaves of a green and an aurea mutant of tobacco by reflectance measurements. Journal of Plant Physiology 148: 483–493[Web of Science]
McMurtrey III, J. E., E. W. Chappelle, M. S. Kim, J. J. Meisinger, and L. A. Corp. 1994 Distinguishing nitrogen fertilization levels in field corn (Zea mays L.) with actively induced fluorescence and passive reflectance measurements. Remote Sensing of Environment 47: 36–44[CrossRef][Web of Science]
Merzlyak, M. N., A. A. Gitelson, O. B. Chivkunova, and V. Y. Rakitin. 1999 Non-destructive optical changes during leaf senescence and fruit ripening. Physiologia Plantarum 106: 135–141
Moss, R. A., and W. E. Loomis. 1952 Absorption spectra of leaves. I. The visible spectrum. Plant Physiology 27: 370–391
Munden, R., P. J. Curran, and J. A. Catt. 1994 The relationship between red edge and chlorophyll concentration in the Broadbalk winter wheat experiment at Rothamsted. International Journal of Remote Sensing 15: 705–709[CrossRef][Web of Science]
Myers, V. I., M. E. Bauer, H. W. Gausman, W. G. Hart, J. L. Heilman, R. B. Macdonald, A. B. Park, R. A. Ryerson, T. J. Schmugge, and F. C. Westin. 1983 Remote sensing applications in agriculture. In R. N. Colwell [ed.], Manual of remote sensing, vol. 2, 2nd ed., 2111–2228. American Society of Photogrammetry, Falls Church, Virginia, USA
Pinar, A., and P. J. Curran. 1996 Grass chlorophyll and the reflectance red edge. International Journal of Remote Sensing 17: 351–357[CrossRef][Web of Science]
Porra, R. J., W. A. Thompson, and P. E. Kriedemann. 1989 Determination of accurate extinction coefficients and simultaneous equations for assaying chlorophylls a and b extracted with four different solvents: verification of the concentration of chlorophyll standards by atomic absorption spectroscopy. Biochimica et Biophysica Acta 975: 384–394[CrossRef]
Rabideau, G. S., C. S. French, and A. S. Holt. 1946 The absorption and reflection spectra of leaves, chloroplast suspensions, and chloroplast fragments as measured in an Ulbricht sphere. American Journal of Botany 33: 769–777[CrossRef][Web of Science]
Rock, B. N., T. Hoshizaki, and J. R. Miller. 1988 Comparison of in situ and airborne spectral measurements of the blue shift associated with forest decline. Remote Sensing of Environment 24: 109–127
Schepers, J. S., T. M. Blackmer, W. W. Wilhelm, and M. Resende. 1996 Transmittance and reflectance measurements of corn leaves from plants with different nitrogen and water supply. Journal of Plant Physiology 148: 523–529[Web of Science]
Sinclair, T. R., M. M. Schreiber, and R. M. Hoffer. 1973 Diffuse reflectance hypothesis for the pathway of solar radiation through leaves. Agronomy Journal 65: 276–283
Sorby, H. C. 1873 On comparative vegetable chromatography. Proceedings of the Royal Society of London 21: 442–483[CrossRef]
Shull, C. A. 1929 A spectrophotometric study of reflection of light from leaf surfaces. Botanical Gazette 87: 583–607[CrossRef][Web of Science]
Thomas, J. R., and H. W. Gausman. 1977 Leaf reflectance vs. leaf chlorophyll and carotenoid concentrations for eight crops. Agronomy Journal 69: 799–802
Thorn, A. J. 1993 Assessing foliar nutrient status of Pinus radiata D. Don and Pinus taeda L. using multispectral and digital image analysis in the visible spectrum. M.S. thesis, University of Georgia, Athens, Georgia, USA
Tsay, M. L., D. H. Gjerstad, and G. R. Glover. 1982 Tree leaf reflectance: a promising technique to rapidly determine nitrogen and chlorophyll content. Canadian Journal of Forest Research 12: 788–792
Vogelmann, J. E., B. N. Rock, and D. M. Moss. 1993 Red edge spectral measurements from sugar maple leaves. International Journal of Remote Sensing 14: 1563–1575[CrossRef][Web of Science]
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| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
) at which leaf optical properties differed most in stressed vs. control leaves