Estimating Wheat Leaves Chlorophyll Content Using Hyperspectral Technology and Integrated Inversion

Approach

Liang Liang*, Zhang Lianpeng, Su Shu, Liu Xiao, Qian Xiaojin, Shen Qiu

School of Geodesy and Geomatics Jiangsu Normal University

Xuzhou, China

Zhao Shuhe, Qin Zhihao School of Goegraphic and Oceanographic Sciences

Nanjing University Nanjing, China

Abstract—Leaf chlorophyll content (LCC) is an important indicator for wheat growth monitor. In this paper, the integrated inversion approach was developed to estimate the wheat LCC. Based on the PROSAIL simulation data set, three hyperspectral vegetation indices (VIs), PRI, TCARI and TCARI/OSVAI, were comparative analyzed to screen out optimal VI for wheat LCC estimation. The integrated inversion models to estimate wheat LCC were built using the curve fit and least squares support vector regression (LS-SVR) algorithms as the modeling methods, respectively. Finally, using the observation dataset, the accuracy of LS-SVR model for wheat LCC inversion was verified. The result showed that TCARI/OSVAI was an optimal VI for LCC estimating, as it not only exhibit a good sensitivity to the change of LCC but also showed a least sensitivity to the change of LAI values among the three indices and therefore least affected by canopy density when used to estimate the wheat LCC; Comparative curve fitting algorithm, LS-SVR was a optimal algorithm for modeling, as indicated by higher R2 (0.932 for LS-SVR model and 0.923 for curve fitting model) and lower RMSE (3.065 for LS-SVR model and 3.209 for curve fitting model); The R2 of the fitting model between the estimated values and measured values reached 0.763, indicated the similarity between estimated and measured value was high, and it was feasible to obtain the wheat LCC accurately by using hyperspectral VIs and integrated inversion approach.

Keywords—hyperspectra; PROSAIL; chlorophyll; integrated inversion; wheat; support vector regression

I. INTRODUCTION Measurements of leaf chlorophyll content (LCC) in plants

provide an important indicator to evaluate crops growth, productivity, photosynthetic capacity, stress conditions and nutritional status [1, 2]. Through set up the estimated models, hyperspectral technology can be applied to estimate the vegetation physical and chemical parameters including LCC, at a relatively low cost compared to biochemical measurements in the field [1].

Traditional hyperspectral technology for vegetation biophysical and biochemical characteristics extracting rely on the observed spectral features via an empirical relationship

linking the variables of interest to the sensitive bands, spectral indices, or spectral transform values (i.e., PCA) [3-5]. However, empirical relationships are site, time, and sampling specific, so the validity of the statistical model based on empirical relationship is not universally applicable to different environmental conditions [6]. An alternative to empirical models are radiative transfer models, which describe the radiation transfer and interactions of the plant canopy based on physical laws. Radiative transfer models aim to generalize empirical results and improve the robustness of vegetation parameters retrieval, so can get a general applicability in different situations [2, 7, 8].

In all sorts of radiative transfer model, the PROSAIL model are most popular for the advantages of simplicity, accuracy and availability, and it’s reliability have been extensively tested by ground, airborne and spaceborne data sets with different sensors/platform yet [9]. However, as the same as other radiative transfer model, PROSAIL model inversion routines, such as iterative optimisation technique, are complex, computationally-demanding and often highly sensitive to initial model parameters. To overcome the limitations above, integrated inversion approach was put forward to structure inversion model. Integrated inversion approach utilize the radiative transfer model to generate simulation database, including the simulated spectra data and corresponding simulated parameters data, and then use the parametric or nonparametric regression method, such as ANN or SVM, to link the relationship between spectral variables and canopy parameters [9]. Therefore, it has the advantages of empirical method’s simplicity and physical model’s universality and able to estimate the properties of vegetation more accurately and quickly. On the other hand, considering vegetation indices(VI) has a role to eliminate interference factors and refine target information, using the VI as the model variable instead of the full band to construct the model can be better applied in remote sensing mapping.

In this paper, the simulated spectra database generated by PROSAIL model were used to produce simulated vegetation indices (VIs), and the integrated inversion model for estimating wheat LCC were established with the simulated VIs and simulated LCC.

* corresponding author Sponsored by National Natural Science Foundation of China (No. 41401473),

the Natural Science Foundation of Jiangsu, China (No. BK2012145), the Natural Science Research Project for Universities of Jiangsu Province, China(No.12KJB420001) and the National Innovation and Entrepreneurship Training Program for Undergraduate (No.201310320030 and No. 201310320048).

II. MATERIALS AND METHODS

A. PROSAIL model and simulated spectra database A combination of the PROSPECT leaf optical properties

model and SAILH canopy reflectance model, that is, the so-called PROSAIL model, has been validated for different kinds of vegetation with a homogeneous canopy and is therefore deemed to be suitable for inverting wheat properties accurately and time-saving [10]. ROSPECT has been coupled with several versions of canopy reflectance models based on SAIL [10-13]. In this study, the version of PROSAIL5B, combined by PROSPECT5 and 4SAIL, was used to simulate the canopy reflectance spectra of the wheat field in various conditions [10, 14] .

PROSAIL model can suffer from the ill-posed problem during the inversion process [15], where the inversion solution is not always unique, as various combinations of canopy parameters may yield the almost similar spectra [16]. Previous studies suggested that utilizing prior information is an efficient way to solve the problem and improve the accuracy [17]. According to prior knowledge and the published literatures [5, 8, 18, 19], the ranges of various parameter used for simulating database are shown in Table1. In this study, 10000 parameter combinations were drawn randomly within the specific ranges and a total of 10000 canopy spectra were generated based on the parameter combinations. The wavelength range of the simulation spectra was 450~2500 nm at 1 nm steps (table I).

TABLE I. THE VALUE FOR INPUT PARAMETERS USED FOR GENERATING PROSAIL SIMULATION DATABASE

Model parameters Abb. Value Unit

PROSPECT

Leaf chlorophyll content Cab 15~55 µg.cm-2

Leaf carotenoid content Car fixed µg.cm-2

Leaf brown pigment content Cbrown fixed µg.cm-2

Equivalent water thickness Cw 0.01-0.02 cm

Dry matter content Cm 0.0025-0.009 g.cm-2

Leaf structural parameter N 1.5~2.0 unitless

SAIL

Leaf area index LAI 0.2~10 unitless

mean leaf inclination angle angl 15~45 deg

Soil brightness parameter, Psoil fixed unitless Fraction of diffuse incoming solar radiation skyl fixed unitless

Hot spot parameter hspot fixed unitless

ihot fixed unitless

Sun zenith angle tts fixed deg

Sensor viewing angle tto fixed deg

Relative azimuth angle psi fixed deg

B. Computation of hyperspectral vegetation indices Simulation canopy spectral data were used for calculating

three vegetation indices, including photochemical reflectance index (PRI), transformation chlorophyll absorption reflectance

index (TCARI), optimized soil adjusted vegetation index (OSAVI) and TCARI/OSAVI, which have been proposed as surrogates for vegetation chlorophyll estimation. They are defined by the following equation, where Rx is the reflectance at the central wavelength of spectra [20-23].

531 570 531 570PRI ( ) ( )R R R R= − + (1)

700 670 700 550 700 670TCARI 3[( ) 0.2( )( )]R R R R R R= − − − (2)

800 670 800 670OSAVI (1 0.16) ( ) ( 0.16)R R R R= + − + + (3)

700 670 700 550 700 670

800 670 800 670

3[( ) 0.2( )( )]TCARI/OSAVI(1 0.16) ( ) ( 0.16)

R R R R R RR R R R

− − −=+ − + +

(4)

C. Modeling methods Acting the spectral index as the independent variable (x),

canopy leaf nitrogen content as the dependent variable (y), we selected the best method in the algorithms of linear regression, exponential regression, logarithmic regression and quadratic regression to set up the estimation model. And then we evaluated the estimation model by the indicators of coefficient of determination (R2) and root mean square error of calibration (RMSEC).

However, the function types must be pre-defined if using these methods. And this process was difficult if the samples scatter diagram did not show a regular pattern of a certain function form. As a machine learning algorithm based on the principle of structural risk minimization, the Support Vector Machine (SVM) algorithm has no need to pre-define the function types, and can ensure the accuracy of calibration model while reducing the complexity of machine learning to obtain good generalization ability and high prediction accuracy

[24]. For that reason, it has been widely used in recent years for solving hyperspectral regression problem. The ultimate purpose of estimating the biophysical and biochemical parameters of crops is just to get a model with good predictive ability. Therefore, we will attempt to optimize the estimation model with the algorithm of least square support vector machines regression (LS-SVR) in this paper.

D. Observation experiments and validation dataset The observation data, which involved a comprehensive

dataset of hyperspectral data and biochemical measurements, were provided by a field experiment to validated the inversion model. The experiment area situated in National Experiment Station for Precision Agriculture (latitude 40°10′31″N to 40°11′18″N, longitude 116°26′10″E to 116°27′05″E), which covers an area of 167 hm2, about 20 km northeast of Beijing, China. The dataset included: 1) A total of 163 samples of wheat were scanned by a Fieldspec Pro FR spectroradiometer (the data were pretreated by wavelet threshold denoising before analysis); 2) collection of leaf tissue for laboratory determination of leaf chlorophyll concentration; and 3) LAI measurements using the dry weight method and Plant Canopy Analyzer (Li-Cor model LAI- 2000).

III. RESULTS AND ANALYSIS

A. Sensitivity analysis 1) For Sensitivity to LCC

In a preliminary analysis for canopy spectra, PRI, TCARI and TCARI/OSAVI were plotted as a function of LCC for leaf reflectance derived by simulations with the PROSAIL model (Fig.1). All other variables are kept constant to highlight canopy reflectance spectra changes due to chlorophyll variation only (5~90 with 2.5 step length) .

As LCC increases, PRI increases, TCARI and TCARI/OSAVI decreases. All of them exhibit a better sensitivity at moderate and low LCC (<50 µg.cm-2), then become somewhat inactive with the increasing of LCC. In general, three indices were sensitivity to the change of LCC in the range 5~90 µg.cm-2, and it indicated that there was a strong potential for using PRI, TCARI and TCARI/OSAVI to predicted real data. However, the result remained the uncertainty regarding the effects of structural development of the crops. LAI as a measure of the structural changes will be used in the next section to examine whether the indices could resist those effects.

2) Sensitivity to LAI changes

The effects of plant growth (LAI) and LCC on PRI, TCARI and TCARI/OSAVI were illustrated in Fig.2. It could be seen that the scaling up to the canopy level produced an impact to the sensitivity of TCARI at low LCC, and played a greater impact the sensitivity of PRI both at low and high content. For indices of PRI and TCARI, LAI produced a strong influence on the relationships between indices and LCC, and contributed to more variability of PRI than TCARI because of the relationship of TCARI and chlorophyll was insensitivity at high LCC. Fortunately, this occurred only at low foliage cover (LAI<1.0) for both PRI and TCARI, when LAI values equal or greater than 1.5, two indices showed less variability and more resistance to the changes of LAI in the range of all possible observable LCC (5~90 µg.cm-2). Despite all this, as for PRI and TCARI , the problems related to low LAI values and LAI interaction with LCC remain unsolved, and that may cause the uncertainty when use it to make prediction in early vegetation growth stages.

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Fig.1. Sensitivity to LCC variability of PRI, TCARI and TCARI/OSAVI. Note: Cab is the general terms of chlorophyll a and b (unit:µg.cm-2)

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Fig. 2. Effects of LAI on PRI, TCARI and TACRI/OSAVI sensitivity to LCC variation. Note: Cab is the general terms of chlorophyll a and b (unit:µg.cm-2)

Fortunately, the effects of LAI and LCC on TCARI/OSAVI was virtually non-existent, except very low foliage cover (LAI<0.1) , with a smaller change at low LCC. As a whole, the sensitivity of TCARI/OSAVI had no significant changes with the increase or decrease of the LAI value. Therefore, considered the lower responsively to LAI variations and its sensitivity to LCC changes, index TCARI/OSAVI held a ability of estimating the LCC status of crop canopies consistently and accurately. It indicated that the TCARI/OSAVI was the optimal index to estimate the wheat LCC for its best accuracy and reliability.

B. LCC inversion modeling Acting the TCARI/OSAVI as the independent variable (x),

LCC as the dependent variable (y), we selected the best method in the algorithms of linear regression, exponential regression, logarithmic regression and polynomial regression to build the estimated model. And then we evaluated the estimated model by the indicators R2 and RMSE. The best fits were included the exponential and quadratic functions, with R2 0.920 and 0.923, RMSE 3.274 and 3.209, respectively. Although quadratic functions fit the observed points better, the exponential reflect the trend of the data better. Consequently, both relationships can been chosen as consistent estimates of LCC according to specific circumstances (Fig.3).

To estimate wheat LCC more accurately, the LS-SVR algorithm was used for optimizing the model. The parameters of LS-SVR model were determined as following: Radial Basis Function (RBF) was selected to be the kernel function of the model; penalty coefficient C and the RBF kernel function parameter g which had a significantly effects to the modeling result were selected by cross validation; and the remaining parameters were used default values [24]. The method of grid search was used for cross-validation, and the search process was divided into two steps to reduce search difficulty and save computation time. In the first step, we used a larger step length in a larger value range for optimization; in the second step, we set a smaller step length based on the result of the first step to get the best parameter values. The results of grid-search were

shown in Table II. While the grid-search got the optimal values of parameters C and g (C=6.4; g=128), the LS-SVR model had a highest accuracy, with R2 and RMSEC 0.932 and 3.065, respectively, better than that of the exponential and quadratic model as mentioned above. It indicated that compared to parameter regression method, the LS-SVR algorithm was a optimal method for modeling.

TABLE II. THE OPTIMIZATION RESULTS OF PARAMETERS C AND G FOR LS-SVR MODEL

Parameter name Parameter value

Step one Step two

Penalty coefficient（C） 0.001≤C≤100000 0.1≤C≤100

RBF parameter（g） 0.001≤g≤100000 1≤g≤1000

Step-size in search 10 2

Optimization Results C=10; g=100 C=6.4; g=128

C. Validation with observation data The field spectral data, as mentioned in section 2.4, was

truncated and re-sampled to a spectrum from 400 nm to 2500 nm with a bandwidth of 1 nm and therefore can be used to calculated the index TCARI/OSAVI as well as the simulated spectra which generated by PROSAIL5B. Therefore, utilizing the index TCARI/OSVAI and the LS-SVR model, the estimation of wheat chlorophyll content was achieved. The measured chlorophyll content of synchronous sampling at the time of spectra acquiring was used for inversion accuracy test. The inversion and measured values were compared by the method of regression fitting. Compared to the measured wheat LCC, the estimation yields an accuracy of R2=0.763 and RMSE=5.50 for the 163 samples with a range of 15.07~49.31 µg.cm-2. Furthermore, the relationship between the chlorophyll estimates from the model and ground-based measurements has a slope close to 1 (0.802) and a small interception (3.512), indicating that the modeling approach produces systematically fair estimation (Fig.4).

y = 84.18exp(-4.014x )R 2 = 0.920

RMSE =3.274

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Fig.3. The exponential (a) and polynomial (b) regression model between LCC and TCARI/OSVAI (n=10000; chlorophyll content unit: µg.cm-2)

y = 0.802x + 3.512R² = 0.763

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IV. CONCLUSION AND DISCUSSION LCC is an essential indicator for assessing the crops

growth. And this indicator can be estimated rapidly and non-destructively by using the hyperspectral indices. In this paper, the wheat LCC was estimated accurately with index TCARI/OSAVI using PROSAIL model and the LS-SVR algorithm, as indicated by the R2 and RMSE of the model were 0.932 and 3.065, respectively. The inversion model applied to observation data also obtained a satisfaction result, as indicated by the goodness of fit between remote sensing inversion values and measured values reached 0.763. It means integrated inversion approach can be used as a rapid and non-destructive approach for getting wheat chlorophyll content.

An ideal vegetation index should be as insensitive as possible to interference factors and as sensitive as possible to parameters that need to estimate [25]. LAI is the dominant factor for canopy reflectance, and it lead to most of the variation in LCC which estimated by many VIs stems from the LAI variability. In order to test the indices sensitivity to LAI changes, PROSAIL model was employed for crop canopy reflectance simulation in the sensitivity study with a wide range of chlorophyll content and LAI variations in this paper. The result demonstrated that TCARI/OSAVI was sensitivity to chlorophyll changes and insensitivity to LAI variations and could estimate chlorophyll accurately and prevent the factors such as plant crown density to interfere the estimation model most effectively. Therefore, TCARI/OSAVI was an appropriate candidate for LCC estimation.

Different modeling method has a great influence on accuracy of estimation. LS-SVR model appears to be a powerful alternative to parameters regression statistical method. Compared to the curve fit method, it achieved relatively better result, indicated by higher R2 (0.932) and lower RMSEC (3.065) . However, the accuracy of LS-SVR algorithm was significantly affects by parameters C (penalty coefficient) and g (RBF kernel function parameter), which

general need the grid search method to optimized, along with the shortcomings of computationally intensive and time-consuming. To solve the problem, a step-search procedure in which a long step size was set first to determine the range of values and then followed by a short step size to determine the specific values, was carried out for rapid optimization of the penalty coefficient C and the RBF kernel function parameter g of LS-SVR model.

The results of this paper confirm that crop physiological and biochemical parameters such as chlorophyll content can be estimated through the inversion of a radiative transfer model using hyperspectral indices with accuracies comparable or even higher to those of empirical approaches [26-28]. Contrast with empirical approaches need a lot of ground measurement data for modeling, integrated inversion approach only need a few ground measured biophysical data for model accuracy validation and model parameters ranges setting. Moreover, because radiative transfer models can get a general applicability in different situations, once an appropriate inversion model has been built, it can in principle be applied to different remote sensing data which covered with similar crop types.

ACKNOWLEDGMENT The authors thank to Mr. Shicheng Chen, Miss Weiwei

Ma and Miss Meng Wang from the School of Geodesy and Geomatics, Jiangsu Normal University, China for their assistance to generate the simulated data.

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