Monitoring canopy chemistry with HyspIRI and other planned imaging spectrometers

A significant literature has developed over the past 20 years that demonstrates that imaging spectroscopy can quantify concentrations of plant pigments, water, nitrogen, and structural carbohydrates (cell wall material and other dry matter in plants, e.g., cellulose and lignin) based on physical principles of spectroscopy rather than empirical relationships that are derived using multiband data. Several governments have announced plans to launch imaging spectrometers over the next few years. With exception of NASA's planned HyspIRI mission (http://hyspiri.jpl.nasa.gov/), all will be sampling missions, limited to a few acquisitions per day and some will only measure the visible to near-infrared region and not the full solar spectrum through the shortwave-infrared. The availability of these data will facilitate far greater understanding of biogeochemical cycles, species distributions, and monitoring the health of the ecosystem.

[1]  L. Johnson,et al.  LCM 2 : A Coupled Leaf / Canopy Radiative Transfer Model , 1999 .

[2]  Stéphane Jacquemoud,et al.  Simulation of photon transport in a three‐dimensional leaf: implications for photosynthesis , 2001 .

[3]  Roberta E. Martin,et al.  Spectral and chemical analysis of tropical forests: Scaling from leaf to canopy levels , 2008 .

[4]  K. Huemmrich The GeoSail model: a simple addition to the SAIL model to describe discontinuous canopy reflectance , 2001 .

[5]  D. Roberts,et al.  Spectral and Structural Measures of Northwest Forest Vegetation at Leaf to Landscape Scales , 2004, Ecosystems.

[6]  S. Ollinger,et al.  A generalizable method for remote sensing of canopy nitrogen across a wide range of forest ecosystems , 2008 .

[7]  Nicholas C. Coops,et al.  Prediction of eucalypt foliage nitrogen content from satellite-derived hyperspectral data , 2003, IEEE Trans. Geosci. Remote. Sens..

[8]  C. Daughtry,et al.  Cellulose absorption index (CAI) to quantify mixed soil-plant litter scenes , 2003 .

[9]  D. Lobell,et al.  A Biogeophysical Approach for Automated SWIR Unmixing of Soils and Vegetation , 2000 .

[10]  S. Ollinger,et al.  Net Primary Production and Canopy Nitrogen in a Temperate Forest Landscape: An Analysis Using Imaging Spectroscopy, Modeling and Field Data , 2005, Ecosystems.

[11]  Peter R. J. North,et al.  The Propagation of Foliar Biochemical Absorption Features in Forest Canopy Reflectance , 1999 .

[12]  W. Verhoef Light scattering by leaf layers with application to canopy reflectance modelling: The SAIL model , 1984 .

[13]  R. Kokaly,et al.  Characterizing canopy biochemistry from imaging spectroscopy and its application to ecosystem studies , 2009 .

[14]  Susan L Ustin,et al.  Remote sensing of plant functional types. , 2010, The New phytologist.

[15]  W. Verhoef,et al.  Simulation of hyperspectral and directional radiance images using coupled biophysical and atmospheric radiative transfer models , 2003 .

[16]  Albert Olioso,et al.  Modeling directional–hemispherical reflectance and transmittance of fresh and dry leaves from 0.4 μm to 5.7 μm with the PROSPECT-VISIR model , 2011 .

[17]  W. Verhoef,et al.  Coupled soil–leaf-canopy and atmosphere radiative transfer modeling to simulate hyperspectral multi-angular surface reflectance and TOA radiance data , 2007 .

[18]  Roberta E. Martin,et al.  Spectroscopy of canopy chemicals in humid tropical forests , 2011 .

[19]  D. Roberts,et al.  Green vegetation, nonphotosynthetic vegetation, and soils in AVIRIS data , 1993 .

[20]  David Riaño,et al.  Estimating canopy water content from spectroscopy , 2012 .

[21]  A. Kuusk The Hot Spot Effect in Plant Canopy Reflectance , 1991 .

[22]  A. Gitelson,et al.  Remote estimation of chlorophyll content in higher plant leaves , 1997 .

[23]  Michael E. Schaepman,et al.  Retrieval of foliar information about plant pigment systems from high resolution spectroscopy , 2009 .

[24]  D. Roberts,et al.  Deriving Water Content of Chaparral Vegetation from AVIRIS Data , 2000 .

[25]  Roberta E. Martin,et al.  PROSPECT-4 and 5: Advances in the leaf optical properties model separating photosynthetic pigments , 2008 .

[26]  William G. Lee,et al.  Modulation of leaf economic traits and trait relationships by climate , 2005 .

[27]  S. Ustin,et al.  Three-dimensional radiation transfer modeling in a dicotyledon leaf. , 1996, Applied optics.

[28]  P. Curran,et al.  LIBERTY—Modeling the Effects of Leaf Biochemical Concentration on Reflectance Spectra , 1998 .

[29]  S. Ollinger Sources of variability in canopy reflectance and the convergent properties of plants. , 2011, The New phytologist.

[30]  James E. McMurtrey,et al.  Assessing crop residue cover using shortwave infrared reflectance , 2004 .

[31]  Sean C. Thomas,et al.  The worldwide leaf economics spectrum , 2004, Nature.

[32]  Josep Peñuelas,et al.  The photochemical reflectance index (PRI) and the remote sensing of leaf, canopy and ecosystem radiation use efficiencies: A review and meta-analysis , 2011 .

[33]  A. Gitelson,et al.  Nondestructive estimation of anthocyanins and chlorophylls in anthocyanic leaves. , 2009, American journal of botany.

[34]  A. Gitelson,et al.  Three‐band model for noninvasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leaves , 2006 .

[35]  L. Johnson,et al.  LEAFMOD : A new within-leaf radiative transfer model , 1998 .

[36]  C. Field,et al.  A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency , 1992 .

[37]  W. Verhoef Earth observation modelling based on layer scattering matrices , 1984 .

[38]  Barry D Ganapol,et al.  LCM2: A coupled leaf/canopy radiative transfer model , 1999 .

[39]  Shruti Khanna,et al.  Image spectroscopy and stable isotopes elucidate functional dissimilarity between native and nonnative plant species in the aquatic environment. , 2012, The New phytologist.

[40]  C. Elvidge Visible and near infrared reflectance characteristics of dry plant materials , 1990 .

[41]  W. Verhoef,et al.  PROSPECT+SAIL models: A review of use for vegetation characterization , 2009 .