Estimating canopy water content from spectroscopy

References

• 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.
   Google Scholar
• 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.
   Google Scholar
• Allen, W.A., Gausman, H.W., Richardson, A.J. 1970. Mean effective constants of cotton leaves. J. Optical Soc. Am. 60: 542-547.
   Google Scholar
• Allen, W.A., Gausman, H.W., Richardson, A.J., J.W., T. 1969. Interaction of isotropic light with a compact plant leaf. J. Optical Soc. Am. 59: 1376-1379.
   Google Scholar
• 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.
   Google Scholar
• Asner, G.P. 1998. Biophysical and biochemical sources of variability in canopy reflectance. Remote Sens. Environ. 64: 234-253.
   Google Scholar
• Asner, G.P., Martin, R.E., Knapp, D.E., Kennedy-Bowdoin, T. 2010. Effects of -Morellafaya tree invasion on aboveground carbon storage in Hawaii. Biol. Invasions 12: 477-494.
   Google Scholar
• 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.
   Google Scholar
• 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.
   Google Scholar
• Baumgardner, MF, Silva, LF, Biehl, LL, Stoner, ER. 1985. Reflectance properties of soils. Adv. Agron. 38: 1-44.
   Google Scholar
• 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.
   Google Scholar
• Carter, G.A. 1991. Primary and secondary effects of water content on the spectral reflectance of leaves. Am. J. Bot. 78: 916-924.
   Google Scholar
• Carter, G.A. 1993. Responses of leaf spectral reflectance to plant stress. Am. J. Bot. 80: 239-243.
   Google Scholar
• 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.
   Google Scholar
• Chandler, C., Cheney, P., Thomas, P., Trabaud, L., Williams, D. 1983. Fire in forestry: forest fire behavior and effects. John Wiley & Sons, NY.
   Google Scholar
• Chapin, F.S., Matson, P.A., Mooney, H.A. 2002. Principles of terrestrial ecosystem ecology. Springer, NY.
   Google Scholar
• Cheng, Y.B., Riaño, D., Zarco-Tejada, P.J., Ustin, S.L. 2006a. 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.
   Google Scholar
• Cheng, Y.B., Zarco-Tejada, P.J., Riaño, D., Rueda, C.A., Ustin, S.L. 2006b. Estimating vegetation water content with hyperspectral data for different canopy scenarios: relationships between AVIRIS and MODIS indexes. Remote Sens. Environ. 105: 354-366.
   Google Scholar
• 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.
   Google Scholar
• 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.
   Google Scholar
• 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.
   Google Scholar
• Cohen, W.B. 1991a. Response of vegetation indices to changes in three measures of leaf water stress. Photogram. Eng. Remote Sens. 57: 195-202.
   Google Scholar
• Cohen, W.B. 1991b. Temporal versus spatial variation in leaf reflectance under changing water stress conditions. Int. J. Remote Sens. 12: 1865-1876.
   Google Scholar
• 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.
   Google Scholar
• Collins, J.R. 1925. Change in the infrared absorption spectrum of water with temperature. Phys. Rev. 25: 771-779.
   Google Scholar
• Curcio, J.A., Petty, C.C. 1951. The near infrared absorption spectrum of liquid water. J. Optical Soc. Am. 41: 302-304.
   Google Scholar
• 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.
   Google Scholar
• Danson, F.M., Bowyer, P. 2004. Estimating live fuel moisture content from remotely sensed reflectance. Remote Sens. Environ. 92: 309-321.
   Google Scholar
• Datt, B. 1999. Remote sensing of water content in Eucalyptus leaves. Aust. J. Bot. 47: 909-923.
   Google Scholar
• 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.
   Google Scholar
• 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.
   Google Scholar
• Dorsey, N.E. 1940. Properties of ordinary water substance, Reinhold, NY.
   Google Scholar
• 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.
   Google Scholar
• 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.
   Google Scholar
• 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.
   Google Scholar
• 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.
   Google Scholar
• 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.
   Google Scholar
• 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.
   Google Scholar
• 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.
   Google Scholar
• 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.
   Google Scholar
• 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.
   Google Scholar
• 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.
   Google Scholar
• 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.
   Google Scholar
• 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. 152: 69-83.
   Google Scholar
• Gates, D.M., Keegan, H.J., Schleter, J.C., Weidner, V.R. 1965. Spectral properties of plants. Appl. Optics 4: 11-20.
   Google Scholar
• 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.
   Google Scholar
• 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.
   Google Scholar
• 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.
   Google Scholar
• 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.
   Google Scholar
• 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).
   Google Scholar
• 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.
   Google Scholar
• 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.
   Google Scholar
• 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.
   Google Scholar
• Hunt, E.R. 1991. Airborne remote sensing of canopy water thickness scaled from leaf spectrometer data. Int. J. Remote Sens. 12: 643-649.
   Google Scholar
• 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.
   Google Scholar
• 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.
   Google Scholar
• 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.
   Google Scholar
• Jacquemoud, S., Baret, F. 1990. PROSPECT—a model of leaf optical-properties spectra. Remote Sen. Environ. 34: 75-91.
   Google Scholar
• 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.
   Google Scholar
• 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.
   Google Scholar
• 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.
   Google Scholar
• 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.
   Google Scholar
• 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.
   Google Scholar
• 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.
   Google Scholar
• 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.
   Google Scholar
• 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.
   Google Scholar
• Kuusk, A. 1995a. A fast, invertible canopy reflectance model. Remote Sens. Environ. 51: 342-350.
   Google Scholar
• Kuusk, A. 1995b. A Markov chain model of canopy reflectance. Ag. Forest Met., 76: 221-236.
   Google Scholar
• Larcher, W. 1975. Physiological plant ecology. Springer, NY.
   Google Scholar
• 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.
   Google Scholar
• 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.
   Google Scholar
• 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.
   Google Scholar
• Nagler, P.L., Daughtry, C.S.T., Goward, S.N. 2000. Plant litter and soil reflectance. Remote Sens. Environ. 71: 207-215.
   Google Scholar
• North, P.R.J. 1996. Three-dimensional forest light interaction model using a Monte Carlo method. IEEE Trans. Geosci. Remote Sens. 34: 946-956.
   Google Scholar
• 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.
   Google Scholar
• Oki, T., Kanae, S. 2006. Global hydrological cycles and world water resources. Sci. 313: 1068-1072.
   Google Scholar
• Palmer, K.F., Williams, D. 1974. Optical properties of water in near-infrared. J. Opt. Soc. Am. 64: 1107-1110.
   Google Scholar
• Peñuelas, J., Inoue, Y. 1999. Reflectance indices indicative of changes in water and pigment contents of peanut and wheat leaves. Photosyn. 36: 355-360.
   Google Scholar
• 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.
   Google Scholar
• 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.
   Google Scholar
• 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.
   Google Scholar
• 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
   Google Scholar
• Pockman, W.T., Sperry, J.S. 2000. Vulnerability to xylem cavitation and the distribution of Sonoran desert vegetation. Am. J. Bot. 87: 1287-1299.
   Google Scholar
• 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.
   Google Scholar
• 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.
   Google Scholar
• Pyne, S.J., Andrews, P.L., Laven, R.D. 1996. Introduction to Wildland fire. Jonh Wiley & Sons, Inc., New York.
   Google Scholar
• 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.
   Google Scholar
• 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.
   Google Scholar
• 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
   Google Scholar
• 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.
   Google Scholar
• 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.
   Google Scholar
• 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.
   Google Scholar
• 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.
   Google Scholar
• 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.
   Google Scholar
• 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
   Google Scholar
• 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.
   Google Scholar
• 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
   Google Scholar
• 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.
   Google Scholar
• Sarlikioti V, Driever SM, Marcelis LFM. 2010. Photochemical reflectance index as a mean of monitoring early water stress. Ann. Appl. Biol. 157: 81-89.
   Google Scholar
• 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.
   Google Scholar
• 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.
   Google Scholar
• 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.
   Google Scholar
• 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.
   Google Scholar
• 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.
   Google Scholar
• 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.
   Google Scholar
• Stoner ER, Baumgardner MF. 1981. Characteristic variations in reflectance of surface soils. Soil Sci. Soc. Am. J. 45: 1161-1165.
   Google Scholar
• Tam, A.C., Patel, C.K.N. 1979. Optical absorptions of light and heavy water by laser optoacoustic spectroscopy. Appl. Optics 18: 3348-3358.
   Google Scholar
• 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.
   Google Scholar
• Tucker, C.J. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 8: 127-150.
   Google Scholar
• Tucker, C.J., Garratt, M.W. 1977. Leaf optical system modeled as a stochastic-process. Appl. Optics 16: 635-642.
   Google Scholar
• Turner, N.C. 1981. Techniques and experimental approaches for the measurement of plant water status. Plant and Soil 58: 339-366.
   Google Scholar
• Ustin, S.L., Gamon, J.A. 2010. Remote sensing of plant functional types. New Phy. 186: 795-816.
   Google Scholar
• 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.
   Google Scholar
• 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.
   Google Scholar
• Ustin, S.L., Roberts, D.A., Pinzón, J., Jacquemoud, S., Gardner, M., Scheer, G., Castaneda, C.M., Palacios-Orueta, A. 1998a. Estimating canopy water content of chaparral shrubs using optical methods. Remote Sens. Environ. 65: 280-291.
   Google Scholar
• Ustin, S.L., Roberts, D.S., Pinzán, J., Jacquemoud, S., Gardner, M., Scheer, B., Castañeda, C.M., Palacios-Orueta, A. 1998b. Estimating canopy water content of chaparral shrubs using optical methods. Remote Sens. Environ. 65: 280-291.
   Google Scholar
• Verhoef, W. 1984. Light scattering by leaf layers with application to canopy reflectance modeling: the SAIL model. Remote Sens. Environ. 16: 125-141.
   Google Scholar
• Vierling, LA, Martinuzzi, S, Asner, GP, Stoker, J, Johnson, BR. 2011. LiDAR: providing structure. Frontiers Ecol. Environ. 9: 261-262.
   Google Scholar
• 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.
   Google Scholar
• 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.
   Google Scholar
• 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.
   Google Scholar
• 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.
   Google Scholar
• Waring, R.H., Landsberg, J.J. 2011. Generalizing plant-water relations to landscapes. J. Plant Ecol. 4: 101-113.
   Google Scholar
• 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.
   Google Scholar
• Willstättler, R., Stoll, A. 1913. Untersuchungen über die Assimilation der Kohlensäure. Spriner, Berlin.
   Google Scholar
• 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.
   Google Scholar
• Yilmaz, M.T., Hunt, ER, 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.
   Google Scholar
• 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.
   Google Scholar
• Zhang, M., Ustin, S.L., Rejmankova, E., Sanderson, E.W. 1997. Monitoring pacific coast salt marshes using remote sensing. Ecol. Appl. 7: 1039-1053.
   Google Scholar
• 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.
   Google Scholar