Estimating Canopy Nitrogen Concentration in Sugarcane Using Field Imaging Spectroscopy

Abstract

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) 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.

Vegetation Indices for Mapping Canopy Foliar Nitrogen in a Mixed Temperate Forest

“…The spectral wavelengths near 550 nm and 700 nm as well as the red-edge region (680-780 nm) have been utilized for assessing chlorophyll [35,42], resulting in a large number of indices [43][44][45]. Compared with chlorophyll, there are a limited number of studies that propose indices specifically for nitrogen estimation, most of which were developed for crops [37,[46][47][48], with few developed for forest [49].…”

“…Empirical techniques are dominant in the retrieval of different vegetation variables from remote sensing data such as nitrogen, ranging from vegetation indices [37] and traditional regression techniques such as stepwise multiple linear regression [17] and partial least square regression [18], to a number of artificial intelligence methods such as support vector regression, neural network, and Bayesian model averaging [32,38,39]. Among the empirical techniques, vegetation indices are one of the simplest and most widely used approaches to estimate leaf biochemical contents such as nitrogen.…”

Mapping Crop Planting Quality in Sugarcane from UAV Imagery: A Pilot Study in Nicaragua

“…It is worthwhile to note that studies conducted with hand-held spectrometers (i.e., [26,27]) achieved good results. Research carried out with spectrometers indicates that it is possible to retrieve nitrogen content [28] and the nitrogen/silicon ratio [23], which are critical data for fertilizing plans and yield forecasting. Schmidt et al [29] conducted a study using a Digital Multi-Spectral Video camera mounted on a microlite, which is a small fixed-wing mono motor plane, over sugarcane fields.…”

Dados hiperespectrais de dossel e sua correlação com nitrogênio aplicado a cultura da cana-de-açúcar

“…Em pesquisa desenvolvida por Miphokasap et al (2012) para desenvolver um modelo que explique a variação do nitrogênio na cana-de-açúcar também teve como resultados a seleção dos comprimentos de onda 720 nm, mais o 410nm, 426 nm, 754 nm e 1216 nm.…”

“…Em trabalho realizado por Vigneau et al (2011) VAN DEN BERG, 2010;MIPHOKASAP et al, 2012;AMARAL et al, 2015;ROSA et al, 2015).…”