Detection of nitrogen-overfertilized rice plants with leaf positional difference in hyperspectral vegetation index

Detection of nitrogen-overfertilized rice plants with leaf positional difference in hyperspectral vegetation index 1 first-author †E-mail:zqifa2002@yahoo.comhjf@zju.edu.cn †E-mail:zqifa2002@yahoo.comhjf@zju.edu.cn zqifa2002@yahoo.com 2 2 corresp ‡Corresponding Author ‡Corresponding Author †E-mail:zqifa2002@yahoo.comhjf@zju.edu.cn †E-mail:zqifa2002@yahoo.comhjf@zju.edu.cn zqifa2002@yahoo.com College of Life Sciences, Zhejiang University, Hangzhou 310058, China College of Life Sciences, Zhejiang University, Hangzhou 310058, China College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310029, China College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310029, China The main objective of this work was to compare the applicability of the single leaf (the uppermost leaf L1 and the third uppermost leaf L3) modified simple ratio (mSR 705 index) and the leaf positional difference in the vegetation index between L1 and L3 (mSR 705L1 −mSR 705L3 ) in detecting nitrogen (N)-overfertilized rice plants. A field experiment consisting of three rice genotypes and five N fertilization levels (0, 75, 180, 285, and 390 kg N/ha) was conducted at Xiaoshan, Hangzhou, Zhejiang Province, China in 2008. The hyperspectral reflectance (350–2500 nm) and the chlorophyll concentration (ChlC) of L1 and L3 were measured at different stages. The mSR 705L1 and mSR 705L3 indices appeared not to be highly sensitive to the N rates, especially when the N rate was high (above 180 kg N/ha). The mean mSR 705L1 −mSR 705L3 across the genotypes increased significantly ( P < 0.05) or considerably from 180 to 285 kg N/ha treatment and from 285 to 390 kg N/ha treatment at all the stages. Also, use of the difference (mSR 705L1 −mSR 705L3 ) greatly reduced the influence of the stages and genotypes in assessing the N status with reflectance data. The results of this study show that the N-overfertilized rice plants can be effectively detected with the leaf positional difference in the mSR 705 index. Rice Nitrogen (N) Overfertilization Leaf position Hyperspectral reflectance Introduction 1 Rice is the main food staple for more than 50% of the world’s population (Fageria and Baligar, 2003 2003 ). Nitrogen (N) fertilizer is one of the most important inputs for rice production in the world. Increasing the N fertilizer rate for rice plants, however, does not always increase grain yield, due to diminishing returns. The excessive use of N fertilizers poses potential adverse environmental and health concerns (Bohlool et al., 1992 1992 ), and increases the incidence of foliar pathogens and plant lodging (Stroppiana et al., 2009 2009 ). Farmers tend to use more N fertilizer than needed mainly because of its subsidized price and its immediate visible impact on plant growth and leaf color (Islam et al., 2007 2007 ). Approximately 30% of the N used as fertilizer in the world is consumed in China, rice crop use accounting for about 37% use (Peng et al., 2002 2002 ). Therefore, rapid and real time detection of N overfertilization during rice production could be very helpful for site-specific N management. In recent years, spectral measurements have been used for rapid and non-destructive estimation of crop N status. The use of radiometric data for N estimation has been reported over a wide range of crops (Blackmer et al., 1996 1996 ; Boegh et al., 2002 2002 ; Hansen and Schjoerring, 2003 2003 ). Controversy exists, however, in the use of radiometric data for rice (Shibayama and Akiyama, 1986 1986 ; Takebe et al., 1990 1990 ; Xue et al., 2004 2004 ; Nguyen and Lee, 2006 2006 ; Zhu et al., 2007 2007 ; Yi et al., 2007 2007 ; Stroppiana et al., 2009 2009 ). Estimating plant N status by spectral data is sometimes difficult as values may vary according to plant age, location, cultivar selection, and soil characteristic (Spaner et al., 2005 2005 ). Particularly, N estimation for crops of high N and chlorophyll (Chl) contents using reflectance data may be difficult because crop reflectance may be insensitive to crops of high N and chlorophyll contents due to the saturated light adsorption in the chlorophyll adsorption region (Thomas and Gausman, 1977 1977 ). N availability profoundly influences the distribution of leaf N, and thus leaf chlorophyll within a canopy profile as N is a highly variable element in the plant. Wang et al. ( 2006 2006 ) showed that the leaf positional differences in N concentration in response to varied N rates could be a useful indication of plant N status in rice. It was implied that the leaf positional differences in N-sensitive or chlorophyll-sensitive hyperspectral indices might be useful for reducing the influences of some factors (e.g., locations and cultivar) on the critical hyperspectral index, thereby allowing for detection of plants with high N and chlorophyll contents by spectral data. Previous research has paid little attention to detecting N overfertilization when assessing plant N status with the spectral approach. The main objectives of this work are (1) to test the applicability of the selected chlorophyll-sensitive hyperspectral index in detecting N-overfertilized rice plants and (2) to explore the feasibility of using the leaf positional differences in the chlorophyll-sensitive hyperspectral index for detecting N-overfertilized rice plants. Materials and methods 1 Field experiment 2 A field experiment was conducted on a sandy loam soil from June to October in 2008 at the research farm of Xiaoshan Agricultural Sciences Research Institute, Xiaoshan, Hangzhou, Zhejiang Province, China (30°20′ N, 120°31′ E). Prior to planting, the original soil had 13.1 g/kg organic C, 5.6 mg/kg bicarbonate extractable P, 35.2 mg/kg exchangeable K, and 1.26 g/kg total N with pH 7.5 (soil:water 1:1 (w/v)). Three genotypes, Yongyou 8, Zhongzheyou 1, and Zhejing 22 of rice ( Oryza sativa L.), were selected for study. A random block design was used with the three genotypes and five N rates (0, 75, 180, 285, and 390 kg N/ha). Forty percent of the N fertilizer (urea) was applied at pre-planting, 30% at the tillering stage, and 30% at initial heading. Each treatment was replicated three times. The plants were transplanted on July 8, 2008 and harvested on October 20, 2008. Spectral measurement 2 One whole rice plant from each plot was collected at panicle initiation, heading and milk stages, and transported to the lab for leaf reflectance measurements. The reflectance of the single leaf (the uppermost leaf L1 and the third uppermost leaf L3) on a randomly selected main stem was measured with an integrating sphere (Model LI-1800, LiCor Inc., Lincoln, NE, USA) coupled to a FieldSpec® model spectral radiometer (Analytical Spectral Devices, Boulder, CO, USA) in the wavelength range of 350–2500 nm around the midpoint of each leaf, each measurement of a leaf being the average of ten scans. The white reference was taken before each spectral measurement. Leaf chlorophyll concentration determination 2 After spectral measurements, leaves of 0.20 g from the middle part of each leaf were sampled for determination of leaf chlorophyll concentration (ChlC). The Chl a and Chl b concentrations per unit mass were measured spectrophotometrically using 80% (v/v) acetone as a solvent, employing the equations of Lichtenhaler and Wellburn ( 1983 1983 ). Data analysis 2 Modified simple ratio [mSR 705 =( R 750 − R 445 )/( R 705 − R 445 ), where R 750 , R 445 , and R 705 are the reflectance at 750, 445, and 705 nm, respectively (Sims and Gamon, 2002 2002 )] was used as the vegetation index (VI) for retrieving leaf ChlC. This VI was selected because it can reduce the effect of differences in leaf surface reflectance and is relatively insensitive to leaf structure (Sims and Gamon, 2002 2002 ). Statistical analysis was done with SPSS 16.0 (Chicago, IL, USA). An analysis of variance (ANOVA) was done to determine the significance of the effects of genotypes, stages, N level, and leaf position on the spectral index and ChlC. Factorial analyses of variance were conducted to determine the significance of the effects of genotypes, stages, and N levels on the VI and ChlC differences between L1 and L3. A linear contrast test was used to test the significance of the differences among the five N levels. For the convenience of illustration, 0, 75, 180, 285, and 390 kg N/ha are designated as N0, N1, N2, N3, and N4, respectively, VIs of L1 and L3 are expressed as VI L1 and VI L3 , respectively, and the leaf ChlCs in L1 and L3 are expressed as ChlC L1 and ChlC L3 , respectively. The leaf positional difference in the VI between L1 and L3 is expressed as mSR 705L1 −mSR 705L3 or VI L1 −VI L3 , and the ChlC difference between L1 and L3 is expressed as ChlC L1 −ChlC L3 . Results 1 Response of leaf chlorophyll to N rate 2 The leaf ChlC ranged from 0.20 to 6.18 mg/g, indicating the leaf ChlC range was wide due to the genotypes, N treatments, stages, and leaf positions. As shown in Table 1 1 , the genotype, the stage, and the N rate significantly affected ( P < 0.05) the leaf ChlC. A significant ( P < 0.05) variation due to stage×genotype, stage×N rate, N rate×leaf position, N rate×genotype, stage×genotype×N rate, and genotype×N rate×leaf position interactions was also observed, although the effect of the leaf position on the leaf ChlC was insignificant ( P > 0.05). The N rate also affected significantly ( P < 0.05) ChlC L1 −ChlC L3 (Table 2 2 ). As presented in Fig. 1 1 and Table 3 3 , both the mean ChlC L1 and the mean ChlC L3 across the three stages and the three genotypes and across the three genotypes at each stage tended to increase with the N rate, although the N2 treatment appeared to have the highest ChlC L1 among the N treatments at the milk stage. In particular, the N4 treatment had the highest ChlC L3 and the lowest ChlC L1 −ChlC L3 among the five N levels at all the stages (Table 3 3 ). Values are means of all three genotypes and three stages for a given N level ( n =27). Bars indicate standard errors significant at 0.05 level significant at 0.01 level significant at 0.001 level Source df mSR 705 ChlC S 2 20.16 *** *** 127.60 *** *** G 2 72.25 *** *** 41.25 *** *** N 4 27.61 *** *** 67.28 *** *** LP 1 3.18 1.50 S×G 4 9.07 *** *** 7.15 *** *** S×N 8 2.29 * * 4.23 *** *** S×LP 2 0.69 1.75 G×N 8 2.15 * * 3.98 *** *** G×LP 2 0.52 0.02 N×LP 4 7.59 *** *** 10.86 *** *** S×G×N 16 1.77 * * 2.21 * * S×G×LP 4 1.20 0.41 S×N×LP 8 0.73 0.51 G×N×LP 8 0.56 2.34 * * S×G×N×LP 16 0.30 0.51 4 Error 180 significant at 0.05 level significant at 0.01 level significant at 0.001 level Source df mSR 705L1 −mSR 705L3 ChlC L1 −ChlC L3 S 2 2.16 4.25 * * G 2 1.48 0.16 N 4 21.98 *** *** 20.14 *** *** S×G 4 3.73 ** ** 0.95 S×N 8 2.55 * * 5.34 *** *** G×N 8 1.65 3.90 ** ** S×G×N 16 0.87 0.87 4 Error 90 Values are means of all three genotypes for a given N level ( n =9). Comparison of the means was done by linear contrast test. Values denoted with different letters are significant among the five N levels at the 0.05 level Stage N level mSR 705L1 mSR 705L3 mSR 705L1 −mSR 705L3 ChlC L1 (mg/g) ChlC L3 (mg/g) ChlC L1 −ChlC L3 (mg/g) 5 Panicle initiation N0 2.39a 2.27a 0.12a 2.27a 1.85a 0.42a N1 2.65b 2.51b 0.14a 2.76a 2.56b 0.20a N2 2.62b 2.64b −0.02b 2.68a 3.03c −0.35b N3 2.59b 2.67b −0.08b 2.66a 3.02c −0.36b N4 2.59b 2.72b −0.13b 2.96b 3.33d −0.37b 8 5 Heading N0 2.69a 2.36a 0.33a 2.27a 1.89a 0.38a N1 2.62a 2.36a 0.26a 2.62a 2.30a 0.32a N2 3.06b 2.96b 0.10b 3.23b 3.07b 0.16a N3 2.93b 2.91b 0.02b 3.67cd 3.10b 0.57a N4 2.89b 3.06b −0.15c 3.97d 4.63c −0.66b 8 5 Milk N0 2.32a 2.04a 0.28a 1.31a 0.72a 0.59a N1 2.57a 2.18a 0.39a 1.67ab 0.98a 0.69a N2 2.83b 2.67b 0.16a 2.48c 1.53b 0.95a N3 2.71b 2.72b −0.01b 2.08bc 2.07bc 0.01b N4 2.46a 2.96b −0.50c 1.94b 3.02c −1.08c Response of the VI to the N rate 2 As shown in Table 1 1 , the genotype, stage, and N rate affected significantly ( P < 0.05) the mSR 705 index. A significant ( P < 0.05) variation due to stage×genotype, stage×N rate, N rate×leaf position, N rate×genotype, and stage×genotype×N rate interactions was also observed for the VI, although the effect of the leaf position on the VI was insignificant ( P > 0.05). The results indicated that the VI varied greatly with the four factors. The N rate had a significant ( P < 0.05) effect on mSR 705L1 −mSR 705L3 , but the effects of stage and genotype on mSR 705L1 −mSR 705L3 became insignificant ( P > 0.05) (Table 2 2 ). The results indicated that the influences of stage and genotype could be greatly reduced when mSR 705L1 −mSR 705L3 instead of mSR 705L1 or mSR 705L3 was used in assessing the N status by reflectance data. As shown in Table 3 3 , the values of mSR 705 tended to increased with the N level from N0 to N2 at all the stages for both L1 and L3. The differences in the VI were considerable, although not always significant, between N0 and N1 treatments. The mean values of the VI were not significantly ( P > 0.05) different among N2, N3, and N4 except that the N4 treatment had significantly ( P < 0.05) lower IV than N2 or N3 treatment at milk stage, indicating that the VI was not sensitive to the high N levels (above N2). The mean mSR 705L1 −mSR 705L3 across the genotypes decreased from N2 treatment to N4 treatment at all the stages, and was significantly ( P < 0.05) higher at heading and milk in N2 and N3 treatments than in N4 treatment. The difference in the mean mSR 705L1 −mSR 705L3 was considerable, although not always significant, between N2 and N3 treatments at all the stages. The mSR 705L1 −mSR 705L3 appeared, however, to be poor in separating N0 and N1 treatments. The results indicated that mSR 705L1 −mSR 705L3 was sensitive to the high N levels, although it was not sensitive to changes at low N levels (N0 and N1). The N-overfertilized rice plants (N4 treatment) were characterized with the lowest and most negative values of mSR 705L1 −mSR 705L3 . The ChlC-mSR 705 relationships were positive and significant ( P < 0.05), but not robust as the coefficient was 0.49 and 0.64 ( n =135), respectively, in L1 and L3, implying that the single leaf VI was not a robust estimator of the leaf ChlC across the genotypes and stages. Fig. 2 2 shows that the mSR 705 index tends to increase with the leaf ChlC, but becomes insensitive to changes in high leaf ChlC (above 3.5 mg/g). (a) The first uppermost leaf L1; (b) The third uppermost leaf L3 Discussion 1 The chlorophylls contain a large proportion of total leaf N. Therefore measurements of ChlC can provide an accurate indirect assessment of plant nutrient status (Moran et al., 2000 2000 ). The results of this study further confirm that leaf ChlC can be used to assess rice N status. At the milk stage, N2 treatment appeared to have the highest ChlC in the flag leaves, but N3 and N4 treatments still had a remarkably higher ChlC than N2 treatment in L3, which might be attributed to a feedback mechanism in response to N remobilization from the leaves to the grain. The single leaf hyperspectral index appeared not to be highly sensitive to the N rates, especially under high N conditions. This might be partly explained by the fact that VI might not be sensitive to high ChlC, since a relatively low content of chlorophyll is sufficient to saturate absorption in the chlorophyll adsorption region (Thomas and Gausman, 1977 1977 ). The leaf position did not statistically influence mSR 705 or leaf ChlC. The N rate×leaf position interaction, however, significantly ( P < 0.05) influenced both the mSR 705 and leaf ChlC (Table 1 1 ), and the N rate was found to significantly ( P < 0.05) influence both mSR 705L1 −mSR 705L3 and ChlC L1 −ChlC L3 (Table 2 2 ). Therefore, the VI L1 −VI L3 can be used to assess rice N status. The VI L1 −VI L3 appeared to be useful, while both the VI L1 and the VI L3 performed poorly in separating N2, N3, and N4 treatments, possibly because the use of the VI L1 −VI L3 , instead of the single leaf hyperspectral index, can greatly reduce the influence of the genotypes and the stages, and relatively enlarge the VI differences among the high N treatments (Table 3 3 ). The VI L1 −VI L3 appeared, however, not to be as effective as the single leaf VI in separating N0 and N1 treatments, corresponding to the insignificant ( P > 0.05) difference in the ChlC L1 −ChlC L3 between the two treatments. The results suggest that combined analyses of the VI L1 , VI L3 , and VI L1 −VI L3 are needed when assessing plant N status with hyperspectral reflectance data. The ChlC-VI relationship was not robust in both L1 and L3. The relationship would be improved if the ChlC was expressed on the basis of area. The wide range of leaf ChlC might also influence the ChlC-VI relationship, since the VI was not sensitive to high leaf ChlC. Conclusions 1 The single leaf hyperspectral index mSR 705 appeared not to be highly sensitive to the N rates, especially when the N rate was high (above N2). The leaf positional differences in the VI appear to be sensitive to the high N rates at all the stages, which could be explained by the fact that the influence of stages and genotypes can be greatly reduced, and that the treatment differences among the high N level treatments are relatively enlarged by using the VI L1 −VI L3 instead of the VI L1 or the VI L3 in assessing the N status via reflectance data. The N-overfertilized rice plant (N4 treatment) was characterized with the lowest and most negative value of the VI L1 −VI L3 at all the stages. The results in this study suggest that the leaf positional differences in the VI can be used to effectively detect the rice plants with N overfertilization. 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Quantitative relationships of leaf nitrogen status to canopy spectral reflectance in rice 10.1631/jzus.B0900348 Project (Nos. 2007AA10Z102 and 2006AA10Z201) supported by the National High-Tech R & D Program (863) of China 2009-10-31 2010-05-11 2010-03-18 2022-01-05 Copyright © Zhejiang University and Springer-Verlag Berlin Heidelberg 2010 2010 Journal of Zhejiang University-SCIENCE B (Biomedicine and Biotechnology) 06 11