Predicting leaf chlorophyll content and its nonuniform vertical distribution of summer maize by using a radiation transfer model

Abstract. Remote sensing technology is an effective method of monitoring chlorophyll content, an important parameter for vegetation health. The chlorophyll information based on spectral information needs to consider the vertical characteristics of plants. Hyperspectral features [spectral reflectance (SR), spectral indices, and wavelet coefficients (WC)] were first selected to construct the cost function in the PROSPECT model-optimized inversion to improve the accuracy and efficiency of leaf chlorophyll content (LCC) inversion. Second, the sensitivity of LCC to leaf SR, vegetation index (VI), and WC were analyzed. Finally, LCC was inverted by the PROSPECT model using the iterative inversion algorithm, and the chlorophyll content of the vertical profile in maize was monitored. The following results were obtained. (1) According to the extended Fourier amplitude sensitivity test (EFAST) used to construct the cost-function inversion of LCC, the normalized difference vegetation index canste (NDVIcanste) and WC based on EFAST method which was used to construct the cost-function inversion of LCC, yielded higher accuracy than other spectral features. These two methods, combined with sensitivity analysis can provide accurate inversion results by weakening the influence of other parameters on spectral changes and eliminating the interference information between bands. (2) The cost function based on NDVIcanste in the LCC optimization exhibited overestimation, whereas WC can solve the problem efficiently. The continuous wavelet transform can extract weak information among the spectra, whereas the other two methods of SR and VI cannot easily obtain this information. (3) The vertical distribution of LCC in different maize varieties and treatments is in accordance with the parabola law, but the chlorophyll content of the different leaf positions on the vertical profile is somewhat different. The results reflect the vertical distribution of chlorophyll content through the radiation transfer of leaves, thus providing a theoretical basis for their further combination.

[1]  He Wei,et al.  EFAST Method for Global Sensitivity Analysisof Remote Sensing Model’s Parameters , 2014 .

[2]  Anatoly Gitelson,et al.  Non-destructive determination of maize leaf and canopy chlorophyll content. , 2009, Journal of plant physiology.

[3]  J. Dungan,et al.  Reflectance spectroscopy of fresh whole leaves for the estimation of chemical concentration , 1992 .

[4]  Chunjiang Zhao,et al.  Vertical Distribution of Nitrogen in Different Layers of Leaf and Stem and Their Relationship with Grain Quality of Winter Wheat , 2005 .

[5]  L. D. Miller,et al.  Remote mapping of standing crop biomass for estimation of the productivity of the shortgrass prairie, Pawnee National Grasslands, Colorado , 1972 .

[6]  Frédéric Baret,et al.  Estimation of leaf traits from reflectance measurements: comparison between methods based on vegetation indices and several versions of the PROSPECT model , 2018, Plant Methods.

[7]  Li-wen Wang,et al.  Revised normalized difference nitrogen index (NDNI) for estimating canopy nitrogen concentration in wetlands , 2016 .

[8]  Zheng Niu,et al.  An evaluation of EO-1 hyperspectral Hyperion data for chlorophyll content and leaf area index estimation , 2010 .

[9]  John R. Miller,et al.  Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture , 2004 .

[10]  Chaoyang Wu,et al.  Estimating chlorophyll content from hyperspectral vegetation indices : Modeling and validation , 2008 .

[11]  A. Huete,et al.  Development of a two-band enhanced vegetation index without a blue band , 2008 .

[12]  Ling Zhang,et al.  Exploring the Vertical Distribution of Structural Parameters and Light Radiation in Rice Canopies by the Coupling Model and Remote Sensing , 2015, Remote. Sens..

[13]  H. Eckersten,et al.  Prediction of grain protein in spring malting barley grown in northern Europe , 2007 .

[14]  Chen Yang-bo,et al.  Global Sensitivity Analysis of Xinanjiang Model Parameters Based on Extend FAST Method , 2010 .

[15]  Jihua Wang,et al.  Comparison of spectral indices and wavelet transform for estimating chlorophyll content of maize from hyperspectral reflectance , 2013 .

[16]  Weixing Cao,et al.  Exploring hyperspectral bands and estimation indices for leaf nitrogen accumulation in wheat , 2010, Int. J. Appl. Earth Obs. Geoinformation.

[17]  Ling Wu,et al.  [Simulation of vegetation indices optimizing under retrieval of vegetation biochemical parameters based on PROSPECT + SAIL model]. , 2012, Ying yong sheng tai xue bao = The journal of applied ecology.

[18]  A. Huete,et al.  A Modified Soil Adjusted Vegetation Index , 1994 .

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

[20]  F. M. Danson,et al.  Sensitivity of spectral reflectance to variation in live fuel moisture content at leaf and canopy level , 2004 .

[21]  R. Geary.  THE RATIO OF THE MEAN DEVIATION TO THE STANDARD DEVIATION AS A TEST OF NORMALITY , 1935 .

[22]  D. Haboudane,et al.  New spectral indicator assessing the efficiency of crop nitrogen treatment in corn and wheat , 2010 .

[23]  Wang Xiuzhen.  The Robustness of Linear Regression Model in Rice Leaf Chlorophyll Concentration Prediction , 2003 .

[24]  Weiwei Liu,et al.  Estimation and Mapping of Winter Oilseed Rape LAI from High Spatial Resolution Satellite Data Based on a Hybrid Method , 2017, Remote. Sens..

[25]  J. Schjoerring,et al.  Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression , 2003 .

[26]  Jorge Nocedal,et al.  A trust region method based on interior point techniques for nonlinear programming , 2000, Math. Program..

[27]  Ronghua Ma,et al.  Estimating the Total Nitrogen Concentration of Reed Canopy with Hyperspectral Measurements Considering a Non-Uniform Vertical Nitrogen Distribution , 2016, Remote. Sens..

[28]  Jagadish Timsina,et al.  Performance of CERES-Rice and CERES-Wheat models in rice–wheat systems: A review , 2006 .

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

[30]  J. Pisek,et al.  Comparison and validation of MODIS and VEGETATION global LAI products over four BigFoot sites in North America , 2007 .

[31]  B. Rivard,et al.  Spectroscopic determination of leaf water content using continuous wavelet analysis , 2011 .

[32]  Pablo J. Zarco-Tejada,et al.  Improved nitrogen retrievals with airborne-derived fluorescence and plant traits quantified from VNIR-SWIR hyperspectral imagery in the context of precision agriculture , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[33]  G. Metternicht.  Vegetation indices derived from high-resolution airborne videography for precision crop management , 2003 .

[34]  F. Baret,et al.  Potentials and limits of vegetation indices for LAI and APAR assessment , 1991 .

[35]  Chen Zhongxin,et al.  Global sensitivity analysis of growth simulation parameters of winter wheat based on EPIC model. , 2009 .

[36]  A. Saltelli,et al.  A quantitative model-independent method for global sensitivity analysis of model output , 1999 .

[37]  C. Rush,et al.  Remote detection of rhizomania in sugar beets. , 2003, Phytopathology.

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

[39]  N. Broge,et al.  Airborne multispectral data for quantifying leaf area index, nitrogen concentration, and photosynthetic efficiency in agriculture , 2002 .

[40]  A. Gitelson,et al.  Signature Analysis of Leaf Reflectance Spectra: Algorithm Development for Remote Sensing of Chlorophyll , 1996 .

[41]  Yu Chang,et al.  [Sensitivity analysis in ecological modeling]. , 2004, Ying yong sheng tai xue bao = The journal of applied ecology.

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

[43]  Alexander F. H. Goetz,et al.  Terrestrial imaging spectrometry - Current status, future trends , 1993 .

[44]  W. Verhoef,et al.  Leaf area index estimation with MODIS reflectance time series and model inversion during full rotations of Eucalyptus plantations , 2011 .

[45]  Bodo Mistele,et al.  Assessing the vertical footprint of reflectance measurements to characterize nitrogen uptake and biomass distribution in maize canopies , 2012 .

[46]  T. M. Lillesand,et al.  Estimating the leaf area index of North Central Wisconsin forests using the landsat thematic mapper , 1997 .