Retrieving Leaf Chlorophyll Content by Incorporating Variable Leaf Surface Reflectance in the PROSPECT Model

Leaf chlorophyll content plays a vital role in plant photosynthesis. The PROSPECT model has been widely used for retrieving leaf chlorophyll content from remote sensing data over various plant species. However, despite wide variations in leaf surface reflectance across different plant species and environmental conditions, leaf surface reflectance is assumed to be the same for different leaves in the PROSPECT model. This work extends the PROSPECT model by taking into account the variation of leaf surface reflection. In the modified model named PROSPECT-Rsurf, an additional surface layer with a variable refractive index is bounded on the N elementary layers. Leaf surface reflectance (Rs) is characterized by the difference between the refractive indices of leaf surface and interior layers. The specific absorption coefficients of the leaf total chlorophyll and carotenoids were recalibrated using a cross-calibration method and the refractive indices of leaf surface and interior layers were obtained during model inversion. Chlorophyll content (Cab) retrieval and spectral reconstruction in the visible spectral region (VIS, 400–750 nm) were greatly improved using PROSPECT-Rsurf, especially for leaves covered by heavy wax or hard cuticles that lead to high surface reflectance. The root mean square error (RMSE) of chlorophyll estimates decreased from 11.1 μg/cm2 to 8.9 μg/cm2 and the Pearson’s correlation coefficient (r) increased from 0.81 to 0.88 (p < 0.01) for broadleaf samples in validation, compared to PROSPECT-5. For needle leaves, r increased from 0.71 to 0.89 (p < 0.01), but systematic overestimation of Cab was found due to the edge effects of needles. After incorporating the edge effects in PROSPECT-Rsurf, the overestimation of Cab was alleviated and its estimation was improved for needle leaves. This study explores the influence of leaf surface reflectance on Cab estimation at the leaf level. By coupling PROSPECT-Rsurf with canopy models, the influence of leaf surface reflectance on canopy reflectance and therefore canopy chlorophyll content retrieval can be investigated across different spatial and temporal scales.

[1]  H. T. Breece Iii,et al.  Bidirectional scattering characteristics of healthy green soybean and corn leaves in vivo. , 1971, Applied optics.

[2]  Stephanie Long,et al.  Development of a standardized methodology for quantifying total chlorophyll and carotenoids from foliage of hardwood and conifer tree species , 2009 .

[3]  Jan G. P. W. Clevers,et al.  Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties - A review , 2015 .

[4]  Karen M. Barry,et al.  Estimation of chlorophyll content in Eucalyptus globulus foliage with the leaf reflectance model PROSPECT , 2009 .

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

[6]  V. Lucarini Kramers-Kronig relations in optical materials research , 2005 .

[7]  Fuzhong Weng,et al.  Kramers‐Kronig analysis of leaf refractive index with the PROSPECT leaf optical property model , 2012 .

[8]  H. Lichtenthaler CHLOROPHYLL AND CAROTENOIDS: PIGMENTS OF PHOTOSYNTHETIC BIOMEMBRANES , 1987 .

[9]  N. Goel,et al.  Needle chlorophyll content estimation through model inversion using hyperspectral data from boreal conifer forest canopies , 2004 .

[10]  F. Baret,et al.  PROSPECT: A model of leaf optical properties spectra , 1990 .

[11]  Glenn Newnham,et al.  Quantification of Chlorophyll and Carotenoid Pigments in Eucalyptus Foliage with the Radiative Transfer Model PROSPECT 5 is Affected by Anthocyanin and Epicuticular Waxes , 2012, GSR.

[12]  Z. Cerovic,et al.  Optical Properties of Plant Surfaces , 2007 .

[13]  L. Grant,et al.  Polarization photometer to measure bidirectional reflectance factor R(55∞, 0∞; 55∞, 180∞) of leaves , 1986 .

[14]  Weixing Cao,et al.  PROCWT: Coupling PROSPECT with continuous wavelet transform to improve the retrieval of foliar chemistry from leaf bidirectional reflectance spectra , 2018 .

[15]  M. G. Holmes,et al.  Effects of pubescence and waxes on the reflectance of leaves in the ultraviolet and photosynthetic wavebands: a comparison of a range of species , 2002 .

[16]  Quevedo Amaya,et al.  Caracterización fisiológica y bioquímica de cuatro genotipos de algodón (Gossypium hirsutum L.) en condiciones de déficit hídrico , 2020 .

[17]  Pablo J. Zarco-Tejada,et al.  Chlorophyll Fluorescence Effects on Vegetation Apparent Reflectance: I. Leaf-Level Measurements and Model Simulation , 2000 .

[18]  A. Wellburn The Spectral Determination of Chlorophylls a and b, as well as Total Carotenoids, Using Various Solvents with Spectrophotometers of Different Resolution* , 1994 .

[19]  H. Gausman,et al.  Refractive index of plant cell walls. , 1974, Applied optics.

[20]  J. Clark,et al.  Photosynthetic action spectra of trees: I. Comparative photosynthetic action spectra of one deciduous and four coniferous tree species as related to photorespiration and pigment complements. , 1975, Plant physiology.

[21]  Holly Croft,et al.  The applicability of empirical vegetation indices for determining leaf chlorophyll content over different leaf and canopy structures , 2014 .

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

[23]  J. Féret,et al.  A physically-based model for retrieving foliar biochemistry and leaf orientation using close-range imaging spectroscopy , 2016 .

[24]  Vern C. Vanderbilt,et al.  Polarized and specular reflectance variation with leaf surface features , 1993 .

[25]  Holly Croft,et al.  Leaf Pigment Content , 2018 .

[26]  Osvaldo Facini,et al.  Leaf characteristics and optical properties of different woody species , 1997, Trees.

[27]  V. Vanderbilt,et al.  Polarization photometer to measure bidirectional reflectance factor R(55 deg, 0 deg, 55 deg, 180 deg) of leaves , 1986 .

[28]  H. Gausman,et al.  Interaction of Isotropic Light with a Compact Plant Leaf , 1969 .

[29]  E. Levizou,et al.  Nondestructive assessment of leaf chemistry and physiology through spectral reflectance measurements may be misleading when changes in trichome density co-occur. , 2004, The New phytologist.

[30]  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 .

[31]  G. Agati,et al.  New vegetation indices for remote measurement of chlorophylls based on leaf directional reflectance spectra. , 2001, Journal of photochemistry and photobiology. B, Biology.

[32]  Stéphane Jacquemoud,et al.  PROSPECT-D: towards modeling leaf optical properties through a complete lifecycle , 2017 .

[33]  S. Jacquemoud,et al.  Leaf BRDF measurements and model for specular and diffuse components differentiation , 2005 .

[34]  H. Mooney,et al.  Leaf Pubescence: Effects on Absorptance and Photosynthesis in a Desert Shrub , 1976, Science.

[35]  M. Teece,et al.  Increased Accumulation of Cuticular Wax and Expression of Lipid Transfer Protein in Response to Periodic Drying Events in Leaves of Tree Tobacco1[W] , 2005, Plant Physiology.

[36]  J. Clark,et al.  Photosynthetic Action Spectra of Trees: II. The Relationship of Cuticle Structure to the Visible and Ultraviolet Spectral Properties of Needles from Four Coniferous Species. , 1975, Plant physiology.

[37]  D. Sims,et al.  Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages , 2002 .

[38]  Yuri A. Gritz,et al.  Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. , 2003, Journal of plant physiology.

[39]  J. Chen,et al.  Retrieving chlorophyll content in conifer needles from hyperspectral measurements , 2008, Canadian Journal of Remote Sensing.

[40]  S. Ustin,et al.  Estimating leaf biochemistry using the PROSPECT leaf optical properties model , 1996 .

[41]  Françoise Viénot,et al.  ACT: A leaf BRDF model taking into account the azimuthal anisotropy of monocotyledonous leaf surface , 2014 .

[42]  Flavio Cannavó,et al.  Sensitivity analysis for volcanic source modeling quality assessment and model selection , 2012, Comput. Geosci..

[43]  A. Skidmore,et al.  Inversion of a radiative transfer model for estimating vegetation LAI and chlorophyll in a heterogeneous grassland , 2008 .

[44]  Yuri Knyazikhin,et al.  Retrieval of canopy biophysical variables from bidirectional reflectance Using prior information to solve the ill-posed inverse problem , 2003 .