Scattering Impact Analysis and Correction for Leaf Biochemical Parameter Estimation Using Vis–NIR Spectroscopy

Jul 01, 2011
By Spectroscopy Editors
Volume 26, Issue 7

In this article, the scattering impact on the estimation of leaf biochemical parameter content such as chlorophyll and water from visible–near infrared (vis–NIR) spectra was systematically evaluated. Simulated leaf spectral data involving scattering levels and interesting biochemical concentrations were generated by a leaf property model (PROSPECT) to analyze the scattering impact. Experimental data from six levels of Epipremnum aureum leaves were examined to validate the conclusion obtained from the simulated situation. To quantitatively describe the scattering impact, we defined the sensitivity function of model errors caused by scattering. Then we applied four preprocessing methods for scattering correction to eliminate the scattering effect: multiplicative scattering correction (MSC), extended multiplicative scatter correction (EMSC), optical pathlength estimation and correction (OPLEC), and orthogonal signal correction (OSC). The results show that scattering impact has a larger and more sensitive influence on water estimation than chlorophyll estimation. Simultaneously, results indicate that OPLEC is an optimal scatter correction method for chlorophyll estimation with the raising of model prediction capability by 39.04% for simulated data and 27.7% for actual data. However, OSC and OPLEC have similar correction effects for water estimation in theory.

Leaf biochemical parameters such as chlorophyll and water content can provide valuable insight into the physiological performance of plants. Traditional wet chemical analysis methods, such as extraction and high performance liquid chromatography (HPLC) techniques, require destruction of measured leaves. Hence, they are not suitable for inspecting the change of physiological state or biochemical parameters for a single leaf over time. In addition, these processes are time consuming, expensive, and impractical for making wide assessments on the health of the plant. In contrast, visible–near infrared (vis–NIR) spectroscopy analysis is a nondestructive, rapid, and applicable technique in different spatial scales. Accordingly, it is widely used in the plant parameter estimation in the level of plant canopy and leaf (1–6).

To successfully use vis–NIR spectroscopy techniques in agriculture applications, the calibration model must provide a stable, predictive capacity against perturbations from different measured objects and measurement conditions; thatis, the model must be robust. However, when taking the physical characteristics of leaves into account, a number of issues arise to prevent the requirements from being satisfied, such as differences in species, healthy states, and growing states. These differences complicate the leaf spectrum and induce additional mendacious or nonlinear factors. A total calibration model with a large number of representative samples can eliminate the influence, to a certain extent, by using multivariate soft modeling techniques such as principal component analysis (PCA), partial least squares (PLS), and least squares support vector regression (LS-SVR). Although the multivariate soft modeling techniques can more or less compensate for the effects caused by scattering light, the model robustness is sacrificed (7). From the view of the scattering mechanism, this has two effects on the spectra (8): firstly, it increases photon losses and the illusive absorption information is thus added to the absorbance spectra; secondly, the more a photon is diffused, the more absorption possibility will be obtained. Thus, the shape of the spectra is additionally modified based on the wavelength. An appropriate preprocessing method to correct the spectra must be used before building the model.

Presently, scattering correction methods are widely applied, discussed, and compared in the prediction of powder or turbid medium (9–11). But there are fewer publications about influences of scattering on plant leaf reflectance spectra. Leaf analysis is the most important tool for evaluating the nutrient and water status of plants and for guiding its fertilization and irrigation. Studies have indicated that scattering of leaves is mainly from the fluctuating state of surface and organelle distribution existing in the cells (12–15).

This article will focus on the analysis of scattering effect influence and the way to correct it effectively when the chlorophyll and water content are estimated from leaf reflectance data. To obtain a comprehensive analysis, large numbers of simulative leaf reflectance data sets with varying biochemical information by a leaf optical properties model (PROSPECT) were employed. In addition, experimental data was used to validate the conclusion. The goals of this study are to analyze the influence and sensitivity caused by light scattering in the leaf biochemical parameters estimation from vis–NIR spectroscopy and to compare four preprocessing methods to present an effective preprocessing method for correcting the light scattering effects.


Simulated Data Acquisition

The experimental strategy adopted in this study was to obtain the leaf diffuse spectral data simultaneously, which included a wide range of chemical matter concentrations and large variability in all of other factors that influence leaf reflectance spectra. Our previous experiences in collecting samples with a large range of biochemical parameter concentrations indicated that the required time and resources were difficult to carry out. Therefore, the leaf optical properties model, PROSPECT, was used for generating spectral data to satisfy the analysis requirement.

PROSPECT is a useful radiant transfer model used to simulate leaf directional-hemispherical reflectance and transmittance over the whole optical domain (400–2500 nm) (16,17). In the model, scattering is described by the refractive index of leaf materials (n) and the parameter (N) characterizing the mesophyll structure; absorbance is modeled using pigment concentration (C ab), water depth (C w), dry matter concentration (C m), and the corresponding specific absorption coefficients (K ab, K w, and K m). It is indicated when the reference spectra were obtained using the integrating sphere coated with BaSO4 attached to a PerkinElmer spectrophotometer (Waltham, Massachusetts), that the root-mean-square errors (RMSEs) of simulated reflectance spectra were less than 0.03 (17). The PROSPECT model has been used in many studies for estimation of leaf water content (18) and leaf chlorophyll content (19,20). Such studies have exploited the ability of the PROSPECT model to rapidly produce large data sets required for supporting the necessary statistical analysis, which was of particular importance in the present study.

Table I: Input variable ranges of PROSEPCT model used for scattering effect analysis
As mentioned above, it is known that scattering information in leaf reflectance spectra is decided by N and n together. Relevant research indicates that leaf equivalent refraction index n is almost constant for different species and the differences between wavelengths are also small. Therefore, the structure parameter N only was considered in this paper. The parameter ranges for PROSPECT were derived from PROSPECT-4 (21), which provides distinction for in vivo specific absorption coefficients for each biochemical constituent and determines an average refractive index of the leaf interior. It has the advantage of the veracity of spectral simulation and the precision of model inversion.

Figure 1: Typical leaf reflectance spectra with different scattering levels.
To analyze how the scattering effects influence the estimation of biochemical parameter contents from leaf diffuse spectra, seven data sets of leaf reflectance (100 samples per set) were generated by PROSPECT. The concentrations of biochemical parameters, which are the same for each data set distributed normally in the given range. N changed from 1 to 4 with the interval of 0.5 (shown in Table I). The typical spectra for different scattering levels with the same biochemical parameter concentrations (C ab = 67.94 µg/cm2 , C w= 0.0225 cm, C m= 0.1002 µg/cm2 ) are shown in Figure 1.

Table II: Input variable ranges of PROSPECT model used for scattering methods comparison
To compare the efficiency of the four scattering correction methods adopted in the estimation of leaf chlorophyll and water content, another set of representative data containing a wide range of chemical matter concentrations with random distributing of N (Table II) was generated. In addition, the number of calibration samples had to be large enough to eliminate the improvement of model prediction ability afforded by the total PLS model. In this study, 1100 reflectance spectra were simulated, and 1000 spectra were employed for building the calibration model; another 100 spectra were used for testing the model performance as the outside test data.

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