# Estimating Canopy Nitrogen Concentration in Sugarcane Using Field Imaging Spectroscopy

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

_{(750/700)}and Ratio Spectral Index (RVI) based on FDS

_{(724/700)}are best suited for characterizing the nitrogen concentration. The modified estimation model, generated by the Stepwise Multiple Linear Regression (SMLR) technique from FDS centered at 410, 426, 720, 754, and 1,216 nm, yielded the highest correlation coefficient value of 0.86 and Root Mean Square Error of the Estimate (RMSE) value of 0.033%N (n = 90) with nitrogen concentration in sugarcane. The results of this research demonstrated that the estimation model developed by SMLR yielded a higher correlation coefficient with nitrogen content than the model computed by narrow vegetation indices. The strong correlation between measured and estimated nitrogen concentration indicated that the methods proposed in this study could be used for the reliable diagnosis of nitrogen quantity in sugarcane. Finally, the success of the field spectroscopy used for estimating the nutrient quality of sugarcane allowed an additional experiment using the polar orbiting hyperspectral data for the timely determination of crop nutrient status in rangelands without any requirement of prior cultivar information.

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Field Experimental Design

#### 2.2. Measurements of Hyperspectral Reflectance

#### 2.3. Determination of Nitrogen Concentration

#### 2.4. Spectral Transformations

#### 2.4.1. First-Derivative Transformation

_{λ(}

_{j}

_{)}is the reflectance at the j waveband, R

_{λ(}

_{j}

_{+ 1)}is the reflectance at the j + 1 waveband and Δλ is the difference in wavelength between j and j + 1.

#### 2.4.2. Calculation of Absorption Features

_{(λi)}is obtained by dividing the reflectance value R

_{(λi)}of each waveband i in the absorption feature by the reflectance level of the continuum line (convex hull) R

_{c(λi)}at the corresponding wavelength i:

- Continuum-Removed Derivative Reflectance (CRDR) was calculated by applying a first-derivative transformation to the continuum-removed reflectance spectrum R′.$$\mathit{CRDR}=({{R}^{\prime}}_{\lambda (j+1)}-{{R}^{\prime}}_{\lambda (j)})/\mathrm{\Delta}\lambda $$
- The band depth (BD) was calculated by subtracting the continuum-removed reflectance at wavelength i from 1:$$B{D}_{(\lambda i)}=1-{{R}^{\prime}}_{\lambda i}$$

#### 2.5. Univariate Approach: Narrow Vegetation Indices

_{1}: FDS, CRDR and BD between 400–2,500 nm., and λ

_{2}: FDS, CRDR and BD between 400–2,500 nm.

#### 2.6. Multivariate Approach: Stepwise Multiple Linear Regression

#### 2.7. Model Validation

^{2}), the Root Mean Square Error of the Estimate (RMSE) and the Relative Error (RE), as illustrated by Equations (7) and (8) [47].

**ŷ**

_{i}and y

_{i}are the estimated and measured crop variables, respectively, and n is the number of samples. The RMSE provides an estimate of the modeling error and is expressed in original measurement units.

## 3. Results and Discussion

#### 3.1. Variations in Nitrogen Concentration and Hyperspectral Canopy Reflectance

#### 3.1.1. Variations in Nitrogen Concentration

#### 3.1.2. Hyperspectral Canopy Reflectance

#### 3.2. Relationships between the Nitrogen Concentration and Narrow Vegetation Index

^{2}≥ 0.6 are displayed in a correlation plot (Figure 3). The highest correlation regions between the NDI and RVI based on the FDS and the nitrogen concentration were highlighted in the range of 630–750 nm, i.e., the near-infrared and red edge regions. Wavelengths between 675 and 750 nm in the CRDR also constitute the sensitive band for determining the nitrogen status. Table 4 illustrates the model accuracy estimated by the NDI and the RVI, using the combined cultivar data set. The predictive performance of the NDI was slightly higher than that of the RVI. In this experiment, the NDI based on the FDS

_{(750/700)}and the RVI based on the FDS

_{(724/700)}yielded the highest accuracy, with R

^{2}values of 0.73 and 0.78, RMSE

_{cv}values of 0.044 and 0.043% N and RE values of 3.34 and 3.27%N for the validation data set and the pooled data set, respectively. The regression equations are Y = 0.37x + 1.39 and Y = 0.14x + 1.04.

#### 3.3. Relationships between the Nitrogen Concentration and Spectral Wavelength Determined by a SMLR Technique

^{2}values of 0.80 and 0.86, RMSE values of 0.038 and 0.033%N and RE values of 2.88 and 2.50%N validated by an independent data set and a pooled data set, respectively. The regression equation is Y = 212.76x

_{1}– 471.9x

_{2}– 31.98x

_{3}+ 133.78x

_{4}– 115.86x

_{5}+ 1.4. The selected wavelengths for each data set are summarized in Table 7. The CRDR and BD variables cannot be used to improve the model precision for determining the nitrogen concentration compared with the FDS. Relationships between the measured and estimated nitrogen concentration were determined from the combined and separated cultivar data sets and are depicted in Figure 5(a,b). However, the predictive models developed by a SMLR technique exhibited a higher accuracy than those developed based on the narrow vegetation indices, as indicated by the higher R

^{2}and lower RMSE associated with the former.

#### 3.4. Discussion

#### 3.4.1. Utility of the Methods Used in this Study in Estimating the Nitrogen Concentration

#### 3.4.2. Wavelength Selection

#### 3.4.3. Effects of Plant Morphology and Structure on the Spectral Response

#### 3.4.4. Performance Comparison of Proposed Models with Previous Models

## 4. Conclusions

- Stepwise multiple linear regression could explain the nitrogen variations in sugarcane canopy better than a narrow vegetation index. This technique utilizes more than two wavelengths from the entire spectral region (400–2,500 nm) to estimate the dependent variable.
- First Derivative Spectrum (FDS) showed a better relationship with canopy nitrogen concentration than Continuum-Removed Derivative Reflectance (CRDR) and Band Depth (BD) when the models were developed with multivariate approaches and validated with a combined cultivar data set.
- It was concluded that a First Derivative Spectrum (FDS) has potential when used to estimate the nitrogen content in sugarcane with combined cultivars at the maturity stage (9–12 months).
- Visible, red edge and far near-infrared regions contain more information on canopy nitrogen concentration of combined sugarcane cultivars compared to other parts of the electromagnetic spectrum.
- Canopy architecture directly influences the spectral response and the predictive precision. Canopy structure, therefore, should be taken into consideration when mapping sugarcane nutrient quality in rangelands with combined cultivars.
- In the case of a known cultivar, partitioning the data into cultivars could increase the estimation capability of the method applied in this research.
- The modified estimation model, generated by SMLR technique from FDS centered at 410, 426, 720, 754, and 1,216 nm, yields the highest correlation coefficient value of 0.86 and RMSE value of 0.033%N (n = 90) with nitrogen concentration in sugarcane. This result is much better than those of the previous studies.

## Acknowledgments

## References

- Xavier, A.C.; Rudorff, B.F.T.; Shimabukuro, Y.E.; Berka, L.M.S.; Moreira, M.A. Multi-temporal analysis of MODIS data to classify sugarcane crop. Int. J. Remote Sens
**2006**, 27, 755–768. [Google Scholar] - Abdel-Rahman, E.M.; Ahmed, F.B. The application of remote sensing techniques to sugarcane (Saccharum spp. hybrid) production: A review of the literature. Int. J. Remote Sens
**2008**, 29, 3753–3767. [Google Scholar] - Fageria, N.K. The Use of Nutrients in Crop Plants; Taylor and Francis Group: London, UK, 2009; p. 430. [Google Scholar]
- Thompson, G.D. The Growth of Sugarcane Variety N14 at Pongola, Mount Edgecombe Research Report No. 7; South African Sugar Association Experiment Station: Mount Edgecombe, South Africa, 1991.
- Wiedenfeld, R.P. Effects of irrigation and N fertilizer application on sugarcane yield and quality. Field Crops Res
**1995**, 43, 101–108. [Google Scholar] - Spaner, D.; Todd, A.; Navabi, A.; Mckenzie, D.; Goonewardene, L. Cane leaf chlorophyll measures at differing growth stages be used as an indicator of winter wheat and spring barley nitrogen requirements in eastern Canada. J. Agron. Crop. Sci
**2005**, 91, 393–399. [Google Scholar] - Roth, G.W.; Fox, R.H.; Marshall, H.G. Plant tissue tests for predicting nitrogen fertilizer requirements of winter Wheat. Agron. J
**1989**, 81, 50. [Google Scholar] - Turner, F.T.; Jund, M.F. Assessing the nitrogen requirements of rice crops with a chlorophyll meter. Aust. J. Exp. Agric
**1994**, 34, 1001–1005. [Google Scholar] - Wang, S.; Zhu, Y.; Jiang, H.; Cao, W. Positional differences in nitrogen and sugar concentrations of upper leaves relate to plant N status in rice under different N rates. Field Crops Res
**2006**, 96, 224–234. [Google Scholar] - Mutanga, O.; Skidmore, A.K.; Prins, H.H.T. Predicting in situ pasture quality in the Kruger National Park, South Africa, using continuum removed absorption features. Remote Sens. Environ
**2004**, 89, 393–408. [Google Scholar] - Clevers, J.G.P.W.; Kooistra, L.; Schaepman, M.E. Using spectral information from the NIR water absorption features for the retrieval of canopy water content. Int. J. Appl. Earth Obs. Geoinf
**2008**, 10, 388–397. [Google Scholar] - Stroppiana, D.; Boschetti, M.; Brivio, P.A.; Bocchi, S. Plant nitrogen concentration in paddy rice from field canopy hyperspectral radiometry. Field Crops Res
**2009**, 111, 119–129. [Google Scholar] - Delegido, J.; Alonso, L.; Gonzalez, G.; Moreno, J. Estimating chlorophyll content of crops from hyperspectral data using a normalized area over reflectance curve (NAOC). Int. J. Appl. Earth Obs. Geoinf
**2010**, 12, 165–174. [Google Scholar] - Jain, N.; Ray, S.S.; Sinph, J.P.; Panigrahy, S. Use of hyperspectral data to assess the effects of different nitrogen application on a potato crop. Prec. Agr
**2007**, 8, 225–239. [Google Scholar] - Yao, X.; Zhu, Y.; Tian, Y.C.; Feng, W.; Cao, W. Exploring hyperspectral bands and estimation indices for leaf nitrogen accumulation in wheat. Int. J. Appl. Earth Obs. Geoinf
**2010**, 12, 89–100. [Google Scholar] - Galvao, L.S.; Formaggio, A.R.; Tisot, D.A. Discrimination of sugarcane varieties in Southeastern Brazil with EO-1 Hyperion data. Remote Sens. Environ
**2005**, 94, 523–534. [Google Scholar] - Vaiphasa, C.; Skidmore, A.K.; De Boer, W.F.; Vaiphasa, T. A hyperspectral band selector for plant species discrimination. ISPRS J. Photogramm
**2007**, 62, 225–235. [Google Scholar] - Kamal, M.; Phinn, S. Hyperspectral data for mangrove species mapping: A comparison of pixel-based and object-based approach. Remote Sens
**2011**, 3, 2222–2242. [Google Scholar] - Apan, A.; Held, A.; Phinn, S.; Markley, J. Detecting sugarcane ‘Orange Rust’ disease using EO-1 hyperion hyperspectral imagery. Int. J. Remote Sens
**2004**, 25, 489–498. [Google Scholar] - Liu, Z.; Huang, J.; Tao, R.; Zhou, W.; Zhang, L. Characterizing and estimating fungal disease severity of rice brown spot with hyperspectral reflectance data. Rice Sci
**2008**, 15, 232–242. [Google Scholar] - Vigneau, N.; Ecarnot, M.; Rabatel, G.; Roumet, P. Potential of field hyperspectral imaging as a non destructive method to assess leaf nitrogen content in Wheat. Field Crops Res
**2011**, 122, 25–31. [Google Scholar] - Kumar, L.; Schmidt, K.; Dury, S.; Skidmore, A. Imaging Spectrometry and Vegetation Science. In Image Spectrometry; van der Meer, F.D., de Jong, S.M., Eds.; Kluwer Academic Publishers: London, UK, 2001; Volume 4, pp. 111–155. [Google Scholar]
- Curran, P.J.; Dungan, J.L.; Peterson, D.L. Estimating the foliar biochemical concentration of leaves with reflectance spectrometry: Testing the Kokaly and Clark methodologies. Remote Sens. Environ
**2001**, 76, 349–359. [Google Scholar] - Read, J.J.; Tarpley, L.; Mc Kinion, J.M.; Reddy, K.R. Narrow waveband reflectance ratios for remote estimation of nitrogen status in cotton. J. Environ. Qual
**2002**, 31, 1442–1452. [Google Scholar] - Zarco-Tejada, P.J.; Miller, J.R.; Mohammed, G.H.; Noland, T.L.; Sampson, P.H. Vegetation stress detection through chlorophyll a + b estimation and fluorescence effects on hyperspectral imagery. J. Environ. Qual
**2002**, 31, 1433–1441. [Google Scholar] - Abdel-Rahman, E.M.; Ahmed, F.B.; Van den berg, M. Estimation of sugarcane leaf nitrogen concentration using in situ spectroscopy. Int. J. Appl. Earth Obs. Geoinf
**2010**, 12, S52–S57. [Google Scholar] - Johnson, R.M.; Richard, E.P., Jr. Prediction of sugarcane sucrose content with high resolution, hyperspectral leaf reflectance measurements. Int. Sugar J
**2011**, 113, 48–55. [Google Scholar] - Mokhele, A.; Ahmed, F.B. Estimation of leaf nitrogen and silicon using hyperspectral remote sensing. J. Appl. Remote Sens
**2010**, 4, 1–18. [Google Scholar] - Begue, A.; Lebourgeois, V.; Bappel, E.; Todoroff, P.; Pellegrino, A.; Baillarin, F.; Siegmund, B. Spatio-temporal variability of sugarcane fields and recommendations for yield forecast using NDVI. Int. J. Remote Sens
**2010**, 21, 5391–5407. [Google Scholar] - Roder, A.; Kuemmerle, T.; Hill, J.; Papanastasis, V.P.; Tsiourlis, G.M. Adaptaetion of a grazing gradient concept to heterogeneous Mediterranean rangelands using cost surface modelling. Ecol. Model
**2007**, 204, 387–398. [Google Scholar] - ASD. Field Spec Pro spectrometer; Analytical Spectral Devices: Boulder, CO, USA, 1995. [Google Scholar]
- Mutanga, O.; Skidmore, A.K. Narrow band vegetation indices overcome the saturation problem in biomass estimation. Int. J. Remote Sens
**2004**, 25, 1–16. [Google Scholar] - Darvishzadeh, R.; Skidmore, A.; Schlerf, M.; Atzberger, C.; Corsi, F.; Cho, M. LAI and chlorophyll estimation for a heterogeneous grassland using hyperspectral measurements. ISPRS J. Photogramm
**2008**, 63, 409–426. [Google Scholar] - Muchovej, R.M.; Newman, P.R. Nitrogen fertilization of Sugarcane on sandy soil: Yield and leaf nutrient composition. J. Am. Soc. Sugar Cane Tech
**2004**, 24, 210–224. [Google Scholar] - SASRI. Leaf Sampling. Information Sheet. South African Sugarcane Research Institute: Mount Edgecombe, South Africa, 2003. [Google Scholar]
- Muchovej, R.M.; Newman, P.R.; Luo, Y. Sugarcane leaf nutrient concentrations: With or without midribs tissue. J. Plant Nutr
**2005**, 28, 1271–1286. [Google Scholar] - FOSS. Kjeltec™ 2200Auto Distillation; FOSS Analytical: Hilleroed, Denmark, 2005. [Google Scholar]
- Dawson, T.P.; Curran, P.J. A new technique for interpolating the reflectance red edge position. Int. J. Remote Sens
**1998**, 19, 2133–2139. [Google Scholar] - de Jong, S.M. Imaging spectrometry for monitoring tree damage caused by volcanic activity in the long valley caldera, California. Int. J. Appl. Earth Obs. Geoinf
**1998**, 1, 1–10. [Google Scholar] - Mutanga, O.; Skidmore, A.K. Continuum-Removed Absorption Features Estimate Tropical Savanna Grass Quality in situ. Proceedings of 3rd EARSeL Workshop on Imaging Spectroscopy, Hersching, Germany, 13–16 May 2003.
- Kokaly, R.F.; Clark, R.N. Spectroscopic determination of leaf biochemistry using band-depth analysis of absorption features and stepwise multiple linear regression. Remote Sens. Environ
**1999**, 67, 267–287. [Google Scholar] - Thenkabail, P.S.; Smith, R.B.; De Pauw, E. Hyperspectral vegetation indices and their relationships with agricultural crop characteristics. Remote Sens. Environ
**2000**, 71, 158–182. [Google Scholar] - Thenkabail, P.S.; Smith, R.B.; De Pauw, E. Evaluation of narrowband and broadband vegetation indices for determining optimal hyperspectral wavebands for agricultural crop characterization. Photogramm. Eng. Remote Sensing
**2002**, 68, 607–621. [Google Scholar] - Motohka, T.; Nasahara, K.N.; Oguma, H.; Tsuchida, S. Applicability of green-red vegetation index for remote sensing of vegetation phenology. Remote Sens
**2010**, 2, 2369–2387. [Google Scholar] - Martin, M.E.; Aber, J.D. Estimation of forest canopy lignin and nitrogen concentration and ecosystem processes by high spectral resolution remote sensing. Ecol. Appl
**1997**, 7, 431–443. [Google Scholar] - Serrano, L.; Penuelas, J.; Ustin, S.L. Remote sensing of nitrogen and lignin in Mediterranean vegetation for AVIRIS data: decomposing biochemical from structural signals. Remote Sens. Environ
**2002**, 81, 355–364. [Google Scholar] - Yi, Q.X.; Huang, J.F.; Wang, F.M.; Wang, X.Z.; Liu, Z.Y. Monitoring rice nitrogen status using hyperspectral reflectance and artificial neural network. Environ. Sci. Tech
**2007**, 41, 6770–6775. [Google Scholar] - Yoder, B.J.; Pettigrew-Crosby, R.E. Predicting nitrogen and chlorophyll content and concentrations from reflectance spectra (400–2500 nm) at the leaf and canopy scale. Remote Sens. Environ
**1995**, 53, 199–211. [Google Scholar] - Elvidge, C.D.; Chen, Z. Comparison of broadband and narrow-band red and near-infrared vegetation indices. Remote Sens. Environ
**1995**, 54, 38–48. [Google Scholar] - Thenkabail, P.S.; Enclona, E.A.; Ashton, M.S.; van der Meer, V. Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications. Remote Sens. Environ
**2004**, 91, 354–376. [Google Scholar] - Thenkabail, P.S.; Enclona, E.A.; Ashton, M.S.; Legg, C.; Jean De Dieu, M. Hyperion, KONOS, ALI, and ETM+ sensors in the study of African rainforests. Remote Sens. Environ
**2004**, 90, 23–43. [Google Scholar] - Tejera, N.A.; Rodes, R.; Ortega, E. Comparative analysis of physiological characteristics and yield components in sugarcane cultivars. Field Crops Res
**2007**, 102, 67–72. [Google Scholar] - Belward, A.S. Spectral Characteristics of Vegetation, Soil and Water in the Visible, Near-Infrared and Middle-Infrared Wavelengths. In Remote Sensing and Geographical Information Systems for Resource Management in Developing Countries; Belward, A.S., Valenzuela, C.R., Eds.; Kluwer: Dordrecht, The Netherlands, 1991. [Google Scholar]
- Hoffer, R.M. Biological and Physical Considerations in Applying Computer Aided Analysis Techniques to Remote Sensor Data. In Remote Sensing: The Quantitative Approach; Swain, P.H., Davis, S.M., Eds.; McGraw Hill: New York, NY, USA, 1978; pp. 227–289. [Google Scholar]

**Figure 2.**Comparison of the mean canopy reflectance spectra of sugarcane (N = 60 for each cultivar) between the three different cultivars: (

**a**) original reflectance; and (

**b**) first derivative spectrum.

**Figure 3.**Contour plots showing the regions with high correlation coefficients (R

^{2}≥ 0.6) between narrow vegetation index value and nitrogen concentration (n = 90) calculated from all possible combination wavelengths: (

**a**) Normalized Differential Index (NDI) based on First-Derivative Spectrum (FDS); (

**b**) NDI based on Continuum-Removed Derivative Reflectance (CRDR); (

**c**) Ratio Spectral Index (RVI) based on FDS; and (

**d**) RVI based on CRDR.

**Figure 4.**Measured versus estimated nitrogen concentration in sugarcane with combined cultivars, using narrow vegetation indices (n = 90): (

**a**) NDI (FDS

**, FDS**

_{750}**); (**

_{700}**b**) NDI (CRDR

_{748}, CRDR

_{700}); (

**c**) RVI (FDS

_{724}, FDS

_{700}); and (

**d**) RVI (CRDR

_{748}, CRDR

_{630}).

**Figure 5.**Measured versus estimated sugarcane nitrogen concentration of combined cultivars; the model was developed by a Stepwise Multiple Linear Regression (SMLR) approach with two independent variables (n = 90): (

**a**) FDS; (

**b**) Band Depth (BD).

Plot 1 | Plot 2 | |
---|---|---|

Location | 13.866°N 102.287°E | 13.710°N 102.214°E |

Soil texture | loamy sand | loamy clay |

Annual precipitation, mm (2010) | 1,268.2 | 1,296.6 |

Annual air temperature, °C (2010) | 22.91/35.14 (Min/Max) | 22.8/34.08 (Min/Max) |

Plot size | 36 m × 76 m | 36 m × 76 m |

**Table 2.**Characteristics of the sensor, wavelength covering and spectral resolution used in this study.

Sensor | Wavelength (nm) | Spectral Resolution (nm) | Number of Bands |
---|---|---|---|

Fieldspec^{®} 3 spectroradiometer | 400–1,050 | 1.4 | 465 |

1,050–2,500 * | 2 | 560 |

^{*}The spectral regions between 1355–1450 nm, 1800–1950 nm and 2420–2500 nm, which are associated with the water absorption, were excluded from the analysis.

Data Set | Sample | Min (%N) | Max (%N) | Mean (%N) | Std Deviation (%N) |
---|---|---|---|---|---|

Calibration | 90 | 1.142 | 1.483 | 1.313 | 0.098 |

Validation | 90 | 1.148 | 1.457 | 1.319 | 0.084 |

Pooled | 180 | 1.142 | 1.483 | 1.316 | 0.091 |

By Cultivar | |||||

LK92-11 | 60 | 1.251 | 1.415 | 1.333 | 0.046 |

KK-3 | 60 | 1.275 | 1.483 | 1.395 | 0.062 |

K84-200 | 60 | 1.142 | 1.322 | 1.221 | 0.051 |

**Table 4.**Performances of narrow vegetation indices calculated from different independent variables for estimating nitrogen concentration in sugarcane with the combined cultivars.

VI | Variable | λ_{1}/λ_{2} (nm) | Calibration Data Set (N = 90) | Validation Data Set (N = 90) | Pooled Data Set (N = 180) | |||
---|---|---|---|---|---|---|---|---|

R^{2}_{c} | RMSE_{c} | R^{2}_{v} | RMSE_{v} | R^{2}_{cv} | RMSE_{cv} | |||

NDI | FDS | 750/700 | 0.82 | 0.041 | 0.73 | 0.044 | 0.78 | 0.043 |

CRDR | 748/690 | 0.82 | 0.041 | 0.73 | 0.043 | 0.78 | 0.043 | |

BD | 748/680 | 0.81 | 0.042 | 0.70 | 0.047 | 0.76 | 0.045 | |

RVI | FDS | 724/700 | 0.80 | 0.043 | 0.73 | 0.044 | 0.78 | 0.043 |

CRDR | 748/630 | 0.83 | 0.041 | 0.73 | 0.044 | 0.78 | 0.043 | |

BD | 748/670 | 0.82 | 0.042 | 0.70 | 0.048 | 0.76 | 0.045 |

_{c}, RMSE

_{v}and RMSE

_{cv}: root mean square error of calibration, validation and relative cross-validation with a 10-fold cross technique, respectively, expressed as %N; R

^{2}

_{cv}: relative cross-validated coefficient of determination with a 10-fold cross technique; fit between estimated and observed values at the 0.01 level was considered highly significant.

**Table 5.**Performances of narrow vegetation indices calculated from different independent variables for estimating nitrogen concentration in sugarcane with the separated cultivar.

VI | Variable | LK92-11 (N = 60) | KK-3 (N = 60) | K84-200 (N = 60) | ||||||
---|---|---|---|---|---|---|---|---|---|---|

λ_{1} | λ_{2} | R^{2}_{v} | λ_{1} | λ_{2} | R^{2}_{v} | λ_{1} | λ_{2} | R^{2}_{v} | ||

NDI | FDS | 748 | 728 | 0.81 | 718 | 710 | 0.91 | 712 | 706 | 0.80 |

CRDR | 570 | 748 | 0.80 | 736 | 712 | 0.90 | 712 | 700 | 0.79 | |

BD | 748 | 584 | 0.78 | 716 | 710 | 0.92 | 746 | 680 | 0.75 | |

RVI | FDS | 748 | 728 | 0.79 | 718 | 710 | 0.90 | 724 | 692 | 0.82 |

CRDR | 748 | 510 | 0.78 | 712 | 574 | 0.91 | 712 | 700 | 0.82 | |

BD | 748 | 586 | 0.78 | 716 | 710 | 0.91 | 712 | 678 | 0.76 |

^{2}

_{v}: root mean square error of calibration, validation; fit between estimated and observed values at the 0.01 level was considered highly significant.

**Table 6.**List of regression models developed by the vegetation indices using the different data sets.

Data set | VI | Variable_{wavelength} | Regression Model | R^{2}_{v} |
---|---|---|---|---|

Combined cultivars | NDI | FDS_{750,700} | Y = 0.37x + 1.39 | 0.73 |

CRDR_{748,690} | Y = 0.26x + 1.38 | 0.73 | ||

BD_{748,680} | Y = 9.64× + 10.77 | 0.70 | ||

RVI | FDS_{724,700} | Y = 0.14x + 1.04 | 0.73 | |

CRDR_{748,630} | Y = −0.09x + 1.15 | 0.73 | ||

BD_{748,670} | Y = 18.79x + 1.12 | 0.70 | ||

Separated cultivar - LK92-11 | NDI | FDS_{748,728} | Y = 0.95x + 1.71 | 0.79 |

CRDR_{570,748} | Y = 0.11x + 1.12 | 0.80 | ||

BD_{748,584} | Y = 4.58x + 5.72 | 0.78 | ||

RVI | FDS_{748,728} | Y = 0.93x + 0.93 | 0.79 | |

CRDR_{748,510} | Y = 0.24x + 1.21 | 0.78 | ||

BD_{748,586} | Y = 9.05x + 1.14 | 0.78 | ||

Separated cultivar - KK-3 | NDI | FDS_{718,710} | Y = 1.59x + 1.09 | 0.91 |

CRDR_{736,712} | Y = 0.44x + 1.39 | 0.90 | ||

BD_{716,710} | Y = 4.18x + 1.73 | 0.92 | ||

RVI | FDS_{718,710} | Y = 0.53x + 0.61 | 0.90 | |

CRDR_{712,574} | Y = 0.61x + 2.05 | 0.91 | ||

BD_{716,710} | Y = 2.45x – 0.7 | 0.91 | ||

Separated cultivar - K84-200 | NDI | FDS_{712,706} | Y = 1.45x + 1.18 | 0.80 |

CRDR_{712,700} | Y = 0.75x + 1.23 | 0.79 | ||

BD_{746,680} | Y = 3.47x + 4.59 | 0.75 | ||

RVI | FDS_{724,692} | Y = 0.08x + 1.07 | 0.82 | |

CRDR_{712,700} | Y = 0.39x + 0.84 | 0.82 | ||

BD_{712,678} | Y = 0.63x + 0.86 | 0.76 |

**Table 7.**Performance of stepwise multiple linear regression in estimating the nitrogen concentration in sugarcane with combined cultivar.

Variable | Wavelength (nm) | Calibration Data set (N = 90) | Validation Data set (N = 90) | Pooled Data Set (N = 180) | |||
---|---|---|---|---|---|---|---|

R^{2}_{c} | RMSE_{c} | R^{2}_{v} | RMSE_{v} | R^{2}_{cv} | RMSE_{cv} | ||

FDS | 410, 430, 720, 754,1216 | 0.90 | 0.030 | 0.80 | 0.038 | 0.86 | 0.033 |

CRDR | 748, 1158, 1184, 1216, 1276 | 0.89 | 0.033 | 0.64 | 0.053 | 0.78 | 0.043 |

BD | 748, 1262 | 0.83 | 0.040 | 0.70 | 0.047 | 0.77 | 0.044 |

_{c}, RMSE

_{v}and RMSE

_{cv}: root mean square error of calibration, validation and relative cross-validation with a 10-fold cross technique, respectively, expressed as %N; R

^{2}

_{cv}: relative cross-validated coefficient of determination with a 10-fold cross technique; fit between estimated and observed values at the 0.01 level was considered highly significant.

**Table 8.**Selected wavelengths and coefficients of determination between the observed nitrogen concentration and the spectral reflectance of separated cultivar.

Variable | LK92-11 (N = 60) | KK-3 (N = 60) | K84-200 (N = 60) | |||
---|---|---|---|---|---|---|

Wavelength (nm) | R^{2}_{cv} | Wavelength (nm) | R^{2}_{cv} | Wavelength (nm) | R^{2}_{cv} | |

FDS | 670, 754, 1,266, 1,494, 2,313 | 0.86 | 750, 1,104, 1,572, 1,586, 2,153 | 0.93 | 552, 1,032, 1,284, 1,604, 2,359 | 0.80 |

CRDR | 656, 704, 1,266 | 0.79 | 598, 674, 740, 1,228 | 0.92 | 678, 746, 1,128 | 0.80 |

BD | 684, 748, 1,228 | 0.82 | 552, 736 | 0.92 | 746 | 0.78 |

^{2}

_{cv}: relative cross-validated coefficient of determination with a 10-fold cross technique; fit between estimated and observed values at the 0.01 level was considered highly significant.

Data set | Variable | Regression Model | R^{2}_{v} |
---|---|---|---|

Combined cultivars | FDS | Y = 212.76x_{1} – 471.9x_{2} – 31.98x_{3} + 133.78x _{4} – 115.86x_{5} + 1.4 | 0.80 |

CRDR | Y = 55.77x_{1} + 19.0x_{2} + 61.92x_{3} – 73.5x _{4} – 34.25x_{5} + 1.26 | 0.64 | |

BD | Y = 25.26x_{1} – 3.7x_{2} + 1.19 | 0.70 | |

Separated cultivar -LK92-11 | FDS | Y = 520.59x_{1} + 14.794x_{2} – 97.004x_{3} + 95.08x _{4} – 63.49x_{5} + 1.347 | 0.86 |

CRDR | Y = 33.04x_{1} – 18.84x_{2} – 32.51x_{3} + 1.72 | 0.79 | |

BD | Y = −0.47x_{1} + 17.86x_{2} – 0.7845x_{3} + 1.62 | 0.82 | |

Separated cultivar-KK-3 | FDS | Y = 67.4x_{1} – 24.64x_{2} – 136.2x_{3} – 168.34x_{4} – 42.007x _{5} + 1.4153 | 0.93 |

CRDR | Y = −14.27x_{1} – 105.25x_{2} + 23.31x_{3} + 8.84x_{4} + 1.004 | 0.92 | |

BD | Y = −3.31x_{1} + 2.26x_{2} + 1.17 | 0.92 | |

Separated cultivar - K84-200 | FDS | Y = −194.8x_{1} + 138.92x_{2} – 94.52x_{3} – 213.97x _{4} – 22.31x_{5} + 1.29 | 0.80 |

CRDR | Y = −64.57x_{1} + 25.36x_{2} + 6.34x_{3} + 28.04x_{4} + 1.08 | 0.80 | |

BD | Y = 7.86x_{1} + 1.12 | 0.78 |

**Table 10.**The estimation models for estimating nitrogen concentration in sugarcane from field spectroscopy compared with previous publications (unit: % nitrogen).

Spectral Parameter | Regression Equation | Validation | No. of Samples | Study Level | Reference | |
---|---|---|---|---|---|---|

R^{2} | RMSE | |||||

RVI (FDS741, FDS1323) | Y = −0.237x + 0.587 | 0.74 | 0.084 | 25 | Leaf | [26] |

RVI (R740, R720) | Y = 0.73x + 0.753 | 0.81 | 0.103 | 37 | Leaf | [28] |

NDI (FDS750, FDS700) | Y = 0.37x + 1.39 | 0.78 | 0.043 | 90 | Canopy | This paper |

RVI (FDS724, FDS700) | Y = 0.14x + 1.04 | 0.78 | 0.043 | 90 | Canopy | This paper |

FDS (410, 430, 720, 754,1216) * | Y = 212.76x_{1} – 471.9x2 – 31.98x_{3} + 133.78x 4 – 115.86x_{5} + 1.4 | 0.86 | 0.033 | 90 | Canopy | This paper |

^{*}developed by SMLR technique.

## Share and Cite

**MDPI and ACS Style**

Miphokasap, P.; Honda, K.; Vaiphasa, C.; Souris, M.; Nagai, M.
Estimating Canopy Nitrogen Concentration in Sugarcane Using Field Imaging Spectroscopy. *Remote Sens.* **2012**, *4*, 1651-1670.
https://doi.org/10.3390/rs4061651

**AMA Style**

Miphokasap P, Honda K, Vaiphasa C, Souris M, Nagai M.
Estimating Canopy Nitrogen Concentration in Sugarcane Using Field Imaging Spectroscopy. *Remote Sensing*. 2012; 4(6):1651-1670.
https://doi.org/10.3390/rs4061651

**Chicago/Turabian Style**

Miphokasap, Poonsak, Kiyoshi Honda, Chaichoke Vaiphasa, Marc Souris, and Masahiko Nagai.
2012. "Estimating Canopy Nitrogen Concentration in Sugarcane Using Field Imaging Spectroscopy" *Remote Sensing* 4, no. 6: 1651-1670.
https://doi.org/10.3390/rs4061651