Estimation of leaf nitrogen levels in sugarcane using hyperspectral models/Modelagem hiperespectral na quantificacao de nitrogenio foliar em cana-de-aciicar

• Published in: Ciência rural 2022-07, Vol.52 (7), p.1
• Main Authors: Barros, Pedro Paulo da Silva, Fiorio, Peterson Ricardo, Jose Dematte, Alexandre de Melo, Ma, Montezano, Zaqueu Fernando, Dias, Fabio Luis Ferreira
• Format: Article
• Language: English
• ISSN: 0103-8478
• DOI: 10.1590/0103-8478cr20200630

Sugarcane is a good source of renewable energy and helps reduce the emission of greenhouse gases. Nitrogen has a critical role in plant growth; therefore,estimating nitrogen levels is essential, and remote sensing can improve fertilizer management. This field study selects wavelengths from hyperspectral data on a sugarcane canopy to generate models for estimating leaf nitrogen concentrations. The study was carried out in the municipalities of Piracicaba, Jau, and Santa Maria da Serra, state of Sao Paulo, in the 2013/2014 growing season. The experiments were carried out using a completely randomized block design with split plots (three sugarcane varieties per plot [variety SP 81-3250 was common to all plots] and four nitrogen concentrations [0, 50, 100, and 150 kg[ha.sup.-1]] per subplot) and four repetitions. The wavelengths that best correlated with leaf nitrogen were selected usingsparse partial least square regression. The wavelength regionswere combinedby stepwise multiple linear regression. Spectral bands in the visible (700-705 nm), red-edge (710-720 nm), near-infrared (725, 925, 955, and 980 nm), and short-wave infrared (1355, 1420, 1595, 1600, 1605, and 1610 nm) regions were identified. The [R.sup.2] and RMSE of the model were 0.50 and 1.67 g.[kg.sup.-1], respectively. The adjusted R2 and RMSE of the models for Piracicaba, Jau, and Santa Maria were 0.31 (unreliable) and 1.30 g.[kg.sup.-1], 0.53 and 1.96 g.[kg.sup.-1], and 0.54 and 1.46 g.[kg.sup.-1], respectively. Our results showed that canopy hyperspectral reflectance can estimate leaf nitrogen concentrations and manage nitrogen application in sugarcane. Key words: remote sensing, Saccharumspp, nitrogen fertilization, reflectance, sPLS, regression model. A cana-de-agucar se destaca como uma das fontes de energia renovdvel frente as estrategias para reduzir a emissao de gases causadores do efeito estufa. O nitrogenio e um dos mais significativos devido ao seu impacto no crescimento de folhas e colmos. Portanto, o monitoramento eficiente do nitrogenio aplicado e essencial e o sensoriamento remoto se apresenta como uma alternativa na melhoria do gerenciamento da adubagao. O presente trabalho teve por objetivo selecionar comprimentos de onda a partir de dados hiperespectrais de dossel da cana-de-agucar para geragao de modelos na predigao da concentragao de Nitrogenio. O estudo foi realizado em experimentos de campo instalados nos municipios de Piracicaba, Jau e Santa Maria da Serra, estado Sao Paulo,