UAV hyperspectral response characteristics and estimation model construction of soil total nitrogen

Abstract: Soil total nitrogen (STN) content can be accurately and rapidly estimated to better reflect the response relationship between spectrum and STN content. In this study, an unmanned aerial vehicle (UAV) equipped with a hyperspectral sensor was used to obtain the soil hyperspectral images in farmland. The original spectral reflectance (R) was then transformed into the reciprocal of reflectance (RR), logarithm of reflectance (LR), the first derivative of reflectance (FDR), the first derivative of reciprocal reflectance (FRR), and the first derivative of logarithm of reflectance (FLR). Grey correlation degree and Pearson correlation coefficient were also selected to extract the sensitive band of STN content in each spectrum. The hyperspectral inversion model of STN was finally constructed using the sensitive band, the partial least squares regression (PLSR), ridge regression (RR), and random forest regression (RF). The determination coefficient R2, root mean square error RMSE, and mean absolute error MAE were used to evaluate the accuracy of the model. After that, the model with the highest accuracy was selected for the inversion mapping of STN content in the study area. The availability of the model was tested, according to the distribution of STN content. The optimal model was selected to invert and map the STN content. The results showed that: 1) The sensitive band (996-1 003 nm) of the RR spectrum was concentrated in the near-infrared long wave range, according to the gray correlation degree and Pearson correlation coefficient. The sensitive bands in the FRR spectrum (39-459, 469, and 472-1 003 nm), and FLR spectrum (398-459, 463-973, and 978-1 003 nm) were distributed in the visible and near-infrared range. The sensitive bands (615-625, 632, and 666-670 nm) in the FDR spectrum were concentrated mainly in the red range of visible light. 2) Pearson correlation coefficient was used to better reflect the response relationship between spectrum and STN content. The STN inversion model R2, RMSE, and MAE were in the range of 0.058-0.693, 0.226-0.477, and 0.171-0.416 g/kg, respectively, in terms of Pearson correlation coefficient. In grey correlation degree, the STN inversion model R2, RMSE, and MAE were within the range of 0.693-0.859, 0.123-0.276, and 0.107-0.209 g/kg, respectively. The accuracy of the model with the Pearson correlation coefficient was higher than that with the grey correlation degree, indicating that the Pearson correlation coefficient performed better on the response relationship between spectrum and STN content. 3) The RF-FDR model was used to estimate the STN content in the field. Among the 12 inversion models of STN content, the RF-FDR model shared the highest accuracy, with R2 of 0.859, RMSE of 0.143 g/kg, and MAE of 0.114 g/kg. The inversion mapping of STN content showed that the STN content in most areas was in the range of 1.50-2.00 g/kg, according to the RF-FDR model. There was the consistency with the average value of STN content in the 72 soil samples, together with the actual situation of planting in one season, low soil fertility consumption, and annual fertilization. As such, the RF-FDR model can be expected to estimate the STN content in fields. Therefore, the Pearson correlation coefficient can be used to extract the sensitive bands for the soil spectrum from UAV hyperspectral. The inversion model of STN content can be constructed with higher accuracy for the effective estimation of STN content.