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Detecting leaf nitrogen content in wheat with canopy hyperspectrum under different soil backgrounds
International Journal of Applied Earth Observation and Geoinformation 32 (2014) 114–124
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Detecting leaf nitrogen content in wheat with canopy hyperspectrum under different soil backgrounds X. Yao, H. Ren, Z. Cao, Y. Tian, W. Cao, Y. Zhu ∗ , T. Cheng National Engineering and Technology Center for Information Agriculture, Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, 1 Weigang Road, Nanjing, Jiangsu 210095, P.R. China
a r t i c l e
i n f o
Article history: Received 12 November 2013 Accepted 20 March 2014 Available online 4 May 2014 Keywords: Wheat canopy Leaf nitrogen content Vegetation coverage Soil background Spectral index Detecting model
a b s t r a c t Hyperspectral sensing techniques can be effective for rapid, non-destructive detecting of the nitrogen (N) status in crop plants; however, their accuracy is often affected by the soil background. Under different fractions of soil background, the canopy spectra and leaf nitrogen content (LNC) in winter wheat (Triticum aestivum L.) were obtained from field experiments with different N rates and planting densities over 3 growing seasons. Five types of vegetation index (VIs: normalized difference vegetation index (NDVI), ratio vegetation index (RVI), soil adjusted vegetation index (SAVI), optimize soil adjusted vegetation index (OSAVI), and perpendicular vegetation index (PVI)) were constructed based on three types of spectral information: (1) the original and the first derivative (FD) spectrum, (2) the spectrum adjusted with the vegetation coverage (FVcover ), and (3) the pure spectrum extracted by a linear mixed model. Comprehensive relationships of above five types of VI with LNC were quantified for LNC detecting under different soil backgrounds. The results indicated that all five types of VI were significantly affected by the soil background, with R2 values of around 0.55 for LNC detecting, with the OSAVI (R514 , R469 )L=0.04 producing the best performance of all five indices. However, based on the FVcover , the coverage adjusted spectral index (CASI = NDVI(R513 , R481 )/(1 + FVcover )) produced the higher R2 value of 0.62 and the lower RRMSE of 13%, and was less sensitive to the leaf area index (LAI), leaf dry weight (LDW), FVcover , and leaf nitrogen accumulation (LNA). The results demonstrate that the newly developed CASI could improve the performance of LNC estimation under different soil backgrounds. © 2014 Elsevier B.V. All rights reserved.
1. Introduction Timely quantitative estimation of plant nitrogen (N) status could not only improve N use efficiency and crop yields but also reduce environmental pollution from excessive N fertilization (Fitzgerald et al., 2010). Rapid, non-destructive, and accurate detecting of N status in crop plants is important for N diagnosing in modern crop production. Spectral remote sensing is considered as a useful tool for real-time, non-destructive estimation of plant N status (Takebe et al., 1990), thus providing a scientific basis for the in-season precision N recommendation and dynamic yield prediction. Crop N content is closely related to canopy spectra (Johnson et al., 1994;). Spectral wavebands in regions of the near-infrared (760–900 nm) and visible (630–660 nm, 530–560 nm) bands can be used to estimate crop N content (N g−1 leaf) (Blackmer et al.,
∗ Corresponding author. Tel.: +86 25 84396598; fax: +86 25 84396672. E-mail address: [email protected] (Y. Zhu). http://dx.doi.org/10.1016/j.jag.2014.03.014 0303-2434/© 2014 Elsevier B.V. All rights reserved.
1996). By combining the reference at different wavebands with statistical methods, several types of vegetation index such as the normalized difference vegetation index (NDVI) and ratio vegetation index (RVI) were developed for detecting crop N content (Lyon et al., 1998; Hansen and Schjoerring, 2003). NDVI (R573 , R440 ), RVI (R660 , R440 ), and NDVI (R1220 , R710 ) have been used to develop exponential or linear models for monitoring leaf N content (LNC) in wheat (Triticum aestivum L.) (Xue et al., 2004; Li et al., 2006). NDVI (R1220 , R610 ) was found to be significantly correlated (R2 = 0.889) with LNC in wheat and rice (Oryza sativa L.) (Zhu et al., 2007). Additionally, mND705 , REPLE , FD729 , AVHRR–GVI, RVI (R1350 , R700 ), and SAVI (R1350 , R700 )L=0.09 indices (Feng et al., 2008; Yao et al., 2009) can effectively monitor the N content in wheat. The classical spectral indices built on statistical models have a simple formulation for easy use, but they do not show good stability and robustness due to the spectral characteristics of the crop. Soil background inevitably coexists with the vegetation in the canopy spectra mentioned above, especially in the early growth stages (such as tillering and jointing) or on low-density fields.
X. Yao et al. / International Journal of Applied Earth Observation and Geoinformation 32 (2014) 114–124
Existing vegetation indices are unable to effectively eliminate the soil background influence (Huete et al., 1985), which reduces the accuracy and reliability of the monitoring models and limits their extrapolation and application. Many studies have been conducted to construct, adjust, or enhance vegetation indices with the aim of eliminating or gradually reducing the impact of soil background. Based on soil line theory, Richardson and Wiegand (1977) developed the perpendicular vegetation index (PVI), which is less affected by soil brightness than other indices are. Based on the, Huete et al. (1985) proposed the soil adjusted vegetation index (SAVI) to describe the soil-vegetation system and to correct for the sensitivity of NDVI to the soil background. The transformational soil adjusted vegetation index (TSAVI) (Baret et al., 1989) and the modified soil adjusted vegetation index (MSAVI) (Rondeaux et al., 1996) were developed from SAVI. Based on the soil moisture and the changes in solar incidence angle, Major et al. (1990) developed three new forms of SAVI (SAVI2 , SAVI3 , and SAVI4 ) to reduce the effects of soil background. Fitzgerald et al. (2010) developed the canopy chlorophyll content index (CCCI) to estimate plant N concentration by eliminating the effect of soil background through a normalized difference procedure that could be used under different types of vegetation cover. The normalized area over reflectance curve (NAOC) provides accurate plant N estimations for various species, canopies, and soil types (Delegido et al., 2010). Haboudane et al. (2002) developed a new spectral index, TCARI/OSAVI, with simulated spectra by combining the PROSPECT and SAILH radiation transfer models. The index is sensitive to chlorophyll content and is less affected by soil background and leaf area index (LAI). The modified chlorophyll absorption ratio index (MCARI) and the triangular vegetation index (TVI) were further developed, which were highly related to chlorophyll while minimizing soil background and LAI effects (Guan and Liu, 2009). To achieve a pure spectrum of canopy plants in which the soil background has been eliminated or minimized, researchers have attempted to decompose the mixed spectra obtained from the field plot and then calculate the area of each end member with a high degree of accuracy (Chen and Vierling, 2006). Attempts to minimize the effects of the soil background from canopy hyperspectra in cereal crops have progressed through three approaches: (1) developing the classic or existed spectrum indices with new wavebands, (2) constructing new spectral indices with novel wavebands and forms, and (3) establishing a model for decomposing the mixed spectra. However, little research has focused on the methods of constructing fractional vegetation on cover indices and evaluating their ability to eliminate the effects of soil background. Some studies have been undertaken in greenhouses (Darvishzadeh et al., 2008); however, this differs from the field conditions where the plant canopy and the soil background usually coexist during N topdressing at the tillering or jointing stages, and the ratio of vegetation to soil coverage changes over the growth period. Thus, it is crucial to develop new technologies for nondestructive monitoring of canopy N status under different fractions of plant cover and soil background in cereal crops. This study investigated potential methodologies and new spectral indices to minimize soil noise when monitoring canopy LNC in winter wheat under different fractions of vegetation cover and soil background. The main objectives were (1) to construct five types of vegetation indices (VIs) of RVI, NDVI, SAVI, OSAVI, and PVI with three types of spectral information; (2) to quantify the relationships of the newly developed spectral indices with LNC under different soil backgrounds in wheat and (3) to compare the performance of the newly established spectral indices and published spectral indices. The results would be assisted in the effective monitoring and diagnosis of the N status during wheat growth by spectral remote sensing.
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2. Materials and methods 2.1. Experiment design Three experiments involving different varieties, N rates, planting densities and sowing dates were carried out in the year of 2009–2012. The N applications were split 50% prior to seeding and 50% at jointing. Monocalcium phosphate and potassium chloride were applied at 120 kg P2 O5 hm−2 and 135 kg K2 O hm−2 at sowing. All treatments were located within a randomized complete block with three replications for each plot of 24 m2 with dimensions of 4 m × 6 m. More detailed information was listed in Table 1. 2.2. Data acquisition 2.2.1. Measurement of spectral reflectance The canopy and soil spectrum were continuously measured with a FieldSpec PRO FR2500 spectra-radiometer (Analytical Spectral Devices, Boulder, CO, USA) (Hatchell, 1999). This instrument records reflectance between 350 and 1000 nm with a 1.40nm sampling interval, and 3-nm resolution, and 1000–2500-nm reflectance at a sampling interval of 2 nm and a resolution of 10 nm. 2.2.1.1. Canopy spectra. The time-course spectral measurements from the spring regrowth to grain filling stage were carried out during cloud-free and low wind speed conditions at mid-day between 10:00 and 14:00. The spectrometer was placed vertically at a nadir of 1 m above the wheat canopy with a 25◦ field of view (FOV), so that it measured a surface diameter of 0.44 m. Measurements of vegetation radiance were obtained at five sites in each plot, with three scans of each site, and their average value was deemed to be the final canopy spectra for each plot. A white spectral reference panel (Labsphere, North Sutton, NH, USA) was used under the same illumination conditions to convert the spectral radiance measurements to reflectance. 2.2.1.2. Soil spectra. After measuring the canopy spectra, soil spectra were measured at two sites in each plot with three scans at each site, and the average value was taken as the soil spectrum of the plot. The soil spectrum was used to construct the soil line and drive the linear mixed model (Richardson and Wiegand, 1977; Vikhamar and Solberg, 2003). 2.2.2. Extraction of FVcover The fractional vegetation cover was extracted by digital image analysis (Li et al., 2004; Jia et al., 2010). The digital single-lens reflex (SLR) camera used in this research was a lower case OLYMPUS E620 (auto exposure and auto white balance). An iron square frame (the area of the inscribed circle was equivalent to the FOV of the spectrometer, 0.44 m × 0.44 m) was placed in the spectra sampling area before photography commenced. When the camera was in a vertical position with the frame facing downward, a digital image containing the frame was taken. The inscribed circular area was then cut using Photoshop CS5 and the color space of the image was transformed from RGB to HSL (Zhou and Shi, 1998) with MATLAB, finally the vegetation cover was extracted through the threshold (Li et al., 2004) (Fig. 1). The formulas are as follows: FVcover (fractional vegetation cover) = Av /Atotal
(1)
FScover (fractional soil cover) = 1 − Av /Atotal ,
(2)
where FVcover is the fractional vegetation crop cover, FScover is the fractional soil cover, and Av and Atotal are the areas of wheat vegetation and the entire visual field, respectively.
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Table 1 Basic information about three experiments conducted at two eco-sites. Exp. number
Year
Soil characteristics (clay soil)
Variety
Treatment (Sowing date (S); Planting density (D): cm; nitrogen rates (N): kg/ha2 )
Date for spectrum detecting
Exp. 1 (validation data set) Yizheng
2009–2010
Yangmai 16 Aikang 58
Exp. 2 (calibration data set) Yizheng
2010–2011
Organic matter: 17.9 g kg−1 , total N: 1.4 g kg−1 , available P: 36 mg kg−1 , available K: 89 mg kg−1 . Organic matter:13.5 g kg−1 , total N: 1.1 g kg−1 , available P: 43 mg kg−1 , available K: 82 mg kg−1 .
Exp. 3 (calibration data set) Rugao
2011–2012
3 D: D1 (45 cm, 1.33 × 106 plants ha−2 ), D3 (35 cm, 1.71 × 106 plants ha−2 ), D5 (25 cm, 2.4 × 106 plants ha−2 ) 2 N: N1 (150), N2 (225) 3 D: D2 (40 cm, 1.50 × 106 plants ha−2 ), D4 (30 cm, 2.00 × 106 plants ha−2 ), D6 (20 cm, 3.00 × 106 plants ha−2 ) 2 N: N1 (150), N3 (300) 3 S: S1 (October 15), S2 (October 30), S3 (November 14) 6 D: D1 (7.5 × 105 plants ha−2 ), D2 (1.5 × 106 plants ha−2 ), D3 (2.25 × 106 plants ha−2 ), D4 (3.0 × 106 plants ha−2 ), D5 (3.75 × 106 plants ha−2 ), D6 (4.5 × 106 plants ha−2 )
Once every 7–10 days from Spring re-growth to filling except rainy or cloudy Once every 7–10 days from Spring re-growth to filling except rainy or cloudy Once every 7–10 days from Spring re-growth to filling except rainy or cloudy
Yangmai 16
Yangmai 16
2.2.3. Determination of agronomic parameters After each measurement of canopy spectral reflectance, 10 plants from each plot were randomly selected and destructively sampled for the determination of leaf dry weight (LDW), LNC and LAI. For each sample, all green leaves were separated from their stems and oven-dried at 105 ◦ C for 30 min and then at 80 ◦ C until a constant weight was achieved. Dried leaf samples were ground to pass through a 1-mm-mesh screen and then stored in plastic bags until to chemical analysis. Total N concentration was determined by Micro-Keldjahl method, and LNC (% or g N g−1 DW) is expressed on a dry weight basis. LNA (g N m−2 ) was calculated as the product of LNC on dry weight basis (g 100 g−1 ) and LDW per unit ground area (g DW m−2 ). The green leaf area was measured using an LI-3000 meter (LI-COR).
2.3. Data analysis and utilization Five types of spectral index (RVI, NDVI, SAVI, OSAVI, and PVI) were developed with all possible two-band combinations between 350 and 2500 nm to determine the optimal combination of center bands for LNC detecting. Each spectral index was calculated based on three types of spectral information as the original spectrum and the first derivative spectrum, the spectrum adjusted by the fractional vegetation cover (FVcover ), and the pure spectrum extracted by a linear mixed model. A comprehensive analysis was undertaken to determine the relationships of the five types of spectral index with LNC to derive monitoring models in wheat. The analysis was based on a selfdeveloped computer program of MATLAB (The MathWorks, 2000). The detailed formulation of spectral indices with the original and the first derivative spectrum is summarized in Table 2.
Fractional vegetation cover (FVcover ), expressed as the proportion of vegetation area to ground area in the microenvironment of a field crop, is a very important parameter for analyzing the effect of the soil background. Previous studies have reported that the parameter L of the soil adjusted vegetation index (SAVI) was 1 for low-density vegetation, 0.5 for medium-density vegetation, and 0.25 for high-density vegetation (Huete et al., 1988; Rondeaux et al., 1996). Our observations indicated that the value of L has a dynamic relationship with vegetation cover. Therefore, the coverage-adjusted spectral index (CASI) was proposed to quantitatively correct for the effect of changing vegetation density and soil cover on the retrieval of LNC from canopy spectra. The CASI was derived by adding the factor of FVcover into the formula of Vis in four ways: VIs*FVcover , VIs/FVcover , VIs*(1 + FVcover ), and VIs/(1 + FVcover ). A mixed model is able to reveal the complex optical properties of the surface of an object and then decompose it into a pure spectral end member, which is useful for estimating the biochemical components of the surface (Van Leeuwen et al., 1997). Mixed models can be divided into linear and nonlinear models. The linear model is widely used due to its simple structure, ease of calculation, and high accuracy (Painter et al., 1998; Vikhamar and Solberg, 2003). The formula of constructing the linear mixed model and extracting pure wheat spectrum is referred by Adams et al. (1993). 2.4. Calibration and validation of the model The two datasets from Exp. 2 and Exp. 3 were used for model development, and the other, independent datasets (Exp. 1) were used for model testing. The determination coefficient (R2 ), standard error (SE), relative root mean square error (RRMSE), and slope were calculated to evaluate the fit between the predicted and observed values.
Fig. 1. Flowchart for extracting fractional vegetation cover.
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Table 2 Algorithms of five VIs used in this study. Vegetation index
Pretreatment of spectrum
Algorithm
Reference
RVI NDVI SAVI OSAVI
Original/FD Original/FD Original/FD Original/FD
Pearson and Miller (1972) Rouse et al. (1974) Huete (1988) Rondeaux et al. (1996)
PVI
Original/FD
R1 /R2 R1 − R2 /R1 + R2 (1 + L)*(R1 − R2 )/(R1 + R2 + L) (1 + X)*(R1 − R2 )/(R1 − R2 + X) RNIR −a×RRED −b √ 1+a2
Richardson and Wiegand (1977)
Note: 1 and 2 are the random wavelengths; 1 and 2 are the reflectance of 1 and 2 in the range 350–2500 nm, respectively; L&X are the adjustment factors defined for SAVI and OSAVI, respectively; a and b are the slope and intercept for the soil line. FD is the first derivative.
3. Results
3.4. Quantitative relationship of canopy LNC with spectral indices
3.1. Patterns of change in FVcvoer and LNC
3.4.1. Classical spectral indices The five types of spectral index (RVI, NDVI, SAVI, OSAVI and PVI) were calculated with all available two wavebands combination in the range of 350–2500 nm, and the relationships between LNC and spectral indices with data from Exp. 2 and 3 were analyzed to identify the optimum waveband combination for LNC detecting. The optimal spectral index of each type of spectral index for LNC detecting was extracted based on the higher R2 value and lowest RRMSE value. The optimum wavebands combinations were consistent among different types of spectral index investigated, except for PVI (Fig. 7). Thus, the different types of spectral index had similar potential for LNC monitoring, although there were significant differences in structure and form of spectral index. The optimal wavebands of different types of spectral index were located in the spectral regions at 460–520 nm. The optimum bands were the same for RVI and NDVI, and OSAVI and PVI had similar band combinations. The optimal bands for SAVI were a combination of the bands in RVI and PVI. Among the five types of spectral index investigated, OSAVI showed the highest correlation with LNC, followed by RVI, NDVI, and PVI, whereas SAVI was the lowest. Table 3 shows the optimal band combinations, and the performance of the spectral indices with optimal band combinations for LNC detecting. The results show that five types of spectral index performed similar under different soil backgrounds, with a R2 of approximately 0.56, among which the OSAVI (R514 , R469 )X=0.04 performed best. The 1:1 plotting with the observed and predicted values exhibited the reliability and accuracy of the derived models, as shown in Fig. 8. It should be noted that the LNC monitoring models based on the first derivative spectrum did not improve the model performance, as compared with the models based on the original spectrum (Table 4). Though they have higher accuracy in calibration, worse reliability in validation with lower R2 and higher RRMSE in similar waveband combinations, when compared with original spectra. The reason may be due to the more noise from the derivative spectra.
Figs. 2 and 3 revealed the dynamic patterns of FVcover of Yangmai 16 under different N rates and planting densities during different growth stages. FVcover increased rapidly, reached a plateau, and then decreased as growth progressed, with the peak at the booting or heading stage. FVcover under the high-planting density treatment was significantly greater than that under the low-planting density treatment with an identical rate of N rates (Fig. 3A). The difference between planting density treatments was reduced when the N application rate was increased (Fig. 3B). Fig. 4 showed that the changes in wheat LNC at different planting densities (D2, D4, and D6) and rates of N application (N1, N3) over the growing degree days were similar, although there were differences in LNC values. LNC decreased over time under different planting densities at the N1 application rate, in the order of D6 < D4 < D2 after 1022 ◦ C d growing degree days (Fig. 4A). At constant density (D4), LNC increased with increasing N application rate (Fig. 4B). 3.2. Features of canopy spectra Canopy spectral reflectance was affected by the concentration of plant biochemical, LAI, canopy architecture, and the soil background. Fig. 5A implied that canopy spectra were reduced with increasing vegetation cover in the visible wavebands (350–720 nm). However, the spectra obtained for C1 (50%) was lower than that for C2 (57%) due to the influence of the soil background. In the near-infrared bands (740–1350 nm), the canopy spectra were elevated due to considerable scattering among leaves, canopy, and soil. Fig. 5B showed the features of canopy hyperspectral reflectance under varied LNC with the same FVcover from Exp. 2. The canopy spectra were reduced with increasing LNC in visible wavebands while dropped in near-infrared bands. There was a consistent trend over all bands with varied LNC. 3.3. Correlation between canopy spectra and LNC According the Exp. 2, Fig. 6A shows that, in most cases, LNC was positively correlated with canopy spectra under different densities. Fig. 6B indicates that the trend in the correlation between the original canopy spectra and LNC was similar for different rates of N application, but the correlation was higher at the lower rate of N application, especially in the infrared band. This indicates that the soil background seriously affected the relationship between reflectance and LNC. A previous study reported that in the visible wavebands (460–710 nm) and near-infrared long wavebands (1480–1650 nm), spectral reflectance decreased with rising rates of N application, whereas in the near-infrared short wavebands (760–1220 nm), the reflectance increased with increasing N application (Zhu et al., 2008).
3.4.2. The cover-adjusted spectral index (CASI) According to the method described in Section 2.3, the CASI based on four forms was developed and the NDVI/(1 + FVcover ) performed best on LNC detecting. Fig. 9A shows a contour map of R2 values between NDVI/(1 + FVcover ) and LNC. It was observed that the higher R2 values between NDVI/(1 + FVcover ) and LNC were focused in the range 400–500 nm, whereas for NDVI, the higher R2 values were in the range 400–700 nm (Fig. 9B). The center bands are 513 nm and 481 nm for NDVI/(1 + FVcover ), which are similar as that for SAVI (514 nm, 481 nm) on the original spectra (Table 3). The newly developed CASI = NDVI(R513 , R481 )/(1 + FVcover ) performed better, with an R2 of 0.6223 and RRMSE of 13% (Fig. 10), compared with that of NDVI(R513 , R481 ) and SAVI(R514 , R481 )L=0.1 (Fig. 9B and Table 3). This result indicated that the newly developed CASI, NDVI(R513 ,
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1
1
0.8
0.8
FVcover
FVcover
Fig. 2. The field pictures of different plant densities and nitrogen levels at varied growth stages in Exp. 2.
0.6
N3D2 N3D4 N3D6
A 0.4
0.2 700
1000
1300
1600
1900
Growing degree day
0.6
N1D3
B
0.4
N3D3
2200
0.2 700
(oC· d)
1000
1300
1600
1900
2200
Growing degree day (o C· d)
Fig. 3. Changes of fractional vegetation cover over growth progress under different plant densities (A) and nitrogen levels (B) in Yangmai 16.
5 Leaf nitrogen concentration (%)
Leaf nitrogen concentration (%)
5 N1D6 N1D4
4
N1D2 3 2
A
1 0
700
1000
1300
1600
Growing degre e day
1900 (o C· d)
2200
N1D4 4
N3D4
3 2
B
1 0 700
1000
1300
1600
1900
2200
Growing degree day (oC·d)
Fig. 4. Changes of LNC over growing degree days under different plant densities (A) and nitrogen treatments (B) in Yangmai16.
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Fig. 5. Features of canopy hyperspectral reflectance at jointing in Exp. 2. 0.6
0.6
B 0.4
0.2
0 350 -0.2
78 0
121 0 164 0 Wavelenth(nm)
207 0
250 0
Correlation coefficent
Correlation coefficent
A 0.4
D6 D4 D2
-0.4
0.2
0 350
780
1210
1640
2070
2500
Wavelenth(nm)
-0.2
N1
-0.4
N3 -0.6
-0.6
Fig. 6. Correlations of canopy original (A, B) and first derivative spectrum (C, D) to LNC in wheat canopy (A, C: varied plant cover; B, D: varied nitrogen content). Table 3 Quantitative relationship between LNC and original spectral index (n = 167) and its testing performance (n = 162). Spectral parameter
OSAVI (R514 , R469 )X=0.04 RVI (R507 , R481 ) NDVI (R507 , R481 ) PVI (R514 , R465 ) SAVI (R514 , R481 )L = 0.1
Regression equation
y = −26.63x + 5.234 y = −11.16x + 15.742 y = −26.01x + 4.713 y = 293.81x − 3.054 y = −40.82x + 5.375
Calibration
Validation
R2
SE
R2
RRMSE
Slope
0.564 0.559 0.557 0.551 0.545
0.175 0.174 0.170 0.180 0.182
0.521 0.522 0.523 0.475 0.456
0.145 0.142 0.141 0.894 0.160
1.395 1.338 1.334 1.277 1.287
The best performance of the five VIs was marked in bold.
R481 )/(1 + FVcover ), effectively reduced the effect of the soil background. Further analysis was conducted to evaluate whether NDVI(R513 , R481 )/(1 + FVcover ) is only sensitive to LNC and less affected by LAI, LDW, FVcover , and LNA. The top 5% area (95%R2 max < R2 LNC < R2 max ) of the R2 contour map between NDVI (R1 , R2 )/(1 + FVcover ) and LNC was extracted, as was the bottom 5% (e.g., LAI, R2 min < R2 LAI < 5%R2 max ) area of the R2 contour map between
NDVI(R1 , R2 )/(1 + FVcover ) and LAI, LDW, FVcover , and LNA. The overlap area between R2 LNC and R2 LAI , R2 LDW , R2 FVcover was in the range 435–526 nm (Fig. 11), whereas the overlap region between R2 LNA and R2 LNC was smaller, in the range 452–521 nm. This suggested that the band combinations in this region (452–521 nm) were sensitive to LNC and less affected by the LAI, LDW, FVcover , and LNA. The coefficient of determination (R2 ) between NDVI(R513 , R481 )/(1 + FVcover ) and LNC, LAI, LDW, FVcover , and LNA were 0.622,
Table 4 Quantitative relationship of LNC to spectral indices with optimum first derivative spectrum (n = 167) and its testing performance (n = 162). Spectral index
PVI(FD505 , FD403 ) OSAVI(FD505 , FD403 ) X = −0.01 SAVI(FD505 , FD403 ) L = −0.1 RVI (FD690 , FD419 ) NDVI(FD684 , FD419 ) Note: FD is the first derivative spectrum.
Regression equation
y = 6261x − 148.8 y = 41.40x + 4.012 y = 492.1x + 4.025 y = −0.372x + 4.493 y = −2.641x + 3.359
Calibration (n = 167)
Validation (n = 162)
R2
SE
R2
RRMSE
Slope
0.598 0.594 0.593 0.552 0.521
0.161 0.162 0.163 0.179 0.192
0.317 0.300 0.286 0.132 0.171
0.184 0.185 0.186 0.238 0.202
0.833 0.805 0.778 0.399 0.700
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Fig. 7. Regions with correlation coefficients over 0.3 between spectral index (RVI, NDVI, PVI, OSAVI and SAVI) and LNC.
0.002, 0.004, 0.002, and 0.030, respectively. This indicates that NDVI(R513 , R481 )/(1 + FVcover ) could be well used for LNC detecting and less affected by LAI, LDW, FVcover , and LNA. 3.4.3. Linear mixed model The accuracy of LNC detecting was enhanced by modifying the spectral index with FVcover ; however, this accuracy is still lower in the early growth stage of crops than after complete canopy cover. Thus a linear mixed model was used to further minimize the effect of soil background by coupling FVcover and soil spectra. Table 5 shows that the R2 of the calibrated model on an unmixed spectrum increased slightly for RVI, NDVI, and SAVI, whereas it decreased for OSAVI and PVI, with the worst performance in PVI.
The factor X of OSAVI changed from 0.03 to 0; however, the R2 value decreased from 0.638 to 0.624. This indicated that the linear mixed model partly eliminated the soil effects, although it lost some of the vegetation information. The optimum band combinations listed in Table 5 after unmixing were 506 nm and 478 nm, which were the same as those for RVI on the original spectrum. The sensitive wavelengths for LNC did not change after linear mixed processing. A linear decomposition could partly eliminate the effects of the soil background. Further analysis of optimum band combination on mixed and unmixed spectra for estimating LNC was conducted. As an example, the R2 contour map (Fig. 12) between RVI and LNC shows that, compared with the mixed spectrum (the original spectra from a
Fig. 8. Calibration (n = 167) and validation (n = 162) of LNC model on OSAVI (R514 , R469 )X=0.04 .
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Fig. 9. Contour map for coefficient of determination (R2 ) between VIs and LNC (A: NDVI; B: NDVI/(1 + FVcover)).
Fig. 10. Performance of calibration (A, n = 167) and validation (B, n = 162) for LNC model on NDVI(R513 , R481 )/(1 + FVcover ).
Fig. 11. Sensitive and insensitive regions of LNC and other relevant parameters based on FVcover adjusted vegetation spectral index in wheat.
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Table 5 Selected wavebands and determination coefficient (R2 ) between LNC and different spectral indices. Spectral spectral index
OSAVI RVI NDVI SAVI PVI
Mixed spectral index (n = 124)
Unmixed spectral index (n = 124)
R2
1
2
L(X)
R2
1
2
L(X)
0.638 0.619 0.618 0.618 0.617
514 507 507 507 513
469 481 481 481 469
0.03
0.624 0.626 0.624 0.624 0.557
506 506 506 506 504
478 478 478 478 476
0
field plot) (Fig. 12A), the R2 value (Fig. 12B) increased and had a larger region of good performance (R2 > 0.3) from 400 to 700 nm, with a stronger correlation (R2 > 0.5) around 500 nm. The validation performance of the spectral index with unmixed spectra was poor compared with that for the mixed spectra (the original spectra). The validated R2 decreased from 0.5 to 0.03, and the RRMSE increased to 200% (Table 6). This result may be because a previous crop (rice straw) was present on the soil surface during the wheat experiment in the 2009–2010 seasons. Only two end members of wheat vegetation and soil background were used in this paper, and senescent leaves and other crop residues, which are important to the accuracy of linear decomposition, were not considered. 3.5. Comparison of newly developed spectral indices with published spectral indices Optimum spectral indices reported in previous studies were selected for comparison with the newly established indices using the validation data set from Exp. 1 (Table 7). The result showed that the new spectral index, NDVI(R513 , R481 )/(1 + FVcover ), generally performed better than several published indices. Overall, the new spectral index is expected to perform well in predicting LNC in wheat under various soil backgrounds.
0
0
canopy spectra in current study and previous one. The condition of the wheat field, a coexistence environment of vegetation and soil, was well presented by the experimental design during dressing, tillering, jointing, and booting periods. The NDVI has been successfully applied to monitor the leaf N status of wheat. Subsequently, the SAVI, TSAVI, and OSAVI indices were proposed to reduce the effects of soil background for N estimation with a soil-adjustable parameter (Gilabert et al., 2002). A statistical model based on the LOPEX’93 spectral database was used to analyze the ability of reducing the effect of soil background (Sun and Cheng, 2011). The spectral index TCARI/OSAVI simulated by the PROSPECTSAILH model can reduce the effects of the soil background from a canopy spectrum (Zarco-Tejada et al., 2004). The results of the present study showed that OSAVI was the best performing index, followed by RVI, NDVI, and PVI; and the poorest performance was observed in SAVI. This supports the assumption that the PVI or the SAVI were not sufficient to explain or predict vegetation responses when there was serious interference (Huete et al., 1985) and some noise from the soil background resulted in lower R2 for the five spectral indices. However, the newly established parameter NDVI (R513 , R481 )/(1 + FVcover ) performed well. The optimal band combinations were located in the green spectral region (460–520 nm), which is consistent with the conclusions of previous studies (Stroppiana et al., 2009; Tian et al., 2011). The results also confirmed that the 530 nm wavelength is sensitive to LNC (Tian et al., 2011) and was less affected by soil background.
4. Discussion 4.1. The performance of different vegetation indices with soil background interference
4.2. The ability of different methods to mitigate against soil background interference
Soil background affects the canopy spectra due to the combined effects of planting density, row spacing, canopy structure, wind, and various other factors (Rondeaux et al., 1996). Our results indicate that the correlation between LNC and canopy spectra is greatly affected by the soil background, with other obvious limitations of the canopy spectrum for monitoring LNC. The influence of soil spectra might be a reason for difference of correlation between LNC and
In this study the model calibration for the soil adjusted vegetation indices (SAVI, OSAVI, and PVI) based on derivative spectra performed better than those derived from original spectra, but the model performance of validation data was significantly worse when using the original spectral data. Although the derived spectra can improve the efficiency of extracting an objective spectrum and help to eliminate interfering factors, they were unstable among
Fig. 12. Contour map for coefficient of determination (R2 ) between RVI (A: Mixed; B: Unmixed) and LNC.
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Table 6 Testing performance of LNC estimation model based on spectral indexes of original spectrum and linear decomposing spectrum. Spectral index
RVI NDVI SAVI OSAVI PVI
Mixed spectral index
Unmixed spectral index
R2
RRMSE
Slope
R2
RRMSE
Slope
0.522 0.523 0.523 0.522 0.475
0.141 0.140 0.140 0.143 1.031
1.245 1.236 1.236 1.291 1.049
0.085 0.035 0.035 0.035 0.118
0.630 2.101 2.101 2.101 0.984
0.087 0.017 0.017 0.017 0.342
Table 7 Comparison of the performance of the newly developed spectral indices with published spectral indices. Spectral index
NDVI(R513 , R481 )/(1 + FVcover ) SIPI:(R800 -R445 )/(R800 -R680 ) NDVI(R573 , R440 ) RVI(R870 , R660 ) SASI(R1350 , R700 )L=0.09 R705 /(R717 + R491 ) MTCI
Regression equation
y = −29.03x + 4.961 y = −3.948x + 7.630 y = −8.09x + 5.752 y = −0.003x + 3.025 y = 1.275x + 2.464 y = 0.307x + 2.854 y = 0.029x + 2.903
Calibration
Validation
Source
R2
SE
R2
RRMSE
Slope
0.622 0.080 0.409 0.001 0.036 0.001 0.003
0.132 0.368 0.236 0.400 0.386 0.400 0.399
0.540 0.082 0.273 0.008 0.016 0.001 0
0.130 0.212 0.168 0.200 0.197 0.200 0.199
1.247 1.441 1.014 −3.889 0.741 0.975 0.112
the different experiment datasets and provided no consistency of prediction. In view of the correlation of the adjusted parameter L in SAVI and OSAVI with FVcover , FVcover was used to correct the soil-adjusted factor. The results showed that the newly developed index NDVI(R513 , R481 )/(1 + FVcover ) was strongly correlated with LNC, with the calibrated R2 increasing from 0.56 to 0.62 and the validated RRMSE decreasing from 14% to 13%. A sensitivity analysis indicated that NDVI(R513 , R481 )/(1 + FVcover ) was a good indicator of LNC that was less affected by other factors such as LAI, LDW, FVcover , and LNA. Finally, a linear mixed model was introduced to eliminate the effects of soil background. The linear mixed model assumes that each photon only touches one surface and that they do not mix before being received by a sensor (Adams et al., 1993). However, the performance of linear model in our paper I was poor, although the R2 was slightly higher, and the validated R2 decreased. This may due to the previous crop residues, thus plant shadows, and falling leaves should be considered in the linear mixed model in the future. Moreover, the actual canopy spectrum is a combination of several complex optical physical processes, and it is difficult to quantify the impact of the soil background in practical applications (Stroppiana et al., 2009). In the future, a non-linear mixed model (Delegido et al., 2010) may be introduced to further elucidate the light-scattering effects among the various components of the mixed environment to propose new spectral indices (Rondeaux et al., 1996; Zarco-Tejada et al., 2004). Thus, further research is needed to determine how the soil background affects the canopy spectra when combined with field conditions and to construct new indices taking into account harvest residues, leaf fall, and the composition of other non-photosynthetic material. 4.3. Center waveband and monitoring model for estimating LNC in wheat This study analyzed the relationships of five types of spectral indices with LNC using three methods to reduce the influence of soil background in a wheat crop. The results revealed that the center bands of the wheat canopy LNC were focused on the green regions between 510 and 480 nm. Compared with the original spectra, the center bands of the derivative spectra extended to both sides of the short and long wavelength located at 605 nm and 403 nm, and
This paper ˜ Penuelas et al. (1995) Hansen and Schjoerring (2003) Zhu et al. (2007) Yao et al. (2009) Tian et al. (2011) Dash and Curran (2007)
at 690 nm and 419 nm. The optimal center bands of the CASI were 513 nm and 481 nm, which was similar to the optimum bands after mixture decomposing (506 nm, 478 nm). Regardless of how the data were analyzed, the center bands indicating LNC were always in the range of 478 (±3) nm and 510 (±3) nm. However, it was still necessary to further eliminate the soil influence to increase the crop N monitoring accuracy. The best LNC monitoring model was the linear model (y = –29.025x + 4.9613, where y is the estimated LNC, and x is the spectral index) based on the spectral index of NDVIFVcover , which was better than other methods used in this study. In addition, measuring the canopy spectra obliquely could partly avoid the soil background, but the shadow of the leaves and the bidirectional reflectance would affect the detecting value of canopy spectra. In fact, we have measured the reflectance at different angles in a separate recent study, and our results showed that the 60◦ backward maybe a better observation angle than the 0◦ of vertical of this paper. Oblique measurement could not largely enhance the monitoring accuracy, as compared to the vertical measurement. And we will continue to focusing on researching the obliquely measurement in the future. 5. Conclusions In field experiments under different N rates and plant densities in winter wheat over 3 growing seasons, the canopy spectra and LNC were obtained under different vegetation coverage. A systematic analysis was conducted to examine the relationships of canopy LNC with five types of spectral index (RVI, NDVI, SAVI, OSAVI, and PVI) using three different spectral information (1) the original and first derivative of the spectrum, (2) the fractional vegetation cover, and (3) the pure spectrum extracted by a linear mixed model. Among five types of spectral index, through the performance of the OSAVI was the best for estimating LNC. Of three methods, the newly spectral index, NDVI(R513 , R481 )/(1 + FVcover ), which decreased the impact of the soil background to a certain extent, was strongly correlated (R2 = 0.62) with the canopy LNC of wheat. Additionally, it was less affected by LAI, LDW, FVcover , and LNA. Although the performance of the linear mixed model was not as good as anticipated because of the complex field environment, it would have greater potential if accurate end members were selected and crop residence were considered.
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Acknowledgments This work was supported by the National High-tech Research and Development Program of China (863 Program) (2013AA102404, 2011AA100703), National Natural Science Foundation of China (31201130, 31201131), Natural Science Foundation of Jiangsu Province (BK2010453), Science and Technology Support Plan of Jiangsu Province (BE2011351), Jiangsu Agriculture Science and Technology Innovation Fund SCX (12)3272, and Academic Program Development of Jiangsu Higher Education Institutions (PAPD). References Adams, J., Smith, M., Gillespie, A., 1993. Imaging spectroscopy: interpretation based on spectral mixture analysis. In: Pieters, C.M., Englert, P. (Eds.), Remote Geochemical Analysis: Elemental and Mineralogical Composition, 7. Cambridge University Press, New York, pp. 145–166. Baret, F., Guyot, G., Major, D., 1989. TSAVI: a vegetation index which minimizes soil brightness effects on LAI and APAR estimation. In: Proc. IGARRS’89. 12th Canadian Symposium on Remote Sensing, vol. 3, No. 1, Vancouver, Canada, pp. 1355–1358. Blackmer, T., Schepers, J., Varvel, G., Walter-Shea, E., 1996. Nitrogen deficiency detection using reflected shortwave radiation from irrigated corn canopies. Agron. J. 88 (1), 1–5. Chen, X., Vierling, L., 2006. Spectral mixture analyses of hyperspectral data acquired using a tethered balloon. Remote Sens. Environ. 103 (3), 338–350. Darvishzadeh, R., Skidmore, A., Atzberger, C., Van Wieren, S., 2008. Estimation of vegetation LAI from hyperspectral reflectance data: effects of soil type and plant architecture. Int. J. Appl. Earth Observ. Geoinform. 10 (3), 358–373. Dash, J., Curran, P., 2007. Evaluation of the MERIS terrestrial chlorophyll index (MTCI). Adv. Space Res. 39 (1), 100–104. Delegido, J., Alonso, L., González, G., Moreno, J., 2010. Estimating chlorophyll content of crops from hyperspectral data using a normalized area over reflectance curve (NAOC). Int. J. Appl. Earth Observ. Geoinform. 12 (3), 165–174. Feng, W., Yao, X., Zhu, Y., Tian, Y., Cao, W., 2008. Monitoring leaf nitrogen status with hyperspectral reflectance in wheat. Eur. J. Agron. 28 (3), 394–404. Fitzgerald, G., Rodriguez, D., O’Leary, G., 2010. Measuring and predicting canopy nitrogen nutrition in wheat using a spectral index—the canopy chlorophyll content index (CCCI). Field Crops Res. 116 (3), 318–324. Gilabert, M., González-Piqueras, J., Garcı´ıa-Haro, F., Meliá, J., 2002. A generalized soil-adjusted vegetation index. Remote Sens. Environ. 82 (2–3), 303–310. Guan, L., Liu, X., 2009. Two modified spectral indices for extraction of crop canopy chlorophyll content. Adv. Earth Sci. 24 (5), 548–554. Haboudane, D., Miller, J., Tremblay, N., Zarco-Tejada, P.J., Dextraze, L., 2002. Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sens. Environ. 81 (2), 416–426. Hansen, P., Schjoerring, J., 2003. Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression. Remote Sens. Environ. 86 (4), 542–553. Hatchell, D., 1999. Analytical Spectral Devices. Analytical Spectral Devices, Inc., ASD. Huete, A., 1988. A soil-adjusted vegetation index (SAVI). Remote Sens. Environ. 25 (3), 295–309. Huete, A., Jackson, R., Post, D., 1985. Spectral response of a plant canopy with different soil backgrounds. Remote Sens. Environ. 17 (1), 37–53. Jia, J., Hu, Y., Liu, L., 2010. Using digital photography to measure vegetation coverage in Qinghai–Tibet plateau. J. Geo-Inform. Sci. 12 (6), 880–885.
Johnson, L., Hlavka, C., Peterson, D., 1994. Multivariate analysis of AVIRIS data for canopy biochemical estimation along the Oregon transect. Remote Sens. Environ. 47 (2), 216–230. Li, C., Wang, J., Liu, L., Wang, R., 2004. Automated digital image analyses for estimating percent ground cover of winter wheat based on object features. J. Zhejiang Univ. 30 (6), 650–656. Li, Y., Zhu, Y., Tian, Y., Yao, X., Qin, X., Cao, W., 2006. Quantitative relationship between leaf nitrogen concentration and canopy reflectance spectra in wheat. Acta Agron. Sin. 32 (3), 358–362. Lyon, J., Yuan, D., Lunetta, R., Elvidge, C., 1998. A change detection experiment using vegetation indices. Photogram. Eng. Remote Sens. 64 (2), 143–150. Major, D., Baret, F., Guyo, t.G., 1990. A ratio vegetation index adjusted for soil brightness. Int. J. Remote Sens. 11 (5), 727–740. Painter, T., Roberts, D., Green, R., Dozier, J., 1998. The effect of grain size on spectral mixture analysis of snow-covered area from AVIRIS data. Remote Sens. Environ. 65 (3), 320–332. Pearson, R., Miller, L., 1972. Remote mapping of standing crop biomass for estimation of the productivity of the short-grass Prairie, Pawnee National Grasslands. In: Proc. of the 8th International Symposium on Remote Sensing of Environment, ERIM, Ann Arbor, MI, 2, pp. 1357–1381. ˜ Penuelas, J., Baret, F., Filella, I., 1995. Semi-empirical indices to assess carotenoids/chlorophyll a ratio from leaf spectral reflectance. Photosynthetica 31, 221–230. Richardson, A., Wiegand, C., 1977. Distinguishing vegetation from soil background information (by gray mapping of Landsat MSS data). Photogram. Eng. Remote Sens. 43 (12), 1541–1552. Rondeaux, G., Steven, M., Baret, F., 1996. Optimization of soil adjusted vegetation indices. Remote Sens. Environ. 55 (2), 95–107. Rouse, J., Haas, R., Schell, J., Deering, D., Harlan, J., 1974. Monitoring the Vernal Advancement of Retrogradation of Natural Vegetation. Final Report, Type III, NASA/GSFC, Greenbelt, MD, USA. 1–371. Stroppiana, D., Boschetti, M., Brivio, P., Bocchi, S., 2009. Plant nitrogen concentration in paddy rice from field canopy hyperspectral radiometry. Field Crops Res. 111 (1), 119–129. Sun, L., Cheng, L., 2011. Anti-soil background capacity with vegetation biochemical component spectral model. Acta Ecol. Sin. 31 (6), 1641–1652. Takebe, M., Yoneyama, T., Inada, K., Murakami, T., 1990. Spectral reflectance ratio of rice canopy for estimating crop nitrogen status. Plant Soil 122 (2), 295– 297. The MathWorks, 2000. MATLAB 6. 0. The MathWorks, Inc., Natick, MA. Tian, Y., Yao, X., Yang, J., Cao, W., Hannaway, D., Zhu, Y., 2011. Assessing newly developed and published vegetation indices for estimating rice leaf nitrogen concentration with ground-and-space-based hyperspectral reflectance. Field Crops Res. 120 (2), 299–310. Van Leeuwen, W., Huete, A., Walthall, C., Prince, S., Bégué, A., Roujean, J., 1997. Deconvolution of remotely sensed spectral mixtures for retrieval of LAI, FAPAR and soil brightness. J. Hydrol. 188, 697–724. Vikhamar, D., Solberg, R., 2003. Snow-cover mapping in forests by constrained linear spectral unmixing of MODIS data. Remote Sens. Environ. 88 (3), 309–323. Xue, L., Cao, W., Luo, W., Zhang, X., 2004. Correlation between leaf nitrogen status and canopy spectral characteristics in wheat. Acta Phytoecol. Sin. 28 (2), 172–177. Yao, X., Zhu, Y., Tian, Y., Feng, W., Cao, W., 2009. Research of optimun hypersepctral vegetation indices on monitoring the nitrogen content in wheat leaves. Sci. Agric. Sin. 42 (8), 2716–2725. Zarco-Tejada, P., Miller, J., Morales, A., Berjón, A., Agüera, J., 2004. Hyperspectral indices and model simulation for chlorophyll estimation in open-canopy tree crops. Remote Sens. Environ. 90 (4), 463–476. Zhou, X., Shi, P., 1998. Fast HLS transformation in color image processing. J. Shanghai Jiaotong Univ. 32 (9), 75–78. Zhu, Y., Yao, X., Tian, Y., Liu, X., Cao, W., 2008. Analysis of common canopy vegetation indices for indicating leaf nitrogen accumulations in wheat and rice. Int. J. Appl. Earth Observ. Geoinform. 10 (1), 1–10. Zhu, Y., Tian, Y., Yao, X., Liu, X., Cao, W., 2007. Analysis of common canopy reflectance spectra for indicating leaf nitrogen concentrations in wheat and rice. Plant Prod. Sci. 10 (4), 400–411.
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International Journal of Applied Earth Observation and Geoinformation journal homepage: www.elsevier.com/locate/jag
Detecting leaf nitrogen content in wheat with canopy hyperspectrum under different soil backgrounds X. Yao, H. Ren, Z. Cao, Y. Tian, W. Cao, Y. Zhu ∗ , T. Cheng National Engineering and Technology Center for Information Agriculture, Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, 1 Weigang Road, Nanjing, Jiangsu 210095, P.R. China
a r t i c l e
i n f o
Article history: Received 12 November 2013 Accepted 20 March 2014 Available online 4 May 2014 Keywords: Wheat canopy Leaf nitrogen content Vegetation coverage Soil background Spectral index Detecting model
a b s t r a c t Hyperspectral sensing techniques can be effective for rapid, non-destructive detecting of the nitrogen (N) status in crop plants; however, their accuracy is often affected by the soil background. Under different fractions of soil background, the canopy spectra and leaf nitrogen content (LNC) in winter wheat (Triticum aestivum L.) were obtained from field experiments with different N rates and planting densities over 3 growing seasons. Five types of vegetation index (VIs: normalized difference vegetation index (NDVI), ratio vegetation index (RVI), soil adjusted vegetation index (SAVI), optimize soil adjusted vegetation index (OSAVI), and perpendicular vegetation index (PVI)) were constructed based on three types of spectral information: (1) the original and the first derivative (FD) spectrum, (2) the spectrum adjusted with the vegetation coverage (FVcover ), and (3) the pure spectrum extracted by a linear mixed model. Comprehensive relationships of above five types of VI with LNC were quantified for LNC detecting under different soil backgrounds. The results indicated that all five types of VI were significantly affected by the soil background, with R2 values of around 0.55 for LNC detecting, with the OSAVI (R514 , R469 )L=0.04 producing the best performance of all five indices. However, based on the FVcover , the coverage adjusted spectral index (CASI = NDVI(R513 , R481 )/(1 + FVcover )) produced the higher R2 value of 0.62 and the lower RRMSE of 13%, and was less sensitive to the leaf area index (LAI), leaf dry weight (LDW), FVcover , and leaf nitrogen accumulation (LNA). The results demonstrate that the newly developed CASI could improve the performance of LNC estimation under different soil backgrounds. © 2014 Elsevier B.V. All rights reserved.
1. Introduction Timely quantitative estimation of plant nitrogen (N) status could not only improve N use efficiency and crop yields but also reduce environmental pollution from excessive N fertilization (Fitzgerald et al., 2010). Rapid, non-destructive, and accurate detecting of N status in crop plants is important for N diagnosing in modern crop production. Spectral remote sensing is considered as a useful tool for real-time, non-destructive estimation of plant N status (Takebe et al., 1990), thus providing a scientific basis for the in-season precision N recommendation and dynamic yield prediction. Crop N content is closely related to canopy spectra (Johnson et al., 1994;). Spectral wavebands in regions of the near-infrared (760–900 nm) and visible (630–660 nm, 530–560 nm) bands can be used to estimate crop N content (N g−1 leaf) (Blackmer et al.,
∗ Corresponding author. Tel.: +86 25 84396598; fax: +86 25 84396672. E-mail address: [email protected] (Y. Zhu). http://dx.doi.org/10.1016/j.jag.2014.03.014 0303-2434/© 2014 Elsevier B.V. All rights reserved.
1996). By combining the reference at different wavebands with statistical methods, several types of vegetation index such as the normalized difference vegetation index (NDVI) and ratio vegetation index (RVI) were developed for detecting crop N content (Lyon et al., 1998; Hansen and Schjoerring, 2003). NDVI (R573 , R440 ), RVI (R660 , R440 ), and NDVI (R1220 , R710 ) have been used to develop exponential or linear models for monitoring leaf N content (LNC) in wheat (Triticum aestivum L.) (Xue et al., 2004; Li et al., 2006). NDVI (R1220 , R610 ) was found to be significantly correlated (R2 = 0.889) with LNC in wheat and rice (Oryza sativa L.) (Zhu et al., 2007). Additionally, mND705 , REPLE , FD729 , AVHRR–GVI, RVI (R1350 , R700 ), and SAVI (R1350 , R700 )L=0.09 indices (Feng et al., 2008; Yao et al., 2009) can effectively monitor the N content in wheat. The classical spectral indices built on statistical models have a simple formulation for easy use, but they do not show good stability and robustness due to the spectral characteristics of the crop. Soil background inevitably coexists with the vegetation in the canopy spectra mentioned above, especially in the early growth stages (such as tillering and jointing) or on low-density fields.
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Existing vegetation indices are unable to effectively eliminate the soil background influence (Huete et al., 1985), which reduces the accuracy and reliability of the monitoring models and limits their extrapolation and application. Many studies have been conducted to construct, adjust, or enhance vegetation indices with the aim of eliminating or gradually reducing the impact of soil background. Based on soil line theory, Richardson and Wiegand (1977) developed the perpendicular vegetation index (PVI), which is less affected by soil brightness than other indices are. Based on the, Huete et al. (1985) proposed the soil adjusted vegetation index (SAVI) to describe the soil-vegetation system and to correct for the sensitivity of NDVI to the soil background. The transformational soil adjusted vegetation index (TSAVI) (Baret et al., 1989) and the modified soil adjusted vegetation index (MSAVI) (Rondeaux et al., 1996) were developed from SAVI. Based on the soil moisture and the changes in solar incidence angle, Major et al. (1990) developed three new forms of SAVI (SAVI2 , SAVI3 , and SAVI4 ) to reduce the effects of soil background. Fitzgerald et al. (2010) developed the canopy chlorophyll content index (CCCI) to estimate plant N concentration by eliminating the effect of soil background through a normalized difference procedure that could be used under different types of vegetation cover. The normalized area over reflectance curve (NAOC) provides accurate plant N estimations for various species, canopies, and soil types (Delegido et al., 2010). Haboudane et al. (2002) developed a new spectral index, TCARI/OSAVI, with simulated spectra by combining the PROSPECT and SAILH radiation transfer models. The index is sensitive to chlorophyll content and is less affected by soil background and leaf area index (LAI). The modified chlorophyll absorption ratio index (MCARI) and the triangular vegetation index (TVI) were further developed, which were highly related to chlorophyll while minimizing soil background and LAI effects (Guan and Liu, 2009). To achieve a pure spectrum of canopy plants in which the soil background has been eliminated or minimized, researchers have attempted to decompose the mixed spectra obtained from the field plot and then calculate the area of each end member with a high degree of accuracy (Chen and Vierling, 2006). Attempts to minimize the effects of the soil background from canopy hyperspectra in cereal crops have progressed through three approaches: (1) developing the classic or existed spectrum indices with new wavebands, (2) constructing new spectral indices with novel wavebands and forms, and (3) establishing a model for decomposing the mixed spectra. However, little research has focused on the methods of constructing fractional vegetation on cover indices and evaluating their ability to eliminate the effects of soil background. Some studies have been undertaken in greenhouses (Darvishzadeh et al., 2008); however, this differs from the field conditions where the plant canopy and the soil background usually coexist during N topdressing at the tillering or jointing stages, and the ratio of vegetation to soil coverage changes over the growth period. Thus, it is crucial to develop new technologies for nondestructive monitoring of canopy N status under different fractions of plant cover and soil background in cereal crops. This study investigated potential methodologies and new spectral indices to minimize soil noise when monitoring canopy LNC in winter wheat under different fractions of vegetation cover and soil background. The main objectives were (1) to construct five types of vegetation indices (VIs) of RVI, NDVI, SAVI, OSAVI, and PVI with three types of spectral information; (2) to quantify the relationships of the newly developed spectral indices with LNC under different soil backgrounds in wheat and (3) to compare the performance of the newly established spectral indices and published spectral indices. The results would be assisted in the effective monitoring and diagnosis of the N status during wheat growth by spectral remote sensing.
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2. Materials and methods 2.1. Experiment design Three experiments involving different varieties, N rates, planting densities and sowing dates were carried out in the year of 2009–2012. The N applications were split 50% prior to seeding and 50% at jointing. Monocalcium phosphate and potassium chloride were applied at 120 kg P2 O5 hm−2 and 135 kg K2 O hm−2 at sowing. All treatments were located within a randomized complete block with three replications for each plot of 24 m2 with dimensions of 4 m × 6 m. More detailed information was listed in Table 1. 2.2. Data acquisition 2.2.1. Measurement of spectral reflectance The canopy and soil spectrum were continuously measured with a FieldSpec PRO FR2500 spectra-radiometer (Analytical Spectral Devices, Boulder, CO, USA) (Hatchell, 1999). This instrument records reflectance between 350 and 1000 nm with a 1.40nm sampling interval, and 3-nm resolution, and 1000–2500-nm reflectance at a sampling interval of 2 nm and a resolution of 10 nm. 2.2.1.1. Canopy spectra. The time-course spectral measurements from the spring regrowth to grain filling stage were carried out during cloud-free and low wind speed conditions at mid-day between 10:00 and 14:00. The spectrometer was placed vertically at a nadir of 1 m above the wheat canopy with a 25◦ field of view (FOV), so that it measured a surface diameter of 0.44 m. Measurements of vegetation radiance were obtained at five sites in each plot, with three scans of each site, and their average value was deemed to be the final canopy spectra for each plot. A white spectral reference panel (Labsphere, North Sutton, NH, USA) was used under the same illumination conditions to convert the spectral radiance measurements to reflectance. 2.2.1.2. Soil spectra. After measuring the canopy spectra, soil spectra were measured at two sites in each plot with three scans at each site, and the average value was taken as the soil spectrum of the plot. The soil spectrum was used to construct the soil line and drive the linear mixed model (Richardson and Wiegand, 1977; Vikhamar and Solberg, 2003). 2.2.2. Extraction of FVcover The fractional vegetation cover was extracted by digital image analysis (Li et al., 2004; Jia et al., 2010). The digital single-lens reflex (SLR) camera used in this research was a lower case OLYMPUS E620 (auto exposure and auto white balance). An iron square frame (the area of the inscribed circle was equivalent to the FOV of the spectrometer, 0.44 m × 0.44 m) was placed in the spectra sampling area before photography commenced. When the camera was in a vertical position with the frame facing downward, a digital image containing the frame was taken. The inscribed circular area was then cut using Photoshop CS5 and the color space of the image was transformed from RGB to HSL (Zhou and Shi, 1998) with MATLAB, finally the vegetation cover was extracted through the threshold (Li et al., 2004) (Fig. 1). The formulas are as follows: FVcover (fractional vegetation cover) = Av /Atotal
(1)
FScover (fractional soil cover) = 1 − Av /Atotal ,
(2)
where FVcover is the fractional vegetation crop cover, FScover is the fractional soil cover, and Av and Atotal are the areas of wheat vegetation and the entire visual field, respectively.
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Table 1 Basic information about three experiments conducted at two eco-sites. Exp. number
Year
Soil characteristics (clay soil)
Variety
Treatment (Sowing date (S); Planting density (D): cm; nitrogen rates (N): kg/ha2 )
Date for spectrum detecting
Exp. 1 (validation data set) Yizheng
2009–2010
Yangmai 16 Aikang 58
Exp. 2 (calibration data set) Yizheng
2010–2011
Organic matter: 17.9 g kg−1 , total N: 1.4 g kg−1 , available P: 36 mg kg−1 , available K: 89 mg kg−1 . Organic matter:13.5 g kg−1 , total N: 1.1 g kg−1 , available P: 43 mg kg−1 , available K: 82 mg kg−1 .
Exp. 3 (calibration data set) Rugao
2011–2012
3 D: D1 (45 cm, 1.33 × 106 plants ha−2 ), D3 (35 cm, 1.71 × 106 plants ha−2 ), D5 (25 cm, 2.4 × 106 plants ha−2 ) 2 N: N1 (150), N2 (225) 3 D: D2 (40 cm, 1.50 × 106 plants ha−2 ), D4 (30 cm, 2.00 × 106 plants ha−2 ), D6 (20 cm, 3.00 × 106 plants ha−2 ) 2 N: N1 (150), N3 (300) 3 S: S1 (October 15), S2 (October 30), S3 (November 14) 6 D: D1 (7.5 × 105 plants ha−2 ), D2 (1.5 × 106 plants ha−2 ), D3 (2.25 × 106 plants ha−2 ), D4 (3.0 × 106 plants ha−2 ), D5 (3.75 × 106 plants ha−2 ), D6 (4.5 × 106 plants ha−2 )
Once every 7–10 days from Spring re-growth to filling except rainy or cloudy Once every 7–10 days from Spring re-growth to filling except rainy or cloudy Once every 7–10 days from Spring re-growth to filling except rainy or cloudy
Yangmai 16
Yangmai 16
2.2.3. Determination of agronomic parameters After each measurement of canopy spectral reflectance, 10 plants from each plot were randomly selected and destructively sampled for the determination of leaf dry weight (LDW), LNC and LAI. For each sample, all green leaves were separated from their stems and oven-dried at 105 ◦ C for 30 min and then at 80 ◦ C until a constant weight was achieved. Dried leaf samples were ground to pass through a 1-mm-mesh screen and then stored in plastic bags until to chemical analysis. Total N concentration was determined by Micro-Keldjahl method, and LNC (% or g N g−1 DW) is expressed on a dry weight basis. LNA (g N m−2 ) was calculated as the product of LNC on dry weight basis (g 100 g−1 ) and LDW per unit ground area (g DW m−2 ). The green leaf area was measured using an LI-3000 meter (LI-COR).
2.3. Data analysis and utilization Five types of spectral index (RVI, NDVI, SAVI, OSAVI, and PVI) were developed with all possible two-band combinations between 350 and 2500 nm to determine the optimal combination of center bands for LNC detecting. Each spectral index was calculated based on three types of spectral information as the original spectrum and the first derivative spectrum, the spectrum adjusted by the fractional vegetation cover (FVcover ), and the pure spectrum extracted by a linear mixed model. A comprehensive analysis was undertaken to determine the relationships of the five types of spectral index with LNC to derive monitoring models in wheat. The analysis was based on a selfdeveloped computer program of MATLAB (The MathWorks, 2000). The detailed formulation of spectral indices with the original and the first derivative spectrum is summarized in Table 2.
Fractional vegetation cover (FVcover ), expressed as the proportion of vegetation area to ground area in the microenvironment of a field crop, is a very important parameter for analyzing the effect of the soil background. Previous studies have reported that the parameter L of the soil adjusted vegetation index (SAVI) was 1 for low-density vegetation, 0.5 for medium-density vegetation, and 0.25 for high-density vegetation (Huete et al., 1988; Rondeaux et al., 1996). Our observations indicated that the value of L has a dynamic relationship with vegetation cover. Therefore, the coverage-adjusted spectral index (CASI) was proposed to quantitatively correct for the effect of changing vegetation density and soil cover on the retrieval of LNC from canopy spectra. The CASI was derived by adding the factor of FVcover into the formula of Vis in four ways: VIs*FVcover , VIs/FVcover , VIs*(1 + FVcover ), and VIs/(1 + FVcover ). A mixed model is able to reveal the complex optical properties of the surface of an object and then decompose it into a pure spectral end member, which is useful for estimating the biochemical components of the surface (Van Leeuwen et al., 1997). Mixed models can be divided into linear and nonlinear models. The linear model is widely used due to its simple structure, ease of calculation, and high accuracy (Painter et al., 1998; Vikhamar and Solberg, 2003). The formula of constructing the linear mixed model and extracting pure wheat spectrum is referred by Adams et al. (1993). 2.4. Calibration and validation of the model The two datasets from Exp. 2 and Exp. 3 were used for model development, and the other, independent datasets (Exp. 1) were used for model testing. The determination coefficient (R2 ), standard error (SE), relative root mean square error (RRMSE), and slope were calculated to evaluate the fit between the predicted and observed values.
Fig. 1. Flowchart for extracting fractional vegetation cover.
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Table 2 Algorithms of five VIs used in this study. Vegetation index
Pretreatment of spectrum
Algorithm
Reference
RVI NDVI SAVI OSAVI
Original/FD Original/FD Original/FD Original/FD
Pearson and Miller (1972) Rouse et al. (1974) Huete (1988) Rondeaux et al. (1996)
PVI
Original/FD
R1 /R2 R1 − R2 /R1 + R2 (1 + L)*(R1 − R2 )/(R1 + R2 + L) (1 + X)*(R1 − R2 )/(R1 − R2 + X) RNIR −a×RRED −b √ 1+a2
Richardson and Wiegand (1977)
Note: 1 and 2 are the random wavelengths; 1 and 2 are the reflectance of 1 and 2 in the range 350–2500 nm, respectively; L&X are the adjustment factors defined for SAVI and OSAVI, respectively; a and b are the slope and intercept for the soil line. FD is the first derivative.
3. Results
3.4. Quantitative relationship of canopy LNC with spectral indices
3.1. Patterns of change in FVcvoer and LNC
3.4.1. Classical spectral indices The five types of spectral index (RVI, NDVI, SAVI, OSAVI and PVI) were calculated with all available two wavebands combination in the range of 350–2500 nm, and the relationships between LNC and spectral indices with data from Exp. 2 and 3 were analyzed to identify the optimum waveband combination for LNC detecting. The optimal spectral index of each type of spectral index for LNC detecting was extracted based on the higher R2 value and lowest RRMSE value. The optimum wavebands combinations were consistent among different types of spectral index investigated, except for PVI (Fig. 7). Thus, the different types of spectral index had similar potential for LNC monitoring, although there were significant differences in structure and form of spectral index. The optimal wavebands of different types of spectral index were located in the spectral regions at 460–520 nm. The optimum bands were the same for RVI and NDVI, and OSAVI and PVI had similar band combinations. The optimal bands for SAVI were a combination of the bands in RVI and PVI. Among the five types of spectral index investigated, OSAVI showed the highest correlation with LNC, followed by RVI, NDVI, and PVI, whereas SAVI was the lowest. Table 3 shows the optimal band combinations, and the performance of the spectral indices with optimal band combinations for LNC detecting. The results show that five types of spectral index performed similar under different soil backgrounds, with a R2 of approximately 0.56, among which the OSAVI (R514 , R469 )X=0.04 performed best. The 1:1 plotting with the observed and predicted values exhibited the reliability and accuracy of the derived models, as shown in Fig. 8. It should be noted that the LNC monitoring models based on the first derivative spectrum did not improve the model performance, as compared with the models based on the original spectrum (Table 4). Though they have higher accuracy in calibration, worse reliability in validation with lower R2 and higher RRMSE in similar waveband combinations, when compared with original spectra. The reason may be due to the more noise from the derivative spectra.
Figs. 2 and 3 revealed the dynamic patterns of FVcover of Yangmai 16 under different N rates and planting densities during different growth stages. FVcover increased rapidly, reached a plateau, and then decreased as growth progressed, with the peak at the booting or heading stage. FVcover under the high-planting density treatment was significantly greater than that under the low-planting density treatment with an identical rate of N rates (Fig. 3A). The difference between planting density treatments was reduced when the N application rate was increased (Fig. 3B). Fig. 4 showed that the changes in wheat LNC at different planting densities (D2, D4, and D6) and rates of N application (N1, N3) over the growing degree days were similar, although there were differences in LNC values. LNC decreased over time under different planting densities at the N1 application rate, in the order of D6 < D4 < D2 after 1022 ◦ C d growing degree days (Fig. 4A). At constant density (D4), LNC increased with increasing N application rate (Fig. 4B). 3.2. Features of canopy spectra Canopy spectral reflectance was affected by the concentration of plant biochemical, LAI, canopy architecture, and the soil background. Fig. 5A implied that canopy spectra were reduced with increasing vegetation cover in the visible wavebands (350–720 nm). However, the spectra obtained for C1 (50%) was lower than that for C2 (57%) due to the influence of the soil background. In the near-infrared bands (740–1350 nm), the canopy spectra were elevated due to considerable scattering among leaves, canopy, and soil. Fig. 5B showed the features of canopy hyperspectral reflectance under varied LNC with the same FVcover from Exp. 2. The canopy spectra were reduced with increasing LNC in visible wavebands while dropped in near-infrared bands. There was a consistent trend over all bands with varied LNC. 3.3. Correlation between canopy spectra and LNC According the Exp. 2, Fig. 6A shows that, in most cases, LNC was positively correlated with canopy spectra under different densities. Fig. 6B indicates that the trend in the correlation between the original canopy spectra and LNC was similar for different rates of N application, but the correlation was higher at the lower rate of N application, especially in the infrared band. This indicates that the soil background seriously affected the relationship between reflectance and LNC. A previous study reported that in the visible wavebands (460–710 nm) and near-infrared long wavebands (1480–1650 nm), spectral reflectance decreased with rising rates of N application, whereas in the near-infrared short wavebands (760–1220 nm), the reflectance increased with increasing N application (Zhu et al., 2008).
3.4.2. The cover-adjusted spectral index (CASI) According to the method described in Section 2.3, the CASI based on four forms was developed and the NDVI/(1 + FVcover ) performed best on LNC detecting. Fig. 9A shows a contour map of R2 values between NDVI/(1 + FVcover ) and LNC. It was observed that the higher R2 values between NDVI/(1 + FVcover ) and LNC were focused in the range 400–500 nm, whereas for NDVI, the higher R2 values were in the range 400–700 nm (Fig. 9B). The center bands are 513 nm and 481 nm for NDVI/(1 + FVcover ), which are similar as that for SAVI (514 nm, 481 nm) on the original spectra (Table 3). The newly developed CASI = NDVI(R513 , R481 )/(1 + FVcover ) performed better, with an R2 of 0.6223 and RRMSE of 13% (Fig. 10), compared with that of NDVI(R513 , R481 ) and SAVI(R514 , R481 )L=0.1 (Fig. 9B and Table 3). This result indicated that the newly developed CASI, NDVI(R513 ,
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1
1
0.8
0.8
FVcover
FVcover
Fig. 2. The field pictures of different plant densities and nitrogen levels at varied growth stages in Exp. 2.
0.6
N3D2 N3D4 N3D6
A 0.4
0.2 700
1000
1300
1600
1900
Growing degree day
0.6
N1D3
B
0.4
N3D3
2200
0.2 700
(oC· d)
1000
1300
1600
1900
2200
Growing degree day (o C· d)
Fig. 3. Changes of fractional vegetation cover over growth progress under different plant densities (A) and nitrogen levels (B) in Yangmai 16.
5 Leaf nitrogen concentration (%)
Leaf nitrogen concentration (%)
5 N1D6 N1D4
4
N1D2 3 2
A
1 0
700
1000
1300
1600
Growing degre e day
1900 (o C· d)
2200
N1D4 4
N3D4
3 2
B
1 0 700
1000
1300
1600
1900
2200
Growing degree day (oC·d)
Fig. 4. Changes of LNC over growing degree days under different plant densities (A) and nitrogen treatments (B) in Yangmai16.
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Fig. 5. Features of canopy hyperspectral reflectance at jointing in Exp. 2. 0.6
0.6
B 0.4
0.2
0 350 -0.2
78 0
121 0 164 0 Wavelenth(nm)
207 0
250 0
Correlation coefficent
Correlation coefficent
A 0.4
D6 D4 D2
-0.4
0.2
0 350
780
1210
1640
2070
2500
Wavelenth(nm)
-0.2
N1
-0.4
N3 -0.6
-0.6
Fig. 6. Correlations of canopy original (A, B) and first derivative spectrum (C, D) to LNC in wheat canopy (A, C: varied plant cover; B, D: varied nitrogen content). Table 3 Quantitative relationship between LNC and original spectral index (n = 167) and its testing performance (n = 162). Spectral parameter
OSAVI (R514 , R469 )X=0.04 RVI (R507 , R481 ) NDVI (R507 , R481 ) PVI (R514 , R465 ) SAVI (R514 , R481 )L = 0.1
Regression equation
y = −26.63x + 5.234 y = −11.16x + 15.742 y = −26.01x + 4.713 y = 293.81x − 3.054 y = −40.82x + 5.375
Calibration
Validation
R2
SE
R2
RRMSE
Slope
0.564 0.559 0.557 0.551 0.545
0.175 0.174 0.170 0.180 0.182
0.521 0.522 0.523 0.475 0.456
0.145 0.142 0.141 0.894 0.160
1.395 1.338 1.334 1.277 1.287
The best performance of the five VIs was marked in bold.
R481 )/(1 + FVcover ), effectively reduced the effect of the soil background. Further analysis was conducted to evaluate whether NDVI(R513 , R481 )/(1 + FVcover ) is only sensitive to LNC and less affected by LAI, LDW, FVcover , and LNA. The top 5% area (95%R2 max < R2 LNC < R2 max ) of the R2 contour map between NDVI (R1 , R2 )/(1 + FVcover ) and LNC was extracted, as was the bottom 5% (e.g., LAI, R2 min < R2 LAI < 5%R2 max ) area of the R2 contour map between
NDVI(R1 , R2 )/(1 + FVcover ) and LAI, LDW, FVcover , and LNA. The overlap area between R2 LNC and R2 LAI , R2 LDW , R2 FVcover was in the range 435–526 nm (Fig. 11), whereas the overlap region between R2 LNA and R2 LNC was smaller, in the range 452–521 nm. This suggested that the band combinations in this region (452–521 nm) were sensitive to LNC and less affected by the LAI, LDW, FVcover , and LNA. The coefficient of determination (R2 ) between NDVI(R513 , R481 )/(1 + FVcover ) and LNC, LAI, LDW, FVcover , and LNA were 0.622,
Table 4 Quantitative relationship of LNC to spectral indices with optimum first derivative spectrum (n = 167) and its testing performance (n = 162). Spectral index
PVI(FD505 , FD403 ) OSAVI(FD505 , FD403 ) X = −0.01 SAVI(FD505 , FD403 ) L = −0.1 RVI (FD690 , FD419 ) NDVI(FD684 , FD419 ) Note: FD is the first derivative spectrum.
Regression equation
y = 6261x − 148.8 y = 41.40x + 4.012 y = 492.1x + 4.025 y = −0.372x + 4.493 y = −2.641x + 3.359
Calibration (n = 167)
Validation (n = 162)
R2
SE
R2
RRMSE
Slope
0.598 0.594 0.593 0.552 0.521
0.161 0.162 0.163 0.179 0.192
0.317 0.300 0.286 0.132 0.171
0.184 0.185 0.186 0.238 0.202
0.833 0.805 0.778 0.399 0.700
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Fig. 7. Regions with correlation coefficients over 0.3 between spectral index (RVI, NDVI, PVI, OSAVI and SAVI) and LNC.
0.002, 0.004, 0.002, and 0.030, respectively. This indicates that NDVI(R513 , R481 )/(1 + FVcover ) could be well used for LNC detecting and less affected by LAI, LDW, FVcover , and LNA. 3.4.3. Linear mixed model The accuracy of LNC detecting was enhanced by modifying the spectral index with FVcover ; however, this accuracy is still lower in the early growth stage of crops than after complete canopy cover. Thus a linear mixed model was used to further minimize the effect of soil background by coupling FVcover and soil spectra. Table 5 shows that the R2 of the calibrated model on an unmixed spectrum increased slightly for RVI, NDVI, and SAVI, whereas it decreased for OSAVI and PVI, with the worst performance in PVI.
The factor X of OSAVI changed from 0.03 to 0; however, the R2 value decreased from 0.638 to 0.624. This indicated that the linear mixed model partly eliminated the soil effects, although it lost some of the vegetation information. The optimum band combinations listed in Table 5 after unmixing were 506 nm and 478 nm, which were the same as those for RVI on the original spectrum. The sensitive wavelengths for LNC did not change after linear mixed processing. A linear decomposition could partly eliminate the effects of the soil background. Further analysis of optimum band combination on mixed and unmixed spectra for estimating LNC was conducted. As an example, the R2 contour map (Fig. 12) between RVI and LNC shows that, compared with the mixed spectrum (the original spectra from a
Fig. 8. Calibration (n = 167) and validation (n = 162) of LNC model on OSAVI (R514 , R469 )X=0.04 .
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Fig. 9. Contour map for coefficient of determination (R2 ) between VIs and LNC (A: NDVI; B: NDVI/(1 + FVcover)).
Fig. 10. Performance of calibration (A, n = 167) and validation (B, n = 162) for LNC model on NDVI(R513 , R481 )/(1 + FVcover ).
Fig. 11. Sensitive and insensitive regions of LNC and other relevant parameters based on FVcover adjusted vegetation spectral index in wheat.
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Table 5 Selected wavebands and determination coefficient (R2 ) between LNC and different spectral indices. Spectral spectral index
OSAVI RVI NDVI SAVI PVI
Mixed spectral index (n = 124)
Unmixed spectral index (n = 124)
R2
1
2
L(X)
R2
1
2
L(X)
0.638 0.619 0.618 0.618 0.617
514 507 507 507 513
469 481 481 481 469
0.03
0.624 0.626 0.624 0.624 0.557
506 506 506 506 504
478 478 478 478 476
0
field plot) (Fig. 12A), the R2 value (Fig. 12B) increased and had a larger region of good performance (R2 > 0.3) from 400 to 700 nm, with a stronger correlation (R2 > 0.5) around 500 nm. The validation performance of the spectral index with unmixed spectra was poor compared with that for the mixed spectra (the original spectra). The validated R2 decreased from 0.5 to 0.03, and the RRMSE increased to 200% (Table 6). This result may be because a previous crop (rice straw) was present on the soil surface during the wheat experiment in the 2009–2010 seasons. Only two end members of wheat vegetation and soil background were used in this paper, and senescent leaves and other crop residues, which are important to the accuracy of linear decomposition, were not considered. 3.5. Comparison of newly developed spectral indices with published spectral indices Optimum spectral indices reported in previous studies were selected for comparison with the newly established indices using the validation data set from Exp. 1 (Table 7). The result showed that the new spectral index, NDVI(R513 , R481 )/(1 + FVcover ), generally performed better than several published indices. Overall, the new spectral index is expected to perform well in predicting LNC in wheat under various soil backgrounds.
0
0
canopy spectra in current study and previous one. The condition of the wheat field, a coexistence environment of vegetation and soil, was well presented by the experimental design during dressing, tillering, jointing, and booting periods. The NDVI has been successfully applied to monitor the leaf N status of wheat. Subsequently, the SAVI, TSAVI, and OSAVI indices were proposed to reduce the effects of soil background for N estimation with a soil-adjustable parameter (Gilabert et al., 2002). A statistical model based on the LOPEX’93 spectral database was used to analyze the ability of reducing the effect of soil background (Sun and Cheng, 2011). The spectral index TCARI/OSAVI simulated by the PROSPECTSAILH model can reduce the effects of the soil background from a canopy spectrum (Zarco-Tejada et al., 2004). The results of the present study showed that OSAVI was the best performing index, followed by RVI, NDVI, and PVI; and the poorest performance was observed in SAVI. This supports the assumption that the PVI or the SAVI were not sufficient to explain or predict vegetation responses when there was serious interference (Huete et al., 1985) and some noise from the soil background resulted in lower R2 for the five spectral indices. However, the newly established parameter NDVI (R513 , R481 )/(1 + FVcover ) performed well. The optimal band combinations were located in the green spectral region (460–520 nm), which is consistent with the conclusions of previous studies (Stroppiana et al., 2009; Tian et al., 2011). The results also confirmed that the 530 nm wavelength is sensitive to LNC (Tian et al., 2011) and was less affected by soil background.
4. Discussion 4.1. The performance of different vegetation indices with soil background interference
4.2. The ability of different methods to mitigate against soil background interference
Soil background affects the canopy spectra due to the combined effects of planting density, row spacing, canopy structure, wind, and various other factors (Rondeaux et al., 1996). Our results indicate that the correlation between LNC and canopy spectra is greatly affected by the soil background, with other obvious limitations of the canopy spectrum for monitoring LNC. The influence of soil spectra might be a reason for difference of correlation between LNC and
In this study the model calibration for the soil adjusted vegetation indices (SAVI, OSAVI, and PVI) based on derivative spectra performed better than those derived from original spectra, but the model performance of validation data was significantly worse when using the original spectral data. Although the derived spectra can improve the efficiency of extracting an objective spectrum and help to eliminate interfering factors, they were unstable among
Fig. 12. Contour map for coefficient of determination (R2 ) between RVI (A: Mixed; B: Unmixed) and LNC.
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Table 6 Testing performance of LNC estimation model based on spectral indexes of original spectrum and linear decomposing spectrum. Spectral index
RVI NDVI SAVI OSAVI PVI
Mixed spectral index
Unmixed spectral index
R2
RRMSE
Slope
R2
RRMSE
Slope
0.522 0.523 0.523 0.522 0.475
0.141 0.140 0.140 0.143 1.031
1.245 1.236 1.236 1.291 1.049
0.085 0.035 0.035 0.035 0.118
0.630 2.101 2.101 2.101 0.984
0.087 0.017 0.017 0.017 0.342
Table 7 Comparison of the performance of the newly developed spectral indices with published spectral indices. Spectral index
NDVI(R513 , R481 )/(1 + FVcover ) SIPI:(R800 -R445 )/(R800 -R680 ) NDVI(R573 , R440 ) RVI(R870 , R660 ) SASI(R1350 , R700 )L=0.09 R705 /(R717 + R491 ) MTCI
Regression equation
y = −29.03x + 4.961 y = −3.948x + 7.630 y = −8.09x + 5.752 y = −0.003x + 3.025 y = 1.275x + 2.464 y = 0.307x + 2.854 y = 0.029x + 2.903
Calibration
Validation
Source
R2
SE
R2
RRMSE
Slope
0.622 0.080 0.409 0.001 0.036 0.001 0.003
0.132 0.368 0.236 0.400 0.386 0.400 0.399
0.540 0.082 0.273 0.008 0.016 0.001 0
0.130 0.212 0.168 0.200 0.197 0.200 0.199
1.247 1.441 1.014 −3.889 0.741 0.975 0.112
the different experiment datasets and provided no consistency of prediction. In view of the correlation of the adjusted parameter L in SAVI and OSAVI with FVcover , FVcover was used to correct the soil-adjusted factor. The results showed that the newly developed index NDVI(R513 , R481 )/(1 + FVcover ) was strongly correlated with LNC, with the calibrated R2 increasing from 0.56 to 0.62 and the validated RRMSE decreasing from 14% to 13%. A sensitivity analysis indicated that NDVI(R513 , R481 )/(1 + FVcover ) was a good indicator of LNC that was less affected by other factors such as LAI, LDW, FVcover , and LNA. Finally, a linear mixed model was introduced to eliminate the effects of soil background. The linear mixed model assumes that each photon only touches one surface and that they do not mix before being received by a sensor (Adams et al., 1993). However, the performance of linear model in our paper I was poor, although the R2 was slightly higher, and the validated R2 decreased. This may due to the previous crop residues, thus plant shadows, and falling leaves should be considered in the linear mixed model in the future. Moreover, the actual canopy spectrum is a combination of several complex optical physical processes, and it is difficult to quantify the impact of the soil background in practical applications (Stroppiana et al., 2009). In the future, a non-linear mixed model (Delegido et al., 2010) may be introduced to further elucidate the light-scattering effects among the various components of the mixed environment to propose new spectral indices (Rondeaux et al., 1996; Zarco-Tejada et al., 2004). Thus, further research is needed to determine how the soil background affects the canopy spectra when combined with field conditions and to construct new indices taking into account harvest residues, leaf fall, and the composition of other non-photosynthetic material. 4.3. Center waveband and monitoring model for estimating LNC in wheat This study analyzed the relationships of five types of spectral indices with LNC using three methods to reduce the influence of soil background in a wheat crop. The results revealed that the center bands of the wheat canopy LNC were focused on the green regions between 510 and 480 nm. Compared with the original spectra, the center bands of the derivative spectra extended to both sides of the short and long wavelength located at 605 nm and 403 nm, and
This paper ˜ Penuelas et al. (1995) Hansen and Schjoerring (2003) Zhu et al. (2007) Yao et al. (2009) Tian et al. (2011) Dash and Curran (2007)
at 690 nm and 419 nm. The optimal center bands of the CASI were 513 nm and 481 nm, which was similar to the optimum bands after mixture decomposing (506 nm, 478 nm). Regardless of how the data were analyzed, the center bands indicating LNC were always in the range of 478 (±3) nm and 510 (±3) nm. However, it was still necessary to further eliminate the soil influence to increase the crop N monitoring accuracy. The best LNC monitoring model was the linear model (y = –29.025x + 4.9613, where y is the estimated LNC, and x is the spectral index) based on the spectral index of NDVIFVcover , which was better than other methods used in this study. In addition, measuring the canopy spectra obliquely could partly avoid the soil background, but the shadow of the leaves and the bidirectional reflectance would affect the detecting value of canopy spectra. In fact, we have measured the reflectance at different angles in a separate recent study, and our results showed that the 60◦ backward maybe a better observation angle than the 0◦ of vertical of this paper. Oblique measurement could not largely enhance the monitoring accuracy, as compared to the vertical measurement. And we will continue to focusing on researching the obliquely measurement in the future. 5. Conclusions In field experiments under different N rates and plant densities in winter wheat over 3 growing seasons, the canopy spectra and LNC were obtained under different vegetation coverage. A systematic analysis was conducted to examine the relationships of canopy LNC with five types of spectral index (RVI, NDVI, SAVI, OSAVI, and PVI) using three different spectral information (1) the original and first derivative of the spectrum, (2) the fractional vegetation cover, and (3) the pure spectrum extracted by a linear mixed model. Among five types of spectral index, through the performance of the OSAVI was the best for estimating LNC. Of three methods, the newly spectral index, NDVI(R513 , R481 )/(1 + FVcover ), which decreased the impact of the soil background to a certain extent, was strongly correlated (R2 = 0.62) with the canopy LNC of wheat. Additionally, it was less affected by LAI, LDW, FVcover , and LNA. Although the performance of the linear mixed model was not as good as anticipated because of the complex field environment, it would have greater potential if accurate end members were selected and crop residence were considered.
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