Estimating leaf chlorophyll content in tobacco based on various canopy hyperspectral parameters

Hyperspectra is a non-destructive measure for estimating crop leaf chlorophyll content (LCC). In this paper, the quantitative relations between LCC and three kinds of canopy hyperspectral parameters in tobacco were investigated. The results indicated that a linear relationship of LCC with the raw spectral reflectance at 732 nm and an exponential relationship of LCC with first order differential spectra at 837 nm were performed to estimate LCC, giving R2 of 0.845 and 0.881, RMSE of 0.366 mg g− 1 and 0.301 mg g− 1, and RE of 18.34% and 15.62%, respectively, and both could serve as optimal techniques to estimate tobacco LCC. Nevertheless, the better one was (SDr−SDy)/(SDr + SDy) with R2 = 0.948, RMSE = 0.127 mg g− 1, and RE = 9.31%, respectively, indicating that (SDr−SDy)/(SDr + SDy) was suitable to estimate LCC. These results suggest that the new normalized variable (SDr−SDy)/(SDr + SDy) to estimate LCC, which is more effective than raw spectral reflectance, first order differential spectra and red edge spectral parameters.

[1]  B. Yoder,et al.  Predicting nitrogen and chlorophyll content and concentrations from reflectance spectra (400–2500 nm) at leaf and canopy scales , 1995 .

[2]  Gerrit Hoogenboom,et al.  A potential of the growth stage estimation for paddy rice by using chlorophyll absorption bands in the 400-1100 nm region , 2015 .

[3]  Craig S. T. Daughtry,et al.  A visible band index for remote sensing leaf chlorophyll content at the canopy scale , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[4]  J. Chen.  Evaluation of Vegetation Indices and a Modified Simple Ratio for Boreal Applications , 1996 .

[5]  Prasad S. Thenkabail,et al.  Hyperspectral narrowband and multispectral broadband indices for remote sensing of crop evapotranspiration and its components (transpiration and soil evaporation) , 2016 .

[6]  L. M. Kawchuk,et al.  Inheritance and mapping of a light green mutant in cultivated diploid potatoes , 1998, Euphytica.

[7]  Samuel I. Beale,et al.  Enzymes of chlorophyll biosynthesis , 1999, Photosynthesis Research.

[8]  Pierre Roumet,et al.  Assessing leaf nitrogen content and leaf mass per unit area of wheat in the field throughout plant cycle with a portable spectrometer , 2013 .

[9]  C. François,et al.  Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements , 2004 .

[10]  J. Suzuki,et al.  Genetic analysis of chlorophyll biosynthesis. , 1997, Annual review of genetics.

[11]  Changwei Tan,et al.  Remotely Assessing Fraction of Photosynthetically Active Radiation (FPAR) for Wheat Canopies Based on Hyperspectral Vegetation Indexes , 2018, Front. Plant Sci..

[12]  Jan Piekarczyk,et al.  Winter Oilseed-Rape Yield Estimates from Hyperspectral Radiometer Measurements , 2011 .

[13]  T. S. Prasad,et al.  Comparative analysis of red-edge hyperspectral indices , 2003 .

[14]  C. Daughtry,et al.  Remote Sensing Leaf Chlorophyll Content Using a Visible Band Index , 2011 .

[15]  Bill Robertson,et al.  The effect of adopting green SLA on key parameters of optical WDM networks , 2016, J. Ambient Intell. Humaniz. Comput..

[16]  John Tenhunen,et al.  A model separating leaf structural and physiological effects on carbon gain along light gradients for the shade‐tolerant species Acer saccharum , 1997 .

[17]  Mary E. Martin,et al.  HIGH SPECTRAL RESOLUTION REMOTE SENSING OF FOREST CANOPY LIGNIN, NITROGEN, AND ECOSYSTEM PROCESSES , 1997 .

[18]  Mohammadmehdi Saberioon,et al.  Assessment of rice leaf chlorophyll content using visible bands at different growth stages at both the leaf and canopy scale , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[19]  Pablo J. Zarco-Tejada,et al.  Hyperspectral indices and model simulation for chlorophyll estimation in open-canopy tree crops , 2004 .

[20]  P. Gong,et al.  Analysis of in situ hyperspectral data for nutrient estimation of giant sequoia , 2002 .

[21]  A. Gitelson,et al.  Application of Spectral Remote Sensing for Agronomic Decisions , 2008 .

[22]  J. Melack,et al.  Remote sensing of foliar chemistry of inundated rice with imaging spectrometry , 1996 .

[23]  Karem Chokmani,et al.  In‐Season Nitrogen Status Assessment and Yield Estimation Using Hyperspectral Vegetation Indices in a Potato Crop , 2015 .

[24]  Byeungwoo Jeon,et al.  Hyperspectral classification employing spatial–spectral low rank representation in hidden fields , 2017 .

[25]  V. Demarez,et al.  A Modeling Approach for Studying Forest Chlorophyll Content , 2000 .

[26]  N. Broge,et al.  Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density , 2001 .

[27]  Andrew D. Richardson,et al.  An evaluation of noninvasive methods to estimate foliar chlorophyll content , 2002 .

[28]  Moon S. Kim,et al.  Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance , 2000 .

[29]  El-Sayed M. El-Alfy,et al.  Automated gait-based gender identification using fuzzy local binary patterns with tuned parameters , 2019, J. Ambient Intell. Humaniz. Comput..

[30]  S. Reinbothe,et al.  The regulation of enzymes involved in chlorophyll biosynthesis. , 1996, European journal of biochemistry.

[31]  Ning Su,et al.  A Chlorophyll-Deficient Rice Mutant with Impaired Chlorophyllide Esterification in Chlorophyll Biosynthesis1[W][OA] , 2007, Plant Physiology.