Influence of spectral transformation methods on nutrient content inversion accuracy by hyperspectral remote sensing in black soil

In order to improve the precision and stability of the soil nutrient content inversion model in black soil area, taking Jiansanjiang area in Heilongjiang province as the study area, and the airborne hyperspectral imaging system CASI-1500 (380-1 050 nm) as the analysis data, the influence of different spectral transformation methods on the accuracy was researched. 60 samples were evenly sampled, and the contents of organic matter, total nitrogen, total phosphorus and total potassium were obtained through laboratory tests. The content of organic matter was determined by potassium dichromate capacity external heating method. The content of total nitrogen, total phosphorus and total potassium was determined by Kjeldahl method, NaOH alkali antimony colorimetric method and potassium flame atomic absorption spectrophotometry. 60 black soil samples were sorted according to nutrient content, and the spectral transformation in the visible near red range was analyzed. The change rule of organic matter is that the reflectance decreases with the increase of content. The change rule of nitrogen is similar to the spectral curve of organic matter. With the increase of nitrogen content, the reflectance decreases. The transformation of phosphorus and potassium in the visible near red spectrum is not significant. The nutrient correlation coefficients of 60 samples at different sampling points were calculated by spectral reflectance. The results show that the correlation coefficient of each band is the highest, the mean value is 0.39, the correlation coefficients of nitrogen and phosphorus are close to 0.28 and 0.30, and the correlation coefficient of potassium is the lowest, which is 0.05. The first 5 bands with high correlation coefficient are selected as modeling bands, that of organic matter is 933.6, 914.5, 905, 866.8 and 943.1 nm, and that of nitrogen is 933.6, 866.8, 876.3, 847.7 and 914.5 nm. The content of organic matter and support vector machine were used to model nitrogen, phosphorus and potassium contents. The extraction accuracies of 5 spectral transformations which are resampling (RE), logarithmic reciprocal (LR), first order derivative (FD), continuum removal (CR) and multivariate scatter correction (MSC) transformation are compared. The most accurate methods for the spectral transformation of organic matter, nitrogen, phosphorus and potassium are MSC, MSC, LR and RE, respectively. Five spectral transformation methods are used to calculate the R2 of each model, and the order of modeling accuracy for soil organic matter prediction is MSC (0.922) > RE (0.529) > LR (0.432) > CR (0.414) > FD (0.018). The modeling accuracy of multiple scattering correction transformation is significantly higher than that of the other four methods. The order of prediction accuracy or total phosphorus is MSC (0.872) > CR (0.387) > RE (0.256) > LR (0.029) > FD (0.012), and the prediction accuracy of the multivariate scattering correction transformation is also the highest. The highest prediction accuracies of total phosphorus and total potassium are LR (0.621) and RE (0.423). In turn, the MSC, MSC, LR and RE spectral transformation methods with high coefficient of determination are applied to the combined operation of the characteristics of organic matter, nitrogen, phosphorus and potassium, and the spatial distribution of nutrient content in black soil is obtained. The results show that the spectral transformation methods of MSC, MSC, LR and RE are applied to calculate soil organic matter, nitrogen, phosphorus and potassium, respectively, the spatial distribution accuracy of nutrient content in black soil is the highest, and the determination coefficients of predicted samples are 0.748, 0.673, 0.631 and 0.420, respectively.