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Sustainability 2018, 10(4), 1266;

Estimations of Nitrogen Concentration in Sugarcane Using Hyperspectral Imagery

Department of Geography, Faculty of Social Science, Kasetsart University, 50 Ngam Wong Wan Rd, Ladyaow, Chatuchak, Bangkok 10900, Thailand
Geography Field of Study, Faculty of Liberal Arts, Thammasat University, 99 Moo 18, Klong Luang, Rangsit, Pathumthani 12121, Thailand
Author to whom correspondence should be addressed.
Received: 5 March 2018 / Revised: 10 April 2018 / Accepted: 17 April 2018 / Published: 20 April 2018
(This article belongs to the Section Sustainable Agriculture, Food and Wildlife)
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This study aims to estimate the spatial variation of sugarcane Canopy Nitrogen Concentration (CNC) using spectral data, which were measured from a spaceborne hyperspectral image. Stepwise Multiple Linear Regression (SMLR) and Support Vector Regression (SVR) were applied to calibrate and validate the CNC estimation models. The raw spectral reflectance was transformed into a First-Derivative Spectrum (FDS) and absorption features to remove the spectral noise and finally used as input variables. The results indicate that the estimation models developed by non-linear SVR based Radial Basis Function (RBF) kernel yield the higher correlation coefficient with CNC compared with the models computed by SMLR. The best model shows the coefficient of determination value of 0.78 and Root Mean Square Error (RMSE) value of 0.035% nitrogen. The narrow sensitive spectral wavelengths for quantifying nitrogen content in the combined cultivar environments existed mainly in the electromagnetic spectrum of the visible-red, longer portion of red edge, shortwave infrared regions and far-near infrared. The most important conclusion from this experiment is that spectral signals from the space hyperspectral data contain the meaningful information for quantifying sugarcane CNC across larger geographic areas. The nutrient deficient areas could be corrected by applying suitable farm management. View Full-Text
Keywords: hyperspectral; nitrogen concentration; sugarcane; support vector regression hyperspectral; nitrogen concentration; sugarcane; support vector regression

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Miphokasap, P.; Wannasiri, W. Estimations of Nitrogen Concentration in Sugarcane Using Hyperspectral Imagery. Sustainability 2018, 10, 1266.

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