Enhancing Estimation of Leaf Nitrogen Content in Ginkgo Saplings Using Wavelet-Based Indices Derived From UAV Hyperspectral Imagery

Abstract

The estimation of leaf nitrogen content (LNC) is important for assessing the growth status of Ginkgo saplings. UAV hyperspectral imagery provides an effective data source for estimating LNC. However, canopy structural variation can affect hyperspectral signals. Although canopy scattering coefficient (CSC) correction can reduce such effects, extracting nitrogen-sensitive spectral features remains challenging. In this study, we applied CSC correction and continuous wavelet transform (CWT) to UAV hyperspectral imagery. Six models were first evaluated to determine the optimal modeling strategy after CSC correction. CWT was then performed at five scales, and the top 10% nitrogen-sensitive wavelet coefficients were selected according to their correlation with LNC. Based on the selected coefficients, we constructed and optimized three types of wavelet indices using an exhaustive search strategy. We then compared the optimized index with existing reflectance-based vegetation indices. The results showed that the optimized wavelet index achieved the best LNC prediction performance (R ${}^{2}=0.691$ , root mean square error (RMSE) = 1.856 g $\cdot $ kg−1). This study highlights the potential of combining CSC correction with CWT for UAV-based LNC estimation in Ginkgo saplings.