Hyperspectral UAV Images at Different Altitudes for Monitoring the Leaf Nitrogen Content in Cotton Crops

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2022-05, Vol.14 (11), p.2576
• Main Authors: Yin, Caixia, Lv, Xin, Zhang, Lifu, Ma, Lulu, Wang, Huihan, Zhang, Linshan, Zhang, Ze
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Altitude; Classification; Cotton; Crops; Environmental impact; Feasibility studies; Fertilizers; Flight; Flight altitude; Hyperspectral imaging; I.R. radiation; Image classification; leaf nitrogen content (LNC); Leaves; Machine learning; Monitoring; Nitrogen; Nutrition; Radiation; Red shift; Regression; Regression analysis; Remote sensing; Root-mean-square errors; Sensors; Spectral reflectance; spectral reflectance (SR); Support vector machines; UAV; Unmanned aerial vehicles; Vegetation
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs14112576

The accurate assessment of cotton nitrogen (N) content over a large area using an unmanned aerial vehicle (UAV) and a hyperspectral meter has practical significance for the precise management of cotton N fertilizer. In this study, we tested the feasibility of the use of a UAV equipped with a hyperspectral spectrometer for monitoring cotton leaf nitrogen content (LNC) by analyzing spectral reflectance (SR) data collected by the UAV flying at altitudes of 60, 80, and 100 m. The experiments performed included two cotton varieties and six N treatments, with applications ranging from 0 to 480 kg ha−1. The results showed the following: (i) With the increase in UAV flight altitude, SR at 500–550 nm increases. In the near-infrared range, SR decreases with the increase in UAV flight altitude. The unique characteristics of vegetation comprise a decrease in the “green peak”, a “red valley” increase, and a redshift appearing in the “red edge” position. (ii) We completed the unsupervised classification of images and found that after classification, the SR was significantly correlated to the cotton LNC in both the visible and near-infrared regions. Before classification, the relationship between spectral data and LNC was not significant. (iii) Fusion modeling showed improved performance when UAV data were collected at three different heights. The model established by multiple linear regression (MLR) had the best performance of those tested in this study, where the model-adjusted the coefficient of determination (R2), root-mean-square error (RMSE), and mean absolute error (MAE) reached 0.96, 1.12, and 1.57, respectively. This was followed by support vector regression (SVR), for which the adjusted_R2, RMSE, and MAE reached 0.71, 1.48, and 1.08, respectively. The worst performance was found for principal component regression (PCR), for which the adjusted_R2, RMSE, and MAE reached 0.59, 1.74, and 1.36, respectively. Therefore, we can conclude that taking UAV hyperspectral images at multiple heights results in a more comprehensive reflection of canopy information and, thus, has greater potential for monitoring cotton LNC.