Absorption spectrum estimating rice chlorophyll concentration: Preliminary investigations

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1 Journal of Plant Breeding and Crop Science Vol. (5). pp. -9, July, 9 vailable online 8 cademic Journals Full Length Research Paper bsorption spectrum estimating rice chlorophyll concentration: Preliminary investigations Jinheng Zhang *, Chao Han and Zhiheng Liu, Institute of Eco-environment and griculture Information and College of Environment and Safety Engineering, Qingdao University of Science and Technology, Qingdao, Shandong 664, China. College of Science, Guizhou University, Guiyang, Guizhou 555, China. ccepted June, 9 Our objective in this study was to develop spectral absorption indices for prediction of leaf chlorophyll concentration based on blue/yellow/red/ edge absorption spectrum. Two field experiments were conducted to study the response of chlorophyll index based on leaf absorption spectra to chlorophyll concentration in rice. The ultimate, penultimate and third expanded leaves were sampled for chlorophyll measurements and the absorption spectra of the leaves on the main stem for three rice varieties at different growth stages to select the absorption wavelength position near zero and develop better algorithms for estimating chlorophyll concentration. Some indices called blue/yellow/red/ edge absorption spectra chlorophyll index (BECI/ YECI/ RECI) were calculated from elected absorption wavelength positions. For the st experiment the correlation coefficients were similar between chlorophyll concentration and single leaf spectral absorption and between chlorophyll concentration and these indices. But the chlorophyll concentration had significant correlations (P<.) to these indices than single leaf spectral absorption in the nd experiment. The liner regression models with single leaf spectral absorption y = , y= and y = had higher power prediction total chlorophyll, chlororphyll a and chlorophyll b concentrations, respectively. Compared with BECI and RECI, stepwise regression analysis showed that YECI 6.6, YECI and YECI 65. were good predictive power for predicting chlorophyll total concentration, chlorophyll a concentration and chlororphyll b concentration respectively. Key words: Rice, chlorophyll concentration, leaf absorption spectrum, vegetation index. INTRODCTION Nondestructive determination of leaf chlorophyll content permits the measurements of changes in pigments over time for leaves and avoids time-consuming and expensive traditional chlorophyll concentration measurements (Gao et al., 8). Spectral bands in the visible and nearinfrared regions of the spectrum have been used to develop a number of indices for estimating chlorophyll content. Vegetation index is a simple, effective and experiential measurement of terrestrial vegetation activity, and plays a very important role in qualitative and quantitative remote sensing. For remote sensing purposes, almost all indices *Corresponding author. zhangjinheng@qust.edu.cn, zjhnhl@6.com. Fax: based on reflectance spectrum including single band spectral reflectance and reflectance band ratios or differences have been used as indicators of chlorophyll content of leaves. Various vegetation indices (VIs) have been related to chlorophyll content. The ratio vegetation index (RVI; Pearson and Miller, 97) and the normalized difference vegetation index (NDVI; Rouse et al., 974) are the best-known indices. NDVI is defined as (reflectance of NIR-reflectance of RED)/ (reflectance of NIR+ reflectance of RED). Some studies have developed RVI (Bisun, 998; Guli, 7). For example ndrea M et al. () reported the best indices for Chl, Chl-a and Chl-b determination of four different plants leaves were R / 54/R / 75, R / 76/R / 75 and R / 556/R / 75, respectively. Some spectral indices were reported such as the transformed chlorophyll absorption in reflectance index (TCRI; Kim et al.,

2 4 J. Plant. Breed. Crop Sci. 994), the modified chlorophyll absorption in reflectance index (MCRI; Daughtry et al., ), the modified chlorophyll absorption continuum index (MCCI; Yang et al., 6) and so on. By developing a new spectral index that reduces the effect of differences in leaf surface reflectance, Sima et al. () were able to significantly improve the correlations with chlorophyll content. Their results demonstrate that spectral indices can be applied across species with widely varying leaf structure without the necessity for extensive calibration for each species. Cheng () showed that the most suitable estimated model of chlorophyll a of upper leaves was obtained by using some hyper-spectral variables such as SDr, SDb and their integration. natoly G () reported that reciprocal reflectance (R ) in the spectral range from 5 to 55 nm and 695 to 75 nm related closely to the total chlorophyll content in leaves of all studied species. Subtraction of near infra-red reciprocal reflectance, (R NIR ), from (R ) made index [(R ) -(R NIR ) ] linearly proportional to the total chlorophyll content in spectral ranges from 55 to 555nm and from 695 to 75 nm with coefficient of determination r >.94.The continuum index using the spectral continuum on which the analyses are based on the area of the troughs spanned by the spectral continuum were reported (Zhang JH 6). The position of the inflexion point in the red edge region (68 to 78 nm) of the spectral reflectance signature, termed the red edge position (REP), was affected by biochemical and biophysical parameters and had been used as a means to estimate foliar chlorophyll or nitrogen content. Many studies have focused on estimation of chlorophyll concentration using red edge characteristics. The rededge, centered at the largest change in reflectance per wavelength change, is located between the red trough and the NIR plateau. Strong correlations have been found between the red edge and the chlorophyll concentration of leaves or canopy (Pinar996). Some studies reported that among red edge parameters (such as red ; Min 6-7;d ; d min ; d red / d min; d and nir ), the red can be used to estimate chlorophyll content satisfactorily, Zhao, ; Tang, 996; 4; Seager, 5). Dash (7) reported a new index called the MERIS terrestrial chlorophyll index (MTCI) (MERIS denotations the medium resolution imaging spectrometer, has fine spectral resolution, moderate spatial resolution and a -day repeat cycle). MTCI uses data in three red/nir wave-bands centered at 68.5, and nm, which lie in red edge range. Preliminary indirect evaluation us-ing model, field and MERIS data suggested its sensitivity to chlorophyll content, notably at high values (Dash, 7). Spectroscopy can provide information about a substance by relating the interaction of electromagnetic radiation as a function of wavelength to its chemical com-position and physical properties. ll vegetation contains the same basic constituents: chlorophyll and other light-absorbing pigments, water, proteins, starches, waxes, and structural biochemical molecules such as lignin and cellulose (Elvidge, 99). ll of these components contribute to the re- flectance, transmissivity and absorption spectra of vegetation. Chlorophyll, the green pigment common to all photosynthetic cells, absorbs all wavelengths of visible light except green. ll photosynthetic organisms have chlorophyll a. Chlorophyll a absorbs its energy from the violet-blue and reddish orange-red wavelengths, and little from the intermediate (green-yellow-orange) wavelengths. Due to chlorophyll absorptions, the visible region of green plants shows a maximum reflectance at approximately 55 nm and lower reflectance in the blue (45 nm) and red (68 nm) (Pu, ). Green plants spectral curve from blue to green and from green to red called blue edge and yellow edge respectively. So chlorophyll concentration will affect green plants visible light spectral curve, which is response for the change of blue edge and yellow edge in visible light region. However, most of the vegetation indices or algorithms reported in the literature have not been developed using blue edge and yellow edge, and fewer studies predict chlorophyll concentration by the absorption spectrum from fresh leaves. Reported investigations about absorption spectrum prediction chlorophyll concentration including chlorophyll a, b and a+b pay attention to the absorption of the extracts at some wavelengths with spectrophotometer. The main aims of this study were to determine blue, yellow and red edge positions to develop new models or vegetation indices for predicting chlorophyll concentration using fresh leaf absorption. MTERILS ND METHODS Experiment description The first field experiment was laid out in a split plot design with three replications. The main plots were arranged in split plot design block and comprised six basal nitrogen (N) rates:, 45,5, 65, 5, kg N ha -. Subplots were three varieties of rice (Oryza sativa L) Shengdao, Lindao and Yangguang, respectively with the similar growth stages. Main plot sizes were m. The second experiment was conducted in a ploughed field with two varieties of rice (Yangguang and Lindao). The field was divided into four N supply areas as follows: kg urea per hectare ; 7 kg urea per hectare; 585 kg urea per hectare; 75 kg urea per hectare. For two field experiments, 5% N fertilizer was applied before transplanting, 4% nitrogen fertilizer at tillering and % at heading, respectively. Leaf absorption spectrum measurements and plant sampling Twenty clumps of rice were obtained from each main plot at tillering, booting and heading stage in the two experiments, and leaf absorption spectrum of the st, nd and rd expanded leaves were measured between 5- nm in spectral resolution.4 nm by an vaspec-48ft-spu with light source of valight-hl. Their leaves were taken serving as subsamples being detached to measure chlorophyll a, chlorophyll b and total chlorophyll concentrations, respectively Determination of chlorophyll concentrations in leaves The fresh leaf mass was determined for the leaf samples prior to

3 Zhang et al. 5 Table. Statistics for chlorophyll a, chlorophyll b, and total chlorophyll concentrations. The st field experiment The nd field experiment N Mean Range Standard Coefficient of Deviation Variation (%) Chl-a Chl-b Chl-t Chl-a Chl-b Chl-t chlorophyll measurement. The st, nd and rd expanded leaves of each N supply level were weighed respectively to obtain the leaf mass. The chlorophyll was extracted in 8% acetone. The absorption of the extracts at wavelengths of 66nm (D 66) and 645 nm (D 645) were measured with a SP 7E spectrophotometer. The concentrations of chlorophyll a (Chl-a), chlorophyll b (Chl-b), and total chlorophyll (Chl-t) were then calculated using the equations as follow (rnon, 949). Chl-a = Chl-b = Chl-t= Statistical methods ll statistical analyses were conducted using the SPSS statistical software and p <.5 was used to determine significance in all tests. number of statistics such as the mean, range, and standard deviation were used to describe the distribution of leaf absorption and chlorophyll data. The pearson correlation with -tailed significance tests were used to characterize the relationship between chlorophyll concentrations and vegetation indices, and the coefficient of variation was also calculated as the ratio of the standard deviation to the mean. Multiple stepwise regressions were used to build and assess the chlorophyll prediction models with vegetation indices. RESULTS ND DISCUSSION Leaf chlorophyll concentration The data on chlorophyll concentration showed a large range of chlorophyll concentrations. The chlorophyll a concentration lied between 7.4% for the st field experiment and 7.45% for the nd field experiment, respectively (Table ), which consisted with some repoted conclusions. Comar (94) reported that chlorophyll a usually lies between 67% and 78% for the normal green tissues of land higher plants, and formed much more rapidly than chlorophyll b. So prediction total chlorophyll and chlorophyll a concentration was higher than chlororphyll b. Gross (99) reported that in higher plants, chlorophyll a was the major pigment and chlorophyll b was an accessory pigment, and the a/b ratio was generally around :. nd Lin () reported that the chlorophyll a / chlorophyll b ratio were generally around :. Selection characteristic wavelengths and development of vegetation indices In order to develop better algorithms for estimating chlo- rophyll concentration, characteristic wavelengths were selected according to the peaks and dips of spectral absorption curves and the st derivative curve. The mean (n = 7) absorption spectra of the leaf samples of the first field experiment was showed in figure. The spectra of all samples were visually similar in shape and absorptions. Large variations in absorption magnitude could be observed in the blue, yellow and red edge position at the st derivative curve and correspond to the peaks and dips of spectral absorption curve (Figure ), while wavelength positions were included in the Table. Characteristics wavelengths were indicated by significant negative correlation coefficients and absorption of characteristics wavelengths were increased along with increased chlorophyll concentration. The correlations between chlorophyll concentrations and the characteristics wavelength positions were shown in Table. ll reported correlation coefficients were significant at P <. except for (Table ). Three edge position chlorophyll indices were designed to estimate of chlorophyll concentration and was calculated using the ratio of the difference in absorption among blue, yellow and red edge position. The blue/yellow/red/ edge absorption spectra chlorophyll index (BECI/ YECI/RECI) standard bands were selected according to Table, Table and Figure. The following equations were developed for the estimation of chlorophyll a, b and total chlorophyll concentrations: BECI RECI YECI = () = () = () x i Where indicates absorption of 66.9 nm (x ), x i 65. nm (x ), nm (x ), 6.6 nm (x 4 ), 6.5 nm (x 5 ), 6.7 nm (x 6 ) and nm (x 7 ), respectively. The relationship between chlorophyll a, b and total concentrations and these indices are shown in Table 4. Com- x i

4 6 J. Plant. Breed. Crop Sci..8.6 absorption The st derivative...8 absorption( values) the st derivative w avelength(nm) Figure. Selection charactistics wavelengths from spectral absorption scurve and its st derivative scurve. Table. Wavelength positions were selected from Figure. Spectral regions (nm) Blue edge Yellow edge Red edge pared with the relationship between single band spectra absorption and chlorophyll concentrations, the correlation coefficients increased significantly between chlorophyll concentrations and these indices with significant correlation at P <. (Tables and 4). Negative correlations were found between chlorophyll concentration and BECI, and between chlorophyll concentration and RECI. s for YECI, positive correlations were found with chlorophyll concentration. The correlation coefficients between these indices and total chlorophyll concentrations were higher than these indices and concentrations of chlorophyll a or b. Therefore, based on the strong correlation, these indices could be used to predict chlorophyll concentrations especially total chlorophyll concentrations. Chlorophyll prediction models The relationship between these single band absorption and chlorophyll concentration were best described by multiple stepwise regression models (Table 5). Multiple stepwise regression models obtained between leaf absorption at different wavelengths (model-: 48.88, 65., and ;model-: 48.88, 75.55, and 66.9 ; model- : 65., 75.55,and 66.9 ) and chlorophyll concentrations were highly correlated, suggesting that these models were better predictors to estimate total chlorophyll, chlorophyll a and chlorophyll b concentration respectively (Table 5). The relationship between blue/yellow/red/ edge absorption spectra chlorophyll index (BECI/YECI/RECI) and chlorophyll concentrations were best described by liner regression models. Liner regression models obtained between chlorophyll concentrations and blue/red edge absorption spectra chlorophyll index (BECI, RECI) had similar correlation coefficients, suggesting that BECI and RECI had similar perdition power to predict chlorophyll concentrations. However, compare with BECI, RECI and single band models (model-, model- and model-), regression analysis indicated that better predictive liner regression models with YECI 6.6, YECI 6.7 and YECI 65. for predicting Chl-t, Chl-a and Chl-b concentrations with the higher R (.74,.656 and.6) respectively (Table 6). Validation The models described in Table 5 and algorithm described

5 Zhang et al. 7 predi ct ed chl t concent rat i on by Model R = pr edi ct ed chl a concent r at i on by Model R = pr edi ct ed chl b concent r at i on by Model R = measured chl b concent rat i on predi ct ed chl t concent rat i on by YETCI R = pr edi ct ed chl a concent r at i on by YETCI R = measured chl a concent rat i on pr edi ct ed chl b concent r at i on by YETCI R = measured chl b concent rat i on predi ct ed chl t concent rat i on predi ct ed chl t by BECI R =. 467 predi ct ed chl t by RECI R = pr edi ct ed chl t concent r at i on pr edi ct ed chl a by BECI pr edi ct ed chl a by RECI R =. 576 R = predi ct ed chl t concent rat i on pr edi ct ed chl b by BECI R =. 9 pr edi ct ed chl b by RECI R = Figure. The relationship between the predicted and measured values for testing power of prediction models.

6 8 J. Plant. Breed. Crop Sci. Table. The correlation coefficients between chlorophyll concentrations (Chl-a, Chl-b and Chl-t) and vegetation indices. (n=7.* and ** indicated that correlation is significant at the.5 level and. level respectively. indicated absorption of waveband.) The st experiment The nd experiment Chl-a Chl-b Chl-t Chl-a Chl-b Chl-t (**).65(**).7(**).68(**).9(**).576(**) (**).68(**).7(**).67(**).88(**).58(**) (**).586(**).667(**).56(**).4(*).49(**) (**).59(**).684(**).589(**).4(*).56(**) (**).65(**).76(**).64(**).7(**).566(**) (**).68(**).7(**).6(**).8(**).575(**) 6.6.6(**).6(**).7(**).646(**).95(**).59(**) (**).67(**).78(**).665(**).9(**).64(**) 65..6(**).69(**).7(**).67(**).8(**).6(**) (**).68(**).79(**).68(**).7(**).6(**) (**).589(**).7(**).666(**).6(**).67(**) (**).6(**).7(**).654(**).6(**).6(**) (**).7(**) Table 4. The correlation between chlorophyll concentrations (Chl-a, Chl-b and Chl-t) and vegetation indices. (.* and ** (n = 7) indicate that correlation is significant at the.5 level and. level respectively). Chl-a Chl-b Chl-t BECI.67(**).594(**).75(**) RECI.59(**).68(**).7(**) YECI x -.66(**) -.64(**) -.7(**) YECI x -.67(**) -.6(**) -.78(**) YECI x -.6(**) -.65(**) -.79(**) YECI x4 -.67(**) -.66(**) -.74(**) YECI x5 -.6(**) -.64(**) -.76(**) YECI x6 -.67(**) -.595(**) -.76(**) YECI x (**) -.54(**) -.7(**) Table 5. Chlorophyll prediction models using single band absorption. Dependent variable Model R F Sig. Chl-t Model y= Chl-a Model y= Chl-b Model y= Table 6. Chlorophyll prediction models with these indices. (n = 7). Dependent variable Predictor Model R R F Sig. YECI 6.6 Y = -.7x Chl-t BECI Y = x RECI Y =.84x YECI 6.7 Y = -.86x Chl-a BECI Y =.85x RECI Y =.47x YECI 65. Y = -.58x Chl-b BECI Y =.79x RECI Y =.5x

7 Zhang et al. 9 in Esq ()-() was tested using a validation data set(n = 95) of the second experiment. The absorption measurements of the validation samples were used to calculate the YECI 6.6, YECI 6.7, YECI 65., BECI and RECI, and then the models and algorithm equations were used to predict the chlorophyll concentrations. The highly significant correlation between the predicted and measured values was indicated by the scatter-plots (Figure 5). High positive correlations between the predicted chlorophyll concentrations and actual chlorophyll concentrations showed good predictive power of predicting chlorophyll total concentration using modeland YECI 6.6 with the higher R (.655,.56), predicting chlorophyll a concentrations using model and YECI 6.7 with the higher R (.694,.678). Good predictive power of predicting Chl-a and Chl-t using BECI and RECI were reported with higher R (Figure ). Conclusion nalysis of visible/near-infrared absorption spectrum and chlorophyll data for a wide range of rice leaves has developed better algorithms for the quantification of chlorophyll concentration by spectral absorption. It was found that five vegetation indices (BECI, RECI, YECI 6.6, YECI 6.7, and YECI 65. ) were reported higher significant correlation with chlorophyll concentration. Three better chlorophyll concentration predictors were found as follow: YECI 6.6 predicting chlorophyll total concentration; YECI 6.7 predicting chlorophyll a concentration; YECI 65. predicting chlorophyll b concentration. From single band spectral absorption, three models (chlt = ; chla = ; chlb = ) were reported having good predictive power with higher correlation between predicted and measured chlorophyll concentrations. This study indicated that satisfactory results have been obtained to predict chlorophyll concentrations using selected characteristic wavelengths and developed vegetation indices. 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