This paper evaluates the potential of integrating textural and spectral information from unmanned aerial vehicle (UAV)-based multispectral imagery for improving the quantification of nitrogen (N) status in rice crops. Vegetation indices (VIs), normalized difference texture indices (NDTIs), and their combination were used to estimate four N nutrition parameters leaf nitrogen concentration (LNC), leaf nitrogen accumulation (LNA), plant nitrogen concentration (PNC), and plant nitrogen accumulation (PNA). Results demonstrated that the normalized difference red-edge index (NDRE) performed best in estimating the N nutrition parameters among all the VI candidates. The optimal texture indices had comparable performance in N nutrition parameters estimation as compared to NDRE. Significant improvement for all N nutrition parameters could be obtained by integrating VIs with NDTIs using multiple linear regression. While tested across years and growth stages, the multivariate models also exhibited satisfactory estimation accuracy. For texture analysis, texture metrics calculated in the direction D3 (perpendicular to the row orientation) are recommended for monitoring row-planted crops. These findings indicate that the addition of textural information derived from UAV multispectral imagery could reduce the effects of background materials and saturation and enhance the N signals of rice canopies for the entire season.

Chlorophyll fluorescence (ChlF) parameters, especially the quantum efficiency of photosystem II (PSII) in dark- and light-adapted conditions (Fv/Fm and Fv’/Fm’), have been used extensively to indicate photosynthetic activity, physiological function, as well as healthy and early stress conditions. Previous studies have demonstrated the potential of applying hyperspectral data for the detection of ChlF parameters in vegetation. However, the performance of spectral features that have been documented to estimate ChlF is not ideal and is poorly understood. In this study, ChlF parameters and leaf reflectance were collected in two field experiments involving various wheat cultivars, nitrogen (N) applications, and plant densities, during the growing seasons of 2014 to 2015 and 2015 to 2016. Three types of spectral features, including vegetation indices (VIs), red edge position (REP), and wavelet features, were used to quantify ChlF parameters Fv/Fm and Fv’/Fm’. The results indicated that traditional chlorophyll fluorescence vegetation indices (ChlF VIs), such as the curvature index (CUR) and D705/D722 were capable of detecting Fv/Fm and Fv’/Fm’ under various scenarios. However, the wavelet-based REP (WREP-S4) and the wavelet feature (WF) (704 nm, scale 4) yielded higher accuracy than other spectral features in calibration and validation datasets. Moreover, the bands used to calculate WREP-S4 and WF (704 nm, scale 4) were all centered in the red edge region (680 to 760 nm), which highlighted the role of the red edge region in tracking the change of active ChlF signal. Our results are supported by previous studies, which have shown that the red edge region is vital for estimating the chlorophyll content, and also the ChlF parameters. These findings could help to improve our understanding of the relationships among active ChlF signal and reflectance spectra.