Estimations of Nitrogen Concentration in Sugarcane Using Hyperspectral Imagery
<p>The study experimental plots in Sakaeo Province, the eastern region of Thailand.</p> "> Figure 2
<p>Sampling design for field data collection.</p> "> Figure 3
<p>Measured versus estimated CNC in each sugarcane cultivar; the model was developed by a Stepwise Multiple Linear Regression (SMLR) approach with three different datasets: (<b>a</b>) First derivative spectrum (FDS); (<b>b</b>) Continuum-Removed Derivative Reflectance (CRDR); and (<b>c</b>) Band depth (BD).</p> "> Figure 4
<p>Spatial distribution of canopy nitrogen concentration (% nitrogen) in sugarcane using a Stepwise Multiple Linear Regression approach (SMLR) calculated from CRDR data sets.</p> "> Figure 5
<p>Measured versus estimated CNC in each sugarcane cultivar; the model was developed with the SVR/KPCA approach with three different datasets; (<b>a</b>) FDS; (<b>b</b>) CRDR and (<b>c</b>) BD.</p> "> Figure 5 Cont.
<p>Measured versus estimated CNC in each sugarcane cultivar; the model was developed with the SVR/KPCA approach with three different datasets; (<b>a</b>) FDS; (<b>b</b>) CRDR and (<b>c</b>) BD.</p> "> Figure 6
<p>Spatial distribution of canopy nitrogen concentration (% nitrogen) in sugarcane using Support Vector Regression (SVR) based RBF Kernel calculated from 11 KPCA components of BD data set, SVR free optimal parameters are <span class="html-italic">C</span> = 1.3, <span class="html-italic">ε</span> = 0.01, <span class="html-italic">γ</span> = 0.04.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Research Methodology Overview
2.3. Data Acquisition
2.3.1. Field Data Collection
2.3.2. Chemical Analysis of Foliar Samples
2.4. Image Pre-Processing
2.4.1. Geometric Correction
2.4.2. Radiance Transformation
2.4.3. Atmospheric Correction
2.4.4. Band Noise Reduction
2.5. Spectral Transformation
2.5.1. First-Derivative Spectrum: FDS
2.5.2. Absorption Features
- (i)
- Continuum-Removed Derivative Reflectance (CRDR) was derived using a first-derivative transformation to the continuum-removed reflectance spectrum R′ as described in Equation (3).
- (ii)
- Band depth (BD) was derived by omitting the continuum-removed reflectance at specific wavelength i from 1 shown as in Equation (4)
2.6. Feature Dimensionality Reduction
2.7. Mapping of Sugarcane CNC Using Multivariate Based Statistical Methods
2.8. Mapping of Sugarcane CNC Using SVR Based Machine Learning Approach
2.9. Model Validation
3. Results
3.1. Descriptive Statistics of Measured Nitrogen Concentration
3.2. Estimation of Sugarcane CNC from Hyperion Satellite Image using a Multiple Linear Regression Approach
3.3. Estimation of Sugarcane CNC from Hyperion Satellite Image Using Support Vector Regression
4. Discussion
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
Abbreviations
CNC | Canopy Nitrogen Concentration |
SVR | Support Vector Regression |
RBF | Radial Basis Function |
SMLR | Stepwise Multiple Linear Regression |
KPCA | Kernel Principal Component Analysis |
FDS | First Derivative Spectrum |
CRDR | Continuum-Removed Derivative Reflectance |
BD | Band Depth |
LOO | Leave One Out cross validation |
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Sensor | Acquisition Date | Spatial Resolution (m) | Spectral Resolution (nm) | Bands 1 | Wavelength (nm) |
---|---|---|---|---|---|
EO-1- Hyperion | 29 January 2012 (winter) | 30 | 10 (approx.) | 8 to 57 | 426.8–925.4 |
79 to 120 | 932.6–1346.2 | ||||
128 to 166 | 1426.9–1810.3 | ||||
179 to 223 | 1941.5–2385.4 |
Data Set | Min | Max | Mean | Std Deviation |
---|---|---|---|---|
K84-200 | 0.508 | 0.663 | 0.591 | 0.065 |
KK-3 | 0.626 | 0.843 | 0.728 | 0.090 |
LK92-11 | 0.597 | 0.835 | 0.720 | 0.083 |
UT-8 | 0.513 | 0.644 | 0.586 | 0.047 |
Pooled | 0.508 | 0.843 | 0.669 | 0.067 |
Variable | Wavelengths λ (nm) | Pooled Data Set | |
---|---|---|---|
R2cv | RMSEcv | ||
FDS | 660/721/1134/1265 | 0.73 | 0.039 |
CRDR | 721/1205/1265/1769 | 0.74 | 0.038 |
BD | 721/742 | 0.60 | 0.042 |
Estimation Model | Data Set | No. of PCs | Optimal SVR Parameters | R2cv | RMSEcv |
---|---|---|---|---|---|
SVR/Linear | FDS | 15 | C = 0.82, ε = 0.03 | 0.63 | 0.043 |
SVR/RBF | FDS | 17 | C = 0.47, ε = 0.03, γ = 0.03 | 0.65 | 0.041 |
SVR/Linear | CRDR | 11 | C = 0.65, ε= 0.04 | 0.66 | 0.042 |
SVR/RBF | CRDR | 13 | C = 0.8, ε = 0.02, γ = 0.06 | 0.74 | 0.038 |
SVR/Linear | BD | 10 | C = 0.42, ε = 0.04 | 0.73 | 0.039 |
SVR/RBF | BD | 11 | C = 1.3, ε = 0.01, γ = 0.04 | 0.78 | 0.035 |
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Miphokasap, P.; Wannasiri, W. Estimations of Nitrogen Concentration in Sugarcane Using Hyperspectral Imagery. Sustainability 2018, 10, 1266. https://doi.org/10.3390/su10041266
Miphokasap P, Wannasiri W. Estimations of Nitrogen Concentration in Sugarcane Using Hyperspectral Imagery. Sustainability. 2018; 10(4):1266. https://doi.org/10.3390/su10041266
Chicago/Turabian StyleMiphokasap, Poonsak, and Wasinee Wannasiri. 2018. "Estimations of Nitrogen Concentration in Sugarcane Using Hyperspectral Imagery" Sustainability 10, no. 4: 1266. https://doi.org/10.3390/su10041266