Inversion analysis of soil nitrogen content using hyperspectral images with different preprocessing methods

Unmanned aerial vehicle (UAV) hyperspectral data can provide accurate and detailed spectral information on the surface of features, leading to a better understanding of the physiological characteristics and optical properties of objects. Compared to satellite hyperspectral, UAV hyperspectral has a greater image spatial resolution, but it also collects more impurity information along with more image detail information. The nitrogen (N) content in shallow soils is of great significance for crop growth, and most of its remote sensing inversions are currently based on satellite images, whereas relatively few studies have used hyperspectral UAV images in the near-infrared band for inversions. Based on this, we propose a high-accuracy inversion model based on UAV hyperspectral imagery using an elastic net (EN) dimensionality reduction algorithm combined with a decision tree (DT) model. In this study, a hyperspectral imager carried by a drone was used to acquire the surface soil spectral data of winter wheat in the study area after sowing and fertilizing, and the measured N content of the sampled soil was determined by chemical analysis methods. We used various pre-processing methods to denoise and reduce the dimensionality of the original hyperspectral images and utilized a DT regression model to construct an inversion model of N content with different preprocessing results. Then we compared the accuracy of the models and used the optimal model to obtain the spatial distribution map of soil N content in the study area. The results show that (1) the dimensionality reduction of hyperspectral data can effectively remove redundant information and prevent data overfitting. Among them, the successive projections algorithm and correlation analysis method are more suitable for analyzing pre-processed spectral data, whereas the Lasso and EN algorithms are more suitable for processing original spectral data. (2) For the original spectral images, the EN dimensionality reduction algorithm (R2 of the EN-Unt-DT inversion model is 0.930, RMSE is 0.045) performed better than the other three algorithms. For the pre-processed spectral images, the successive projections algorithm (R2 of the SPA-Pre-DT inversion model is 0.906, RMSE is 0.055) performed better than the other three algorithms. The constructed EN-Unt-DT inversion model had the highest fitting accuracy. The research results provide a corresponding technical reference for the inversion of soil N content using UAV NIR hyperspectral data.