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CHANGES IN WINTER WHEAT GROWN UNDER IRRIGATED AND RAINFED CONDITIONS.

Byline: E. H. Rong, S.C. Li, Z. G. Zhao, M. C. Feng, C. Wang, M. J. Zhang, R. Li, R. Y. Wang and W. D. Yang

ABSTRACT

Changes of the canopy spectral reflectance, leaf area index (LAI) and chlorophyll density (CHLD) of winter water in irrigated and dry land at different growth stage were studied to analyze the correlation between hyperspectral vegetation indices and CHLD, and to confirm the optimum vegetation indices for estimating CHLD of winter wheat in irrigated and dry land. The result showed the trend of changes in LAI similar to CHLD in irrigated and dry land, the values increased at first and then decreased, but the maximum value of CHLD and LAI appeared at different growth stages.

The changes in canopy reflectance were also similar in irrigated and dry land, canopy reflectance formed an obvious reflection peaks at a waveband (510-680nm), but with the grain filling stage progressed, the reflection peaks gradually became unobvious, and disappeared at maturity. NDVI (780,670) might be better to predict CHLD of winter wheat in irrigated land, with the determination coefficient of 0.9451, but DVI (1200, 670) might be to predict CHLD of winter wheat in dry land, with the determination coefficient of 0.7346.

Key words: Leaf area index; Chlorophyll density; Spectral parameters; Irrigated; Dry land _: INTRODUCTION

Winter wheat (Triticum aestivum L.) is planted in about 71% of the cultivated area of Shanxi Province in China, which is the most frequently grown crop in Loess Plateau (Gao et al., 2009). Therefore, more study about winter wheat in irrigated and dry land is imperative. Recently, Ma et al. (2008) suggested remote sensing is a reliable method for estimating wheat growth (Ma et al., 2008)because it is characterized as non-destructive, fast and relatively inexpensive(Darvishzadeh et al., 2008), especially for specific vegetation variables, such as leaf area index (LAI)(Ren et al., 2010), chlorophyll density (CHLD)(Feng et al., 2008; Eitel et al., 2009), yield and quality (Wang et al., 2011).

CHLD is an index for characterizing photosynthetic capacity and growth condition of many crops because chlorophyll is the major pigment in photosynthesis responsible for absorption and reaction with most of the visible red light (Arunyanark et al., 2009). Therefore, field CHLD measurement is important for evaluation of wheat growing environment and condition (Schepers et al., 1992; Daughtry et al., 2000). With the development of remote sensing, many observed values could be obtained from multi-spectral, multi- azimuth, multi-date, and composite index, which loaded the corresponding waveband information of different crop and vegetation structure. In field experiment, LAI is also an important dynamic determinant for plant community structure and various of ecosystem processes, such as transpiration, photosynthesis, and nutrient cycling (Brantley and Young,

2007). The crop yield increases along with increasing LAI. However, when the LAI increases to some degree, canopy closure is formed, and results in insufficient light, and decreased photosynthetic efficiency, and finally the crop yield is reduced (Xinyou et al., 2011). In addition, study also indicated that nitrogen application could promote crop growth and developmental processes by an increase in LAI to counterbalance decrease in net assimilation rate. Therefore, LAI is an important consideration for high yielding cultivation of wheat plant(XUE et al., 2004).

In brief, it is helpful to obtain qualitative and quantitative data for crop production management by studying the relationship between spectrum vegetation index and various agriculture parameters, such as LAI and CHLD. However, up to date, few studies were reported to reflect the changes in CHLD and LAI of winter wheat in irrigated land and dry land through canopy reflectance spectrum. In this study, we comprehensively analyzed the correlation ship between canopy spectral parameter and CHLD and LAI of winter wheat in irrigated land and dry land, and compared the effect of eight spectral parameters on CHLD and LAI of winter wheat in irrigated land and dry land. It is being comprehended to establish the quantitative detection model of CHLD and LAI of winter wheat in irrigated land and dry land, and lay a theoretical and technique basis for monitoring wheat growth by remote sensing.

MATERIALS AND METHODS

Study site: The study site was located in Wenxi county, Shanxi province (longitude110deg59' to 111deg37'E, latitude 35deg9'3to 35deg34'N). Table 1 depicts the villages selected as irrigated and dry land including north to Xi-aodi, west to Chaijiashan and Guojiagou, south to Guojiazhuang and Xiayangzhuang, east to Nanwang and Shangyuan, and the centre of Xiguanzhuang and Shangshaowang.

The study area lies in a temperate continental climate zone, with the mean annual temperature of 8-14degC. The winter of study area is cold (averaged of -3.2degC in January) and dry, while the summer is hot (averaged of 26.5degC in July) and rainy. The area have an average annual precipitation of450-600mm and a frost-free period of160-190 days. There is an average of 2461 h sunlight annually, with the most from May to June. The texture of soil was calcareous cinnamon soil with neutral or alkalescent PH, relatively higher mineral matter, organic matter content, humus layer, and fertility. Canopy spectral measurements: We used a portable multi-spectral radiometer MSR-16R (CROPSCAN Inc, USA) to measure canopy reflectance spectrumat wheat re-greening stage, jointing stage, heading stage, grain filling stage, and maturity, respectively.

This spectrometer was fitted with 31.1deg field-of-view fiber optics, operating in the 60-1700 nm spectral region, with band positions centered at 460, 500, 550, 600, 650, 670, 730, 780, 800, 850, 950, 1050, 1100, 1200, 1260, and 1460 nm, respectively.

The optical head should be held perpendicular to land surface with a distance of 2 m between the plant canopy and the optical head of the spectro-radiometer. Before performing each reflectance measurement, the target measurements were normalized by recording the radiance of a white standard panel coated with BaSO4 and of known reflectivity. To minimize atmospheric perturbations, all spectral measurements were conducted between 10:00 and 14:00 on clear sunny days. In order to suppress the measurement noise, we taken the average value of 15 replicate spectral measurements from each subplot as the final results.

LAI measurements: The LAI measurements and canopy spectral measurements were taken on the same day. Five plants were randomly collected from each plot and five leaves were arranged neatly to measure the total width. And then the middle part of leaves (4 cm) was accurately cut off to measure the partial leaves area(S) and dry weight (W1). The other partial leaves were also dried and obtained the dry weight W2. The total leaf area of the five leaves was calculated according to the following equation: S1=S (W1+W2)/W2(Breda, 2003).

Chlorophyll destiny measurements: The chlorophyll destiny measurements and canopy spectral measurements were taken on the same day. All of the upper fully expanded leaves and adjacent lower leaves were collected to determine chlorophyll concentration. Chlorophyll in the sampled leaves were extracted in 25 ml of 80% (v/v) acetone solution and 95% ethanol (1:1), and then kept in dark for 24h before determining chlorophyll using a spectrophotometer (Shimadzu UV-1800 type) with the absorbance at 440nm, 645nm and 663nm, respectively. Chlorophyll destiny was then calculated with the following equations(Arnon, 1949): Chlorophyll a=(12.71A663-2.59A645) V/W 1000 Chlorophyll b=(22.88A645-4.67A663) V/W 1000 Total chlorophyll content =(8.04A663+20.29A645) V/W 1000 Note: V,10ml; W, 0.05g Vegetation indices used in this analysis: In this section, we described eight vegetation indices (Table 2) those were examined for their potential to estimate CHLD and optimum spectral wavebands.

RESULTS AND DISCUSSION

Changes in LAI and chlorophyll destiny of irrigated and dry land winter wheat at different growth stages: The LAI is suggested high related to light energy utilization, dry matter production and accumulation, and grain yield. As the major pigment in photosynthesis, the chlorophyll content may reflect the growth condition and production capacity of winter wheat. Therefore, they may directly influence the economic yield of wheat. Results from our study showed a parabola trend in the changes of LAI and chlorophyll destiny of winter wheat in irrigated as well dry land (Figure 1). However, the peak value of them occurred at different stages. Sufficient water supply was present at the whole growth stage in irrigated land, and thus LAI increased from emergence stage and reached the peak value at heading stage (Figure 1A). Due to deficiency water in dry land, the increase of LAI was relatively slower and arrived at the peak value until to grain-filling stage.

The LAI showed a rapid decrease after grain-filling stage since the leaves could not carry on the photosynthesis, and senesced and withered subsequently. From Figure 1B, chlorophyll destiny of dry land wheat reached the maximum at jointing stage, but at grain- filling stage in irrigated land. This result may be because that nutrient supply and rainfall were sufficient before jointing stage, and abundant chlorophyll was synthesized to accomplish photosynthesis and make organics to meet the need of vegetative growth and reproductive growth. However, after jointing stage, nutrient gradually decreased due to hot weather and lack of rainfall, and thus chlorophyll synthesis was reduced correspondingly. Identically, sufficient rainfall and nutrient results in the peak value of chlorophyll destiny was at grain-filling stage in irrigated land. Chlorophyll destiny sharply decreased after grain-filling stage because of leaf senescence and abscission.

Our results found the changes trend of LAI and CHLD of winter wheat in irrigated land was consistent with them in dry land, that is, increased first and then declined. However, the peak value of LAI and CHLD of winter wheat arrived at different growth stage in irrigated land and dry land.

Changes in canopy spectrum characteristic of winter wheat in irrigated land and dry land at different growth stage: In this study, we investigated the changes incanopy spectrum characteristic of winter wheat in irrigated land and dry land at two crucial stages, jointing stage and filling stage (Figure 2). The results showed that the canopy spectrum characteristic of winter wheat was similar in irrigated land and dry land. Spectral reflectance gradually increased along with the development of winter wheat in visible waveband (460-730nm), but decreased in infrared waveband (780-1100nm).

Two absorption bands were present in visible blue(450nm) and red(670nm) waveband, but a small spectral reflectance peak occurred between these two absorption bands, which lead to the plant green. Spectral reflectance peak disappeared near 540 nm at maturity, therefore the plant became yellow. A absorption valley was present in visible red waveband (680nm). If the absorption valley reduced, the wheat would grow yellow. Spectral reflectance sharply increased from 680 nm visible red waveband and then peaked at 1100 nm infrared waveband.

In addition, spectral reflectance of dry land wheat was significantly higher than that in irrigated land in visible waveband (460-730nm), but lower in infrared waveband (780-1100nm). This indicated that the chlorophyll destiny of winter wheat in dryland was lower than that in irrigated land, which was also in accordance with changes trend observed in Figure 1. In brief, the reflectance varied due to different moisture and nutrient in the same vegetation. Therefore, we could monitor the growth and nutrient status of wheat according to above changes.

Overall, the changes in canopy spectral reflectance of winter wheat were similar in irrigated land and dryland at different growth stage. A spectral reflectance was present in 510-680 nm wave band, but with the development of grain filling, this spectral reflectance peak was gradually unconspicuous and disappeared at maturity. Obvious changes also could be observed in near-infrared waveband (780-1100 nm). Further analysis of jointing stage and grain filling stage indicated that spectral reflectance in dryland was significantly higher than that in irrigated land at visible waveband(460-730 nm), but lower than that in irrigated land at near-infrared waveband(780-1100 nm).

Correlation analysis between CHLD and canopy spectral reflectance at various bands: The results of correlation analysis showed that CHLD of winter wheat in irrigated land was significant and had negative correlation with the spectral reflectance in visible waveband (460-730nm) and near-infrared waveband (1200 nm), but was significant and positive correlated with the spectral reflectance in near-infrared waveband (780-1100 nm) (Figure 3A).

The negative correlation between CHLD and spectral reflectance in 1460 nm waveband as well as the positive correlation between CHLD and spectral reflectance in 1260 nm waveband was not statistically significant. However, CHLD of winter wheat in dry land was significant and had negative correlation with the spectral reflectance in visible waveband (460-730nm) and near- infrared waveband (1460 nm), but significant and positive correlated with the spectral reflectance in near-infrared waveband(780- 1100 nm) (Figure 3B). The positive correlation between CHLD and spectral reflectance in 1200 nm as well as 1260 nm was not significant.

Further, it can be observed from Figure 3 that correlation coefficient between CHLD and canopy spectral reflectance arrived the peak value at 670 nm waveband, with -0.90 of irrigated land and -0.59 of dry land. Therefore, we drew regression equations by setting spectral reflectance at 670 nm as x and CHLD as y (Table 3). As a result, the regression equation for irrigated land is y = 12.364e-0.3382x, with R2= 0.9208, while the regression equation for dry land is y= -0.0188x + 0.1998x + 3.3992, with R= 0.4057.. Correlation analysis between CHLD and vegetation index: Eight vegetation index were collected to confirm the correlation analysis between CHLD and vegetation index here (Table 4). Our results showed that eight vegetation index were all significant and correlated with CHLD. And The NDVI was the highest in irrigated land, with correlation coefficient of 0.92, while DVI was the highest in dryland, with correlation coefficient of 0.70. Therefore, NDVI and DVI may be used to estimate the CHLD in irrigated land and dryland, respectively.

In addition, our results indicated that in dryland, TSAVI, PVI, NDVI, SAVI, DVI, OSAVI, and RDVI were significantly and had positive correlation with CHLD in near-infrared waveband (780-1260 nm) and visible waveband (460-730nm), but gave negative correlation with CHLD in near-infrared waveband (1460nm) and visible waveband (460-730nm).

However, in irrigated land, PVI was significantly and positive correlation with CHLD in near-infrared waveband (780-1460 nm) and visible waveband (460-730nm). NDVI, OSAVI, TSAVI, SAVI, and RVI were significantly positive correlation with CHLD in near-infrared waveband (780-1260 nm) and visible waveband (460-730nm). Significantly positive correlation was present between NDVI, OSAVI, SAVI and CHLD in 1460 nm waveband and visible waveband (500, 600, 650, 670 nm). But no significantly positive correlation was present between NDVI, OSAVI, SAVI and CHLD in 1460 nm waveband and visible waveband (460, 550 nm). No significantly positive correlation was also observed between TSAVI and CHLD in 1460 nm waveband and visible waveband (460-670nm).

Table1. The geographic location of irrigated and dry land

Treatments###Study sites

###Xizhang village###Xiangyang village

###Xiaositou village###Jinzhuang village

###Hedi village###Goudong village

###Nanwang village###Renhe village

###Shangyuan village###Xiyangquantou village

###Xinyangzhuang village###Dongzhen

###Dongyu village###Hou village

Irrigated land

###Sidi village###Xiguanzhuang village

###Fengjiazhuang village###Daze village

###Jilu village###Xinxizhang village

###Dingdian village###Poshen village

###Xihan village###Shangshaowang village

###Guojiazhuang village###Guodian village

###Xifu village###Yangzhuang village

###Kengdong village 1###Hutouzhuang1

###Kengdong village 2###Hutouzhuang2

###Zhujiabao village###Bolin village

###Dongdama village###langjiawan

###Beiyuan###Guojiagou

Dry land

###Xiaodi village###Gaoyu

###Xuedian###Chaijiashan

###Xin village###Qiujiazhuang

###Li village###Zhaojialing

###Xiaozhang village###Dongwang

Table 2. Algorithm and references of different spectral parameters

Spectral parameter###Abbreviation###Algorithm###Reference

###Cropscan

Reflectance###R###_

###(2000)(Inc, 2000)

###Pearson et al

Ratio vegetation index###RVI###RNIR/RRed

###(1972)(Pearson and Miller, 1972)

###Jordan

Difference vegetation index###DVI###RNIR-RRed

###(1969)(Jordan, 1969)

Normalized###difference###Rouse et al.

###NDVI###RNIR-RRed/RNIR+RRed

vegetation index###(1974)(Rouse, 1974)

###Richardson et al.

Perpendicular###vegetation

index

###PVI###(RNIR-aRRedb)/ 1+a2###(1977)(Richardson and Wiegand,

###1977)

Transformed###Chlorophyll###3[(R700-R670)-0.2(R700-R550)###Haboudane et al.

###TCARI

Absorption ratio Index###(R700/R670)]###(2004)(Hu et al., 2004)

Transformed soil adjusted###a(NIR-aRed-b)###Baret et al

###TSAVI###a=10.489, b=6.604

vegetation index###aNIR+Redab###(1989)(Baret et al., 1989)

Soil adjusted vegetation###(1+L) (RNIR-RRed)/ (RNIR+RRed+L)###Huete et al.

###SAVI

index###L=0.5###(1988)(Huete, 1988)

Optimized###soil-adjusted###Ronddeaux et al.(1996)(Rondeaux

###OSAVI###(1+0.16) (R800-R670)/ (R800-R670+0.16)

vegetation index###et al., 1996)

Renormalized###difference###Reujean et al.

vegetation index

Table 3. Quantitative relationships between CHLD(y) and canopy reflectance of 670nm(x)in winter wheat

Treatment###Regression equation###Determination coefficient R2

Irrigated land###y=12.364e-0.3382x###0.9208

Dry land###y=-0.0188x2+0.1998x+3.3992###0.4057

Table 4. Correlation analysis between CHLD and eight vegetation index

###Water land###Dry land

spectral index

###CHLD###CHLD

TSAVI###0.90###0.68

NDVI###0.92###0.65

OSAVI###0.90###0.65

SAVI###0.90###0.65

PVI###0.90###0.62

RDVI###0.87###0.67

DVI###0.84###0.70

RVI###0.73###0.51

indicates P less than 0.01.

Table 5. Quantitative relationships of CHLD(y) to individual spectral index(x)in water and dry land of winter wheat

Treatment###Spectral parameter(x)###Linear regression equation###R2

###NDVI(780, 670)###y=7.1019x2.6863###0.9451

###OSAVI(780, 670)###y=4.8185x2.6852###0.9449

Irrigated land

###SAVI(780, 670)###y=2.4735x2.6829###0.9447

###TSAVI(780, 670)###y=4.7808e0.4644x###0.9332

###DVI(1200, 670)###y=1E-05x4.1715###0.7346

###RDVI(1200, 600)###y=0.0112x5.0458###0.7255

Dry land

###TSAVI(1200, 600)###y=60.92e0.8726x###0.683

###SAVI(1200, 600)###y=11.092x4.293###0.6298

Establishment of spectrum model based on CHLD: Regression analysis was performed between eight spectrum vegetation index and CHLD of winter wheat in irrigated land and dryland. The results indicated NDVI, OSAVI, SAVI, and TSAVI were better spectral parameters to monitor in irrigated land, but DVI, RDVI, TSAVI, and SAVI were better spectral parameters to monitor in dryland (Table 5). Of them, NDVI (780, 670) could predict the CHLD of winter wheat in irrigated land, with 92.36% precision, but DVI (1200, 670) could predict the CHLD of winter wheat in dryland, with 85.78% precision(Figure 4).

Recent studies have demonstrated that spectrum parameter is associated with LAI and CHLD. For example, vegetation indices, such as NDVI, RDVI, and SAVI, etc were sensitive to changes of chlorophyll concentration and were affected by saturation at high LAI (Haboudane et al., 2004). Bannari et al (2007) investigated the correlation between chlorophyll content and a wide range of hyperspectral chlorophyll indices and found that normalized pigments chlorophyll ratio index (NPCI) showed the best results when R2= 0.84 and root mean squared (RMSE) = 11.0 hyperspectral chlorophyll indices (Bannari et al., 2007). Both of NDVI and MTVI2 indices provided good relationships with LAI (R2 = 0.70- 0.90). And the MTVI2 performed better than the NDVI at full canopy closure.

Estimation of LAI using the MTVI2 was underestimated late in the season during the seed- filling period (Smith et al., 2008). Identically, in this study, our results suggested that NDVI(780, 670) was more effective to predict the CHLD of winter wheat in irrigated land, when the determination coefficient (R2) was 0.9451, but DVI(1200, 670) was for CHLD of winter wheat in dry land, when the determination coefficient (R2) was 0.7346. The corresponding regression model was y irrigated land =7.1019x NDVI (780, 670)2.6863 and y dry land = 1E-05x 4.1715, respectively.

Conclusion: Our monitor model is demonstrated suitable for field experiment in Yuncheng town. Comprehensive analysis of LAI and CHLD in irrigated and dry land through remote sensing may provide technique for wheat dynamic management in Shanxi Province. However, a difference in canopy reflectance spectrum may be caused by some factors, such as irrigation times, and sampling time, etc. Many years, many regions, and many position experiments are still indispensable to further confirm.

Acknowledgements: This study was supported by Postdoctoral Foundation of China (No.125375); The Key Technologies R and D Program of Shan Xi Province, China (No.20060311140; 20110311038; 20120311001-2; 20120311013-4; 20130311011-5)), the National Natural Science Foundation of China (No. 31201168), the Specialized Research Fund for the Doctoral Program of Higher Education of China (No.20111403110002), Shanxi Scholarship Council of China (No.2013-key-6),

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