Abstract:Nitrogen (N) deficiency can directly reflect the degree of crop N nutrient deficiency, and it is important to obtain the information of rice N deficiency quickly and in a large area to achieve accurate fertilization of rice. Most of the existing studies focused on the use of UAV remote sensing to monitor rice N nutrition, and less research was conducted on N deficiency itself. Based on the canopy spectral data obtained by UAV hyperspectral remote sensing and rice agronomic data obtained by field sampling, the method of constructing the critical nitrogen concentration curve of northeastern rice was studied, and the nitrogen deficit of rice on this basis was determined; the spectrum in the state of nitrogen deficit approximately equal to 0 was used as the standard spectrum, and ratio, difference and normalized difference transformations on the spectral reflectance data were carried out respectively, and then the competitive adaptive re-weighting sampling method was used to the inversion models of rice nitrogen deficit based on the multivariable linear regression (MLR), extreme learning machine（ELM）and the bat algorithm optimized extreme learning machine（BA-ELM） were constructed by taking the extracted feature bands as input variables and the nitrogen deficit as output variables. The results showed that the equation coefficients a and b of the critical nitrogen concentration curve of northeastern rice were 2.026 and -0.4603, respectively, based on field data, which were consistent with previous studies; compared with other transformation methods, the normalized difference transformation and feature band extraction of the rice canopy spectrum significantly improved the correlation between the canopy spectral reflectance and rice nitrogen deficit, and also improved the inversion of the subsequent inversion model. The BA-ELM inversion model with normalized difference spectra as input predicted significantly better than the rest of the models, with the validation set R2 of 0.8306，RMSE of 0.8141kg/hm2, which had better estimation of N deficit.
