Objectives Canopy hyper-spectral reflectance has been used for the estimation of nitrogen in the key growing stages of crop rapidly and non-destructively. In this paper, the successive project algorithm (SPA) and the partial least squares (PLS) were used to help screening more sensitive wave bands, so as to increase the accuracy of hyper-spectral remote sensing in the estimation of nitrogen contents in winter wheat at jointing stage.
Methods Based on a plot experiment of winter wheat in Guanzhong area of Shaanxi Province in 2015–2016, the canopy spectrum sensitive wave bands of leaf nitrogen contents of winter wheat were calculated by SPA. The leaf nitrogen contents estimation model of winter wheat based on the sensitive feature bands at the jointing stage was established by using PLS regression method.
Results Through the SPA, 8 sensitive feature wave bands of leaf nitrogen contents were selected in canopy spectrum ranging from 338 nm to 2510 nm of winter wheat, including 1985 nm, 2474 nm, 1751 nm, 1916 nm, 2507 nm, 1955 nm, 2465 nm and 344 nm. The result decreased the number of bands by 98.9%, and effectively reduced the redundancy of spectral information. The determination coefficient and root-mean-square error of the leaf nitrogen contents regression model that was established by PLS based on the sensitive feature bands were 0.82 and 0.28, respectively. The determination coefficient of the verification equation of model was 0.84, the root-mean-square error was 0.21 and the residual prediction deviation was large than 2, which indicated that the model had high precision and good prediction ability.
Conclusions Compared with the leaf nitrogen contents estimation model developed using common vegetation indices, the SPA combining with PLS has higher precision and more stability, which can be used as the hyper-spectral reflectance estimation method for the leaf nitrogen contents of winter wheat at the jointing stage.
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