Abstract
The retrieval of nutrient concentration in sugarcane through hyperspectral remote sensing is widely known to be affected by canopy architecture. The goal of this research was to develop an estimation model that could explain the nitrogen variations in sugarcane with combined cultivars. Reflectance spectra were measured over the sugarcane canopy using a field spectroradiometer. The models were calibrated by a vegetation index and multiple linear regression. The original reflectance was transformed into a First-Derivative Spectrum (FDS) and two absorption features. The results indicated that the sensitive spectral wavelengths for quantifying nitrogen content existed mainly in the visible, red edge and far near-infrared regions of the electromagnetic spectrum. Normalized Differential Index (NDI) based on FDS(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.
Highlights
Sugarcane (Saccharum spp. hybrid) is one of the most important economic crops in Thailand
Reflectance spectra were measured over the sugarcane canopy
The most important conclusions that could be drawn from this study are as follows: (i) Stepwise multiple linear regression could explain the nitrogen variations in sugarcane canopy better than a narrow vegetation index
Summary
Sugarcane (Saccharum spp. hybrid) is one of the most important economic crops in Thailand. The precise estimation of the annual sugarcane yield is necessary to balance the amount of sugarcane used by these two competing industries and, to establish proper policies regarding its use. Several physical and chemical factors, such as nitrogen, cultivars, climate, soil and water availability, influence sugarcane growth [2] and need to be considered in any yield estimation model. Nitrogen is one of the most significant macronutrients associated with sugarcane yield due to its impact on leaf and stalk growth [3]. Sugarcane accumulates most of its nitrogen from the initial growth stages up to canopy closure [4,5]. An adequate nitrogen supply will improve the leaf area index and the chlorophyll concentration [6]
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