Estimating canopy water content from spectroscopy

Israel Journal of Plant Sciences

Volumn 60, Issue 1-2, 2012, Pages 9-23

  Ustin, Susan L ^a   Riaño, David ^a,b   Hunt, E Raymond ^c  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b INSTITUTO DE HISTORIA   (Spain)

^c AGRICULTURAL RESEARCH SERVICE   (United States)

Author keywords

imaging spectroscopy; radiative transfer models; spectral features; spectral indexes; water absorption features

Indexed keywords

ABSORPTION; CANOPY; DATABASE; ERROR ANALYSIS; ESTIMATION METHOD; FREQUENCY ANALYSIS; GENETIC ALGORITHM; SHORTWAVE RADIATION; SPECTRAL REFLECTANCE; TRANSPIRATION; WATER BUDGET; WATER CONTENT; WATER UPTAKE;

EID: 84874274382     PISSN: 07929978     EISSN: None     Source Type: Journal    
DOI: 10.1560/IJPS.60.1-2.9     Document Type: Article

References (130)

1. Allen, C.D., Breshears, D.D. 1998. Drought-induced shift of a forest woodland ecotone: Rapid landscape response to climate variation. Proc. Nat. Acad. Sci. USA 95: 14839-14842.
2. Allen, T.R., Kupfer, J.A. 2000. Application of spherical statistics to change vector analysis of Landsat data: Southern Appalachian spruce-fir forests. Remote Sens. Environ. 74: 482-493.
3. Allen, W.A., Gausman, H.W., Richardson, A.J. 1970. Mean effective constants of cotton leaves. J. Optical Soc. Am. 60: 542-547.
4. Allen, W.A., Gausman, H.W., Richardson, A.J., 1969. Interaction of isotropic light with a compact plant leaf. J. Optical Soc. Am. 59: 1376-1379.
5. Asbjornsen, H., Goldsmith, G.R., Alvarado-Barrientos, M.S., Rebel, K., Van Osch, F.P., Rietkerk, M., Chen, J.Q., Gotsch, S., Tobon, C., Geissert, D.R., Gomez-Tagle, A., Vache, K., Dawson, T.E. 2011. Ecohydrological advances and applications in plant-water relations research: A review. J. Plant Ecol. 4: 3-22.
6. Asner, G.P. 1998. Biophysical and biochemical sources of variability in canopy reflectance. Remote Sens. Environ. 64: 234-253.
7. Asner, G.P., Martin, R.E., Knapp, D.E.,Kennedy-Bowdoin, T. 2010. Effects of Morella faya tree invasion on aboveground carbon storage in Hawaii. Biol. Invasions 12: 477-494.
8. Baret, F., Clevers, J., Steven, M.D. 1995. The robustness of canopy gap fraction estimates from red and near-infrared reflectances - A comparison of approaches. Remote Sens. Environ. 54: 141-151.
9. Baret, F., Hagolle, O., Geiger, B., Bicheron, P., Miras, B., Huc, M., Berthelot, B., Nino, F., Weiss, M., Samain, O., Roujean, J.L., Leroy, M. 2007. LAI, fAPAR and fCover CYCLOPES global products derived from VEGETATION - Part 1: principles of the algorithm. Remote Sens. Environ. 110: 275-286.
10. Baumgardner, MF, Silva, LF, Biehl, LL, Stoner, ER. 1985. Reflectance properties of soils. Adv. Agron. 38: 1-44.
11. Camia, A., Bovio, G., Aguado, I., Stach, N. 1999. Meteorological fire danger indices and remote sensing. In: Chuvieco, E., ed. Remote sensing of large wildfires in the European Mediterranean Basin. Springer-Verlag, Berlin, pp. 39-59.
12. Carter, G.A. 1991. Primary and secondary effects of water content on the spectral reflectance of leaves. Am. J. Bot. 78: 916-924.
13. Carter, G.A. 1993. Responses of leaf spectral reflectance to plant stress. Am. J. Bot. 80: 239-243.
14. Ceccato, P., Gobron, N., Flasse, S., Pinty, B., Tarantola, S. 2002. Designing a spectral index to estimate vegetation water content from remote sensing data: Part 1 - theoretical approach. Remote Sens. Environ. 82: 188-197.
15. Chandler, C., Cheney, P., Thomas, P., Trabaud, L., Williams, D. 1983. Fire in forestry: forest fire behavior and effects. John Wiley & Sons, NY.
16. Chapin, F.S., Matson, P.A., Mooney, H.A. 2002. Principles of terrestrial ecosystem ecology. Springer, NY.
17. Cheng, Y.B., Riaño, D., Zarco-Tejada, P.J., Ustin, S.L. 2006. Canopy water content estimates with AVIRIS imagery and MODIS reflectance products. The International Symposium on Optical Science and Technology. Proc., SPIE Vol. 6298, p. 629809.
18. Cheng, Y.B., Zarco-Tejada, P.J., Riaño, D., Rueda, C.A., Ustin, S.L. 2006. Estimating vegetation water content with hyperspectral data for different canopy scenarios: relationships between AVIRIS and MODIS indexes. Remote Sens. Environ. 105: 354-366.
19. Cheng, Y.B., Ustin, S.L., Riaño, D., Vanderbilt, V.C. 2008. Water content estimation from hyperspectral images and MODIS indexes in southeastern Arizona. Remote Sens. Environ. 112: 363-374.
20. Chuvieco, E., Aguado, I., Cocero, D., Riaño, D. 2003. Design of an empirical index to estimate fuel moisture content from NOAA-AVHRR analysis in forest fire danger studies. Int. J. Remote Sens. 24: 1621-1637.
21. Chuvieco, E., Cocero, D., Riaño, D., Martin, P., Martínez-Vega, J., de la Riva, J., Pérez, F. 2004. Combining NDVI and surface temperature for the estimation of live fuels moisture content in forest fire danger rating. Remote Sens. Environ. 92: 322-331.
22. Cohen, W.B. 1991. Response of vegetation indices to changes in three measures of leaf water stress. Photogram. Eng. Remote Sens. 57: 195-202.
23. Cohen, W.B. 1991. Temporal versus spatial variation in leaf reflectance under changing water stress conditions. Int. J. Remote Sens. 12: 1865-1876.
24. Collins, J.B., Woodcock, C.E. 1996. An assessment of several linear change detection techniques for mapping forest mortality using multitemporal landsat TM data. Remote Sens. Environ. 56: 66-77.
25. Collins, J.R. 1925. Change in the infrared absorption spectrum of water with temperature. Phys. Rev. 25: 771-779.
26. Curcio, J.A., Petty, C.C. 1951. The near infrared absorption spectrum of liquid water. J. Optical Soc. Am. 41: 302-304.
27. Dai, A., Trenberth, K.E., Qian, T.T. 2004. A global dataset of Palmer drought severity index for 1870-2002: relationship with soil moisture and effects of surface warming. J. Hydromet. 5: 1117-1130.
28. Danson, F.M., Bowyer, P. 2004. Estimating live fuel moisture content from remotely sensed reflectance. Remote Sens. Environ. 92: 309-321.
29. Datt, B. 1999. Remote sensing of water content in Eucalyptus leaves. Aust. J. Bot. 47: 909-923.
30. Daughtry, C.S.T., Hunt, E.R. 2008. Mitigating the effects of soil and residue water contents on remotely sensed estimates of crop residue cover. Remote Sens. Environ. 112: 1647-1657.
31. Daughtry, C.S.T., Hunt, E.R., Doraiswamy, P.C., McMurtrey, J.E. 2005. Remote sensing the spatial distribution of crop residues. Agron. J. 97: 864-871.
32. Dorsey, N.E 1940. Properties of ordinary water substance, Reinhold, NY.
33. Downing, H.G., Carter, G.A., Holladay, K.W., Cibula, W.G. 1993. The radiative-equivalent water thickness of leaves. Remote Sens. Environ. 46: 103-107.
34. Fang, H.L., Liang, S.L. 2003. Retrieving leaf area index with a neural network method: Simulation and validation. IEEE Trans. Geosci. Remote Sens. 41: 2052-2062.
35. Féret, J.B., Francois, C., Gitelson, A., Asner, G.P., Barry, K.M., Panigada, C., Richardson, A.D., Jacquemoud, S. 2011. Optimizing spectral indices and chemometric analysis of leaf chemical properties using radiative transfer modeling. Remote Sens. Environ. 115: 2742-2750.
36. Foley, J.A., DeFries, R., Asner, G.P., Barford, C., Bonan, G., Carpenter, S.R., Chapin, F.S., Coe, M.T., Daily, G.C., Gibbs, H.K., Helkowski, J.H., Holloway, T., Howard, E.A., Kucharik, C.J., Monfreda, C., Patz, J.A., Prentice, I.C., Ramankutty, N., Snyder, P.K. 2005. Global consequences of land use. Sci. 309: 570-574.
37. Foley, J.A., Levis, S., Prentice, I.C., Pollard, D., Thompson, S.L. 1998. Coupling dynamic models of climate and vegetation. Global Change Biol. 4: 561-579.
38. Fourty, T., Baret, F. 1997. Vegetation water and dry matter contents estimated from top-of-the atmosphere reflectance data: A simulation study. Remote Sens. Environ. 61: 34-45.
39. Gamon, J.A., Peñuelas, J., Field, C.B. 1992. A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sens. Environ. 41: 35-44.
40. Gao, B.C. 1996. NDWI. A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens. Environ. 58: 257-266.
41. Gao, B.C., Goetz, A.F.H. 1994. Extraction of dry leaf spectral features from reflectance spectra of green vegetation. Remote Sens. Environ. 47: 369-374.
42. Gao, B.C., Goetz, A.F.H. 1995. Retrieval of equivalent water thickness and information related to biochemical components of vegetation canopies from AVIRIS data. Remote Sens. Environ. 52: 155-162.
43. Garbulsky M, Peñuelas J, Gamon J, Inoue Y, Filella I. 2011. The photochemical reflectance index (PRI) and the remote sensing of leaf, canopy and ecosystem radiation use efficiencies: A review and meta-analysis. Remote Sens. Environ. 115: 281-297.
44. Garnier, E., Laurent, G., Bellmann, A., Debain, S., Berthelier, P., Ducout, B., Roumet, C., Navas, M.L. 2001. Consistency of species ranking based on functional leaf traits. New Phy. 69-83.
45. Gates, D.M., Keegan, H.J., Schleter, J.C., Weidner, V.R. 1965. Spectral properties of plants. Appl. Optics 4: 11-20.
46. Gausman, H.W., Allen, W.A., Cardenas, R., Richardson, A.J. 1970. Relation of light reflectance to histological and physical evaluations of cotton leaf maturity. Appl. Optics 9: 545-552.
47. Goetz, A.F.H. 1992. Imaging spectrometry for earth remote sensing. In: Toselli, F., Bodechtel, J., eds. Imaging spectroscopy: fundamentals and prospective applications. Kluwer, Brussels, pp. 1-19.
48. Govaerts, Y.M., Jacquemoud, S., Verstraete, M.M., Ustin, S.L. 1996. Three-dimensional radiation transfer modeling in a dicotyledon leaf. Appl. Optics 35: 6585-6598.
49. Green, R.O., Conel, J.E., Margolis, J.S., Bruegge, C.J., Hoover, G.L. 1991. An inversion algorithm for retrieval of atmospheric and leaf water absorption from AVIRIS radiance with compensation for atmospheric scatting. Proc. 3rd Airborne Sci. Workshop: AVIRIS. NASA JPL, Pasadena, CA.
50. Green, R.O., Conel, J.E., Roberts, D.A. 1993. Estimation of aerosol optical depth & calculation of apparent surface reflectance from radiance measured by the airborne visible-infrared imaging spectrometer (AVIRIS) using MODTRAN2. SPIE Conf. 1937: Imaging Spectrometry of the Terrestrial Environment, the Int. Soc. Opt. Eng. (SPIE).
51. Haboudane D, Miller JR, Tremblay N, Zarco-Tejada PJ, Dextraze L. 2002. Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sens. Environ. 81: 416-426.
52. Hardisky, M.A., Klemas, V., Smart, R.M. 1983. The influence of soil salinity, growth form, and leaf moisture on the spectral radiance of Spartina alterniflora canopies. Photogram. Eng. Remote Sens. 49: 77-83.
53. Huemmrich, K.F. 2001. The GeoSAIL model: A simple addition to the SAIL model to describe discontinuous canopy reflectance. Remote Sens. Environ. 75: 423-431.
54. Hunt, E.R. 1991. Airborne remote sensing of canopy water thickness scaled from leaf spectrometer data. Int. J. Remote Sens. 12: 643-649.
55. Hunt, E.R., Rock, B.N. 1989. Detection of changes in leaf water content using near and middle-infrared reflectances. Remote Sens. Environ. 30: 43-54.
56. Hunt, E.R., Rock, B.N., Nobel, P.S. 1987. Measurement of leaf relative water content by infrared reflectance. Remote Sens. Environ. 22: 429-435.
57. IPCC 2007. The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.
58. Jacquemoud, S., Baret, F. 1990. PROSPECT - a model of leaf optical-properties spectra. Remote Sen. Environ. 34: 75-91.
59. Jacquemoud, S., Baret, F., Andrieu, B., Danson, F.M., Jaggard, K. 1995. Extraction of vegetation biophysical parameters by inversion of the PROSPECT plus SAIL models in sugar-beet canopy reflectance data - application to TM and AVIRIS sensors. Remote Sens. Environ. 52: 163-172.
60. Jacquemoud, S., Ustin, S.L. 2003. Application of radiative transfer models to moisture content estimation and burned land mapping. Fourth International Workshop on Remote Sensing and GIS Applications to Forest Fire Management. Innovative Concepts and Methods in Fire Danger Estimation. European Association of Remote Sensing Laboratoies (EARSeL) Symposium, jointly with GOSC/GOLD, Global Observations of Forest and Land Cover Dynamics, a project of Global Terrestrial Observing system (GTOS) sponsored by International Global Observing Strategy (ITOS), 5-7 June, 2003, Ghent, Belgium, pp. 8-17.
61. Jacquemoud, S., Ustin, S.L., Verdebout, J., Schmuck, G., Andreoli, G., Hosgood, B. 1996. Estimating leaf biochemistry using the PROSPECT leaf optical properties model. Remote Sens. Environ. 56: 194-202.
62. Jacquemoud, S., Verhoef, W., Baret, F., Bacour, C., Zarco-Tejada, P.J., Asner, G.P., Francois, C., Ustin, S.L. 2009. PROSPECT plus SAIL models: A review of use for vegetation characterization. Remote Sens. Environ. 113: S56-S66.
63. Kasischke, E.S., Goetz, S., Hansen, M., Ozdogan, M., Rogan, J., Ustin, S.L., Woodcock, C.E. 2004. Temperate and boreal forests. In: Ustin, S.L. ed. Manual of remote sensing, Vol. 4. Remote sensing for natural resource management and environmental monitoring. ASPRS. John Wiley & Sons, NY, pp. 147-238.
64. Knipling, E.B. 1970. Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation. Remote Sens. Environ. 1: 155-159.
65. Kötz, B., Schaepman, M., Morsdorf, F., Bowyer, P., Itten, K., Allgower, B. 2004. Radiative transfer modeling within a heterogeneous canopy for estimation of forest fire fuel properties. Remote Sens. Environ. 92: 332-344.
66. Kou, L.H., Labrie, D., Chylek, P. 1993. Refractive indexes of water and ice in the 0.65 μm to 2.5 μm spectral range. Appl. Optics 32: 3531-3540.
67. Kuusk, A. 1995. A fast, invertible canopy reflectance model. Remote Sens. Environ. 51: 342-350.
68. Kuusk, A. 1995. A Markov chain model of canopy reflectance. Ag. Forest Met., 76: 221-236.
69. Larcher, W. 1975. Physiological plant ecology. Springer, NY.
70. Li, L., Ustin, S.L., Riaño, D. 2007. Retrieval of fresh leaf fuel moisture content using genetic algorithm - partial least squares modeling (GA-PLS). IEEE Geosci. Remote Sens. Let. 4: 216-220.
71. Luck, W. 1963. Beitrag zur Assoziation des flussigen Wassers. 1. Die Temperaturabhangigkeit der ultrarotbanden des Wassers. Berichte Der Bunsen-Gesellschaft Fur Physikalische Chemie 67: 186-189.
72. Moran, M.S., Pinter, P.J., Clothier, B.E., Allen, S.G. 1989. Effect of water-stress on the canopy architecture and spectral indexes of irrigated alfalfa. Remote Sens. Environ. 29: 251-261.
73. Nagler, P.L., Daughtry, C.S.T., Goward, S.N. 2000. Plant litter and soil reflectance. Remote Sens. Environ. 71: 207-215.
74. North, P.R.J. 1996. Three-dimensional forest light interaction model using a Monte Carlo method. IEEE Trans. Geosci. Remote Sens. 34: 946-956.
75. NRC, 2007. Earth science and applications from space: National imperatives for the next decade and beyond. Committee on Earth Science and Applications from Space: A Community Assessment and Strategy for the Future, National Research Council.
76. Oki, T., Kanae, S. 2006. Global hydrological cycles and world water resources. Sci. 313: 1068-1072.
77. Palmer, K.F., Williams, D. 1974. Optical properties of water in near-infrared. J. Opt. Soc. Am. 64: 1107-1110.
78. Peñuelas, J., Inoue, Y. 1999. Reflectance indices indicative of changes in water and pigment contents of peanut and wheat leaves. Photosyn. 36: 355-360.
79. Peñuelas, J., Filella, I., Biel, C., Serrano, L., Savé, R. 1993. The reflectance at the 950-970 nm region as an indicator of plant water status. Int. J. Remote Sens. 14: 1887-1905.
80. Peñuelas J., Gamon JA, Fredeen AL, Merino J, Field CB. 1994. Reflectance indices associated with physiological changes in nitrogen- and water-limited sunflower leaves. Remote Sens. Environ. 48: 135-146.
81. 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: 2869-2875.
82. Pinty, B., Widlowski, J.L., Taberner, M., Gobron, N., Verstraete, M.M., Disney, M., Gascon, F., Gastellu, J.P., Jiang, L., Kuusk, A., Lewis, P., Li, X., Ni-Meister, W., Nilson, T., North, P., Qin, W., Su, L., Tang, S., Thompson, R., Verhoef, W., Wang, H., Wang, J., Yan, G., Zang, H. 2004. Radiation transfer model intercomparison (RAMI) exercise: results from the second phase. J. Geophys. Res.-Atm. 109
83. Pockman, W.T., Sperry, J.S. 2000. Vulnerability to xylem cavitation and the distribution of Sonoran desert vegetation. Am. J. Bot. 87: 1287-1299.
84. Pontius, J., Hallett, R., Martin, M. 2005. Using AVIRIS to assess hemlock abundance and early decline in the Catskills, New York. Remote Sens. Environ. 97: 163-173.
85. Potter, C.S., Klooster, S., Brooks, V. 1999. Interannual variability in terrestrial net primary production: Exploration of trends and controls on regional to global scales. Ecosys., 2: 36-48.
86. Pyne, S.J., Andrews, P.L., Laven, R.D. 1996. Introduction to Wildland fire. Jonh Wiley & Sons, Inc., New York.
87. Ramoelo, A., Skidmore, A.K., Schlerf, M., Mathieu, R., Heitkonig, I.M.A. 2011. Water-removed spectra increase the retrieval accuracy when estimating savanna grass nitrogen and phosphorus concentrations. ISPRS J. Photogram. Remote Sens. 66: 408-417.
88. Riaño, D., Vaughan, P., Chuvieco, E., Zarco-Tejada, P.J., Ustin, S.L. 2005. Estimation of fuel moisture content by inversion of radiative transfer models to simulate equivalent water thickness and dry matter content. Analysis at leaf and canopy level. IEEE Trans. Geosci. Remote Sens. 43: 819-826.
89. Roberts, D.A., Brown, K., Green, R., Ustin, S.L., Hinckley, T. 1998. Investigating the relationship between liquid water and leaf area in clonal populus. Summaries of the 7th Annual JPL Earth Science Workshop. Jet Propulsion Laboratory, Pasadena, California,
90. Roberts, D.A., Dennison, P.E., Gardner, M.E., Hetzel, Y., Ustin, S.L., Lee, C.T. 2003. Evaluation of the potential of Hyperion for fire danger assessment by comparison to the Airborne Visible/Infrared Imaging Spectrometer. IEEE Trans. Geosci. Remote Sens., 41: 1297-1310.
91. Roberts, D.A., Green, R.O., Adams, J.B. 1997. Temporal and spatial patterns in vegetation and atmospheric properties from AVIRIS. Remote Sens. Environ. 62: 223-240.
92. Roberts, D.A., Ustin, S.L., Ogunjemiyo, S., Greenberg, J., Dobrowski, S.Z., Chen, J., Hinckley, T.M. 2004. Spectral and structural measures of northwest forest vegetation at leaf to landscape scale. Ecosys. 7: 545-562.
93. Rodríguez Pérez, J.R., Riaño, D., Carlisle, E., Ustin, S.L., Smart, D.R. 2007. Remote sensing of grapevine water status in vineyards using hyperspectral reflectance indices. American Journal of Enol. Vit. 58: 302-317.
94. Rosema, A., Verhoef, W., Noorbergen, H., Borgesius, J.J. 1992. A new forest light interaction model in support of forest monitoring. Remote Sens. Environ. 42: 23-41.
95. Rouse, J.W., Haas, R.W., Schell, J.A., Deering, D.H., Harlan, J.C. 1974. Monitoring the vernal advancement and retrogadation (Greenwave effect) of natural vegetation. NASA/GSFCT, Greenbelt, MD, USA
96. Royle, D.D., Lathrop, R.G. 1997. Monitoring hemlock forest health in New Jersey using Landsat TM data and change detection techniques. Forest Sci. 42: 327-335.
97. Rubio, M.A., Riaño, D., Cheng, Y.B., Ustin, S.L. 2006. Estimation of canopy water content from MODIS using artificial neural networks trained with radiative transfer models. 6th EMS/6th ECAC. European Meteorological Society, Ljubljana, Slovenia,
98. Sanderson, E.W., Zhang, M., Ustin, S.L., Rejmankova, E. 1998. Geostatistical scaling of canopy water content in a California salt marsh. Landscape Ecol. 13: 79-92.
99. Sarlikioti V, Driever SM, Marcelis LFM. 2010. Photochemical reflectance index as a mean of monitoring early water stress. Ann. Appl. Biol. 157: 81-89.
100. Schaepman, M.E., Koetz, B., Schaepman-Strub, G., Itten, K.I. 2005. Spectrodirectional remote sensing for the improved estimation of biophysical and chemical variables: Two case studies. Int. J. Appl. Earth Obser. Geoinfor. 6: 271-282.
101. Serrano, L., Ustin, S.L., Roberts, D.A., Gamon, J.A., Peñuelas, J. 2000. Deriving water content of chaparral vegetation from AVIRIS data. Remote Sens. Environ. 74: 570-581.
102. Shibayama, M., Takahashi, W., Morinaga, S., Akiyama, T. 1993. Canopy water deficit detection in paddy rice using a high resolution field spectroradiometer. Remote Sens. Environ. 45: 117-126.
103. Sims, D.A., Gamon, J.A. 2002. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sens. Environ. 81: 337-354.
104. Sims, D.A., Gamon, J.A. 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: 526-537.
105. Stimson, H.C., Breshears, D.D., Ustin, S.L., Kefauver, S.C. 2005. Spectral sensing of foliar water conditions in two co-occurring conifer species: Pinus edulis and Juniperus monosperma. Remote Sens. Environ. 96: 108-118.
106. Stoner ER, Baumgardner MF. 1981. Characteristic variations in reflectance of surface soils. Soil Sci. Soc. Am. J. 45: 1161-1165.
107. Tam, A.C., Patel, C.K.N. 1979. Optical absorptions of light and heavy water by laser optoacoustic spectroscopy. Appl. Optics 18: 3348-3358.
108. Trombetti, M., Riaño, D., Rubio, M.A., Cheng, Y.B., Ustin, S.L. 2008. Multitemporal vegetation canopy water content retrieval using artificial neural networks for the USA. Remote Sens. Environ. 112: 203-215.
109. Tucker, C.J. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 8: 127-150.
110. Tucker, C.J., Garratt, M.W. 1977. Leaf optical system modeled as a stochastic-process. Appl. Optics 16: 635-642.
111. Turner, N.C. 1981. Techniques and experimental approaches for the measurement of plant water status. Plant and Soil 58: 339-366.
112. Ustin, S.L., Gamon, J.A. 2010. Remote sensing of plant functional types. New Phy. 186: 795-816.
113. Ustin, S.L., Jacquemoud, S., Govaerts, Y. 2001. Simulation of photon transport in a three-dimensional leaf: Implications for photosynthesis. Plant Cell Environ. 24: 1095-1103.
114. Ustin, S.L., Roberts, D.A., Gamon, J.A., Asner, G.P., Green, R.O. 2004. Using imaging spectroscopy to study ecosystem processes and properties. Biosci. 54: 523-534.
115. Ustin, S.L., Roberts, D.A., Pinzón, J., Jacquemoud, S., Gardner, M., Scheer, G., Castaneda, C.M., Palacios-Orueta, A. 1998. Estimating canopy water content of chaparral shrubs using optical methods. Remote Sens. Environ. 65: 280-291.
116. Ustin, S.L., Roberts, D.S., Pinzón, J., Jacquemoud, S., Gardner, M., Scheer, B., Castañeda, C.M., Palacios-Orueta, A. 1998. Estimating canopy water content of chaparral shrubs using optical methods. Remote Sens. Environ. 65: 280-291.
117. Verhoef, W. 1984. Light scattering by leaf layers with application to canopy reflectance modeling: The SAIL model. Remote Sens. Environ. 16: 125-141.
118. Vierling, LA, Martinuzzi, S, Asner, GP, Stoker, J, Johnson, BR. 2011. LiDAR: providing structure. Frontiers Ecol. Environ. 9: 261-262.
119. Vitousek, P.M., Tweiten, M.A., Kellner J., Hotchkiss, S.C., Chadwick, O.A., Asner, G.P. 2010. Top-down analysis of forest structure and biogeochemistry across Hawaiian landscapes. Pacific Sci. 64: 359-366.
120. Vogelmann, J.E., Rock, B.N. 1989. Use of Thematic Mapper data for the detection of forest damage caused by the pear thrips. Remote Sens. Environ. 30: 217-225.
121. Vorosmarty, C.J., Green, P., Salisbury, J., Lammers, R.B. 2000. Global water resources: vulnerability from climate change and population growth. Sci. 289: 284-288.
122. Wang, L.L., Hunt, E.R., Qu, J.J., Hao, X.J., Daughtry, C.S.T. 2011. Estimating dry matter content of fresh leaves from the residuals between leaf and water reflectance. Remote Sens. Let. 2: 137-145.
123. Waring, R.H., Landsberg, J.J. 2011. Generalizing plant-water relations to landscapes. J. Plant Ecol. 4: 101-113.
124. Weiss, M., Baret, F., Leroy, M., Hautecoeur, O., Bacour, C., Prevot, L., Bruguier, N. 2002. Validation of neural net techniques to estimate canopy biophysical variables from remote sensing data. Agron. 22: 547-553.
125. Willstättler, R., Stoll, A. 1913. Untersuchungen über die Assimilation der Kohlensäure. Spriner, Berlin.
126. Yebra, M., Chuvieco, E., Riaño, D. 2008. Estimation of live fuel moisture content from MODIS images for fire risk assessment. Ag. Forest Met. 148: 523-536.
127. Yilmaz, M.T., Hunt, E.R., Goins, L.D., Ustin, S.L., Vanderbilt, V.C., Jackson, T.J. 2008. Vegetation water content during SMEX04 from ground data and Landsat 5 Thematic Mapper imagery. Remote Sens. Environ. 112: 350-362.
128. Zarco-Tejada, P.J., Rueda, C.A., Ustin, S.L. 2003. Water content estimation in vegetation with MODIS reflectance data and model inversion methods. Remote Sens. Environ. 85: 109-124.
129. Zhang, M., Ustin, S.L., Rejmankova, E., Sanderson, E.W. 1997. Monitoring pacific coast salt marshes using remote sensing. Ecol. Appl. 7: 1039-1053.
130. Zhang, Q.X., Li, Q.B., Zhang, G.J. 2011. Scattering impact analysis and correction for leaf biochemical parameter estimation using Vis-NIR spectroscopy. Spectroscopy 26: 28-39.