Irrigation Science

, Volume 26, Issue 5, pp 407–414 | Cite as

Extraneous variables and their influence on reflectance-based measurements of leaf water content

  • Hans-Dieter SeeligEmail author
  • William W. AdamsIII
  • Alexander Hoehn
  • Louis S. Stodieck
  • David M. Klaus
  • William J. Emery
Original Paper


Leaf water indices based on leaf reflectance may depend not only on the variable of interest, leaf water content, but may also be influenced by a variety of extraneous variables, leading to considerable data variability if such extraneous variables are not eliminated or taken into account. Here, we examined the nature of three potential extraneous variables: homogeneity of the leaf target area, the distance between a primary reflecting leaf and background material, and measurement sensitivity at various wavelengths. Although leaf water indices appear to be homogeneously distributed between major leaf veins, they may fluctuate substantially in areas where major veins are present. Leaf water indices may also depend to some extent on the distance between a primary reflecting leaf and any reflecting background material, at least for small distances. Leaf water indices utilizing the 970 or 1200 nm water absorption bands have been shown to be rather insensitive to changes in leaf water content, potentially resulting in low signal-to-noise ratios, an additional source of data variability.


Leaf Water Extraneous Variable Background Material Data Variability Water Deficit Stress 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



H.-D.S. gratefully acknowledges financial assistance from BioServe Space Technologies (NASA NCC8-242) at the University of Colorado.


  1. Allen WA, Gausman HW, Richardson AJ, Thomas JR (1969) Interaction of isotropic light with a compact plant leaf. J Opt Soc Am 59:1376–1379CrossRefGoogle Scholar
  2. Bowman WD (1989) The relationship between leaf water status, gas exchange, and spectral reflectance in cotton leaves. Remote Sens Environ 30:249–255CrossRefGoogle Scholar
  3. Carter GA (1991) Primary and secondary effects of water content on the spectral reflectance of leaves. Am J Bot 78:919–924CrossRefGoogle Scholar
  4. Carter GA (1994) Ratios of leaf reflectance in narrow wavebands as indicators of plant stress. Int J Remote Sens 15:697–703CrossRefGoogle Scholar
  5. Ceccato P, Flasse S, Tarantola S, Jacquemoud S, Grégoire J-M (2001) Detecting vegetation leaf water content using reflectance in the optical domain. Remote Sens Environ 77:22–33CrossRefGoogle Scholar
  6. Ceccato P, Gobron N, Flasse S, Pinty B, Tarantola S (2002a) Designing a spectral index to estimate vegetation water content from remote sensing data: part 1 theoretical approach. Remote Sens Environ 82:188–197CrossRefGoogle Scholar
  7. Ceccato P, Flasse S, Grégoire J-M (2002b) Designing a spectral index to estimate vegetation water content from remote sensing data: part 2. Validation and applications. Remote Sens Environ 82:198–207CrossRefGoogle Scholar
  8. Cibula WG, Zetka EF, Rickman DL (1992) Response of thematic mapper bands to plant water stress. Int J Remote Sens 13:1869–1880CrossRefGoogle Scholar
  9. Cohen WB (1991) Temporal versus spatial variation in leaf reflectance under changing water stress conditions. Int J Remote Sens 12:1865–1876CrossRefGoogle Scholar
  10. Danson FM, Steven MD, Malthus TJ, Clark JA (1992) High-spectral resolution data for determining leaf water content. Int J Remote Sens 13:461–470CrossRefGoogle Scholar
  11. Dawson TP, Curran PJ, North PR, Plummer SE (1999) The propagation of foliar biochemical absorption features in forest canopy reflectance: a theoretical analysis. Remote Sens Environ 67:147–159CrossRefGoogle Scholar
  12. Downing HG, Carter GA, Holladay KW, Cibula WG (1993) The radiative-equivalent water thickness of leaves. Remote Sens Environ 46:103–107CrossRefGoogle Scholar
  13. Gausman HW (1974) Leaf reflectance of near-infrared. Photogramm Eng 40:183–191Google Scholar
  14. Gausman HW, Allen WA, Cardenas R, Richardson AJ (1970) Relation of light reflectance to histological and physical evaluations of cotton leaf maturity. Appl Opt 9:545–552Google Scholar
  15. Grant L (1987) Diffuse and specular characteristics of leaf reflectance. Remote Sens Environ 22:309–322CrossRefGoogle Scholar
  16. Holben BN, Schutt JB, McMurtrey JE III (1983) Leaf water stress detection utilizing thematic mapper bands 3, 4 and 5 in soybean plants. Int J Remote Sens 4:289–297CrossRefGoogle Scholar
  17. Hopkins WG (1999) Introduction to plant physiology, 2nd ed. Wiley, New YorkGoogle Scholar
  18. Jackson RD, Pinter PJ, Reginato RJ, Idso SB (1986) Detection and evaluation of plant stress for crop management decisions. IEEE Trans Geosci Remote Sens GE-24:99–106CrossRefGoogle Scholar
  19. Knipling EB (1970) Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation. Remote Sens Environ 1:155–159CrossRefGoogle Scholar
  20. Kou L, Labrie D, Chylek P (1993) Refractive indices of water and ice in the 0.65 to 2.5 μm spectral range. Appl Opt 32:3531–3540Google Scholar
  21. Peñuelas J, Filella I, Biel C, Serrano L, Save R (1993) The reflectance at the 950–970 nm region as an indicator of plant water status. Int J Remote Sens 14:1887–1905CrossRefGoogle Scholar
  22. Peñuelas J, Piñol J, Ogaya R, Filella I (1997) Estimation of plant water concentration by the reflectance water index WI (R900/R970). Int J Remote Sens 18:2869–2875CrossRefGoogle Scholar
  23. Pierce LL, Running SW, Riggs GA (1990) Remote detection of canopy water stress in conferous forests using the NS001 thematic mapper simulator and the thermal infrared multispectral scenner. Photogramm Eng Remote Sens 56:579–586Google Scholar
  24. Piñol J, Filella I, Ogaya R, Peñuelas J (1998) Ground-based spectroradiometric estimation of live fine fuel moisture of mediterranean plants. Agric For Meteorol 90:173–186CrossRefGoogle Scholar
  25. Riggs GA, Running SW (1991) Detection of canopy water stress in conifers using the airborne imaging spectrometer. Remote Sens Environ 35:51–68CrossRefGoogle Scholar
  26. Ripple WJ (1986) Spectral reflectance relationships to leaf water stress. Photogramm Eng Remote Sens 52:1669–1675Google Scholar
  27. Rollin EM, Milton EJ (1998) Processing of high spectral resolution reflectance data for the retrieval of canopy water content information. Remote Sens Environ 65:86–92CrossRefGoogle Scholar
  28. Serrano L, Ustin SL, Roberts DA, Gamon JA, Peñuelas J (2000) Deriving water content of chaparral vegetation from AVIRIS data. Remote Sens Environ 74:570–581CrossRefGoogle Scholar
  29. Sims DA, Gamon JA (2003) Estimation of vegetation water content and photosynthetic tissue area from spectral reflectance: a comparison of indices based on liquid water and chlorophyll absorption features. Remote Sens Environ 84:526–537CrossRefGoogle Scholar
  30. Sinclair TR (1968) Pathway of solar radiation through leaves. M.S. Thesis. Purdue University, LafayetteGoogle Scholar
  31. Strachan IB, Pattey E, Boisvert J (2002) Impact of nitrogen and environmental conditions on corn as detected by hyperspectral reflectance. Remote Sens Environ 80:213–224CrossRefGoogle Scholar
  32. Thomas JR, Namken LN, Oerther GG, Brown RG (1971) Estimating leaf water content by reflectance measurements. Agron J 63:845–847Google Scholar
  33. Tucker CJ (1980) Remote sensing of leaf water content in the near infrared. Remote Sens Environ 10:23–32CrossRefGoogle Scholar
  34. Ustin SL, Roberts DA, Pinzón J, Jacquemoud S, Gardner M, Scheer G, Castañeda C, Palacios-Orueta A (1998) Estimating canopy water content of chaparral shrubs using optical methods. Remote Sens Environ 65:280–291CrossRefGoogle Scholar
  35. Vogelmann TC, Nishio JN, Smith WK (1996) Leaves and light capture: light propagation and gradients of carbon fixation within leaves. Trends Plant Sci 1:65–70CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  • Hans-Dieter Seelig
    • 1
    Email author
  • William W. AdamsIII
    • 2
  • Alexander Hoehn
    • 1
  • Louis S. Stodieck
    • 1
  • David M. Klaus
    • 1
  • William J. Emery
    • 1
  1. 1.Department of Aerospace Engineering SciencesUniversity of Colorado at BoulderBoulderUSA
  2. 2.Department of Ecology and Evolutionary BiologyUniversity of Colorado at BoulderBoulderUSA

Personalised recommendations